---
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: 0.1076
- Accuracy: 0.8503
- F1: 0.7896
- Recall: 0.7959
- Precision: 0.7839

## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1548        | 1.0   | 35   | 0.1456          | 0.7411   | 0.4571 | 0.5112 | 0.6227    |
| 0.1572        | 2.0   | 70   | 0.1354          | 0.7411   | 0.6588 | 0.6570 | 0.6607    |
| 0.1305        | 3.0   | 105  | 0.1212          | 0.7768   | 0.6402 | 0.6251 | 0.7194    |
| 0.1069        | 4.0   | 140  | 0.1155          | 0.8393   | 0.7857 | 0.7794 | 0.7930    |
| 0.0937        | 5.0   | 175  | 0.1216          | 0.8304   | 0.7764 | 0.7734 | 0.7798    |
| 0.0639        | 6.0   | 210  | 0.1257          | 0.8482   | 0.7899 | 0.7742 | 0.8125    |
| 0.0437        | 7.0   | 245  | 0.1610          | 0.8393   | 0.7614 | 0.7345 | 0.8195    |
| 0.0254        | 8.0   | 280  | 0.2101          | 0.8482   | 0.7842 | 0.7630 | 0.8197    |
| 0.0067        | 9.0   | 315  | 0.2555          | 0.8482   | 0.7899 | 0.7742 | 0.8125    |


### Framework versions

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