English Chunking in Flair (default model)
This is the standard phrase chunking model for English that ships with Flair.
F1-Score: 96,48 (CoNLL-2000)
Predicts 4 tags:
| tag | meaning |
|---|---|
| ADJP | adjectival |
| ADVP | adverbial |
| CONJP | conjunction |
| INTJ | interjection |
| LST | list marker |
| NP | noun phrase |
| PP | prepositional |
| PRT | particle |
| SBAR | subordinate clause |
| VP | verb phrase |
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/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".
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 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)
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.