token-classification
mask_token: 
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gilf/french-postag-model
pytorch
tf
How to use this model directly from the
🤗/transformers library:
from transformers import AutoTokenizer, AutoModelForTokenClassification tokenizer = AutoTokenizer.from_pretrained("gilf/french-postag-model") model = AutoModelForTokenClassification.from_pretrained("gilf/french-postag-model")
Update on GitHub
The french-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 'bert-base-multilingual-cased'.
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
The usage of this model follows the common transformers patterns. Here is a short example of its usage:
API endpoint
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("gilf/french-postag-model")
model = AutoModelForTokenClassification.from_pretrained("gilf/french-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': 'PONCT', 'score': 0.0742340236902237, 'word': '[CLS]'},
{'entity_group': 'U', 'score': 0.9995399713516235, 'word': 'Face'},
{'entity_group': 'P', 'score': 0.9999609589576721, 'word': 'à'},
{'entity_group': 'DET', 'score': 0.9999597072601318, 'word': 'un'},
{'entity_group': 'NC', 'score': 0.9998948276042938, 'word': 'choc'},
{'entity_group': 'ADJ', 'score': 0.995318204164505, 'word': 'inédit'},
{'entity_group': 'PONCT', 'score': 0.9999793171882629, 'word': ','},
{'entity_group': 'DET', 'score': 0.999964714050293, 'word': 'les'},
{'entity_group': 'NC', 'score': 0.999936580657959, 'word': 'mesures'},
{'entity_group': 'VPP', 'score': 0.9995776414871216, 'word': 'mises'},
{'entity_group': 'P', 'score': 0.99996417760849, 'word': 'en'},
{'entity_group': 'NC', 'score': 0.999882161617279, 'word': 'place'},
{'entity_group': 'P', 'score': 0.9999671578407288, 'word': 'par'},
{'entity_group': 'DET', 'score': 0.9999637603759766, 'word': 'le'},
{'entity_group': 'NC', 'score': 0.9999350309371948, 'word': 'gouvernement'},
{'entity_group': 'V', 'score': 0.9999298453330994, 'word': 'ont'},
{'entity_group': 'VPP', 'score': 0.9998740553855896, 'word': 'permis'},
{'entity_group': 'DET', 'score': 0.9999625086784363, 'word': 'une'},
{'entity_group': 'NC', 'score': 0.9999420046806335, 'word': 'protection'},
{'entity_group': 'ADJ', 'score': 0.9998913407325745, 'word': 'forte'},
{'entity_group': 'CC', 'score': 0.9998615980148315, 'word': 'et'},
{'entity_group': 'ADJ', 'score': 0.9998483657836914, 'word': 'efficace'},
{'entity_group': 'P+D', 'score': 0.9987645149230957, 'word': 'des'},
{'entity_group': 'NC', 'score': 0.8720395267009735, 'word': 'ménages [SEP]'}]