Update camembert-base-README.md (#3836)

model_cards/camembert-base-README.md

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 language: french
 ---
 
-# CamemBERT 
+# CamemBERT: a Tasty French Language Model
 
-CamemBERT is a state-of-the-art language model for French based on the RoBERTa architecture pretrained on the French subcorpus of the newly available multilingual corpus OSCAR.  
+## Introduction
 
-CamemBERT was originally evaluated on four different downstream tasks for French: part-of-speech (POS) tagging, dependency parsing, named entity recognition (NER) and natural language inference (NLI); improving the state of the art for most tasks over previous monolingual and multilingual approaches, which confirms the effectiveness of large pretrained language models for French.   
+[CamemBERT](https://arxiv.org/abs/1911.03894) is a state-of-the-art language model for French based on the RoBERTa architecture. 
 
-CamemBERT was trained and evaluated by Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.  
+It is now available on Hugging Face in 6 different versions varying the number of parameters, the amount of pretraining data and the pretraining data source domains. 
+
+For further information or requests, please go to [Camembert Website](https://camembert-model.fr/)
+
+## Pre-trained models
+
+| Model                          | #params                                                                                                                  | Arch. | Training data                     |
+|--------------------------------|---------|--------------------------------------------------------------------------------------------------------------------------|-------|-----------------------------------|
+| `camembert-base` | 110M   | Base  | OSCAR (138 GB of text)            |
+| `camembert` / `camembert-large`              | 335M    | Large | CCNet (135 GB of text)            |
+| `camembert` / `camembert-base-ccnet`         | 110M    | Base  | CCNet (135 GB of text)            |
+| `camembert` / `camembert-base-wikipedia-4gb` | 110M    | Base  | Wikipedia (4 GB of text)          |
+| `camembert` / `camembert-base-oscar-4gb`     | 110M    | Base  | Subsample of OSCAR (4 GB of text) |
+| `camembert` / `camembert-base-ccnet-4gb`     | 110M    | Base  | Subsample of CCNet (4 GB of text) |
+
+## How to use CamemBERT with HuggingFace
+
+##### Load CamemBERT and its sub-word tokenizer :
+```python
+from transformers import CamembertModel, CamembertTokenizer
+
+tokenizer = CamembertTokenizer.from_pretrained("camembert-base")
+camembert = CamembertModel.from_pretrained("camembert-base")
+
+camembert.eval()  # disable dropout (or leave in train mode to finetune)
+
+```
+
+##### Filling masks using pipeline 
+```python
+from transformers import pipeline 
+
+camembert_fill_mask  = pipeline("fill-mask",model="camembert-base",tokenizer="camembert-base")
+results = camembert_fill_mask("Le camembert est <mask> :)")
+# results
+#[{'sequence': '<s> Le camembert est délicieux :)</s>', 'score': 0.4909103214740753, 'token': 7200},
+# {'sequence': '<s> Le camembert est excellent :)</s>', 'score': 0.10556930303573608, 'token': 2183}, 
+# {'sequence': '<s> Le camembert est succulent :)</s>', 'score': 0.03453315049409866, 'token': 26202}, 
+# {'sequence': '<s> Le camembert est meilleur :)</s>', 'score': 0.03303130343556404, 'token': 528}, 
+# {'sequence': '<s> Le camembert est parfait :)</s>', 'score': 0.030076518654823303, 'token': 1654}]
+
+```
+
+##### Extract contextual embedding features from Camembert output 
+```python
+import torch
+# Tokenize in sub-words with SentencePiece
+tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
+# ['▁J', "'", 'aime', '▁le', '▁ca', 'member', 't', '▁!'] 
+
+# 1-hot encode and add special starting and end tokens 
+encoded_sentence = tokenizer.encode(tokenized_sentence)
+# [5, 121, 11, 660, 16, 730, 25543, 110, 83, 6] 
+# NB: can do in one step : tokenize.encode("J'aime le camembert !")
+
+# Feed to Camembert as a torch tensor (batch dim 1)
+encoded_sentence = torch.tensor(encoded_sentence).unsqueeze(0)
+embeddings, _ = camembert(encoded_sentence)
+# embeddings.detach()
+# embeddings.size torch.Size([1, 10, 768])
+# tensor([[[-0.0254,  0.0235,  0.1027,  ..., -0.1459, -0.0205, -0.0116],
+#         [ 0.0606, -0.1811, -0.0418,  ..., -0.1815,  0.0880, -0.0766],
+#         [-0.1561, -0.1127,  0.2687,  ..., -0.0648,  0.0249,  0.0446],
+#         ...,
+```
+
+##### Extract contextual embedding features from all Camembert layers
+```python
+from transformers import CamembertConfig
+# (Need to reload the model with new config)
+config = CamembertConfig.from_pretrained("camembert-base", output_hidden_states=True)
+camembert = CamembertModel.from_pretrained("camembert-base",config=config)
+
+embeddings, _, all_layer_embeddings = camembert(encoded_sentence)
+#  all_layer_embeddings list of len(all_layer_embeddings) == 13 (input embedding layer + 12 self attention layers)
+all_layer_embeddings[5]
+# layer 5 contextual embedding : size torch.Size([1, 10, 768])
+#tensor([[[-0.0032,  0.0075,  0.0040,  ..., -0.0025, -0.0178, -0.0210],
+#         [-0.0996, -0.1474,  0.1057,  ..., -0.0278,  0.1690, -0.2982],
+#         [ 0.0557, -0.0588,  0.0547,  ..., -0.0726, -0.0867,  0.0699],
+#         ...,
+```
+
+
+## Authors 
+
+CamemBERT was trained and evaluated by Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
+
+
+## Citation
+If you use our work, please cite:
+
+```bibtex
+@inproceedings{martin2020camembert,
+  title={CamemBERT: a Tasty French Language Model},
+  author={Martin, Louis and Muller, Benjamin and Su{\'a}rez, Pedro Javier Ortiz and Dupont, Yoann and Romary, Laurent and de la Clergerie, {\'E}ric Villemonte and Seddah, Djam{\'e} and Sagot, Beno{\^\i}t},
+  booktitle={Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
+  year={2020}
+}
+```
 
-Preprint can be found [CamemBERT: a Tasty French Language Model](https://arxiv.org/abs/1911.03894)