estudiante_Swin3D_profesor_MViT_akl_RWF2000

DanJoshua/estudiante_Swin3D_profesor_MViT_akl_RWF2000

estudiante_Swin3D_profesor_MViT_akl_RWF2000 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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Model card

From the Hugging Face model README

estudiante_Swin3D_profesor_MViT_akl_RWF2000

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

Loss: 0.2862
Accuracy: 0.9113
F1: 0.9112
Precision: 0.9113
Recall: 0.9113
Roc Auc: 0.9627

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: 10
eval_batch_size: 10
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: 560
training_steps: 5600
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	F1	Precision	Recall	Roc Auc
4.1936	1.0214	280	0.3434	0.8575	0.8563	0.8702	0.8575	0.9537
3.4616	3.0143	560	0.2829	0.8775	0.8774	0.8791	0.8775	0.9587
2.8886	5.0071	840	0.3145	0.8775	0.8770	0.8835	0.8775	0.9586
2.4481	6.0286	1120	0.3595	0.8675	0.8666	0.8778	0.8675	0.9492
2.0603	8.0214	1400	0.3079	0.8875	0.8873	0.8903	0.8875	0.9513
1.7483	10.0143	1680	0.2810	0.8975	0.8974	0.8987	0.8975	0.9631
1.808	12.0071	1960	0.2715	0.9	0.9000	0.9000	0.9	0.9630
1.7373	13.0286	2240	0.3319	0.875	0.8744	0.8825	0.875	0.9652
1.621	15.0214	2520	0.2721	0.91	0.9100	0.9102	0.91	0.9680
1.5803	17.0143	2800	0.2640	0.9175	0.9174	0.9193	0.9175	0.9733
1.6703	19.0071	3080	0.2754	0.9075	0.9074	0.9087	0.9075	0.9675
1.3204	20.0286	3360	0.2532	0.9175	0.9175	0.9175	0.9175	0.9691
1.2447	22.0214	3640	0.3191	0.89	0.8896	0.8957	0.89	0.9721
1.4993	24.0143	3920	0.3287	0.8875	0.8871	0.8937	0.8875	0.9680
1.1415	26.0071	4200	0.2641	0.9225	0.9225	0.9225	0.9225	0.9663
1.6365	27.0286	4480	0.2984	0.9025	0.9025	0.9025	0.9025	0.9573
0.9572	29.0214	4760	0.3362	0.89	0.8898	0.8932	0.89	0.9610
1.093	31.0143	5040	0.2720	0.91	0.9100	0.9100	0.91	0.9654
1.2306	33.0071	5320	0.2865	0.9125	0.9125	0.9126	0.9125	0.9655
1.0559	34.0286	5600	0.2807	0.905	0.905	0.905	0.905	0.9632

Framework versions

Transformers 4.46.2
Pytorch 2.0.1+cu118
Datasets 3.1.0
Tokenizers 0.20.3

Library: transformers
Created: Nov 8, 2024
Updated: Nov 8, 2024