entity-english-4class

English NER in Flair (4-class)

This is the 4-class NER model for English for Flair.

F1-Score: 95.6

Predicts 4 tags:

⚠️ Default license: noncommercial use only. This model is released under the Flukes NC 1.0 License. Commercial use — including using this model's predictions in a commercial product or service — requires a separate license. Contact [email protected].

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Demo: How to use in Flair

Requires: Flair (pip install flair)

from flair.data import Sentence
from flair.models import SequenceTagger

# load tagger
tagger = SequenceTagger.load("flair/entity-english-4class")

# make example sentence
sentence = Sentence("Santos star Pelé won the 1970 World Cup in Mexico.")

# predict NER tags
tagger.predict(sentence)

# print sentence
print(sentence)

# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
    print(entity)

This yields the following output:

Span[0:1]: "Santos" → ORG (1.0000)
Span[2:3]: "Pelé" → PER (1.0000)
Span[5:8]: "1970 World Cup" → MISC (1.0000)
Span[9:10]: "Mexico" → LOC (1.0000)

So, the entities "Santos" (team name labeled as a organization), "Pelé" (labeled as a person), "Mexico" (labeled as a location) are found, together with the entity "1970 World Cup", labeled as other (miscellaneous, MISC).

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Cite

Please cite the following paper when using this model.

@inproceedings{akbik2019flair,
    title={{FLAIR}: An easy-to-use framework for state-of-the-art {NLP}},
    author={Akbik, Alan and Bergmann, Tanja and Blythe, Duncan and Rasul, Kashif and Schweter, Stefan and Vollgraf, Roland},
    booktitle={{NAACL} 2019, 2019 Annual Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)},
    pages={54--59},
    year={2019}
}

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License

Model weights: Flukes Noncommercial License 1.0. Personal, academic, and other noncommercial use permitted. Commercial use requires a separate license — contact [email protected].