---
tags:
- classification
- generated_from_trainer
datasets:
- hate_speech_offensive
metrics:
- accuracy
model-index:
- name: clasificador-hate_speech_offensive-BERTweet
  results:
  - task:
      name: Text Classification
      type: text-classification
    dataset:
      name: hate_speech_offensive
      type: hate_speech_offensive
      config: default
      split: train
      args: default
    metrics:
    - name: Accuracy
      type: accuracy
      value: 0.9195077667944321
---

<!-- 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. -->

# clasificador-hate_speech_offensive-BERTweet

This model is a fine-tuned version of [vinai/bertweet-base](https://huggingface.co./vinai/bertweet-base) on the hate_speech_offensive dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2951
- Accuracy: 0.9195

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3129        | 1.0   | 2479 | 0.3258          | 0.9112   |
| 0.2877        | 2.0   | 4958 | 0.2844          | 0.9124   |
| 0.235         | 3.0   | 7437 | 0.2951          | 0.9195   |


### Framework versions

- Transformers 4.27.2
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2