---
license: mit
base_model: neuralmind/bert-large-portuguese-cased
tags:
- generated_from_trainer
datasets:
- hate_speech_portuguese
metrics:
- accuracy
model-index:
- name: bertimbau_hate_speech
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: hate_speech_portuguese
      type: hate_speech_portuguese
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.781433607520564
---

<!-- 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. -->

# bertimbau_hate_speech

This model is a fine-tuned version of [neuralmind/bert-large-portuguese-cased](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the hate_speech_portuguese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5009
- Accuracy: 0.7814

## 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: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log        | 1.0   | 302  | 0.4593          | 0.7756   |
| 0.4361        | 2.0   | 604  | 0.5009          | 0.7814   |


### Framework versions

- Transformers 4.31.0
- Pytorch 2.1.0.dev20230816+cu121
- Datasets 2.14.4
- Tokenizers 0.13.3