Teacher: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.
Teacher: Now, understand the problem? Solve this instance: pretrained word embeddings (WE): For the PDT experiments, we generate the word embeddings with word2vec on a concatenation of large raw Czech corpora available from the LINDAT/CLARIN repository. For UD Czech, we use FastText word embeddings BIBREF27 of dimension 300, which we pretrain on Czech Wikipedia using segmentation and tokenization trained from the UD data.

BERT BIBREF1: Pretrained contextual word embeddings of dimension 768 from the Base model. We average the last four layers of the BERT model to produce the embeddings. Because BERT utilizes word pieces, we decompose UD words into appropriate subwords and then average the generated embeddings over subwords belonging to the same word.

Flair BIBREF2: Pretrained contextual word embeddings of dimension 4096.
Question: What data is used to build the embeddings?
Student:
large raw Czech corpora available from the LINDAT/CLARIN repository Czech Wikipedia