README.md

metadata

tags:
  - flair
  - token-classification
  - sequence-tagger-model
language:
  - en
  - de
  - nl
  - es
datasets:
  - conll2003
widget:
  - text: George Washington ging nach Washington

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

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

F1-Score: 91,51 (CoNLL-03 English), 85,72 (CoNLL-03 German revised), 86,22 (CoNLL-03 Dutch), 85,78 (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-fast")

# 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:

Span [1,2]: "George Washington"   [− Labels: PER (0.9977)]
Span [5]: "Washington"   [− Labels: LOC (0.9895)]

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. get the multi-language corpus
corpus: Corpus = MultiCorpus([
    CONLL_03(),         # English corpus
    CONLL_03_GERMAN(),  # German corpus
    CONLL_03_DUTCH(),   # Dutch corpus
    CONLL_03_SPANISH(), # Spanish corpus
    ])

# 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('glove'),

    # FastText embeddings
    WordEmbeddings('de'),

    # contextual string embeddings, forward
    FlairEmbeddings('multi-forward-fast'),

    # contextual string embeddings, backward
    FlairEmbeddings('multi-backward-fast'),
]

# 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-multi-fast',
              train_with_dev=True,
              max_epochs=150)

---

Cite

Please cite the following papers when using this model.

@misc{akbik2019multilingual,
  title={Multilingual sequence labeling with one model},
  author={Akbik, Alan and Bergmann, Tanja and Vollgraf, Roland}
  booktitle = {{NLDL} 2019, Northern Lights Deep Learning Workshop},
  year      = {2019}
}

@inproceedings{akbik2018coling,
  title={Contextual String Embeddings for Sequence Labeling},
  author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
  booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
  pages     = {1638--1649},
  year      = {2018}
}