模型:

flair/upos-multi-fast

英文

Flair中的多语言通用词性标注(快速模型)

这是Flair中附带的快速多语言通用词性标注模型。

F1得分:92.88(覆盖英语、德语、法语、意大利语、荷兰语、波兰语、西班牙语、瑞典语、丹麦语、挪威语、芬兰语和捷克语的12个UD Treebanks)

预测通用词性标签:

tag meaning
ADJ adjective
ADP adposition
ADV adverb
AUX auxiliary
CCONJ coordinating conjunction
DET determiner
INTJ interjection
NOUN noun
NUM numeral
PART particle
PRON pronoun
PROPN proper noun
PUNCT punctuation
SCONJ subordinating conjunction
SYM symbol
VERB verb
X other

基于 Flair embeddings 和LSTM-CRF。

示例: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/upos-multi-fast")

# make example sentence
sentence = Sentence("Ich liebe Berlin, as they say. ")

# 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)

这将产生以下输出:

Span [1]: "Ich"   [− Labels: PRON (0.9999)]
Span [2]: "liebe"   [− Labels: VERB (0.9999)]
Span [3]: "Berlin"   [− Labels: PROPN (0.9997)]
Span [4]: ","   [− Labels: PUNCT (1.0)]
Span [5]: "as"   [− Labels: SCONJ (0.9991)]
Span [6]: "they"   [− Labels: PRON (0.9998)]
Span [7]: "say"   [− Labels: VERB (0.9998)]
Span [8]: "."   [− Labels: PUNCT (1.0)]

因此,在多语言句子“我爱柏林,就像他们说的那样”中,“我”和“they”被标记为代词(PRON),而“liebe”和“say”被标记为动词(VERB)。

训练:训练此模型的脚本

使用以下Flair脚本训练此模型:

from flair.data import MultiCorpus
from flair.datasets import UD_ENGLISH, UD_GERMAN, UD_FRENCH, UD_ITALIAN, UD_POLISH, UD_DUTCH, UD_CZECH, \
    UD_DANISH, UD_SPANISH, UD_SWEDISH, UD_NORWEGIAN, UD_FINNISH
from flair.embeddings import StackedEmbeddings, FlairEmbeddings

# 1. make a multi corpus consisting of 12 UD treebanks (in_memory=False here because this corpus becomes large)
corpus = MultiCorpus([
    UD_ENGLISH(in_memory=False),
    UD_GERMAN(in_memory=False),
    UD_DUTCH(in_memory=False),
    UD_FRENCH(in_memory=False),
    UD_ITALIAN(in_memory=False),
    UD_SPANISH(in_memory=False),
    UD_POLISH(in_memory=False),
    UD_CZECH(in_memory=False),
    UD_DANISH(in_memory=False),
    UD_SWEDISH(in_memory=False),
    UD_NORWEGIAN(in_memory=False),
    UD_FINNISH(in_memory=False),
])

# 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('multi-forward-fast'),

    # contextual string embeddings, backward
    FlairEmbeddings('multi-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,
                        use_crf=False)

# 6. initialize trainer
from flair.trainers import ModelTrainer

trainer = ModelTrainer(tagger, corpus)

# 7. run training
trainer.train('resources/taggers/upos-multi-fast',
              train_with_dev=True,
              max_epochs=150)

引用

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?

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