模型:
flair/upos-english
这是随 Flair 一起提供的英语标准通用词性标注模型。
F1 分数:98.6(Ontonotes)
预测通用词性标签:
pronoun、verb 和 proper noun基于 以及 LSTM-CRF。
需要:通过 pip install flair 安装 Flair
from flair.data import Sentence from flair.models import SequenceTagger # load tagger tagger = SequenceTagger.load("flair/upos-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)
这会产生以下输出:
Span [1]: "I" [− Labels: PRON (0.9996)] Span [2]: "love" [− Labels: VERB (1.0)] Span [3]: "Berlin" [− Labels: PROPN (0.9986)] Span [4]: "." [− Labels: PUNCT (1.0)]
因此,在句子"I love Berlin"中,单词"I"被标记为代词(PRON),"love"被标记为动词(VERB),"Berlin"被标记为专有名词(PROPN)。
使用以下的 Flair 脚本来训练此模型:
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 = 'upos' # 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/upos-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 问题跟踪器可在此处找到 here 。