python - 使用 Hugging Face Transformers 库你怎么能 POS_TAG 法语文本

Question

我正在尝试使用 Hugging Face Transformers 库对法语进行 POS_TAG。在英语中，我能够给出一个句子，例如:

The weather is really great. So let us go for a walk.

结果是:

    token   feature
0   The     DET
1   weather NOUN
2   is      AUX
3   really  ADV
4   great   ADJ
5   .       PUNCT
6   So      ADV
7   let     VERB
8   us      PRON
9   go      VERB
10  for     ADP
11  a       DET
12  walk    NOUN
13  .       PUNCT

有没有人知道如何为法语实现类似的事情？

这是我在 Jupyter notebook 中用于英文版的代码:

!git clone https://github.com/bhoov/spacyface.git
!python -m spacy download en_core_web_sm

from transformers import pipeline
import numpy as np
import pandas as pd

nlp = pipeline('feature-extraction')
sequence = "The weather is really great. So let us go for a walk."
result = nlp(sequence)
# Just displays the size of the embeddings. The sequence
# In this case there are 16 tokens and the embedding size is 768
np.array(result).shape

import sys
sys.path.append('spacyface')

from spacyface.aligner import BertAligner

alnr = BertAligner.from_pretrained("bert-base-cased")
tokens = alnr.meta_tokenize(sequence)
token_data = [{'token': tok.token, 'feature': tok.pos} for tok in tokens]
pd.DataFrame(token_data)

这个笔记本的输出如上。

Answer 1

我们最终用 Hugging Face Transformers 训练了词性标注(词性标注)模型图书馆。生成的模型可在此处获得:

https://huggingface.co/gilf/french-postag-model?text=En+Turquie%2C+Recep+Tayyip+Erdogan+ordonne+la+reconversion+de+Sainte-Sophie+en+mosqu%C3%A9e

你基本上可以在上面提到的网页上看到它是如何分配POS标签的。如果您安装了 Hugging Face Transformers 库，您可以使用以下代码在 Jupyter 笔记本中试用它:

from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline

tokenizer = AutoTokenizer.from_pretrained("gilf/french-postag-model")
model = AutoModelForTokenClassification.from_pretrained("gilf/french-postag-model")

nlp_token_class = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True)
nlp_token_class('En Turquie, Recep Tayyip Erdogan ordonne la reconversion de Sainte-Sophie en mosquée')

这是控制台上的结果:

[{'entity_group': 'PONCT', 'score': 0.11994100362062454, 'word': '[CLS]'},
{'entity_group': 'P', 'score': 0.9999570250511169, 'word': 'En'}, 
{'entity_group': 'NPP', 'score': 0.9998692870140076, 'word': 'Turquie'},
{'entity_group': 'PONCT', 'score': 0.9999769330024719, 'word': ','},
{'entity_group': 'NPP',   'score': 0.9996993020176888,  'word': 'Recep Tayyip Erdogan'},
{'entity_group': 'V', 'score': 0.9997997283935547, 'word': 'ordonne'},  
{'entity_group': 'DET', 'score': 0.9999586343765259, 'word': 'la'},
{'entity_group': 'NC', 'score': 0.9999251365661621, 'word': 'reconversion'},  
{'entity_group': 'P', 'score': 0.9999709129333496, 'word': 'de'},
{'entity_group': 'NPP', 'score': 0.9985082149505615, 'word': 'Sainte'},  
{'entity_group': 'PONCT', 'score': 0.9999614357948303, 'word': '-'},
{'entity_group': 'NPP', 'score': 0.9461128115653992, 'word': 'Sophie'},
{'entity_group': 'P', 'score': 0.9999079704284668, 'word': 'en'},
{'entity_group': 'NC', 'score': 0.8998225331306458, 'word': 'mosquée [SEP]'}]