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1ddf2f39c7
* Switch converters to generator functions To reduce the memory usage when converting large corpora, refactor the convert methods to be generator functions. * Update tests
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
from wasabi import Printer
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from .conll_ner_to_docs import n_sents_info
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from ...vocab import Vocab
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from ...training import iob_to_biluo, tags_to_entities
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from ...tokens import Doc, Span
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from ...errors import Errors
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from ...util import minibatch
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def iob_to_docs(input_data, n_sents=10, no_print=False, *args, **kwargs):
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"""
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Convert IOB files with one sentence per line and tags separated with '|'
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into Doc objects so they can be saved. IOB and IOB2 are accepted.
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Sample formats:
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I|O like|O London|I-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O
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I|O like|O London|B-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O
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I|PRP|O like|VBP|O London|NNP|I-GPE and|CC|O New|NNP|B-GPE York|NNP|I-GPE City|NNP|I-GPE .|.|O
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I|PRP|O like|VBP|O London|NNP|B-GPE and|CC|O New|NNP|B-GPE York|NNP|I-GPE City|NNP|I-GPE .|.|O
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"""
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vocab = Vocab() # need vocab to make a minimal Doc
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msg = Printer(no_print=no_print)
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if n_sents > 0:
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n_sents_info(msg, n_sents)
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yield from read_iob(input_data.split("\n"), vocab, n_sents)
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def read_iob(raw_sents, vocab, n_sents):
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for group in minibatch(raw_sents, size=n_sents):
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tokens = []
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words = []
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tags = []
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iob = []
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sent_starts = []
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for line in group:
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if not line.strip():
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continue
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sent_tokens = [t.split("|") for t in line.split()]
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if len(sent_tokens[0]) == 3:
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sent_words, sent_tags, sent_iob = zip(*sent_tokens)
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elif len(sent_tokens[0]) == 2:
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sent_words, sent_iob = zip(*sent_tokens)
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sent_tags = ["-"] * len(sent_words)
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else:
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raise ValueError(Errors.E902)
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words.extend(sent_words)
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tags.extend(sent_tags)
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iob.extend(sent_iob)
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tokens.extend(sent_tokens)
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sent_starts.append(True)
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sent_starts.extend([False for _ in sent_words[1:]])
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doc = Doc(vocab, words=words)
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for i, tag in enumerate(tags):
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doc[i].tag_ = tag
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for i, sent_start in enumerate(sent_starts):
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doc[i].is_sent_start = sent_start
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biluo = iob_to_biluo(iob)
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entities = tags_to_entities(biluo)
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doc.ents = [Span(doc, start=s, end=e + 1, label=L) for (L, s, e) in entities]
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yield doc
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