estudiante_MC318_profesor_MViT_akl_RLVS

DanJoshua/estudiante_MC318_profesor_MViT_akl_RLVS

estudiante_MC318_profesor_MViT_akl_RLVS is a machine learning model from DanJoshua. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for transformers.

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

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At a glance

Library: transformers
Access: Public
Created: Nov 28, 2024
Updated: Nov 28, 2024
SHA: 94805fe0

Model card

From the Hugging Face model README

estudiante_MC318_profesor_MViT_akl_RLVS

This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0597
Accuracy: 0.9803
F1: 0.9803
Precision: 0.9803
Recall: 0.9803
Roc Auc: 0.9979

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: 1e-05
train_batch_size: 30
eval_batch_size: 30
seed: 42
optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 159
training_steps: 1590
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc
22.7945	1.0145	79	0.4204	0.8984	0.8984	0.8985	0.8984	0.9730
14.8284	2.0289	158	0.2329	0.9251	0.9251	0.9259	0.9251	0.9836
10.0047	4.0082	237	0.1401	0.9492	0.9492	0.9492	0.9492	0.9905
7.3642	5.0226	316	0.1023	0.9733	0.9733	0.9733	0.9733	0.9932
5.6128	7.0019	395	0.1193	0.9733	0.9733	0.9733	0.9733	0.9947
4.5908	8.0164	474	0.0743	0.9733	0.9733	0.9735	0.9733	0.9958
3.4695	9.0308	553	0.0811	0.9759	0.9759	0.9760	0.9759	0.9968
3.3759	11.0101	632	0.0575	0.9759	0.9759	0.9761	0.9759	0.9975
3.4995	12.0245	711	0.0492	0.9840	0.9840	0.9840	0.9840	0.9982
2.5838	14.0038	790	0.0470	0.9840	0.9840	0.9840	0.9840	0.9980
2.8276	15.0182	869	0.0544	0.9759	0.9759	0.9761	0.9759	0.9986
2.3636	16.0327	948	0.0441	0.9786	0.9786	0.9791	0.9786	0.9989
2.5376	18.0119	1027	0.0431	0.9840	0.9840	0.9842	0.9840	0.9988
2.3264	19.0264	1106	0.0620	0.9706	0.9706	0.9717	0.9706	0.9989

Framework versions

Transformers 4.46.3
Pytorch 2.0.1+cu118
Datasets 3.1.0
Tokenizers 0.20.3

Library: transformers
Created: Nov 28, 2024
Updated: Nov 28, 2024