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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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- **Developed by:** ctrlbuzz |
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- **Model type:** Bert |
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- **Language(s) (NLP):** Named Entity recognition |
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- **Finetuned from model [optional]:** bert-base-cased |
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## Uses |
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### Direct Use |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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from transformers import pipeline |
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tokenizer = AutoTokenizer.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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print(nlp(example)) |
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``` |
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