Chunk English

🚀 English Chunking in Flair (default model)

This is a standard English phrase chunking model provided by Flair, which can effectively identify and classify different types of phrases in English text.

✨ Features

• High Accuracy: Achieves an F1-Score of 96.48 on the CoNLL-2000 dataset.
• Rich Tag Prediction: Predicts 4 tags, including ADJP, ADVP, CONJP, etc., covering a wide range of phrase types.
• Advanced Technology: Based on Flair embeddings and LSTM - CRF technology.

📦 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")

# 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 model is based on Flair embeddings and LSTM - CRF. 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'),

    # 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/chunk-english',
              train_with_dev=True,
              max_epochs=150)

📄 License

No license information provided in the original document.

📚 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.