|
|
--- |
|
|
library_name: transformers |
|
|
tags: |
|
|
- generated_from_trainer |
|
|
metrics: |
|
|
- accuracy |
|
|
- f1 |
|
|
- precision |
|
|
- recall |
|
|
model-index: |
|
|
- name: profesor_MViT_N_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_N_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: 0.0466 |
|
|
- Accuracy: 0.9908 |
|
|
- F1: 0.9908 |
|
|
- Precision: 0.9910 |
|
|
- Recall: 0.9908 |
|
|
- Roc Auc: 0.9996 |
|
|
|
|
|
## 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 | |
|
|
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:| |
|
|
| 0.4675 | 2.0333 | 240 | 0.2770 | 0.9687 | 0.9687 | 0.9691 | 0.9687 | 0.9955 | |
|
|
| 0.1526 | 5.0333 | 480 | 0.1237 | 0.9661 | 0.9661 | 0.9667 | 0.9661 | 0.9974 | |
|
|
| 0.0744 | 8.0333 | 720 | 0.0744 | 0.9817 | 0.9817 | 0.9818 | 0.9817 | 0.9989 | |
|
|
| 0.0579 | 11.0333 | 960 | 0.0711 | 0.9817 | 0.9817 | 0.9818 | 0.9817 | 0.9989 | |
|
|
| 0.0437 | 14.0333 | 1200 | 0.0465 | 0.9896 | 0.9896 | 0.9896 | 0.9896 | 0.9995 | |
|
|
| 0.0328 | 17.0333 | 1440 | 0.0478 | 0.9869 | 0.9869 | 0.9871 | 0.9869 | 0.9996 | |
|
|
| 0.021 | 20.0333 | 1680 | 0.0465 | 0.9869 | 0.9869 | 0.9871 | 0.9869 | 0.9995 | |
|
|
| 0.0394 | 23.0333 | 1920 | 0.0374 | 0.9922 | 0.9922 | 0.9923 | 0.9922 | 0.9997 | |
|
|
| 0.0189 | 26.0333 | 2160 | 0.0514 | 0.9896 | 0.9896 | 0.9898 | 0.9896 | 0.9994 | |
|
|
| 0.0295 | 29.0333 | 2400 | 0.0522 | 0.9896 | 0.9896 | 0.9896 | 0.9896 | 0.9996 | |
|
|
|
|
|
|
|
|
### Framework versions |
|
|
|
|
|
- Transformers 4.46.1 |
|
|
- Pytorch 2.0.1+cu118 |
|
|
- Datasets 3.0.2 |
|
|
- Tokenizers 0.20.1 |
|
|
|
