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.

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?
unshuffled version of the French OSCAR corpus

The first part is described as Task A, the purpose of which is to identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set. We chose the best 3 results from each word embeddings group (EE, EP, EC) from Table TABREF19 presenting F1-scores for all models. Then we evaluated these results using more detailed measures for timexes, presented in BIBREF27 . 
 Question: What experiments are presented?
identify the boundaries of timexes and assign them to one of the following classes: date, time, duration, set  Then we evaluated these results using more detailed measures for timexes

We evaluate our newly proposed models and related baselines in several low-resource settings across different languages with real, distantly supervised data with non-synthetic noise. The advanced modeling of the noisy labels substantially improves the performance up to 36% over methods without noise-handling and up to 9% over all other noise-handling baselines. 
 Question: Did they evaluate against baseline?

Yes