Text-Classification-PyTorch 🐋

Here is a new boy 🙇 who wants to become a NLPer and his repository for Text Classification. Besides TextCNN and TextAttnBiLSTM, more models will be added in the near future.

Thanks for you Star:star:, Fork and Watch！

Dataset

• Stanford Sentiment Treebank(SST)
  • SST-1: 5 classes(fine-grained), SST-2: 2 classes(binary)
• Preprocess
  • Map sentiment values to labels
  • Remove tokens consisting of all non-alphanumeric characters, such as ...

Pre-trained Word Vectors

• Word2Vec : GoogleNews-vectors-negative300.bin
• GloVe : glove.840B.300d.txt
  • Because the OOV Rate of GloVe is lower than Word2Vec and the experiment performance is also better than the other one, we use GloVe as pre-trained word vectors.
  • Options for different format word vectors are still preserved in the code.

Model

• TextCNN

  • Paper: Convolutional Neural Networks for Sentence Classification
  • See：models/TextCNN.py

  

• TextAttnBiLSTM

  • Paper: Attention-Based Bidirection LSTM for Text Classification
  • See: models/TextAttnBiLSTM.py

Result

• Baseline from the paper

model	SST-1	SST-2
CNN-rand	45.0	82.7
CNN-static	45.5	86.8
CNN-non-static	48.0	87.2
CNN-multichannel	47.4	88.1

• Re-Implementation

model	SST-1	SST-2
CNN-rand	34.841	74.500
CNN-static	45.056	84.125
CNN-non-static	46.974	85.886
CNN-multichannel	45.129	85.993
Attention + BiLSTM	47.015	85.632
Attention + BiGRU	47.854	85.102

Requirement

Please install the following library requirements first.

pandas==0.24.2
torch==1.1.0
fire==0.1.3
numpy==1.16.2
gensim==3.7.3

Structure

│  .gitignore
│  config.py            # Global Configuration
│  datasets.py          # Create Dataloader
│  main.py 
│  preprocess.py
│  README.md
│  requirements.txt
│  utils.py   
│  
├─checkpoints           # Save checkpoint and best model
│      
├─data                  # pretrained word vectors and datasets
│  │  glove.6B.300d.txt
│  │  GoogleNews-vectors-negative300.bin
│  └─stanfordSentimentTreebank # datasets folder
│          
├─models
│      TextAttnBiLSTM.py
│      TextCNN.py
│      __init__.py
│      
└─output_data           # Preprocessed data and vocabulary, etc.

Usage

• Set global configuration parameters in config.py

• Preprocess the datasets

$python preprocess.py

• Train

$python main.py run

You can set the parameters in the config.py and models/TextCNN.py or models/TextAttnBiLSTM.py in the command line.

$python main.py run [--option=VALUE]

For example，

$python main.py run --status='train' --use_model="TextAttnBiLSTM"

• Test

$python main.py run --status='test' --best_model="checkpoints/BEST_checkpoint_SST-2_TextCNN.pth"

Conclusion

• The TextCNN model uses the n-gram-like convolution kernel extraction feature, while the TextAttnBiLSTM model uses BiLSTM to capture semantics and long-term dependencies, combined with the attention mechanism for classification.
• TextCNN Parameter tuning:
  • glove is better than word2vec
  • Use a smaller batch size
  • Add weight decay ($l_2$ constraint), learning rate decay, early stop, etc.
  • Do not set padding_idx=0 in embedding layer
• TextAttnBiLSTM
  • Apply dropout on embedding layer, LSTM layer, and fully-connected layer

Acknowledge

• Motivated by https://github.com/TobiasLee/Text-Classification
• Thanks to https://github.com/bigboNed3/chinese_text_cnn
• Thanks to https://github.com/ShawnyXiao/TextClassification-Keras

Reference

[1] Convolutional Neural Networks for Sentence Classification

[2] A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

[3] Attention-Based Bidirection LSTM for Text Classification