Add api playground to chat completion template doc + run generate script

huggingface · Oct 23, 2024 · 869c1c1 · 869c1c1
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diff --git a/docs/api-inference/tasks/image-segmentation.md b/docs/api-inference/tasks/image-segmentation.md
@@ -24,7 +24,7 @@ For more details about the `image-segmentation` task, check out its [dedicated p
 
 ### Recommended models
 
-- [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512): Semantic segmentation model trained on ADE20k benchmark dataset with 512x512 resolution.
+- [nvidia/segformer-b0-finetuned-ade-512-512](https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512): Semantic segmentation model trained on ADE20k dataset.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=image-segmentation&sort=trending).
 

diff --git a/docs/api-inference/tasks/object-detection.md b/docs/api-inference/tasks/object-detection.md
@@ -24,8 +24,7 @@ For more details about the `object-detection` task, check out its [dedicated pag
 
 ### Recommended models
 
-- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50): Solid object detection model trained on the benchmark dataset COCO 2017.
-- [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k): Strong object detection model trained on ImageNet-21k dataset.
+- [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50): Solid object detection model pre-trained on the COCO 2017 dataset.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=object-detection&sort=trending).
 

diff --git a/docs/api-inference/tasks/question-answering.md b/docs/api-inference/tasks/question-answering.md
@@ -26,6 +26,7 @@ For more details about the `question-answering` task, check out its [dedicated p
 
 - [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2): A robust baseline model for most question answering domains.
 - [distilbert/distilbert-base-cased-distilled-squad](https://huggingface.co/distilbert/distilbert-base-cased-distilled-squad): Small yet robust model that can answer questions.
+- [google/tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq): A special model that can answer questions from tables.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=question-answering&sort=trending).
 

diff --git a/docs/api-inference/tasks/table-question-answering.md b/docs/api-inference/tasks/table-question-answering.md
@@ -24,6 +24,7 @@ For more details about the `table-question-answering` task, check out its [dedic
 
 ### Recommended models
 
+- [google/tapas-base-finetuned-wtq](https://huggingface.co/google/tapas-base-finetuned-wtq): A robust table question answering model.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=table-question-answering&sort=trending).
 
@@ -34,7 +35,7 @@ Explore all available models and find the one that suits you best [here](https:/
 
 <curl>
 ```bash
-curl https://api-inference.huggingface.co/models/<REPO_ID> \
+curl https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq \
 	-X POST \
 	-d '{"inputs": { "query": "How many stars does the transformers repository have?", "table": { "Repository": ["Transformers", "Datasets", "Tokenizers"], "Stars": ["36542", "4512", "3934"], "Contributors": ["651", "77", "34"], "Programming language": [ "Python", "Python", "Rust, Python and NodeJS" ] } }}' \
 	-H 'Content-Type: application/json' \
@@ -46,7 +47,7 @@ curl https://api-inference.huggingface.co/models/<REPO_ID> \
 ```py
 import requests
 
-API_URL = "https://api-inference.huggingface.co/models/<REPO_ID>"
+API_URL = "https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq"
 headers = {"Authorization": "Bearer hf_***"}
 
 def query(payload):
@@ -77,7 +78,7 @@ To use the Python client, see `huggingface_hub`'s [package reference](https://hu
 ```js
 async function query(data) {
 	const response = await fetch(
-		"https://api-inference.huggingface.co/models/<REPO_ID>",
+		"https://api-inference.huggingface.co/models/google/tapas-base-finetuned-wtq",
 		{
 			headers: {
 				Authorization: "Bearer hf_***"

diff --git a/docs/api-inference/tasks/token-classification.md b/docs/api-inference/tasks/token-classification.md
@@ -131,7 +131,8 @@ If `stream` is `false` (default), the response will be a JSON object with the fo
 | Body |  |
 | :--- | :--- | :--- |
 | **(array)** | _object[]_ | Output is an array of objects. |
-| **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;entity_group** | _string_ | The predicted label for that group of tokens |
+| **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;entity_group** | _string_ | The predicted label for a group of one or more tokens |
+| **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;entity** | _string_ | The predicted label for a single token |
 | **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;score** | _number_ | The associated score / probability |
 | **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;word** | _string_ | The corresponding text |
 | **&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;start** | _integer_ | The character position in the input where this group begins. |

diff --git a/docs/api-inference/tasks/zero-shot-classification.md b/docs/api-inference/tasks/zero-shot-classification.md
@@ -25,6 +25,7 @@ For more details about the `zero-shot-classification` task, check out its [dedic
 ### Recommended models
 
 - [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli): Powerful zero-shot text classification model.
+- [MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7](https://huggingface.co/MoritzLaurer/mDeBERTa-v3-base-xnli-multilingual-nli-2mil7): Powerful zero-shot multilingual text classification model that can accomplish multiple tasks.
 
 Explore all available models and find the one that suits you best [here](https://huggingface.co/models?inference=warm&pipeline_tag=zero-shot-classification&sort=trending).
 

diff --git a/scripts/api-inference/templates/task/chat-completion.handlebars b/scripts/api-inference/templates/task/chat-completion.handlebars
@@ -17,6 +17,21 @@ This is a subtask of [`text-generation`](https://huggingface.co/docs/api-inferen
 - [{{this.id}}](https://huggingface.co/{{this.id}}): {{this.description}}
 {{/each}}
 
+### API Playground
+
+For Chat Completion models, we provide an interactive UI Playground for easier testing:
+
+- Quickly iterate on your prompts from the UI.
+- Set and override system, assistant and user messages.
+- Browse and select models currently available on the Inference API.
+- Compare the output of two models side-by-side.
+- Adjust requests parameters from the UI.
+- Easily switch between UI view and code snippets.
+
+<a href="https://huggingface.co/playground" target="blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/5f17f0a0925b9863e28ad517/9_Tgf0Tv65srhBirZQMTp.png" style="max-width: 400px; width: 100%;"/></a>
+
+Access the Inference UI Playground and start exploring: [https://huggingface.co/playground](https://huggingface.co/playground)
+
 ### Using the API
 
 The API supports: