---
library_name: transformers
license: mit
base_model: belisards/congretimbau
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: belisards/congretimbau
  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. -->

# belisards/congretimbau

This model is a fine-tuned version of [belisards/congretimbau](https://huggingface.co/belisards/congretimbau) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1176
- Accuracy: 0.8027
- F1: 0.7358
- Recall: 0.7544
- Precision: 0.7236

## 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: 64
- eval_batch_size: 64
- seed: 5151
- 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: 200
- num_epochs: 15

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.3405        | 1.0   | 18   | 0.2083          | 0.7232   | 0.4751 | 0.5104 | 0.5393    |
| 0.1467        | 2.0   | 36   | 0.1258          | 0.4107   | 0.4105 | 0.5463 | 0.5486    |
| 0.1198        | 3.0   | 54   | 0.1127          | 0.6607   | 0.5988 | 0.6140 | 0.5964    |
| 0.107         | 4.0   | 72   | 0.0999          | 0.6696   | 0.6339 | 0.6762 | 0.6380    |
| 0.0987        | 5.0   | 90   | 0.0943          | 0.6339   | 0.6113 | 0.6745 | 0.6339    |
| 0.0911        | 6.0   | 108  | 0.0930          | 0.6875   | 0.6492 | 0.6882 | 0.6492    |
| 0.078         | 7.0   | 126  | 0.0953          | 0.7321   | 0.6883 | 0.7183 | 0.6805    |
| 0.0671        | 8.0   | 144  | 0.0934          | 0.7232   | 0.6850 | 0.7235 | 0.6798    |
| 0.0534        | 9.0   | 162  | 0.1065          | 0.8036   | 0.7441 | 0.7441 | 0.7441    |
| 0.0355        | 10.0  | 180  | 0.1363          | 0.8214   | 0.7724 | 0.7786 | 0.7670    |
| 0.0263        | 11.0  | 198  | 0.1411          | 0.8214   | 0.7724 | 0.7786 | 0.7670    |
| 0.013         | 12.0  | 216  | 0.2712          | 0.8214   | 0.7560 | 0.7449 | 0.7710    |
| 0.0074        | 13.0  | 234  | 0.3198          | 0.7946   | 0.7294 | 0.7268 | 0.7321    |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0