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Tensorflow implementations of Text Classification Models.
| Date | Stars |
|---|---|
| 2026-07-24 | 503 |
| 2026-07-25 | 503 |
| 2026-07-28 | 503 |
| 2026-07-30 | 503 |
| 2026-08-06 | 503 |
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# Text Classification Models with Tensorflow Tensorflow implementation of Text Classification Models. Implemented Models: 1. Word-level CNN [[paper](https://arxiv.org/abs/1408.5882)] 2. Character-level CNN [[paper](https://arxiv.org/abs/1509.01626)] 3. Very Deep CNN [[paper](https://arxiv.org/abs/1606.01781)] 4. Word-level Bidirectional RNN 5. Attention-Based Bidirectional RNN [[paper](http://www.aclweb.org/anthology/P16-2034)] 6. RCNN [[paper](https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9745/9552)] Semi-supervised text classification(Transfer learning) models are implemented at [[dongjun-Lee/transfer-learning-text-tf]](https://github.com/dongjun-Lee/transfer-learning-text-tf). ## Requirements - Python3 - Tensorflow - pip install -r requirements.txt ## Usage ### Train To train classification models for dbpedia dataset, ``` $ python train.py --model="<MODEL>" ``` (\<Model>: word_cnn | char_cnn | vd_cnn | word_rnn | att_rnn | rcnn) ### Test To test classification accuracy for test data after training, ``` $ python test.py --model="<TRAINED_MODEL>" ``` ### Sample Test Results 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]](https://github.com/dongjun-Lee/transfer-learning-text-tf).) ## Models ### 1. Word-level CNN Implementation of [Convolutional Neural Networks for Sentence Classification](https://arxiv.org/abs/1408.5882). <img width="600" src="https://user-images.githubusercontent.com/6512394/41590312-b1c28fca-73f1-11e8-9123-e26a03853cc7.png"> ### 2. Char-level CNN Implementation of [Character-level Convolutional Networks for Text Classification](https://arxiv.org/abs/1509.01626). <img width="600" src="https://user-images.githubusercontent.com/6512394/41590359-c6c94f8a-73f1-11e8-8bda-976ddf09e817.png"> ### 3. Very Deep CNN (VDCNN) Implementation of [Very Deep Convolutional Networks for Text Classification](https://arxiv.org/abs/1606.01781). <img height="600" src="https://user-images.githubusercontent.com/6512394/41590802-e68f71cc-73f2-11e8-88c6-4bf84bf3410e.png"> ### 4. Word-level Bi-RNN Bi-directional RNN for Text Classification. 1. Embedding layer 2. Bidirectional RNN layer 3. Concat all the outputs from RNN layer 4. Fully-connected layer ### 5. Attention-Based Bi-RNN Implementation of [Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification](http://www.aclweb.org/anthology/P16-2034). <img width="600" src="https://user-images.githubusercontent.com/6512394/41424160-42520358-7038-11e8-8db0-859346a1fa3a.PNG"> ### 6. RCNN Implementation of [Recurrent Convolutional Neural Networks for Text Classification](https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/download/9745/9552). <img width="600" src="https://user-images.githubusercontent.com/6512394/42731530-c4ff3c34-8849-11e8-89fb-a49743255b0a.png"> ## References - [dennybritz/cnn-text-classification-tf](https://github.com/dennybritz/cnn-text-classification-tf) - [zonetrooper32/VDCNN](https://github.com/zonetrooper32/VDCNN)
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matched fp:aa23c29ecffaafe6, topic:text-classification, name:text classification, desc:text classification
matched fp:aa23c29ecffaafe6, topic:tensorflow