Instructions to use raruidol/ArgumentMining-CAT-E2E-VivesDebate with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use raruidol/ArgumentMining-CAT-E2E-VivesDebate with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="raruidol/ArgumentMining-CAT-E2E-VivesDebate")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("raruidol/ArgumentMining-CAT-E2E-VivesDebate") model = AutoModelForTokenClassification.from_pretrained("raruidol/ArgumentMining-CAT-E2E-VivesDebate", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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Check out the documentation for more information.
Argument Mining E2E
Argument Mining model trained with Catalan (CAT) data for the Argument Segmentation + Argument Classification (E2E) task using the VivesDebate-speech corpus (ArgumentMining-CAT-AC-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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