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			69 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			69 lines
		
	
	
		
			2.3 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
| # coding: utf8
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| from __future__ import unicode_literals
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| 
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| from wasabi import Printer
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| 
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| from ...gold import iob_to_biluo
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| from ...util import minibatch
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| from .conll_ner2json import n_sents_info
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| 
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| 
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| def iob2json(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 JSON format for use with train cli. IOB and IOB2 are accepted.
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| 
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|     Sample formats:
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| 
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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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|     msg = Printer(no_print=no_print)
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|     docs = read_iob(input_data.split("\n"))
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|     if n_sents > 0:
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|         n_sents_info(msg, n_sents)
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|         docs = merge_sentences(docs, n_sents)
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|     return docs
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| 
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| 
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| def read_iob(raw_sents):
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|     sentences = []
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|     for line in raw_sents:
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|         if not line.strip():
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|             continue
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|         tokens = [t.split("|") for t in line.split()]
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|         if len(tokens[0]) == 3:
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|             words, pos, iob = zip(*tokens)
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|         elif len(tokens[0]) == 2:
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|             words, iob = zip(*tokens)
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|             pos = ["-"] * len(words)
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|         else:
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|             raise ValueError(
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|                 "The sentence-per-line IOB/IOB2 file is not formatted correctly. Try checking whitespace and delimiters. See https://spacy.io/api/cli#convert"
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|             )
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|         biluo = iob_to_biluo(iob)
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|         sentences.append(
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|             [
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|                 {"orth": w, "tag": p, "ner": ent}
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|                 for (w, p, ent) in zip(words, pos, biluo)
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|             ]
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|         )
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|     sentences = [{"tokens": sent} for sent in sentences]
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|     paragraphs = [{"sentences": [sent]} for sent in sentences]
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|     docs = [{"id": i, "paragraphs": [para]} for i, para in enumerate(paragraphs)]
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|     return docs
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| 
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| 
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| def merge_sentences(docs, n_sents):
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|     merged = []
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|     for group in minibatch(docs, size=n_sents):
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|         group = list(group)
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|         first = group.pop(0)
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|         to_extend = first["paragraphs"][0]["sentences"]
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|         for sent in group:
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|             to_extend.extend(sent["paragraphs"][0]["sentences"])
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|         merged.append(first)
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|     return merged
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