Instructions to use raruidol/ArgumentMining-EN-ARI-Financial with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raruidol/ArgumentMining-EN-ARI-Financial with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raruidol/ArgumentMining-EN-ARI-Financial")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("raruidol/ArgumentMining-EN-ARI-Financial") model = AutoModelForSequenceClassification.from_pretrained("raruidol/ArgumentMining-EN-ARI-Financial") - Notebooks
- Google Colab
- Kaggle
Argument Relation Mining
Argument Relation Identification (ARI) model trained with English (EN) data belonging to the Financial domain as part of the paper titled "Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain".
Code available in https://github.com/raruidol/RobustArgumentMining-LREC-COLING-2024
Cite:
@inproceedings{ruiz2024learning,
title={Learning Strategies for Robust Argument Mining: An Analysis of Variations in Language and Domain},
author={Ruiz-Dolz, Ramon and Chiu, Chr-Jr and Chen, Chung-Chi and Kando, Noriko and Chen, Hsin-Hsi},
booktitle={Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
pages={10286--10292},
year={2024}
}
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