Argument Mining E N A R I U S2016

Introduction

The Argument Mining model, specifically designed for Argument Relation Identification (ARI), is trained on English data using the US2016 corpus. It is recognized for its performance in the paper "Transformer-Based Models for Automatic Detection of Argument Relations: A Cross-Domain Evaluation."

Architecture

This model leverages the transformer architecture, specifically based on the RoBERTa model, and is implemented in PyTorch. It is designed for text classification tasks related to argument mining.

Training

The model was developed and evaluated as part of research detailed in the IEEE Intelligent Systems journal. The training involved cross-domain evaluation to ensure robust performance across different datasets.

Guide: Running Locally

1. Clone the Repository: Access the code from the GitHub repository at ArgumentRelationMining.
2. Install Dependencies: Ensure you have PyTorch and the Hugging Face Transformers library installed.
3. Download the Model: Obtain the model files from the Hugging Face model card.
4. Run the Model: Use a script to load the model and perform inference on your data.

For optimal performance, consider using a cloud GPU service such as AWS EC2, Google Cloud, or Azure to handle computational demands.

License

The model is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (cc-by-nc-sa-4.0) license, which allows for sharing and adaptation with attribution, for non-commercial purposes, under similar license terms.