Update README.md

README.md

@@ -137,15 +137,6 @@ text = "Pierre retrouva sa femme au restaurant. <Le couple> dina jusqu'à tard d
 
 
 ## Risks, Limitations and Biases
-**CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.**
-
-Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)).
-
-This model was pretrained on a subcorpus of OSCAR multilingual corpus. Some of the limitations and risks associated with the OSCAR dataset, which are further detailed in the [OSCAR dataset card](https://huggingface.co/datasets/oscar), include the following: 
-
-> The quality of some OSCAR sub-corpora might be lower than expected, specifically for the lowest-resource languages.
-
-> Constructed from Common Crawl, Personal and sensitive information might be present.
 
 
 
@@ -153,103 +144,19 @@ This model was pretrained on a subcorpus of OSCAR multilingual corpus. Some of t
 
 
 #### Training Data
-OSCAR or Open Super-large Crawled Aggregated coRpus is a multilingual corpus obtained by language classification and filtering of the Common Crawl corpus using the Ungoliant architecture.
 
 
-#### Training Procedure
 
-| 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) |
+#### Training Procedure
 
-## Evaluation
 
 
-The model developers evaluated CamemBERT using four different downstream tasks for French: part-of-speech (POS) tagging, dependency parsing, named entity recognition (NER) and natural language inference (NLI).
+## Evaluation
 
 
 
 ## Citation Information
 
-```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}
-}
-```
 
 ## How to Get Started With the Model
 
-##### Load CamemBERT and its sub-word tokenizer :
-```python
-from transformers import CamembertModel, CamembertTokenizer
-
-# You can replace "camembert-base" with any other model from the table, e.g. "camembert/camembert-large".
-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 be done in one step : tokenize.encode("J'aime le camembert !")
-
-# Feed tokens 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],
-#         ...,
-```