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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
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### Model Description
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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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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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```python
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from transformers import
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from transformers import pipeline
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tokenizer =
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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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# 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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- **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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