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--- |
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language: fr |
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widget: |
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- text: "Face à un choc inédit, les mesures mises en place par le gouvernement ont permis une protection forte et efficace des ménages" |
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--- |
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## About |
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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 |
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[github](https://github.com/nicolashernandez/free-french-treebank). The base tokenizer and model used for training is *'camembert-base'*. |
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## Supported Tags |
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It uses the following tags: |
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| Tag | Category | Extra Info | |
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|----------|:------------------------------:|------------:| |
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| ADJ | adjectif | | |
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| ADJWH | adjectif | | |
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| ADV | adverbe | | |
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| ADVWH | adverbe | | |
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| CC | conjonction de coordination | | |
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| CLO | pronom | obj | |
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| CLR | pronom | refl | |
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| CLS | pronom | suj | |
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| CS | conjonction de subordination | | |
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| DET | déterminant | | |
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| DETWH | déterminant | | |
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| ET | mot étranger | | |
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| I | interjection | | |
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| NC | nom commun | | |
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| NPP | nom propre | | |
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| P | préposition | | |
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| P+D | préposition + déterminant | | |
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| PONCT | signe de ponctuation | | |
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| PREF | préfixe | | |
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| PRO | autres pronoms | | |
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| PROREL | autres pronoms | rel | |
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| PROWH | autres pronoms | int | |
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| U | ? | | |
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| V | verbe | | |
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| VIMP | verbe imperatif | | |
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| VINF | verbe infinitif | | |
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| VPP | participe passé | | |
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| VPR | participe présent | | |
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| VS | subjonctif | | |
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More information on the tags can be found here: |
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http://alpage.inria.fr/statgram/frdep/Publications/crabbecandi-taln2008-final.pdf |
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## Usage |
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The usage of this model follows the common transformers patterns. Here is a short example of its usage: |
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```python |
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from transformers import AutoTokenizer, AutoModelForTokenClassification |
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tokenizer = AutoTokenizer.from_pretrained("gilf/french-camembert-postag-model") |
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model = AutoModelForTokenClassification.from_pretrained("gilf/french-camembert-postag-model") |
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from transformers import pipeline |
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nlp_token_class = pipeline('ner', model=model, tokenizer=tokenizer, grouped_entities=True) |
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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') |
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``` |
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The lines above would display something like this on a Jupyter notebook: |
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``` |
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[{'entity_group': 'NC', 'score': 0.5760144591331482, 'word': '<s>'}, |
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{'entity_group': 'U', 'score': 0.9946700930595398, 'word': 'Face'}, |
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{'entity_group': 'P', 'score': 0.999615490436554, 'word': 'à'}, |
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{'entity_group': 'DET', 'score': 0.9995906352996826, 'word': 'un'}, |
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{'entity_group': 'NC', 'score': 0.9995531439781189, 'word': 'choc'}, |
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{'entity_group': 'ADJ', 'score': 0.999183714389801, 'word': 'inédit'}, |
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{'entity_group': 'P', 'score': 0.3710663616657257, 'word': ','}, |
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{'entity_group': 'DET', 'score': 0.9995903968811035, 'word': 'les'}, |
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{'entity_group': 'NC', 'score': 0.9995649456977844, 'word': 'mesures'}, |
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{'entity_group': 'VPP', 'score': 0.9988670349121094, 'word': 'mises'}, |
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{'entity_group': 'P', 'score': 0.9996246099472046, 'word': 'en'}, |
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{'entity_group': 'NC', 'score': 0.9995329976081848, 'word': 'place'}, |
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{'entity_group': 'P', 'score': 0.9996233582496643, 'word': 'par'}, |
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{'entity_group': 'DET', 'score': 0.9995935559272766, 'word': 'le'}, |
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{'entity_group': 'NC', 'score': 0.9995369911193848, 'word': 'gouvernement'}, |
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{'entity_group': 'V', 'score': 0.9993771314620972, 'word': 'ont'}, |
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{'entity_group': 'VPP', 'score': 0.9991101026535034, 'word': 'permis'}, |
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{'entity_group': 'DET', 'score': 0.9995885491371155, 'word': 'une'}, |
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{'entity_group': 'NC', 'score': 0.9995636343955994, 'word': 'protection'}, |
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{'entity_group': 'ADJ', 'score': 0.9991781711578369, 'word': 'forte'}, |
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{'entity_group': 'CC', 'score': 0.9991298317909241, 'word': 'et'}, |
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{'entity_group': 'ADJ', 'score': 0.9992275238037109, 'word': 'efficace'}, |
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{'entity_group': 'P+D', 'score': 0.9993300437927246, 'word': 'des'}, |
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{'entity_group': 'NC', 'score': 0.8353511393070221, 'word': 'ménages</s>'}] |
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``` |
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