Danish NER in Flair (default model)
This is the standard 4-class NER model for Danish that ships with Flair.
F1-Score: 81.78 (DaNER)
Predicts 4 tags:
tag | meaning |
---|---|
PER | person name |
LOC | location name |
ORG | organization name |
MISC | other name |
Based on Transformer 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-danish")
# make example sentence
sentence = Sentence("Jens Peter Hansen kommer fra Danmark")
# 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 [1,2,3]: "Jens Peter Hansen" [โ Labels: PER (0.9961)]
Span [6]: "Danmark" [โ Labels: LOC (0.9816)]
So, the entities "Jens Peter Hansen" (labeled as a person) and "Danmark" (labeled as a location) are found in the sentence "Jens Peter Hansen kommer fra Danmark".
Training: Script to train this model
The model was trained by the DaNLP project using the DaNE corpus. Check their repo for more information.
The following Flair script may be used to train such a model:
from flair.data import Corpus
from flair.datasets import DANE
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings
# 1. get the corpus
corpus: Corpus = DANE()
# 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 = [
# GloVe embeddings
WordEmbeddings('da'),
# contextual string embeddings, forward
FlairEmbeddings('da-forward'),
# contextual string embeddings, backward
FlairEmbeddings('da-backward'),
]
# embedding stack consists of Flair and GloVe embeddings
embeddings = StackedEmbeddings(embeddings=embedding_types)
# 5. initialize sequence tagger
from flair.models import SequenceTagger
tagger = SequenceTagger(hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type=tag_type)
# 6. initialize trainer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus)
# 7. run training
trainer.train('resources/taggers/ner-danish',
train_with_dev=True,
max_epochs=150)
Cite
Please cite the following papers when using this model.
@inproceedings{akbik-etal-2019-flair,
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 = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics (Demonstrations)",
year = "2019",
url = "https://www.aclweb.org/anthology/N19-4010",
pages = "54--59",
}
And check the DaNLP project for more information.
Issues?
The Flair issue tracker is available here.
- Downloads last month
- 92
This model does not have enough activity to be deployed to Inference API (serverless) yet. Increase its social
visibility and check back later, or deploy to Inference Endpoints (dedicated)
instead.