biaffineparser is a PyTorch implementation of "Deep Biaffine Attention for Neural Dependency Parsing."
biaffineparser works on PyTorch.
$ git clone https://github.com/chantera/biaffineparser
$ cd biaffineparser
$ pip install -r requirements.txtusage: main.py train [-h] --train_file FILE [--eval_file FILE]
[--embed_file FILE] [--max_steps NUM]
[--eval_interval NUM] [--batch_size NUM]
[--learning_rate VALUE] [--cuda] [--save_dir DIR]
[--cache_dir DIR] [--seed VALUE]usage: main.py evaluate [-h] --eval_file FILE --checkpoint_file FILE
--preprocessor_file FILE [--batch_size NUM] [--cuda]
[--verbose]$ mkdir models
$ python3 src/main.py train --train_file $DATA/train.conll --eval_file $DATA/dev.conll --embed_file $DATA/glove.6B.100d.txt --cuda --save_dir ./models
$ python3 src/main.py evaluate --eval_file $DATA/test.conll --ckpt ./models/step-[num].ckpt --proc ./models/preprocessor.pt --cudaThe model achieves UAS: 95.77 and LAS: 94.10 in wsj 23 (test set) in PTB-SD 3.3.0 with the reported hyperparameters.
- Dozat, T., Manning, C. D., 2016. Deep Biaffine Attention for Neural Dependency Parsing. arXiv preprint arXiv:1611.01734. https://arxiv.org/abs/1611.01734
Apache License Version 2.0
© Copyright 2021 Hiroki Teranishi