100 lines
3.3 KiB
Python
100 lines
3.3 KiB
Python
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from flair.data import Sentence
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from flair.datasets import FlairDatapointDataset
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def nolabel2o(line, i):
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return 'O' if line[i] == 'NoLabel' else line[i]
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def conllu2flair(sentences, label=None):
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if label == "frame":
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return conllu2flair_frame(sentences, label)
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else:
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return conllu2flair_slot(sentences, label)
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def conllu2flair_frame(sentences, label=None):
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fsentences = []
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for sentence in sentences:
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tokens = [token["form"] for token in sentence]
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fsentence = Sentence(' '.join(tokens), use_tokenizer=False)
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for i in range(len(fsentence)):
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fsentence[i:i+1].add_label(label, sentence[i][label])
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fsentences.append(fsentence)
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return FlairDatapointDataset(fsentences)
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def conllu2flair_slot(sentences, label=None):
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fsentences = []
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for sentence in sentences:
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fsentence = Sentence(' '.join(token['form'] for token in sentence), use_tokenizer=False)
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start_idx = None
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end_idx = None
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tag = None
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if label:
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for idx, (token, ftoken) in enumerate(zip(sentence, fsentence)):
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if token[label].startswith('B-'):
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start_idx = idx
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end_idx = idx
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tag = token[label][2:]
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elif token[label].startswith('I-'):
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end_idx = idx
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elif token[label] == 'O':
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if start_idx is not None:
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fsentence[start_idx:end_idx+1].add_label(label, tag)
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start_idx = None
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end_idx = None
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tag = None
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if start_idx is not None:
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fsentence[start_idx:end_idx+1].add_label(label, tag)
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fsentences.append(fsentence)
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return FlairDatapointDataset(fsentences)
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def __predict(model, csentence):
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fsentence = conllu2flair([csentence])[0]
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model.predict(fsentence)
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return fsentence
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def __csentence(sentence, label_type):
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if label_type == "frame":
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return [{'form': word } for word in sentence]
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else:
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return [{'form': word, 'slot': 'O'} for word in sentence]
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def predict_single(model, sentence, label_type):
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csentence = __csentence(sentence, label_type)
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fsentence = __predict(model, csentence)
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intent = {}
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for span in fsentence.get_spans(label_type):
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tag = span.get_label(label_type).value
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if tag in intent:
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intent[tag] += 1
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else:
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intent[tag] = 1
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return max(intent, key=intent.get)
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def predict_multiple(model, sentence, label_type):
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csentence = __csentence(sentence, label_type)
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fsentence = __predict(model, csentence)
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return set(span.get_label(label_type).value for span in fsentence.get_spans(label_type))
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def predict_and_annotate(model, sentence, label_type):
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csentence = __csentence(sentence, label_type)
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fsentence = __predict(model, csentence)
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for span in fsentence.get_spans(label_type):
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tag = span.get_label(label_type).value
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if label_type == "frame":
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csentence[span.tokens[0].idx-1]['frame'] = tag
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else:
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csentence[span.tokens[0].idx - 1]['slot'] = f'B-{tag}'
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for token in span.tokens[1:]:
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csentence[token.idx - 1]['slot'] = f'I-{tag}'
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return csentence
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