Added first description of the model (#5672)

Added general description, information about the tags and also some example usage code.

model_cards/gilf/french-postag-model/README.md

@@ -0,0 +1,90 @@
+## About
+
+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](https://github.com/nicolashernandez/free-french-treebank). The base tokenizer and model used for training is *'bert-base-multilingual-cased'*.
+
+## 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-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]'}]
+```