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Machine Learning Project

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├── data                        # Auto-downloaded datasets
│   └── ...
├── notebooks                   # Your notebooks with experimental results
│   └── ...
├── models                      # Trained models (probably saved with pickle)
│   └── ...
├── report                      # Project report (including source files, figures, etc.)
│   └── ...
├── scripts                     # Utility scripts (download, deploy, etc.)
│   └── ...
└── src                         # Source files (including train.py & test.py)
    └── ...

Description of each file

File Description
data/train_data.json The original dataset auto-downloaded by fetch_train_data() in src/utils.py
scripts/download.py Script to download all datasets (MiraLab's version, UCSD's version)
src/train.py Script to train our model (not necessary, since we may finish training all in notebooks)
src/test.py Script to test our model (needed to generate submission file)
src/models.py Implementation of our model(s), should be wrapped as a class
src/preprocess.py Source code for data cleansing, transforming, and feature engineering
src/utils.py Utility functions that cannot be put in other source files (e.g. fetch_train_data())
src/main.ipynb An example notebook to show how to import and use our library. You may have your own versions in other branches.

TODO

  • Implement an oversampling method to deal with the imbalanced dataset

Reference

Please share your references here.

Title Comment
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) (Minneapolis, Minnesota, Jun. 2019), 4171–4186. Refered in homepage
Convolutional Neural Networks for Sentence Classification. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) (Doha, Qatar, Oct. 2014), 1746–1751. Refered in homepage
Recommending Product Sizes to Customers. Proceedings of the Eleventh ACM Conference on Recommender Systems (New York, NY, USA, Aug. 2017), 243–250.
Decomposing fit semantics for product size recommendation in metric spaces. Proceedings of the 12th ACM Conference on Recommender Systems (New York, NY, USA, Sep. 2018), 422–426. Original dataset
Learning Embeddings for Product Size Recommendations. (2019), 9.
PreSizE: Predicting Size in E-Commerce using Transformers. Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval (New York, NY, USA, Jul. 2021), 255–264.
A Hierarchical Bayesian Model for Size Recommendation in Fashion. Proceedings of the 12th ACM Conference on Recommender Systems (Sep. 2018), 392–396.
Bayesian Models for Product Size Recommendations. Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW ’18 (Lyon, France, 2018), 679–687.

White List

torch.sqrt
torch.Tensor
torch.where
torch.randn
torch.zeros
torch.ones
torch.abs
torch.add
torch.mul
torch.matmul
torch.div
torch.nn.functional
torch.exp
torch.log
torch.mean
torch.sum
torch.max
torch.min
torch.transpose
torch.cat
torch.chunk
torch.split
torch.stack
torch.unsqueeze
torch.squeeze
torch.gather
torch.save
torch.load
torch.cuda
torch.distributed
torch.distributions
torch.multiprocessing
torch.ge
csv
nltk.tokenize
numpy
math
json
pandas
scipy
sklearn.metrics 
os
sys
collections
random
abc
gtimer
datetime
time
copy
tqdm
argparse
matplotlib
importlib
pickle