模型:
flair/ner-english-ontonotes-large
这是用于英语的大型18类NER模型,随附于 Flair 。
F1-Score: 90.93(Ontonotes)
预测18个标签:
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 |
基于文档级别的XLM-R嵌入和 FLERT 。
需求: Flair (pip install flair)
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)
这将产生以下输出:
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)]
因此,在句子“On September 1st George Washington won 1 dollar while watching Game of Thrones”中找到了实体“September 1st”(标记为日期),“George”(标记为人物),“1 dollar”(标记为货币)和“Game of Thrones”(标记为艺术作品)。
使用以下Flair脚本训练了该模型:
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 = '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., )
在使用该模型时,请引用以下论文。
@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} }
Flair问题跟踪器可在 here 中找到。