Text Classification
Transformers
Safetensors
distilbert
Generated from Trainer
text-embeddings-inference
Instructions to use Gab-P79/resultados_fine_tuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Gab-P79/resultados_fine_tuning with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Gab-P79/resultados_fine_tuning")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Gab-P79/resultados_fine_tuning") model = AutoModelForSequenceClassification.from_pretrained("Gab-P79/resultados_fine_tuning", device_map="auto") - Notebooks
- Google Colab
- Kaggle
resultados_fine_tuning
This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3344
- Accuracy: 0.897
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: 16
- seed: 21407463
- 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: 2
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| No log | 1.0 | 313 | 0.3318 | 0.896 |
| 0.3666 | 2.0 | 626 | 0.3344 | 0.897 |
Framework versions
- Transformers 5.20.2
- Pytorch 2.11.0+cu130
- Datasets 4.8.5
- Tokenizers 0.23.3
- Downloads last month
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Model tree for Gab-P79/resultados_fine_tuning
Base model
distilbert/distilbert-base-uncased