Update README.md

README.md

@@ -13,6 +13,20 @@ CamemBERT is a state-of-the-art language model for French based on the RoBERTa m
 
 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).
 
+## Limitations and bias
+
+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 pretrinaed 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.
+
+## 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.
+
 ## How to use
 
 -**Filling masks using pipeline**
@@ -42,18 +56,4 @@ The model developers evaluated CamemBERT using four different downstream tasks f
   'token': 1654,
   'token_str': 'parfait',
   'sequence': 'Le camembert est parfait :)'}]
-```
-
-## Limitations and bias
-
-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 pretrinaed 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.
-
-## 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.
+```