https://github.com/yuchenlin/lstm_sentence_classifier

LSTM-based Models for Sentence Classification in PyTorch
https://github.com/yuchenlin/lstm_sentence_classifier

lstm-model nlp pytorch sentence-classification

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LSTM-based Models for Sentence Classification in PyTorch

• Host: GitHub
• URL: https://github.com/yuchenlin/lstm_sentence_classifier
• Owner: yuchenlin
• License: mit
• Created: 2017-07-03T08:27:30.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2020-10-05T22:32:32.000Z (about 4 years ago)
• Last Synced: 2024-10-11T21:11:22.449Z (about 1 month ago)
• Topics: lstm-model, nlp, pytorch, sentence-classification
• Language: Python
• Homepage:
• Size: 103 MB
• Stars: 76
• Watchers: 6
• Forks: 31
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# LSTM-based Models for Sentence Classification in PyTorch

This repo is aiming for reproducing the sentence classifcation experiments in Mou et al. (EMNLP 2016).

https://aclweb.org/anthology/D16-1046

## Datasets

### 1. IMDB Sentiment Classification

    https://drive.google.com/file/d/0B8yp1gOBCztyN0JaMDVoeXhHWm8/

    Train+dev = 600,000 (imdb.neg+imdb.pos)  550,000 for train and 50,000 for dev

    Test = 2,000 (rt_critics.test)

### 2. MR Sentiment Classification

    https://www.cs.cornell.edu/people/pabo/movie-review-data/

    Train+dev+test = rt-polarity.neg + rt-polarity.pos 

    all = 5331*2 = 10662 = 8500(train) + 1100(dev) + 1062(test) 

### 3. QC Question Classification (6 types)

    http://cogcomp.cs.illinois.edu/Data/QA/QC/

    Train(train_5500.label, 5452 = 4,800(train) + 652(dev)) + test(TREC_10.label, 500) 

    

## Performance 

### 1. LSTM-Softmax Classifier without MiniBatch or Pretrained Embedding

    "LSTM_sentence_classifier.py"

    Remark: 

    This model is the simplest version of LSTM-Softmax Classifier. 

    It doesn't use mini-batch or pretrained word embedding. 

    Note that there is not fixed lenght of the sentences.

    Its performance with Adam(lr = 1e-3) is 76.1 in terms of accuracy on MR dataset. 

    It is slower with Adam than with SGD, but the performance is much better. 

    However, it's unreasonable to use such code to train a very large dataset like IMDB.