Text Classification
Transformers
PyTorch
TensorFlow
bert
generated_from_keras_callback
text-embeddings-inference
Instructions to use z-dickson/CAP_coded_UK_statutory_instruments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use z-dickson/CAP_coded_UK_statutory_instruments with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="z-dickson/CAP_coded_UK_statutory_instruments")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("z-dickson/CAP_coded_UK_statutory_instruments") model = AutoModelForSequenceClassification.from_pretrained("z-dickson/CAP_coded_UK_statutory_instruments") - Notebooks
- Google Colab
- Kaggle
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model-index:
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- name: CAP_coded_UK_statutory_instruments
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results: []
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model-index:
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- name: CAP_coded_UK_statutory_instruments
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results: []
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widget:
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- text: "The National Health Service (Charges for Drugs and Appliances) (Scotland) Regulations 2007"
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example_title: "'label': 'health', 'score': 0.9882584810256958"
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- text: "The Licensing of Relevant Permanent Sites (Scotland) Regulations 2016"
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example_title: "'label': 'housing', 'score': 0.6133455038070679"
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