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52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
import srsly
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from ...gold import docs_to_json
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from ...util import get_lang_class, minibatch
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def ner_jsonl2docs(input_data, lang=None, n_sents=10, use_morphology=False, **_):
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if lang is None:
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raise ValueError("No --lang specified, but tokenization required")
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docs = []
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input_examples = [srsly.json_loads(line) for line in input_data.strip().split("\n")]
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nlp = get_lang_class(lang)()
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sentencizer = nlp.create_pipe("sentencizer")
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for i, batch in enumerate(minibatch(input_examples, size=n_sents)):
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docs = []
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# TODO: Should we be merging these? We're disrespecting the n_sents
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# currently.
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for record in batch:
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raw_text = record["text"]
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if "entities" in record:
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ents = record["entities"]
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else:
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ents = record["spans"]
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ents = [(e["start"], e["end"], e["label"]) for e in ents]
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doc = nlp.make_doc(raw_text)
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sentencizer(doc)
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spans = [doc.char_span(s, e, label=L) for s, e, L in ents]
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doc.ents = _cleanup_spans(spans)
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docs.append(doc)
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return docs
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def _cleanup_spans(spans):
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output = []
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seen = set()
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for span in spans:
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if span is not None:
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# Trim whitespace
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while len(span) and span[0].is_space:
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span = span[1:]
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while len(span) and span[-1].is_space:
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span = span[:-1]
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if not len(span):
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continue
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for i in range(span.start, span.end):
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if i in seen:
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break
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else:
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output.append(span)
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seen.update(range(span.start, span.end))
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return output
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