ner-english EU AI Act Compliance Profile

English NER in Flair (default model)

This is the standard 4-class NER model for English that ships with Flair.

F1-Score: 93,06 (corrected CoNLL-03)

Predicts 4 tags:

| tag | meaning | |---------------------------------|-----------| | PER | person name | | LOC | location name | | ORG | organization name | | MISC | other name |

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")

# 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:

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".

Training: Script to train this model

The following Flair script was used to train this model:

from flair.data import Corpus
from flair.datasets import CONLL_03
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings

# 1. get the corpus
corpus: Corpus = CONLL_03()

# 2. what tag do we want to predict?
tag_type = 'ner'

# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)

# 4. initialize each embedding we use
embedding_types = [

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