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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This model predicts the CAP code of parliamentary bills/instruments (https://www.comparativeagendas.net/pages/master-codebook)
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The model is trained on ~40k UK Parliamentary Statutory Instruments from the UK House of Commons and the Scottish Parliament.
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See the README for more information on the model and training data
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- Train Loss: 0.1188
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This model predicts the CAP code of parliamentary bills/instruments (https://www.comparativeagendas.net/pages/master-codebook)
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The model is trained on ~40k UK Parliamentary Statutory Instruments from the UK House of Commons and the Scottish Parliament.
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The model is cased (case sensitive)
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See the README for more information on the model and training data or message me on twitter - @sachary_
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- Train Loss: 0.1188
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