---
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: 0.1877
- Accuracy: 0.7891
- F1: 0.7273
- Recall: 0.7564
- Precision: 0.7128

## 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: 120
- num_epochs: 18

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.29          | 1.0   | 35   | 0.2717          | 0.5625   | 0.5209 | 0.5478 | 0.5371    |
| 0.2615        | 2.0   | 70   | 0.2353          | 0.5357   | 0.5344 | 0.6643 | 0.6468    |
| 0.2189        | 3.0   | 105  | 0.1945          | 0.8036   | 0.7637 | 0.7889 | 0.7506    |
| 0.1579        | 4.0   | 140  | 0.1931          | 0.7857   | 0.7375 | 0.7545 | 0.7273    |
| 0.1078        | 5.0   | 175  | 0.2402          | 0.8036   | 0.7496 | 0.7553 | 0.7447    |
| 0.0596        | 6.0   | 210  | 0.2657          | 0.7946   | 0.7591 | 0.7941 | 0.7458    |


### Framework versions

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