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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

# 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'):

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:

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(
                column_format={0: "text", 1: "pos", 2: "upos", 3: "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

    # contextual string embeddings, 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,

# 6. initialize trainer
from flair.trainers import ModelTrainer

trainer = ModelTrainer(tagger, corpus)

# 7. run training


Please cite the following paper when using this model.

  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}


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