---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
datasets:
- told-br
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: bert-base-multilingual-cased-finetuned-hate-speech-ptbr
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: told-br
      type: told-br
      config: binary
      split: validation
      args: binary
    metrics:
    - name: Precision
      type: precision
      value: 0.702020202020202
    - name: Recall
      type: recall
      value: 0.7654185022026432
    - name: Accuracy
      type: accuracy
      value: 0.758095238095238
    - name: F1
      type: f1
      value: 0.7590123199739615
---

<!-- 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. -->

# bert-base-multilingual-cased-finetuned-hate-speech-ptbr

This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the told-br dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6224
- Precision: 0.7020
- Recall: 0.7654
- Accuracy: 0.7581
- F1: 0.7590

## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5

### Training results

| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 0.5127        | 1.0   | 1050 | 0.4978          | 0.6500    | 0.8756 | 0.7424   | 0.7418 |
| 0.4415        | 2.0   | 2100 | 0.5206          | 0.7143    | 0.7104 | 0.7519   | 0.7518 |
| 0.3623        | 3.0   | 3150 | 0.6204          | 0.6747    | 0.8293 | 0.7533   | 0.7542 |
| 0.283         | 4.0   | 4200 | 0.6224          | 0.7020    | 0.7654 | 0.7581   | 0.7590 |
| 0.2196        | 5.0   | 5250 | 0.7572          | 0.6954    | 0.7742 | 0.7557   | 0.7568 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3