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bert-base-uncased-city-country-ner
ml6team/bert-base-uncased-city-country-ner
bert-base-uncased-city-country-ner is a token classification model from ml6team. Use it when you need labels on individual words, such as names. It is set up for transformers.
A bert-base-uncased model finetuned on a custom dataset to detect Country and City names from a given sentence.
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At a glance
- Task
- Token Classification
- Library
- transformers
- Model type
- bert
- Access
- Public
- Created
- Mar 2, 2022
- Updated
- Jul 1, 2022
- SHA
- e38e683a
Try a prompt
Model card
From the Hugging Face model README
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.")
- Task
- Token Classification
- Library
- transformers
- Type
- bert
- Languages
- en
- Created
- Mar 2, 2022
- Updated
- Jul 1, 2022