Instructions to use Nikte1/modelo-beto-burla with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Nikte1/modelo-beto-burla with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Nikte1/modelo-beto-burla")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Nikte1/modelo-beto-burla") model = AutoModelForSequenceClassification.from_pretrained("Nikte1/modelo-beto-burla", device_map="auto") - Notebooks
- Google Colab
- Kaggle
modelo-beto-burla
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0574
- Accuracy: 0.9825
- Precision: 0.9524
- Recall: 1.0
- F1: 0.9756
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: 8
- eval_batch_size: 8
- 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 | Precision | Recall | F1 |
|---|---|---|---|---|---|---|---|
| 0.4113 | 1.0 | 29 | 0.1153 | 0.9825 | 1.0 | 0.95 | 0.9744 |
| 0.0446 | 2.0 | 58 | 0.0558 | 0.9825 | 0.9524 | 1.0 | 0.9756 |
| 0.0279 | 3.0 | 87 | 0.0574 | 0.9825 | 0.9524 | 1.0 | 0.9756 |
Framework versions
- Transformers 5.13.1
- Pytorch 2.11.0+cpu
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for Nikte1/modelo-beto-burla
Base model
dccuchile/bert-base-spanish-wwm-cased