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
add model
Browse files- config.json +1 -1
- pytorch_model.bin +3 -0
config.json
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"torch_dtype": "float32",
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"transformers_version": "4.19.
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 28996
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"seq_classif_dropout": 0.2,
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"sinusoidal_pos_embds": false,
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"torch_dtype": "float32",
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"transformers_version": "4.19.3",
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"type_vocab_size": 2,
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"use_cache": true,
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"vocab_size": 28996
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:ccba8d66ccb03e5db5c9a747c755cb534823a8c05556c046f4e125252a9ff602
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size 433360817
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