Abstractive_summarization_PEGASUS_XSUM_&_large_model.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "gpuType": "T4",
      "authorship_tag": "ABX9TyOlXeyGa0AE/G95mKy9o9Jo",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "3a59bde9093b45bea99c0648aee16e70": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_50326cdd3881487aa73187ea2e6ac2a0",
              "IPY_MODEL_2c390d5bcf50485793f4aae4d2955b28",
              "IPY_MODEL_30d2471df8b14042985947a65c9e8f5d"
            ],
            "layout": "IPY_MODEL_b46ee5693fea4d89b0e4d55678de1864"
          }
        },
        "50326cdd3881487aa73187ea2e6ac2a0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_49a86b0f124c4abda54f5239cfa6bbac",
            "placeholder": "​",
            "style": "IPY_MODEL_288063f6d31548d8a5986801e4ecb536",
            "value": "Downloading (…)ve/main/spiece.model: 100%"
          }
        },
        "2c390d5bcf50485793f4aae4d2955b28": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_51d0ceb47c244529ab20ca8916ed8f1b",
            "max": 1912529,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_afe1cb762cff4cc6bbe4cadd8e846d7a",
            "value": 1912529
          }
        },
        "30d2471df8b14042985947a65c9e8f5d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_18920cd590134d939e7ea5b26286658c",
            "placeholder": "​",
            "style": "IPY_MODEL_d78de53b169849a0bd2dd2574237991c",
            "value": " 1.91M/1.91M [00:01&lt;00:00, 1.83MB/s]"
          }
        },
        "b46ee5693fea4d89b0e4d55678de1864": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "49a86b0f124c4abda54f5239cfa6bbac": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "288063f6d31548d8a5986801e4ecb536": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "51d0ceb47c244529ab20ca8916ed8f1b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "afe1cb762cff4cc6bbe4cadd8e846d7a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "18920cd590134d939e7ea5b26286658c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d78de53b169849a0bd2dd2574237991c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "b6131e2170524d01b4c904aa1fd77d76": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_8462bac52aa8448881d2b3cd87f67bc3",
              "IPY_MODEL_76d0ea6d2d634e1391829894b9d031ff",
              "IPY_MODEL_c97bf50fe0f04243b2620c9d47f241e3"
            ],
            "layout": "IPY_MODEL_02c416188b7a4bb38c79cd5fae0e34f9"
          }
        },
        "8462bac52aa8448881d2b3cd87f67bc3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3ae5916ce80d480fa4895b4ad6d1d3db",
            "placeholder": "​",
            "style": "IPY_MODEL_7cdd0d5ab86d4abba030195a579d3a30",
            "value": "Downloading (…)cial_tokens_map.json: 100%"
          }
        },
        "76d0ea6d2d634e1391829894b9d031ff": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_a235115a5536437f9ad41ab6d535d2ea",
            "max": 65,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_59ebe66061f74f5fbe6b6fd5e6cb6b51",
            "value": 65
          }
        },
        "c97bf50fe0f04243b2620c9d47f241e3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_29e6aaa5ad0745d292276511e8253e68",
            "placeholder": "​",
            "style": "IPY_MODEL_de2d8f20c32f4a788f8327fcfd8cc2cd",
            "value": " 65.0/65.0 [00:00&lt;00:00, 2.19kB/s]"
          }
        },
        "02c416188b7a4bb38c79cd5fae0e34f9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3ae5916ce80d480fa4895b4ad6d1d3db": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7cdd0d5ab86d4abba030195a579d3a30": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "a235115a5536437f9ad41ab6d535d2ea": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "59ebe66061f74f5fbe6b6fd5e6cb6b51": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "29e6aaa5ad0745d292276511e8253e68": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "de2d8f20c32f4a788f8327fcfd8cc2cd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "2dc33220dcf744fdb8e671a03eda7f85": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_264236fa802246d283f1e70e1a1f6c44",
              "IPY_MODEL_ce2a0be1baa94dc4a7f560f16946f52d",
              "IPY_MODEL_af64cb57876f4d42b5a51255b11fcd98"
            ],
            "layout": "IPY_MODEL_6062e7515e1d46e09adae3d61f10dab9"
          }
        },
        "264236fa802246d283f1e70e1a1f6c44": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_46c4836c9f4247af9d58cd9ef0a232f3",
            "placeholder": "​",
            "style": "IPY_MODEL_a1a970e85e38415087c22978d406c1ef",
            "value": "Downloading (…)okenizer_config.json: 100%"
          }
        },
        "ce2a0be1baa94dc4a7f560f16946f52d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_beee3555c75e453b9763536e9320f163",
            "max": 87,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_829ee2f5cd0043d4baf0af7eb3ed3d49",
            "value": 87
          }
        },
        "af64cb57876f4d42b5a51255b11fcd98": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_9452718eea9e43baa1719fee1031324c",
            "placeholder": "​",
            "style": "IPY_MODEL_aa461c098bf24a9b8e04c56dda8614c1",
            "value": " 87.0/87.0 [00:00&lt;00:00, 3.33kB/s]"
          }
        },
        "6062e7515e1d46e09adae3d61f10dab9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "46c4836c9f4247af9d58cd9ef0a232f3": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a1a970e85e38415087c22978d406c1ef": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "beee3555c75e453b9763536e9320f163": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "829ee2f5cd0043d4baf0af7eb3ed3d49": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "9452718eea9e43baa1719fee1031324c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "aa461c098bf24a9b8e04c56dda8614c1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "da605f29d7234a08a03bfe24ec8a9d62": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_e25bec17697a4743b52d8376a260cfa3",
              "IPY_MODEL_d019fc77139d49a9855e7239591d81bc",
              "IPY_MODEL_e2186d50bbdb4054bcaa5dee9d65f056"
            ],
            "layout": "IPY_MODEL_4b82de99ba2c484aa4b646adfed14bf3"
          }
        },
        "e25bec17697a4743b52d8376a260cfa3": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c74a67526e3a464bae54b1e20f9d8f53",
            "placeholder": "​",
            "style": "IPY_MODEL_78ce9d4b213a470ba5a5503827b2d962",
            "value": "Downloading (…)lve/main/config.json: 100%"
          }
        },
        "d019fc77139d49a9855e7239591d81bc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_c61aae76235d4d7880aef87eafc9bad1",
            "max": 1392,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_f4f6541bcb2a4c91a582b861167837c9",
            "value": 1392
          }
        },
        "e2186d50bbdb4054bcaa5dee9d65f056": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_edb7398aaa554a3fa35861825b10103d",
            "placeholder": "​",
            "style": "IPY_MODEL_2758272204804ca09ecd88713e602e34",
            "value": " 1.39k/1.39k [00:00&lt;00:00, 75.6kB/s]"
          }
        },
        "4b82de99ba2c484aa4b646adfed14bf3": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c74a67526e3a464bae54b1e20f9d8f53": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "78ce9d4b213a470ba5a5503827b2d962": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "c61aae76235d4d7880aef87eafc9bad1": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "f4f6541bcb2a4c91a582b861167837c9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "edb7398aaa554a3fa35861825b10103d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "2758272204804ca09ecd88713e602e34": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "0a186e0d848b44c68125b5272f3351ba": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_f01dde672e614aaeb7a13683f3e4a39b",
              "IPY_MODEL_15b9fa7607c44e97be17736e1839d070",
              "IPY_MODEL_bcc914fadb504a69b4b54b291252f318"
            ],
            "layout": "IPY_MODEL_6ce740d85bc5412f9c5a8902e49f122f"
          }
        },
        "f01dde672e614aaeb7a13683f3e4a39b": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_aae5917ef1044cc78e24fadc3370e30b",
            "placeholder": "​",
            "style": "IPY_MODEL_64ffd2a0adf147e8a705ff136fa29f22",
            "value": "Downloading pytorch_model.bin: 100%"
          }
        },
        "15b9fa7607c44e97be17736e1839d070": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_1ddffa21afec47688e6a67a8a522436c",
            "max": 2275329241,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_578d1f31b39f4d649e8da28364374ca1",
            "value": 2275329241
          }
        },
        "bcc914fadb504a69b4b54b291252f318": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7e543e9ebde74c15838a5f077466bd06",
            "placeholder": "​",
            "style": "IPY_MODEL_1e4a808da740406cb2236d5cad09a9a0",
            "value": " 2.28G/2.28G [00:30&lt;00:00, 43.6MB/s]"
          }
        },
        "6ce740d85bc5412f9c5a8902e49f122f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "aae5917ef1044cc78e24fadc3370e30b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "64ffd2a0adf147e8a705ff136fa29f22": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "1ddffa21afec47688e6a67a8a522436c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "578d1f31b39f4d649e8da28364374ca1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "7e543e9ebde74c15838a5f077466bd06": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "1e4a808da740406cb2236d5cad09a9a0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "685c8fc8cfc147bb820236a6daa262b7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_63946659736c4d1ba4e3d6cb657a338a",
              "IPY_MODEL_80350a3fa45840cfbe321f798a7f70ed",
              "IPY_MODEL_eef3ff552e22419db03f8fe9eecebcb1"
            ],
            "layout": "IPY_MODEL_5eef2a5b4fdb439694a493f676698696"
          }
        },
        "63946659736c4d1ba4e3d6cb657a338a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_97d55ffe17c540c9ad804fda661ab51c",
            "placeholder": "​",
            "style": "IPY_MODEL_be5273a552004165a765eca03d64e973",
            "value": "Downloading (…)neration_config.json: 100%"
          }
        },
        "80350a3fa45840cfbe321f798a7f70ed": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_0070b38218d14a8db4c67ca6155da8ad",
            "max": 259,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_6b26265fd53d4bd08025ffc89542d4cb",
            "value": 259
          }
        },
        "eef3ff552e22419db03f8fe9eecebcb1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_09dddfff7ad34a0bb896f47c598f5d51",
            "placeholder": "​",
            "style": "IPY_MODEL_d2f2558e658c44459df1a6db41f3547f",
            "value": " 259/259 [00:00&lt;00:00, 4.59kB/s]"
          }
        },
        "5eef2a5b4fdb439694a493f676698696": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "97d55ffe17c540c9ad804fda661ab51c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "be5273a552004165a765eca03d64e973": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "0070b38218d14a8db4c67ca6155da8ad": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6b26265fd53d4bd08025ffc89542d4cb": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "09dddfff7ad34a0bb896f47c598f5d51": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d2f2558e658c44459df1a6db41f3547f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "7544f0f7528d4b8cb7d7561e81c4ff04": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_b677ec0633a8407a9c2fd8d1d88aef48",
              "IPY_MODEL_b73d8338077b4199a7df1af8e75ed4bf",
              "IPY_MODEL_2e0e0be0284d414e87b9e8e0285fa21e"
            ],
            "layout": "IPY_MODEL_fda3296fb00d46a5b0733c39d0998b44"
          }
        },
        "b677ec0633a8407a9c2fd8d1d88aef48": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_7e601a74700c4a748f39f85511d13cdc",
            "placeholder": "​",
            "style": "IPY_MODEL_6b567aa9a1294e8e85a9634a5d8c9b38",
            "value": "Downloading (…)ve/main/spiece.model: 100%"
          }
        },
        "b73d8338077b4199a7df1af8e75ed4bf": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_fca8715508034aadab8e6b4b9fecbfd2",
            "max": 1912529,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_9da5ce93f11946fdb85e498e8249e34a",
            "value": 1912529
          }
        },
        "2e0e0be0284d414e87b9e8e0285fa21e": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5188929a6d6b456fb0316090895e9b9a",
            "placeholder": "​",
            "style": "IPY_MODEL_41b142c4cfbf47f0b674a38dfd7ec130",
            "value": " 1.91M/1.91M [00:00&lt;00:00, 2.26MB/s]"
          }
        },
        "fda3296fb00d46a5b0733c39d0998b44": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "7e601a74700c4a748f39f85511d13cdc": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "6b567aa9a1294e8e85a9634a5d8c9b38": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "fca8715508034aadab8e6b4b9fecbfd2": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9da5ce93f11946fdb85e498e8249e34a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "5188929a6d6b456fb0316090895e9b9a": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "41b142c4cfbf47f0b674a38dfd7ec130": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "cb55bf4b32c948a5858da2b8f591952d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_5bcf05b66d0349909f86474e1e6fc6d6",
              "IPY_MODEL_2e4769b264cb469ea52581f1e541e641",
              "IPY_MODEL_38c4d0c1d7ee4f999539afa0c41d0ad6"
            ],
            "layout": "IPY_MODEL_8aec947b6f794538b9779cbf1d1253f4"
          }
        },
        "5bcf05b66d0349909f86474e1e6fc6d6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_e3fd6f94fdf545a38067843204c55875",
            "placeholder": "​",
            "style": "IPY_MODEL_27d3d589450e45a1bc7bd977164da181",
            "value": "Downloading (…)cial_tokens_map.json: 100%"
          }
        },
        "2e4769b264cb469ea52581f1e541e641": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_f5fd771117274080949a10d8d23ba912",
            "max": 65,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_4f0096e4e50d4176b75a845361eb0ecd",
            "value": 65
          }
        },
        "38c4d0c1d7ee4f999539afa0c41d0ad6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_902def0ffaa54f7e9b399382ea2f1054",
            "placeholder": "​",
            "style": "IPY_MODEL_a8942c99c973476cb0d923ed91e3a0a4",
            "value": " 65.0/65.0 [00:00&lt;00:00, 3.58kB/s]"
          }
        },
        "8aec947b6f794538b9779cbf1d1253f4": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e3fd6f94fdf545a38067843204c55875": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "27d3d589450e45a1bc7bd977164da181": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "f5fd771117274080949a10d8d23ba912": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "4f0096e4e50d4176b75a845361eb0ecd": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "902def0ffaa54f7e9b399382ea2f1054": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "a8942c99c973476cb0d923ed91e3a0a4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "191adaba9cfb4611ae8c150e37d239c5": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_cafe8f14fc934d10931b7145cf1a0cf9",
              "IPY_MODEL_7af8654bb5f847108e5d59168646edcc",
              "IPY_MODEL_f49da29452224d5aac905b41ddc6cea1"
            ],
            "layout": "IPY_MODEL_3362d3b0da86433fa725737a7b3965cc"
          }
        },
        "cafe8f14fc934d10931b7145cf1a0cf9": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_b765f16a533c48aabb3ebf50a4eb7857",
            "placeholder": "​",
            "style": "IPY_MODEL_9f72563febef4d099bdff1eef8b32197",
            "value": "Downloading (…)okenizer_config.json: 100%"
          }
        },
        "7af8654bb5f847108e5d59168646edcc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3fb6807f493f4ce9951c7d7e3f76200c",
            "max": 88,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_d0ba23b5621b441a88717ec1e3715479",
            "value": 88
          }
        },
        "f49da29452224d5aac905b41ddc6cea1": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bc74817649084a1580bbf1c12f5d8174",
            "placeholder": "​",
            "style": "IPY_MODEL_04177f29dd234b7086b75d18e8d9e695",
            "value": " 88.0/88.0 [00:00&lt;00:00, 5.32kB/s]"
          }
        },
        "3362d3b0da86433fa725737a7b3965cc": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "b765f16a533c48aabb3ebf50a4eb7857": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9f72563febef4d099bdff1eef8b32197": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "3fb6807f493f4ce9951c7d7e3f76200c": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d0ba23b5621b441a88717ec1e3715479": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "bc74817649084a1580bbf1c12f5d8174": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "04177f29dd234b7086b75d18e8d9e695": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "57cee14502db417c89223ab0e78f0f01": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_8383eded655642cd8ea20e9aece1c48d",
              "IPY_MODEL_23c3adc2f5a84b998e80e73c46d25077",
              "IPY_MODEL_d1c67386614b48e4bd9eeb10443742fa"
            ],
            "layout": "IPY_MODEL_7f4f4986c0574882a5d78c129383810d"
          }
        },
        "8383eded655642cd8ea20e9aece1c48d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_679fc3ef1e104a4f924b555bfe0625ed",
            "placeholder": "​",
            "style": "IPY_MODEL_56b33a4f63dc4bedb2cae375fde09df8",
            "value": "Downloading (…)lve/main/config.json: 100%"
          }
        },
        "23c3adc2f5a84b998e80e73c46d25077": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_aff8e4e2e7a84ab5ae530d1b371036b7",
            "max": 3094,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_876021c1eb244d43a62d82fbeb097ac8",
            "value": 3094
          }
        },
        "d1c67386614b48e4bd9eeb10443742fa": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_47ad0aafe1a649a0a7cc9ecf98441894",
            "placeholder": "​",
            "style": "IPY_MODEL_cc465f82ffb04698be4cc6f624249700",
            "value": " 3.09k/3.09k [00:00&lt;00:00, 173kB/s]"
          }
        },
        "7f4f4986c0574882a5d78c129383810d": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "679fc3ef1e104a4f924b555bfe0625ed": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "56b33a4f63dc4bedb2cae375fde09df8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "aff8e4e2e7a84ab5ae530d1b371036b7": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "876021c1eb244d43a62d82fbeb097ac8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "47ad0aafe1a649a0a7cc9ecf98441894": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "cc465f82ffb04698be4cc6f624249700": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "33c0098ab4814031aa2b4113ff625581": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_da4af786c24d4dcabf60ceb1c8a1730a",
              "IPY_MODEL_64be57d26bf648f4b3f6f0efb3f7d058",
              "IPY_MODEL_09d0ea56874740458f2bad033e67d0e8"
            ],
            "layout": "IPY_MODEL_a26237c74a314f10b30e5fb2a9a98ebe"
          }
        },
        "da4af786c24d4dcabf60ceb1c8a1730a": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_865551b35c784f599e885e02914db7da",
            "placeholder": "​",
            "style": "IPY_MODEL_c7cd4e3288b34deb9de2586b720b86d2",
            "value": "Downloading pytorch_model.bin: 100%"
          }
        },
        "64be57d26bf648f4b3f6f0efb3f7d058": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3e4bb21e61d74c86ba985d6d79523eab",
            "max": 2275327883,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_00a8550b4f9b4c6f9106b973688f35f7",
            "value": 2275327883
          }
        },
        "09d0ea56874740458f2bad033e67d0e8": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_6dccb168296c4eeeaeb4da403d215e84",
            "placeholder": "​",
            "style": "IPY_MODEL_de6a62f2c2c24be990255e8f4c9307dc",
            "value": " 2.28G/2.28G [00:43&lt;00:00, 79.7MB/s]"
          }
        },
        "a26237c74a314f10b30e5fb2a9a98ebe": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "865551b35c784f599e885e02914db7da": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c7cd4e3288b34deb9de2586b720b86d2": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "3e4bb21e61d74c86ba985d6d79523eab": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "00a8550b4f9b4c6f9106b973688f35f7": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "6dccb168296c4eeeaeb4da403d215e84": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "de6a62f2c2c24be990255e8f4c9307dc": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "75208905e70f40f1882ec79ef4108f74": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_c50b1fc521a2467db35982062813d36f",
              "IPY_MODEL_90aa3bcfb739477ca1412f22d1ef26ad",
              "IPY_MODEL_4e40bbc354844c45a25445942c5c5a81"
            ],
            "layout": "IPY_MODEL_a179d3879c804b20a81ca7a594563228"
          }
        },
        "c50b1fc521a2467db35982062813d36f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_bd2c4c5791324e4d8a7a7221e3677a25",
            "placeholder": "​",
            "style": "IPY_MODEL_9609415dd28e47b2bf6819b2a5145579",
            "value": "Downloading (…)neration_config.json: 100%"
          }
        },
        "90aa3bcfb739477ca1412f22d1ef26ad": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_54607b9e937945f88ea840bfad70c31e",
            "max": 260,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_d887be792072433aa486d8069c43618f",
            "value": 260
          }
        },
        "4e40bbc354844c45a25445942c5c5a81": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_5c39926e23ba4897b2d094cf3d6df03b",
            "placeholder": "​",
            "style": "IPY_MODEL_c71037e8c52e49da95f75dab45e960ee",
            "value": " 260/260 [00:00&lt;00:00, 11.1kB/s]"
          }
        },
        "a179d3879c804b20a81ca7a594563228": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "bd2c4c5791324e4d8a7a7221e3677a25": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "9609415dd28e47b2bf6819b2a5145579": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "54607b9e937945f88ea840bfad70c31e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "d887be792072433aa486d8069c43618f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "5c39926e23ba4897b2d094cf3d6df03b": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c71037e8c52e49da95f75dab45e960ee": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jannat0718/Text-Summarizations-/blob/main/Abstractive_summarization_PEGASUS_XSUM_%26_large_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install Packages and Import libraries"
      ],
      "metadata": {
        "id": "sjj9bm20_9ey"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tjtqsJbMX7tX",
        "outputId": "2b5beb97-ea1d-4316-f460-63e398a28af9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Looking in links: https://download.pytorch.org/whl/lts/1.8/torch_lts.html\n",
            "Collecting torch==1.8.2+cu111\n",
            "  Downloading https://download.pytorch.org/whl/lts/1.8/cu111/torch-1.8.2%2Bcu111-cp39-cp39-linux_x86_64.whl (1982.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 GB\u001b[0m \u001b[31m910.4 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchvision==0.9.2+cu111\n",
            "  Downloading https://download.pytorch.org/whl/lts/1.8/cu111/torchvision-0.9.2%2Bcu111-cp39-cp39-linux_x86_64.whl (17.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.4/17.4 MB\u001b[0m \u001b[31m85.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting torchaudio===0.8.2\n",
            "  Downloading https://download.pytorch.org/whl/lts/1.8/torchaudio-0.8.2-cp39-cp39-linux_x86_64.whl (1.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.9/1.9 MB\u001b[0m \u001b[31m86.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from torch==1.8.2+cu111) (4.5.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from torch==1.8.2+cu111) (1.22.4)\n",
            "Requirement already satisfied: pillow>=4.1.1 in /usr/local/lib/python3.9/dist-packages (from torchvision==0.9.2+cu111) (8.4.0)\n",
            "Installing collected packages: torch, torchvision, torchaudio\n",
            "  Attempting uninstall: torch\n",
            "    Found existing installation: torch 2.0.0+cu118\n",
            "    Uninstalling torch-2.0.0+cu118:\n",
            "      Successfully uninstalled torch-2.0.0+cu118\n",
            "  Attempting uninstall: torchvision\n",
            "    Found existing installation: torchvision 0.15.1+cu118\n",
            "    Uninstalling torchvision-0.15.1+cu118:\n",
            "      Successfully uninstalled torchvision-0.15.1+cu118\n",
            "  Attempting uninstall: torchaudio\n",
            "    Found existing installation: torchaudio 2.0.1+cu118\n",
            "    Uninstalling torchaudio-2.0.1+cu118:\n",
            "      Successfully uninstalled torchaudio-2.0.1+cu118\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "torchtext 0.15.1 requires torch==2.0.0, but you have torch 1.8.2+cu111 which is incompatible.\n",
            "torchdata 0.6.0 requires torch==2.0.0, but you have torch 1.8.2+cu111 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed torch-1.8.2+cu111 torchaudio-0.8.2 torchvision-0.9.2+cu111\n"
          ]
        }
      ],
      "source": [
        "##Install Pytorch\n",
        "!pip3 install torch==1.8.2+cu111 torchvision==0.9.2+cu111 torchaudio===0.8.2 -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H6cU3n5sotJ-",
        "outputId": "bbacd133-806c-45d5-9615-9c921c640174"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.98-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m49.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.98\n"
          ]
        }
      ],
      "source": [
        "#Install sentencepiece for Pegasus Tokenizer\n",
        "!pip3 install sentencepiece"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Install Transformmers for using Hugging face Pegasus-xsum model\n",
        "!pip3 install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KpCpFC-gp9iA",
        "outputId": "0a676b55-112b-4de5-efa1-3c5b8a296552"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.28.1-py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m90.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from transformers) (3.11.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.9/dist-packages (from transformers) (23.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.9/dist-packages (from transformers) (4.65.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (1.22.4)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0\n",
            "  Downloading huggingface_hub-0.13.4-py3-none-any.whl (200 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m200.1/200.1 kB\u001b[0m \u001b[31m25.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tokenizers!=0.11.3,<0.14,>=0.11.1\n",
            "  Downloading tokenizers-0.13.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m79.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from transformers) (2.27.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.9/dist-packages (from transformers) (2022.10.31)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.9/dist-packages (from transformers) (6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers) (4.5.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2022.12.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (3.4)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->transformers) (2.0.12)\n",
            "Installing collected packages: tokenizers, huggingface-hub, transformers\n",
            "Successfully installed huggingface-hub-0.13.4 tokenizers-0.13.3 transformers-4.28.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Install Gradio for Interface\n",
        "! pip install gradio"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dnvqjjr4Camx",
        "outputId": "0db7efb7-8659-4423-f298-7d0a2a2da419"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting gradio\n",
            "  Downloading gradio-3.27.0-py3-none-any.whl (17.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m17.3/17.3 MB\u001b[0m \u001b[31m43.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pydantic in /usr/local/lib/python3.9/dist-packages (from gradio) (1.10.7)\n",
            "Collecting aiohttp\n",
            "  Downloading aiohttp-3.8.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m62.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting uvicorn\n",
            "  Downloading uvicorn-0.21.1-py3-none-any.whl (57 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.8/57.8 kB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: markdown-it-py[linkify]>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from gradio) (2.2.0)\n",
            "Collecting semantic-version\n",
            "  Downloading semantic_version-2.10.0-py2.py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: markupsafe in /usr/local/lib/python3.9/dist-packages (from gradio) (2.1.2)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.9/dist-packages (from gradio) (6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from gradio) (2.27.1)\n",
            "Collecting pydub\n",
            "  Downloading pydub-0.25.1-py2.py3-none-any.whl (32 kB)\n",
            "Collecting fastapi\n",
            "  Downloading fastapi-0.95.1-py3-none-any.whl (56 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m57.0/57.0 kB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting mdit-py-plugins<=0.3.3\n",
            "  Downloading mdit_py_plugins-0.3.3-py3-none-any.whl (50 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.5/50.5 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting python-multipart\n",
            "  Downloading python_multipart-0.0.6-py3-none-any.whl (45 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.7/45.7 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: huggingface-hub>=0.13.0 in /usr/local/lib/python3.9/dist-packages (from gradio) (0.13.4)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from gradio) (1.5.3)\n",
            "Requirement already satisfied: altair>=4.2.0 in /usr/local/lib/python3.9/dist-packages (from gradio) (4.2.2)\n",
            "Collecting httpx\n",
            "  Downloading httpx-0.24.0-py3-none-any.whl (75 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.3/75.3 kB\u001b[0m \u001b[31m10.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pillow in /usr/local/lib/python3.9/dist-packages (from gradio) (8.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from gradio) (1.22.4)\n",
            "Collecting orjson\n",
            "  Downloading orjson-3.8.10-cp39-cp39-manylinux_2_28_x86_64.whl (140 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m140.5/140.5 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: jinja2 in /usr/local/lib/python3.9/dist-packages (from gradio) (3.1.2)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.9/dist-packages (from gradio) (3.7.1)\n",
            "Collecting gradio-client>=0.1.3\n",
            "  Downloading gradio_client-0.1.3-py3-none-any.whl (286 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m286.2/286.2 kB\u001b[0m \u001b[31m33.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting ffmpy\n",
            "  Downloading ffmpy-0.3.0.tar.gz (4.8 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting websockets>=10.0\n",
            "  Downloading websockets-11.0.2-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (129 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.7/129.7 kB\u001b[0m \u001b[31m1.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.9/dist-packages (from gradio) (4.5.0)\n",
            "Collecting aiofiles\n",
            "  Downloading aiofiles-23.1.0-py3-none-any.whl (14 kB)\n",
            "Requirement already satisfied: jsonschema>=3.0 in /usr/local/lib/python3.9/dist-packages (from altair>=4.2.0->gradio) (4.3.3)\n",
            "Requirement already satisfied: toolz in /usr/local/lib/python3.9/dist-packages (from altair>=4.2.0->gradio) (0.12.0)\n",
            "Requirement already satisfied: entrypoints in /usr/local/lib/python3.9/dist-packages (from altair>=4.2.0->gradio) (0.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.9/dist-packages (from gradio-client>=0.1.3->gradio) (2023.4.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.9/dist-packages (from gradio-client>=0.1.3->gradio) (23.1)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.9/dist-packages (from huggingface-hub>=0.13.0->gradio) (3.11.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.9/dist-packages (from huggingface-hub>=0.13.0->gradio) (4.65.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.9/dist-packages (from markdown-it-py[linkify]>=2.0.0->gradio) (0.1.2)\n",
            "Collecting linkify-it-py<3,>=1\n",
            "  Downloading linkify_it_py-2.0.0-py3-none-any.whl (19 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->gradio) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->gradio) (2022.7.1)\n",
            "Collecting yarl<2.0,>=1.0\n",
            "  Downloading yarl-1.9.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (269 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m269.3/269.3 kB\u001b[0m \u001b[31m26.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting frozenlist>=1.1.1\n",
            "  Downloading frozenlist-1.3.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl (158 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.8/158.8 kB\u001b[0m \u001b[31m19.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting async-timeout<5.0,>=4.0.0a3\n",
            "  Downloading async_timeout-4.0.2-py3-none-any.whl (5.8 kB)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->gradio) (23.1.0)\n",
            "Collecting aiosignal>=1.1.2\n",
            "  Downloading aiosignal-1.3.1-py3-none-any.whl (7.6 kB)\n",
            "Collecting multidict<7.0,>=4.5\n",
            "  Downloading multidict-6.0.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (114 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.2/114.2 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.9/dist-packages (from aiohttp->gradio) (2.0.12)\n",
            "Collecting starlette<0.27.0,>=0.26.1\n",
            "  Downloading starlette-0.26.1-py3-none-any.whl (66 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.9/66.9 kB\u001b[0m \u001b[31m4.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting httpcore<0.18.0,>=0.15.0\n",
            "  Downloading httpcore-0.17.0-py3-none-any.whl (70 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m70.6/70.6 kB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: sniffio in /usr/local/lib/python3.9/dist-packages (from httpx->gradio) (1.3.0)\n",
            "Requirement already satisfied: idna in /usr/local/lib/python3.9/dist-packages (from httpx->gradio) (3.4)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.9/dist-packages (from httpx->gradio) (2022.12.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->gradio) (1.4.4)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->gradio) (3.0.9)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->gradio) (4.39.3)\n",
            "Requirement already satisfied: importlib-resources>=3.2.0 in /usr/local/lib/python3.9/dist-packages (from matplotlib->gradio) (5.12.0)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.9/dist-packages (from matplotlib->gradio) (1.0.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.9/dist-packages (from matplotlib->gradio) (0.11.0)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->gradio) (1.26.15)\n",
            "Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.9/dist-packages (from uvicorn->gradio) (8.1.3)\n",
            "Collecting h11>=0.8\n",
            "  Downloading h11-0.14.0-py3-none-any.whl (58 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: anyio<5.0,>=3.0 in /usr/local/lib/python3.9/dist-packages (from httpcore<0.18.0,>=0.15.0->httpx->gradio) (3.6.2)\n",
            "Requirement already satisfied: zipp>=3.1.0 in /usr/local/lib/python3.9/dist-packages (from importlib-resources>=3.2.0->matplotlib->gradio) (3.15.0)\n",
            "Requirement already satisfied: pyrsistent!=0.17.0,!=0.17.1,!=0.17.2,>=0.14.0 in /usr/local/lib/python3.9/dist-packages (from jsonschema>=3.0->altair>=4.2.0->gradio) (0.19.3)\n",
            "Collecting uc-micro-py\n",
            "  Downloading uc_micro_py-1.0.1-py3-none-any.whl (6.2 kB)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->gradio) (1.16.0)\n",
            "Building wheels for collected packages: ffmpy\n",
            "  Building wheel for ffmpy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ffmpy: filename=ffmpy-0.3.0-py3-none-any.whl size=4707 sha256=73ff24dbb489183aef4a1604bc6e4053a78000e78a36f1d5dc4941bda218efa7\n",
            "  Stored in directory: /root/.cache/pip/wheels/91/e2/96/f676aa08bfd789328c6576cd0f1fde4a3d686703bb0c247697\n",
            "Successfully built ffmpy\n",
            "Installing collected packages: pydub, ffmpy, websockets, uc-micro-py, semantic-version, python-multipart, orjson, multidict, h11, frozenlist, async-timeout, aiofiles, yarl, uvicorn, starlette, mdit-py-plugins, linkify-it-py, httpcore, aiosignal, httpx, fastapi, aiohttp, gradio-client, gradio\n",
            "Successfully installed aiofiles-23.1.0 aiohttp-3.8.4 aiosignal-1.3.1 async-timeout-4.0.2 fastapi-0.95.1 ffmpy-0.3.0 frozenlist-1.3.3 gradio-3.27.0 gradio-client-0.1.3 h11-0.14.0 httpcore-0.17.0 httpx-0.24.0 linkify-it-py-2.0.0 mdit-py-plugins-0.3.3 multidict-6.0.4 orjson-3.8.10 pydub-0.25.1 python-multipart-0.0.6 semantic-version-2.10.0 starlette-0.26.1 uc-micro-py-1.0.1 uvicorn-0.21.1 websockets-11.0.2 yarl-1.9.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Import BeautifulSoup libraries\n",
        "from bs4 import BeautifulSoup\n",
        "import requests\n",
        "##Import Pegasus models and tokenizer\n",
        "from transformers import PegasusForConditionalGeneration,PegasusTokenizer\n",
        "import gradio as gr"
      ],
      "metadata": {
        "id": "TyjR4JVSBUBy"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Scrape Scientific Article "
      ],
      "metadata": {
        "id": "3UhKtDxvINEC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "url='https://towardsdatascience.com/my-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524'\n",
        "response=requests.get(url)"
      ],
      "metadata": {
        "id": "NCU0mZLOBizV"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Perse content\n",
        "soup=BeautifulSoup(response.text, 'html')\n",
        "print(soup.prettify())"
      ],
      "metadata": {
        "id": "J6X9qsDfBi8N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6fd30d8c-f383-442c-af8f-31ab5bb7ff4e"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<!DOCTYPE html>\n",
            "<html lang=\"en\">\n",
            " <head>\n",
            "  <title data-rh=\"true\">\n",
            "   Sentiment Analysis using TextBlob | by Parthvi Shah | Towards Data Science\n",
            "  </title>\n",
            "  <meta charset=\"utf-8\" data-rh=\"true\"/>\n",
            "  <meta content=\"width=device-width,minimum-scale=1,initial-scale=1,maximum-scale=1\" data-rh=\"true\" name=\"viewport\"/>\n",
            "  <meta content=\"#000000\" data-rh=\"true\" name=\"theme-color\"/>\n",
            "  <meta content=\"Medium\" data-rh=\"true\" name=\"twitter:app:name:iphone\"/>\n",
            "  <meta content=\"828256236\" data-rh=\"true\" name=\"twitter:app:id:iphone\"/>\n",
            "  <meta content=\"Medium\" data-rh=\"true\" property=\"al:ios:app_name\"/>\n",
            "  <meta content=\"828256236\" data-rh=\"true\" property=\"al:ios:app_store_id\"/>\n",
            "  <meta content=\"com.medium.reader\" data-rh=\"true\" property=\"al:android:package\"/>\n",
            "  <meta content=\"542599432471018\" data-rh=\"true\" property=\"fb:app_id\"/>\n",
            "  <meta content=\"Medium\" data-rh=\"true\" property=\"og:site_name\"/>\n",
            "  <meta content=\"article\" data-rh=\"true\" property=\"og:type\"/>\n",
            "  <meta content=\"2020-11-06T16:53:36.702Z\" data-rh=\"true\" property=\"article:published_time\"/>\n",
            "  <meta content=\"Sentiment Analysis using TextBlob | by Parthvi Shah | Towards Data Science\" data-rh=\"true\" name=\"title\"/>\n",
            "  <meta content=\"My Absolute Go-To for Sentiment Analysis — TextBlob.\" data-rh=\"true\" property=\"og:title\"/>\n",
            "  <meta content=\"medium://p/3ac3a11d524\" data-rh=\"true\" property=\"al:android:url\"/>\n",
            "  <meta content=\"medium://p/3ac3a11d524\" data-rh=\"true\" property=\"al:ios:url\"/>\n",
            "  <meta content=\"Medium\" data-rh=\"true\" property=\"al:android:app_name\"/>\n",
            "  <meta content=\"Sentiment Analysis can help us decipher the mood and emotions of general public and gather insightful information regarding the context. Sentiment Analysis is a process of analyzing data and…\" data-rh=\"true\" name=\"description\"/>\n",
            "  <meta content=\"Sentiment Analysis using TextBlob.\" data-rh=\"true\" property=\"og:description\"/>\n",
            "  <meta content=\"https://towardsdatascience.com/my-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524\" data-rh=\"true\" property=\"og:url\"/>\n",
            "  <meta content=\"https://towardsdatascience.com/my-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524\" data-rh=\"true\" property=\"al:web:url\"/>\n",
            "  <meta content=\"https://miro.medium.com/v2/resize:fit:1200/1*vkqmLESSfaYMmHvXznGk5Q.jpeg\" data-rh=\"true\" property=\"og:image\"/>\n",
            "  <meta content=\"https://shahparthvi22.medium.com\" data-rh=\"true\" property=\"article:author\"/>\n",
            "  <meta content=\"Parthvi Shah\" data-rh=\"true\" name=\"author\"/>\n",
            "  <meta content=\"index,follow,max-image-preview:large\" data-rh=\"true\" name=\"robots\"/>\n",
            "  <meta content=\"unsafe-url\" data-rh=\"true\" name=\"referrer\"/>\n",
            "  <meta content=\"My Absolute Go-To for Sentiment Analysis — TextBlob.\" data-rh=\"true\" property=\"twitter:title\"/>\n",
            "  <meta content=\"@TDataScience\" data-rh=\"true\" name=\"twitter:site\"/>\n",
            "  <meta content=\"medium://p/3ac3a11d524\" data-rh=\"true\" name=\"twitter:app:url:iphone\"/>\n",
            "  <meta content=\"Sentiment Analysis using TextBlob.\" data-rh=\"true\" property=\"twitter:description\"/>\n",
            "  <meta content=\"https://miro.medium.com/v2/resize:fit:1200/1*vkqmLESSfaYMmHvXznGk5Q.jpeg\" data-rh=\"true\" name=\"twitter:image:src\"/>\n",
            "  <meta content=\"summary_large_image\" data-rh=\"true\" name=\"twitter:card\"/>\n",
            "  <meta content=\"Reading time\" data-rh=\"true\" name=\"twitter:label1\"/>\n",
            "  <meta content=\"3 min read\" data-rh=\"true\" name=\"twitter:data1\"/>\n",
            "  <meta content=\"2\" data-rh=\"true\" name=\"twitter:tile:template:testing\"/>\n",
            "  <meta content=\"https://miro.medium.com/v2/resize:fit:1200/1*vkqmLESSfaYMmHvXznGk5Q.jpeg\" data-rh=\"true\" name=\"twitter:tile:image\"/>\n",
            "  <meta content=\"Person\" data-rh=\"true\" name=\"twitter:tile:info1:icon\"/>\n",
            "  <meta content=\"Parthvi Shah\" data-rh=\"true\" name=\"twitter:tile:info1:text\"/>\n",
            "  <meta content=\"Calendar\" data-rh=\"true\" name=\"twitter:tile:info2:icon\"/>\n",
            "  <meta content=\"Nov 6, 2020\" data-rh=\"true\" name=\"twitter:tile:info2:text\"/>\n",
            "  <meta content=\"Read on Medium\" data-rh=\"true\" name=\"twitter:cta\"/>\n",
            "  <link data-rh=\"true\" href=\"https://miro.medium.com/v2/resize:fill:256:256/1*VzTUkfeGymHP4Bvav-T-lA.png\" rel=\"icon\"/>\n",
            "  <link data-rh=\"true\" href=\"/osd.xml\" rel=\"search\" title=\"Medium\" type=\"application/opensearchdescription+xml\"/>\n",
            "  <link data-rh=\"true\" href=\"https://miro.medium.com/v2/resize:fill:152:152/1*sHhtYhaCe2Uc3IU0IgKwIQ.png\" rel=\"apple-touch-icon\" sizes=\"152x152\"/>\n",
            "  <link data-rh=\"true\" href=\"https://miro.medium.com/v2/resize:fill:120:120/1*sHhtYhaCe2Uc3IU0IgKwIQ.png\" rel=\"apple-touch-icon\" sizes=\"120x120\"/>\n",
            "  <link data-rh=\"true\" href=\"https://miro.medium.com/v2/resize:fill:76:76/1*sHhtYhaCe2Uc3IU0IgKwIQ.png\" rel=\"apple-touch-icon\" sizes=\"76x76\"/>\n",
            "  <link data-rh=\"true\" href=\"https://miro.medium.com/v2/resize:fill:60:60/1*sHhtYhaCe2Uc3IU0IgKwIQ.png\" rel=\"apple-touch-icon\" sizes=\"60x60\"/>\n",
            "  <link color=\"#171717\" data-rh=\"true\" href=\"https://cdn-static-1.medium.com/_/fp/icons/Medium-Avatar-500x500.svg\" rel=\"mask-icon\"/>\n",
            "  <link as=\"style\" data-rh=\"true\" href=\"https://glyph.medium.com/css/unbound.css\" id=\"glyph_preload_link\" rel=\"preload\" type=\"text/css\"/>\n",
            "  <link data-rh=\"true\" href=\"https://glyph.medium.com/css/unbound.css\" id=\"glyph_link\" rel=\"stylesheet\" type=\"text/css\"/>\n",
            "  <link data-rh=\"true\" href=\"https://shahparthvi22.medium.com\" rel=\"author\"/>\n",
            "  <link data-rh=\"true\" href=\"https://towardsdatascience.com/my-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524\" rel=\"canonical\"/>\n",
            "  <link data-rh=\"true\" href=\"android-app://com.medium.reader/https/medium.com/p/3ac3a11d524\" rel=\"alternate\"/>\n",
            "  <script data-rh=\"true\" type=\"application/ld+json\">\n",
            "   {\"@context\":\"http:\\u002F\\u002Fschema.org\",\"@type\":\"NewsArticle\",\"image\":[\"https:\\u002F\\u002Fmiro.medium.com\\u002Fv2\\u002Fresize:fit:1200\\u002F1*vkqmLESSfaYMmHvXznGk5Q.jpeg\"],\"url\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524\",\"dateCreated\":\"2020-06-27T17:57:11.758Z\",\"datePublished\":\"2020-06-27T17:57:11.758Z\",\"dateModified\":\"2021-12-15T01:50:37.877Z\",\"headline\":\"Sentiment Analysis using TextBlob - Towards Data Science\",\"name\":\"Sentiment Analysis using TextBlob - Towards Data Science\",\"description\":\"Sentiment Analysis can help us decipher the mood and emotions of general public and gather insightful information regarding the context. Sentiment Analysis is a process of analyzing data and…\",\"identifier\":\"3ac3a11d524\",\"author\":{\"@type\":\"Person\",\"name\":\"Parthvi Shah\",\"url\":\"https:\\u002F\\u002Fshahparthvi22.medium.com\"},\"creator\":[\"Parthvi Shah\"],\"publisher\":{\"@type\":\"Organization\",\"name\":\"Towards Data Science\",\"url\":\"towardsdatascience.com\",\"logo\":{\"@type\":\"ImageObject\",\"width\":165,\"height\":60,\"url\":\"https:\\u002F\\u002Fmiro.medium.com\\u002Fv2\\u002Fresize:fit:330\\u002F1*mG6i4Bh_LgixUYXJgQpYsg@2x.png\"}},\"mainEntityOfPage\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524\"}\n",
            "  </script>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"STATIC\" type=\"text/css\">\n",
            "   html{box-sizing:border-box;-webkit-text-size-adjust:100%}*, *:before, *:after{box-sizing:inherit}body{margin:0;padding:0;text-rendering:optimizeLegibility;-webkit-font-smoothing:antialiased;color:rgba(0,0,0,0.8);position:relative;min-height:100vh}h1, h2, h3, h4, h5, h6, dl, dd, ol, ul, menu, figure, blockquote, p, pre, form{margin:0}menu, ol, ul{padding:0;list-style:none;list-style-image:none}main{display:block}a{color:inherit;text-decoration:none}a, button, input{-webkit-tap-highlight-color:transparent}img, svg{vertical-align:middle}button{background:transparent;overflow:visible}button, input, optgroup, select, textarea{margin:0}:root{--reach-tabs:1;--reach-menu-button:1}#speechify-root{font-family:Sohne, sans-serif}div[data-popper-reference-hidden=\"true\"]{visibility:hidden;pointer-events:none}\n",
            "/*XCode style (c) Angel Garcia <angelgarcia.mail@gmail.com>*/.hljs {background: #fff;color: black;\n",
            "}/* Gray DOCTYPE selectors like WebKit */\n",
            ".xml .hljs-meta {color: #c0c0c0;\n",
            "}.hljs-comment,\n",
            ".hljs-quote {color: #007400;\n",
            "}.hljs-tag,\n",
            ".hljs-attribute,\n",
            ".hljs-keyword,\n",
            ".hljs-selector-tag,\n",
            ".hljs-literal,\n",
            ".hljs-name {color: #aa0d91;\n",
            "}.hljs-variable,\n",
            ".hljs-template-variable {color: #3F6E74;\n",
            "}.hljs-code,\n",
            ".hljs-string,\n",
            ".hljs-meta .hljs-string {color: #c41a16;\n",
            "}.hljs-regexp,\n",
            ".hljs-link {color: #0E0EFF;\n",
            "}.hljs-title,\n",
            ".hljs-symbol,\n",
            ".hljs-bullet,\n",
            ".hljs-number {color: #1c00cf;\n",
            "}.hljs-section,\n",
            ".hljs-meta {color: #643820;\n",
            "}.hljs-title.class_,\n",
            ".hljs-class .hljs-title,\n",
            ".hljs-type,\n",
            ".hljs-built_in,\n",
            ".hljs-params {color: #5c2699;\n",
            "}.hljs-attr {color: #836C28;\n",
            "}.hljs-subst {color: #000;\n",
            "}.hljs-formula {background-color: #eee;font-style: italic;\n",
            "}.hljs-addition {background-color: #baeeba;\n",
            "}.hljs-deletion {background-color: #ffc8bd;\n",
            "}.hljs-selector-id,\n",
            ".hljs-selector-class {color: #9b703f;\n",
            "}.hljs-doctag,\n",
            ".hljs-strong {font-weight: bold;\n",
            "}.hljs-emphasis {font-style: italic;\n",
            "}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" type=\"text/css\">\n",
            "   .a{font-family:medium-content-sans-serif-font, -apple-system, BlinkMacSystemFont, \"Segoe UI\", Roboto, Oxygen, Ubuntu, Cantarell, \"Open Sans\", \"Helvetica Neue\", sans-serif}.b{font-weight:400}.c{background-color:rgba(255, 255, 255, 1)}.l{display:block}.m{position:sticky}.n{top:0}.o{z-index:500}.p{padding:0 24px}.q{align-items:center}.r{border-bottom:solid 1px rgba(242, 242, 242, 1)}.y{height:41px}.z{line-height:20px}.ab{display:flex}.ac{height:57px}.ad{flex:1 0 auto}.ae{color:inherit}.af{fill:inherit}.ag{font-size:inherit}.ah{border:inherit}.ai{font-family:inherit}.aj{letter-spacing:inherit}.ak{font-weight:inherit}.al{padding:0}.am{margin:0}.an{cursor:pointer}.ao:disabled{cursor:not-allowed}.ap:disabled{color:rgba(117, 117, 117, 1)}.aq:disabled{fill:rgba(117, 117, 117, 1)}.at{height:25px}.au{fill:rgba(41, 41, 41, 1)}.av{margin-left:16px}.aw{border:none}.ax{border-radius:20px}.ay{width:240px}.az{background:rgba(250, 250, 250, 1)}.ba path{fill:rgba(117, 117, 117, 1)}.bc{outline:none}.bd{font-family:sohne, \"Helvetica Neue\", Helvetica, Arial, sans-serif}.be{font-size:14px}.bf{width:100%}.bg{padding:10px 20px 10px 0}.bh{background-color:transparent}.bi{color:rgba(41, 41, 41, 1)}.bj::placeholder{color:rgba(117, 117, 117, 1)}.bk{display:inline-block}.bl{margin-left:12px}.bm{margin-right:12px}.bn{border-radius:4px}.bo{margin-left:24px}.bp{height:24px}.bv{background-color:rgba(250, 250, 250, 1)}.bw{border-radius:50%}.bx{height:32px}.by{width:32px}.bz{margin:auto}.ca{max-width:1336px}.cb{flex-direction:row}.cc{justify-content:space-evenly}.cj{flex:1 1 auto}.ck{height:100vh}.cl{justify-content:center}.cm{padding-right:24px}.de{box-sizing:border-box}.df{border-left:1px solid rgba(242, 242, 242, 1)}.dg{min-height:100vh}.dh{height:100%}.di{position:relative}.dj{margin-left:8px}.dk{color:rgba(117, 117, 117, 1)}.dl{font-size:13px}.eb{color:rgba(255, 255, 255, 1)}.ec{fill:rgba(255, 255, 255, 1)}.ed{background:rgba(102, 138, 170, 1)}.ee{border-color:rgba(102, 138, 170, 1)}.ei:disabled{cursor:inherit !important}.ej:disabled{opacity:0.3}.ek:disabled:hover{background:rgba(102, 138, 170, 1)}.el:disabled:hover{border-color:rgba(102, 138, 170, 1)}.em{border-radius:99em}.eo{border-width:1px}.ep{border-style:solid}.eq{text-decoration:none}.et{margin-right:32px}.eu{fill:rgba(117, 117, 117, 1)}.ex{background:transparent}.ey svg{margin-left:4px}.ez svg{fill:rgba(117, 117, 117, 1)}.fb{box-shadow:inset 0 0 0 1px rgba(0, 0, 0, 0.05)}.fc{position:absolute}.fe{background-color:rgba(242, 242, 242, 1)}.ff{border-bottom:1px solid rgba(230, 230, 230, 1)}.fg{min-width:0}.fh{margin:0 24px}.fl{height:56px}.fm{margin-right:16px}.fn{margin-right:3px}.fo{flex:0 0 auto}.fp{overflow:hidden}.fq{max-height:20px}.fr{text-overflow:ellipsis}.fs{display:-webkit-box}.ft{-webkit-line-clamp:1}.fu{-webkit-box-orient:vertical}.fv{word-break:break-all}.gh{margin-left:auto}.gi{margin-right:auto}.gj{max-width:728px}.gu{align-items:flex-start}.gv{justify-content:space-between}.gw{height:48px}.gx{width:48px}.gy{font-size:16px}.gz{line-height:24px}.ha{margin-bottom:4px}.hb{padding-left:12px}.hf{padding:0px 8px 1px}.hg{flex-wrap:wrap}.hh{padding:0 8px}.hi{padding-right:4px}.hj{padding:8px 2px}.hk path{fill:rgba(168, 168, 168, 1)}.hm{margin:0 16px 0 28px}.hn path{fill:rgba(41, 41, 41, 1)}.hq svg path{fill:rgba(117, 117, 117, 1)}.hr{padding-top:24px}.hs{border:1px solid rgba(230, 230, 230, 1)}.ht{padding:6px 15px 6px 10px}.hv svg{margin-right:8px}.hw{padding-right:12px}.hx{background:rgba(255, 255, 255, 1)}.hy{box-shadow:0 1px 4px rgba(230, 230, 230, 1)}.hz{max-height:100vh}.ia{overflow-y:auto}.ib{left:0}.ic{top:calc(100vh + 100px)}.id{bottom:calc(100vh + 100px)}.ie{width:10px}.if{pointer-events:none}.ig{word-break:break-word}.ih{word-wrap:break-word}.ii:after{display:block}.ij:after{content:\"\"}.ik:after{clear:both}.il{line-height:1.23}.im{letter-spacing:0}.in{font-style:normal}.io{font-weight:700}.jj{margin-bottom:-0.27em}.jk{line-height:1.394}.jl{font-size:24px}.kb{margin-bottom:-0.42em}.kc{max-width:2560px}.kh{clear:both}.kj{cursor:zoom-in}.kk{z-index:auto}.km{max-width:100%}.kn{height:auto}.ko{margin-top:10px}.kp{text-align:center}.ks{text-decoration:underline}.kt{line-height:1.31}.ku{letter-spacing:-0.022em}.kv{font-weight:600}.lk{margin-bottom:-0.37em}.ll{max-width:650px}.lm{line-height:1.58}.ln{letter-spacing:-0.004em}.lo{font-family:source-serif-pro, Georgia, Cambria, \"Times New Roman\", Times, serif}.mh{margin-bottom:-0.46em}.mi{line-height:1.18}.mt{margin-bottom:-0.31em}.mz{font-style:italic}.na{overflow-x:auto}.nb{font-family:source-code-pro, Menlo, Monaco, \"Courier New\", Courier, monospace}.nc{padding:20px}.nd{border-radius:0}.ne{background:rgba(242, 242, 242, 1)}.nf{margin-top:-0.09em}.ng{margin-bottom:-0.09em}.nh{white-space:pre-wrap}.ni{min-width:fit-content}.nj{padding-bottom:100%}.nk{height:0}.nl{padding-left:30px}.nm{line-height:40px}.nn{letter-spacing:-0.009em}.no{font-weight:300}.np{font-size:28px}.oa{line-height:28px}.ob{letter-spacing:-0.003em}.of{list-style-type:decimal}.og{margin-left:30px}.oh{padding-left:0px}.oi{font-size:18px}.oo{margin-bottom:26px}.op{margin-top:6px}.oq{margin-top:8px}.or{margin-right:8px}.os{padding:8px 16px}.ot{border-radius:100px}.ou{transition:background 300ms ease}.ow{white-space:nowrap}.ox{border-top:none}.pd{height:52px}.pe{max-height:52px}.pf{box-sizing:content-box}.pg{position:static}.ph{z-index:1}.pj{max-width:155px}.pm{margin-right:4px}.pp{-webkit-user-select:none}.pq{border:0}.pt{outline:0}.pu{user-select:none}.pv> svg{pointer-events:none}.qe{cursor:progress}.qh{margin-top:0px}.qi{opacity:1}.qj{padding:4px 0}.ql svg{color:rgba(117, 117, 117, 1)}.qo{margin-left:20px}.qp{margin-right:20px}.qq{padding-bottom:4px}.qr{padding-top:32px}.qs{font-weight:500}.qy{padding:7px 16px 9px}.qz{padding-top:5px}.ra{padding-top:25px}.rb{padding-bottom:96px}.rc{padding-top:40px}.rd{padding:30px 0}.re{margin-bottom:0}.rf{min-width:100vw}.rg{right:0}.rh{bottom:0}.ri{background-color:rgba(0, 0, 0, 1)}.rp{max-width:1192px}.rq:disabled{color:rgba(255, 255, 255, 0.6)}.rr:disabled{fill:rgba(255, 255, 255, 0.45)}.ru{height:22px}.rv{margin-top:20px}.rw{color:rgba(255, 255, 255, 0.95)}.ry{height:1px}.rz{background-color:rgba(255, 255, 255, 0.4)}.sa{margin:28px 0 20px}.sb{top:57px}.sc{min-height:calc(100vh - 57px)}.sd{flex-direction:column}.se{padding-bottom:0px}.sf{border-bottom:none}.sg{margin-top:40px}.sh{height:88px}.si{width:88px}.sj{margin-top:16px}.sk{margin-top:4px}.sl{margin-top:12px}.sm{margin-bottom:40px}.sn{margin-top:24px}.so{width:auto}.sp{stroke:rgba(242, 242, 242, 1)}.sq{height:36px}.sr{width:36px}.ss{color:rgba(242, 242, 242, 1)}.st{fill:rgba(242, 242, 242, 1)}.su{border-color:rgba(242, 242, 242, 1)}.ta{padding:24px 0}.tb{margin-right:6px}.tc{font-size:11px}.td{line-height:16px}.ar:hover:not(:disabled){color:rgba(25, 25, 25, 1)}.as:hover:not(:disabled){fill:rgba(25, 25, 25, 1)}.ef:hover{background:rgba(90, 118, 144, 1)}.eg:hover{border-color:rgba(90, 118, 144, 1)}.eh:hover{cursor:pointer}.ev:hover{color:rgba(41, 41, 41, 1)}.ew:hover{fill:rgba(41, 41, 41, 1)}.fa:hover svg{fill:rgba(41, 41, 41, 1)}.fd:hover{background-color:none}.hl:hover path{fill:rgba(8, 8, 8, 1)}.ho:hover:not(:disabled) svg path{fill:rgba(8, 8, 8, 1)}.hu:hover{border-color:rgba(204, 204, 204, 1)}.ov:hover{background-color:rgba(230, 230, 230, 1)}.ps:hover{fill:rgba(8, 8, 8, 1)}.qk:hover p{color:rgba(8, 8, 8, 1)}.qm:hover svg{color:rgba(8, 8, 8, 1)}.rs:hover:not(:disabled){color:rgba(255, 255, 255, 1)}.rt:hover:not(:disabled){fill:rgba(255, 255, 255, 0.9)}.rx:hover{text-decoration:underline}.sv:hover{background:rgba(242, 242, 242, 1)}.sw:hover{border-color:rgba(242, 242, 242, 1)}.sx:hover{cursor:wait}.sy:hover{color:rgba(242, 242, 242, 1)}.sz:hover{fill:rgba(242, 242, 242, 1)}.bb:focus-within path{fill:rgba(41, 41, 41, 1)}.hp:focus svg path{fill:rgba(8, 8, 8, 1)}.kl:focus{transform:scale(1.01)}.pr:focus{fill:rgba(8, 8, 8, 1)}.qn:focus svg{color:rgba(8, 8, 8, 1)}.pw:active{border-style:none}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"all and (min-width: 1080px)\" type=\"text/css\">\n",
            "   .d{display:none}.bu{width:64px}.cd{max-width:728px}.ce{min-width:728px}.da{padding-left:clamp(24px, 24px + 100vw - 1080px, 40px)}.db{display:block}.dc{min-width:368px}.dd{max-width:368px}.du{font-size:14px}.dv{line-height:20px}.ea{padding:4px 12px 6px}.es{display:flex}.fk{max-width:680px}.gb{margin-bottom:68px}.gg{padding:0 16px}.gs{margin-bottom:32px}.gt{margin-top:56px}.jf{font-size:32px}.jg{margin-top:0.6em}.jh{line-height:40px}.ji{letter-spacing:-0.016em}.jy{margin-top:0.79em}.jz{font-size:22px}.ka{line-height:28px}.li{margin-top:3.14em}.lj{letter-spacing:0}.md{font-size:20px}.me{margin-top:2em}.mf{line-height:32px}.mg{letter-spacing:-0.003em}.mr{margin-top:2.37em}.ms{line-height:24px}.my{margin-top:0.86em}.nu{margin-top:2.75em}.nz{margin-top:2.48em}.oe{margin-top:2.14em}.on{margin-top:1.14em}.pc{margin-bottom:26px}.qd{margin-top:0px}.qg{display:inline-block}.qx{max-height:24px}.ro{margin:0 64px}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"all and (max-width: 1079.98px)\" type=\"text/css\">\n",
            "   .e{display:none}.kq{margin-left:auto}.kr{text-align:center}.qc{margin-top:0px}.qf{display:inline-block}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"all and (max-width: 903.98px)\" type=\"text/css\">\n",
            "   .f{display:none}.pl{display:inline-block}.qb{margin-top:0px}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"all and (max-width: 727.98px)\" type=\"text/css\">\n",
            "   .g{display:none}.pk{display:inline-block}.pz{margin-top:0px}.qa{margin-right:0px}.rj{padding:24px 0}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"all and (max-width: 551.98px)\" type=\"text/css\">\n",
            "   .h{display:none}.s{display:flex}.t{justify-content:space-between}.bq{width:24px}.ci{min-width:0}.cn{padding-left:24px}.co{min-width:352px}.cp{max-width:352px}.dm{font-size:13px}.dn{line-height:20px}.dw{padding:0px 8px 1px}.fx{margin-bottom:52px}.gc{padding:0 8px}.gk{margin-bottom:24px}.gl{margin-top:32px}.hc{display:inline-block}.ip{font-size:32px}.iq{margin-top:0.64em}.ir{line-height:40px}.is{letter-spacing:-0.016em}.jm{margin-top:0.46em}.jn{font-size:22px}.jo{line-height:28px}.kd{margin-top:40px}.kw{font-size:20px}.kx{margin-top:1.9em}.ky{line-height:24px}.kz{letter-spacing:0}.lp{font-size:18px}.lq{margin-top:1.56em}.lr{letter-spacing:-0.003em}.mj{font-size:16px}.mk{margin-top:2.07em}.mu{margin-top:0.67em}.nq{margin-top:1.42em}.nv{margin-top:2em}.oj{margin-top:1.34em}.oy{margin-bottom:10px}.pn{margin-left:0px}.px{margin-top:0px}.py{margin-right:0px}.qt{max-height:20px}.rk{margin:0 24px}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"all and (min-width: 904px) and (max-width: 1079.98px)\" type=\"text/css\">\n",
            "   .i{display:none}.bt{width:64px}.cf{min-width:0}.cw{padding-left:24px}.cx{display:block}.cy{min-width:352px}.cz{max-width:352px}.ds{font-size:14px}.dt{line-height:20px}.dz{padding:4px 12px 6px}.er{display:flex}.fj{max-width:680px}.ga{margin-bottom:68px}.gf{padding:0 16px}.gq{margin-bottom:24px}.gr{margin-top:32px}.jb{font-size:32px}.jc{margin-top:0.6em}.jd{line-height:40px}.je{letter-spacing:-0.016em}.jv{margin-top:0.79em}.jw{font-size:22px}.jx{line-height:28px}.kg{margin-top:56px}.lg{margin-top:3.14em}.lh{letter-spacing:0}.lz{font-size:20px}.ma{margin-top:2em}.mb{line-height:32px}.mc{letter-spacing:-0.003em}.mp{margin-top:2.37em}.mq{line-height:24px}.mx{margin-top:0.86em}.nt{margin-top:2.75em}.ny{margin-top:2.48em}.od{margin-top:2.14em}.om{margin-top:1.14em}.pb{margin-bottom:26px}.qw{max-height:24px}.rn{margin:0 64px}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"all and (min-width: 728px) and (max-width: 903.98px)\" type=\"text/css\">\n",
            "   .j{display:none}.w{display:flex}.x{justify-content:flex-end}.bs{width:64px}.cg{min-width:0}.ct{padding-left:24px}.cu{min-width:352px}.cv{max-width:352px}.dq{font-size:13px}.dr{line-height:20px}.dy{padding:0px 8px 1px}.fi{max-width:680px}.fz{margin-bottom:68px}.ge{padding:0 16px}.go{margin-bottom:24px}.gp{margin-top:32px}.he{display:inline-block}.ix{font-size:32px}.iy{margin-top:0.6em}.iz{line-height:40px}.ja{letter-spacing:-0.016em}.js{margin-top:0.79em}.jt{font-size:22px}.ju{line-height:28px}.kf{margin-top:56px}.le{margin-top:3.14em}.lf{letter-spacing:0}.lv{font-size:20px}.lw{margin-top:2em}.lx{line-height:32px}.ly{letter-spacing:-0.003em}.mn{margin-top:2.37em}.mo{line-height:24px}.mw{margin-top:0.86em}.ns{margin-top:2.75em}.nx{margin-top:2.48em}.oc{margin-top:2.14em}.ol{margin-top:1.14em}.pa{margin-bottom:26px}.qv{max-height:24px}.rm{margin:0 48px}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"all and (min-width: 552px) and (max-width: 727.98px)\" type=\"text/css\">\n",
            "   .k{display:none}.u{display:flex}.v{justify-content:space-between}.br{width:24px}.ch{min-width:0}.cq{padding-left:24px}.cr{min-width:352px}.cs{max-width:352px}.do{font-size:13px}.dp{line-height:20px}.dx{padding:0px 8px 1px}.fy{margin-bottom:52px}.gd{padding:0 8px}.gm{margin-bottom:24px}.gn{margin-top:32px}.hd{display:inline-block}.it{font-size:32px}.iu{margin-top:0.64em}.iv{line-height:40px}.iw{letter-spacing:-0.016em}.jp{margin-top:0.46em}.jq{font-size:22px}.jr{line-height:28px}.ke{margin-top:40px}.la{font-size:20px}.lb{margin-top:1.9em}.lc{line-height:24px}.ld{letter-spacing:0}.ls{font-size:18px}.lt{margin-top:1.56em}.lu{letter-spacing:-0.003em}.ml{font-size:16px}.mm{margin-top:2.07em}.mv{margin-top:0.67em}.nr{margin-top:1.42em}.nw{margin-top:2em}.ok{margin-top:1.34em}.oz{margin-bottom:10px}.po{margin-left:0px}.qu{max-height:20px}.rl{margin:0 24px}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"print\" type=\"text/css\">\n",
            "   .pi{display:none}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"(orientation: landscape) and (max-width: 903.98px)\" type=\"text/css\">\n",
            "   .fw{max-height:none}\n",
            "  </style>\n",
            "  <style data-fela-rehydration=\"523\" data-fela-type=\"RULE\" media=\"(prefers-reduced-motion: no-preference)\" type=\"text/css\">\n",
            "   .ki{transition:transform 300ms cubic-bezier(0.2, 0, 0.2, 1)}\n",
            "  </style>\n",
            " </head>\n",
            " <body>\n",
            "  <div id=\"root\">\n",
            "   <div class=\"a b c\">\n",
            "    <div class=\"d e f g h i j k\">\n",
            "    </div>\n",
            "    <script>\n",
            "     document.domain = document.domain;\n",
            "    </script>\n",
            "    <div class=\"l c\">\n",
            "     <div class=\"l m n o c\">\n",
            "      <div class=\"p q r s t u v w x i d y z\">\n",
            "       <a class=\"dk af dl bd aj b al am an ao ap aq ar as s u j i d q dh z\" href=\"https://rsci.app.link/?%24canonical_url=https%3A%2F%2Fmedium.com%2Fp%2F3ac3a11d524&amp;%7Efeature=LoOpenInAppButton&amp;%7Echannel=ShowPostUnderCollection&amp;source=---two_column_layout_nav----------------------------------\" rel=\"noopener follow\">\n",
            "        Open in app\n",
            "        <svg class=\"dj\" fill=\"none\" height=\"10\" viewbox=\"0 0 10 10\" width=\"10\">\n",
            "         <path d=\"M.98 8.48a.37.37 0 1 0 .54.54l-.54-.54zm7.77-7.23h.38c0-.2-.17-.38-.38-.38v.38zM8.37 6.5a.37.37 0 1 0 .76 0h-.76zM3.5.87a.37.37 0 1 0 0 .76V.88zM1.52 9.03l7.5-7.5-.54-.54-7.5 7.5.54.54zm6.86-7.77V6.5h.74V1.25h-.74zm-4.88.38h5.25V.88H3.5v.74z\" fill=\"currentColor\">\n",
            "         </path>\n",
            "        </svg>\n",
            "       </a>\n",
            "       <div class=\"ab q\">\n",
            "        <p class=\"bd b dm dn do dp dq dr ds dt du dv dk\">\n",
            "         <span>\n",
            "          <a class=\"bd b dm dn dw do dp dx dq dr dy ds dt dz du dv ea eb ec ed ee ef eg eh ei ej ek el em eo ep de bk eq\" href=\"https://medium.com/m/signin?operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=post_page---two_column_layout_nav-----------------------global_nav-----------\" rel=\"noopener follow\">\n",
            "           Sign up\n",
            "          </a>\n",
            "         </span>\n",
            "        </p>\n",
            "        <div class=\"av l\">\n",
            "         <p class=\"bd b dm dn do dp dq dr ds dt du dv dk\">\n",
            "          <span>\n",
            "           <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?operation=login&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=post_page---two_column_layout_nav-----------------------global_nav-----------\" rel=\"noopener follow\">\n",
            "            Sign In\n",
            "           </a>\n",
            "          </span>\n",
            "         </p>\n",
            "        </div>\n",
            "       </div>\n",
            "      </div>\n",
            "      <div class=\"p q r ab ac\">\n",
            "       <div class=\"ab q ad\">\n",
            "        <a aria-label=\"Homepage\" class=\"ae af ag ah ai aj ak al am an ao ap aq ar as ab\" href=\"https://medium.com/?source=---two_column_layout_nav----------------------------------\" rel=\"noopener follow\">\n",
            "         <svg class=\"at au\" viewbox=\"0 0 1043.63 592.71\">\n",
            "          <g data-name=\"Layer 2\">\n",
            "           <g data-name=\"Layer 1\">\n",
            "            <path d=\"M588.67 296.36c0 163.67-131.78 296.35-294.33 296.35S0 460 0 296.36 131.78 0 294.34 0s294.33 132.69 294.33 296.36M911.56 296.36c0 154.06-65.89 279-147.17 279s-147.17-124.94-147.17-279 65.88-279 147.16-279 147.17 124.9 147.17 279M1043.63 296.36c0 138-23.17 249.94-51.76 249.94s-51.75-111.91-51.75-249.94 23.17-249.94 51.75-249.94 51.76 111.9 51.76 249.94\">\n",
            "            </path>\n",
            "           </g>\n",
            "          </g>\n",
            "         </svg>\n",
            "        </a>\n",
            "        <div class=\"av h\">\n",
            "         <div class=\"ab aw ax ay az q ba bb\">\n",
            "          <div aria-describedby=\"searchResults\" aria-hidden=\"false\" aria-labelledby=\"searchResults\" class=\"bk\">\n",
            "          </div>\n",
            "          <div class=\"bl bm ab\">\n",
            "           <svg fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "            <path clip-rule=\"evenodd\" d=\"M4.1 11.06a6.95 6.95 0 1 1 13.9 0 6.95 6.95 0 0 1-13.9 0zm6.94-8.05a8.05 8.05 0 1 0 5.13 14.26l3.75 3.75a.56.56 0 1 0 .8-.79l-3.74-3.73A8.05 8.05 0 0 0 11.04 3v.01z\" fill=\"currentColor\" fill-rule=\"evenodd\">\n",
            "            </path>\n",
            "           </svg>\n",
            "          </div>\n",
            "          <input aria-controls=\"searchResults\" aria-expanded=\"false\" aria-label=\"search\" class=\"aw bc bd be z bf bg bh bi bj\" placeholder=\"Search Medium\" role=\"combobox\" tabindex=\"0\" value=\"\"/>\n",
            "         </div>\n",
            "        </div>\n",
            "       </div>\n",
            "       <div class=\"h k w er es\">\n",
            "        <div class=\"et ab\">\n",
            "         <span>\n",
            "          <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?operation=register&amp;redirect=https%3A%2F%2Fmedium.com%2Fnew-story&amp;source=---two_column_layout_nav-----------------------new_post_sidenav-----------\" rel=\"noopener follow\">\n",
            "           <div class=\"bd b be z dk di eu ab q ev ew\">\n",
            "            <svg aria-label=\"Write\" fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "             <path d=\"M14 4a.5.5 0 0 0 0-1v1zm7 6a.5.5 0 0 0-1 0h1zm-7-7H4v1h10V3zM3 4v16h1V4H3zm1 17h16v-1H4v1zm17-1V10h-1v10h1zm-1 1a1 1 0 0 0 1-1h-1v1zM3 20a1 1 0 0 0 1 1v-1H3zM4 3a1 1 0 0 0-1 1h1V3z\" fill=\"currentColor\">\n",
            "             </path>\n",
            "             <path d=\"M17.5 4.5l-8.46 8.46a.25.25 0 0 0-.06.1l-.82 2.47c-.07.2.12.38.31.31l2.47-.82a.25.25 0 0 0 .1-.06L19.5 6.5m-2-2l2.32-2.32c.1-.1.26-.1.36 0l1.64 1.64c.1.1.1.26 0 .36L19.5 6.5m-2-2l2 2\" stroke=\"currentColor\">\n",
            "             </path>\n",
            "            </svg>\n",
            "            <div class=\"dj l\">\n",
            "             Write\n",
            "            </div>\n",
            "           </div>\n",
            "          </a>\n",
            "         </span>\n",
            "        </div>\n",
            "       </div>\n",
            "       <div class=\"k j i d\">\n",
            "        <div class=\"et ab\">\n",
            "         <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/search?source=---two_column_layout_nav----------------------------------\" rel=\"noopener follow\">\n",
            "          <div class=\"bd b be z dk di eu ab q ev ew\">\n",
            "           <svg aria-label=\"Search\" fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "            <path clip-rule=\"evenodd\" d=\"M4.1 11.06a6.95 6.95 0 1 1 13.9 0 6.95 6.95 0 0 1-13.9 0zm6.94-8.05a8.05 8.05 0 1 0 5.13 14.26l3.75 3.75a.56.56 0 1 0 .8-.79l-3.74-3.73A8.05 8.05 0 0 0 11.04 3v.01z\" fill=\"currentColor\" fill-rule=\"evenodd\">\n",
            "            </path>\n",
            "           </svg>\n",
            "          </div>\n",
            "         </a>\n",
            "        </div>\n",
            "       </div>\n",
            "       <div class=\"et h k j\">\n",
            "        <div class=\"ab q\">\n",
            "         <p class=\"bd b dm dn do dp dq dr ds dt du dv dk\">\n",
            "          <span>\n",
            "           <a class=\"bd b dm dn dw do dp dx dq dr dy ds dt dz du dv ea eb ec ed ee ef eg eh ei ej ek el em eo ep de bk eq\" href=\"https://medium.com/m/signin?operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=post_page---two_column_layout_nav-----------------------global_nav-----------\" rel=\"noopener follow\">\n",
            "            Sign up\n",
            "           </a>\n",
            "          </span>\n",
            "         </p>\n",
            "         <div class=\"av l\">\n",
            "          <p class=\"bd b dm dn do dp dq dr ds dt du dv dk\">\n",
            "           <span>\n",
            "            <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?operation=login&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=post_page---two_column_layout_nav-----------------------global_nav-----------\" rel=\"noopener follow\">\n",
            "             Sign In\n",
            "            </a>\n",
            "           </span>\n",
            "          </p>\n",
            "         </div>\n",
            "        </div>\n",
            "       </div>\n",
            "       <div aria-hidden=\"false\" class=\"l\">\n",
            "        <button aria-label=\"user options menu\" class=\"aw ex al ab q an ey ez fa\">\n",
            "         <div class=\"l di\">\n",
            "          <img alt=\"\" class=\"l de bw bx by fe\" height=\"32\" loading=\"lazy\" role=\"presentation\" src=\"https://miro.medium.com/v2/resize:fill:64:64/1*dmbNkD5D-u45r44go_cf0g.png\" width=\"32\"/>\n",
            "          <div class=\"fb bw l bx by fc n aw fd\">\n",
            "          </div>\n",
            "         </div>\n",
            "         <svg height=\"12px\" viewbox=\"0 0 15 15\" width=\"12px\">\n",
            "          <path d=\"M3.85 5.15a.5.5 0 0 0-.7.7l4.35 4.36 4.35-4.36a.5.5 0 1 0-.7-.7L7.5 8.79 3.85 5.15z\" fill-rule=\"evenodd\">\n",
            "          </path>\n",
            "         </svg>\n",
            "        </button>\n",
            "       </div>\n",
            "      </div>\n",
            "     </div>\n",
            "     <div class=\"bz ca l\">\n",
            "      <div class=\"ab cb cc\">\n",
            "       <main class=\"cd ce cf cg ch ci l cj\">\n",
            "        <div class=\"l\">\n",
            "         <div class=\"ff l\">\n",
            "          <div class=\"ab cl\">\n",
            "           <div class=\"fg bf fh fi fj fk\">\n",
            "            <div class=\"fl ab q\">\n",
            "             <div class=\"fm l\">\n",
            "              <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://towardsdatascience.com/?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "               <div class=\"l di\">\n",
            "                <img alt=\"Towards Data Science\" class=\"l de bw bx by fe\" height=\"32\" loading=\"lazy\" src=\"https://miro.medium.com/v2/resize:fill:64:64/1*CJe3891yB1A1mzMdqemkdg.jpeg\" width=\"32\"/>\n",
            "                <div class=\"fb bw l bx by fc n aw fd\">\n",
            "                </div>\n",
            "               </div>\n",
            "              </a>\n",
            "             </div>\n",
            "             <div class=\"fn l fo\">\n",
            "              <div class=\"bd b be z dk\">\n",
            "               Published in\n",
            "              </div>\n",
            "             </div>\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://towardsdatascience.com/?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "              <p class=\"bd b be z fp fq fr fs ft fu fv fw bi\">\n",
            "               Towards Data Science\n",
            "              </p>\n",
            "             </a>\n",
            "            </div>\n",
            "           </div>\n",
            "          </div>\n",
            "         </div>\n",
            "         <div class=\"fx fy fz ga gb l\">\n",
            "          <div class=\"ab cl\">\n",
            "           <div class=\"fg bf fh fi fj fk\">\n",
            "            <article>\n",
            "             <div class=\"l\">\n",
            "              <div class=\"gc gd ge gf gg gh gi bf gj de l\">\n",
            "              </div>\n",
            "              <div class=\"l\">\n",
            "               <header class=\"pw-post-byline-header gk gl gm gn go gp gq gr gs gt l\">\n",
            "                <div class=\"ab gu gv\">\n",
            "                 <div class=\"ab\">\n",
            "                  <div class=\"fm l\">\n",
            "                   <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://shahparthvi22.medium.com/?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "                    <div class=\"l di\">\n",
            "                     <img alt=\"Parthvi Shah\" class=\"l de bw gw gx fe\" height=\"48\" loading=\"lazy\" src=\"https://miro.medium.com/v2/resize:fill:96:96/1*-WmnCAUlTAjdn2C2uFbr_w.jpeg\" width=\"48\"/>\n",
            "                     <div class=\"fb bw l gw gx fc n aw fd\">\n",
            "                     </div>\n",
            "                    </div>\n",
            "                   </a>\n",
            "                  </div>\n",
            "                  <div class=\"l\">\n",
            "                   <div class=\"pw-author bd b gy gz bi\">\n",
            "                    <div class=\"ha ab q\">\n",
            "                     <div>\n",
            "                      <div aria-hidden=\"false\" class=\"bk\">\n",
            "                       <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://shahparthvi22.medium.com/?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "                        <div class=\"ab q\">\n",
            "                         Parthvi Shah\n",
            "                        </div>\n",
            "                       </a>\n",
            "                      </div>\n",
            "                     </div>\n",
            "                     <div class=\"hb hc hd he i d\">\n",
            "                      <span>\n",
            "                       <a class=\"bd b dl z eb hf ec ed ee ef eg eh ei ej ek el em eo ep de bk eq\" href=\"https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fsubscribe%2Fuser%2Fe2a3c138d982&amp;operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;user=Parthvi+Shah&amp;userId=e2a3c138d982&amp;source=post_page-e2a3c138d982----3ac3a11d524---------------------follow_byline-----------\" rel=\"noopener follow\">\n",
            "                        Follow\n",
            "                       </a>\n",
            "                      </span>\n",
            "                     </div>\n",
            "                    </div>\n",
            "                   </div>\n",
            "                   <div class=\"ab q hg\">\n",
            "                    <p class=\"pw-published-date bd b be z dk\">\n",
            "                     <span>\n",
            "                      Jun 27, 2020\n",
            "                     </span>\n",
            "                    </p>\n",
            "                    <div aria-hidden=\"true\" class=\"hh bk\">\n",
            "                     <span aria-hidden=\"true\" class=\"l\">\n",
            "                      <span class=\"bd b be z dk\">\n",
            "                       ·\n",
            "                      </span>\n",
            "                     </span>\n",
            "                    </div>\n",
            "                    <div class=\"pw-reading-time bd b be z dk\">\n",
            "                     3 min read\n",
            "                    </div>\n",
            "                   </div>\n",
            "                  </div>\n",
            "                 </div>\n",
            "                 <div class=\"h k w er es q\">\n",
            "                  <div class=\"hi l fo\">\n",
            "                   <div>\n",
            "                    <div aria-hidden=\"false\" class=\"bk\">\n",
            "                     <button aria-label=\"Share on twitter\" class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\">\n",
            "                      <span class=\"bk hj hk hl\">\n",
            "                       <svg fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                        <path d=\"M20 5.34c-.67.41-1.4.7-2.18.87a3.45 3.45 0 0 0-5.02-.1 3.49 3.49 0 0 0-1.02 2.47c0 .28.03.54.07.8a9.91 9.91 0 0 1-7.17-3.66 3.9 3.9 0 0 0-.5 1.74 3.6 3.6 0 0 0 1.56 2.92 3.36 3.36 0 0 1-1.55-.44V10c0 1.67 1.2 3.08 2.8 3.42-.3.06-.6.1-.94.12l-.62-.06a3.5 3.5 0 0 0 3.24 2.43 7.34 7.34 0 0 1-4.36 1.49l-.81-.05a9.96 9.96 0 0 0 5.36 1.56c6.4 0 9.91-5.32 9.9-9.9v-.5c.69-.49 1.28-1.1 1.74-1.81-.63.3-1.3.48-2 .56A3.33 3.33 0 0 0 20 5.33\" fill=\"#A8A8A8\">\n",
            "                        </path>\n",
            "                       </svg>\n",
            "                      </span>\n",
            "                     </button>\n",
            "                    </div>\n",
            "                   </div>\n",
            "                  </div>\n",
            "                  <div class=\"hi l fo\">\n",
            "                   <div>\n",
            "                    <div aria-hidden=\"false\" class=\"bk\">\n",
            "                     <button aria-label=\"Share on facebook\" class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\">\n",
            "                      <span class=\"bk hj hk hl\">\n",
            "                       <svg fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                        <path d=\"M19.75 12.04c0-4.3-3.47-7.79-7.75-7.79a7.77 7.77 0 0 0-5.9 12.84 7.77 7.77 0 0 0 4.69 2.63v-5.49h-1.9v-2.2h1.9v-1.62c0-1.88 1.14-2.9 2.8-2.9.8 0 1.49.06 1.69.08v1.97h-1.15c-.91 0-1.1.43-1.1 1.07v1.4h2.17l-.28 2.2h-1.88v5.52a7.77 7.77 0 0 0 6.7-7.71\" fill=\"#A8A8A8\">\n",
            "                        </path>\n",
            "                       </svg>\n",
            "                      </span>\n",
            "                     </button>\n",
            "                    </div>\n",
            "                   </div>\n",
            "                  </div>\n",
            "                  <div class=\"hi l fo\">\n",
            "                   <div>\n",
            "                    <div aria-hidden=\"false\" class=\"bk\">\n",
            "                     <button aria-label=\"Share on linkedin\" class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\">\n",
            "                      <span class=\"bk hj hk hl\">\n",
            "                       <svg fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                        <path d=\"M19.75 5.39v13.22a1.14 1.14 0 0 1-1.14 1.14H5.39a1.14 1.14 0 0 1-1.14-1.14V5.39a1.14 1.14 0 0 1 1.14-1.14h13.22a1.14 1.14 0 0 1 1.14 1.14zM8.81 10.18H6.53v7.3H8.8v-7.3zM9 7.67a1.31 1.31 0 0 0-1.3-1.32h-.04a1.32 1.32 0 0 0 0 2.64A1.31 1.31 0 0 0 9 7.71v-.04zm8.46 5.37c0-2.2-1.4-3.05-2.78-3.05a2.6 2.6 0 0 0-2.3 1.18h-.07v-1h-2.14v7.3h2.28V13.6a1.51 1.51 0 0 1 1.36-1.63h.09c.72 0 1.26.45 1.26 1.6v3.91h2.28l.02-4.43z\" fill=\"#A8A8A8\">\n",
            "                        </path>\n",
            "                       </svg>\n",
            "                      </span>\n",
            "                     </button>\n",
            "                    </div>\n",
            "                   </div>\n",
            "                  </div>\n",
            "                  <div class=\"l fo\">\n",
            "                   <div>\n",
            "                    <div aria-hidden=\"false\" class=\"bk\">\n",
            "                     <button class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\">\n",
            "                      <span class=\"bk hj hk hl\">\n",
            "                       <svg fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                        <path clip-rule=\"evenodd\" d=\"M3.57 14.67c0-.57.13-1.11.38-1.6l.02-.02v-.02l.02-.02c0-.02 0-.02.02-.02.12-.26.3-.52.57-.8L7.78 9v-.02l.01-.02c.44-.41.91-.7 1.44-.85a4.87 4.87 0 0 0-1.19 2.36A5.04 5.04 0 0 0 8 11.6L6.04 13.6c-.19.19-.32.4-.38.65a2 2 0 0 0 0 .9c.08.2.2.4.38.57l1.29 1.31c.27.28.62.42 1.03.42.42 0 .78-.14 1.06-.42l1.23-1.25.79-.78 1.15-1.16c.08-.09.19-.22.28-.4.1-.2.15-.42.15-.67 0-.16-.02-.3-.06-.45l-.02-.02v-.02l-.07-.14s0-.03-.04-.06l-.06-.13-.02-.02c0-.02 0-.03-.02-.05a.6.6 0 0 0-.14-.16l-.48-.5c0-.04.02-.1.04-.15l.06-.12 1.17-1.14.09-.09.56.57c.02.04.08.1.16.18l.05.04.03.06.04.05.03.04.04.06.1.14.02.02c0 .02.01.03.03.04l.1.2v.02c.1.16.2.38.3.68a1 1 0 0 1 .04.25 3.2 3.2 0 0 1 .02 1.33 3.49 3.49 0 0 1-.95 1.87l-.66.67-.97.97-1.56 1.57a3.4 3.4 0 0 1-2.47 1.02c-.97 0-1.8-.34-2.49-1.03l-1.3-1.3a3.55 3.55 0 0 1-1-2.51v-.01h-.02v.02zm5.39-3.43c0-.19.02-.4.07-.63.13-.74.44-1.37.95-1.87l.66-.67.97-.98 1.56-1.56c.68-.69 1.5-1.03 2.47-1.03.97 0 1.8.34 2.48 1.02l1.3 1.32a3.48 3.48 0 0 1 1 2.48c0 .58-.11 1.11-.37 1.6l-.02.02v.02l-.02.04c-.14.27-.35.54-.6.8L16.23 15l-.01.02-.01.02c-.44.42-.92.7-1.43.83a4.55 4.55 0 0 0 1.23-3.52L18 10.38c.18-.21.3-.42.35-.65a2.03 2.03 0 0 0-.01-.9 1.96 1.96 0 0 0-.36-.58l-1.3-1.3a1.49 1.49 0 0 0-1.06-.42c-.42 0-.77.14-1.06.4l-1.2 1.27-.8.8-1.16 1.15c-.08.08-.18.21-.29.4a1.66 1.66 0 0 0-.08 1.12l.02.03v.02l.06.14s.01.03.05.06l.06.13.02.02.01.02.01.02c.05.08.1.13.14.16l.47.5c0 .04-.02.09-.04.15l-.06.12-1.15 1.15-.1.08-.56-.56a2.3 2.3 0 0 0-.18-.19c-.02-.01-.02-.03-.02-.04l-.02-.02a.37.37 0 0 1-.1-.12c-.03-.03-.05-.04-.05-.06l-.1-.15-.02-.02-.02-.04-.08-.17v-.02a5.1 5.1 0 0 1-.28-.69 1.03 1.03 0 0 1-.04-.26c-.06-.23-.1-.46-.1-.7v.01z\" fill=\"#A8A8A8\" fill-rule=\"evenodd\">\n",
            "                        </path>\n",
            "                       </svg>\n",
            "                      </span>\n",
            "                     </button>\n",
            "                    </div>\n",
            "                   </div>\n",
            "                  </div>\n",
            "                  <div class=\"hm ab q\">\n",
            "                   <span>\n",
            "                    <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fbookmark%2Fp%2F3ac3a11d524&amp;operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=--------------------------bookmark_header-----------\" rel=\"noopener follow\">\n",
            "                     <button aria-controls=\"addToCatalogBookmarkButton\" aria-expanded=\"false\" aria-label=\"Add to list bookmark button\" class=\"ae eu ag ah ai aj ak hj am an ao ej ho hp hq\">\n",
            "                      <svg aria-label=\"Add to list bookmark button\" class=\"hn\" fill=\"none\" height=\"25\" viewbox=\"0 0 25 25\" width=\"25\">\n",
            "                       <path d=\"M18 2.5a.5.5 0 0 1 1 0V5h2.5a.5.5 0 0 1 0 1H19v2.5a.5.5 0 1 1-1 0V6h-2.5a.5.5 0 0 1 0-1H18V2.5zM7 7a1 1 0 0 1 1-1h3.5a.5.5 0 0 0 0-1H8a2 2 0 0 0-2 2v14a.5.5 0 0 0 .8.4l5.7-4.4 5.7 4.4a.5.5 0 0 0 .8-.4v-8.5a.5.5 0 0 0-1 0v7.48l-5.2-4a.5.5 0 0 0-.6 0l-5.2 4V7z\" fill=\"#292929\">\n",
            "                       </path>\n",
            "                      </svg>\n",
            "                     </button>\n",
            "                    </a>\n",
            "                   </span>\n",
            "                  </div>\n",
            "                  <div>\n",
            "                   <div aria-hidden=\"false\" class=\"bk\">\n",
            "                   </div>\n",
            "                  </div>\n",
            "                 </div>\n",
            "                </div>\n",
            "                <div class=\"hr s u j i d\">\n",
            "                 <div class=\"fm l\">\n",
            "                  <span>\n",
            "                   <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fbookmark%2Fp%2F3ac3a11d524&amp;operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=--------------------------bookmark_header-----------\" rel=\"noopener follow\">\n",
            "                    <button aria-controls=\"addToCatalogBookmarkButton\" aria-expanded=\"false\" aria-label=\"Add to list bookmark button\" class=\"ae eu ag hs ai aj ak ht am an ao ej em ab q hu hv hq\">\n",
            "                     <svg aria-label=\"Add to list bookmark button\" class=\"hn\" fill=\"none\" height=\"25\" viewbox=\"0 0 25 25\" width=\"25\">\n",
            "                      <path d=\"M18 2.5a.5.5 0 0 1 1 0V5h2.5a.5.5 0 0 1 0 1H19v2.5a.5.5 0 1 1-1 0V6h-2.5a.5.5 0 0 1 0-1H18V2.5zM7 7a1 1 0 0 1 1-1h3.5a.5.5 0 0 0 0-1H8a2 2 0 0 0-2 2v14a.5.5 0 0 0 .8.4l5.7-4.4 5.7 4.4a.5.5 0 0 0 .8-.4v-8.5a.5.5 0 0 0-1 0v7.48l-5.2-4a.5.5 0 0 0-.6 0l-5.2 4V7z\" fill=\"#292929\">\n",
            "                      </path>\n",
            "                     </svg>\n",
            "                     <p class=\"bd b be z dk\">\n",
            "                      Save\n",
            "                     </p>\n",
            "                    </button>\n",
            "                   </a>\n",
            "                  </span>\n",
            "                 </div>\n",
            "                 <div class=\"hw l fo\">\n",
            "                  <div>\n",
            "                   <div aria-hidden=\"false\" class=\"bk\">\n",
            "                    <button aria-label=\"Share on twitter\" class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\">\n",
            "                     <span class=\"bk hj hk hl\">\n",
            "                      <svg fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                       <path d=\"M20 5.34c-.67.41-1.4.7-2.18.87a3.45 3.45 0 0 0-5.02-.1 3.49 3.49 0 0 0-1.02 2.47c0 .28.03.54.07.8a9.91 9.91 0 0 1-7.17-3.66 3.9 3.9 0 0 0-.5 1.74 3.6 3.6 0 0 0 1.56 2.92 3.36 3.36 0 0 1-1.55-.44V10c0 1.67 1.2 3.08 2.8 3.42-.3.06-.6.1-.94.12l-.62-.06a3.5 3.5 0 0 0 3.24 2.43 7.34 7.34 0 0 1-4.36 1.49l-.81-.05a9.96 9.96 0 0 0 5.36 1.56c6.4 0 9.91-5.32 9.9-9.9v-.5c.69-.49 1.28-1.1 1.74-1.81-.63.3-1.3.48-2 .56A3.33 3.33 0 0 0 20 5.33\" fill=\"#A8A8A8\">\n",
            "                       </path>\n",
            "                      </svg>\n",
            "                     </span>\n",
            "                    </button>\n",
            "                   </div>\n",
            "                  </div>\n",
            "                 </div>\n",
            "                 <div class=\"hw l fo\">\n",
            "                  <div>\n",
            "                   <div aria-hidden=\"false\" class=\"bk\">\n",
            "                    <button aria-label=\"Share on facebook\" class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\">\n",
            "                     <span class=\"bk hj hk hl\">\n",
            "                      <svg fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                       <path d=\"M19.75 12.04c0-4.3-3.47-7.79-7.75-7.79a7.77 7.77 0 0 0-5.9 12.84 7.77 7.77 0 0 0 4.69 2.63v-5.49h-1.9v-2.2h1.9v-1.62c0-1.88 1.14-2.9 2.8-2.9.8 0 1.49.06 1.69.08v1.97h-1.15c-.91 0-1.1.43-1.1 1.07v1.4h2.17l-.28 2.2h-1.88v5.52a7.77 7.77 0 0 0 6.7-7.71\" fill=\"#A8A8A8\">\n",
            "                       </path>\n",
            "                      </svg>\n",
            "                     </span>\n",
            "                    </button>\n",
            "                   </div>\n",
            "                  </div>\n",
            "                 </div>\n",
            "                 <div class=\"hw l fo\">\n",
            "                  <div>\n",
            "                   <div aria-hidden=\"false\" class=\"bk\">\n",
            "                    <button aria-label=\"Share on linkedin\" class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\">\n",
            "                     <span class=\"bk hj hk hl\">\n",
            "                      <svg fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                       <path d=\"M19.75 5.39v13.22a1.14 1.14 0 0 1-1.14 1.14H5.39a1.14 1.14 0 0 1-1.14-1.14V5.39a1.14 1.14 0 0 1 1.14-1.14h13.22a1.14 1.14 0 0 1 1.14 1.14zM8.81 10.18H6.53v7.3H8.8v-7.3zM9 7.67a1.31 1.31 0 0 0-1.3-1.32h-.04a1.32 1.32 0 0 0 0 2.64A1.31 1.31 0 0 0 9 7.71v-.04zm8.46 5.37c0-2.2-1.4-3.05-2.78-3.05a2.6 2.6 0 0 0-2.3 1.18h-.07v-1h-2.14v7.3h2.28V13.6a1.51 1.51 0 0 1 1.36-1.63h.09c.72 0 1.26.45 1.26 1.6v3.91h2.28l.02-4.43z\" fill=\"#A8A8A8\">\n",
            "                       </path>\n",
            "                      </svg>\n",
            "                     </span>\n",
            "                    </button>\n",
            "                   </div>\n",
            "                  </div>\n",
            "                 </div>\n",
            "                 <div class=\"l fo\">\n",
            "                  <div>\n",
            "                   <div aria-hidden=\"false\" class=\"bk\">\n",
            "                    <button class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\">\n",
            "                     <span class=\"bk hj hk hl\">\n",
            "                      <svg fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                       <path clip-rule=\"evenodd\" d=\"M3.57 14.67c0-.57.13-1.11.38-1.6l.02-.02v-.02l.02-.02c0-.02 0-.02.02-.02.12-.26.3-.52.57-.8L7.78 9v-.02l.01-.02c.44-.41.91-.7 1.44-.85a4.87 4.87 0 0 0-1.19 2.36A5.04 5.04 0 0 0 8 11.6L6.04 13.6c-.19.19-.32.4-.38.65a2 2 0 0 0 0 .9c.08.2.2.4.38.57l1.29 1.31c.27.28.62.42 1.03.42.42 0 .78-.14 1.06-.42l1.23-1.25.79-.78 1.15-1.16c.08-.09.19-.22.28-.4.1-.2.15-.42.15-.67 0-.16-.02-.3-.06-.45l-.02-.02v-.02l-.07-.14s0-.03-.04-.06l-.06-.13-.02-.02c0-.02 0-.03-.02-.05a.6.6 0 0 0-.14-.16l-.48-.5c0-.04.02-.1.04-.15l.06-.12 1.17-1.14.09-.09.56.57c.02.04.08.1.16.18l.05.04.03.06.04.05.03.04.04.06.1.14.02.02c0 .02.01.03.03.04l.1.2v.02c.1.16.2.38.3.68a1 1 0 0 1 .04.25 3.2 3.2 0 0 1 .02 1.33 3.49 3.49 0 0 1-.95 1.87l-.66.67-.97.97-1.56 1.57a3.4 3.4 0 0 1-2.47 1.02c-.97 0-1.8-.34-2.49-1.03l-1.3-1.3a3.55 3.55 0 0 1-1-2.51v-.01h-.02v.02zm5.39-3.43c0-.19.02-.4.07-.63.13-.74.44-1.37.95-1.87l.66-.67.97-.98 1.56-1.56c.68-.69 1.5-1.03 2.47-1.03.97 0 1.8.34 2.48 1.02l1.3 1.32a3.48 3.48 0 0 1 1 2.48c0 .58-.11 1.11-.37 1.6l-.02.02v.02l-.02.04c-.14.27-.35.54-.6.8L16.23 15l-.01.02-.01.02c-.44.42-.92.7-1.43.83a4.55 4.55 0 0 0 1.23-3.52L18 10.38c.18-.21.3-.42.35-.65a2.03 2.03 0 0 0-.01-.9 1.96 1.96 0 0 0-.36-.58l-1.3-1.3a1.49 1.49 0 0 0-1.06-.42c-.42 0-.77.14-1.06.4l-1.2 1.27-.8.8-1.16 1.15c-.08.08-.18.21-.29.4a1.66 1.66 0 0 0-.08 1.12l.02.03v.02l.06.14s.01.03.05.06l.06.13.02.02.01.02.01.02c.05.08.1.13.14.16l.47.5c0 .04-.02.09-.04.15l-.06.12-1.15 1.15-.1.08-.56-.56a2.3 2.3 0 0 0-.18-.19c-.02-.01-.02-.03-.02-.04l-.02-.02a.37.37 0 0 1-.1-.12c-.03-.03-.05-.04-.05-.06l-.1-.15-.02-.02-.02-.04-.08-.17v-.02a5.1 5.1 0 0 1-.28-.69 1.03 1.03 0 0 1-.04-.26c-.06-.23-.1-.46-.1-.7v.01z\" fill=\"#A8A8A8\" fill-rule=\"evenodd\">\n",
            "                       </path>\n",
            "                      </svg>\n",
            "                     </span>\n",
            "                    </button>\n",
            "                   </div>\n",
            "                  </div>\n",
            "                 </div>\n",
            "                 <div class=\"bl l\">\n",
            "                  <div>\n",
            "                   <div aria-hidden=\"false\" class=\"bk\">\n",
            "                   </div>\n",
            "                  </div>\n",
            "                 </div>\n",
            "                </div>\n",
            "               </header>\n",
            "               <span class=\"l\">\n",
            "               </span>\n",
            "               <section>\n",
            "                <div>\n",
            "                 <div class=\"fc ib ic id ie if\">\n",
            "                 </div>\n",
            "                 <div class=\"ig ih ii ij ik\">\n",
            "                  <div class=\"\">\n",
            "                   <h1 class=\"pw-post-title il im in bd io ip iq ir is it iu iv iw ix iy iz ja jb jc jd je jf jg jh ji jj bi\" id=\"3d1c\">\n",
            "                    Sentiment Analysis using TextBlob\n",
            "                   </h1>\n",
            "                  </div>\n",
            "                  <div class=\"\">\n",
            "                   <h2 class=\"pw-subtitle-paragraph jk im in bd b jl jm jn jo jp jq jr js jt ju jv jw jx jy jz ka kb dk\" id=\"4e7a\">\n",
            "                    Sentiment Analysis using TextBlob and its working!\n",
            "                   </h2>\n",
            "                  </div>\n",
            "                  <figure class=\"kd ke kf kg gt kh gh gi paragraph-image\">\n",
            "                   <div class=\"ki kj di kk bf kl\" role=\"button\" tabindex=\"0\">\n",
            "                    <div class=\"gh gi kc\">\n",
            "                     <picture>\n",
            "                      <source sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px\" srcset=\"https://miro.medium.com/v2/resize:fit:640/format:webp/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 1100w, https://miro.medium.com/v2/resize:fit:1400/format:webp/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 1400w\" type=\"image/webp\"/>\n",
            "                      <source data-testid=\"og\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 700px\" srcset=\"https://miro.medium.com/v2/resize:fit:640/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 640w, https://miro.medium.com/v2/resize:fit:720/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 720w, https://miro.medium.com/v2/resize:fit:750/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 750w, https://miro.medium.com/v2/resize:fit:786/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 786w, https://miro.medium.com/v2/resize:fit:828/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 828w, https://miro.medium.com/v2/resize:fit:1100/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 1100w, https://miro.medium.com/v2/resize:fit:1400/1*vkqmLESSfaYMmHvXznGk5Q.jpeg 1400w\"/>\n",
            "                      <img alt=\"\" class=\"bf km kn c\" height=\"438\" loading=\"eager\" role=\"presentation\" width=\"700\"/>\n",
            "                     </picture>\n",
            "                    </div>\n",
            "                   </div>\n",
            "                   <figcaption class=\"ko kp gj gh gi kq kr bd b be z dk\">\n",
            "                    <a class=\"ae ks\" href=\"https://wallpaperaccess.com/sunny-landscape-desktop\" rel=\"noopener ugc nofollow\" target=\"_blank\">\n",
            "                     source\n",
            "                    </a>\n",
            "                   </figcaption>\n",
            "                  </figure>\n",
            "                  <h1 class=\"kt ku in bd kv kw kx ky kz la lb lc ld jt le ju lf jw lg jx lh jz li ka lj lk bi\" id=\"a7d1\">\n",
            "                   What do you mean by Sentiment?\n",
            "                  </h1>\n",
            "                  <figure class=\"kd ke kf kg gt kh gh gi paragraph-image\">\n",
            "                   <div class=\"gh gi ll\">\n",
            "                    <picture>\n",
            "                     <source sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 650px\" srcset=\"https://miro.medium.com/v2/resize:fit:640/format:webp/1*JOMSVAYTK5OEp4SQHgqoMg.png 640w, https://miro.medium.com/v2/resize:fit:720/format:webp/1*JOMSVAYTK5OEp4SQHgqoMg.png 720w, https://miro.medium.com/v2/resize:fit:750/format:webp/1*JOMSVAYTK5OEp4SQHgqoMg.png 750w, https://miro.medium.com/v2/resize:fit:786/format:webp/1*JOMSVAYTK5OEp4SQHgqoMg.png 786w, https://miro.medium.com/v2/resize:fit:828/format:webp/1*JOMSVAYTK5OEp4SQHgqoMg.png 828w, https://miro.medium.com/v2/resize:fit:1100/format:webp/1*JOMSVAYTK5OEp4SQHgqoMg.png 1100w, https://miro.medium.com/v2/resize:fit:1300/format:webp/1*JOMSVAYTK5OEp4SQHgqoMg.png 1300w\" type=\"image/webp\"/>\n",
            "                     <source data-testid=\"og\" sizes=\"(min-resolution: 4dppx) and (max-width: 700px) 50vw, (-webkit-min-device-pixel-ratio: 4) and (max-width: 700px) 50vw, (min-resolution: 3dppx) and (max-width: 700px) 67vw, (-webkit-min-device-pixel-ratio: 3) and (max-width: 700px) 65vw, (min-resolution: 2.5dppx) and (max-width: 700px) 80vw, (-webkit-min-device-pixel-ratio: 2.5) and (max-width: 700px) 80vw, (min-resolution: 2dppx) and (max-width: 700px) 100vw, (-webkit-min-device-pixel-ratio: 2) and (max-width: 700px) 100vw, 650px\" srcset=\"https://miro.medium.com/v2/resize:fit:640/1*JOMSVAYTK5OEp4SQHgqoMg.png 640w, https://miro.medium.com/v2/resize:fit:720/1*JOMSVAYTK5OEp4SQHgqoMg.png 720w, https://miro.medium.com/v2/resize:fit:750/1*JOMSVAYTK5OEp4SQHgqoMg.png 750w, https://miro.medium.com/v2/resize:fit:786/1*JOMSVAYTK5OEp4SQHgqoMg.png 786w, https://miro.medium.com/v2/resize:fit:828/1*JOMSVAYTK5OEp4SQHgqoMg.png 828w, https://miro.medium.com/v2/resize:fit:1100/1*JOMSVAYTK5OEp4SQHgqoMg.png 1100w, https://miro.medium.com/v2/resize:fit:1300/1*JOMSVAYTK5OEp4SQHgqoMg.png 1300w\"/>\n",
            "                     <img alt=\"\" class=\"bf km kn c\" height=\"406\" loading=\"lazy\" role=\"presentation\" width=\"650\"/>\n",
            "                    </picture>\n",
            "                   </div>\n",
            "                  </figure>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"0b15\">\n",
            "                   Sentiment Analysis can help us decipher the mood and emotions of general public and gather insightful information regarding the context. Sentiment Analysis is a process of analyzing data and classifying it based on the need of the research.\n",
            "                  </p>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"4526\">\n",
            "                   These sentiments can be used for a better understanding of various events and impact caused by it. L. Bing [1] highlights that in the research literature it is possible to see many different names, e.g. “sentiment analysis, opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, review mining”, however all of them have similar purposes and belong to the subject of sentiment analysis or opinion mining. By analysing these sentiments, we may find what people like, what they want and what their major concerns are.\n",
            "                  </p>\n",
            "                  <h2 class=\"mi ku in bd kv mj mk dn kz ml mm dp ld lv mn mo lf lz mp mq lh md mr ms lj mt bi\" id=\"ebfe\">\n",
            "                   Sentiment Analysis using TextBlob\n",
            "                  </h2>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp mu jo lr ls mv jr lu lv mw lx ly lz mx mb mc md my mf mg mh ig bi\" id=\"1ec5\">\n",
            "                   TextBlob is a python library for Natural Language Processing (NLP).TextBlob actively used Natural Language ToolKit (NLTK) to achieve its tasks. NLTK is a library which gives an easy access to a lot of lexical resources and allows users to work with categorization, classification and many other tasks. TextBlob is a simple library which supports complex analysis and operations on textual data.\n",
            "                  </p>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"9c90\">\n",
            "                   For\n",
            "                   <strong class=\"lo io\">\n",
            "                    lexicon-based approaches\n",
            "                   </strong>\n",
            "                   , a sentiment is defined by its semantic orientation and the intensity of each word in the sentence. This requires a pre-defined dictionary classifying negative and positive words. Generally, a text message will be represented by bag of words. After assigning individual scores to all the words, final sentiment is calculated by some pooling operation like taking an average of all the sentiments.\n",
            "                  </p>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"40ca\">\n",
            "                   TextBlob returns\n",
            "                   <strong class=\"lo io\">\n",
            "                    polarity\n",
            "                   </strong>\n",
            "                   and\n",
            "                   <strong class=\"lo io\">\n",
            "                    subjectivity\n",
            "                   </strong>\n",
            "                   of a sentence. Polarity lies between [-1,1], -1 defines a negative sentiment and 1 defines a positive sentiment. Negation words reverse the polarity. TextBlob has semantic labels that help with fine-grained analysis. For example — emoticons, exclamation mark, emojis, etc. Subjectivity lies between [0,1].\n",
            "                   <strong class=\"lo io\">\n",
            "                    Subjectivity quantifies the amount of personal opinion and factual information contained in the text. The higher subjectivity means that the text contains personal opinion rather than factual information\n",
            "                   </strong>\n",
            "                   . TextBlob has one more parameter — intensity. TextBlob calculates subjectivity by looking at the ‘\n",
            "                   <strong class=\"lo io\">\n",
            "                    intensity\n",
            "                   </strong>\n",
            "                   ’. Intensity determines if a word\n",
            "                   <em class=\"mz\">\n",
            "                    modifies\n",
            "                   </em>\n",
            "                   the next word. For English, adverbs are used as modifiers (‘very good’).\n",
            "                  </p>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"eced\">\n",
            "                   For example: We calculated polarity and subjectivity for “I do not like this example at all, it is too boring”. For this particular example, polarity = -1 and subjectivity is 1, which is fair.\n",
            "                  </p>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"3be4\">\n",
            "                   However, for the sentence “This was a helpful example but I would prefer another one”. It returns\n",
            "                   <strong class=\"lo io\">\n",
            "                    0.0 for both subjectivity and polarity\n",
            "                   </strong>\n",
            "                   which is not the finest answer we’d expect.\n",
            "                  </p>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"3741\">\n",
            "                   It is expected that if the library returns exactly 0.0 either if your sentence didn’t contain any words that had a polarity in the NLTK training set or because TextBlob uses a weighted average sentiment score over all the words in each sample. This easily diffuses out the effect of sentences with widely varying polarities between words in our case : ‘helpful’ and ‘but’.\n",
            "                  </p>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"4f3c\">\n",
            "                   More often than not, for simple cases, TextBlob works just fine.\n",
            "                  </p>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"51a6\">\n",
            "                   Steps to apply Sentiment Analysis using TextBlob –\n",
            "                  </p>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"778c\">\n",
            "                   1. Import textblob\n",
            "                  </p>\n",
            "                  <pre class=\"kd ke kf kg gt na nb nc nd aw ne bi\"><span class=\"mi ku in nb b gy nf ng l nh ni\" id=\"2e97\">from textblob import TextBlob</span></pre>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"c643\">\n",
            "                   2. Define a function that calculates subjectivity, polarity and give it a score based on the threshold you want to set.\n",
            "                  </p>\n",
            "                  <figure class=\"kd ke kf kg gt kh\">\n",
            "                   <div class=\"bz fp l di\">\n",
            "                    <div class=\"nj nk l\">\n",
            "                    </div>\n",
            "                   </div>\n",
            "                   <figcaption class=\"ko kp gj gh gi kq kr bd b be z dk\">\n",
            "                    Snippet of code for using TextBlob and analysis your data\n",
            "                   </figcaption>\n",
            "                  </figure>\n",
            "                  <blockquote class=\"nl\">\n",
            "                   <p class=\"nm nn in bd no np nq nr ns nt nu mh dk\" id=\"0097\">\n",
            "                    Another useful article that I came across is:\n",
            "                    <a class=\"ae ks\" href=\"https://neptune.ai/blog/sentiment-analysis-python-textblob-vs-vader-vs-flair\" rel=\"noopener ugc nofollow\" target=\"_blank\">\n",
            "                     https://neptune.ai/blog/sentiment-analysis-python-textblob-vs-vader-vs-flair\n",
            "                    </a>\n",
            "                   </p>\n",
            "                  </blockquote>\n",
            "                  <p class=\"pw-post-body-paragraph lm ln in lo b lp nv jo lr ls nw jr lu lv nx lx ly lz ny mb mc md nz mf mg mh ig bi\" id=\"fe5a\">\n",
            "                   References —\n",
            "                  </p>\n",
            "                  <ol class=\"\">\n",
            "                   <li class=\"oa ob in lo b lp lq ls lt lv oc lz od md oe mh of og oh oi bi\" id=\"dd0f\">\n",
            "                    <a class=\"ae ks\" href=\"https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf\" rel=\"noopener ugc nofollow\" target=\"_blank\">\n",
            "                     https://www.cs.uic.edu/~liub/FBS/SentimentAnalysis-and-OpinionMining.pdf\n",
            "                    </a>\n",
            "                   </li>\n",
            "                   <li class=\"oa ob in lo b lp oj ls ok lv ol lz om md on mh of og oh oi bi\" id=\"a803\">\n",
            "                    <a class=\"ae ks\" href=\"https://github.com/sloria/TextBlob/blob/dev/textblob/en/en-sentiment.xml\" rel=\"noopener ugc nofollow\" target=\"_blank\">\n",
            "                     https://github.com/sloria/TextBlob/blob/dev/textblob/\n",
            "                    </a>\n",
            "                   </li>\n",
            "                  </ol>\n",
            "                 </div>\n",
            "                </div>\n",
            "               </section>\n",
            "              </div>\n",
            "             </div>\n",
            "            </article>\n",
            "           </div>\n",
            "          </div>\n",
            "         </div>\n",
            "         <div class=\"ab cl\">\n",
            "          <div class=\"fg bf fh fi fj fk\">\n",
            "           <div class=\"oo op ab hg\">\n",
            "            <div class=\"oq ab\">\n",
            "             <a class=\"or aw al an\" href=\"https://medium.com/tag/sentiment-analysis?source=post_page-----3ac3a11d524---------------sentiment_analysis-----------------\" rel=\"noopener follow\">\n",
            "              <div class=\"os di fe ot ou ov bd b be z bi ow\">\n",
            "               Sentiment Analysis\n",
            "              </div>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"oq ab\">\n",
            "             <a class=\"or aw al an\" href=\"https://medium.com/tag/textblob?source=post_page-----3ac3a11d524---------------textblob-----------------\" rel=\"noopener follow\">\n",
            "              <div class=\"os di fe ot ou ov bd b be z bi ow\">\n",
            "               Textblob\n",
            "              </div>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"oq ab\">\n",
            "             <a class=\"or aw al an\" href=\"https://medium.com/tag/subjectivity?source=post_page-----3ac3a11d524---------------subjectivity-----------------\" rel=\"noopener follow\">\n",
            "              <div class=\"os di fe ot ou ov bd b be z bi ow\">\n",
            "               Subjectivity\n",
            "              </div>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"oq ab\">\n",
            "             <a class=\"or aw al an\" href=\"https://medium.com/tag/polarity?source=post_page-----3ac3a11d524---------------polarity-----------------\" rel=\"noopener follow\">\n",
            "              <div class=\"os di fe ot ou ov bd b be z bi ow\">\n",
            "               Polarity\n",
            "              </div>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"oq ab\">\n",
            "             <a class=\"or aw al an\" href=\"https://medium.com/tag/towards-data-science?source=post_page-----3ac3a11d524---------------towards_data_science-----------------\" rel=\"noopener follow\">\n",
            "              <div class=\"os di fe ot ou ov bd b be z bi ow\">\n",
            "               Towards Data Science\n",
            "              </div>\n",
            "             </a>\n",
            "            </div>\n",
            "           </div>\n",
            "          </div>\n",
            "         </div>\n",
            "         <div class=\"l\">\n",
            "         </div>\n",
            "         <footer class=\"ox oy oz pa pb pc pd pe pf ab q pg ph c\">\n",
            "          <div class=\"l ad\">\n",
            "           <div class=\"ab cl\">\n",
            "            <div class=\"fg bf fh fi fj fk\">\n",
            "             <div class=\"ab gv pi\">\n",
            "              <div class=\"ab q cb\">\n",
            "               <div class=\"pj l\">\n",
            "                <span class=\"l hc pk pl e d\">\n",
            "                 <div class=\"ab q cb\">\n",
            "                  <div class=\"pw-multi-vote-icon di pm pn po pp\">\n",
            "                   <span>\n",
            "                    <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fvote%2Ftowards-data-science%2F3ac3a11d524&amp;operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;user=Parthvi+Shah&amp;userId=e2a3c138d982&amp;source=-----3ac3a11d524---------------------clap_footer-----------\" rel=\"noopener follow\">\n",
            "                     <div class=\"pq an eu pr ps pt al pu pv pw pp\">\n",
            "                      <svg aria-label=\"clap\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                       <path clip-rule=\"evenodd\" d=\"M11.37.83L12 3.28l.63-2.45h-1.26zM13.92 3.95l1.52-2.1-1.18-.4-.34 2.5zM8.59 1.84l1.52 2.11-.34-2.5-1.18.4zM18.52 18.92a4.23 4.23 0 0 1-2.62 1.33l.41-.37c2.39-2.4 2.86-4.95 1.4-7.63l-.91-1.6-.8-1.67c-.25-.56-.19-.98.21-1.29a.7.7 0 0 1 .55-.13c.28.05.54.23.72.5l2.37 4.16c.97 1.62 1.14 4.23-1.33 6.7zm-11-.44l-4.15-4.15a.83.83 0 0 1 1.17-1.17l2.16 2.16a.37.37 0 0 0 .51-.52l-2.15-2.16L3.6 11.2a.83.83 0 0 1 1.17-1.17l3.43 3.44a.36.36 0 0 0 .52 0 .36.36 0 0 0 0-.52L5.29 9.51l-.97-.97a.83.83 0 0 1 0-1.16.84.84 0 0 1 1.17 0l.97.97 3.44 3.43a.36.36 0 0 0 .51 0 .37.37 0 0 0 0-.52L6.98 7.83a.82.82 0 0 1-.18-.9.82.82 0 0 1 .76-.51c.22 0 .43.09.58.24l5.8 5.79a.37.37 0 0 0 .58-.42L13.4 9.67c-.26-.56-.2-.98.2-1.29a.7.7 0 0 1 .55-.13c.28.05.55.23.73.5l2.2 3.86c1.3 2.38.87 4.59-1.29 6.75a4.65 4.65 0 0 1-4.19 1.37 7.73 7.73 0 0 1-4.07-2.25zm3.23-12.5l2.12 2.11c-.41.5-.47 1.17-.13 1.9l.22.46-3.52-3.53a.81.81 0 0 1-.1-.36c0-.23.09-.43.24-.59a.85.85 0 0 1 1.17 0zm7.36 1.7a1.86 1.86 0 0 0-1.23-.84 1.44 1.44 0 0 0-1.12.27c-.3.24-.5.55-.58.89-.25-.25-.57-.4-.91-.47-.28-.04-.56 0-.82.1l-2.18-2.18a1.56 1.56 0 0 0-2.2 0c-.2.2-.33.44-.4.7a1.56 1.56 0 0 0-2.63.75 1.6 1.6 0 0 0-2.23-.04 1.56 1.56 0 0 0 0 2.2c-.24.1-.5.24-.72.45a1.56 1.56 0 0 0 0 2.2l.52.52a1.56 1.56 0 0 0-.75 2.61L7 19a8.46 8.46 0 0 0 4.48 2.45 5.18 5.18 0 0 0 3.36-.5 4.89 4.89 0 0 0 4.2-1.51c2.75-2.77 2.54-5.74 1.43-7.59L18.1 7.68z\" fill-rule=\"evenodd\">\n",
            "                       </path>\n",
            "                      </svg>\n",
            "                     </div>\n",
            "                    </a>\n",
            "                   </span>\n",
            "                  </div>\n",
            "                  <div class=\"pw-multi-vote-count l px py pz qa qb qc qd\">\n",
            "                   <p class=\"bd b dl z dk\">\n",
            "                    <span class=\"qe\">\n",
            "                     --\n",
            "                    </span>\n",
            "                   </p>\n",
            "                  </div>\n",
            "                 </div>\n",
            "                </span>\n",
            "                <span class=\"l h g f qf qg\">\n",
            "                 <div class=\"ab q cb\">\n",
            "                  <div class=\"pw-multi-vote-icon di pm pn po pp\">\n",
            "                   <span>\n",
            "                    <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fvote%2Ftowards-data-science%2F3ac3a11d524&amp;operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;user=Parthvi+Shah&amp;userId=e2a3c138d982&amp;source=-----3ac3a11d524---------------------clap_footer-----------\" rel=\"noopener follow\">\n",
            "                     <div class=\"pq an eu pr ps pt al pu pv pw pp\">\n",
            "                      <svg aria-label=\"clap\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                       <path clip-rule=\"evenodd\" d=\"M11.37.83L12 3.28l.63-2.45h-1.26zM13.92 3.95l1.52-2.1-1.18-.4-.34 2.5zM8.59 1.84l1.52 2.11-.34-2.5-1.18.4zM18.52 18.92a4.23 4.23 0 0 1-2.62 1.33l.41-.37c2.39-2.4 2.86-4.95 1.4-7.63l-.91-1.6-.8-1.67c-.25-.56-.19-.98.21-1.29a.7.7 0 0 1 .55-.13c.28.05.54.23.72.5l2.37 4.16c.97 1.62 1.14 4.23-1.33 6.7zm-11-.44l-4.15-4.15a.83.83 0 0 1 1.17-1.17l2.16 2.16a.37.37 0 0 0 .51-.52l-2.15-2.16L3.6 11.2a.83.83 0 0 1 1.17-1.17l3.43 3.44a.36.36 0 0 0 .52 0 .36.36 0 0 0 0-.52L5.29 9.51l-.97-.97a.83.83 0 0 1 0-1.16.84.84 0 0 1 1.17 0l.97.97 3.44 3.43a.36.36 0 0 0 .51 0 .37.37 0 0 0 0-.52L6.98 7.83a.82.82 0 0 1-.18-.9.82.82 0 0 1 .76-.51c.22 0 .43.09.58.24l5.8 5.79a.37.37 0 0 0 .58-.42L13.4 9.67c-.26-.56-.2-.98.2-1.29a.7.7 0 0 1 .55-.13c.28.05.55.23.73.5l2.2 3.86c1.3 2.38.87 4.59-1.29 6.75a4.65 4.65 0 0 1-4.19 1.37 7.73 7.73 0 0 1-4.07-2.25zm3.23-12.5l2.12 2.11c-.41.5-.47 1.17-.13 1.9l.22.46-3.52-3.53a.81.81 0 0 1-.1-.36c0-.23.09-.43.24-.59a.85.85 0 0 1 1.17 0zm7.36 1.7a1.86 1.86 0 0 0-1.23-.84 1.44 1.44 0 0 0-1.12.27c-.3.24-.5.55-.58.89-.25-.25-.57-.4-.91-.47-.28-.04-.56 0-.82.1l-2.18-2.18a1.56 1.56 0 0 0-2.2 0c-.2.2-.33.44-.4.7a1.56 1.56 0 0 0-2.63.75 1.6 1.6 0 0 0-2.23-.04 1.56 1.56 0 0 0 0 2.2c-.24.1-.5.24-.72.45a1.56 1.56 0 0 0 0 2.2l.52.52a1.56 1.56 0 0 0-.75 2.61L7 19a8.46 8.46 0 0 0 4.48 2.45 5.18 5.18 0 0 0 3.36-.5 4.89 4.89 0 0 0 4.2-1.51c2.75-2.77 2.54-5.74 1.43-7.59L18.1 7.68z\" fill-rule=\"evenodd\">\n",
            "                       </path>\n",
            "                      </svg>\n",
            "                     </div>\n",
            "                    </a>\n",
            "                   </span>\n",
            "                  </div>\n",
            "                  <div class=\"pw-multi-vote-count l px py pz qa qb qc qd\">\n",
            "                   <p class=\"bd b dl z dk\">\n",
            "                    <span class=\"qe\">\n",
            "                     --\n",
            "                    </span>\n",
            "                   </p>\n",
            "                  </div>\n",
            "                 </div>\n",
            "                </span>\n",
            "               </div>\n",
            "               <div class=\"bo ab\">\n",
            "                <div>\n",
            "                 <div aria-hidden=\"false\" class=\"bk\">\n",
            "                  <button aria-label=\"responses\" class=\"an pq qi qj ab q eu ps qk\">\n",
            "                   <svg aria-label=\"responses\" class=\"qh\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                    <path d=\"M18 16.8a7.14 7.14 0 0 0 2.24-5.32c0-4.12-3.53-7.48-8.05-7.48C7.67 4 4 7.36 4 11.48c0 4.13 3.67 7.48 8.2 7.48a8.9 8.9 0 0 0 2.38-.32c.23.2.48.39.75.56 1.06.69 2.2 1.04 3.4 1.04.22 0 .4-.11.48-.29a.5.5 0 0 0-.04-.52 6.4 6.4 0 0 1-1.16-2.65v.02zm-3.12 1.06l-.06-.22-.32.1a8 8 0 0 1-2.3.33c-4.03 0-7.3-2.96-7.3-6.59S8.17 4.9 12.2 4.9c4 0 7.1 2.96 7.1 6.6 0 1.8-.6 3.47-2.02 4.72l-.2.16v.26l.02.3a6.74 6.74 0 0 0 .88 2.4 5.27 5.27 0 0 1-2.17-.86c-.28-.17-.72-.38-.94-.59l.01-.02z\">\n",
            "                    </path>\n",
            "                   </svg>\n",
            "                  </button>\n",
            "                 </div>\n",
            "                </div>\n",
            "               </div>\n",
            "              </div>\n",
            "              <div class=\"ab q\">\n",
            "               <div aria-describedby=\"postFooterSocialMenu\" aria-hidden=\"false\" aria-labelledby=\"postFooterSocialMenu\" class=\"bk\">\n",
            "                <div>\n",
            "                 <div aria-hidden=\"false\" class=\"bk\">\n",
            "                  <button class=\"ae eu ag ah ai aj ak hj am an ao ej ql qm qk qn\">\n",
            "                   <svg fill=\"none\" height=\"24\" viewbox=\"0 0 24 24\" width=\"24\">\n",
            "                    <path clip-rule=\"evenodd\" d=\"M15.22 4.93a.42.42 0 0 1-.12.13h.01a.45.45 0 0 1-.29.08.52.52 0 0 1-.3-.13L12.5 3v7.07a.5.5 0 0 1-.5.5.5.5 0 0 1-.5-.5V3.02l-2 2a.45.45 0 0 1-.57.04h-.02a.4.4 0 0 1-.16-.3.4.4 0 0 1 .1-.32l2.8-2.8a.5.5 0 0 1 .7 0l2.8 2.8a.42.42 0 0 1 .07.5zm-.1.14zm.88 2h1.5a2 2 0 0 1 2 2v10a2 2 0 0 1-2 2h-11a2 2 0 0 1-2-2v-10a2 2 0 0 1 2-2H8a.5.5 0 0 1 .35.14c.1.1.15.22.15.35a.5.5 0 0 1-.15.35.5.5 0 0 1-.35.15H6.4c-.5 0-.9.4-.9.9v10.2a.9.9 0 0 0 .9.9h11.2c.5 0 .9-.4.9-.9V8.96c0-.5-.4-.9-.9-.9H16a.5.5 0 0 1 0-1z\" fill=\"currentColor\" fill-rule=\"evenodd\">\n",
            "                    </path>\n",
            "                   </svg>\n",
            "                  </button>\n",
            "                 </div>\n",
            "                </div>\n",
            "               </div>\n",
            "               <div class=\"qo qp l fo\">\n",
            "                <span>\n",
            "                 <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fbookmark%2Fp%2F3ac3a11d524&amp;operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=--------------------------bookmark_footer-----------\" rel=\"noopener follow\">\n",
            "                  <button aria-controls=\"addToCatalogBookmarkButton\" aria-expanded=\"false\" aria-label=\"Add to list bookmark button\" class=\"ae eu ag ah ai aj ak hj am an ao ej ho hp hq\">\n",
            "                   <svg aria-label=\"Add to list bookmark button\" class=\"hn\" fill=\"none\" height=\"25\" viewbox=\"0 0 25 25\" width=\"25\">\n",
            "                    <path d=\"M18 2.5a.5.5 0 0 1 1 0V5h2.5a.5.5 0 0 1 0 1H19v2.5a.5.5 0 1 1-1 0V6h-2.5a.5.5 0 0 1 0-1H18V2.5zM7 7a1 1 0 0 1 1-1h3.5a.5.5 0 0 0 0-1H8a2 2 0 0 0-2 2v14a.5.5 0 0 0 .8.4l5.7-4.4 5.7 4.4a.5.5 0 0 0 .8-.4v-8.5a.5.5 0 0 0-1 0v7.48l-5.2-4a.5.5 0 0 0-.6 0l-5.2 4V7z\" fill=\"#292929\">\n",
            "                    </path>\n",
            "                   </svg>\n",
            "                  </button>\n",
            "                 </a>\n",
            "                </span>\n",
            "               </div>\n",
            "              </div>\n",
            "             </div>\n",
            "            </div>\n",
            "           </div>\n",
            "          </div>\n",
            "         </footer>\n",
            "         <div class=\"ab cl\">\n",
            "          <div class=\"fg bf fh fi fj fk\">\n",
            "          </div>\n",
            "         </div>\n",
            "         <div class=\"l\">\n",
            "          <div class=\"l bv pi\">\n",
            "           <div class=\"l pi\">\n",
            "            <div class=\"qq qr l bv\">\n",
            "             <div class=\"ab cl\">\n",
            "              <div class=\"fg bf fh fi fj fk\">\n",
            "               <div class=\"ab q gv\">\n",
            "                <h2 class=\"bd qs mj dn qt kz ml dp qu ld lv mo qv lf lz mq qw lh md ms qx lj fp fr fs ft fu fv fw bi\">\n",
            "                 <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://towardsdatascience.com/?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "                  More from Towards Data Science\n",
            "                 </a>\n",
            "                </h2>\n",
            "                <span>\n",
            "                 <a class=\"bd b be z eb qy ec ed ee ef eg eh ei ej ek el em eo ep de bk eq\" href=\"https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fsubscribe%2Fcollection%2Ftowards-data-science%2F3ac3a11d524&amp;operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;collection=Towards+Data+Science&amp;collectionId=7f60cf5620c9&amp;source=post_page-----3ac3a11d524---------------------follow_footer-----------\" rel=\"noopener follow\">\n",
            "                  Follow\n",
            "                 </a>\n",
            "                </span>\n",
            "               </div>\n",
            "               <div class=\"qz l ig\">\n",
            "                <p class=\"bd b be z dk\">\n",
            "                 Your home for data science. A Medium publication sharing concepts, ideas and codes.\n",
            "                </p>\n",
            "               </div>\n",
            "              </div>\n",
            "             </div>\n",
            "            </div>\n",
            "           </div>\n",
            "           <div class=\"ra l\">\n",
            "            <div class=\"rb rc l\">\n",
            "             <div class=\"km l kp\">\n",
            "              <a class=\"bd b be z eb qy ec ed ee ef eg eh ei ej ek el em eo ep de bk eq\" href=\"https://towardsdatascience.com/?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "               Read more from\n",
            "               <!-- -->\n",
            "               Towards Data Science\n",
            "              </a>\n",
            "             </div>\n",
            "            </div>\n",
            "           </div>\n",
            "          </div>\n",
            "         </div>\n",
            "         <div class=\"d\">\n",
            "          <div class=\"rd re rf l pg rg rh ib ri rj\">\n",
            "           <div class=\"ab cl\">\n",
            "            <div class=\"rk rl rm rn ro rp fg bf\">\n",
            "             <a aria-label=\"Go to homepage\" class=\"ae af ag ah ai aj ak al am an ao rq rr rs rt\" href=\"https://medium.com/?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "              <svg class=\"ec ru\" viewbox=\"0 0 3940 610\">\n",
            "               <path d=\"M594.79 308.2c0 163.76-131.85 296.52-294.5 296.52S5.8 472 5.8 308.2 137.65 11.69 300.29 11.69s294.5 132.75 294.5 296.51M917.86 308.2c0 154.16-65.93 279.12-147.25 279.12s-147.25-125-147.25-279.12S689.29 29.08 770.61 29.08s147.25 125 147.25 279.12M1050 308.2c0 138.12-23.19 250.08-51.79 250.08s-51.79-112-51.79-250.08 23.19-250.08 51.8-250.08S1050 170.09 1050 308.2M1862.77 37.4l.82-.18v-6.35h-167.48l-155.51 365.5-155.51-365.5h-180.48v6.35l.81.18c30.57 6.9 46.09 17.19 46.09 54.3v434.45c0 37.11-15.58 47.4-46.15 54.3l-.81.18V587H1327v-6.35l-.81-.18c-30.57-6.9-46.09-17.19-46.09-54.3V116.9L1479.87 587h11.33l205.59-483.21V536.9c-2.62 29.31-18 38.36-45.68 44.61l-.82.19v6.3h213.3v-6.3l-.82-.19c-27.71-6.25-43.46-15.3-46.08-44.61l-.14-445.2h.14c0-37.11 15.52-47.4 46.08-54.3m97.43 287.8c3.49-78.06 31.52-134.4 78.56-135.37 14.51.24 26.68 5 36.14 14.16 20.1 19.51 29.55 60.28 28.09 121.21zm-2.11 22h250v-1.05c-.71-59.69-18-106.12-51.34-138-28.82-27.55-71.49-42.71-116.31-42.71h-1c-23.26 0-51.79 5.64-72.09 15.86-23.11 10.7-43.49 26.7-60.45 47.7-27.3 33.83-43.84 79.55-47.86 130.93-.13 1.54-.24 3.08-.35 4.62s-.18 2.92-.25 4.39a332.64 332.64 0 0 0-.36 21.69C1860.79 507 1923.65 600 2035.3 600c98 0 155.07-71.64 169.3-167.8l-7.19-2.53c-25 51.68-69.9 83-121 79.18-69.76-5.22-123.2-75.95-118.35-161.63m532.69 157.68c-8.2 19.45-25.31 30.15-48.24 30.15s-43.89-15.74-58.78-44.34c-16-30.7-24.42-74.1-24.42-125.51 0-107 33.28-176.21 84.79-176.21 21.57 0 38.55 10.7 46.65 29.37zm165.84 76.28c-30.57-7.23-46.09-18-46.09-57V5.28L2424.77 60v6.7l1.14-.09c25.62-2.07 43 1.47 53.09 10.79 7.9 7.3 11.75 18.5 11.75 34.26v71.14c-18.31-11.69-40.09-17.38-66.52-17.38-53.6 0-102.59 22.57-137.92 63.56-36.83 42.72-56.3 101.1-56.3 168.81C2230 518.72 2289.53 600 2378.13 600c51.83 0 93.53-28.4 112.62-76.3V588h166.65v-6.66zm159.29-505.33c0-37.76-28.47-66.24-66.24-66.24-37.59 0-67 29.1-67 66.24s29.44 66.24 67 66.24c37.77 0 66.24-28.48 66.24-66.24m43.84 505.33c-30.57-7.23-46.09-18-46.09-57h-.13V166.65l-166.66 47.85v6.5l1 .09c36.06 3.21 45.93 15.63 45.93 57.77V588h166.8v-6.66zm427.05 0c-30.57-7.23-46.09-18-46.09-57V166.65L3082 212.92v6.52l.94.1c29.48 3.1 38 16.23 38 58.56v226c-9.83 19.45-28.27 31-50.61 31.78-36.23 0-56.18-24.47-56.18-68.9V166.66l-166.66 47.85V221l1 .09c36.06 3.2 45.94 15.62 45.94 57.77v191.27a214.48 214.48 0 0 0 3.47 39.82l3 13.05c14.11 50.56 51.08 77 109 77 49.06 0 92.06-30.37 111-77.89v66h166.66v-6.66zM3934.2 588v-6.67l-.81-.19c-33.17-7.65-46.09-22.07-46.09-51.43v-243.2c0-75.83-42.59-121.09-113.93-121.09-52 0-95.85 30.05-112.73 76.86-13.41-49.6-52-76.86-109.06-76.86-50.12 0-89.4 26.45-106.25 71.13v-69.87l-166.66 45.89v6.54l1 .09c35.63 3.16 45.93 15.94 45.93 57V588h155.5v-6.66l-.82-.2c-26.46-6.22-35-17.56-35-46.66V255.72c7-16.35 21.11-35.72 49-35.72 34.64 0 52.2 24 52.2 71.28V588h155.54v-6.66l-.82-.2c-26.46-6.22-35-17.56-35-46.66v-248a160.45 160.45 0 0 0-2.2-27.68c7.42-17.77 22.34-38.8 51.37-38.8 35.13 0 52.2 23.31 52.2 71.28V588z\">\n",
            "               </path>\n",
            "              </svg>\n",
            "             </a>\n",
            "             <div class=\"rv l\">\n",
            "              <p class=\"bd b dl z rw\">\n",
            "               <a class=\"ae af ag ah ai aj ak al am an ao rq rr rx qp\" href=\"https://medium.com/about?autoplay=1&amp;source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "                About\n",
            "               </a>\n",
            "               <a class=\"ae af ag ah ai aj ak al am an ao rq rr rx qp\" href=\"https://help.medium.com/hc/en-us?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "                Help\n",
            "               </a>\n",
            "               <a class=\"ae af ag ah ai aj ak al am an ao rq rr rx qp\" href=\"https://policy.medium.com/medium-terms-of-service-9db0094a1e0f?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "                Terms\n",
            "               </a>\n",
            "               <a class=\"ae af ag ah ai aj ak al am an ao rq rr rx\" href=\"https://policy.medium.com/medium-privacy-policy-f03bf92035c9?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "                Privacy\n",
            "               </a>\n",
            "              </p>\n",
            "             </div>\n",
            "             <div class=\"j i d\">\n",
            "              <hr aria-hidden=\"true\" class=\"pq ry rz sa\"/>\n",
            "              <h2 class=\"bd qs gy z im rw\">\n",
            "               Get the Medium app\n",
            "              </h2>\n",
            "              <div class=\"rv ab\">\n",
            "               <div class=\"fm l\">\n",
            "                <a class=\"ae af ag ah ai aj ak al am an ao rq rr rs rt\" href=\"https://itunes.apple.com/app/medium-everyones-stories/id828256236?pt=698524&amp;mt=8&amp;ct=post_page&amp;source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "                 <img alt=\"A button that says 'Download on the App Store', and if clicked it will lead you to the iOS App store\" class=\"\" height=\"41\" loading=\"lazy\" src=\"https://miro.medium.com/v2/resize:fit:270/1*Crl55Tm6yDNMoucPo1tvDg.png\" width=\"135\"/>\n",
            "                </a>\n",
            "               </div>\n",
            "               <a class=\"ae af ag ah ai aj ak al am an ao rq rr rs rt\" href=\"https://play.google.com/store/apps/details?id=com.medium.reader&amp;source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">\n",
            "                <img alt=\"A button that says 'Get it on, Google Play', and if clicked it will lead you to the Google Play store\" class=\"\" height=\"41\" loading=\"lazy\" src=\"https://miro.medium.com/v2/resize:fit:270/1*W_RAPQ62h0em559zluJLdQ.png\" width=\"135\"/>\n",
            "               </a>\n",
            "              </div>\n",
            "             </div>\n",
            "            </div>\n",
            "           </div>\n",
            "          </div>\n",
            "         </div>\n",
            "        </div>\n",
            "       </main>\n",
            "       <div class=\"cm cn h co cp cq k cr cs ct j cu cv cw cx cy cz da db dc dd de c df dg\">\n",
            "        <div class=\"dh bf bk di\">\n",
            "         <div class=\"l m sb\">\n",
            "          <div class=\"sc ab sd\">\n",
            "           <div class=\"l ad\">\n",
            "            <div class=\"se sf sg l\">\n",
            "            </div>\n",
            "            <div class=\"se sf sg l\">\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://shahparthvi22.medium.com/?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "              <div class=\"l di\">\n",
            "               <img alt=\"Parthvi Shah\" class=\"l de bw sh si fe\" height=\"88\" loading=\"lazy\" src=\"https://miro.medium.com/v2/resize:fill:176:176/1*-WmnCAUlTAjdn2C2uFbr_w.jpeg\" width=\"88\"/>\n",
            "               <div class=\"fb bw l sh si fc n aw fd\">\n",
            "               </div>\n",
            "              </div>\n",
            "             </a>\n",
            "             <div class=\"sj l\">\n",
            "              <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://shahparthvi22.medium.com/?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "               <h2 class=\"pw-author-name bd qs gy z im bi\">\n",
            "                <span class=\"ig\">\n",
            "                 Parthvi Shah\n",
            "                </span>\n",
            "               </h2>\n",
            "              </a>\n",
            "             </div>\n",
            "             <div class=\"sk l\">\n",
            "              <span class=\"pw-follower-count bd b gy gz dk\">\n",
            "               <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://shahparthvi22.medium.com/followers?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "                106 Followers\n",
            "               </a>\n",
            "              </span>\n",
            "             </div>\n",
            "             <div class=\"sl l\">\n",
            "              <p class=\"bd b be z dk\">\n",
            "               <span class=\"ig\">\n",
            "                Masters in Data Science @ NYU\n",
            "               </span>\n",
            "              </p>\n",
            "             </div>\n",
            "             <div class=\"sm sn ab\">\n",
            "              <span>\n",
            "               <a class=\"bd b be z eb qy ec ed ee ef eg eh ei ej ek el em so eo ep de bk eq\" href=\"https://medium.com/m/signin?actionUrl=https%3A%2F%2Fmedium.com%2F_%2Fsubscribe%2Fuser%2Fe2a3c138d982&amp;operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;user=Parthvi+Shah&amp;userId=e2a3c138d982&amp;source=post_page-e2a3c138d982--two_column_layout_sidebar-----------------------follow_profile-----------\" rel=\"noopener follow\">\n",
            "                Follow\n",
            "               </a>\n",
            "              </span>\n",
            "              <div class=\"dj l\">\n",
            "               <div>\n",
            "                <div>\n",
            "                 <div aria-hidden=\"false\" class=\"bk\">\n",
            "                  <div class=\"l\">\n",
            "                   <span>\n",
            "                    <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?actionUrl=%2F_%2Fapi%2Fsubscriptions%2Fnewsletters%2F642229a86fbf&amp;operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;newsletterV3=e2a3c138d982&amp;newsletterV3Id=642229a86fbf&amp;user=Parthvi+Shah&amp;userId=e2a3c138d982&amp;source=---two_column_layout_sidebar-----------------------subscribe_user-----------\" rel=\"noopener follow\">\n",
            "                     <button aria-label=\"Subscribe\" class=\"bd b be z ss al st ne su sv sw sx sy sz ei ej ek el em eo ep de bk eq\">\n",
            "                      <svg class=\"sp sq sr\" fill=\"none\" height=\"38\" viewbox=\"0 0 38 38\" width=\"38\">\n",
            "                       <rect height=\"6.5\" rx=\"0.25\" width=\"0.5\" x=\"26.25\" y=\"9.25\">\n",
            "                       </rect>\n",
            "                       <rect height=\"6.5\" rx=\"0.25\" transform=\"rotate(90 29.75 12.25)\" width=\"0.5\" x=\"29.75\" y=\"12.25\">\n",
            "                       </rect>\n",
            "                       <path d=\"M19.5 12.5h-7a1 1 0 0 0-1 1v11a1 1 0 0 0 1 1h13a1 1 0 0 0 1-1v-5\">\n",
            "                       </path>\n",
            "                       <path d=\"M11.5 14.5L19 20l4-3\">\n",
            "                       </path>\n",
            "                      </svg>\n",
            "                     </button>\n",
            "                    </a>\n",
            "                   </span>\n",
            "                  </div>\n",
            "                 </div>\n",
            "                </div>\n",
            "               </div>\n",
            "              </div>\n",
            "             </div>\n",
            "            </div>\n",
            "            <div class=\"se sf sg l\">\n",
            "            </div>\n",
            "            <div class=\"se sf sg l\">\n",
            "            </div>\n",
            "           </div>\n",
            "           <div class=\"ta ab cb hg\">\n",
            "            <div class=\"tb l\">\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://help.medium.com/hc/en-us?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "              <p class=\"bd b tc td dk\">\n",
            "               Help\n",
            "              </p>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"tb l\">\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.statuspage.io/?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "              <p class=\"bd b tc td dk\">\n",
            "               Status\n",
            "              </p>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"tb l\">\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://about.medium.com/creators/?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "              <p class=\"bd b tc td dk\">\n",
            "               Writers\n",
            "              </p>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"tb l\">\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://blog.medium.com/?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "              <p class=\"bd b tc td dk\">\n",
            "               Blog\n",
            "              </p>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"tb l\">\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/jobs-at-medium/work-at-medium-959d1a85284e?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "              <p class=\"bd b tc td dk\">\n",
            "               Careers\n",
            "              </p>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"tb l\">\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://policy.medium.com/medium-privacy-policy-f03bf92035c9?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "              <p class=\"bd b tc td dk\">\n",
            "               Privacy\n",
            "              </p>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"tb l\">\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://policy.medium.com/medium-terms-of-service-9db0094a1e0f?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "              <p class=\"bd b tc td dk\">\n",
            "               Terms\n",
            "              </p>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"tb l\">\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/about?autoplay=1&amp;source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "              <p class=\"bd b tc td dk\">\n",
            "               About\n",
            "              </p>\n",
            "             </a>\n",
            "            </div>\n",
            "            <div class=\"l\">\n",
            "             <a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://speechify.com/medium?source=---two_column_layout_sidebar----------------------------------\" rel=\"noopener follow\">\n",
            "              <p class=\"bd b tc td dk\">\n",
            "               Text to speech\n",
            "              </p>\n",
            "             </a>\n",
            "            </div>\n",
            "           </div>\n",
            "          </div>\n",
            "         </div>\n",
            "        </div>\n",
            "       </div>\n",
            "      </div>\n",
            "     </div>\n",
            "    </div>\n",
            "   </div>\n",
            "  </div>\n",
            "  <script>\n",
            "   window.__BUILD_ID__=\"main-20230421-232146-7ecfeddcf4\"\n",
            "  </script>\n",
            "  <script>\n",
            "   window.__GRAPHQL_URI__ = \"https://towardsdatascience.com/_/graphql\"\n",
            "  </script>\n",
            "  <script>\n",
            "   window.__PRELOADED_STATE__ = {\"algolia\":{\"queries\":{}},\"auroraPage\":{\"isAuroraPageEnabled\":false},\"cache\":{\"experimentGroupSet\":true,\"reason\":\"This request is not using the cache middleware worker\",\"group\":\"disabled\",\"tags\":[\"group-edgeCachePosts\",\"post-3ac3a11d524\",\"user-e2a3c138d982\",\"collection-7f60cf5620c9\"],\"serverVariantState\":\"\",\"middlewareEnabled\":false,\"cacheStatus\":\"DYNAMIC\",\"shouldUseCache\":false,\"vary\":[],\"inDisabledExperiment\":false,\"postPageSingleColumnLayoutEnabled\":false},\"client\":{\"hydrated\":false,\"isUs\":false,\"isNativeMedium\":false,\"isSafariMobile\":false,\"isSafari\":false,\"isFirefox\":false,\"routingEntity\":{\"type\":\"COLLECTION\",\"id\":\"7f60cf5620c9\",\"explicit\":true},\"viewerIsBot\":false},\"debug\":{\"requestId\":\"53bcd723-99cf-4ed1-9af8-05b4e9164119\",\"hybridDevServices\":[],\"originalSpanCarrier\":{\"ot-tracer-spanid\":\"5309596b2b89e3ae\",\"ot-tracer-traceid\":\"da83a6aac113344\",\"ot-tracer-sampled\":\"true\"}},\"meter\":{},\"multiVote\":{\"clapsPerPost\":{}},\"navigation\":{\"branch\":{\"show\":null,\"hasRendered\":null,\"blockedByCTA\":false},\"hideGoogleOneTap\":false,\"hasRenderedAlternateUserBanner\":null,\"currentLocation\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524\",\"host\":\"towardsdatascience.com\",\"hostname\":\"towardsdatascience.com\",\"referrer\":\"\",\"hasSetReferrer\":false,\"susiModal\":{\"step\":null,\"operation\":\"register\"},\"postRead\":false},\"config\":{\"nodeEnv\":\"production\",\"version\":\"main-20230421-232146-7ecfeddcf4\",\"target\":\"production\",\"productName\":\"Medium\",\"publicUrl\":\"https:\\u002F\\u002Fcdn-client.medium.com\\u002Flite\",\"authDomain\":\"medium.com\",\"authGoogleClientId\":\"216296035834-k1k6qe060s2tp2a2jam4ljdcms00sttg.apps.googleusercontent.com\",\"favicon\":\"production\",\"glyphUrl\":\"https:\\u002F\\u002Fglyph.medium.com\",\"branchKey\":\"key_live_ofxXr2qTrrU9NqURK8ZwEhknBxiI6KBm\",\"algolia\":{\"appId\":\"MQ57UUUQZ2\",\"apiKeySearch\":\"394474ced050e3911ae2249ecc774921\",\"indexPrefix\":\"medium_\",\"host\":\"-dsn.algolia.net\"},\"recaptchaKey\":\"6Lfc37IUAAAAAKGGtC6rLS13R1Hrw_BqADfS1LRk\",\"recaptcha3Key\":\"6Lf8R9wUAAAAABMI_85Wb8melS7Zj6ziuf99Yot5\",\"datadog\":{\"applicationId\":\"6702d87d-a7e0-42fe-bbcb-95b469547ea0\",\"clientToken\":\"pub853ea8d17ad6821d9f8f11861d23dfed\",\"rumToken\":\"pubf9cc52896502b9413b68ba36fc0c7162\",\"context\":{\"deployment\":{\"target\":\"production\",\"tag\":\"main-20230421-232146-7ecfeddcf4\",\"commit\":\"7ecfeddcf4f6e9949f1cd6d2424dede80d4d584b\"}},\"datacenter\":\"us\"},\"googleAnalyticsCode\":\"UA-24232453-2\",\"googlePay\":{\"apiVersion\":\"2\",\"apiVersionMinor\":\"0\",\"merchantId\":\"BCR2DN6TV7EMTGBM\",\"merchantName\":\"Medium\",\"instanceMerchantId\":\"13685562959212738550\"},\"applePay\":{\"version\":3},\"signInWallCustomDomainCollectionIds\":[\"3a8144eabfe3\",\"336d898217ee\",\"61061eb0c96b\",\"138adf9c44c\",\"819cc2aaeee0\"],\"mediumMastodonDomainName\":\"me.dm\",\"mediumOwnedAndOperatedCollectionIds\":[\"8a9336e5bb4\",\"b7e45b22fec3\",\"193b68bd4fba\",\"8d6b8a439e32\",\"54c98c43354d\",\"3f6ecf56618\",\"d944778ce714\",\"92d2092dc598\",\"ae2a65f35510\",\"1285ba81cada\",\"544c7006046e\",\"fc8964313712\",\"40187e704f1c\",\"88d9857e584e\",\"7b6769f2748b\",\"bcc38c8f6edf\",\"cef6983b292\",\"cb8577c9149e\",\"444d13b52878\",\"713d7dbc99b0\",\"ef8e90590e66\",\"191186aaafa0\",\"55760f21cdc5\",\"9dc80918cc93\",\"bdc4052bbdba\",\"8ccfed20cbb2\"],\"tierOneDomains\":[\"medium.com\",\"thebolditalic.com\",\"arcdigital.media\",\"towardsdatascience.com\",\"uxdesign.cc\",\"codeburst.io\",\"psiloveyou.xyz\",\"writingcooperative.com\",\"entrepreneurshandbook.co\",\"prototypr.io\",\"betterhumans.coach.me\",\"theascent.pub\"],\"topicsToFollow\":[\"d61cf867d93f\",\"8a146bc21b28\",\"1eca0103fff3\",\"4d562ee63426\",\"aef1078a3ef5\",\"e15e46793f8d\",\"6158eb913466\",\"55f1c20aba7a\",\"3d18b94f6858\",\"4861fee224fd\",\"63c6f1f93ee\",\"1d98b3a9a871\",\"decb52b64abf\",\"ae5d4995e225\",\"830cded25262\"],\"topicToTagMappings\":{\"accessibility\":\"accessibility\",\"addiction\":\"addiction\",\"android-development\":\"android-development\",\"art\":\"art\",\"artificial-intelligence\":\"artificial-intelligence\",\"astrology\":\"astrology\",\"basic-income\":\"basic-income\",\"beauty\":\"beauty\",\"biotech\":\"biotech\",\"blockchain\":\"blockchain\",\"books\":\"books\",\"business\":\"business\",\"cannabis\":\"cannabis\",\"cities\":\"cities\",\"climate-change\":\"climate-change\",\"comics\":\"comics\",\"coronavirus\":\"coronavirus\",\"creativity\":\"creativity\",\"cryptocurrency\":\"cryptocurrency\",\"culture\":\"culture\",\"cybersecurity\":\"cybersecurity\",\"data-science\":\"data-science\",\"design\":\"design\",\"digital-life\":\"digital-life\",\"disability\":\"disability\",\"economy\":\"economy\",\"education\":\"education\",\"equality\":\"equality\",\"family\":\"family\",\"feminism\":\"feminism\",\"fiction\":\"fiction\",\"film\":\"film\",\"fitness\":\"fitness\",\"food\":\"food\",\"freelancing\":\"freelancing\",\"future\":\"future\",\"gadgets\":\"gadgets\",\"gaming\":\"gaming\",\"gun-control\":\"gun-control\",\"health\":\"health\",\"history\":\"history\",\"humor\":\"humor\",\"immigration\":\"immigration\",\"ios-development\":\"ios-development\",\"javascript\":\"javascript\",\"justice\":\"justice\",\"language\":\"language\",\"leadership\":\"leadership\",\"lgbtqia\":\"lgbtqia\",\"lifestyle\":\"lifestyle\",\"machine-learning\":\"machine-learning\",\"makers\":\"makers\",\"marketing\":\"marketing\",\"math\":\"math\",\"media\":\"media\",\"mental-health\":\"mental-health\",\"mindfulness\":\"mindfulness\",\"money\":\"money\",\"music\":\"music\",\"neuroscience\":\"neuroscience\",\"nonfiction\":\"nonfiction\",\"outdoors\":\"outdoors\",\"parenting\":\"parenting\",\"pets\":\"pets\",\"philosophy\":\"philosophy\",\"photography\":\"photography\",\"podcasts\":\"podcast\",\"poetry\":\"poetry\",\"politics\":\"politics\",\"privacy\":\"privacy\",\"product-management\":\"product-management\",\"productivity\":\"productivity\",\"programming\":\"programming\",\"psychedelics\":\"psychedelics\",\"psychology\":\"psychology\",\"race\":\"race\",\"relationships\":\"relationships\",\"religion\":\"religion\",\"remote-work\":\"remote-work\",\"san-francisco\":\"san-francisco\",\"science\":\"science\",\"self\":\"self\",\"self-driving-cars\":\"self-driving-cars\",\"sexuality\":\"sexuality\",\"social-media\":\"social-media\",\"society\":\"society\",\"software-engineering\":\"software-engineering\",\"space\":\"space\",\"spirituality\":\"spirituality\",\"sports\":\"sports\",\"startups\":\"startup\",\"style\":\"style\",\"technology\":\"technology\",\"transportation\":\"transportation\",\"travel\":\"travel\",\"true-crime\":\"true-crime\",\"tv\":\"tv\",\"ux\":\"ux\",\"venture-capital\":\"venture-capital\",\"visual-design\":\"visual-design\",\"work\":\"work\",\"world\":\"world\",\"writing\":\"writing\"},\"defaultImages\":{\"avatar\":{\"imageId\":\"1*dmbNkD5D-u45r44go_cf0g.png\",\"height\":150,\"width\":150},\"orgLogo\":{\"imageId\":\"1*OMF3fSqH8t4xBJ9-6oZDZw.png\",\"height\":106,\"width\":545},\"postLogo\":{\"imageId\":\"1*kFrc4tBFM_tCis-2Ic87WA.png\",\"height\":810,\"width\":1440},\"postPreviewImage\":{\"imageId\":\"1*hn4v1tCaJy7cWMyb0bpNpQ.png\",\"height\":386,\"width\":579}},\"collectionStructuredData\":{\"8d6b8a439e32\":{\"name\":\"Elemental\",\"data\":{\"@type\":\"NewsMediaOrganization\",\"ethicsPolicy\":\"https:\\u002F\\u002Fhelp.medium.com\\u002Fhc\\u002Fen-us\\u002Farticles\\u002F360043290473\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https:\\u002F\\u002Fcdn-images-1.medium.com\\u002Fmax\\u002F980\\u002F1*9ygdqoKprhwuTVKUM0DLPA@2x.png\",\"width\":980,\"height\":159}}},\"3f6ecf56618\":{\"name\":\"Forge\",\"data\":{\"@type\":\"NewsMediaOrganization\",\"ethicsPolicy\":\"https:\\u002F\\u002Fhelp.medium.com\\u002Fhc\\u002Fen-us\\u002Farticles\\u002F360043290473\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https:\\u002F\\u002Fcdn-images-1.medium.com\\u002Fmax\\u002F596\\u002F1*uULpIlImcO5TDuBZ6lm7Lg@2x.png\",\"width\":596,\"height\":183}}},\"ae2a65f35510\":{\"name\":\"GEN\",\"data\":{\"@type\":\"NewsMediaOrganization\",\"ethicsPolicy\":\"https:\\u002F\\u002Fhelp.medium.com\\u002Fhc\\u002Fen-us\\u002Farticles\\u002F360043290473\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https:\\u002F\\u002Fmiro.medium.com\\u002Fmax\\u002F264\\u002F1*RdVZMdvfV3YiZTw6mX7yWA.png\",\"width\":264,\"height\":140}}},\"88d9857e584e\":{\"name\":\"LEVEL\",\"data\":{\"@type\":\"NewsMediaOrganization\",\"ethicsPolicy\":\"https:\\u002F\\u002Fhelp.medium.com\\u002Fhc\\u002Fen-us\\u002Farticles\\u002F360043290473\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https:\\u002F\\u002Fmiro.medium.com\\u002Fmax\\u002F540\\u002F1*JqYMhNX6KNNb2UlqGqO2WQ.png\",\"width\":540,\"height\":108}}},\"7b6769f2748b\":{\"name\":\"Marker\",\"data\":{\"@type\":\"NewsMediaOrganization\",\"ethicsPolicy\":\"https:\\u002F\\u002Fhelp.medium.com\\u002Fhc\\u002Fen-us\\u002Farticles\\u002F360043290473\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https:\\u002F\\u002Fcdn-images-1.medium.com\\u002Fmax\\u002F383\\u002F1*haCUs0wF6TgOOvfoY-jEoQ@2x.png\",\"width\":383,\"height\":92}}},\"444d13b52878\":{\"name\":\"OneZero\",\"data\":{\"@type\":\"NewsMediaOrganization\",\"ethicsPolicy\":\"https:\\u002F\\u002Fhelp.medium.com\\u002Fhc\\u002Fen-us\\u002Farticles\\u002F360043290473\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https:\\u002F\\u002Fmiro.medium.com\\u002Fmax\\u002F540\\u002F1*cw32fIqCbRWzwJaoQw6BUg.png\",\"width\":540,\"height\":123}}},\"8ccfed20cbb2\":{\"name\":\"Zora\",\"data\":{\"@type\":\"NewsMediaOrganization\",\"ethicsPolicy\":\"https:\\u002F\\u002Fhelp.medium.com\\u002Fhc\\u002Fen-us\\u002Farticles\\u002F360043290473\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https:\\u002F\\u002Fmiro.medium.com\\u002Fmax\\u002F540\\u002F1*tZUQqRcCCZDXjjiZ4bDvgQ.png\",\"width\":540,\"height\":106}}}},\"embeddedPostIds\":{\"coronavirus\":\"cd3010f9d81f\"},\"sharedCdcMessaging\":{\"COVID_APPLICABLE_TAG_SLUGS\":[],\"COVID_APPLICABLE_TOPIC_NAMES\":[],\"COVID_APPLICABLE_TOPIC_NAMES_FOR_TOPIC_PAGE\":[],\"COVID_MESSAGES\":{\"tierA\":{\"text\":\"For more information on the novel coronavirus and Covid-19, visit cdc.gov.\",\"markups\":[{\"start\":66,\"end\":73,\"href\":\"https:\\u002F\\u002Fwww.cdc.gov\\u002Fcoronavirus\\u002F2019-nCoV\"}]},\"tierB\":{\"text\":\"Anyone can publish on Medium per our Policies, but we don’t fact-check every story. For more info about the coronavirus, see cdc.gov.\",\"markups\":[{\"start\":37,\"end\":45,\"href\":\"https:\\u002F\\u002Fhelp.medium.com\\u002Fhc\\u002Fen-us\\u002Fcategories\\u002F201931128-Policies-Safety\"},{\"start\":125,\"end\":132,\"href\":\"https:\\u002F\\u002Fwww.cdc.gov\\u002Fcoronavirus\\u002F2019-nCoV\"}]},\"paywall\":{\"text\":\"This article has been made free for everyone, thanks to Medium Members. For more information on the novel coronavirus and Covid-19, visit cdc.gov.\",\"markups\":[{\"start\":56,\"end\":70,\"href\":\"https:\\u002F\\u002Fmedium.com\\u002Fmembership\"},{\"start\":138,\"end\":145,\"href\":\"https:\\u002F\\u002Fwww.cdc.gov\\u002Fcoronavirus\\u002F2019-nCoV\"}]},\"unbound\":{\"text\":\"This article is free for everyone, thanks to Medium Members. For more information on the novel coronavirus and Covid-19, visit cdc.gov.\",\"markups\":[{\"start\":45,\"end\":59,\"href\":\"https:\\u002F\\u002Fmedium.com\\u002Fmembership\"},{\"start\":127,\"end\":134,\"href\":\"https:\\u002F\\u002Fwww.cdc.gov\\u002Fcoronavirus\\u002F2019-nCoV\"}]}},\"COVID_BANNER_POST_ID_OVERRIDE_WHITELIST\":[\"3b31a67bff4a\"]},\"sharedVoteMessaging\":{\"TAGS\":[\"politics\",\"election-2020\",\"government\",\"us-politics\",\"election\",\"2020-presidential-race\",\"trump\",\"donald-trump\",\"democrats\",\"republicans\",\"congress\",\"republican-party\",\"democratic-party\",\"biden\",\"joe-biden\",\"maga\"],\"TOPICS\":[\"politics\",\"election\"],\"MESSAGE\":{\"text\":\"Find out more about the U.S. election results here.\",\"markups\":[{\"start\":46,\"end\":50,\"href\":\"https:\\u002F\\u002Fcookpolitical.com\\u002F2020-national-popular-vote-tracker\"}]},\"EXCLUDE_POSTS\":[\"397ef29e3ca5\"]},\"embedPostRules\":[],\"recircOptions\":{\"v1\":{\"limit\":3},\"v2\":{\"limit\":8}},\"braintreeClientKey\":\"production_zjkj96jm_m56f8fqpf7ngnrd4\",\"braintree\":{\"enabled\":true,\"merchantId\":\"m56f8fqpf7ngnrd4\",\"merchantAccountId\":{\"usd\":\"AMediumCorporation_instant\",\"eur\":\"amediumcorporation_EUR\",\"cad\":\"amediumcorporation_CAD\"},\"publicKey\":\"ds2nn34bg2z7j5gd\",\"braintreeEnvironment\":\"production\",\"dashboardUrl\":\"https:\\u002F\\u002Fwww.braintreegateway.com\\u002Fmerchants\",\"gracePeriodDurationInDays\":14,\"mediumMembershipPlanId\":{\"monthly\":\"ce105f8c57a3\",\"monthlyWithTrial\":\"d5ee3dbe3db8\",\"yearly\":\"a40ad4a43185\",\"yearlyStaff\":\"d74fb811198a\",\"yearlyWithTrial\":\"b3bc7350e5c7\",\"monthlyCad\":\"p52orjkaceei\",\"yearlyCad\":\"h4q9g2up9ktt\"},\"braintreeDiscountId\":{\"oneMonthFree\":\"MONTHS_FREE_01\",\"threeMonthsFree\":\"MONTHS_FREE_03\",\"sixMonthsFree\":\"MONTHS_FREE_06\",\"fiftyPercentOffOneYear\":\"FIFTY_PERCENT_OFF_ONE_YEAR\"},\"3DSecureVersion\":\"2\",\"defaultCurrency\":\"usd\",\"providerPlanIdCurrency\":{\"4ycw\":\"usd\",\"rz3b\":\"usd\",\"3kqm\":\"usd\",\"jzw6\":\"usd\",\"c2q2\":\"usd\",\"nnsw\":\"usd\",\"fx7w\":\"cad\",\"nwf2\":\"cad\"}},\"paypalClientId\":\"AXj1G4fotC2GE8KzWX9mSxCH1wmPE3nJglf4Z2ig_amnhvlMVX87otaq58niAg9iuLktVNF_1WCMnN7v\",\"paypal\":{\"host\":\"https:\\u002F\\u002Fapi.paypal.com:443\",\"clientMode\":\"production\",\"serverMode\":\"live\",\"webhookId\":\"4G466076A0294510S\",\"monthlyPlan\":{\"planId\":\"P-9WR0658853113943TMU5FDQA\",\"name\":\"Medium Membership (Monthly) with setup fee\",\"description\":\"Unlimited access to the best and brightest stories on Medium. Membership billed monthly.\"},\"yearlyPlan\":{\"planId\":\"P-7N8963881P8875835MU5JOPQ\",\"name\":\"Medium Membership (Annual) with setup fee\",\"description\":\"Unlimited access to the best and brightest stories on Medium. Membership billed annually.\"},\"oneYearGift\":{\"name\":\"Medium Membership (1 Year, Digital Gift Code)\",\"description\":\"Unlimited access to the best and brightest stories on Medium. Gift codes can be redeemed at medium.com\\u002Fredeem.\",\"price\":\"50.00\",\"currency\":\"USD\",\"sku\":\"membership-gift-1-yr\"},\"oldMonthlyPlan\":{\"planId\":\"P-96U02458LM656772MJZUVH2Y\",\"name\":\"Medium Membership (Monthly)\",\"description\":\"Unlimited access to the best and brightest stories on Medium. Membership billed monthly.\"},\"oldYearlyPlan\":{\"planId\":\"P-59P80963JF186412JJZU3SMI\",\"name\":\"Medium Membership (Annual)\",\"description\":\"Unlimited access to the best and brightest stories on Medium. Membership billed annually.\"},\"monthlyPlanWithTrial\":{\"planId\":\"P-66C21969LR178604GJPVKUKY\",\"name\":\"Medium Membership (Monthly) with setup fee\",\"description\":\"Unlimited access to the best and brightest stories on Medium. Membership billed monthly.\"},\"yearlyPlanWithTrial\":{\"planId\":\"P-6XW32684EX226940VKCT2MFA\",\"name\":\"Medium Membership (Annual) with setup fee\",\"description\":\"Unlimited access to the best and brightest stories on Medium. Membership billed annually.\"},\"oldMonthlyPlanNoSetupFee\":{\"planId\":\"P-4N046520HR188054PCJC7LJI\",\"name\":\"Medium Membership (Monthly)\",\"description\":\"Unlimited access to the best and brightest stories on Medium. Membership billed monthly.\"},\"oldYearlyPlanNoSetupFee\":{\"planId\":\"P-7A4913502Y5181304CJEJMXQ\",\"name\":\"Medium Membership (Annual)\",\"description\":\"Unlimited access to the best and brightest stories on Medium. Membership billed annually.\"},\"sdkUrl\":\"https:\\u002F\\u002Fwww.paypal.com\\u002Fsdk\\u002Fjs\"},\"stripePublishableKey\":\"pk_live_7FReX44VnNIInZwrIIx6ghjl\",\"log\":{\"json\":true,\"level\":\"info\"},\"imageUploadMaxSizeMb\":25,\"staffPicks\":{\"title\":\"Staff Picks\",\"catalogId\":\"c7bc6e1ee00f\"}},\"session\":{\"xsrf\":\"\"}}\n",
            "  </script>\n",
            "  <script>\n",
            "   window.__APOLLO_STATE__ = {\"ROOT_QUERY\":{\"__typename\":\"Query\",\"variantFlags\":[{\"__typename\":\"VariantFlag\",\"name\":\"enable_twitter_auth_suggestions\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"web_enable_syntax_highlighting\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"disable_edge_cache\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_ios_offline_reading\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_import\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_recirc_model\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_rito_upstream_deadlines\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_updated_follower_email\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"bevy_rds_double_write\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"reader_fair_distribution_non_qp\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_lite_server_upstream_deadlines\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_tribute_landing_page\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_speechify_ios\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_verifications_service\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_signup_friction\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"android_enable_syntax_highlight\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_updated_new_user_onboarding\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"allow_test_auth\",\"valueType\":{\"__typename\":\"VariantFlagString\",\"value\":\"disallow\"}},{\"__typename\":\"VariantFlag\",\"name\":\"coronavirus_topic_recirc\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_legacy_feed_in_iceland\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_braintree_trial_membership\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_lite_continue_this_thread\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_lite_response_markup\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_author_cards\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_creator_welcome_email\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"glyph_font_set\",\"valueType\":{\"__typename\":\"VariantFlagString\",\"value\":\"m2-unbound-source-serif-pro\"}},{\"__typename\":\"VariantFlag\",\"name\":\"allow_signup\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_automod\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_author_cards_byline\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_rex_reading_history\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"ios_social_share_sheet\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_lite_deprecate_custom_profile\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_tipping_v0_android\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"ios_iceland_nux\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_boosted_notification\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"ios_enable_lock_responses\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"ios_in_app_free_trial\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_braintree_webhook\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"signup_services\",\"valueType\":{\"__typename\":\"VariantFlagString\",\"value\":\"twitter,facebook,google,email,google-fastidv,google-one-tap,apple\"}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_aurora_pub_follower_page\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_auto_follow_on_subscribe\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"android_enable_lists_v2\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"available_monthly_plan\",\"valueType\":{\"__typename\":\"VariantFlagString\",\"value\":\"60e220181034\"}},{\"__typename\":\"VariantFlag\",\"name\":\"covid_19_cdc_banner\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_iceland_forced_android\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"textshots_userid\",\"valueType\":{\"__typename\":\"VariantFlagString\",\"value\":\"\"}},{\"__typename\":\"VariantFlag\",\"name\":\"available_annual_plan\",\"valueType\":{\"__typename\":\"VariantFlagString\",\"value\":\"2c754bcc2995\"}},{\"__typename\":\"VariantFlag\",\"name\":\"skip_fs_cache_user_vals\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"can_receive_tips_v0\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_featurestore_parallel_queries\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_mastodon_for_members\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_tipping_v0_ios\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_aspirational_paywall\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_tick_landing_page\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"onboarding_tags_from_top_views\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_pp_dashboard_referred_earnings\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"can_send_tips_v0\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_braintree_apple_pay\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"skip_sign_in_recaptcha\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_android_dynamic_programming_paywall\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_cache_less_following_feed\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_pill_based_home_feed\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_entities_to_follow_v2\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_app_flirty_thirty\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_braintree_integration\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_footer_app_buttons\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"limit_post_referrers\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_rex_aggregator_v2\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"android_responses_rewrite\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"explicit_signals_android\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_tag_recs\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_ios_dynamic_paywall_aspiriational\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_triton_recirc\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_seamless_social_sharing\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"ios_remove_twitter_onboarding_step\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"disable_partner_program_enrollment\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_braintree_client\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_google_one_tap\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_sprig\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"explicit_signals_ios\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_annual_renewal_reminder_email\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_aurora_nav\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_newsletter_lo_flow_custom_domains\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"android_enable_editor_new_publishing_flow\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_email_sign_in_captcha\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_group_gifting\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_lite_homepage\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_notifications_service\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"ios_display_paywall_after_onboarding\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_android_dynamic_aspirational_paywall\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_speechify_widget\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_apple_webhook\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_android_offline_reading\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_braintree_paypal\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_ios_autorefresh\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_ios_dynamic_paywall_programming\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"android_two_hour_refresh\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_marketing_emails\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"android_notifications_rewrite\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"redefined_top_posts\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"author_fair_distribution_non_qp3\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_android_miro_v2\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_simplified_digest_v2_c\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_google_webhook\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"allow_access\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_digest_generation_pipeline\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"limit_user_follows\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"ios_enable_home_post_menu\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"ios_enable_verified_book_author\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_mastodon_for_members_username_selection\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_starspace\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_medium2_kbfd\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"signin_services\",\"valueType\":{\"__typename\":\"VariantFlagString\",\"value\":\"twitter,facebook,google,email,google-fastidv,google-one-tap,apple\"}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_android_verified_author\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_apple_sign_in\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_rex_new_push_notification_endpoint\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"browsable_stream_config_bucket\",\"valueType\":{\"__typename\":\"VariantFlagString\",\"value\":\"curated-topics\"}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_braintree_google_pay\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_digest_tagline\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_mastodon_avatar_upload\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_branch_io\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_ml_rank_rex_anno\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}},{\"__typename\":\"VariantFlag\",\"name\":\"enable_members_only_audio\",\"valueType\":{\"__typename\":\"VariantFlagBoolean\",\"value\":true}}],\"viewer\":null,\"collectionByDomainOrSlug({\\\"domainOrSlug\\\":\\\"towardsdatascience.com\\\"})\":{\"__ref\":\"Collection:7f60cf5620c9\"},\"postResult({\\\"id\\\":\\\"3ac3a11d524\\\"})\":{\"__ref\":\"Post:3ac3a11d524\"}},\"ImageMetadata:1*VzTUkfeGymHP4Bvav-T-lA.png\":{\"__typename\":\"ImageMetadata\",\"id\":\"1*VzTUkfeGymHP4Bvav-T-lA.png\"},\"Collection:7f60cf5620c9\":{\"__typename\":\"Collection\",\"id\":\"7f60cf5620c9\",\"favicon\":{\"__ref\":\"ImageMetadata:1*VzTUkfeGymHP4Bvav-T-lA.png\"},\"customStyleSheet\":{\"__ref\":\"CustomStyleSheet:514038af8f2f\"},\"colorPalette\":{\"__typename\":\"ColorPalette\",\"highlightSpectrum\":{\"__typename\":\"ColorSpectrum\",\"backgroundColor\":\"#FFFFFFFF\",\"colorPoints\":[{\"__typename\":\"ColorPoint\",\"color\":\"#FFEDF4FC\",\"point\":0},{\"__typename\":\"ColorPoint\",\"color\":\"#FFE9F2FD\",\"point\":0.1},{\"__typename\":\"ColorPoint\",\"color\":\"#FFE6F1FD\",\"point\":0.2},{\"__typename\":\"ColorPoint\",\"color\":\"#FFE2EFFD\",\"point\":0.3},{\"__typename\":\"ColorPoint\",\"color\":\"#FFDFEEFD\",\"point\":0.4},{\"__typename\":\"ColorPoint\",\"color\":\"#FFDBECFE\",\"point\":0.5},{\"__typename\":\"ColorPoint\",\"color\":\"#FFD7EBFE\",\"point\":0.6},{\"__typename\":\"ColorPoint\",\"color\":\"#FFD4E9FE\",\"point\":0.7},{\"__typename\":\"ColorPoint\",\"color\":\"#FFD0E7FF\",\"point\":0.8},{\"__typename\":\"ColorPoint\",\"color\":\"#FFCCE6FF\",\"point\":0.9},{\"__typename\":\"ColorPoint\",\"color\":\"#FFC8E4FF\",\"point\":1}]},\"defaultBackgroundSpectrum\":{\"__typename\":\"ColorSpectrum\",\"backgroundColor\":\"#FFFFFFFF\",\"colorPoints\":[{\"__typename\":\"ColorPoint\",\"color\":\"#FF668AAA\",\"point\":0},{\"__typename\":\"ColorPoint\",\"color\":\"#FF61809D\",\"point\":0.1},{\"__typename\":\"ColorPoint\",\"color\":\"#FF5A7690\",\"point\":0.2},{\"__typename\":\"ColorPoint\",\"color\":\"#FF546C83\",\"point\":0.3},{\"__typename\":\"ColorPoint\",\"color\":\"#FF4D6275\",\"point\":0.4},{\"__typename\":\"ColorPoint\",\"color\":\"#FF455768\",\"point\":0.5},{\"__typename\":\"ColorPoint\",\"color\":\"#FF3D4C5A\",\"point\":0.6},{\"__typename\":\"ColorPoint\",\"color\":\"#FF34414C\",\"point\":0.7},{\"__typename\":\"ColorPoint\",\"color\":\"#FF2B353E\",\"point\":0.8},{\"__typename\":\"ColorPoint\",\"color\":\"#FF21282F\",\"point\":0.9},{\"__typename\":\"ColorPoint\",\"color\":\"#FF161B1F\",\"point\":1}]},\"tintBackgroundSpectrum\":{\"__typename\":\"ColorSpectrum\",\"backgroundColor\":\"#FF355876\",\"colorPoints\":[{\"__typename\":\"ColorPoint\",\"color\":\"#FF355876\",\"point\":0},{\"__typename\":\"ColorPoint\",\"color\":\"#FF4D6C88\",\"point\":0.1},{\"__typename\":\"ColorPoint\",\"color\":\"#FF637F99\",\"point\":0.2},{\"__typename\":\"ColorPoint\",\"color\":\"#FF7791A8\",\"point\":0.3},{\"__typename\":\"ColorPoint\",\"color\":\"#FF8CA2B7\",\"point\":0.4},{\"__typename\":\"ColorPoint\",\"color\":\"#FF9FB3C6\",\"point\":0.5},{\"__typename\":\"ColorPoint\",\"color\":\"#FFB2C3D4\",\"point\":0.6},{\"__typename\":\"ColorPoint\",\"color\":\"#FFC5D2E1\",\"point\":0.7},{\"__typename\":\"ColorPoint\",\"color\":\"#FFD7E2EE\",\"point\":0.8},{\"__typename\":\"ColorPoint\",\"color\":\"#FFE9F1FA\",\"point\":0.9},{\"__typename\":\"ColorPoint\",\"color\":\"#FFFBFFFF\",\"point\":1}]}},\"googleAnalyticsId\":null,\"domain\":\"towardsdatascience.com\",\"name\":\"Towards Data Science\",\"slug\":\"towards-data-science\",\"avatar\":{\"__ref\":\"ImageMetadata:1*CJe3891yB1A1mzMdqemkdg.jpeg\"},\"isAuroraVisible\":true,\"legacyHeaderBackgroundImage\":null,\"logo\":{\"__ref\":\"ImageMetadata:1*mG6i4Bh_LgixUYXJgQpYsg@2x.png\"},\"subscriberCount\":659633,\"newsletterV3\":{\"__ref\":\"NewsletterV3:d6fe9076899\"},\"navItems\":[{\"__typename\":\"NavItem\",\"tagSlug\":null,\"title\":\"Data Science\",\"url\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fdata-science\\u002Fhome\"},{\"__typename\":\"NavItem\",\"tagSlug\":null,\"title\":\"Machine Learning\",\"url\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fmachine-learning\\u002Fhome\"},{\"__typename\":\"NavItem\",\"tagSlug\":null,\"title\":\"Programming\",\"url\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fprogramming\\u002Fhome\"},{\"__typename\":\"NavItem\",\"tagSlug\":null,\"title\":\"Visualization\",\"url\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fdata-visualization\\u002Fhome\"},{\"__typename\":\"NavItem\",\"tagSlug\":null,\"title\":\"Video\",\"url\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fvideo\\u002Fhome\"},{\"__typename\":\"NavItem\",\"tagSlug\":null,\"title\":\"★\",\"url\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Feditors-picks\\u002Fhome\"},{\"__typename\":\"NavItem\",\"tagSlug\":null,\"title\":\"About\",\"url\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fabout-us\\u002Fhome\"},{\"__typename\":\"NavItem\",\"tagSlug\":null,\"title\":\"Contribute\",\"url\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fcontribute\\u002Fhome\"}],\"viewerEdge\":{\"__ref\":\"CollectionViewerEdge:collectionId:7f60cf5620c9-viewerId:lo_9f9c66fb3cec\"},\"twitterUsername\":\"TDataScience\",\"facebookPageId\":null,\"customDomainState\":{\"__typename\":\"CustomDomainState\",\"live\":{\"__typename\":\"CustomDomain\",\"status\":\"ACTIVE\",\"isSubdomain\":false}},\"creator\":{\"__ref\":\"User:7e12c71dfa81\"},\"ptsQualifiedAt\":1616092952992,\"description\":\"Your home for data science. A Medium publication sharing concepts, ideas and codes.\"},\"CustomStyleSheet:514038af8f2f\":{\"__typename\":\"CustomStyleSheet\",\"id\":\"514038af8f2f\",\"global\":{\"__typename\":\"GlobalStyles\",\"colorPalette\":{\"__typename\":\"StyleSheetColorPalette\",\"primary\":{\"__typename\":\"ColorValue\",\"colorPalette\":{\"__typename\":\"ColorPalette\",\"highlightSpectrum\":{\"__typename\":\"ColorSpectrum\",\"backgroundColor\":\"#FFFFFFFF\",\"colorPoints\":[{\"__typename\":\"ColorPoint\",\"color\":\"#FFEDF4FC\",\"point\":0},{\"__typename\":\"ColorPoint\",\"color\":\"#FFE9F2FD\",\"point\":0.1},{\"__typename\":\"ColorPoint\",\"color\":\"#FFE6F1FD\",\"point\":0.2},{\"__typename\":\"ColorPoint\",\"color\":\"#FFE2EFFD\",\"point\":0.3},{\"__typename\":\"ColorPoint\",\"color\":\"#FFDFEEFD\",\"point\":0.4},{\"__typename\":\"ColorPoint\",\"color\":\"#FFDBECFE\",\"point\":0.5},{\"__typename\":\"ColorPoint\",\"color\":\"#FFD7EBFE\",\"point\":0.6},{\"__typename\":\"ColorPoint\",\"color\":\"#FFD4E9FE\",\"point\":0.7},{\"__typename\":\"ColorPoint\",\"color\":\"#FFD0E7FF\",\"point\":0.8},{\"__typename\":\"ColorPoint\",\"color\":\"#FFCCE6FF\",\"point\":0.9},{\"__typename\":\"ColorPoint\",\"color\":\"#FFC8E4FF\",\"point\":1}]},\"defaultBackgroundSpectrum\":{\"__typename\":\"ColorSpectrum\",\"backgroundColor\":\"#FFFFFFFF\",\"colorPoints\":[{\"__typename\":\"ColorPoint\",\"color\":\"#FF668AAA\",\"point\":0},{\"__typename\":\"ColorPoint\",\"color\":\"#FF61809D\",\"point\":0.1},{\"__typename\":\"ColorPoint\",\"color\":\"#FF5A7690\",\"point\":0.2},{\"__typename\":\"ColorPoint\",\"color\":\"#FF546C83\",\"point\":0.3},{\"__typename\":\"ColorPoint\",\"color\":\"#FF4D6275\",\"point\":0.4},{\"__typename\":\"ColorPoint\",\"color\":\"#FF455768\",\"point\":0.5},{\"__typename\":\"ColorPoint\",\"color\":\"#FF3D4C5A\",\"point\":0.6},{\"__typename\":\"ColorPoint\",\"color\":\"#FF34414C\",\"point\":0.7},{\"__typename\":\"ColorPoint\",\"color\":\"#FF2B353E\",\"point\":0.8},{\"__typename\":\"ColorPoint\",\"color\":\"#FF21282F\",\"point\":0.9},{\"__typename\":\"ColorPoint\",\"color\":\"#FF161B1F\",\"point\":1}]},\"tintBackgroundSpectrum\":{\"__typename\":\"ColorSpectrum\",\"backgroundColor\":\"#FF355876\",\"colorPoints\":[{\"__typename\":\"ColorPoint\",\"color\":\"#FF355876\",\"point\":0},{\"__typename\":\"ColorPoint\",\"color\":\"#FF4D6C88\",\"point\":0.1},{\"__typename\":\"ColorPoint\",\"color\":\"#FF637F99\",\"point\":0.2},{\"__typename\":\"ColorPoint\",\"color\":\"#FF7791A8\",\"point\":0.3},{\"__typename\":\"ColorPoint\",\"color\":\"#FF8CA2B7\",\"point\":0.4},{\"__typename\":\"ColorPoint\",\"color\":\"#FF9FB3C6\",\"point\":0.5},{\"__typename\":\"ColorPoint\",\"color\":\"#FFB2C3D4\",\"point\":0.6},{\"__typename\":\"ColorPoint\",\"color\":\"#FFC5D2E1\",\"point\":0.7},{\"__typename\":\"ColorPoint\",\"color\":\"#FFD7E2EE\",\"point\":0.8},{\"__typename\":\"ColorPoint\",\"color\":\"#FFE9F1FA\",\"point\":0.9},{\"__typename\":\"ColorPoint\",\"color\":\"#FFFBFFFF\",\"point\":1}]}}},\"background\":null},\"fonts\":{\"__typename\":\"StyleSheetFonts\",\"font1\":{\"__typename\":\"StyleSheetFont\",\"name\":\"SANS_SERIF_1\"},\"font2\":{\"__typename\":\"StyleSheetFont\",\"name\":\"SANS_SERIF_1\"},\"font3\":{\"__typename\":\"StyleSheetFont\",\"name\":\"SERIF_2\"}}},\"header\":{\"__typename\":\"HeaderStyles\",\"headerScale\":\"HEADER_SCALE_MEDIUM\",\"backgroundImageDisplayMode\":\"IMAGE_DISPLAY_MODE_FILL\",\"backgroundImageVerticalAlignment\":\"CENTER\",\"backgroundColorDisplayMode\":\"COLOR_DISPLAY_MODE_SOLID\",\"backgroundColor\":{\"__typename\":\"ColorValue\",\"alpha\":\"59\",\"rgb\":\"355876\"},\"secondaryBackgroundColor\":null,\"postBackgroundColor\":null,\"backgroundImage\":{\"__ref\":\"ImageMetadata:1*Pxg5mSUxMauqJj07YJmtdg.png\"},\"logoImage\":{\"__ref\":\"ImageMetadata:1*jGgIXzQHi3stXYzPKNA6Qg.png\"},\"appNameColor\":null,\"appNameTreatment\":\"NAME_TREATMENT_LOGO\",\"nameTreatment\":\"NAME_TREATMENT_LOGO\"},\"navigation\":{\"__typename\":\"HeaderNavigation\",\"navItems\":[{\"__typename\":\"HeaderNavigationItem\",\"name\":\"Editors' Picks\",\"href\":null,\"tags\":[{\"__ref\":\"Tag:editors-pick\"}],\"type\":\"NAV_TYPE_TAG\"},{\"__typename\":\"HeaderNavigationItem\",\"name\":\"Features\",\"href\":null,\"tags\":[{\"__ref\":\"Tag:tds-features\"}],\"type\":\"NAV_TYPE_TAG\"},{\"__typename\":\"HeaderNavigationItem\",\"name\":\"Deep Dives\",\"href\":null,\"tags\":[{\"__ref\":\"Tag:deep-dives\"}],\"type\":\"NAV_TYPE_TAG\"},{\"__typename\":\"HeaderNavigationItem\",\"name\":\"Author Resources\",\"href\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fquestions-96667b06af5\",\"tags\":[],\"type\":\"NAV_TYPE_LINK\"}]}},\"UserViewerEdge:userId:e2a3c138d982-viewerId:lo_9f9c66fb3cec\":{\"__typename\":\"UserViewerEdge\",\"isBlocking\":false,\"id\":\"userId:e2a3c138d982-viewerId:lo_9f9c66fb3cec\",\"isFollowing\":false,\"isUser\":false},\"NewsletterV3:642229a86fbf\":{\"__typename\":\"NewsletterV3\",\"id\":\"642229a86fbf\",\"type\":\"NEWSLETTER_TYPE_AUTHOR\",\"slug\":\"e2a3c138d982\",\"name\":\"e2a3c138d982\",\"collection\":null,\"user\":{\"__ref\":\"User:e2a3c138d982\"},\"description\":\"\",\"promoHeadline\":\"\",\"promoBody\":\"\",\"showPromo\":false},\"User:e2a3c138d982\":{\"__typename\":\"User\",\"id\":\"e2a3c138d982\",\"name\":\"Parthvi Shah\",\"username\":\"shahparthvi22\",\"newsletterV3\":{\"__ref\":\"NewsletterV3:642229a86fbf\"},\"imageId\":\"1*-WmnCAUlTAjdn2C2uFbr_w.jpeg\",\"socialStats\":{\"__typename\":\"SocialStats\",\"followerCount\":106,\"followingCount\":44,\"collectionFollowingCount\":24},\"customStyleSheet\":null,\"viewerEdge\":{\"__ref\":\"UserViewerEdge:userId:e2a3c138d982-viewerId:lo_9f9c66fb3cec\"},\"verifications\":{\"__typename\":\"VerifiedInfo\",\"isBookAuthor\":false},\"customDomainState\":{\"__typename\":\"CustomDomainState\",\"live\":{\"__typename\":\"CustomDomain\",\"domain\":\"shahparthvi22.medium.com\",\"status\":\"ACTIVE\",\"isSubdomain\":true}},\"hasSubdomain\":true,\"bio\":\"Masters in Data Science @ NYU\",\"isPartnerProgramEnrolled\":false,\"viewerIsUser\":false,\"postSubscribeMembershipUpsellShownAt\":0,\"authoredBooks\":[],\"linkedAccounts\":{\"__ref\":\"LinkedAccounts:e2a3c138d982\"},\"mediumMemberAt\":1668971679000,\"homepagePostsConnection:{\\\"paging\\\":{\\\"limit\\\":1}}\":{\"__typename\":\"PostConnection\",\"posts\":[{\"__ref\":\"Post:cd26fdc963d8\"}]},\"isSuspended\":false,\"allowNotes\":true,\"twitterScreenName\":\"\",\"atsQualifiedAt\":1612205622633,\"isAuroraVisible\":true},\"Post:3ac3a11d524\":{\"__typename\":\"Post\",\"id\":\"3ac3a11d524\",\"firstPublishedAt\":1593280631758,\"visibility\":\"PUBLIC\",\"creator\":{\"__ref\":\"User:e2a3c138d982\"},\"canonicalUrl\":\"\",\"collection\":{\"__ref\":\"Collection:7f60cf5620c9\"},\"content({\\\"postMeteringOptions\\\":{\\\"forceTruncation\\\":false}})\":{\"__typename\":\"PostContent\",\"isLockedPreviewOnly\":false,\"validatedShareKey\":\"\",\"bodyModel\":{\"__typename\":\"RichText\",\"sections\":[{\"__typename\":\"Section\",\"name\":\"9662\",\"startIndex\":0,\"textLayout\":null,\"imageLayout\":null,\"backgroundImage\":null,\"videoLayout\":null,\"backgroundVideo\":null}],\"paragraphs\":[{\"__ref\":\"Paragraph:73f68cef87e1_0\"},{\"__ref\":\"Paragraph:73f68cef87e1_1\"},{\"__ref\":\"Paragraph:73f68cef87e1_2\"},{\"__ref\":\"Paragraph:73f68cef87e1_3\"},{\"__ref\":\"Paragraph:73f68cef87e1_4\"},{\"__ref\":\"Paragraph:73f68cef87e1_5\"},{\"__ref\":\"Paragraph:73f68cef87e1_6\"},{\"__ref\":\"Paragraph:73f68cef87e1_7\"},{\"__ref\":\"Paragraph:73f68cef87e1_8\"},{\"__ref\":\"Paragraph:73f68cef87e1_9\"},{\"__ref\":\"Paragraph:73f68cef87e1_10\"},{\"__ref\":\"Paragraph:73f68cef87e1_11\"},{\"__ref\":\"Paragraph:73f68cef87e1_12\"},{\"__ref\":\"Paragraph:73f68cef87e1_13\"},{\"__ref\":\"Paragraph:73f68cef87e1_14\"},{\"__ref\":\"Paragraph:73f68cef87e1_15\"},{\"__ref\":\"Paragraph:73f68cef87e1_16\"},{\"__ref\":\"Paragraph:73f68cef87e1_17\"},{\"__ref\":\"Paragraph:73f68cef87e1_18\"},{\"__ref\":\"Paragraph:73f68cef87e1_19\"},{\"__ref\":\"Paragraph:73f68cef87e1_20\"},{\"__ref\":\"Paragraph:73f68cef87e1_21\"},{\"__ref\":\"Paragraph:73f68cef87e1_22\"},{\"__ref\":\"Paragraph:73f68cef87e1_23\"}]}},\"customStyleSheet\":{\"__ref\":\"CustomStyleSheet:514038af8f2f\"},\"isPublished\":true,\"isLocked\":false,\"license\":\"ALL_RIGHTS_RESERVED\",\"collaborators\":[],\"statusForCollection\":\"APPROVED\",\"isMarkedPaywallOnly\":false,\"lockedSource\":\"LOCKED_POST_SOURCE_NONE\",\"mediumUrl\":\"https:\\u002F\\u002Ftowardsdatascience.com\\u002Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524\",\"primaryTopic\":{\"__ref\":\"Topic:ae5d4995e225\"},\"topics\":[{\"__typename\":\"Topic\",\"slug\":\"machine-learning\",\"name\":\"Machine Learning\"},{\"__typename\":\"Topic\",\"slug\":\"data-science\",\"name\":\"Data Science\"}],\"viewerEdge\":{\"__ref\":\"PostViewerEdge:postId:3ac3a11d524-viewerId:lo_9f9c66fb3cec\"},\"latestPublishedVersion\":\"73f68cef87e1\",\"postResponses\":{\"__typename\":\"PostResponses\",\"count\":0},\"allowResponses\":true,\"isLimitedState\":false,\"voterCount\":37,\"recommenders\":[],\"isSeries\":false,\"sequence\":null,\"uniqueSlug\":\"my-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524\",\"title\":\"My Absolute Go-To for Sentiment Analysis — TextBlob.\",\"clapCount\":157,\"layerCake\":3,\"tags\":[{\"__ref\":\"Tag:sentiment-analysis\"},{\"__ref\":\"Tag:textblob\"},{\"__ref\":\"Tag:subjectivity\"},{\"__ref\":\"Tag:polarity\"},{\"__ref\":\"Tag:towards-data-science\"}],\"readingTime\":2.5908805031446542,\"pendingCollection\":null,\"inResponseToEntityType\":null,\"socialTitle\":\"\",\"socialDek\":\"\",\"noIndex\":null,\"curationStatus\":\"CURATION_STATUS_DISTRIBUTED\",\"metaDescription\":\"\",\"latestPublishedAt\":1604681616702,\"previewContent\":{\"__typename\":\"PreviewContent\",\"subtitle\":\"Sentiment Analysis using TextBlob.\"},\"previewImage\":{\"__ref\":\"ImageMetadata:1*vkqmLESSfaYMmHvXznGk5Q.jpeg\"},\"isShortform\":false,\"seoTitle\":\"\",\"updatedAt\":1639533037877,\"shortformType\":\"SHORTFORM_TYPE_LINK\",\"seoDescription\":\"\",\"isIndexable\":true,\"isSuspended\":false,\"pinnedAt\":0,\"awards:countToShowAwardBadge(type:STAFF_PICK,limit:1)\":{\"__typename\":\"AwardConnection\",\"totalCount\":0,\"awards\":[]}},\"ImageMetadata:1*CJe3891yB1A1mzMdqemkdg.jpeg\":{\"__typename\":\"ImageMetadata\",\"id\":\"1*CJe3891yB1A1mzMdqemkdg.jpeg\"},\"ImageMetadata:1*Pxg5mSUxMauqJj07YJmtdg.png\":{\"__typename\":\"ImageMetadata\",\"id\":\"1*Pxg5mSUxMauqJj07YJmtdg.png\",\"originalWidth\":2569},\"ImageMetadata:1*jGgIXzQHi3stXYzPKNA6Qg.png\":{\"__typename\":\"ImageMetadata\",\"id\":\"1*jGgIXzQHi3stXYzPKNA6Qg.png\",\"originalHeight\":429,\"originalWidth\":1376},\"ImageMetadata:1*mG6i4Bh_LgixUYXJgQpYsg@2x.png\":{\"__typename\":\"ImageMetadata\",\"id\":\"1*mG6i4Bh_LgixUYXJgQpYsg@2x.png\",\"originalHeight\":122,\"originalWidth\":337},\"NewsletterV3:d6fe9076899\":{\"__typename\":\"NewsletterV3\",\"slug\":\"the-variable\",\"id\":\"d6fe9076899\",\"name\":\"The Variable\",\"description\":\"Every Thursday, the Variable delivers the very best of Towards Data Science: from hands-on tutorials and cutting-edge research to original features you don't want to miss.\",\"promoHeadline\":\"\",\"promoBody\":\"\",\"type\":\"NEWSLETTER_TYPE_COLLECTION\",\"user\":{\"__ref\":\"User:895063a310f4\"},\"collection\":{\"__ref\":\"Collection:7f60cf5620c9\"},\"showPromo\":true},\"Tag:editors-pick\":{\"__typename\":\"Tag\",\"id\":\"editors-pick\",\"normalizedTagSlug\":\"editors-pick\"},\"Tag:tds-features\":{\"__typename\":\"Tag\",\"id\":\"tds-features\",\"normalizedTagSlug\":\"tds-features\"},\"Tag:deep-dives\":{\"__typename\":\"Tag\",\"id\":\"deep-dives\",\"normalizedTagSlug\":\"deep-dives\"},\"LinkedAccounts:e2a3c138d982\":{\"__typename\":\"LinkedAccounts\",\"mastodon\":null,\"id\":\"e2a3c138d982\"},\"Post:cd26fdc963d8\":{\"__typename\":\"Post\",\"id\":\"cd26fdc963d8\"},\"User:895063a310f4\":{\"__typename\":\"User\",\"id\":\"895063a310f4\",\"name\":\"Ludovic Benistant\",\"username\":\"ludobenistant\",\"newsletterV3\":{\"__ref\":\"NewsletterV3:2375c85c8da7\"}},\"NewsletterV3:2375c85c8da7\":{\"__typename\":\"NewsletterV3\",\"id\":\"2375c85c8da7\"},\"Topic:ae5d4995e225\":{\"__typename\":\"Topic\",\"slug\":\"data-science\",\"id\":\"ae5d4995e225\",\"name\":\"Data Science\"},\"PostViewerEdge:postId:3ac3a11d524-viewerId:lo_9f9c66fb3cec\":{\"__typename\":\"PostViewerEdge\",\"paywall\":{\"__typename\":\"PostPaywall\",\"type\":\"PROGRAMMING\",\"creatorSpotlights\":[{\"__typename\":\"CreatorSpotlight\",\"name\":\"Marc-André Giroux\",\"title\":\"Sr. Software Developer\",\"company\":\"Netflix\",\"miroId\":\"1*uSxyStPJhzmIp7PjJLcpiw.jpeg\",\"hideOnSmallScreen\":true},{\"__typename\":\"CreatorSpotlight\",\"name\":\"Carlos Arguelles\",\"title\":\"Sr. Staff Engineer\",\"company\":\"Google\",\"miroId\":\"1*CM27oO9pXETXjs2M9_dUFg.jpeg\",\"hideOnSmallScreen\":null},{\"__typename\":\"CreatorSpotlight\",\"name\":\"Tony Yiu\",\"title\":\"Director\",\"company\":\"Nasdaq\",\"miroId\":\"2*CSDritfpmHLYxn63arD9sQ.jpeg\",\"hideOnSmallScreen\":null},{\"__typename\":\"CreatorSpotlight\",\"name\":\"Brandeis Marshall\",\"title\":\"CEO\",\"company\":\"DataedX\",\"miroId\":\"1*qLWny7soUL4K4lqhX1wQVw.png\",\"hideOnSmallScreen\":null},{\"__typename\":\"CreatorSpotlight\",\"name\":\"Cassie Kozyrkov\",\"title\":\"Chief Decision Scientist\",\"company\":\"Google\",\"miroId\":\"1*IL0mnvzNcpG2ZD0JBqo7zQ.jpeg\",\"hideOnSmallScreen\":true},{\"__typename\":\"CreatorSpotlight\",\"name\":\"Memo Akten\",\"title\":\"Asst. Professor\",\"company\":\"UCSD\",\"miroId\":\"4*f7EyGRullh3Ih_2tm3k5xw.png\",\"hideOnSmallScreen\":null},{\"__typename\":\"CreatorSpotlight\",\"name\":\"Vitali Zaidman\",\"title\":\"Software Architect\",\"company\":\"Meta\",\"miroId\":\"2*gHhbmOgQGWxZrqDIfLps6A.jpeg\",\"hideOnSmallScreen\":null},{\"__typename\":\"CreatorSpotlight\",\"name\":\"Camille Fournier\",\"title\":\"Head of Engineering\",\"company\":\"JPMorgan Chase\",\"miroId\":\"1*J2fWNTyPbgEhIyvVIjHAXg.jpeg\",\"hideOnSmallScreen\":null}]},\"id\":\"postId:3ac3a11d524-viewerId:lo_9f9c66fb3cec\"},\"Paragraph:73f68cef87e1_0\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_0\",\"name\":\"3d1c\",\"type\":\"H3\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"Sentiment Analysis using TextBlob\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_1\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_1\",\"name\":\"4e7a\",\"type\":\"H4\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"Sentiment Analysis using TextBlob and its working!\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"ImageMetadata:1*vkqmLESSfaYMmHvXznGk5Q.jpeg\":{\"__typename\":\"ImageMetadata\",\"id\":\"1*vkqmLESSfaYMmHvXznGk5Q.jpeg\",\"originalHeight\":1600,\"originalWidth\":2560,\"focusPercentX\":null,\"focusPercentY\":null,\"alt\":null},\"Paragraph:73f68cef87e1_2\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_2\",\"name\":\"06d9\",\"type\":\"IMG\",\"href\":null,\"layout\":\"INSET_CENTER\",\"metadata\":{\"__ref\":\"ImageMetadata:1*vkqmLESSfaYMmHvXznGk5Q.jpeg\"},\"text\":\"source\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[{\"__typename\":\"Markup\",\"type\":\"A\",\"start\":0,\"end\":6,\"href\":\"https:\\u002F\\u002Fwallpaperaccess.com\\u002Fsunny-landscape-desktop\",\"anchorType\":\"LINK\",\"userId\":null,\"linkMetadata\":null}],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_3\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_3\",\"name\":\"a7d1\",\"type\":\"H3\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"What do you mean by Sentiment?\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"ImageMetadata:1*JOMSVAYTK5OEp4SQHgqoMg.png\":{\"__typename\":\"ImageMetadata\",\"id\":\"1*JOMSVAYTK5OEp4SQHgqoMg.png\",\"originalHeight\":406,\"originalWidth\":650,\"focusPercentX\":null,\"focusPercentY\":null,\"alt\":null},\"Paragraph:73f68cef87e1_4\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_4\",\"name\":\"e663\",\"type\":\"IMG\",\"href\":null,\"layout\":\"INSET_CENTER\",\"metadata\":{\"__ref\":\"ImageMetadata:1*JOMSVAYTK5OEp4SQHgqoMg.png\"},\"text\":\"\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_5\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_5\",\"name\":\"0b15\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"Sentiment Analysis can help us decipher the mood and emotions of general public and gather insightful information regarding the context. Sentiment Analysis is a process of analyzing data and classifying it based on the need of the research.\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_6\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_6\",\"name\":\"4526\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"These sentiments can be used for a better understanding of various events and impact caused by it. L. Bing [1] highlights that in the research literature it is possible to see many different names, e.g. “sentiment analysis, opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, review mining”, however all of them have similar purposes and belong to the subject of sentiment analysis or opinion mining. By analysing these sentiments, we may find what people like, what they want and what their major concerns are.\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_7\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_7\",\"name\":\"ebfe\",\"type\":\"H4\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"Sentiment Analysis using TextBlob\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_8\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_8\",\"name\":\"1ec5\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"TextBlob is a python library for Natural Language Processing (NLP).TextBlob actively used Natural Language ToolKit (NLTK) to achieve its tasks. NLTK is a library which gives an easy access to a lot of lexical resources and allows users to work with categorization, classification and many other tasks. TextBlob is a simple library which supports complex analysis and operations on textual data.\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_9\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_9\",\"name\":\"9c90\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"For lexicon-based approaches, a sentiment is defined by its semantic orientation and the intensity of each word in the sentence. This requires a pre-defined dictionary classifying negative and positive words. Generally, a text message will be represented by bag of words. After assigning individual scores to all the words, final sentiment is calculated by some pooling operation like taking an average of all the sentiments.\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[{\"__typename\":\"Markup\",\"type\":\"STRONG\",\"start\":4,\"end\":28,\"href\":null,\"anchorType\":null,\"userId\":null,\"linkMetadata\":null}],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_10\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_10\",\"name\":\"40ca\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"TextBlob returns polarity and subjectivity of a sentence. Polarity lies between [-1,1], -1 defines a negative sentiment and 1 defines a positive sentiment. Negation words reverse the polarity. TextBlob has semantic labels that help with fine-grained analysis. For example — emoticons, exclamation mark, emojis, etc. Subjectivity lies between [0,1]. Subjectivity quantifies the amount of personal opinion and factual information contained in the text. The higher subjectivity means that the text contains personal opinion rather than factual information. TextBlob has one more parameter — intensity. TextBlob calculates subjectivity by looking at the ‘intensity’. Intensity determines if a word modifies the next word. For English, adverbs are used as modifiers (‘very good’).\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[{\"__typename\":\"Markup\",\"type\":\"STRONG\",\"start\":17,\"end\":25,\"href\":null,\"anchorType\":null,\"userId\":null,\"linkMetadata\":null},{\"__typename\":\"Markup\",\"type\":\"STRONG\",\"start\":30,\"end\":42,\"href\":null,\"anchorType\":null,\"userId\":null,\"linkMetadata\":null},{\"__typename\":\"Markup\",\"type\":\"STRONG\",\"start\":349,\"end\":552,\"href\":null,\"anchorType\":null,\"userId\":null,\"linkMetadata\":null},{\"__typename\":\"Markup\",\"type\":\"STRONG\",\"start\":651,\"end\":660,\"href\":null,\"anchorType\":null,\"userId\":null,\"linkMetadata\":null},{\"__typename\":\"Markup\",\"type\":\"EM\",\"start\":694,\"end\":702,\"href\":null,\"anchorType\":null,\"userId\":null,\"linkMetadata\":null}],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_11\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_11\",\"name\":\"eced\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"For example: We calculated polarity and subjectivity for “I do not like this example at all, it is too boring”. For this particular example, polarity = -1 and subjectivity is 1, which is fair.\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_12\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_12\",\"name\":\"3be4\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"However, for the sentence “This was a helpful example but I would prefer another one”. It returns 0.0 for both subjectivity and polarity which is not the finest answer we’d expect.\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[{\"__typename\":\"Markup\",\"type\":\"STRONG\",\"start\":98,\"end\":136,\"href\":null,\"anchorType\":null,\"userId\":null,\"linkMetadata\":null}],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_13\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_13\",\"name\":\"3741\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"It is expected that if the library returns exactly 0.0 either if your sentence didn’t contain any words that had a polarity in the NLTK training set or because TextBlob uses a weighted average sentiment score over all the words in each sample. This easily diffuses out the effect of sentences with widely varying polarities between words in our case : ‘helpful’ and ‘but’.\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_14\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_14\",\"name\":\"4f3c\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"More often than not, for simple cases, TextBlob works just fine.\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_15\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_15\",\"name\":\"51a6\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"Steps to apply Sentiment Analysis using TextBlob –\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_16\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_16\",\"name\":\"778c\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"1. Import textblob\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_17\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_17\",\"name\":\"2e97\",\"type\":\"PRE\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"from textblob import TextBlob\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_18\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_18\",\"name\":\"c643\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"2. Define a function that calculates subjectivity, polarity and give it a score based on the threshold you want to set.\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"MediaResource:ad1bc87c181430fdfc15e62b630d27d7\":{\"__typename\":\"MediaResource\",\"id\":\"ad1bc87c181430fdfc15e62b630d27d7\",\"iframeSrc\":\"\",\"iframeHeight\":0,\"iframeWidth\":0,\"title\":\"\"},\"Paragraph:73f68cef87e1_19\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_19\",\"name\":\"3dcf\",\"type\":\"IFRAME\",\"href\":null,\"layout\":\"INSET_CENTER\",\"metadata\":null,\"text\":\"Snippet of code for using TextBlob and analysis your data\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":{\"__typename\":\"Iframe\",\"mediaResource\":{\"__ref\":\"MediaResource:ad1bc87c181430fdfc15e62b630d27d7\"}},\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_20\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_20\",\"name\":\"0097\",\"type\":\"PQ\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"Another useful article that I came across is: https:\\u002F\\u002Fneptune.ai\\u002Fblog\\u002Fsentiment-analysis-python-textblob-vs-vader-vs-flair\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[{\"__typename\":\"Markup\",\"type\":\"A\",\"start\":46,\"end\":122,\"href\":\"https:\\u002F\\u002Fneptune.ai\\u002Fblog\\u002Fsentiment-analysis-python-textblob-vs-vader-vs-flair\",\"anchorType\":\"LINK\",\"userId\":null,\"linkMetadata\":null}],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_21\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_21\",\"name\":\"fe5a\",\"type\":\"P\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"References —\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_22\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_22\",\"name\":\"dd0f\",\"type\":\"OLI\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"https:\\u002F\\u002Fwww.cs.uic.edu\\u002F~liub\\u002FFBS\\u002FSentimentAnalysis-and-OpinionMining.pdf\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[{\"__typename\":\"Markup\",\"type\":\"A\",\"start\":0,\"end\":72,\"href\":\"https:\\u002F\\u002Fwww.cs.uic.edu\\u002F~liub\\u002FFBS\\u002FSentimentAnalysis-and-OpinionMining.pdf\",\"anchorType\":\"LINK\",\"userId\":null,\"linkMetadata\":null}],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"Paragraph:73f68cef87e1_23\":{\"__typename\":\"Paragraph\",\"id\":\"73f68cef87e1_23\",\"name\":\"a803\",\"type\":\"OLI\",\"href\":null,\"layout\":null,\"metadata\":null,\"text\":\"https:\\u002F\\u002Fgithub.com\\u002Fsloria\\u002FTextBlob\\u002Fblob\\u002Fdev\\u002Ftextblob\\u002F\",\"hasDropCap\":null,\"dropCapImage\":null,\"markups\":[{\"__typename\":\"Markup\",\"type\":\"A\",\"start\":0,\"end\":53,\"href\":\"https:\\u002F\\u002Fgithub.com\\u002Fsloria\\u002FTextBlob\\u002Fblob\\u002Fdev\\u002Ftextblob\\u002Fen\\u002Fen-sentiment.xml\",\"anchorType\":\"LINK\",\"userId\":null,\"linkMetadata\":null}],\"codeBlockMetadata\":null,\"iframe\":null,\"mixtapeMetadata\":null},\"CollectionViewerEdge:collectionId:7f60cf5620c9-viewerId:lo_9f9c66fb3cec\":{\"__typename\":\"CollectionViewerEdge\",\"id\":\"collectionId:7f60cf5620c9-viewerId:lo_9f9c66fb3cec\",\"isEditor\":false},\"Tag:sentiment-analysis\":{\"__typename\":\"Tag\",\"id\":\"sentiment-analysis\",\"displayTitle\":\"Sentiment Analysis\",\"normalizedTagSlug\":\"sentiment-analysis\"},\"Tag:textblob\":{\"__typename\":\"Tag\",\"id\":\"textblob\",\"displayTitle\":\"Textblob\",\"normalizedTagSlug\":\"textblob\"},\"Tag:subjectivity\":{\"__typename\":\"Tag\",\"id\":\"subjectivity\",\"displayTitle\":\"Subjectivity\",\"normalizedTagSlug\":\"subjectivity\"},\"Tag:polarity\":{\"__typename\":\"Tag\",\"id\":\"polarity\",\"displayTitle\":\"Polarity\",\"normalizedTagSlug\":\"polarity\"},\"Tag:towards-data-science\":{\"__typename\":\"Tag\",\"id\":\"towards-data-science\",\"displayTitle\":\"Towards Data Science\",\"normalizedTagSlug\":\"towards-data-science\"},\"User:7e12c71dfa81\":{\"__typename\":\"User\",\"atsQualifiedAt\":1612205680542,\"id\":\"7e12c71dfa81\"}}\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/manifest.e04694fb.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8493.58a29e31.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/main.359a5523.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/instrumentation.c71f0248.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/reporting.bbdcaa9d.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/9658.17030d28.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/799.361fd2fb.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/1860.abea291f.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/3838.7ae103cd.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/2905.21128cd9.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8007.e7e42be3.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8695.ac0f83b3.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8.5980bcd4.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/9683.6f872dd8.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/7702.c5a5a368.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/5203.23a22ad8.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8708.546db97b.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/1957.6c5d9d7a.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/9174.13cf10e7.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/3635.c351368e.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/5472.a7dd22a2.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/4129.9a8d63eb.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8580.e792aa8d.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/1802.0f7ac4a6.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/2371.6ba1ff25.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/4078.182beff5.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8093.f160e4e4.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/1743.42985c62.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/3115.4bc827fe.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/2287.a89f9d21.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/7089.d69c832a.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/2092.0b9868e3.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8824.e4a9134e.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/9225.9cfbe85d.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/6804.9f6509a8.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8051.3f4b510b.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/1069.ec72bd6d.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/5076.1a6c0ef7.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/847.383abb1d.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8316.a8c19480.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8793.496b82a7.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8908.ed51a30d.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/PostPage.MainContent.1bb15d52.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/5627.c45a7fb4.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/8880.97b5ed81.chunk.js\">\n",
            "  </script>\n",
            "  <script src=\"https://cdn-client.medium.com/lite/static/js/PostPage.RightColumnContent.153512e3.chunk.js\">\n",
            "  </script>\n",
            "  <script>\n",
            "   window.main();\n",
            "  </script>\n",
            "  <script crossorigin=\"anonymous\" data-cf-beacon='{\"rayId\":\"7bbe15538ab59e46\",\"version\":\"2023.3.0\",\"b\":1,\"token\":\"0b5f665943484354a59c39c6833f7078\",\"si\":100}' defer=\"\" integrity=\"sha512-jGCTpDpBAYDGNYR5ztKt4BQPGef1P0giN6ZGVUi835kFF88FOmmn8jBQWNgrNd8g/Yu421NdgWhwQoaOPFflDw==\" src=\"https://static.cloudflareinsights.com/beacon.min.js/v52afc6f149f6479b8c77fa569edb01181681764108816\">\n",
            "  </script>\n",
            " </body>\n",
            "</html>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Extract required Titles and text\n",
        "results=soup.find_all(['h1','h2','p','b'])\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ArgtIfeIFDcI",
        "outputId": "8823e279-e768-459a-b8c0-5c79aa1c57d8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<p class=\"bd b dm dn do dp dq dr ds dt du dv dk\"><span><a class=\"bd b dm dn dw do dp dx dq dr dy ds dt dz du dv ea eb ec ed ee ef eg eh ei ej ek el em eo ep de bk eq\" href=\"https://medium.com/m/signin?operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=post_page---two_column_layout_nav-----------------------global_nav-----------\" rel=\"noopener follow\">Sign up</a></span></p>,\n",
              " <p class=\"bd b dm dn do dp dq dr ds dt du dv dk\"><span><a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?operation=login&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=post_page---two_column_layout_nav-----------------------global_nav-----------\" rel=\"noopener follow\">Sign In</a></span></p>,\n",
              " <p class=\"bd b dm dn do dp dq dr ds dt du dv dk\"><span><a class=\"bd b dm dn dw do dp dx dq dr dy ds dt dz du dv ea eb ec ed ee ef eg eh ei ej ek el em eo ep de bk eq\" href=\"https://medium.com/m/signin?operation=register&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=post_page---two_column_layout_nav-----------------------global_nav-----------\" rel=\"noopener follow\">Sign up</a></span></p>,\n",
              " <p class=\"bd b dm dn do dp dq dr ds dt du dv dk\"><span><a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://medium.com/m/signin?operation=login&amp;redirect=https%3A%2F%2Ftowardsdatascience.com%2Fmy-absolute-go-to-for-sentiment-analysis-textblob-3ac3a11d524&amp;source=post_page---two_column_layout_nav-----------------------global_nav-----------\" rel=\"noopener follow\">Sign In</a></span></p>,\n",
              " <p class=\"bd b be z fp fq fr fs ft fu fv fw bi\">Towards Data Science</p>,\n",
              " <p class=\"pw-published-date bd b be z dk\"><span>Jun 27, 2020</span></p>,\n",
              " <p class=\"bd b be z dk\">Save</p>,\n",
              " <h1 class=\"pw-post-title il im in bd io ip iq ir is it iu iv iw ix iy iz ja jb jc jd je jf jg jh ji jj bi\" id=\"3d1c\">Sentiment Analysis using TextBlob</h1>,\n",
              " <h2 class=\"pw-subtitle-paragraph jk im in bd b jl jm jn jo jp jq jr js jt ju jv jw jx jy jz ka kb dk\" id=\"4e7a\">Sentiment Analysis using TextBlob and its working!</h2>,\n",
              " <h1 class=\"kt ku in bd kv kw kx ky kz la lb lc ld jt le ju lf jw lg jx lh jz li ka lj lk bi\" id=\"a7d1\">What do you mean by Sentiment?</h1>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"0b15\">Sentiment Analysis can help us decipher the mood and emotions of general public and gather insightful information regarding the context. Sentiment Analysis is a process of analyzing data and classifying it based on the need of the research.</p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"4526\">These sentiments can be used for a better understanding of various events and impact caused by it. L. Bing [1] highlights that in the research literature it is possible to see many different names, e.g. “sentiment analysis, opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, review mining”, however all of them have similar purposes and belong to the subject of sentiment analysis or opinion mining. By analysing these sentiments, we may find what people like, what they want and what their major concerns are.</p>,\n",
              " <h2 class=\"mi ku in bd kv mj mk dn kz ml mm dp ld lv mn mo lf lz mp mq lh md mr ms lj mt bi\" id=\"ebfe\">Sentiment Analysis using TextBlob</h2>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp mu jo lr ls mv jr lu lv mw lx ly lz mx mb mc md my mf mg mh ig bi\" id=\"1ec5\">TextBlob is a python library for Natural Language Processing (NLP).TextBlob actively used Natural Language ToolKit (NLTK) to achieve its tasks. NLTK is a library which gives an easy access to a lot of lexical resources and allows users to work with categorization, classification and many other tasks. TextBlob is a simple library which supports complex analysis and operations on textual data.</p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"9c90\">For <strong class=\"lo io\">lexicon-based approaches</strong>, a sentiment is defined by its semantic orientation and the intensity of each word in the sentence. This requires a pre-defined dictionary classifying negative and positive words. Generally, a text message will be represented by bag of words. After assigning individual scores to all the words, final sentiment is calculated by some pooling operation like taking an average of all the sentiments.</p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"40ca\">TextBlob returns <strong class=\"lo io\">polarity</strong> and <strong class=\"lo io\">subjectivity</strong> of a sentence. Polarity lies between [-1,1], -1 defines a negative sentiment and 1 defines a positive sentiment. Negation words reverse the polarity. TextBlob has semantic labels that help with fine-grained analysis. For example — emoticons, exclamation mark, emojis, etc. Subjectivity lies between [0,1]. <strong class=\"lo io\">Subjectivity quantifies the amount of personal opinion and factual information contained in the text. The higher subjectivity means that the text contains personal opinion rather than factual information</strong>. TextBlob has one more parameter — intensity. TextBlob calculates subjectivity by looking at the ‘<strong class=\"lo io\">intensity</strong>’. Intensity determines if a word <em class=\"mz\">modifies</em> the next word. For English, adverbs are used as modifiers (‘very good’).</p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"eced\">For example: We calculated polarity and subjectivity for “I do not like this example at all, it is too boring”. For this particular example, polarity = -1 and subjectivity is 1, which is fair.</p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"3be4\">However, for the sentence “This was a helpful example but I would prefer another one”. It returns <strong class=\"lo io\">0.0 for both subjectivity and polarity</strong> which is not the finest answer we’d expect.</p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"3741\">It is expected that if the library returns exactly 0.0 either if your sentence didn’t contain any words that had a polarity in the NLTK training set or because TextBlob uses a weighted average sentiment score over all the words in each sample. This easily diffuses out the effect of sentences with widely varying polarities between words in our case : ‘helpful’ and ‘but’.</p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"4f3c\">More often than not, for simple cases, TextBlob works just fine.</p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"51a6\">Steps to apply Sentiment Analysis using TextBlob –</p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"778c\">1. Import textblob</p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp lq jo lr ls lt jr lu lv lw lx ly lz ma mb mc md me mf mg mh ig bi\" id=\"c643\">2. Define a function that calculates subjectivity, polarity and give it a score based on the threshold you want to set.</p>,\n",
              " <p class=\"nm nn in bd no np nq nr ns nt nu mh dk\" id=\"0097\">Another useful article that I came across is: <a class=\"ae ks\" href=\"https://neptune.ai/blog/sentiment-analysis-python-textblob-vs-vader-vs-flair\" rel=\"noopener ugc nofollow\" target=\"_blank\">https://neptune.ai/blog/sentiment-analysis-python-textblob-vs-vader-vs-flair</a></p>,\n",
              " <p class=\"pw-post-body-paragraph lm ln in lo b lp nv jo lr ls nw jr lu lv nx lx ly lz ny mb mc md nz mf mg mh ig bi\" id=\"fe5a\">References —</p>,\n",
              " <p class=\"bd b dl z dk\"><span class=\"qe\">--</span></p>,\n",
              " <p class=\"bd b dl z dk\"><span class=\"qe\">--</span></p>,\n",
              " <h2 class=\"bd qs mj dn qt kz ml dp qu ld lv mo qv lf lz mq qw lh md ms qx lj fp fr fs ft fu fv fw bi\"><a class=\"ae af ag ah ai aj ak al am an ao ap aq ar as\" href=\"https://towardsdatascience.com/?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">More from Towards Data Science</a></h2>,\n",
              " <p class=\"bd b be z dk\">Your home for data science. A Medium publication sharing concepts, ideas and codes.</p>,\n",
              " <p class=\"bd b dl z rw\"><a class=\"ae af ag ah ai aj ak al am an ao rq rr rx qp\" href=\"https://medium.com/about?autoplay=1&amp;source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">About</a><a class=\"ae af ag ah ai aj ak al am an ao rq rr rx qp\" href=\"https://help.medium.com/hc/en-us?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">Help</a><a class=\"ae af ag ah ai aj ak al am an ao rq rr rx qp\" href=\"https://policy.medium.com/medium-terms-of-service-9db0094a1e0f?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">Terms</a><a class=\"ae af ag ah ai aj ak al am an ao rq rr rx\" href=\"https://policy.medium.com/medium-privacy-policy-f03bf92035c9?source=post_page-----3ac3a11d524--------------------------------\" rel=\"noopener follow\">Privacy</a></p>,\n",
              " <h2 class=\"bd qs gy z im rw\">Get the Medium app</h2>,\n",
              " <h2 class=\"pw-author-name bd qs gy z im bi\"><span class=\"ig\">Parthvi Shah</span></h2>,\n",
              " <p class=\"bd b be z dk\"><span class=\"ig\">Masters in Data Science @ NYU</span></p>,\n",
              " <p class=\"bd b tc td dk\">Help</p>,\n",
              " <p class=\"bd b tc td dk\">Status</p>,\n",
              " <p class=\"bd b tc td dk\">Writers</p>,\n",
              " <p class=\"bd b tc td dk\">Blog</p>,\n",
              " <p class=\"bd b tc td dk\">Careers</p>,\n",
              " <p class=\"bd b tc td dk\">Privacy</p>,\n",
              " <p class=\"bd b tc td dk\">Terms</p>,\n",
              " <p class=\"bd b tc td dk\">About</p>,\n",
              " <p class=\"bd b tc td dk\">Text to speech</p>]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "## Remove all the label from the text and joined back text\n",
        "text=[result.text for result in results]\n",
        "Article=' '.join(text)\n",
        "Article"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "4Ps9ztznFDnl",
        "outputId": "a1598f54-582b-491e-aea3-23514ff7e013"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Sign up Sign In Sign up Sign In Towards Data Science Jun 27, 2020 Save Sentiment Analysis using TextBlob Sentiment Analysis using TextBlob and its working! What do you mean by Sentiment? Sentiment Analysis can help us decipher the mood and emotions of general public and gather insightful information regarding the context. Sentiment Analysis is a process of analyzing data and classifying it based on the need of the research. These sentiments can be used for a better understanding of various events and impact caused by it. L. Bing [1] highlights that in the research literature it is possible to see many different names, e.g. “sentiment analysis, opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, review mining”, however all of them have similar purposes and belong to the subject of sentiment analysis or opinion mining. By analysing these sentiments, we may find what people like, what they want and what their major concerns are. Sentiment Analysis using TextBlob TextBlob is a python library for Natural Language Processing (NLP).TextBlob actively used Natural Language ToolKit (NLTK) to achieve its tasks. NLTK is a library which gives an easy access to a lot of lexical resources and allows users to work with categorization, classification and many other tasks. TextBlob is a simple library which supports complex analysis and operations on textual data. For lexicon-based approaches, a sentiment is defined by its semantic orientation and the intensity of each word in the sentence. This requires a pre-defined dictionary classifying negative and positive words. Generally, a text message will be represented by bag of words. After assigning individual scores to all the words, final sentiment is calculated by some pooling operation like taking an average of all the sentiments. TextBlob returns polarity and subjectivity of a sentence. Polarity lies between [-1,1], -1 defines a negative sentiment and 1 defines a positive sentiment. Negation words reverse the polarity. TextBlob has semantic labels that help with fine-grained analysis. For example — emoticons, exclamation mark, emojis, etc. Subjectivity lies between [0,1]. Subjectivity quantifies the amount of personal opinion and factual information contained in the text. The higher subjectivity means that the text contains personal opinion rather than factual information. TextBlob has one more parameter — intensity. TextBlob calculates subjectivity by looking at the ‘intensity’. Intensity determines if a word modifies the next word. For English, adverbs are used as modifiers (‘very good’). For example: We calculated polarity and subjectivity for “I do not like this example at all, it is too boring”. For this particular example, polarity = -1 and subjectivity is 1, which is fair. However, for the sentence “This was a helpful example but I would prefer another one”. It returns 0.0 for both subjectivity and polarity which is not the finest answer we’d expect. It is expected that if the library returns exactly 0.0 either if your sentence didn’t contain any words that had a polarity in the NLTK training set or because TextBlob uses a weighted average sentiment score over all the words in each sample. This easily diffuses out the effect of sentences with widely varying polarities between words in our case : ‘helpful’ and ‘but’. More often than not, for simple cases, TextBlob works just fine. Steps to apply Sentiment Analysis using TextBlob – 1. Import textblob 2. Define a function that calculates subjectivity, polarity and give it a score based on the threshold you want to set. Another useful article that I came across is: https://neptune.ai/blog/sentiment-analysis-python-textblob-vs-vader-vs-flair References — -- -- More from Towards Data Science Your home for data science. A Medium publication sharing concepts, ideas and codes. AboutHelpTermsPrivacy Get the Medium app Parthvi Shah Masters in Data Science @ NYU Help Status Writers Blog Careers Privacy Terms About Text to speech'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perforrm Abstractive Summarization using Pegasus-xsum"
      ],
      "metadata": {
        "id": "L9F_he4IA_Gc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Create tokenizer\n",
        "tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-xsum')"
      ],
      "metadata": {
        "id": "4FRra3RYpCnF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "3a59bde9093b45bea99c0648aee16e70",
            "50326cdd3881487aa73187ea2e6ac2a0",
            "2c390d5bcf50485793f4aae4d2955b28",
            "30d2471df8b14042985947a65c9e8f5d",
            "b46ee5693fea4d89b0e4d55678de1864",
            "49a86b0f124c4abda54f5239cfa6bbac",
            "288063f6d31548d8a5986801e4ecb536",
            "51d0ceb47c244529ab20ca8916ed8f1b",
            "afe1cb762cff4cc6bbe4cadd8e846d7a",
            "18920cd590134d939e7ea5b26286658c",
            "d78de53b169849a0bd2dd2574237991c",
            "b6131e2170524d01b4c904aa1fd77d76",
            "8462bac52aa8448881d2b3cd87f67bc3",
            "76d0ea6d2d634e1391829894b9d031ff",
            "c97bf50fe0f04243b2620c9d47f241e3",
            "02c416188b7a4bb38c79cd5fae0e34f9",
            "3ae5916ce80d480fa4895b4ad6d1d3db",
            "7cdd0d5ab86d4abba030195a579d3a30",
            "a235115a5536437f9ad41ab6d535d2ea",
            "59ebe66061f74f5fbe6b6fd5e6cb6b51",
            "29e6aaa5ad0745d292276511e8253e68",
            "de2d8f20c32f4a788f8327fcfd8cc2cd",
            "2dc33220dcf744fdb8e671a03eda7f85",
            "264236fa802246d283f1e70e1a1f6c44",
            "ce2a0be1baa94dc4a7f560f16946f52d",
            "af64cb57876f4d42b5a51255b11fcd98",
            "6062e7515e1d46e09adae3d61f10dab9",
            "46c4836c9f4247af9d58cd9ef0a232f3",
            "a1a970e85e38415087c22978d406c1ef",
            "beee3555c75e453b9763536e9320f163",
            "829ee2f5cd0043d4baf0af7eb3ed3d49",
            "9452718eea9e43baa1719fee1031324c",
            "aa461c098bf24a9b8e04c56dda8614c1",
            "da605f29d7234a08a03bfe24ec8a9d62",
            "e25bec17697a4743b52d8376a260cfa3",
            "d019fc77139d49a9855e7239591d81bc",
            "e2186d50bbdb4054bcaa5dee9d65f056",
            "4b82de99ba2c484aa4b646adfed14bf3",
            "c74a67526e3a464bae54b1e20f9d8f53",
            "78ce9d4b213a470ba5a5503827b2d962",
            "c61aae76235d4d7880aef87eafc9bad1",
            "f4f6541bcb2a4c91a582b861167837c9",
            "edb7398aaa554a3fa35861825b10103d",
            "2758272204804ca09ecd88713e602e34"
          ]
        },
        "outputId": "380960c2-78a7-4b2b-9b08-b58478a873ee"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/1.91M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3a59bde9093b45bea99c0648aee16e70"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/65.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6131e2170524d01b4c904aa1fd77d76"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/87.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2dc33220dcf744fdb8e671a03eda7f85"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/1.39k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da605f29d7234a08a03bfe24ec8a9d62"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Load model\n",
        "model=PegasusForConditionalGeneration.from_pretrained(\"google/pegasus-xsum\")"
      ],
      "metadata": {
        "id": "DmRe0i9PJbLk",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "0a186e0d848b44c68125b5272f3351ba",
            "f01dde672e614aaeb7a13683f3e4a39b",
            "15b9fa7607c44e97be17736e1839d070",
            "bcc914fadb504a69b4b54b291252f318",
            "6ce740d85bc5412f9c5a8902e49f122f",
            "aae5917ef1044cc78e24fadc3370e30b",
            "64ffd2a0adf147e8a705ff136fa29f22",
            "1ddffa21afec47688e6a67a8a522436c",
            "578d1f31b39f4d649e8da28364374ca1",
            "7e543e9ebde74c15838a5f077466bd06",
            "1e4a808da740406cb2236d5cad09a9a0",
            "685c8fc8cfc147bb820236a6daa262b7",
            "63946659736c4d1ba4e3d6cb657a338a",
            "80350a3fa45840cfbe321f798a7f70ed",
            "eef3ff552e22419db03f8fe9eecebcb1",
            "5eef2a5b4fdb439694a493f676698696",
            "97d55ffe17c540c9ad804fda661ab51c",
            "be5273a552004165a765eca03d64e973",
            "0070b38218d14a8db4c67ca6155da8ad",
            "6b26265fd53d4bd08025ffc89542d4cb",
            "09dddfff7ad34a0bb896f47c598f5d51",
            "d2f2558e658c44459df1a6db41f3547f"
          ]
        },
        "outputId": "205db5df-cdb2-4ac9-ae84-34c1c1688108"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/2.28G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0a186e0d848b44c68125b5272f3351ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/259 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "685c8fc8cfc147bb820236a6daa262b7"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Create Tokens - number representation of our text\n",
        "tokens=tokenizer(Article,truncation=True,padding=\"longest\",return_tensors=\"pt\")"
      ],
      "metadata": {
        "id": "qo22bV0kJuBK"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokens"
      ],
      "metadata": {
        "id": "6aXIchstBhqA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ce07971e-aa80-4b96-865a-d8a73c5f7756"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[ 6294,   164,  6294,   222,  6294,   164,  6294,   222, 33519,  2331,\n",
              "          2126, 10144,  8358,  7149,  4646, 60607,  6980,   303,  8543,   788,\n",
              "         43740, 60607,  6980,   303,  8543,   788, 43740,   111,   203,   375,\n",
              "           147,   463,   171,   119,  1021,   141, 60607,   152, 60607,  6980,\n",
              "           137,   225,   214, 31637,   109,  4301,   111,  4556,   113,   956,\n",
              "           481,   111,  4699, 15311,   257,  1409,   109,  2956,   107, 60607,\n",
              "          6980,   117,   114,   366,   113, 10850,   335,   111, 57476,   126,\n",
              "           451,   124,   109,   217,   113,   109,   473,   107,   507, 24972,\n",
              "           137,   129,   263,   118,   114,   340,  1301,   113,   623,   702,\n",
              "           111,   979,  2145,   141,   126,   107,  1054,   107, 16694,  1126,\n",
              "         49040,  4461,   120,   115,   109,   473,  4413,   126,   117,   433,\n",
              "           112,   236,   223,   291,  1964,   108,   860,   107,   838,   107,\n",
              "           185, 25724, 54858,  1382,   108,  2500,  3267,   108,  2500,  9463,\n",
              "           108,  9972,  3267,   108, 74685,  1382,   108,  2384,  1382,   108,\n",
              "          8179,  1382,   108,   933,  3267,  2894,   802,   149,   113,   183,\n",
              "           133,   984,  2578,   111,  7263,   112,   109,  1220,   113,  9972,\n",
              "          1382,   132,  2500,  3267,   107,  1060, 26111,   219, 24972,   108,\n",
              "           145,   218,   258,   180,   200,   172,   108,   180,   157,   245,\n",
              "           111,   180,   153,   698,  2084,   127,   107, 60607,  6980,   303,\n",
              "          8543,   788, 43740,  8543,   788, 43740,   117,   114, 25075,  2400,\n",
              "           118,  4284,  7148, 11430,   143, 72237,   250, 18995,   788, 43740,\n",
              "          4785,   263,  4284,  7148,  8042, 21919,   143, 21297, 31784,   158,\n",
              "           112,  1433,   203,  2722,   107, 19778, 31784,   117,   114,  2400,\n",
              "           162,  1106,   142,   400,   489,   112,   114,   367,   113, 67186,\n",
              "          1040,   111,   871,   780,   112,   201,   122, 62495,   108, 10526,\n",
              "           111,   223,   176,  2722,   107,  8543,   788, 43740,   117,   114,\n",
              "           586,  2400,   162,  3000,  1482,  1382,   111,  1875,   124, 26153,\n",
              "           335,   107,   321, 62043,   121,   936,  4166,   108,   114,  9972,\n",
              "           117,  3365,   141,   203, 27055,  8892,   111,   109,  7444,   113,\n",
              "           276,  1172,   115,   109,  5577,   107,   182,  1632,   114,  1133,\n",
              "           121, 15807, 14246, 57476,  2404,   111,  1259,   989,   107, 10879,\n",
              "           108,   114,  1352,  1285,   138,   129,  4543,   141,  1856,   113,\n",
              "           989,   107,   721, 28887,   819,  4198,   112,   149,   109,   989,\n",
              "           108,   976,  9972,   117,  7123,   141,   181, 48863,  2148,   172,\n",
              "           646,   142,  1077,   113,   149,   109, 24972,   107,  8543,   788,\n",
              "         43740,  3115, 56171,   111, 74685,   113,   114,  5577,   107, 22305,\n",
              "          2922,  4269,   317,  1126, 32559, 49040,   108, 52552, 10903,   114,\n",
              "          2404,  9972,   111,   305, 10903,   114,  1259,  9972,   107, 31126,\n",
              "          3580,   989,  5373,   109, 56171,   107,  8543,   788, 43740,   148,\n",
              "         27055,  5672,   120,   225,   122,  1226,   121, 40042,  1382,   107,\n",
              "           321,   587,   492, 67878,   116,   108, 53573,  2689,   108, 42994,\n",
              "           108,   733,   107, 16559, 19467,  4269,   317,   110, 58998,   108,\n",
              "         49040,   107, 16559, 19467, 39783, 21560,   109,   713,   113,   510,\n",
              "          2500,   111, 22915,   257,  4400,   115,   109,  1352,   107,   139,\n",
              "           902, 74685,   495,   120,   109,  1352,  1733,   510,  2500,   880,\n",
              "           197, 22915,   257,   107,  8543,   788, 43740,   148,   156,   154,\n",
              "          9840,   492,  7444,   107,  8543,   788, 43740, 31812, 74685,   141,\n",
              "           383,   134,   109,   402, 37785,   123,   107, 68096, 11917,   175,\n",
              "           114,  1172, 56876,   109,   352,  1172,   107,   321,  1188,   108,\n",
              "         50498,   116,   127,   263,   130, 60171, 24695,  9054,   234,   123,\n",
              "           250,   321,   587,   151,   184,  7123, 56171,   111, 74685,   118,\n",
              "           185,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Summarize\n",
        "summary= model.generate(**tokens)"
      ],
      "metadata": {
        "id": "lnysFU8ZJxih",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3d867b4-743b-4b2c-80ee-d8bfedaa1653"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (64) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "{**tokens}"
      ],
      "metadata": {
        "id": "7iZSC6wsBh5u",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f1281ef-7caf-4135-c428-298e7fba0f0c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[ 6294,   164,  6294,   222,  6294,   164,  6294,   222, 33519,  2331,\n",
              "           2126, 10144,  8358,  7149,  4646, 60607,  6980,   303,  8543,   788,\n",
              "          43740, 60607,  6980,   303,  8543,   788, 43740,   111,   203,   375,\n",
              "            147,   463,   171,   119,  1021,   141, 60607,   152, 60607,  6980,\n",
              "            137,   225,   214, 31637,   109,  4301,   111,  4556,   113,   956,\n",
              "            481,   111,  4699, 15311,   257,  1409,   109,  2956,   107, 60607,\n",
              "           6980,   117,   114,   366,   113, 10850,   335,   111, 57476,   126,\n",
              "            451,   124,   109,   217,   113,   109,   473,   107,   507, 24972,\n",
              "            137,   129,   263,   118,   114,   340,  1301,   113,   623,   702,\n",
              "            111,   979,  2145,   141,   126,   107,  1054,   107, 16694,  1126,\n",
              "          49040,  4461,   120,   115,   109,   473,  4413,   126,   117,   433,\n",
              "            112,   236,   223,   291,  1964,   108,   860,   107,   838,   107,\n",
              "            185, 25724, 54858,  1382,   108,  2500,  3267,   108,  2500,  9463,\n",
              "            108,  9972,  3267,   108, 74685,  1382,   108,  2384,  1382,   108,\n",
              "           8179,  1382,   108,   933,  3267,  2894,   802,   149,   113,   183,\n",
              "            133,   984,  2578,   111,  7263,   112,   109,  1220,   113,  9972,\n",
              "           1382,   132,  2500,  3267,   107,  1060, 26111,   219, 24972,   108,\n",
              "            145,   218,   258,   180,   200,   172,   108,   180,   157,   245,\n",
              "            111,   180,   153,   698,  2084,   127,   107, 60607,  6980,   303,\n",
              "           8543,   788, 43740,  8543,   788, 43740,   117,   114, 25075,  2400,\n",
              "            118,  4284,  7148, 11430,   143, 72237,   250, 18995,   788, 43740,\n",
              "           4785,   263,  4284,  7148,  8042, 21919,   143, 21297, 31784,   158,\n",
              "            112,  1433,   203,  2722,   107, 19778, 31784,   117,   114,  2400,\n",
              "            162,  1106,   142,   400,   489,   112,   114,   367,   113, 67186,\n",
              "           1040,   111,   871,   780,   112,   201,   122, 62495,   108, 10526,\n",
              "            111,   223,   176,  2722,   107,  8543,   788, 43740,   117,   114,\n",
              "            586,  2400,   162,  3000,  1482,  1382,   111,  1875,   124, 26153,\n",
              "            335,   107,   321, 62043,   121,   936,  4166,   108,   114,  9972,\n",
              "            117,  3365,   141,   203, 27055,  8892,   111,   109,  7444,   113,\n",
              "            276,  1172,   115,   109,  5577,   107,   182,  1632,   114,  1133,\n",
              "            121, 15807, 14246, 57476,  2404,   111,  1259,   989,   107, 10879,\n",
              "            108,   114,  1352,  1285,   138,   129,  4543,   141,  1856,   113,\n",
              "            989,   107,   721, 28887,   819,  4198,   112,   149,   109,   989,\n",
              "            108,   976,  9972,   117,  7123,   141,   181, 48863,  2148,   172,\n",
              "            646,   142,  1077,   113,   149,   109, 24972,   107,  8543,   788,\n",
              "          43740,  3115, 56171,   111, 74685,   113,   114,  5577,   107, 22305,\n",
              "           2922,  4269,   317,  1126, 32559, 49040,   108, 52552, 10903,   114,\n",
              "           2404,  9972,   111,   305, 10903,   114,  1259,  9972,   107, 31126,\n",
              "           3580,   989,  5373,   109, 56171,   107,  8543,   788, 43740,   148,\n",
              "          27055,  5672,   120,   225,   122,  1226,   121, 40042,  1382,   107,\n",
              "            321,   587,   492, 67878,   116,   108, 53573,  2689,   108, 42994,\n",
              "            108,   733,   107, 16559, 19467,  4269,   317,   110, 58998,   108,\n",
              "          49040,   107, 16559, 19467, 39783, 21560,   109,   713,   113,   510,\n",
              "           2500,   111, 22915,   257,  4400,   115,   109,  1352,   107,   139,\n",
              "            902, 74685,   495,   120,   109,  1352,  1733,   510,  2500,   880,\n",
              "            197, 22915,   257,   107,  8543,   788, 43740,   148,   156,   154,\n",
              "           9840,   492,  7444,   107,  8543,   788, 43740, 31812, 74685,   141,\n",
              "            383,   134,   109,   402, 37785,   123,   107, 68096, 11917,   175,\n",
              "            114,  1172, 56876,   109,   352,  1172,   107,   321,  1188,   108,\n",
              "          50498,   116,   127,   263,   130, 60171, 24695,  9054,   234,   123,\n",
              "            250,   321,   587,   151,   184,  7123, 56171,   111, 74685,   118,\n",
              "            185,     1]]),\n",
              " 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Decode summary\n",
        "tokenizer.decode(summary[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "_VU1YvqSJ2VK",
        "outputId": "02a37844-8348-4d74-9915-95df31af9cd3"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<pad>TextBlob is a library which supports complex analysis and operations on textual data.</s>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text generator and Interface"
      ],
      "metadata": {
        "id": "EsV-_I15Bb_k"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "UU8T-Jv2DJWI"
      },
      "outputs": [],
      "source": [
        "# Function for text generation\n",
        "def generate_xsum_text(prompt):\n",
        "    input_id = tokenizer(prompt,truncation=True,padding=\"longest\",return_tensors=\"pt\")\n",
        "    output_text = model.generate(**input_id)\n",
        "    P_xsum_summary = tokenizer.decode(output_text[0], skip_special_tokens=True)\n",
        "    return P_xsum_summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        },
        "outputId": "c3e65ab3-4267-4543-fcc2-07422ff9cc8b",
        "id": "LwrMM41MDMwP"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/gradio/inputs.py:27: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.9/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7860, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# Gradio interface\n",
        "input_text = gr.inputs.Textbox(lines=3, label=\"Input Text\")\n",
        "New_text = gr.outputs.Textbox(label=\"Generated Text\")\n",
        "\n",
        "iface = gr.Interface(\n",
        "    fn=generate_xsum_text,\n",
        "    inputs=input_text,\n",
        "    outputs=New_text,\n",
        "    title=\"Pegasus_xsum Text Summarization\",\n",
        "    description=\"Abstractive text summarization using Pegasus_xsum model.\",\n",
        ")\n",
        "\n",
        "iface.launch()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Perforrm Abstractive Summarization using Pegasus-large model"
      ],
      "metadata": {
        "id": "8softb0gBRdW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Create Tokenizar using Pegasus-large model\n",
        "tokenizer = PegasusTokenizer.from_pretrained('google/pegasus-large')"
      ],
      "metadata": {
        "id": "RT4BRN3QJZSI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "7544f0f7528d4b8cb7d7561e81c4ff04",
            "b677ec0633a8407a9c2fd8d1d88aef48",
            "b73d8338077b4199a7df1af8e75ed4bf",
            "2e0e0be0284d414e87b9e8e0285fa21e",
            "fda3296fb00d46a5b0733c39d0998b44",
            "7e601a74700c4a748f39f85511d13cdc",
            "6b567aa9a1294e8e85a9634a5d8c9b38",
            "fca8715508034aadab8e6b4b9fecbfd2",
            "9da5ce93f11946fdb85e498e8249e34a",
            "5188929a6d6b456fb0316090895e9b9a",
            "41b142c4cfbf47f0b674a38dfd7ec130",
            "cb55bf4b32c948a5858da2b8f591952d",
            "5bcf05b66d0349909f86474e1e6fc6d6",
            "2e4769b264cb469ea52581f1e541e641",
            "38c4d0c1d7ee4f999539afa0c41d0ad6",
            "8aec947b6f794538b9779cbf1d1253f4",
            "e3fd6f94fdf545a38067843204c55875",
            "27d3d589450e45a1bc7bd977164da181",
            "f5fd771117274080949a10d8d23ba912",
            "4f0096e4e50d4176b75a845361eb0ecd",
            "902def0ffaa54f7e9b399382ea2f1054",
            "a8942c99c973476cb0d923ed91e3a0a4",
            "191adaba9cfb4611ae8c150e37d239c5",
            "cafe8f14fc934d10931b7145cf1a0cf9",
            "7af8654bb5f847108e5d59168646edcc",
            "f49da29452224d5aac905b41ddc6cea1",
            "3362d3b0da86433fa725737a7b3965cc",
            "b765f16a533c48aabb3ebf50a4eb7857",
            "9f72563febef4d099bdff1eef8b32197",
            "3fb6807f493f4ce9951c7d7e3f76200c",
            "d0ba23b5621b441a88717ec1e3715479",
            "bc74817649084a1580bbf1c12f5d8174",
            "04177f29dd234b7086b75d18e8d9e695",
            "57cee14502db417c89223ab0e78f0f01",
            "8383eded655642cd8ea20e9aece1c48d",
            "23c3adc2f5a84b998e80e73c46d25077",
            "d1c67386614b48e4bd9eeb10443742fa",
            "7f4f4986c0574882a5d78c129383810d",
            "679fc3ef1e104a4f924b555bfe0625ed",
            "56b33a4f63dc4bedb2cae375fde09df8",
            "aff8e4e2e7a84ab5ae530d1b371036b7",
            "876021c1eb244d43a62d82fbeb097ac8",
            "47ad0aafe1a649a0a7cc9ecf98441894",
            "cc465f82ffb04698be4cc6f624249700"
          ]
        },
        "outputId": "f3eb3f17-8143-4ebb-e70f-84543c44e2b2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)ve/main/spiece.model:   0%|          | 0.00/1.91M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7544f0f7528d4b8cb7d7561e81c4ff04"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/65.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb55bf4b32c948a5858da2b8f591952d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/88.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "191adaba9cfb4611ae8c150e37d239c5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/3.09k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "57cee14502db417c89223ab0e78f0f01"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Load model\n",
        "model=PegasusForConditionalGeneration.from_pretrained(\"google/pegasus-large\")"
      ],
      "metadata": {
        "id": "rf9Gfv37o6yn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "33c0098ab4814031aa2b4113ff625581",
            "da4af786c24d4dcabf60ceb1c8a1730a",
            "64be57d26bf648f4b3f6f0efb3f7d058",
            "09d0ea56874740458f2bad033e67d0e8",
            "a26237c74a314f10b30e5fb2a9a98ebe",
            "865551b35c784f599e885e02914db7da",
            "c7cd4e3288b34deb9de2586b720b86d2",
            "3e4bb21e61d74c86ba985d6d79523eab",
            "00a8550b4f9b4c6f9106b973688f35f7",
            "6dccb168296c4eeeaeb4da403d215e84",
            "de6a62f2c2c24be990255e8f4c9307dc",
            "75208905e70f40f1882ec79ef4108f74",
            "c50b1fc521a2467db35982062813d36f",
            "90aa3bcfb739477ca1412f22d1ef26ad",
            "4e40bbc354844c45a25445942c5c5a81",
            "a179d3879c804b20a81ca7a594563228",
            "bd2c4c5791324e4d8a7a7221e3677a25",
            "9609415dd28e47b2bf6819b2a5145579",
            "54607b9e937945f88ea840bfad70c31e",
            "d887be792072433aa486d8069c43618f",
            "5c39926e23ba4897b2d094cf3d6df03b",
            "c71037e8c52e49da95f75dab45e960ee"
          ]
        },
        "outputId": "a1623ae1-8c9c-4793-d4bf-f8a3043508cc"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/2.28G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "33c0098ab4814031aa2b4113ff625581"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/260 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "75208905e70f40f1882ec79ef4108f74"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Create Tokens - number representation of our text\n",
        "tokens=tokenizer(Article,truncation=True,padding=\"longest\",return_tensors=\"pt\")"
      ],
      "metadata": {
        "id": "03h8EshK0850"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tokens"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xqy106QY1a9V",
        "outputId": "3d2a9e63-e253-4ee7-82c3-e294ea55b9de"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[ 6294,   164,  6294,   222,  6294,   164,  6294,   222, 33519,  2331,\n",
              "          2126, 10144,  8358,  7149,  4646, 60607,  6980,   303,  8543,   788,\n",
              "         43740, 60607,  6980,   303,  8543,   788, 43740,   111,   203,   375,\n",
              "           147,   463,   171,   119,  1021,   141, 60607,   152, 60607,  6980,\n",
              "           137,   225,   214, 31637,   109,  4301,   111,  4556,   113,   956,\n",
              "           481,   111,  4699, 15311,   257,  1409,   109,  2956,   107, 60607,\n",
              "          6980,   117,   114,   366,   113, 10850,   335,   111, 57476,   126,\n",
              "           451,   124,   109,   217,   113,   109,   473,   107,   507, 24972,\n",
              "           137,   129,   263,   118,   114,   340,  1301,   113,   623,   702,\n",
              "           111,   979,  2145,   141,   126,   107,  1054,   107, 16694,  1126,\n",
              "         49040,  4461,   120,   115,   109,   473,  4413,   126,   117,   433,\n",
              "           112,   236,   223,   291,  1964,   108,   860,   107,   838,   107,\n",
              "           185, 25724, 54858,  1382,   108,  2500,  3267,   108,  2500,  9463,\n",
              "           108,  9972,  3267,   108, 74685,  1382,   108,  2384,  1382,   108,\n",
              "          8179,  1382,   108,   933,  3267,  2894,   802,   149,   113,   183,\n",
              "           133,   984,  2578,   111,  7263,   112,   109,  1220,   113,  9972,\n",
              "          1382,   132,  2500,  3267,   107,  1060, 26111,   219, 24972,   108,\n",
              "           145,   218,   258,   180,   200,   172,   108,   180,   157,   245,\n",
              "           111,   180,   153,   698,  2084,   127,   107, 60607,  6980,   303,\n",
              "          8543,   788, 43740,  8543,   788, 43740,   117,   114, 25075,  2400,\n",
              "           118,  4284,  7148, 11430,   143, 72237,   250, 18995,   788, 43740,\n",
              "          4785,   263,  4284,  7148,  8042, 21919,   143, 21297, 31784,   158,\n",
              "           112,  1433,   203,  2722,   107, 19778, 31784,   117,   114,  2400,\n",
              "           162,  1106,   142,   400,   489,   112,   114,   367,   113, 67186,\n",
              "          1040,   111,   871,   780,   112,   201,   122, 62495,   108, 10526,\n",
              "           111,   223,   176,  2722,   107,  8543,   788, 43740,   117,   114,\n",
              "           586,  2400,   162,  3000,  1482,  1382,   111,  1875,   124, 26153,\n",
              "           335,   107,   321, 62043,   121,   936,  4166,   108,   114,  9972,\n",
              "           117,  3365,   141,   203, 27055,  8892,   111,   109,  7444,   113,\n",
              "           276,  1172,   115,   109,  5577,   107,   182,  1632,   114,  1133,\n",
              "           121, 15807, 14246, 57476,  2404,   111,  1259,   989,   107, 10879,\n",
              "           108,   114,  1352,  1285,   138,   129,  4543,   141,  1856,   113,\n",
              "           989,   107,   721, 28887,   819,  4198,   112,   149,   109,   989,\n",
              "           108,   976,  9972,   117,  7123,   141,   181, 48863,  2148,   172,\n",
              "           646,   142,  1077,   113,   149,   109, 24972,   107,  8543,   788,\n",
              "         43740,  3115, 56171,   111, 74685,   113,   114,  5577,   107, 22305,\n",
              "          2922,  4269,   317,  1126, 32559, 49040,   108, 52552, 10903,   114,\n",
              "          2404,  9972,   111,   305, 10903,   114,  1259,  9972,   107, 31126,\n",
              "          3580,   989,  5373,   109, 56171,   107,  8543,   788, 43740,   148,\n",
              "         27055,  5672,   120,   225,   122,  1226,   121, 40042,  1382,   107,\n",
              "           321,   587,   492, 67878,   116,   108, 53573,  2689,   108, 42994,\n",
              "           108,   733,   107, 16559, 19467,  4269,   317,   110, 58998,   108,\n",
              "         49040,   107, 16559, 19467, 39783, 21560,   109,   713,   113,   510,\n",
              "          2500,   111, 22915,   257,  4400,   115,   109,  1352,   107,   139,\n",
              "           902, 74685,   495,   120,   109,  1352,  1733,   510,  2500,   880,\n",
              "           197, 22915,   257,   107,  8543,   788, 43740,   148,   156,   154,\n",
              "          9840,   492,  7444,   107,  8543,   788, 43740, 31812, 74685,   141,\n",
              "           383,   134,   109,   402, 37785,   123,   107, 68096, 11917,   175,\n",
              "           114,  1172, 56876,   109,   352,  1172,   107,   321,  1188,   108,\n",
              "         50498,   116,   127,   263,   130, 60171, 24695,  9054,   234,   123,\n",
              "           250,   321,   587,   151,   184,  7123, 56171,   111, 74685,   118,\n",
              "           185,   187,   171,   146,   172,   136,   587,   134,   149,   108,\n",
              "           126,   117,   314,  6880,  1577,   321,   136,   970,   587,   108,\n",
              "         56171,  3092, 52552,   111, 74685,   117,  2628,   162,   117,  2335,\n",
              "           107,   611,   108,   118,   109,  5577,   185,   287,   140,   114,\n",
              "          1908,   587,   155,   125,   192,  2364,   372,   156,  1577,   168,\n",
              "          3115,   110, 19626,   118,   302, 74685,   111, 56171,   162,   117,\n",
              "           146,   109,  3617,  1140,   145,   123,   252,  1337,   107,   168,\n",
              "           117,  1214,   120,   175,   109,  2400,  3115,  1270,   110, 19626,\n",
              "           707,   175,   128,  5577,   595,   123,   144,  2512,   189,   989,\n",
              "           120,   196,   114, 56171,   115,   109, 19778, 31784,   569,   323,\n",
              "           132,   262,  8543,   788, 43740,  1481,   114, 20420,  1077,  9972,\n",
              "          2135,   204,   149,   109,   989,   115,   276,  2630,   107,   182,\n",
              "           831, 28575,   116,   165,   109,  1298,   113,  9750,   122,  3447,\n",
              "          7775, 15169,  7398,   317,   989,   115,   150,   437,   110,   151,\n",
              "           402, 91466,   123,   111,   402,  2819,   123,   107,  1439,   432,\n",
              "           197,   146,   108,   118,   586,  1145,   108,  8543,   788, 43740,\n",
              "           659,   188,  1226,   107, 17338,   112,  1138, 60607,  6980,   303,\n",
              "          8543,   788, 43740,   212,  3898, 20122,  1352, 26409,  1271,   280,\n",
              "           107, 39167,   114,  1434,   120, 31812, 74685,   108, 56171,   111,\n",
              "           361,   126,   114,  2135,   451,   124,   109, 10238,   119,   245,\n",
              "           112,   323,   107,  3314,  1498,   974,   120,   125,   642,   482,\n",
              "           117,   151,  3864,  1228,  2979,  1379, 20619,   107,  8025,   191,\n",
              "         13765,   191, 25724, 54858,   121, 26280,   121, 60896,   121, 13830,\n",
              "         26409,  1271,   121, 28400,   121, 34592,   420,   121, 28400,   121,\n",
              "         20603,  6011, 39673,   492,  1315,  1315,  1439,   135, 33519,  2331,\n",
              "          2126,   673,   238,   118,   335,  1578,   107,   202, 11158,  4157,\n",
              "          1542,  3924,   108,   675,   111,  3412,   107,  3440, 10587, 41686,\n",
              "         45533,  1504,   109, 11158,   998,  7978,   307,  7770, 13604,  7877,\n",
              "           115,  2331,  2126,  2650, 31150,  4596, 14005, 15500,  4952, 31751,\n",
              "          9980,  7787,  3440,  8543,   112,  3442,     1]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "         1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Summarize\n",
        "summary= model.generate(**tokens)"
      ],
      "metadata": {
        "id": "2O8p1JzL1_XO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4a40545-5b68-424d-ec6d-9b82db720dd8"
      },
      "execution_count": 23,
      "outputs": [
        {
          "metadata": {
            "tags": null
          },
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/transformers/generation/utils.py:1313: UserWarning: Using `max_length`'s default (256) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "{**tokens}"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hl0t4blD1_5r",
        "outputId": "231155b1-4aec-4a12-e708-8518cb13ec75"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[ 6294,   164,  6294,   222,  6294,   164,  6294,   222, 33519,  2331,\n",
              "           2126, 10144,  8358,  7149,  4646, 60607,  6980,   303,  8543,   788,\n",
              "          43740, 60607,  6980,   303,  8543,   788, 43740,   111,   203,   375,\n",
              "            147,   463,   171,   119,  1021,   141, 60607,   152, 60607,  6980,\n",
              "            137,   225,   214, 31637,   109,  4301,   111,  4556,   113,   956,\n",
              "            481,   111,  4699, 15311,   257,  1409,   109,  2956,   107, 60607,\n",
              "           6980,   117,   114,   366,   113, 10850,   335,   111, 57476,   126,\n",
              "            451,   124,   109,   217,   113,   109,   473,   107,   507, 24972,\n",
              "            137,   129,   263,   118,   114,   340,  1301,   113,   623,   702,\n",
              "            111,   979,  2145,   141,   126,   107,  1054,   107, 16694,  1126,\n",
              "          49040,  4461,   120,   115,   109,   473,  4413,   126,   117,   433,\n",
              "            112,   236,   223,   291,  1964,   108,   860,   107,   838,   107,\n",
              "            185, 25724, 54858,  1382,   108,  2500,  3267,   108,  2500,  9463,\n",
              "            108,  9972,  3267,   108, 74685,  1382,   108,  2384,  1382,   108,\n",
              "           8179,  1382,   108,   933,  3267,  2894,   802,   149,   113,   183,\n",
              "            133,   984,  2578,   111,  7263,   112,   109,  1220,   113,  9972,\n",
              "           1382,   132,  2500,  3267,   107,  1060, 26111,   219, 24972,   108,\n",
              "            145,   218,   258,   180,   200,   172,   108,   180,   157,   245,\n",
              "            111,   180,   153,   698,  2084,   127,   107, 60607,  6980,   303,\n",
              "           8543,   788, 43740,  8543,   788, 43740,   117,   114, 25075,  2400,\n",
              "            118,  4284,  7148, 11430,   143, 72237,   250, 18995,   788, 43740,\n",
              "           4785,   263,  4284,  7148,  8042, 21919,   143, 21297, 31784,   158,\n",
              "            112,  1433,   203,  2722,   107, 19778, 31784,   117,   114,  2400,\n",
              "            162,  1106,   142,   400,   489,   112,   114,   367,   113, 67186,\n",
              "           1040,   111,   871,   780,   112,   201,   122, 62495,   108, 10526,\n",
              "            111,   223,   176,  2722,   107,  8543,   788, 43740,   117,   114,\n",
              "            586,  2400,   162,  3000,  1482,  1382,   111,  1875,   124, 26153,\n",
              "            335,   107,   321, 62043,   121,   936,  4166,   108,   114,  9972,\n",
              "            117,  3365,   141,   203, 27055,  8892,   111,   109,  7444,   113,\n",
              "            276,  1172,   115,   109,  5577,   107,   182,  1632,   114,  1133,\n",
              "            121, 15807, 14246, 57476,  2404,   111,  1259,   989,   107, 10879,\n",
              "            108,   114,  1352,  1285,   138,   129,  4543,   141,  1856,   113,\n",
              "            989,   107,   721, 28887,   819,  4198,   112,   149,   109,   989,\n",
              "            108,   976,  9972,   117,  7123,   141,   181, 48863,  2148,   172,\n",
              "            646,   142,  1077,   113,   149,   109, 24972,   107,  8543,   788,\n",
              "          43740,  3115, 56171,   111, 74685,   113,   114,  5577,   107, 22305,\n",
              "           2922,  4269,   317,  1126, 32559, 49040,   108, 52552, 10903,   114,\n",
              "           2404,  9972,   111,   305, 10903,   114,  1259,  9972,   107, 31126,\n",
              "           3580,   989,  5373,   109, 56171,   107,  8543,   788, 43740,   148,\n",
              "          27055,  5672,   120,   225,   122,  1226,   121, 40042,  1382,   107,\n",
              "            321,   587,   492, 67878,   116,   108, 53573,  2689,   108, 42994,\n",
              "            108,   733,   107, 16559, 19467,  4269,   317,   110, 58998,   108,\n",
              "          49040,   107, 16559, 19467, 39783, 21560,   109,   713,   113,   510,\n",
              "           2500,   111, 22915,   257,  4400,   115,   109,  1352,   107,   139,\n",
              "            902, 74685,   495,   120,   109,  1352,  1733,   510,  2500,   880,\n",
              "            197, 22915,   257,   107,  8543,   788, 43740,   148,   156,   154,\n",
              "           9840,   492,  7444,   107,  8543,   788, 43740, 31812, 74685,   141,\n",
              "            383,   134,   109,   402, 37785,   123,   107, 68096, 11917,   175,\n",
              "            114,  1172, 56876,   109,   352,  1172,   107,   321,  1188,   108,\n",
              "          50498,   116,   127,   263,   130, 60171, 24695,  9054,   234,   123,\n",
              "            250,   321,   587,   151,   184,  7123, 56171,   111, 74685,   118,\n",
              "            185,   187,   171,   146,   172,   136,   587,   134,   149,   108,\n",
              "            126,   117,   314,  6880,  1577,   321,   136,   970,   587,   108,\n",
              "          56171,  3092, 52552,   111, 74685,   117,  2628,   162,   117,  2335,\n",
              "            107,   611,   108,   118,   109,  5577,   185,   287,   140,   114,\n",
              "           1908,   587,   155,   125,   192,  2364,   372,   156,  1577,   168,\n",
              "           3115,   110, 19626,   118,   302, 74685,   111, 56171,   162,   117,\n",
              "            146,   109,  3617,  1140,   145,   123,   252,  1337,   107,   168,\n",
              "            117,  1214,   120,   175,   109,  2400,  3115,  1270,   110, 19626,\n",
              "            707,   175,   128,  5577,   595,   123,   144,  2512,   189,   989,\n",
              "            120,   196,   114, 56171,   115,   109, 19778, 31784,   569,   323,\n",
              "            132,   262,  8543,   788, 43740,  1481,   114, 20420,  1077,  9972,\n",
              "           2135,   204,   149,   109,   989,   115,   276,  2630,   107,   182,\n",
              "            831, 28575,   116,   165,   109,  1298,   113,  9750,   122,  3447,\n",
              "           7775, 15169,  7398,   317,   989,   115,   150,   437,   110,   151,\n",
              "            402, 91466,   123,   111,   402,  2819,   123,   107,  1439,   432,\n",
              "            197,   146,   108,   118,   586,  1145,   108,  8543,   788, 43740,\n",
              "            659,   188,  1226,   107, 17338,   112,  1138, 60607,  6980,   303,\n",
              "           8543,   788, 43740,   212,  3898, 20122,  1352, 26409,  1271,   280,\n",
              "            107, 39167,   114,  1434,   120, 31812, 74685,   108, 56171,   111,\n",
              "            361,   126,   114,  2135,   451,   124,   109, 10238,   119,   245,\n",
              "            112,   323,   107,  3314,  1498,   974,   120,   125,   642,   482,\n",
              "            117,   151,  3864,  1228,  2979,  1379, 20619,   107,  8025,   191,\n",
              "          13765,   191, 25724, 54858,   121, 26280,   121, 60896,   121, 13830,\n",
              "          26409,  1271,   121, 28400,   121, 34592,   420,   121, 28400,   121,\n",
              "          20603,  6011, 39673,   492,  1315,  1315,  1439,   135, 33519,  2331,\n",
              "           2126,   673,   238,   118,   335,  1578,   107,   202, 11158,  4157,\n",
              "           1542,  3924,   108,   675,   111,  3412,   107,  3440, 10587, 41686,\n",
              "          45533,  1504,   109, 11158,   998,  7978,   307,  7770, 13604,  7877,\n",
              "            115,  2331,  2126,  2650, 31150,  4596, 14005, 15500,  4952, 31751,\n",
              "           9980,  7787,  3440,  8543,   112,  3442,     1]]),\n",
              " 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "          1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "##Output/summary in tokens\n",
        "summary[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "boDPqc-f1_8q",
        "outputId": "2894d1ca-5320-480d-bf7b-6661c7a2665c"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "tensor([    0,  6294,   164,  6294,   222,  6294,   164,  6294,   222, 33519,\n",
              "         2331,  2126, 10144,  8358,  7149,  4646, 60607,  6980,   303,  8543,\n",
              "          788, 43740, 60607,  6980,   303,  8543,   788, 43740,   111,   203,\n",
              "          375,   147,   185, 25724, 54858,  1382,   108,  2500,  3267,   108,\n",
              "         2500,  9463,   108,  9972,  3267,   108, 74685,  1382,   108,  2384,\n",
              "         1382,   108,  8179,  1382,   108,   933,  3267,  2894,   802,   149,\n",
              "          113,   183,   133,   984,  2578,   111,  7263,   112,   109,  1220,\n",
              "          113,  9972,  1382,   132,  2500,  3267,   107, 60607,  6980,   303,\n",
              "         8543,   788, 43740,  8543,   788, 43740,   117,   114,  2400, 25075,\n",
              "          118,  4284,  7148, 11430,   143, 72237,   250, 18995,   788, 43740,\n",
              "         4785,   263,  4284,  7148,  8042, 21919,   143, 21297, 31784,   158,\n",
              "          112,  1433,   203,  2722,   107,     1])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Decode summary\n",
        "tokenizer.decode(summary[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "SLQINFkC2AA6",
        "outputId": "70490098-b50f-4ab1-e461-e65decdb16a6"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<pad>Sign up Sign In Sign up Sign In Towards Data Science Jun 27, 2020 Save Sentiment Analysis using TextBlob Sentiment Analysis using TextBlob and its working! “sentiment analysis, opinion mining, opinion extraction, sentiment mining, subjectivity analysis, affect analysis, emotion analysis, review mining”, however all of them have similar purposes and belong to the subject of sentiment analysis or opinion mining. Sentiment Analysis using TextBlob TextBlob is a library python for Natural Language Processing (NLP).TextBlob actively used Natural Language ToolKit (NLTK) to achieve its tasks.</s>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Text Generator and Interface"
      ],
      "metadata": {
        "id": "9R5yVA0pBkVh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "Fu7Yru6290R7"
      },
      "outputs": [],
      "source": [
        "# Function for text generation\n",
        "def Pegasus_large_text(prompt):\n",
        "    input_id = tokenizer(prompt,truncation=True,padding=\"longest\",return_tensors=\"pt\")\n",
        "    output_text = model.generate(**input_id)\n",
        "    P_large_summary = tokenizer.decode(output_text[0], skip_special_tokens=True)\n",
        "    return P_large_summary"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 775
        },
        "id": "iEc2nL_b4FUs",
        "outputId": "38378f47-fec8-4cdc-8149-c6b129fb6f22"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/gradio/inputs.py:27: UserWarning: Usage of gradio.inputs is deprecated, and will not be supported in the future, please import your component from gradio.components\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `optional` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.9/dist-packages/gradio/deprecation.py:40: UserWarning: `numeric` parameter is deprecated, and it has no effect\n",
            "  warnings.warn(value)\n",
            "/usr/local/lib/python3.9/dist-packages/gradio/outputs.py:22: UserWarning: Usage of gradio.outputs is deprecated, and will not be supported in the future, please import your components from gradio.components\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "Note: opening Chrome Inspector may crash demo inside Colab notebooks.\n",
            "\n",
            "To create a public link, set `share=True` in `launch()`.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "(async (port, path, width, height, cache, element) => {\n",
              "                        if (!google.colab.kernel.accessAllowed && !cache) {\n",
              "                            return;\n",
              "                        }\n",
              "                        element.appendChild(document.createTextNode(''));\n",
              "                        const url = await google.colab.kernel.proxyPort(port, {cache});\n",
              "\n",
              "                        const external_link = document.createElement('div');\n",
              "                        external_link.innerHTML = `\n",
              "                            <div style=\"font-family: monospace; margin-bottom: 0.5rem\">\n",
              "                                Running on <a href=${new URL(path, url).toString()} target=\"_blank\">\n",
              "                                    https://localhost:${port}${path}\n",
              "                                </a>\n",
              "                            </div>\n",
              "                        `;\n",
              "                        element.appendChild(external_link);\n",
              "\n",
              "                        const iframe = document.createElement('iframe');\n",
              "                        iframe.src = new URL(path, url).toString();\n",
              "                        iframe.height = height;\n",
              "                        iframe.allow = \"autoplay; camera; microphone; clipboard-read; clipboard-write;\"\n",
              "                        iframe.width = width;\n",
              "                        iframe.style.border = 0;\n",
              "                        element.appendChild(iframe);\n",
              "                    })(7862, \"/\", \"100%\", 500, false, window.element)"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": []
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "# Gradio interface\n",
        "input_text = gr.inputs.Textbox(lines=3, label=\"Input Text\")\n",
        "New_text = gr.outputs.Textbox(label=\"Generated Text\")\n",
        "\n",
        "iface = gr.Interface(\n",
        "    fn=Pegasus_large_text,\n",
        "    inputs=input_text,\n",
        "    outputs=New_text,\n",
        "    title=\"Pegasus_large Text Summarization\",\n",
        "    description=\"Abstractive text summarization using Pegasus_large model.\",\n",
        ")\n",
        "\n",
        "iface.launch()\n"
      ]
    }
  ]
}