metadata
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- ontonotes
inference: false
English Verb Disambiguation in Flair (default model)
This is the standard verb disambiguation model for English that ships with Flair.
F1-Score: 89,34 (Ontonotes) - predicts Proposition Bank verb frames.
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/pos-english")
# make example sentence
sentence = Sentence("I love Berlin.")
# 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)
This yields the following output:
Span [1]: "I" [− Labels: PRP (1.0)]
Span [2]: "love" [− Labels: VBP (1.0)]
Span [3]: "Berlin" [− Labels: NNP (0.9999)]
Span [4]: "." [− Labels: . (1.0)]
So, the word "I" is labeled as a pronoun (PRP), "love" is labeled as a verb (VBP) and "Berlin" is labeled as a proper noun (NNP) in the sentence "TheI 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 = ColumnCorpus(
"resources/tasks/srl", column_format={1: "text", 11: "frame"}
)
# 2. what tag do we want to predict?
tag_type = 'frame'
# 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 = [
BytePairEmbeddings("en"),
FlairEmbeddings("news-forward-fast"),
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/frame-english',
train_with_dev=True,
max_epochs=150)
Cite
Please cite the following paper when using this model.
@inproceedings{akbik2019flair,
title={FLAIR: An easy-to-use framework for state-of-the-art NLP},
author={Akbik, Alan and Bergmann, Tanja and Blythe, Duncan and Rasul, Kashif and Schweter, Stefan and Vollgraf, Roland},
booktitle={{NAACL} 2019, 2019 Conference of the North American Chapter of the Association for Computational Linguistics (Demonstrations)},
pages={54--59},
year={2019}
}
Issues?
The Flair issue tracker is available here.