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Transfer Learning for Text Classification with Tensorflow

Tensorflow implementation of Semi-supervised Sequence Learning(https://arxiv.org/abs/1511.01432).

Auto-encoder or language model is used as a pre-trained model to initialize LSTM text classification model.

  • SA-LSTM: Use auto-encoder as a pre-trained model.
  • LM-LSTM: Use language model as a pre-trained model.

Requirements

  • Python 3
  • Tensorflow
  • pip install -r requirements.txt

Usage

DBpedia dataset is used for pre-training and training.

Pre-train auto encoder or language model

$ python pre_train.py --model="<MODEL>"

(<Model>: auto_encoder | language_model)

Train LSTM text classification model

$ python train.py --pre_trained="<MODEL>"

(<Model>: none | auto_encoder | language_model)

Experimental Results

  • Orange lines: LSTM
  • Blue lines: SA-LSTM
  • Red lines: LM-LSTM

Loss

Accuracy

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