metadata
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
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?
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.")