{"id":23237003,"url":"https://github.com/SupritYoung/FaiMA","last_synced_at":"2025-08-19T23:31:42.107Z","repository":{"id":224495411,"uuid":"763405452","full_name":"SupritYoung/FaiMA","owner":"SupritYoung","description":"The code of paper \"FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis\" accepted by LREC-COLING 2024.","archived":false,"fork":false,"pushed_at":"2024-02-26T08:33:21.000Z","size":11179,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"master","last_synced_at":"2024-02-26T09:45:50.066Z","etag":null,"topics":[],"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/SupritYoung.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}},"created_at":"2024-02-26T08:29:21.000Z","updated_at":"2024-02-26T09:45:53.798Z","dependencies_parsed_at":"2024-02-26T09:57:05.739Z","dependency_job_id":null,"html_url":"https://github.com/SupritYoung/FaiMA","commit_stats":null,"previous_names":["suprityoung/faima"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SupritYoung%2FFaiMA","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SupritYoung%2FFaiMA/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SupritYoung%2FFaiMA/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/SupritYoung%2FFaiMA/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/SupritYoung","download_url":"https://codeload.github.com/SupritYoung/FaiMA/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":230374271,"owners_count":18216044,"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":[],"created_at":"2024-12-19T04:13:24.170Z","updated_at":"2024-12-19T04:13:29.346Z","avatar_url":"https://github.com/SupritYoung.png","language":"Python","readme":"This is a Pytorch implementation and released dataset of \"FaiMA: Feature-aware In-context Learning for Multi-Domain Aspect-based Sentiment Analysis\" accepted by LREC-COLING 2024.\n\n# Feature-aware In-context Learning for Multi-Domain Aspect-based Sentiment Analysis (FaiMA)\n\nMore details of the paper and dataset will be released after it is published.\n\n# The Code\n\n## Requirements\n\nFollowing is the suggested way to install the dependencies:\n\n    pip install -r requirements.txt\n\n\n## Folder Structure\n\n```tex\n└── SA-LLM\n    ├── data                    # Contains the datasets\n    │   ├── inst/ASPE           # Our MD-ASPE instruction data\n    │   ├── raw/ASPE            # MD-ASPE raw data\n    ├── checkpoints             # Contains the trained checkpoint for model weights\n    ├── src\n    │   ├── gnnencoder          # The code related to MGATE\n    │   ├── Icl                 # The code related to Feature-aware In-context Learning\n    │   ├── llmtuner            # The code related to LLM train, predict etc.\n    ├── run_gnn.py              # The code for training MGATE\n    ├── run_aspe.py             # The code for training FaiMA and baselines\n    └── README.md               # This document\n```\n\n##  Training and Evaluation\n\n[//]: # (首先运行 `run_gnn.py` 训练 MGATE 模型，然后运行 `run_aspe.py` 训练 FaiMA 模型。)\n\n1. Run `run_gnn.py` to train MGATE model.\n2. Run `run_aspe.py` to train FaiMA and baselines, replece `model_name_or_path` with your llama model weight path.\n","funding_links":[],"categories":["Frameworks"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSupritYoung%2FFaiMA","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FSupritYoung%2FFaiMA","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FSupritYoung%2FFaiMA/lists"}