https://github.com/SupritYoung/FaiMA

The code of paper "FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis" accepted by LREC-COLING 2024.
https://github.com/SupritYoung/FaiMA

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The code of paper "FaiMA: Feature-aware In-context Learning for Multi-domain Aspect-based Sentiment Analysis" accepted by LREC-COLING 2024.

• Host: GitHub
• URL: https://github.com/SupritYoung/FaiMA
• Owner: SupritYoung
• Created: 2024-02-26T08:29:21.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2024-02-26T08:33:21.000Z (about 1 year ago)
• Last Synced: 2024-02-26T09:45:50.066Z (about 1 year ago)
• Language: Python
• Homepage:
• Size: 10.7 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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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.

# Feature-aware In-context Learning for Multi-Domain Aspect-based Sentiment Analysis (FaiMA)

More details of the paper and dataset will be released after it is published.

# The Code

## Requirements

Following is the suggested way to install the dependencies:

    pip install -r requirements.txt

## Folder Structure

```tex

└── SA-LLM

    ├── data                    # Contains the datasets

    │   ├── inst/ASPE           # Our MD-ASPE instruction data

    │   ├── raw/ASPE            # MD-ASPE raw data

    ├── checkpoints             # Contains the trained checkpoint for model weights

    ├── src

    │   ├── gnnencoder          # The code related to MGATE

    │   ├── Icl                 # The code related to Feature-aware In-context Learning

    │   ├── llmtuner            # The code related to LLM train, predict etc.

    ├── run_gnn.py              # The code for training MGATE

    ├── run_aspe.py             # The code for training FaiMA and baselines

    └── README.md               # This document

```

##  Training and Evaluation

[//]: # (首先运行 `run_gnn.py` 训练 MGATE 模型，然后运行 `run_aspe.py` 训练 FaiMA 模型。)

1. Run `run_gnn.py` to train MGATE model.

2. Run `run_aspe.py` to train FaiMA and baselines, replece `model_name_or_path` with your llama model weight path.