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gilf/french-camembert-postag-model gilf/french-camembert-postag-model
383 downloads
last 30 days

pytorch

tf

Contributed by

gilf Gil Fernandes
3 models

How to use this model directly from the 🤗/transformers library:

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

About

The french-camembert-postag-model is a part of speech tagging model for French that was trained on the free-french-treebank dataset available on github. The base tokenizer and model used for training is 'camembert-base'.

Supported Tags

It uses the following tags:

Tag Category Extra Info
ADJ adjectif
ADJWH adjectif
ADV adverbe
ADVWH adverbe
CC conjonction de coordination
CLO pronom obj
CLR pronom refl
CLS pronom suj
CS conjonction de subordination
DET déterminant
DETWH déterminant
ET mot étranger
I interjection
NC nom commun
NPP nom propre
P préposition
P+D préposition + déterminant
PONCT signe de ponctuation
PREF préfixe
PRO autres pronoms
PROREL autres pronoms rel
PROWH autres pronoms int
U ?
V verbe
VIMP verbe imperatif
VINF verbe infinitif
VPP participe passé
VPR participe présent
VS subjonctif

More information on the tags can be found here:

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

Usage

The usage of this model follows the common transformers patterns. Here is a short example of its usage:

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('Face à un choc inédit, les mesures mises en place par le gouvernement ont permis une protection forte et efficace des ménages')

The lines above would display something like this on a Jupyter notebook:

[{'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>'}]