transformers safetensors bert text-classification generated_from_trainer dataset:hate_speech_portuguese base_model:google-bert/bert-base-uncased base_model:finetune:google-bert/bert-base-uncased license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: bert-base-uncased
datasets:
- hate_speech_portuguese
library_name: transformers
license: apache-2.0
metrics:
- accuracy
tags:
- generated_from_trainer
model-index:
- name: bert-hate-speech-test
  results:
  - task:
      type: text-classification
      name: Text Classification
    dataset:
      name: hate_speech_portuguese
      type: hate_speech_portuguese
      config: default
      split: train[:10%]
      args: default
    metrics:
    - type: accuracy
      value: 0.5964912280701754
      name: Accuracy
---

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# bert-hate-speech-test

This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the hate_speech_portuguese dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7279
- Accuracy: 0.5965

## 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: Use OptimizerNames.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: 3

### Training results



### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cpu
- Datasets 3.3.0
- Tokenizers 0.21.0
