- tensorflow-gpu 1.12
- jieba
- tqdm
- jupyter notebook
- tensorflow-gpu 1.13.1 (model_bert_basic)
- bert-as-service
- Use
Preprocessing.ipynb
to tokenize sentences and turn them into sequences of int values. - Use
WordEmbedding.ipynb
to pretrain word vectors. - Use
model_bert_basic/preprocessing.py
to generate datasets.
- Use
RNN_basic.ipynb
to train a simple model w/o attention mechanism. - Use
RNN_complex.ipynb
to train a attentional model. - Use
model_bert_basic/predict.py
to train a model based on BERT.
- Add Convolution Layer
- Use
Var1_Simple_CNN_network.ipynb
to train a CNN model w/o attention - Use
Var2_Convolution_before_RNN.ipynb
to train a RNN_basic model w/ additional Convolution Layer - Use
Var3_Convolution_Layer_after_Attentional_bi-LSTM_network.ipynb
to train a RNN_complex mode w/ additional Convolution Layer
- Use
https://www.kaggle.com/c/fake-news-pair-classification-challenge/
- pytorch 1.0.1
- wordninja (pip install wordninja)
- nltk
- sklearn (for metrics)
- tqdm
- jupyter notebook
- To train biLSTM+POS models, open biLSTM-POS/Trainer.ipynb, set the datapaths, then run all cells.