README.md

metadata

tags:
  - flair
  - token-classification
  - sequence-tagger-model
language: da
datasets:
  - DaNE
widget:
  - text: Jens Peter Hansen kommer fra Danmark

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"   [− Labels: PER (0.9961)]
Span [6]: "Danmark"   [− 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.