Upos Multi Fast

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

This is a fast multilingual universal part-of-speech tagging model provided by Flair. It can effectively handle part-of-speech tagging tasks across multiple languages.

Key Information

• Supported Languages: English, German, French, Italian, Dutch, Polish, Spanish, Swedish, Danish, Norwegian, Finnish and Czech.
• F1-Score: 92.88 (evaluated on 12 UD Treebanks covering the above languages)
• Based on: Flair embeddings and LSTM-CRF

✨ Features

• Multilingual Support: Capable of tagging multiple languages, making it highly versatile.
• Fast Performance: Optimized for speed, suitable for real - time applications.
• High Accuracy: Achieves an F1 - Score of 92.88 on relevant datasets.

Predicted Universal POS Tags

📦 Installation

Requires: Flair (pip install flair)

💻 Usage Examples

Basic Usage

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

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

# make example sentence
sentence = Sentence("Ich liebe Berlin, as they say. ")

# 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)

The above code demonstrates how to use the model to perform part - of - speech tagging on a given sentence.

Training Script

The following script shows how to train the model:

from flair.data import MultiCorpus
from flair.datasets import UD_ENGLISH, UD_GERMAN, UD_FRENCH, UD_ITALIAN, UD_POLISH, UD_DUTCH, UD_CZECH, \
    UD_DANISH, UD_SPANISH, UD_SWEDISH, UD_NORWEGIAN, UD_FINNISH
from flair.embeddings import StackedEmbeddings, FlairEmbeddings

# 1. make a multi corpus consisting of 12 UD treebanks (in_memory=False here because this corpus becomes large)
corpus = MultiCorpus([
    UD_ENGLISH(in_memory=False),
    UD_GERMAN(in_memory=False),
    UD_DUTCH(in_memory=False),
    UD_FRENCH(in_memory=False),
    UD_ITALIAN(in_memory=False),
    UD_SPANISH(in_memory=False),
    UD_POLISH(in_memory=False),
    UD_CZECH(in_memory=False),
    UD_DANISH(in_memory=False),
    UD_SWEDISH(in_memory=False),
    UD_NORWEGIAN(in_memory=False),
    UD_FINNISH(in_memory=False),
])

# 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('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,
                        use_crf=False)

# 6. initialize trainer
from flair.trainers import ModelTrainer

trainer = ModelTrainer(tagger, corpus)

# 7. run training
trainer.train('resources/taggers/upos-multi-fast',
              train_with_dev=True,
              max_epochs=150)

📚 Documentation

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.