# Load model directly
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("raruidol/ArgumentMining-MultiLD-ARI")
model = AutoModelForSequenceClassification.from_pretrained("raruidol/ArgumentMining-MultiLD-ARI")Quick Links
Argument Relation Mining
Argument Relation Identification (ARI) model pre-trained with English (EN), Chinese (CN), and Catalan (CAT) data belonging to the Essay, Financial, and debate domains 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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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="raruidol/ArgumentMining-MultiLD-ARI")