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flair/
upos-english-fast

Name: upos-english-fast
Availability: InStock

flair

pytorch

token-classification

sequence-tagger-model

ontonotes

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English Universal Part-of-Speech Tagging in Flair (fast model)

This is the fast universal part-of-speech tagging model for English that ships with Flair.

F1-Score: 98,47 (Ontonotes)

Predicts universal POS tags:

tag	meaning
ADJ	adjective
ADP	adposition
ADV	adverb
AUX	auxiliary
CCONJ	coordinating conjunction
DET	determiner
INTJ	interjection
NOUN	noun
NUM	numeral
PART	particle
PRON	pronoun
PROPN	proper noun
PUNCT	punctuation
SCONJ	subordinating conjunction
SYM	symbol
VERB	verb
X	other

Based on Flair embeddings and LSTM-CRF.

Demo: How to use in Flair

Requires: Flair (pip install flair)

python

from flair.data import Sentence
from flair.models import SequenceTagger

# load tagger
tagger = SequenceTagger.load("flair/upos-english-fast")

# make example sentence
sentence = Sentence("I love Berlin.")

# 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('pos'):
    print(entity)

This yields the following output:

Span [1]: "I"   [− Labels: PRON (0.9996)]
Span [2]: "love"   [− Labels: VERB (1.0)]
Span [3]: "Berlin"   [− Labels: PROPN (0.9986)]
Span [4]: "."   [− Labels: PUNCT (1.0)]

So, the word "I" is labeled as a pronoun (PRON), "love" is labeled as a verb (VERB) and "Berlin" is labeled as a proper noun (PROPN) in the sentence "I love Berlin".

Training: Script to train this model

The following Flair script was used to train this model:

python

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

# 1. load the corpus (Ontonotes does not ship with Flair, you need to download and reformat into a column format yourself)
corpus: Corpus = ColumnCorpus(
                "resources/tasks/onto-ner",
                column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"},
                tag_to_bioes="ner",
            )

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

# 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 = [

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

    # contextual string embeddings, backward
    FlairEmbeddings('news-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/upos-english-fast',
              train_with_dev=True,
              max_epochs=150)

Cite

Please cite the following paper when using this model.

@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}
}

Issues?

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