deberta-semeval25_EN08_WAR_fold5

g-assismoraes/deberta-semeval25_EN08_WAR_fold5

deberta-semeval25_EN08_WAR_fold5 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: 564595cd

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Base models

microsoft/deberta-v3-base

Model card

From the Hugging Face model README

deberta-semeval25_EN08_WAR_fold5

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: 10.3208
Precision Samples: 0.2124
Recall Samples: 0.4636
F1 Samples: 0.2634
Precision Macro: 0.6349
Recall Macro: 0.3430
F1 Macro: 0.2211
Precision Micro: 0.2049
Recall Micro: 0.4258
F1 Micro: 0.2766
Precision Weighted: 0.4312
Recall Weighted: 0.4258
F1 Weighted: 0.2159

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
10.8716	1.0	43	11.1010	0.1764	0.1324	0.1418	0.9676	0.1392	0.1131	0.1792	0.1211	0.1445	0.9047	0.1211	0.0437
9.3457	2.0	86	10.9271	0.2529	0.2659	0.1663	0.8864	0.1928	0.1307	0.1531	0.2422	0.1876	0.7249	0.2422	0.0879
9.2504	3.0	129	10.8593	0.1657	0.2917	0.1887	0.8654	0.2050	0.1341	0.1554	0.2695	0.1971	0.7079	0.2695	0.0907
8.6182	4.0	172	10.7548	0.1653	0.3665	0.2061	0.8101	0.2438	0.1538	0.1616	0.3320	0.2174	0.6140	0.3320	0.1218
10.3126	5.0	215	10.6097	0.1839	0.3906	0.2268	0.7668	0.2733	0.1745	0.1742	0.3633	0.2354	0.5543	0.3633	0.1550
10.024	6.0	258	10.5335	0.2143	0.3844	0.2450	0.7510	0.2770	0.1887	0.2046	0.3477	0.2576	0.5495	0.3477	0.1755
9.0775	7.0	301	10.4518	0.1985	0.3927	0.2402	0.6712	0.3039	0.2036	0.1980	0.3828	0.2610	0.4572	0.3828	0.1927
8.1542	8.0	344	10.4111	0.2090	0.4501	0.2575	0.6283	0.3104	0.2038	0.1943	0.4023	0.2621	0.4116	0.4023	0.1972
7.7217	9.0	387	10.3686	0.2166	0.4562	0.2648	0.6165	0.3358	0.2205	0.2038	0.4219	0.2748	0.4098	0.4219	0.2166
8.4374	10.0	430	10.3208	0.2124	0.4636	0.2634	0.6349	0.3430	0.2211	0.2049	0.4258	0.2766	0.4312	0.4258	0.2159

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