Instructions to use fernandabufon/epochs_1_fold_3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fernandabufon/epochs_1_fold_3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="fernandabufon/epochs_1_fold_3")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("fernandabufon/epochs_1_fold_3") model = AutoModelForSequenceClassification.from_pretrained("fernandabufon/epochs_1_fold_3") - Notebooks
- Google Colab
- Kaggle
epochs_1_fold_3
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: 0.4757
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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Use 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: 1
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.7372 | 0.2020 | 300 | 0.5910 |
| 0.569 | 0.4040 | 600 | 0.5229 |
| 0.5406 | 0.6061 | 900 | 0.4872 |
| 0.5096 | 0.8081 | 1200 | 0.4757 |
Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0
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Model tree for fernandabufon/epochs_1_fold_3
Base model
neuralmind/bert-base-portuguese-cased