Author: Tianxiang Gao
Pytorch implementation of seq2seq French-to-English translation.
Run French-to-English translation on GPU:0, the hyperparameters are defined in the ./experiments/attention/f2e/config.json'
python main.py --gpu 0 --model_dir experiment/attention/f2e/
After the training, the best checkpoint will be stored in ./experiment/attention/f2e/ckpts/best.pth.tar. To evaluate the
model on GPU:0, use the best checkpoint, beam search with width 3, and save attention weights heat map, simply run the
following command:
python main.py --gpu 0 --model_dir experiment/attention/f2e/ --mode test --heatmap --beam_size 3
python main.py --gpu [gpu_id] --model_dir [model_dir]
In the experiments/attention/ folder, there are two configuration json files for French-English and English-French
translation. It is easy to set different hyper-parameters and play around the model.
Optional arguments for training:
--resumewhether to resume training from check point
python main.py --gpu [gpu_id] --model_dir [model_dir] --mode test
Optional arguments:
--beam_size [value]or--bs [value]whether use beam search. Greedy search if the value is 0. Otherwise, the value is the width of beam search.--heatmapor--hmwhether to generate and store the attention weight heat map.