Instructions to use raruidol/ArgumentMining-CAT-AS-VivesDebate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raruidol/ArgumentMining-CAT-AS-VivesDebate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="raruidol/ArgumentMining-CAT-AS-VivesDebate")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("raruidol/ArgumentMining-CAT-AS-VivesDebate") model = AutoModelForTokenClassification.from_pretrained("raruidol/ArgumentMining-CAT-AS-VivesDebate") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Argument Segmentation
Argument Mining model trained with Catalan (CAT) data for the Argument Segmentation (AS) task to identify Begin, Inside, and Outside (BIO) tokens using the VivesDebate-speech corpus (ArgumentMining-CAT-AS-VivesDebate).
Code available in https://github.com/jairsan/VivesDebate-Speech
Cite:
@inproceedings{ruiz2023vivesdebate,
title={VivesDebate-Speech: A Corpus of Spoken Argumentation to Leverage Audio Features for Argument Mining},
author={Ruiz-Dolz, Ramon and Iranzo-S{\'a}nchez, Javier},
booktitle={Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
pages={2071--2077},
year={2023}
}
license: cc-by-nc-sa-4.0
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