English Universal Part-of-Speech Tagging in Flair (default model)
This is the standard universal part-of-speech tagging model for English that ships with Flair.
F1-Score: 98,6 (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
)
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("flair/upos-english")
# 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" [โ Labels: PRON (0.9996)]
Span [2]: "love" [โ Labels: VERB (1.0)]
Span [3]: "Berlin" [โ Labels: PROPN (0.9986)]
Span [4]: "." [โ 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:
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'),
# contextual string embeddings, backward
FlairEmbeddings('news-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/upos-english',
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?
The Flair issue tracker is available here.
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