Text Classification
Transformers
Safetensors
Spanish
bertin_alc
feature-extraction
multi-label-classification
spanish
public-policy
bertin
roberta
custom_code
Instructions to use PENCR/bertin-alc-multilabel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use PENCR/bertin-alc-multilabel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="PENCR/bertin-alc-multilabel", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("PENCR/bertin-alc-multilabel", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
| """Paquete HuggingFace del clasificador multi-label ALC (ALC1/ALC2/ALC3). | |
| Expone el modelo, la config, el pipeline y utilidades de calibración. | |
| """ | |
| from .calibration import PLATT_PARAMS, apply_platt | |
| from .labels import ALC_LABELS, ALC_DESCRIPTIONS | |
| from .modeling_bertin_alc import BertinAlcConfig, BertinAlcModel | |
| from .pipeline_bertin_alc import BertinAlcPipeline, register | |
| __all__ = [ | |
| "ALC_LABELS", | |
| "ALC_DESCRIPTIONS", | |
| "BertinAlcConfig", | |
| "BertinAlcModel", | |
| "BertinAlcPipeline", | |
| "PLATT_PARAMS", | |
| "apply_platt", | |
| "register", | |
| ] | |