Datasets:
jblitzar
/
github-python-corpus

Dataset card Data Studio Files
xet
Community
text
stringlengths
1
93.6k
return None
def kindle_find_lemma(
doc,
lemma_matcher,
start,
mobi_codec,
escaped_text,
lemmas_conn,
ll_conn,
lemma_lang,
prefs,
):
from spacy.util import filter_spans
lemma_starts: set[int] = set()
for span in filter_spans(lemma_matcher(doc, as_spans=True)):
data = get_kindle_lemma_data(
getattr(span, "lemma_", ""),
span.text,
getattr(span.doc[span.start], "pos_", ""),
lemmas_conn,
lemma_lang,
prefs,
)
if data is not None:
kindle_add_lemma(
span.start_char,
span.end_char,
start,
doc.text,
ll_conn,
mobi_codec,
escaped_text,
lemma_starts,
data,
)
def epub_find_lemma(
doc,
lemma_matcher,
paragraph_start,
paragraph_end,
interval_tree,
epub,
xhtml_path,
):
from spacy.util import filter_spans
for span in filter_spans(lemma_matcher(doc, as_spans=True)):
if interval_tree is not None and interval_tree.is_overlap(
Interval(span.start_char, span.end_char - 1)
):
return
pos = getattr(span.doc[span.start], "pos_", "")
epub.add_lemma(
getattr(span, "lemma_", ""),
span.text,
spacy_to_wiktionary_pos(pos) if pos != "" else "",
paragraph_start,
paragraph_end,
span.start_char,
span.end_char,
xhtml_path,
)
def spacy_to_kindle_pos(pos: str) -> str:
# spaCy POS: https://universaldependencies.org/u/pos
match pos:
case "NOUN":
return "noun"
case "VERB":
return "verb"
case "ADJ":
return "adjective"
case "ADV":
return "adverb"
case "CCONJ" | "SCONJ":
return "conjunction"
case "ADP":
return "preposition"
case "PRON":
return "pronoun"
case _:
return "other"
def get_kindle_lemma_data(
lemma: str,
word: str,
pos: str,
conn: sqlite3.Connection,
lemma_lang: str,
prefs: Prefs,
) -> tuple[int, int] | None:
if pos != "":
return get_kindle_lemma_with_pos(lemma, word, pos, conn, lemma_lang, prefs)