In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.
--------
Question: We use the unshuffled version of the French OSCAR corpus, which amounts to 138GB of uncompressed text and 32.7B SentencePiece tokens. 
 Question: What data is used for training CamemBERT?

Answer: unshuffled version of the French OSCAR corpus


Question: The only remaining question is what makes two environments similar enough to infer the existence of a common category. There is, again, a large literature on this question (including the aforementioned language modeling, unsupervised parsing, and alignment work), but in the current work we will make use of a very simple criterion: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same. 
 Question: How do they determine similar environments for fragments in their data augmentation scheme?

Answer: fragments are interchangeable if they occur in at least one lexical environment that is exactly the same


Question: In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated.  Using the word2vec model available in a public repository BIBREF14 , the proposal involves the analysis of the most similar analogies generated before and after the application of the BIBREF3 .  
 Question: What were the word embeddings trained on?

Answer:
large Portuguese corpus