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library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: profesor_MViT_S_RLVS
results: []
---
<!-- 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_S_RLVS
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0191
- Accuracy: 0.9947
- F1: 0.9947
- Precision: 0.9947
- Recall: 0.9947
- Roc Auc: 0.9999
## 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: 20
- eval_batch_size: 20
- 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: 240
- training_steps: 2400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Roc Auc |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.4099 | 2.0333 | 240 | 0.2282 | 0.9634 | 0.9634 | 0.9639 | 0.9634 | 0.9974 |
| 0.1424 | 5.0333 | 480 | 0.1349 | 0.9661 | 0.9660 | 0.9676 | 0.9661 | 0.9955 |
| 0.0659 | 8.0333 | 720 | 0.0890 | 0.9765 | 0.9765 | 0.9768 | 0.9765 | 0.9989 |
| 0.0582 | 11.0333 | 960 | 0.1153 | 0.9687 | 0.9687 | 0.9700 | 0.9687 | 0.9987 |
| 0.0319 | 14.0333 | 1200 | 0.0400 | 0.9896 | 0.9896 | 0.9896 | 0.9896 | 0.9994 |
| 0.0346 | 17.0333 | 1440 | 0.0408 | 0.9896 | 0.9896 | 0.9896 | 0.9896 | 0.9993 |
| 0.0145 | 20.0333 | 1680 | 0.0314 | 0.9922 | 0.9922 | 0.9922 | 0.9922 | 0.9997 |
| 0.0302 | 23.0333 | 1920 | 0.0395 | 0.9896 | 0.9896 | 0.9896 | 0.9896 | 0.9996 |
| 0.0244 | 26.0333 | 2160 | 0.0423 | 0.9896 | 0.9896 | 0.9896 | 0.9896 | 0.9998 |
| 0.0285 | 29.0333 | 2400 | 0.2273 | 0.9608 | 0.9608 | 0.9637 | 0.9608 | 0.9993 |
### Framework versions
- Transformers 4.46.1
- Pytorch 2.0.1+cu118
- Datasets 3.0.2
- Tokenizers 0.20.1
|