revert back of grammarly fix on readme.md (#6)

- revert back of grammarly fix on readme.md (53848133d24d0e7dad5de55df42ac06dc3e15211)


Co-authored-by: Rohit Kumar <[email protected]>

README.md

@@ -10,7 +10,7 @@ datasets:
 ## Table of Contents
 - [Model Details](#model-details)
 - [Uses](#uses)
-- [Risks, Limitations, and Biases](#risks-limitations-and-biases)
+- [Risks, Limitations and Biases](#risks-limitations-and-biases)
 - [Training](#training)
 - [Evaluation](#evaluation)
 - [Citation Information](#citation-information)
@@ -20,7 +20,7 @@ datasets:
 ## Model Details
 - **Model Description:**
 CamemBERT is a state-of-the-art language model for French based on the RoBERTa model.
-It is now available on Hugging Face in 6 different versions with varying numbers of parameters, amount of pretraining data, and pretraining data source domains.
+It is now available on Hugging Face in 6 different versions with varying number of parameters, amount of pretraining data and pretraining data source domains.
 - **Developed by:**  Louis Martin\*, Benjamin Muller\*, Pedro Javier Ortiz Suárez\*, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah and Benoît Sagot.
 - **Model Type:** Fill-Mask
 - **Language(s):** French
@@ -38,7 +38,7 @@ It is now available on Hugging Face in 6 different versions with varying numbers
 This model can be used for Fill-Mask tasks.
 
 
-## Risks, Limitations, and Biases
+## 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)).
@@ -72,7 +72,7 @@ OSCAR or Open Super-large Crawled Aggregated coRpus is a multilingual corpus obt
 ## 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).
+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).
 
 
 
@@ -81,7 +81,7 @@ The model developers evaluated CamemBERT using four different downstream tasks f
 ```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},
+  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}
 }
@@ -126,7 +126,7 @@ tokenized_sentence = tokenizer.tokenize("J'aime le camembert !")
 # 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 !")
+# 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)
@@ -155,3 +155,4 @@ all_layer_embeddings[5]
 #         [ 0.0557, -0.0588,  0.0547,  ..., -0.0726, -0.0867,  0.0699],
 #         ...,
 ```
+