Instructions to use Josealssc/resultados_modelo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Josealssc/resultados_modelo with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Josealssc/resultados_modelo")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Josealssc/resultados_modelo") model = AutoModelForSequenceClassification.from_pretrained("Josealssc/resultados_modelo") - Notebooks
- Google Colab
- Kaggle
resultados_modelo
This model is a fine-tuned version of bertin-project/bertin-roberta-base-spanish on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.2676
- Accuracy: 0.928
- F1: 0.9295
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: 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: 3
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|---|---|---|---|---|---|
| No log | 1.0 | 79 | 0.3780 | 0.851 | 0.8346 |
| No log | 2.0 | 158 | 0.2961 | 0.914 | 0.914 |
| No log | 3.0 | 237 | 0.2955 | 0.918 | 0.9187 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.5.1+cu121
- Datasets 4.8.5
- Tokenizers 0.22.2
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Model tree for Josealssc/resultados_modelo
Base model
bertin-project/bertin-roberta-base-spanish