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

# MyDrive

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: 0.1336
- Accuracy: 0.8776
- F1: 0.8115
- Recall: 0.7919
- Precision: 0.8389

## 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: 150
- num_epochs: 14

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1343        | 2.8333  | 51   | 0.1396          | 0.7679   | 0.5492 | 0.5629 | 0.7832    |
| 0.1057        | 5.6667  | 102  | 0.1280          | 0.8036   | 0.6777 | 0.6543 | 0.7887    |
| 0.053         | 8.5     | 153  | 0.1457          | 0.8482   | 0.7899 | 0.7742 | 0.8125    |
| 0.0159        | 11.3333 | 204  | 0.2345          | 0.8482   | 0.7952 | 0.7854 | 0.8072    |


### Framework versions

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