---
library_name: transformers
license: mit
base_model: neuralmind/bert-base-portuguese-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: e3_lr2e-05
  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. -->

# e3_lr2e-05

This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0753
- Precision: 0.9611
- Recall: 0.9778
- F1: 0.9694
- Accuracy: 0.9817

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4195        | 0.2564 | 50   | 0.2315          | 0.8642    | 0.8460 | 0.8550 | 0.9499   |
| 0.2396        | 0.5128 | 100  | 0.1778          | 0.8971    | 0.8970 | 0.8970 | 0.9517   |
| 0.1717        | 0.7692 | 150  | 0.1330          | 0.9033    | 0.9323 | 0.9176 | 0.9639   |
| 0.1249        | 1.0256 | 200  | 0.1090          | 0.9369    | 0.9554 | 0.9460 | 0.9728   |
| 0.0929        | 1.2821 | 250  | 0.1066          | 0.9397    | 0.9630 | 0.9512 | 0.9739   |
| 0.0954        | 1.5385 | 300  | 0.0831          | 0.9498    | 0.9670 | 0.9583 | 0.9788   |
| 0.0858        | 1.7949 | 350  | 0.0844          | 0.9459    | 0.9727 | 0.9591 | 0.9776   |
| 0.0715        | 2.0513 | 400  | 0.0868          | 0.9512    | 0.9766 | 0.9637 | 0.9796   |
| 0.056         | 2.3077 | 450  | 0.0789          | 0.9616    | 0.9774 | 0.9695 | 0.9818   |
| 0.0592        | 2.5641 | 500  | 0.0768          | 0.9614    | 0.9783 | 0.9698 | 0.9817   |
| 0.0607        | 2.8205 | 550  | 0.0753          | 0.9611    | 0.9778 | 0.9694 | 0.9817   |


### Framework versions

- Transformers 4.45.0
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.20.0