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")# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
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README.md
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license: afl-3.0
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This model was trained on UK parliamentary statutory instruments (www.legislation.gov.uk). The model is trained to predict CAP codes of statutory instruments. The model is trained on the titles of ~40k statutory instruments from the UK House of Commons and the Scottish Parliament.
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Training data available at: https://comparativeagendas.s3.amazonaws.com/datasetfiles/uk_statutoryinstruments_v1_1.csv
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All statutory instruments are available at: https://www.legislation.gov.uk/uksi
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Comparative Agendas Project codes: https://www.comparativeagendas.net/pages/master-codebook
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license: afl-3.0
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