---
library_name: transformers
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: estudiante_S3D_RWF2000
  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. -->

# estudiante_S3D_RWF2000

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: 0.4851
- Accuracy: 0.8925
- F1: 0.8924
- Precision: 0.8937
- Recall: 0.8925
- Roc Auc: 0.9471

## 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: 75
- eval_batch_size: 75
- 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: 189
- training_steps: 1890
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Precision | Recall | Roc Auc |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:-------:|
| 0.4756        | 3.0116  | 94   | 0.4573          | 0.77     | 0.7623 | 0.8102    | 0.77   | 0.9241  |
| 0.3034        | 7.0106  | 188  | 0.4936          | 0.775    | 0.7641 | 0.8374    | 0.775  | 0.9375  |
| 0.2149        | 11.0095 | 282  | 0.7740          | 0.77     | 0.7583 | 0.8348    | 0.77   | 0.9408  |
| 0.1536        | 15.0085 | 376  | 0.7817          | 0.795    | 0.7877 | 0.8418    | 0.795  | 0.9405  |
| 0.1242        | 19.0074 | 470  | 0.7977          | 0.7875   | 0.7793 | 0.8375    | 0.7875 | 0.9390  |
| 0.0884        | 23.0063 | 564  | 0.7113          | 0.83     | 0.8263 | 0.8603    | 0.83   | 0.9438  |
| 0.0742        | 27.0053 | 658  | 0.6809          | 0.8325   | 0.8290 | 0.8619    | 0.8325 | 0.9446  |
| 0.055         | 31.0042 | 752  | 0.5733          | 0.835    | 0.8319 | 0.8613    | 0.835  | 0.9422  |
| 0.0441        | 35.0032 | 846  | 0.5877          | 0.85     | 0.8485 | 0.8646    | 0.85   | 0.9428  |
| 0.0304        | 39.0021 | 940  | 0.4630          | 0.865    | 0.8641 | 0.8746    | 0.865  | 0.9444  |
| 0.0242        | 43.0011 | 1034 | 0.5108          | 0.8675   | 0.8667 | 0.8765    | 0.8675 | 0.9440  |
| 0.0201        | 46.0127 | 1128 | 0.5144          | 0.87     | 0.8694 | 0.8774    | 0.87   | 0.9451  |
| 0.0165        | 50.0116 | 1222 | 0.3905          | 0.8825   | 0.8822 | 0.8860    | 0.8825 | 0.9463  |
| 0.0232        | 54.0106 | 1316 | 0.4735          | 0.8825   | 0.8822 | 0.8860    | 0.8825 | 0.9466  |
| 0.0098        | 58.0095 | 1410 | 0.4823          | 0.8825   | 0.8823 | 0.8847    | 0.8825 | 0.9457  |
| 0.0132        | 62.0085 | 1504 | 0.4508          | 0.88     | 0.8798 | 0.8824    | 0.88   | 0.9475  |
| 0.0105        | 66.0074 | 1598 | 0.4055          | 0.885    | 0.8849 | 0.8869    | 0.885  | 0.9477  |
| 0.0082        | 70.0063 | 1692 | 0.4190          | 0.885    | 0.8849 | 0.8869    | 0.885  | 0.9464  |
| 0.0072        | 74.0053 | 1786 | 0.4812          | 0.8875   | 0.8874 | 0.8891    | 0.8875 | 0.9476  |
| 0.0056        | 78.0042 | 1880 | 0.4603          | 0.8975   | 0.8975 | 0.8980    | 0.8975 | 0.9484  |


### Framework versions

- Transformers 4.46.1
- Pytorch 2.0.1+cu118
- Datasets 3.1.0
- Tokenizers 0.20.1