BioTokens

This is a Transformer based neural machine translation model.

Data

The dataset is generated using our inhouse amino-acid potential analytical model

Data Process

Data is generated automatically when the model is run. Data generation solves the forward problem of amino acid potential fingerprints, whereas the ML model attempts to solve the inverse problem.

Word Segmentation

• Tool：sentencepiece
• Preprocess：Run ./data/get_corpus.py , in which we will get bilingual data to build our training, dev and testing set. The data will be saved in corpus.meas and corpus.ami, with one sentence in each line.
• Word segmentation model training: Run ./tokenizer/tokenize.py, in which the sentencepiece.SentencePieceTrainer.Train() mothed is called to train our word segmentation model. After training, ami.model，ami.vocab，meas.model and meas.vocab will be saved in ./tokenizer. .model is the word segmentation model we need and .vocab is the vocabulary.

Model

We use the open-source code transformer-pytorch developmented by Harvard.

Requirements

This repo was tested on Python 3.6+ and PyTorch 1.5.1. The main requirements are:

• tqdm
• pytorch >= 1.5.1
• sacrebleu >= 1.4.14
• sentencepiece >= 0.1.94

To get the environment settled quickly, run:

pip install -r requirements.txt

Usage

Hyperparameters can be modified in config.py.

To start training, please run:

python main.py

The training log is saved in ./experiment/train.log, and the translation results of testing dataset is in ./experiment/output.txt.