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profesor_MViT_N_VIOPERU
DanJoshua/profesor_MViT_N_VIOPERU
profesor_MViT_N_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 1, 2024
- Updated
- Nov 2, 2024
- SHA
- 463c8590
Model card
From the Hugging Face model README
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
profesor_MViT_N_VIOPERU
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.4752
- Accuracy: 0.8929
- F1: 0.8927
- Precision: 0.8949
- Recall: 0.8929
- Roc Auc: 0.9283
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: 22
- eval_batch_size: 22
- 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: 90
- training_steps: 900
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 0.6465 | 7.0067 | 90 | 0.6553 | 0.7679 | 0.7672 | 0.7710 | 0.7679 | 0.8010 |
| 0.5617 | 14.0133 | 180 | 0.5770 | 0.8036 | 0.8030 | 0.8071 | 0.8036 | 0.8673 |
| 0.4403 | 22.0067 | 270 | 0.4678 | 0.8929 | 0.8927 | 0.8949 | 0.8929 | 0.9031 |
| 0.3043 | 29.0133 | 360 | 0.4475 | 0.8393 | 0.8380 | 0.8505 | 0.8393 | 0.9069 |
| 0.196 | 37.0067 | 450 | 0.4182 | 0.8393 | 0.8380 | 0.8505 | 0.8393 | 0.9247 |
| 0.131 | 44.0133 | 540 | 0.3250 | 0.875 | 0.8746 | 0.8794 | 0.875 | 0.9515 |
| 0.0945 | 52.0067 | 630 | 0.3444 | 0.8929 | 0.8927 | 0.8949 | 0.8929 | 0.9605 |
| 0.0736 | 59.0133 | 720 | 0.3662 | 0.875 | 0.8746 | 0.8794 | 0.875 | 0.9554 |
| 0.0535 | 67.0067 | 810 | 0.4566 | 0.8571 | 0.8564 | 0.8646 | 0.8571 | 0.9681 |
| 0.0513 | 74.0133 | 900 | 0.4785 | 0.875 | 0.8746 | 0.8794 | 0.875 | 0.9554 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.0.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.1
- Library
- transformers
- Created
- Nov 1, 2024
- Updated
- Nov 2, 2024