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FengZiYjun fb806163c3 | 6 years ago | |
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LICENSE | 6 years ago | |
README.md | 6 years ago | |
__init__.py | 6 years ago | |
main.py | 6 years ago | |
model.py | 6 years ago | |
test.py | 6 years ago | |
test.txt | 6 years ago | |
train.py | 6 years ago | |
train.txt | 6 years ago | |
utilities.py | 6 years ago | |
valid.txt | 6 years ago |
This is the PyTorch implementation of character-aware neural language model proposed in this paper by Yoon Kim.
The code is run and tested with Python 3.5.2 and PyTorch 0.3.1.
HyperParam | value |
---|---|
LSTM batch size | 20 |
LSTM sequence length | 35 |
LSTM hidden units | 300 |
epochs | 35 |
initial learning rate | 1.0 |
character embedding dimension | 15 |
Train the model with split train/valid/test data.
python train.py
The trained model will saved in cache/net.pkl
.
Test the model.
python test.py
Best result on test set:
PPl=127.2163
cross entropy loss=4.8459
This implementation borrowed ideas from
https://github.com/jarfo/kchar
https://github.com/cronos123/Character-Aware-Neural-Language-Models
一款轻量级的自然语言处理(NLP)工具包,目标是减少用户项目中的工程型代码,例如数据处理循环、训练循环、多卡运行等
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