mdeberta-semeval25_narratives_fold5

g-assismoraes/mdeberta-semeval25_narratives_fold5

mdeberta-semeval25_narratives_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 27, 2024
Updated: Oct 27, 2024
SHA: 859fc5b5

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Base models

microsoft/mdeberta-v3-base

Model card

From the Hugging Face model README

mdeberta-semeval25_narratives_fold5

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: 4.0196
Precision Samples: 0.3300
Recall Samples: 0.8054
F1 Samples: 0.4401
Precision Macro: 0.6768
Recall Macro: 0.5901
F1 Macro: 0.3682
Precision Micro: 0.2997
Recall Micro: 0.7707
F1 Micro: 0.4316
Precision Weighted: 0.4440
Recall Weighted: 0.7707
F1 Weighted: 0.3890

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
5.5606	1.0	19	5.1746	0.5241	0.0760	0.0945	0.9639	0.1737	0.1596	0.2418	0.0827	0.1232	0.9031	0.0827	0.0450
4.8516	2.0	38	4.9279	0.2575	0.4866	0.3149	0.8592	0.3182	0.2199	0.2573	0.4323	0.3226	0.6527	0.4323	0.1902
5.1102	3.0	57	4.6329	0.3105	0.6392	0.3944	0.7662	0.4137	0.2809	0.3010	0.5827	0.3969	0.5232	0.5827	0.2907
4.5152	4.0	76	4.4162	0.2982	0.7060	0.3926	0.7663	0.4692	0.2933	0.2827	0.6654	0.3969	0.5256	0.6654	0.3110
4.3922	5.0	95	4.2955	0.3114	0.7290	0.4139	0.7003	0.5100	0.3321	0.2961	0.6880	0.4140	0.4569	0.6880	0.3560
4.0885	6.0	114	4.1427	0.3210	0.8169	0.4335	0.6788	0.5895	0.3665	0.2921	0.7820	0.4254	0.4415	0.7820	0.3845
3.9996	7.0	133	4.0937	0.3164	0.7928	0.4286	0.6762	0.5803	0.3656	0.2945	0.7594	0.4244	0.4386	0.7594	0.3814
3.9713	8.0	152	4.0603	0.3159	0.7847	0.4253	0.6727	0.5768	0.3623	0.2935	0.7481	0.4216	0.4375	0.7481	0.3792
4.016	9.0	171	4.0393	0.3189	0.7905	0.4300	0.6750	0.5812	0.3654	0.2978	0.7556	0.4272	0.4418	0.7556	0.3848
3.9635	10.0	190	4.0196	0.3300	0.8054	0.4401	0.6768	0.5901	0.3682	0.2997	0.7707	0.4316	0.4440	0.7707	0.3890

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