ner english ontonotes fast EU AI Act Compliance Profile

English NER in Flair (Ontonotes fast model)

This is the fast version of the 18-class NER model for English that ships with Flair.

F1-Score: 89.3 (Ontonotes)

Predicts 18 tags:

| tag | meaning | |---------------------------------|-----------| | CARDINAL | cardinal value | | DATE | date value | | EVENT | event name | | FAC | building name | | GPE | geo-political entity | | LANGUAGE | language name | | LAW | law name | | LOC | location name | | MONEY | money name | | NORP | affiliation | | ORDINAL | ordinal value | | ORG | organization name | | PERCENT | percent value | | PERSON | person name | | PRODUCT | product name | | QUANTITY | quantity value | | TIME | time value | | WORK_OF_ART | name of work of art |

Based on Flair embeddings and LSTM-CRF.

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/ner-english-ontonotes-fast")

# make example sentence
sentence = Sentence("On September 1st George Washington won 1 dollar.")

# 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:

So, the entities "September 1st" (labeled as a date), "George Washington" (labeled as a person) and "*1 dolla

Your obligations as a ner english ontonotes fast deployer

Track your progress

Sign up to track obligations, receive deadline reminders, and generate audit packages.

Tags

Languages

Library