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flair/
ner-english-ontonotes-large

Name: ner-english-ontonotes-large
Availability: InStock

flair

pytorch

token-classification

sequence-tagger-model

ontonotes

2011.06993

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English NER in Flair (Ontonotes large model)

This is the large 18-class NER model for English that ships with Flair.

F1-Score: 90.93 (Ontonotes)

Predicts 18 tags:

tag	meaning
CARDINAL	cardinal value
DATE	date value
EVENT	event name
FAC	building name
GPE	geo-political entity
LANGUAGE	language name
LAW	law name
LOC	location name
MONEY	money name
NORP	affiliation
ORDINAL	ordinal value
ORG	organization name
PERCENT	percent value
PERSON	person name
PRODUCT	product name
QUANTITY	quantity value
TIME	time value
WORK_OF_ART	name of work of art

Based on document-level XLM-R embeddings and FLERT.

Demo: How to use in Flair

Requires: Flair (pip install flair)

python

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

# load tagger
tagger = SequenceTagger.load("flair/ner-english-ontonotes-large")

# make example sentence
sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")

# 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('ner'):
    print(entity)

This yields the following output:

Span [2,3]: "September 1st"   [− Labels: DATE (1.0)]
Span [4]: "George"   [− Labels: PERSON (1.0)]
Span [6,7]: "1 dollar"   [− Labels: MONEY (1.0)]
Span [10,11,12]: "Game of Thrones"   [− Labels: WORK_OF_ART (1.0)]

So, the entities "September 1st" (labeled as a date), "George" (labeled as a person), "1 dollar" (labeled as a money) and "Game of Thrones" (labeled as a work of art) are found in the sentence "On September 1st George Washington won 1 dollar while watching Game of Thrones".

Training: Script to train this model

The following Flair script was used to train this model:

python

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: Corpus = ColumnCorpus(
                "resources/tasks/onto-ner",
                column_format={0: "text", 1: "pos", 2: "upos", 3: "ner"},
                tag_to_bioes="ner",
            )

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

# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)

# 4. initialize fine-tuneable transformer embeddings WITH document context
from flair.embeddings import TransformerWordEmbeddings

embeddings = TransformerWordEmbeddings(
    model='xlm-roberta-large',
    layers="-1",
    subtoken_pooling="first",
    fine_tune=True,
    use_context=True,
)

# 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
from flair.models import SequenceTagger

tagger = SequenceTagger(
    hidden_size=256,
    embeddings=embeddings,
    tag_dictionary=tag_dictionary,
    tag_type='ner',
    use_crf=False,
    use_rnn=False,
    reproject_embeddings=False,
)

# 6. initialize trainer with AdamW optimizer
from flair.trainers import ModelTrainer

trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)

# 7. run training with XLM parameters (20 epochs, small LR)
from torch.optim.lr_scheduler import OneCycleLR

trainer.train('resources/taggers/ner-english-ontonotes-large',
              learning_rate=5.0e-6,
              mini_batch_size=4,
              mini_batch_chunk_size=1,
              max_epochs=20,
              scheduler=OneCycleLR,
              embeddings_storage_mode='none',
              weight_decay=0.,
              )

Cite

Please cite the following paper when using this model.

@misc{schweter2020flert,
    title={FLERT: Document-Level Features for Named Entity Recognition},
    author={Stefan Schweter and Alan Akbik},
    year={2020},
    eprint={2011.06993},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}

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