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flair_en_ner-fast
acuvity/flair_en_ner-fast
flair_en_ner-fast is a machine learning model from acuvity. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
This is the fast and small 4-class NER model for English that ships with Flair.
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At a glance
- License
- mit
- Access
- Public
- Created
- Feb 12, 2025
- Updated
- Feb 13, 2025
- SHA
- 878a53bf
Model card
From the Hugging Face model README
English NER in Flair
This is the fast and small 4-class NER model for English that ships with Flair.
| Model | Size | Type | Language | Dataset | F1 Score (%) |
|---|---|---|---|---|---|
| ner-fast | ~ 240 mb | NER (4-class) | English | CoNLL-03 | 92.75 |
Predicts 4 tags:
| tag | meaning |
|---|---|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
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("acuvity/flair_ner-fast")
# make example sentence
sentence = Sentence("George Washington went to Washington")
# 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 [1,2]: "George Washington" [− Labels: PER (1.0)]
Span [5]: "Washington" [− Labels: LOC (1.0)]
So, the entities "George Washington" (labeled as a person) and "Washington" (labeled as a location) are found in the sentence "George Washington went to Washington".
- License
- mit
- Created
- Feb 12, 2025
- Updated
- Feb 13, 2025