mirror of https://github.com/explosion/spaCy.git
51 lines
1.7 KiB
Python
51 lines
1.7 KiB
Python
import srsly
|
|
|
|
from ...gold import docs_to_json
|
|
from ...util import get_lang_class, minibatch
|
|
|
|
|
|
def ner_jsonl2json(input_data, lang=None, n_sents=10, use_morphology=False, **_):
|
|
if lang is None:
|
|
raise ValueError("No --lang specified, but tokenization required")
|
|
json_docs = []
|
|
input_examples = [srsly.json_loads(line) for line in input_data.strip().split("\n")]
|
|
nlp = get_lang_class(lang)()
|
|
sentencizer = nlp.create_pipe("sentencizer")
|
|
for i, batch in enumerate(minibatch(input_examples, size=n_sents)):
|
|
docs = []
|
|
for record in batch:
|
|
raw_text = record["text"]
|
|
if "entities" in record:
|
|
ents = record["entities"]
|
|
else:
|
|
ents = record["spans"]
|
|
ents = [(e["start"], e["end"], e["label"]) for e in ents]
|
|
doc = nlp.make_doc(raw_text)
|
|
sentencizer(doc)
|
|
spans = [doc.char_span(s, e, label=L) for s, e, L in ents]
|
|
doc.ents = _cleanup_spans(spans)
|
|
docs.append(doc)
|
|
json_docs.append(docs_to_json(docs, id=i))
|
|
return json_docs
|
|
|
|
|
|
def _cleanup_spans(spans):
|
|
output = []
|
|
seen = set()
|
|
for span in spans:
|
|
if span is not None:
|
|
# Trim whitespace
|
|
while len(span) and span[0].is_space:
|
|
span = span[1:]
|
|
while len(span) and span[-1].is_space:
|
|
span = span[:-1]
|
|
if not len(span):
|
|
continue
|
|
for i in range(span.start, span.end):
|
|
if i in seen:
|
|
break
|
|
else:
|
|
output.append(span)
|
|
seen.update(range(span.start, span.end))
|
|
return output
|