## City-Country-NER

A `bert-base-uncased` model finetuned on a custom dataset to detect `Country` and `City` names from a given sentence. 

### Custom Dataset
We weakly supervised the `Ultra-Fine Entity Typing[https://www.cs.utexas.edu/~eunsol/html_pages/open_entity.html]` dataset to include the `City` and `Country` information. We also did some extra preprocessing to remove false labels. 

The model predicts 3 different tags:

| **Predicted Tag**| **Meaning** |
|------------------|-------------|
| LABEL_0          | Others      | 
| LABEL_2          | Country     | 
| LABEL_3          | City        |



### How to use the finetuned model?

```
from transformers import AutoTokenizer, AutoModelForTokenClassification

tokenizer = AutoTokenizer.from_pretrained("ml6team/bert-base-uncased-city-country-ner", use_auth_token=True)

model = AutoModelForTokenClassification.from_pretrained("ml6team/bert-base-uncased-city-country-ner", use_auth_token=True)

from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("My name is Kermit and I live in London.")
```