mdeberta-semeval25_narratives09_maxf1_fold3

g-assismoraes/mdeberta-semeval25_narratives09_maxf1_fold3

mdeberta-semeval25_narratives09_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: 42ba366b

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Base models

microsoft/mdeberta-v3-base

Model card

From the Hugging Face model README

mdeberta-semeval25_narratives09_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: 4.1972
Precision Samples: 0.3634
Recall Samples: 0.7451
F1 Samples: 0.4586
Precision Macro: 0.6979
Recall Macro: 0.4621
F1 Macro: 0.2857
Precision Micro: 0.3270
Recall Micro: 0.6974
F1 Micro: 0.4452
Precision Weighted: 0.4846
Recall Weighted: 0.6974
F1 Weighted: 0.3864

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
5.6486	1.0	19	5.3335	1.0	0.0	0.0	1.0	0.0476	0.0476	1.0	0.0	0.0	1.0	0.0	0.0
5.1544	2.0	38	5.1485	0.3023	0.3614	0.2993	0.8755	0.1773	0.1289	0.2996	0.3063	0.3029	0.7138	0.3063	0.1870
4.8667	3.0	57	4.9432	0.2776	0.4614	0.3282	0.8665	0.2238	0.1327	0.2853	0.4022	0.3338	0.6952	0.4022	0.1947
4.5126	4.0	76	4.6697	0.3487	0.5930	0.4045	0.7826	0.2967	0.1944	0.3166	0.5129	0.3915	0.5781	0.5129	0.2876
4.6317	5.0	95	4.4823	0.3405	0.6624	0.4196	0.7583	0.3672	0.2290	0.3034	0.5978	0.4025	0.5513	0.5978	0.3219
4.4113	6.0	114	4.3402	0.3574	0.7394	0.4481	0.7165	0.4305	0.2537	0.3140	0.6790	0.4294	0.4928	0.6790	0.3587
3.9755	7.0	133	4.2814	0.3659	0.7354	0.4539	0.7175	0.4258	0.2523	0.3176	0.6716	0.4313	0.4960	0.6716	0.3581
4.0457	8.0	152	4.2820	0.3549	0.7009	0.4398	0.7116	0.4227	0.2646	0.32	0.6494	0.4287	0.4905	0.6494	0.3644
4.0695	9.0	171	4.1947	0.3767	0.7511	0.4662	0.7109	0.4572	0.2863	0.3259	0.7011	0.4450	0.4949	0.7011	0.3884
4.3445	10.0	190	4.1972	0.3634	0.7451	0.4586	0.6979	0.4621	0.2857	0.3270	0.6974	0.4452	0.4846	0.6974	0.3864

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