ner multi EU AI Act Compliance Profile

4-Language NER in Flair (English, German, Dutch and Spanish)

This is the standard 4-class NER model for 4 CoNLL-03 languages that ships with Flair. Also kind of works for related languages like French.

F1-Score: 92,16 (CoNLL-03 English), 87,33 (CoNLL-03 German revised), 88,96 (CoNLL-03 Dutch), 86,65 (CoNLL-03 Spanish)

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-multi")

# make example sentence in any of the four languages
sentence = Sentence("George Washington ging nach 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 ging nach 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, CONLL_03_GERMAN, CONLL_03_DUTCH, CONLL_03_SPANISH
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings

# 1.

Your obligations as a ner multi deployer

Track your progress

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

Tags

Languages

Library