---
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: final
  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. -->

# final

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3507
- Accuracy: 0.8945
- F1: 0.8863
- Recall: 0.8760
- Precision: 0.8968

## 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: 64
- eval_batch_size: 64
- 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: 10

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1     | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.485         | 0.9756 | 80   | 0.3916          | 0.8182   | 0.7984 | 0.7674 | 0.8319    |
| 0.3395        | 1.9512 | 160  | 0.3039          | 0.8764   | 0.8547 | 0.7752 | 0.9524    |
| 0.2139        | 2.9268 | 240  | 0.3122          | 0.8691   | 0.8548 | 0.8217 | 0.8908    |
| 0.084         | 3.9024 | 320  | 0.3507          | 0.8945   | 0.8863 | 0.8760 | 0.8968    |
| 0.058         | 4.8780 | 400  | 0.5087          | 0.8727   | 0.8571 | 0.8140 | 0.9052    |
| 0.0389        | 5.8537 | 480  | 0.4579          | 0.8982   | 0.888  | 0.8605 | 0.9174    |
| 0.0264        | 6.8293 | 560  | 0.5052          | 0.8873   | 0.8765 | 0.8527 | 0.9016    |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1