Text Classification
setfit
Joblib
ONNX
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use kemonito233/intentino with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use kemonito233/intentino with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("kemonito233/intentino") - sentence-transformers
How to use kemonito233/intentino with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("kemonito233/intentino") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3] - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- intent_mapping.json +13 -0
intent_mapping.json
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{
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"0": "A_QUIEN_BUSCA",
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"1": "EQUIVOCADO",
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"2": "ESPERA",
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"3": "FALLECIDO",
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"4": "MOLESTO",
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"5": "MUY_INTERESADO",
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"6": "NO_DISPONIBLE",
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"7": "NO_ESCUCHE",
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"8": "NO_INTERESADO",
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"9": "OCUPADO",
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"10": "QUIEN_HABLA"
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}
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