Instructions to use suarez84/modelo-inclusividad with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use suarez84/modelo-inclusividad with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="suarez84/modelo-inclusividad")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("suarez84/modelo-inclusividad") model = AutoModelForSequenceClassification.from_pretrained("suarez84/modelo-inclusividad", device_map="auto") - Notebooks
- Google Colab
- Kaggle
modelo-inclusividad
This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.5462
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: 10
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.6912 | 1.0 | 30 | 0.6378 |
| 0.5595 | 2.0 | 60 | 0.5138 |
| 0.2955 | 3.0 | 90 | 0.4315 |
| 0.166 | 4.0 | 120 | 0.3883 |
| 0.0806 | 5.0 | 150 | 0.3825 |
| 0.0687 | 6.0 | 180 | 0.4732 |
| 0.0084 | 7.0 | 210 | 0.5182 |
| 0.0553 | 8.0 | 240 | 0.5462 |
Framework versions
- Transformers 4.55.2
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
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Model tree for suarez84/modelo-inclusividad
Base model
distilbert/distilbert-base-uncased