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estudiante_S3D_profesor_MViT_akl_RLVS
DanJoshua/estudiante_S3D_profesor_MViT_akl_RLVS
estudiante_S3D_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 4, 2024
- Updated
- Nov 4, 2024
- SHA
- 73e9ceaf
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. -->
estudiante_S3D_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: 0.0388
- Accuracy: 0.9882
- F1: 0.9882
- Precision: 0.9882
- Recall: 0.9882
- Roc Auc: 0.9994
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: 320
- training_steps: 3200
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc |
|---|---|---|---|---|---|---|---|---|
| 10.8581 | 3.0125 | 160 | 0.1665 | 0.9198 | 0.9196 | 0.9247 | 0.9198 | 0.9854 |
| 4.143 | 7.0125 | 320 | 0.0648 | 0.9759 | 0.9759 | 0.9763 | 0.9759 | 0.9966 |
| 2.3673 | 11.0125 | 480 | 0.0455 | 0.9866 | 0.9866 | 0.9866 | 0.9866 | 0.9985 |
| 1.9476 | 15.0125 | 640 | 0.0464 | 0.9866 | 0.9866 | 0.9866 | 0.9866 | 0.9983 |
| 1.2871 | 19.0125 | 800 | 0.0445 | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 0.9991 |
| 1.4727 | 23.0125 | 960 | 0.0399 | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 0.9988 |
| 1.5091 | 27.0125 | 1120 | 0.0365 | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 0.9991 |
| 1.1539 | 31.0125 | 1280 | 0.0470 | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 0.9990 |
| 1.0804 | 35.0125 | 1440 | 0.0386 | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 0.9993 |
| 1.1841 | 39.0125 | 1600 | 0.0311 | 0.9893 | 0.9893 | 0.9893 | 0.9893 | 0.9992 |
Framework versions
- Transformers 4.46.1
- Pytorch 2.0.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.1
- Library
- transformers
- Created
- Nov 4, 2024
- Updated
- Nov 4, 2024