模型:
flair/ner-english-fast
这是用于英语的快速4类NER模型,附带在 Flair 中。
F1-Score: 92.92(校正的CoNLL-03)
预测4个标签:
tag | meaning |
---|---|
PER | person name |
LOC | location name |
ORG | organization name |
MISC | other name |
基于 Flair embeddings 和LSTM-CRF。
要求: Flair (pip install flair)
from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("flair/ner-english-fast") # make example sentence sentence = Sentence("George Washington went to Washington") # 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 [1,2]: "George Washington" [− Labels: PER (0.9515)] Span [5]: "Washington" [− Labels: LOC (0.992)]
因此,在句子 "George Washington went to Washington" 中找到了实体"George Washington"(标记为person)和"Washington"(标记为location)。
使用以下Flair脚本对该模型进行了训练:
from flair.data import Corpus from flair.datasets import CONLL_03 from flair.embeddings import WordEmbeddings, StackedEmbeddings, FlairEmbeddings # 1. get the corpus corpus: Corpus = CONLL_03() # 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('glove'), # 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', train_with_dev=True, max_epochs=150)
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} }
Flair问题跟踪器可在 here 中找到。