from wasabi import Printer from .conll_ner_to_docs import n_sents_info from ...vocab import Vocab from ...training import iob_to_biluo, tags_to_entities from ...tokens import Doc, Span from ...errors import Errors from ...util import minibatch def iob_to_docs(input_data, n_sents=10, no_print=False, *args, **kwargs): """ Convert IOB files with one sentence per line and tags separated with '|' into Doc objects so they can be saved. IOB and IOB2 are accepted. Sample formats: I|O like|O London|I-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O I|O like|O London|B-GPE and|O New|B-GPE York|I-GPE City|I-GPE .|O 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 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 """ vocab = Vocab() # need vocab to make a minimal Doc msg = Printer(no_print=no_print) if n_sents > 0: n_sents_info(msg, n_sents) docs = read_iob(input_data.split("\n"), vocab, n_sents) return docs def read_iob(raw_sents, vocab, n_sents): docs = [] for group in minibatch(raw_sents, size=n_sents): tokens = [] words = [] tags = [] iob = [] sent_starts = [] for line in group: if not line.strip(): continue sent_tokens = [t.split("|") for t in line.split()] if len(sent_tokens[0]) == 3: sent_words, sent_tags, sent_iob = zip(*sent_tokens) elif len(sent_tokens[0]) == 2: sent_words, sent_iob = zip(*sent_tokens) sent_tags = ["-"] * len(sent_words) else: raise ValueError(Errors.E092) words.extend(sent_words) tags.extend(sent_tags) iob.extend(sent_iob) tokens.extend(sent_tokens) sent_starts.append(True) sent_starts.extend([False for _ in sent_words[1:]]) doc = Doc(vocab, words=words) for i, tag in enumerate(tags): doc[i].tag_ = tag for i, sent_start in enumerate(sent_starts): doc[i].is_sent_start = sent_start biluo = iob_to_biluo(iob) entities = tags_to_entities(biluo) doc.ents = [Span(doc, start=s, end=e + 1, label=L) for (L, s, e) in entities] docs.append(doc) return docs