estudiante_S3D_profesor_MViT_akl_VIOPERU

DanJoshua/estudiante_S3D_profesor_MViT_akl_VIOPERU

estudiante_S3D_profesor_MViT_akl_VIOPERU 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 3, 2024
Updated: Nov 3, 2024
SHA: 7463791f

Model card

From the Hugging Face model README

estudiante_S3D_profesor_MViT_akl_VIOPERU

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

Loss: 0.5499
Accuracy: 0.7411
F1: 0.7363
Precision: 0.7597
Recall: 0.7411
Roc Auc: 0.8501

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: 40
eval_batch_size: 40
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: 40
training_steps: 400
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc
2.7266	2.01	20	0.6842	0.5	0.3875	0.5	0.5	0.5459
2.137	4.02	40	0.6643	0.5893	0.5221	0.7041	0.5893	0.6480
1.6043	7.01	60	0.6242	0.6429	0.6111	0.7121	0.6429	0.7309
1.3277	9.02	80	0.6219	0.6964	0.6842	0.7323	0.6964	0.7704
1.03	12.01	100	0.5769	0.6964	0.6842	0.7323	0.6964	0.8010
0.8458	14.02	120	0.5795	0.6964	0.6884	0.7191	0.6964	0.8087
0.6449	17.01	140	0.5731	0.7143	0.7083	0.7333	0.7143	0.8189
0.5688	19.02	160	0.5663	0.7321	0.7300	0.7398	0.7321	0.8329
0.4625	22.01	180	0.5546	0.7143	0.7110	0.7246	0.7143	0.8380
0.4104	24.02	200	0.5407	0.7143	0.7110	0.7246	0.7143	0.8406
0.5655	27.01	220	0.5199	0.7143	0.7110	0.7246	0.7143	0.8393
0.4936	29.02	240	0.5060	0.6786	0.6748	0.6872	0.6786	0.8316
0.3731	32.01	260	0.5524	0.6964	0.6916	0.7095	0.6964	0.8355

Framework versions

Transformers 4.46.1
Pytorch 2.0.1+cu118
Datasets 3.1.0
Tokenizers 0.20.1

Library: transformers
Created: Nov 3, 2024
Updated: Nov 3, 2024