  Contextualized embeddings, which capture appropriate word meaning depending
on context, have recently been proposed. We evaluate two meth ods for
precomputing such embeddings, BERT and Flair, on four Czech text processing
tasks: part-of-speech (POS) tagging, lemmatization, dependency pars ing and
named entity recognition (NER). The first three tasks, POS tagging,
lemmatization and dependency parsing, are evaluated on two corpora: the Prague
Dependency Treebank 3.5 and the Universal Dependencies 2.3. The named entity
recognition (NER) is evaluated on the Czech Named Entity Corpus 1.1 and 2.0. We
report state-of-the-art results for the above mentioned tasks and corpora.
