{"id":19467503,"url":"https://github.com/thunlp/sememepso-attack","last_synced_at":"2025-04-25T11:31:17.753Z","repository":{"id":39625051,"uuid":"259660591","full_name":"thunlp/SememePSO-Attack","owner":"thunlp","description":"Code and data of the ACL 2020 paper \"Word-level Textual Adversarial Attacking as Combinatorial Optimization\"","archived":false,"fork":false,"pushed_at":"2021-04-11T10:55:12.000Z","size":61519,"stargazers_count":85,"open_issues_count":1,"forks_count":14,"subscribers_count":9,"default_branch":"master","last_synced_at":"2024-04-20T13:04:21.910Z","etag":null,"topics":["adversarial-attacks","adversarial-examples","nlp","pso","sememe"],"latest_commit_sha":null,"homepage":null,"language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/thunlp.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2020-04-28T14:28:38.000Z","updated_at":"2024-04-18T19:08:45.000Z","dependencies_parsed_at":"2022-09-12T15:24:24.517Z","dependency_job_id":null,"html_url":"https://github.com/thunlp/SememePSO-Attack","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thunlp%2FSememePSO-Attack","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thunlp%2FSememePSO-Attack/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thunlp%2FSememePSO-Attack/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/thunlp%2FSememePSO-Attack/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/thunlp","download_url":"https://codeload.github.com/thunlp/SememePSO-Attack/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":223998285,"owners_count":17238716,"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":["adversarial-attacks","adversarial-examples","nlp","pso","sememe"],"created_at":"2024-11-10T18:35:29.226Z","updated_at":"2024-11-10T18:35:29.876Z","avatar_url":"https://github.com/thunlp.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# SememePSO-Attack\nCode and data of the ACL 2020 paper \"Word-level Textual Adversarial Attacking as Combinatorial Optimization\". [[paper]](https://arxiv.org/pdf/1910.12196.pdf)\n## Citation\nPlease cite our paper if you find it helpful.\n\n\n```\n@inproceedings{zang2020word,\n  title={Word-level Textual Adversarial Attacking as Combinatorial Optimization},\n  author={Zang, Yuan and Qi, Fanchao and Yang, Chenghao and Liu, Zhiyuan and Zhang, Meng and Liu, Qun and Sun, Maosong},\n  booktitle={Proceedings of ACL},\n  year={2020}\n}\n```\nThis repository is mainly contributed by Yuan Zang and Chenghao Yang.\n## Requirements\n\n- tensorflow-gpu == 1.14.0   \n- keras == 2.2.4   \n- sklearn == 0.0  \n- anytree == 2.6.0  \n- nltk == 3.4.5  \n- OpenHowNet == 0.0.1a8    \n- pytorch_transformers == 1.0.0  \n- loguru == 0.3.2\n## General Required Data and Tools\n- Download [Glove vectors](http://nlp.stanford.edu/data/glove.840B.300d.zip)\n\u003c!-- ### Download Stanford Pos Tagger --\u003e\n- Download [Stanford POS Tagger](https://nlp.stanford.edu/software/tagger.shtml#Download)\n\n## Reproducibility Support\nSince data processing and models training may take a lot of time and computing resources, we provide the data and models we use for experiments. You can directly download the data and models we used for IMDB-related experiments from [TsinghuaCloud](https://cloud.tsinghua.edu.cn/d/b6b35b7b7fdb43c1bf8c/). The instructions of how to use these files can be found in the `README.md` files in `IMDB/`, `SNLI/` and `SST/`.\n\n## Running Instructions\nPlease see the `README.md` files in `IMDB/`, `SNLI/` and `SST/` for specific running instructions for each attack models on corresponding downstream tasks.","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthunlp%2Fsememepso-attack","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fthunlp%2Fsememepso-attack","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fthunlp%2Fsememepso-attack/lists"}