Instructions to use hellengfranca/resultados_bertimbau with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hellengfranca/resultados_bertimbau with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="hellengfranca/resultados_bertimbau")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("hellengfranca/resultados_bertimbau") model = AutoModelForSequenceClassification.from_pretrained("hellengfranca/resultados_bertimbau", device_map="auto") - Notebooks
- Google Colab
- Kaggle
resultados_bertimbau
This model is a fine-tuned version of neuralmind/bert-base-portuguese-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.2246
- F1 Macro: 0.7527
- Accuracy: 0.7871
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: 32
- 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: 4
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | Accuracy |
|---|---|---|---|---|---|
| 1.6567 | 1.0 | 203 | 1.6277 | 0.4865 | 0.6460 |
| 1.2308 | 2.0 | 406 | 1.2325 | 0.6935 | 0.75 |
| 0.9535 | 3.0 | 609 | 1.1864 | 0.7281 | 0.7599 |
| 0.6850 | 4.0 | 812 | 1.2246 | 0.7527 | 0.7871 |
Framework versions
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 5.0.0
- Tokenizers 0.22.2
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Model tree for hellengfranca/resultados_bertimbau
Base model
neuralmind/bert-base-portuguese-cased