---
license: mit
tags:
- generated_from_trainer
base_model: neuralmind/bert-base-portuguese-cased
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: hate_BERTimbau_v3
  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. -->

# hate_BERTimbau_v3

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: 1.7782
- Precision: 0.7641
- Recall: 0.7690
- F1: 0.7621
- Accuracy: 0.7690

## 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: 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: 10

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1     | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.5887        | 1.0   | 284  | 0.5397          | 0.7478    | 0.7319 | 0.7362 | 0.7319   |
| 0.4617        | 2.0   | 568  | 0.4880          | 0.7842    | 0.7090 | 0.7136 | 0.7090   |
| 0.3256        | 3.0   | 852  | 0.7394          | 0.7646    | 0.7690 | 0.7603 | 0.7690   |
| 0.2023        | 4.0   | 1136 | 0.8880          | 0.7808    | 0.7831 | 0.7816 | 0.7831   |
| 0.1305        | 5.0   | 1420 | 1.0510          | 0.7747    | 0.7619 | 0.7654 | 0.7619   |
| 0.0916        | 6.0   | 1704 | 1.4921          | 0.7452    | 0.7496 | 0.7464 | 0.7496   |
| 0.0494        | 7.0   | 1988 | 1.7782          | 0.7641    | 0.7690 | 0.7621 | 0.7690   |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1