High-Performance Open-Source Archive
spacy_install() and
spacy_uninstall() functions to provide a more modern and
robust installation procedure.pip in spacy_install() and
spacy_upgrade().spacy_parse() and
spacy_tokenize() are now coerced to character,
which allows them to work directly with quanteda v2
corpus objects (which are based on the character class but
with special attributed), without special methods.spacy_parse(x, nounphrase = TRUE) that
occurred when no noun phrases were found in the text. (#153)spacy_initialize() (#157).
spacy_initialize() ignored Python options supplied by users
when a conda environment created through spacy_install()
exists.spacy_tokenize(),
spacy_extract_entity(),
spacy_extract_nounphrases(),
nounphrase_extract(), and
nounphrase_consolidate() for direct extraction of entities,
noun phrases, and tokens, and extraction of noun phrases from spacyr
parsed tests.additional_attributes to
spacy_parse() allowing the return of any tokens-level
attribute available from https://spacy.io/api/token#attributes.spacy_install(),
spacy_install_virtualenv(), and
spacy_upgrade() to make installing or upgrading spaCy (and
Python itself) easy and automatic.spacy_parse() via
the multithreading argument. This uses the “pipes”
functionality in spaCy for improved performance..Rprofilespacy_initialize(entity = FALSE) (#91).bash_profile.ask = FALSE to spacy_initialize(), to
find spaCy installations automatically.spacy_parse(), by changing 1:length() to
seq_along().
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