---
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: 0.2121
- Accuracy: 0.7823
- F1: 0.7252
- Recall: 0.7628
- Precision: 0.7103

## 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: 32
- eval_batch_size: 32
- 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: 18

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.2495        | 1.0   | 35   | 0.3182          | 0.7143   | 0.5311 | 0.5380 | 0.5711    |
| 0.2596        | 2.0   | 70   | 0.2572          | 0.4911   | 0.4818 | 0.5557 | 0.5448    |
| 0.2321        | 3.0   | 105  | 0.2390          | 0.7232   | 0.6754 | 0.7011 | 0.6681    |
| 0.1769        | 4.0   | 140  | 0.2265          | 0.7054   | 0.6773 | 0.7339 | 0.6816    |
| 0.1614        | 5.0   | 175  | 0.2461          | 0.7054   | 0.6735 | 0.7227 | 0.6745    |
| 0.1027        | 6.0   | 210  | 0.2762          | 0.8125   | 0.7764 | 0.8062 | 0.7621    |
| 0.0832        | 7.0   | 245  | 0.3463          | 0.8036   | 0.7441 | 0.7441 | 0.7441    |
| 0.0354        | 8.0   | 280  | 0.6084          | 0.8214   | 0.7673 | 0.7673 | 0.7673    |
| 0.0068        | 9.0   | 315  | 0.6917          | 0.8214   | 0.7673 | 0.7673 | 0.7673    |


### Framework versions

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