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--- |
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language: |
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- en |
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tags: |
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- token-classification |
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- address-NER |
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- NER |
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- bert-base-uncased |
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datasets: |
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- Ultra Fine Entity Typing |
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metrics: |
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- Precision |
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- Recall |
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- F1 Score |
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widget: |
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- text: "Hi, I am Kermit and I live in Berlin" |
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- text: "It is very difficult to find a house in Berlin, Germany." |
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- text: "ML6 is a very cool company from Belgium" |
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- text: "Samuel ppops in a happy plce called Berlin which happens to be Kazakhstan" |
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- text: "My family and I visited Montreal, Canada last week and the flight from Amsterdam took 9 hours" |
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--- |
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## City-Country-NER |
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A `bert-base-uncased` model finetuned on a custom dataset to detect `Country` and `City` names from a given sentence. |
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### Custom Dataset |
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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. |
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The model predicts 3 different tags: `OTHER`, `CITY` and `COUNTRY` |
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### How to use the finetuned model? |
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``` |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("ml6team/bert-base-uncased-city-country-ner") |
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model = AutoModelForTokenClassification.from_pretrained("ml6team/bert-base-uncased-city-country-ner") |
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from transformers import pipeline |
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nlp = pipeline('ner', model=model, tokenizer=tokenizer, aggregation_strategy="simple") |
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nlp("My name is Kermit and I live in London.") |
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``` |
