mdeberta-semeval25_maxf1_fold3

g-assismoraes/mdeberta-semeval25_maxf1_fold3

mdeberta-semeval25_maxf1_fold3 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 27, 2024
Updated: Oct 27, 2024
SHA: 81a284b6

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Base models

microsoft/mdeberta-v3-base

Model card

From the Hugging Face model README

mdeberta-semeval25_maxf1_fold3

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

Loss: 9.3380
Precision Samples: 0.1633
Recall Samples: 0.5122
F1 Samples: 0.2311
Precision Macro: 0.8671
Recall Macro: 0.2933
F1 Macro: 0.2022
Precision Micro: 0.1506
Recall Micro: 0.3994
F1 Micro: 0.2188
Precision Weighted: 0.6317
Recall Weighted: 0.3994
F1 Weighted: 0.1249

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: 32
eval_batch_size: 32
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
11.0696	1.0	19	10.6040	1.0	0.0	0.0	1.0	0.1556	0.1556	1.0	0.0	0.0	1.0	0.0	0.0
10.193	2.0	38	10.2581	0.1747	0.2540	0.1918	0.9816	0.1789	0.1641	0.1753	0.1445	0.1584	0.8838	0.1445	0.0480
9.6612	3.0	57	10.0671	0.1425	0.2939	0.1781	0.9714	0.1889	0.1637	0.1456	0.1700	0.1569	0.8577	0.1700	0.0472
9.0942	4.0	76	9.8758	0.1474	0.3411	0.1891	0.9189	0.2076	0.1690	0.1385	0.2181	0.1694	0.7494	0.2181	0.0607
9.3042	5.0	95	9.6689	0.1613	0.4720	0.2241	0.8971	0.2654	0.1874	0.1473	0.3598	0.2091	0.6810	0.3598	0.1058
9.0703	6.0	114	9.5397	0.1626	0.4743	0.2237	0.8953	0.2670	0.1855	0.1471	0.3626	0.2093	0.6781	0.3626	0.1029
9.5375	7.0	133	9.4382	0.1630	0.4948	0.2286	0.8764	0.2833	0.1983	0.1506	0.3853	0.2166	0.6480	0.3853	0.1220
8.4717	8.0	152	9.3612	0.1642	0.5086	0.2317	0.8738	0.2881	0.1987	0.1481	0.3966	0.2157	0.6417	0.3966	0.1207
9.1265	9.0	171	9.3158	0.1621	0.5051	0.2286	0.8604	0.2921	0.1978	0.1478	0.3994	0.2158	0.6221	0.3994	0.1195
9.0143	10.0	190	9.3380	0.1633	0.5122	0.2311	0.8671	0.2933	0.2022	0.1506	0.3994	0.2188	0.6317	0.3994	0.1249

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 27, 2024
Updated: Oct 27, 2024