Tensorflow implementation of Text Classification Models.
Implemented Models:
Semi-supervised text classification(Transfer learning) models are implemented at [dongjun-Lee/transfer-learning-text-tf].
To train classification models for dbpedia dataset,
$ python train.py --model="<MODEL>"
(\
To test classification accuracy for test data after training,
$ python test.py --model="<TRAINED_MODEL>"
Trained and tested with dbpedia dataset. (dbpedia_csv/train.csv
, dbpedia_csv/test.csv
)
Model | WordCNN | CharCNN | VDCNN | WordRNN | AttentionRNN | RCNN | *SA-LSTM | *LM-LSTM |
---|---|---|---|---|---|---|---|---|
Accuracy | 98.42% | 98.05% | 97.60% | 98.57% | 98.61% | 98.68% | 98.88% | 98.86% |
(SA-LSTM and LM-LSTM are implemented at [dongjun-Lee/transfer-learning-text-tf].)
Implementation of Convolutional Neural Networks for Sentence Classification.
Implementation of Character-level Convolutional Networks for Text Classification.
Implementation of Very Deep Convolutional Networks for Text Classification.
Bi-directional RNN for Text Classification.
Implementation of Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification.
Implementation of Recurrent Convolutional Neural Networks for Text Classification.