Skip to content

Latest commit

 

History

History
51 lines (48 loc) · 2.76 KB

README.md

File metadata and controls

51 lines (48 loc) · 2.76 KB
Raw

Language Translation System

This Repository provides implementation for Language Translation System in Tensorflow 2 using all best practices

Implementations Included:

  • Transformers
  • Encoder Decoder with Attention

Training And Evaluating Transformers

Dataset is needed in order to train Transformer model. Go grab a dataset of your choice from the pool here. Extract the dataset and you are ready to train the model.

Training:

$ python main_transformers.py --path [path to .txt] \
                              --batch [batch size] \
                              --sample [No of lines to train] \
                              --patience [Patience to early stopping] \
                              --epochs [no of epochs]

This will split out the model weights and tokenizers for both the languages. Model weights can be found in checkpoints directory and tokenizers can be found named tok_lang1.subwords and tok_lang2.subwords

Inference:

$ python evaluate_transfomer.py --input_vocab [path to input vocabulary (in this case tok_lang1.subwords)] \
                                --target_vocab [path to target vocabulary] \
                                --checkpoint [path to checkpoint directory (defaults to ./checkpoints/train)]

evaluate_transformer.py script is highly customizable, so you can customize it in your own way. Default configuration will ask you for input and will spit out the prediction.

Training And Evaluating Encoder-Decoder with attention

Similarly, you can grab a dataset here.

Training:

$ python main_attention.py --path [path to .txt] \
                           --batch [batch size] \
                           --sample [No of lines to train on] \
                           --patience [Patience to early stopping] \
                           --epochs [no of epochs]

This will split out the model weights and tokenizers for both the languages. Model weights can be found in checkpoints directory and tokenizers can be found named tok_lang1.subwords and tok_lang2.subwords

Inference:

$ python evaluate_attention.py --input_vocab [path to input vocabulary (in this case tok_lang1.subwords)] \
                                --target_vocab [path to target vocabulary] \
                                --checkpoint [path to checkpoint directory (defaults to ./checkpoints/train)]

evaluate_attention.py script is highly customizable, so you can customize it in your own way. Default configuration will ask you for input and will spit out the prediction.

LICENSE

MIT License