Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/jadore801120/attention-is-all-you-need-pytorch
A PyTorch implementation of the Transformer model in "Attention is All You Need".
https://github.com/jadore801120/attention-is-all-you-need-pytorch
attention attention-is-all-you-need deep-learning natural-language-processing nlp pytorch
Last synced: 27 days ago
JSON representation
A PyTorch implementation of the Transformer model in "Attention is All You Need".
- Host: GitHub
- URL: https://github.com/jadore801120/attention-is-all-you-need-pytorch
- Owner: jadore801120
- License: mit
- Created: 2017-06-14T10:15:20.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2024-04-16T07:27:13.000Z (7 months ago)
- Last Synced: 2024-09-30T15:42:52.477Z (about 1 month ago)
- Topics: attention, attention-is-all-you-need, deep-learning, natural-language-processing, nlp, pytorch
- Language: Python
- Homepage:
- Size: 162 KB
- Stars: 8,777
- Watchers: 97
- Forks: 1,973
- Open Issues: 81
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-pytorch - attention-is-all-you-need-pytorch
- awesome-transformer - code
- awesome-pytorch - attention-is-all-you-need-pytorch
- StarryDivineSky - jadore801120/attention-is-all-you-need-pytorch
- awesome-transformer - code
README
# Attention is all you need: A Pytorch Implementation
This is a PyTorch implementation of the Transformer model in "[Attention is All You Need](https://arxiv.org/abs/1706.03762)" (Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin, arxiv, 2017).
A novel sequence to sequence framework utilizes the **self-attention mechanism**, instead of Convolution operation or Recurrent structure, and achieve the state-of-the-art performance on **WMT 2014 English-to-German translation task**. (2017/06/12)
> The official Tensorflow Implementation can be found in: [tensorflow/tensor2tensor](https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/models/transformer.py).
> To learn more about self-attention mechanism, you could read "[A Structured Self-attentive Sentence Embedding](https://arxiv.org/abs/1703.03130)".
The project support training and translation with trained model now.
Note that this project is still a work in progress.
**BPE related parts are not yet fully tested.**
If there is any suggestion or error, feel free to fire an issue to let me know. :)
# Usage
## WMT'16 Multimodal Translation: de-en
An example of training for the WMT'16 Multimodal Translation task (http://www.statmt.org/wmt16/multimodal-task.html).
### 0) Download the spacy language model.
```bash
# conda install -c conda-forge spacy
python -m spacy download en
python -m spacy download de
```
### 1) Preprocess the data with torchtext and spacy.
```bash
python preprocess.py -lang_src de -lang_trg en -share_vocab -save_data m30k_deen_shr.pkl
```
### 2) Train the model
```bash
python train.py -data_pkl m30k_deen_shr.pkl -log m30k_deen_shr -embs_share_weight -proj_share_weight -label_smoothing -output_dir output -b 256 -warmup 128000 -epoch 400
```
### 3) Test the model
```bash
python translate.py -data_pkl m30k_deen_shr.pkl -model trained.chkpt -output prediction.txt
```
## [(WIP)] WMT'17 Multimodal Translation: de-en w/ BPE
### 1) Download and preprocess the data with bpe:
> Since the interfaces is not unified, you need to switch the main function call from `main_wo_bpe` to `main`.
```bash
python preprocess.py -raw_dir /tmp/raw_deen -data_dir ./bpe_deen -save_data bpe_vocab.pkl -codes codes.txt -prefix deen
```
### 2) Train the model
```bash
python train.py -data_pkl ./bpe_deen/bpe_vocab.pkl -train_path ./bpe_deen/deen-train -val_path ./bpe_deen/deen-val -log deen_bpe -embs_share_weight -proj_share_weight -label_smoothing -output_dir output -b 256 -warmup 128000 -epoch 400
```
### 3) Test the model (not ready)
- TODO:
- Load vocabulary.
- Perform decoding after the translation.
---
# Performance
## Training
- Parameter settings:
- batch size 256
- warmup step 4000
- epoch 200
- lr_mul 0.5
- label smoothing
- do not apply BPE and shared vocabulary
- target embedding / pre-softmax linear layer weight sharing.
## Testing
- coming soon.
---
# TODO
- Evaluation on the generated text.
- Attention weight plot.
---
# Acknowledgement
- The byte pair encoding parts are borrowed from [subword-nmt](https://github.com/rsennrich/subword-nmt/).
- The project structure, some scripts and the dataset preprocessing steps are heavily borrowed from [OpenNMT/OpenNMT-py](https://github.com/OpenNMT/OpenNMT-py).
- Thanks for the suggestions from @srush, @iamalbert, @Zessay, @JulesGM, @ZiJianZhao, and @huanghoujing.