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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 fro Conll, ontonotes5, and a custom address dataset that was self made. Cleaned
out the tags. Detects U.S addresses.
[\"O\", \"B-ORG\", \"I-ORG\", \"B-PER\", \"I-PER\",'B-addr','I-addr']

### Model Description

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

## Uses

### Direct Use

```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
tokenizer = AutoTokenizer.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))
```