Flair中的英文NER（Ontonotes快速模型）

这是英文的18类NER模型的快速版本，内置于 Flair 中。

F1-Score: 89.3（基于Ontonotes）

预测了18个标签：

基于 Flair embeddings 和LSTM-CRF。

演示：如何在Flair中使用

需要： Flair （pip install flair）

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

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

# make example sentence
sentence = Sentence("On September 1st George Washington won 1 dollar.")

# 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)

这将产生以下输出：

Span [2,3]: "September 1st"   [− Labels: DATE (0.9655)]
Span [4,5]: "George Washington"   [− Labels: PERSON (0.8243)]
Span [7,8]: "1 dollar"   [− Labels: MONEY (0.8022)]

因此，实体“September 1st”（标记为日期）、“George Washington”（标记为人物）和“1美元”（标记为货币）出现在句子“On September 1st George Washington won 1 dollar”中。

训练：用于训练此模型的脚本

使用以下Flair脚本对此模型进行训练：

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 each embedding we use
embedding_types = [

    # GloVe embeddings
    WordEmbeddings('en-crawl'),

    # 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/ner-english-ontonotes-fast',
              train_with_dev=True,
              max_epochs=150)

引用

在使用此模型时，请引用以下论文。

@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}
}

问题？

Flair问题跟踪器可在 here 中找到。