{"id":21171643,"url":"https://github.com/anthonymrios/adversarial-relation-classification","last_synced_at":"2025-07-09T19:33:03.711Z","repository":{"id":144847647,"uuid":"111491190","full_name":"AnthonyMRios/adversarial-relation-classification","owner":"AnthonyMRios","description":"Unsupervised domain adaptation method for relation extraction","archived":false,"fork":false,"pushed_at":"2018-07-16T20:55:23.000Z","size":18,"stargazers_count":18,"open_issues_count":1,"forks_count":9,"subscribers_count":1,"default_branch":"master","last_synced_at":"2025-05-09T01:36:29.254Z","etag":null,"topics":["bioinformatics","biomedical-data-science","machine-learning","natural-language-processing","nlp","nlp-machine-learning","relation-extraction"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/AnthonyMRios.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2017-11-21T02:58:32.000Z","updated_at":"2025-01-19T13:18:33.000Z","dependencies_parsed_at":"2023-07-01T16:23:14.391Z","dependency_job_id":null,"html_url":"https://github.com/AnthonyMRios/adversarial-relation-classification","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/AnthonyMRios/adversarial-relation-classification","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnthonyMRios%2Fadversarial-relation-classification","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnthonyMRios%2Fadversarial-relation-classification/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnthonyMRios%2Fadversarial-relation-classification/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnthonyMRios%2Fadversarial-relation-classification/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/AnthonyMRios","download_url":"https://codeload.github.com/AnthonyMRios/adversarial-relation-classification/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/AnthonyMRios%2Fadversarial-relation-classification/sbom","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":264502490,"owners_count":23618617,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bioinformatics","biomedical-data-science","machine-learning","natural-language-processing","nlp","nlp-machine-learning","relation-extraction"],"created_at":"2024-11-20T16:09:26.034Z","updated_at":"2025-07-09T19:33:03.366Z","avatar_url":"https://github.com/AnthonyMRios.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Adversarial\n\nThis repo contains code for our unsupervised domain adaptation method for relation extraction.\n\n**Note:** Examples of the data format can be found in the data/ folder.\n\n## Usage\n### Training\n\n```\npython 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\n```\n\n```\nusage: train_final_cnn.py [-h] [--num_epochs NUM_EPOCHS]\n                          [--hidden_state HIDDEN_STATE]\n                          [--checkpoint_dir CHECKPOINT_DIR]\n                          [--checkpoint_name CHECKPOINT_NAME]\n                          [--min_df MIN_DF] [--lr LR] [--penalty PENALTY]\n                          [--train_data_X TRAIN_DATA_X]\n                          [--train_data TRAIN_DATA] [--test_data TEST_DATA]\n                          [--val_data_X VAL_DATA_X]\n                          [--adv_train_data_X ADV_TRAIN_DATA_X]\n                          [--adv_test_data_X ADV_TEST_DATA_X]\n                          [--num_iters NUM_ITERS] [--grad_clip GRAD_CLIP]\n                          [--num_disc_updates NUM_DISC_UPDATES] [--seed SEED]\n                          [--adv] [--emb_reg] [--pos_reg]\n\nTrain Neural Network.\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --num_epochs NUM_EPOCHS\n                        Number of updates to make.\n  --hidden_state HIDDEN_STATE\n                        LSTM hidden state size.\n  --checkpoint_dir CHECKPOINT_DIR\n                        Checkpoint directory.\n  --checkpoint_name CHECKPOINT_NAME\n                        Checkpoint File Name.\n  --min_df MIN_DF       Min word count.\n  --lr LR               Learning Rate.\n  --penalty PENALTY     Regularization Parameter.\n  --train_data_X TRAIN_DATA_X\n                        Training Data.\n  --train_data TRAIN_DATA\n                        Training Data.\n  --test_data TEST_DATA\n                        Training Data.\n  --val_data_X VAL_DATA_X\n                        Validation Data.\n  --adv_train_data_X ADV_TRAIN_DATA_X\n                        Validation Data.\n  --adv_test_data_X ADV_TEST_DATA_X\n                        Validation Data.\n  --num_iters NUM_ITERS\n                        Validation Data.\n  --grad_clip GRAD_CLIP\n                        Gradient Clip Value.\n  --num_disc_updates NUM_DISC_UPDATES\n                        Number of time to update discriminator.\n  --seed SEED           Random seed.\n  --adv                 Adversarial training?\n  --emb_reg             Regularize word embeddings?\n  --pos_reg             Regularize pos embeddings?\n```\n\n## Acknowledgements\n\n\u003e Anthony Rios, Ramakanth Kavuluru, and Zhiyong Lu. \"Generalizing Biomedical Relation Classification with Neural Adversarial Domain Adaptation\". Bioinformatics 2018\n\n```\n@article{rios2018advrel,\n  title={Generalizing Biomedical Relation Classification with Neural Adversarial Domain Adaptation},\n  author={Rios, Anthony and Kavuluru, Ramakanth and Lu, Zhiyong},\n  journal={Bioinformatics},\n  year={2018}\n}\n```\n\nWritten by Anthony Rios (anthonymrios at gmail dot com)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanthonymrios%2Fadversarial-relation-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanthonymrios%2Fadversarial-relation-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanthonymrios%2Fadversarial-relation-classification/lists"}