deberta-semeval25_EN08_WAR_fold4

g-assismoraes/deberta-semeval25_EN08_WAR_fold4

deberta-semeval25_EN08_WAR_fold4 is a text classification model from g-assismoraes. Use it when you need a label for a piece of text. It is set up for transformers. The card lists the license as mit.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Text Classification
Library: transformers
License: mit
Model type: deberta-v2
Access: Public
Created: Oct 28, 2024
Updated: Oct 28, 2024
SHA: 3c36a429

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Base models

microsoft/deberta-v3-base

Model card

From the Hugging Face model README

deberta-semeval25_EN08_WAR_fold4

This model is a fine-tuned version of microsoft/deberta-v3-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 8.6077
Precision Samples: 0.1720
Recall Samples: 0.4912
F1 Samples: 0.2394
Precision Macro: 0.6271
Recall Macro: 0.3805
F1 Macro: 0.2080
Precision Micro: 0.1669
Recall Micro: 0.4907
F1 Micro: 0.2491
Precision Weighted: 0.4263
Recall Weighted: 0.4907
F1 Weighted: 0.1966

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision Samples	Recall Samples	F1 Samples	Precision Macro	Recall Macro	F1 Macro	Precision Micro	Recall Micro	F1 Micro	Precision Weighted	Recall Weighted	F1 Weighted
10.8033	1.0	43	9.3728	0.1378	0.2438	0.1666	0.9162	0.1942	0.1269	0.1474	0.2593	0.1879	0.7785	0.2593	0.0801
10.1064	2.0	86	9.1982	0.1369	0.2669	0.1707	0.8713	0.2046	0.1301	0.1354	0.2870	0.1840	0.6798	0.2870	0.0892
9.246	3.0	129	9.1481	0.1404	0.3175	0.1842	0.8340	0.2329	0.1419	0.1426	0.3333	0.1997	0.6338	0.3333	0.1101
10.4091	4.0	172	9.0465	0.1517	0.3645	0.2022	0.8028	0.2641	0.1545	0.1517	0.3843	0.2176	0.5867	0.3843	0.1306
9.7993	5.0	215	9.0293	0.1560	0.3716	0.2076	0.7276	0.3003	0.1789	0.1514	0.3981	0.2194	0.5169	0.3981	0.1532
10.5038	6.0	258	8.8051	0.1696	0.4602	0.2349	0.6589	0.3553	0.2003	0.1697	0.4722	0.2497	0.4598	0.4722	0.1771
9.1186	7.0	301	8.7311	0.1773	0.4607	0.2312	0.6367	0.3637	0.2015	0.1643	0.4722	0.2437	0.4362	0.4722	0.1792
9.7935	8.0	344	8.6690	0.1797	0.4680	0.2316	0.6339	0.3667	0.1992	0.1629	0.4676	0.2416	0.4367	0.4676	0.1808
8.1802	9.0	387	8.6158	0.1712	0.4941	0.2400	0.6267	0.3832	0.2082	0.1664	0.4907	0.2485	0.4238	0.4907	0.1938
8.7028	10.0	430	8.6077	0.1720	0.4912	0.2394	0.6271	0.3805	0.2080	0.1669	0.4907	0.2491	0.4263	0.4907	0.1966

Framework versions

Transformers 4.46.0
Pytorch 2.3.1
Datasets 2.21.0
Tokenizers 0.20.1

Task: Text Classification
Library: transformers
Type: deberta-v2
License: mit
Created: Oct 28, 2024
Updated: Oct 28, 2024