


## 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: `OTHER`, `CITY` and `COUNTRY`



### How to use the finetuned model?

```
from transformers import AutoTokenizer, AutoModelForTokenClassification

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

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

from transformers import pipeline

nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple")
nlp("My name is Kermit and I live in London.")
```
{"language": ["en"], "tags": ["token-classification", "address-NER", "NER", "bert-base-uncased"], "datasets": ["Ultra Fine Entity Typing"], "metrics": ["Precision", "Recall", "F1 Score"], "widget": [{"text": "Hi, I am Kermit and I live in Berlin"}, {"text": "It is very difficult to find a house in Berlin, Germany."}, {"text": "ML6 is a very cool company from Belgium"}, {"text": "Samuel ppops in a happy plce called Berlin which happens to be Kazakhstan"}, {"text": "My family and I visited Montreal, Canada last week and the flight from Amsterdam took 9 hours"}]}
transformers pytorch tf bert token-classification address-NER NER bert-base-uncased en autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #tf #bert #token-classification #address-NER #NER #bert-base-uncased #en #autotrain_compatible #endpoints_compatible #has_space #region-us 




## 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 dataset to include the 'City' and 'Country' information. We also did some extra preprocessing to remove false labels. 

The model predicts 3 different tags: 'OTHER', 'CITY' and 'COUNTRY'



### How to use the finetuned model?


## 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 dataset to include the 'City' and 'Country' information. We also did some extra preprocessing to remove false labels. 

The model predicts 3 different tags: 'OTHER', 'CITY' and 'COUNTRY' ### How to use the finetuned model?
TAGS
#transformers #pytorch #tf #bert #token-classification #address-NER #NER #bert-base-uncased #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ## 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 dataset to include the 'City' and 'Country' information. We also did some extra preprocessing to remove false labels. 

The model predicts 3 different tags: 'OTHER', 'CITY' and 'COUNTRY' ### How to use the finetuned model?
passage: TAGS
#transformers #pytorch #tf #bert #token-classification #address-NER #NER #bert-base-uncased #en #autotrain_compatible #endpoints_compatible #has_space #region-us 
## 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 dataset to include the 'City' and 'Country' information. We also did some extra preprocessing to remove false labels. 

The model predicts 3 different tags: 'OTHER', 'CITY' and 'COUNTRY'### How to use the finetuned model?