File size: 4,954 Bytes
20dcf94 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 |
---
language: fr
widget:
- text: "Face à un choc inédit, les mesures mises en place par le gouvernement ont permis une protection forte et efficace des ménages"
---
## 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](https://github.com/nicolashernandez/free-french-treebank). 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:
```python
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>'}]
```
|