Instructions to use JDAlvarez/Beto-Sarcasmo-2026 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JDAlvarez/Beto-Sarcasmo-2026 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="JDAlvarez/Beto-Sarcasmo-2026")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("JDAlvarez/Beto-Sarcasmo-2026") model = AutoModelForSequenceClassification.from_pretrained("JDAlvarez/Beto-Sarcasmo-2026", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Beto-Sarcasmo-2026
This model is a fine-tuned version of dccuchile/bert-base-spanish-wwm-cased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.6084
- F1 Macro: 0.9125
- F1 Weighted: 0.9171
- Accuracy: 0.9173
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
- lr_scheduler_warmup_steps: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Macro | F1 Weighted | Accuracy |
|---|---|---|---|---|---|---|
| 0.3024 | 1.0 | 955 | 0.2617 | 0.8988 | 0.9038 | 0.9037 |
| 0.2290 | 2.0 | 1910 | 0.2183 | 0.9160 | 0.9206 | 0.9209 |
| 0.1226 | 3.0 | 2865 | 0.3910 | 0.9196 | 0.9237 | 0.9236 |
| 0.0658 | 4.0 | 3820 | 0.5219 | 0.9069 | 0.9110 | 0.9105 |
| 0.0156 | 5.0 | 4775 | 0.6126 | 0.9074 | 0.9126 | 0.9131 |
| 0.0340 | 6.0 | 5730 | 0.6084 | 0.9125 | 0.9171 | 0.9173 |
Framework versions
- Transformers 5.12.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for JDAlvarez/Beto-Sarcasmo-2026
Base model
dccuchile/bert-base-spanish-wwm-cased