Instructions to use ctrlbuzz/bert-addresses with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ctrlbuzz/bert-addresses with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("token-classification", model="ctrlbuzz/bert-addresses")# Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ctrlbuzz/bert-addresses") model = AutoModelForTokenClassification.from_pretrained("ctrlbuzz/bert-addresses") - Notebooks
- Google Colab
- Kaggle
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# Model Card for Model ID
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This model is developed to tag Names, Organisations and addresses. I have used a data combined fro Conll, ontonotes5, and a custom address dataset that was self made. Cleaned
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out the tags.
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[\"O\", \"B-ORG\", \"I-ORG\", \"B-PER\", \"I-PER\",'B-addr','I-addr']
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### Model Description
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# Model Card for Model ID
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This model is developed to tag Names, Organisations and addresses. I have used a data combined fro Conll, ontonotes5, and a custom address dataset that was self made. Cleaned
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out the tags. Detects U.S addresses.
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[\"O\", \"B-ORG\", \"I-ORG\", \"B-PER\", \"I-PER\",'B-addr','I-addr']
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### Model Description
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