Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/AnthonyMRios/adversarial-relation-classification
Unsupervised domain adaptation method for relation extraction
https://github.com/AnthonyMRios/adversarial-relation-classification
bioinformatics biomedical-data-science machine-learning natural-language-processing nlp nlp-machine-learning relation-extraction
Last synced: 27 days ago
JSON representation
Unsupervised domain adaptation method for relation extraction
- Host: GitHub
- URL: https://github.com/AnthonyMRios/adversarial-relation-classification
- Owner: AnthonyMRios
- Created: 2017-11-21T02:58:32.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2018-07-16T20:55:23.000Z (over 6 years ago)
- Last Synced: 2024-08-03T05:02:12.562Z (4 months ago)
- Topics: bioinformatics, biomedical-data-science, machine-learning, natural-language-processing, nlp, nlp-machine-learning, relation-extraction
- Language: Python
- Homepage:
- Size: 17.6 KB
- Stars: 19
- Watchers: 2
- Forks: 9
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-domain-adaptation-NLP - [__code__
README
# Adversarial
This repo contains code for our unsupervised domain adaptation method for relation extraction.
**Note:** Examples of the data format can be found in the data/ folder.
## Usage
### Training
```
python train_final_cnn.py --num_epochs 50 --checkpoint_dir /checkpoint/dir/experiments/checkpoints/ --checkpoint_name my_checkpoint --min_df 5 --lr 0.001 --penalty 0. --adv_train_data_X /my/data/data1/all_train.txt --adv_test_data_X /my/data/biogrid_train_test/all_test.txt --test_data /my/data/test_data.txt --train_data /my/data/train_data.txt --train_data_X /my/data/data2/train.txt --val_data_X /my/data/data2/test.txt --num_iters 10000 --num_disc_updates 1 --emb_reg --adv --pos_reg --hidden_state 128 --adv --seed 42
```
```
usage: train_final_cnn.py [-h] [--num_epochs NUM_EPOCHS]
[--hidden_state HIDDEN_STATE]
[--checkpoint_dir CHECKPOINT_DIR]
[--checkpoint_name CHECKPOINT_NAME]
[--min_df MIN_DF] [--lr LR] [--penalty PENALTY]
[--train_data_X TRAIN_DATA_X]
[--train_data TRAIN_DATA] [--test_data TEST_DATA]
[--val_data_X VAL_DATA_X]
[--adv_train_data_X ADV_TRAIN_DATA_X]
[--adv_test_data_X ADV_TEST_DATA_X]
[--num_iters NUM_ITERS] [--grad_clip GRAD_CLIP]
[--num_disc_updates NUM_DISC_UPDATES] [--seed SEED]
[--adv] [--emb_reg] [--pos_reg]
Train Neural Network.
optional arguments:
-h, --help show this help message and exit
--num_epochs NUM_EPOCHS
Number of updates to make.
--hidden_state HIDDEN_STATE
LSTM hidden state size.
--checkpoint_dir CHECKPOINT_DIR
Checkpoint directory.
--checkpoint_name CHECKPOINT_NAME
Checkpoint File Name.
--min_df MIN_DF Min word count.
--lr LR Learning Rate.
--penalty PENALTY Regularization Parameter.
--train_data_X TRAIN_DATA_X
Training Data.
--train_data TRAIN_DATA
Training Data.
--test_data TEST_DATA
Training Data.
--val_data_X VAL_DATA_X
Validation Data.
--adv_train_data_X ADV_TRAIN_DATA_X
Validation Data.
--adv_test_data_X ADV_TEST_DATA_X
Validation Data.
--num_iters NUM_ITERS
Validation Data.
--grad_clip GRAD_CLIP
Gradient Clip Value.
--num_disc_updates NUM_DISC_UPDATES
Number of time to update discriminator.
--seed SEED Random seed.
--adv Adversarial training?
--emb_reg Regularize word embeddings?
--pos_reg Regularize pos embeddings?
```
## Acknowledgements
> Anthony Rios, Ramakanth Kavuluru, and Zhiyong Lu. "Generalizing Biomedical Relation Classification with Neural Adversarial Domain Adaptation". Bioinformatics 2018
```
@article{rios2018advrel,
title={Generalizing Biomedical Relation Classification with Neural Adversarial Domain Adaptation},
author={Rios, Anthony and Kavuluru, Ramakanth and Lu, Zhiyong},
journal={Bioinformatics},
year={2018}
}
```
Written by Anthony Rios (anthonymrios at gmail dot com)