Text Classification
setfit
ONNX
Safetensors
sentence-transformers
bert
generated_from_setfit_trainer
Eval Results (legacy)
text-embeddings-inference
Instructions to use Northell/ros-classifiers with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- setfit
How to use Northell/ros-classifiers with setfit:
from setfit import SetFitModel model = SetFitModel.from_pretrained("Northell/ros-classifiers") - sentence-transformers
How to use Northell/ros-classifiers with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Northell/ros-classifiers") 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- config_setfit.json +5 -5
- heads/domain-router.pkl +1 -1
- model_head.pkl +1 -1
config_setfit.json
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"normalize_embeddings": false,
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"labels": [
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"ShippingAndHandlingDomain",
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"FeatureDomain",
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"AssemblyDomain",
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"SupplierDomain",
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"MaterialDomain",
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"ManufacturingDomain",
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"MeasurementDomain",
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"ProductDomain"
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}
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"normalize_embeddings": false,
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"labels": [
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"ShippingAndHandlingDomain",
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"ManufacturingDomain",
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"AssemblyDomain",
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"MeasurementDomain",
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"ProductDomain",
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"SupplierDomain",
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"FeatureDomain",
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"MaterialDomain"
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}
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heads/domain-router.pkl
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model_head.pkl
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size 28404
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