Chunk English Fast

🚀 English Chunking in Flair (fast model)

This is a fast phrase chunking model for English that comes with Flair. It can effectively perform phrase chunking tasks in English, providing high - quality tagging results.

F1 - Score: 96,22 (CoNLL - 2000)

✨ Features

This model predicts 4 tags:

It is based on Flair embeddings and LSTM - CRF.

🚀 Quick Start

📦 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/chunk-english-fast")

# make example sentence
sentence = Sentence("The happy man has been eating at the diner")

# 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('np'):
    print(entity)

This yields the following output:

Span [1,2,3]: "The happy man"   [− Labels: NP (0.9958)]
Span [4,5,6]: "has been eating"   [− Labels: VP (0.8759)]
Span [7]: "at"   [− Labels: PP (1.0)]
Span [8,9]: "the diner"   [− Labels: NP (0.9991)]

So, the spans "The happy man" and "the diner" are labeled as noun phrases (NP) and "has been eating" is labeled as a verb phrase (VP) in the sentence "The happy man has been eating at the diner".

🔧 Technical Details

The following Flair script was used to train this model:

from flair.data import Corpus
from flair.datasets import CONLL_2000
from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings

# 1. get the corpus
corpus: Corpus = CONLL_2000()

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

# 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/chunk-english-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.