arg_mining_us2016_locutions

Godfrey2712/arg_mining_us2016_locutions

arg_mining_us2016_locutions is a text classification model from Godfrey2712. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as ms-pl.

Classifies Coherent Locution Pairs as either Arguing, Disagreeing, or N/A Illocutionary Force. N/A consists of instances where there are other and no illocutionary forces present in the sentence pair.

Downloads · 30 days

1% of all-time downloads

All-time downloads

521

Public

Parameters

435M

1.7 GB on disk

Likes

Public

Hugging Face

Repo makeup

Click a slice to open those files.

.safetensors1.7 GB · 99%

At a glance

Task: Text Classification
Library: transformers
License: ms-pl
Model type: deberta-v2
Access: Public
Created: Jul 8, 2024
Updated: Mar 31, 2026
SHA: d0298387

Try a prompt

Model card

From the Hugging Face model README

Takes two coherent Locutions and classifies as either Arguing, Disagreeing, or N/A.

Results:

	precision	recall	f1-score	support
Arguing	0.75	0.63	0.69	647
Disagreeing	0.51	0.52	0.52	143
N/A	0.67	0.77	0.72	701

accuracy			0.69	1491
macro avg	0.65	0.64	0.64	1491
weighted avg	0.69	0.69	0.69	1491

Dataset: https://corpora.aifdb.org/US2016 The data preprocessing and fine-tuning technique can be found here: https://discovery.dundee.ac.uk/en/studentTheses/exploiting-illocutionary-forces-in-dialogue-structures-for-enhanc/

Citation:

Inyama, G. (2025). Exploiting Illocutionary Forces in Dialogue Structures for Enhancing Authorship Identification [Master of Philosophy thesis, University of Dundee]. Discovery - the University of Dundee Research Portal. https://doi.org/10.15132/20000713

Task: Text Classification
Library: transformers
Type: deberta-v2
License: ms-pl
Created: Jul 8, 2024
Updated: Mar 31, 2026