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

# modelos

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: 0.5239
- Accuracy: 0.8254
- F1: 0.7442
- Recall: 0.7267
- Precision: 0.7727

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 20

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.5681        | 1.0323 | 32   | 0.5508          | 0.75     | 0.4286 | 0.5    | 0.375     |
| 0.5233        | 2.0645 | 64   | 0.5138          | 0.7381   | 0.5146 | 0.5317 | 0.5897    |
| 0.4339        | 3.0968 | 96   | 0.4529          | 0.7917   | 0.6875 | 0.6706 | 0.7240    |
| 0.3907        | 4.1290 | 128  | 0.4087          | 0.8393   | 0.7683 | 0.75   | 0.7970    |
| 0.2166        | 5.1613 | 160  | 0.4054          | 0.8452   | 0.7867 | 0.7778 | 0.7976    |
| 0.14          | 6.1935 | 192  | 0.4474          | 0.8274   | 0.7716 | 0.7738 | 0.7696    |
| 0.0673        | 7.2258 | 224  | 0.5118          | 0.8393   | 0.7726 | 0.7579 | 0.7932    |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1