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

# content

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4768
- Accuracy: 0.7739
- F1-score: 0.7823
- Recall: 0.9002
- Precision: 0.6917

## 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: 2.5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Accuracy | F1-score | Recall | Precision |
|:-------------:|:------:|:----:|:---------------:|:--------:|:--------:|:------:|:---------:|
| 0.5016        | 0.3814 | 500  | 0.4686          | 0.7736   | 0.7865   | 0.9022 | 0.6971    |
| 0.4628        | 0.7628 | 1000 | 0.4437          | 0.7753   | 0.7769   | 0.8464 | 0.7180    |
| 0.4139        | 1.1442 | 1500 | 0.4633          | 0.7773   | 0.7573   | 0.7517 | 0.7630    |
| 0.3569        | 1.5256 | 2000 | 0.5019          | 0.7831   | 0.7930   | 0.8991 | 0.7093    |
| 0.357         | 1.9069 | 2500 | 0.4498          | 0.7839   | 0.7644   | 0.7585 | 0.7704    |
| 0.2612        | 2.2883 | 3000 | 0.6906          | 0.7665   | 0.7740   | 0.8650 | 0.7003    |
| 0.2292        | 2.6697 | 3500 | 0.6406          | 0.7624   | 0.7711   | 0.8656 | 0.6952    |
| 0.2345        | 3.0511 | 4000 | 0.8274          | 0.7687   | 0.7502   | 0.7511 | 0.7492    |
| 0.1527        | 3.4325 | 4500 | 0.8778          | 0.7602   | 0.7433   | 0.7511 | 0.7356    |
| 0.1613        | 3.8139 | 5000 | 0.8756          | 0.7564   | 0.7220   | 0.6842 | 0.7642    |
| 0.1188        | 4.1953 | 5500 | 1.2264          | 0.7567   | 0.7317   | 0.7176 | 0.7463    |
| 0.0992        | 4.5767 | 6000 | 1.2104          | 0.7636   | 0.7440   | 0.7430 | 0.7449    |
| 0.0938        | 4.9580 | 6500 | 1.1858          | 0.7616   | 0.7461   | 0.7579 | 0.7347    |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1