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bert-addresses

Token Classification
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bert
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Model Card for Model ID

This model is developed to tag Names, Organisations and addresses. I have used a data combined from Conll, ontonotes5, and a custom address dataset that wad self made.

Model Description

  • Developed by: ctrlbuzz
  • Model type: Bert
  • Language(s) (NLP): Named Entity recognition
  • Finetuned from model [optional]: bert-base-cased

Model Sources [optional]

  • Repository: [More Information Needed]
  • Paper [optional]: [More Information Needed]
  • Demo [optional]: [More Information Needed]

Uses

Direct Use 

from transformers import BertTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = BertTokenizer.from_pretrained('bert-base-cased')
model = AutoModelForTokenClassification.from_pretrained("ctrlbuzz/bert-addresses")
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
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.",

print(nlp(example))