Instructions to use dhanymarth/beto-sarcasmo-colombia with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dhanymarth/beto-sarcasmo-colombia with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="dhanymarth/beto-sarcasmo-colombia")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("dhanymarth/beto-sarcasmo-colombia") model = AutoModelForSequenceClassification.from_pretrained("dhanymarth/beto-sarcasmo-colombia", device_map="auto") - Notebooks
- Google Colab
- Kaggle
beto-sarcasmo-colombia
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: 0.0102
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: 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 |
|---|---|---|---|
| 0.3441 | 1.0 | 1910 | 0.1436 |
| 0.1647 | 2.0 | 3820 | 0.0388 |
| 0.0385 | 3.0 | 5730 | 0.0102 |
Framework versions
- Transformers 5.9.0
- Pytorch 2.11.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2
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Model tree for dhanymarth/beto-sarcasmo-colombia
Base model
dccuchile/bert-base-spanish-wwm-cased