关于

french-camembert-postag-model 是一个法语词性标注模型，基于github上的free-french-treebank数据集训练而成。训练所用的基础分词器和模型为*'camembert-base'*。

支持的标签

该模型使用以下标签：

更多关于标签的信息可在此处查阅：

http://alpage.inria.fr/statgram/frdep/Publications/crabbecandi-taln2008-final.pdf

使用方法

该模型的使用遵循常见的transformers模式。以下是其用法的简短示例：

from transformers import AutoTokenizer, AutoModelForTokenClassification

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

from transformers import pipeline

nlp_token_class = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True)

nlp_token_class('面对前所未有的冲击，政府采取的措施为家庭提供了强有力的有效保护')

上述代码在Jupyter笔记本中会显示类似以下内容：

[{'entity_group': 'NC', 'score': 0.5760144591331482, 'word': '<s>'},
 {'entity_group': 'U', 'score': 0.9946700930595398, 'word': 'Face'},
 {'entity_group': 'P', 'score': 0.999615490436554, 'word': 'à'},
 {'entity_group': 'DET', 'score': 0.9995906352996826, 'word': 'un'},
 {'entity_group': 'NC', 'score': 0.9995531439781189, 'word': 'choc'},
 {'entity_group': 'ADJ', 'score': 0.999183714389801, 'word': 'inédit'},
 {'entity_group': 'P', 'score': 0.3710663616657257, 'word': ','},
 {'entity_group': 'DET', 'score': 0.9995903968811035, 'word': 'les'},
 {'entity_group': 'NC', 'score': 0.9995649456977844, 'word': 'mesures'},
 {'entity_group': 'VPP', 'score': 0.9988670349121094, 'word': 'mises'},
 {'entity_group': 'P', 'score': 0.9996246099472046, 'word': 'en'},
 {'entity_group': 'NC', 'score': 0.9995329976081848, 'word': 'place'},
 {'entity_group': 'P', 'score': 0.9996233582496643, 'word': 'par'},
 {'entity_group': 'DET', 'score': 0.9995935559272766, 'word': 'le'},
 {'entity_group': 'NC', 'score': 0.9995369911193848, 'word': 'gouvernement'},
 {'entity_group': 'V', 'score': 0.9993771314620972, 'word': 'ont'},
 {'entity_group': 'VPP', 'score': 0.9991101026535034, 'word': 'permis'},
 {'entity_group': 'DET', 'score': 0.9995885491371155, 'word': 'une'},
 {'entity_group': 'NC', 'score': 0.9995636343955994, 'word': 'protection'},
 {'entity_group': 'ADJ', 'score': 0.9991781711578369, 'word': 'forte'},
 {'entity_group': 'CC', 'score': 0.9991298317909241, 'word': 'et'},
 {'entity_group': 'ADJ', 'score': 0.9992275238037109, 'word': 'efficace'},
 {'entity_group': 'P+D', 'score': 0.9993300437927246, 'word': 'des'},
 {'entity_group': 'NC', 'score': 0.8353511393070221, 'word': 'ménages</s>'}]