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")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("ctrlbuzz/bert-addresses") model = AutoModelForTokenClassification.from_pretrained("ctrlbuzz/bert-addresses", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Create README.md
Browse files
README.md
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from transformers import BertTokenizer, AutoModelForTokenClassification
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from transformers import pipeline
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tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
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model = AutoModelForTokenClassification.from_pretrained("ctrlbuzz/bert-addresses")
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nlp = pipeline("ner", model=model, tokenizer=tokenizer)
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example = "While Maria was representing Johnson & Associates at a conference in Spain, she mailed me a letter from her new office at 123 Elm St., Apt. 4B, Springfield, IL.",
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ner_results = nlp(example)
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print(ner_results)
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