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estudiante_S3D_profesor_MViT_kl_VIOPERU
DanJoshua/estudiante_S3D_profesor_MViT_kl_VIOPERU
estudiante_S3D_profesor_MViT_kl_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 4, 2024
- SHA
- b893a863
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_kl_VIOPERU
This model is a fine-tuned version of on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.5112
- Accuracy: 0.7768
- F1: 0.7737
- Precision: 0.7926
- Recall: 0.7768
- Roc Auc: 0.8568
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 |
|---|---|---|---|---|---|---|---|---|
| 0.5176 | 4.02 | 40 | 0.6982 | 0.5357 | 0.4313 | 0.6346 | 0.5357 | 0.5472 |
| 0.3737 | 9.02 | 80 | 0.6248 | 0.7321 | 0.7214 | 0.7745 | 0.7321 | 0.7602 |
| 0.2609 | 14.02 | 120 | 0.5522 | 0.7143 | 0.7110 | 0.7246 | 0.7143 | 0.8253 |
| 0.2297 | 19.02 | 160 | 0.5293 | 0.7143 | 0.7110 | 0.7246 | 0.7143 | 0.8495 |
| 0.1643 | 24.02 | 200 | 0.5365 | 0.7679 | 0.7672 | 0.7710 | 0.7679 | 0.8533 |
| 0.1424 | 29.02 | 240 | 0.4691 | 0.7679 | 0.7672 | 0.7710 | 0.7679 | 0.8559 |
| 0.1415 | 34.02 | 280 | 0.4561 | 0.7143 | 0.7128 | 0.7188 | 0.7143 | 0.8367 |
| 0.1346 | 39.02 | 320 | 0.5279 | 0.7143 | 0.7128 | 0.7188 | 0.7143 | 0.8380 |
| 0.1223 | 44.02 | 360 | 0.5140 | 0.7143 | 0.7128 | 0.7188 | 0.7143 | 0.8380 |
| 0.1315 | 49.02 | 400 | 0.4849 | 0.6964 | 0.6940 | 0.7029 | 0.6964 | 0.8304 |
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 4, 2024