Text-Classification-PyTorch

This repo implements 7 text classification algorithms(CNN, CNN+Attention, TextCNN, DPCNN, LSTM, Bi-LSTM+Attention, RCNN) and a train-eval pipeline.

Requirements

• python 3.6+
• torch==1.1.0
• pandas
• matplotlib
• nltk
• scikit_learn

IMDB Dataset

This dataset contains movie reviews along with their associated binary sentiment polarity labels. It is intended to serve as a benchmark for sentiment classification. The core dataset contains 50,000 reviews split evenly into 25k train and 25k test sets. The overall distribution of labels is balanced (25k pos and 25k neg). We also include an additional 50,000 unlabeled documents for unsupervised learning.

Get Start

1. Install all the required package.
   $ cd Text-Classification-PyTorch
$ pip install -r requirements.txt
2. Download dataset.
   $ wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
$ tar -zxvf aclImdb_v1.tar.gz
3. Download pre-trained word vectors(Optional).
   $ wget http://nlp.stanford.edu/data/glove.6B.zip
$ unzip glove.6B.zip -d glove
4. Initialize data.
   $ python initialize.py
   Or use $ python initialize.py -h for help.
5. Train.
   $ python train.py
   Or use $ python train.py -h for help.
6. Evaluation.
   $ python eval.py
   Or use $ python eval.py -h for help.
7. Check evaluation results.
   Open --name file to view PR curve.

Training in Google Colab

https://colab.research.google.com/drive/1VJmSx-vThBFlGZYJ9sKWDMINKWOzFNCD

Training in Docker

1. Pull image
   $ docker pull wisedoge/text_clf_pytorch
2. Run
   $ docker run -it wisedoge/text_clf_pytorch

Results

Parameters

#	Param	CNN	TextCNN	DPCNN	CNNAtt	LSTM	BiLSTMAtt	RCNN
1	Vocab size	30000	30000	30000	30000	30000	30000	30000
2	Max seq len	256	256	256	256	256	256	256
3	Embedding dim	256	256	256	256	256	256	256
4	Hidden dim	512	256	250	128	128	512	128
5	Context vec dim	-	-	-	64	-	64	-
6	Dropout prob	-	-	-	-	0.2	-	-
7	Num LSTM layer	-	-	-	-	2	-	-
8	Num DPCNN block	-	-	2	-	-	-	-

* You can also set --max_seq_len=512(longer sequence length) and --glove_path=your glove path/glove.6B.*d.txt(use pre-trained word vectors) to build large model for better accuarcy(>= 0.9).

Accuracy

Model name	Accuracy on test set
CNNAttn	0.82340
LSTM	0.83548
CNN	0.85100
RCNN	0.87732
BiLSTMAttn	0.87780
TextCNN	0.87848
DPCNN	0.87904

PR-Curve