Instructions to use LeoHS04/hateBR_classifier with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LeoHS04/hateBR_classifier with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="LeoHS04/hateBR_classifier")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("LeoHS04/hateBR_classifier") model = AutoModelForSequenceClassification.from_pretrained("LeoHS04/hateBR_classifier", device_map="auto") - Notebooks
- Google Colab
- Kaggle
hateBR_classifier
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3644
- Accuracy: 0.9152
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 OptimizerNames.ADAMW_TORCH_FUSED 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
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.2167 | 1.0 | 307 | 0.2307 | 0.9181 |
| 0.1202 | 2.0 | 614 | 0.2744 | 0.9229 |
| 0.0511 | 3.0 | 921 | 0.3644 | 0.9152 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for LeoHS04/hateBR_classifier
Base model
neuralmind/bert-base-portuguese-cased