parameters: (input_files, tokenizer, max_seq_length,
                                dupe_factor, short_seq_prob, masked_lm_prob,
                                max_predictions_per_seq, rng, must_mask)
return_statement: return instances
docstring: Create `TrainingInstance`s from raw text.
docstring_summary: Create `TrainingInstance`s from raw text.
docstring_tokens: Create TrainingInstance s from raw text .
function: def create_training_instances(input_files, tokenizer, max_seq_length,
                                dupe_factor, short_seq_prob, masked_lm_prob,
                                max_predictions_per_seq, rng, must_mask):
    """Create `TrainingInstance`s from raw text."""
    all_documents = [[]]
  
    # Input file format:
    # (1) One sentence per line. These should ideally be actual sentences, not
    # entire paragraphs or arbitrary spans of text. (Because we use the
    # sentence boundaries for the "next sentence prediction" task).
    # (2) Blank lines between documents. Document boundaries are needed so
    # that the "next sentence prediction" task doesn't span between documents.
    for input_file in input_files:
      with tf.gfile.GFile(input_file, "r") as reader:
        while True:
          line = tokenization.convert_to_unicode(reader.readline())
          if not line:
            break
          line = line.strip()
  
          # Empty lines are used as document delimiters
          if not line:
            all_documents.append([])
          tokens = tokenizer.tokenize(line)
          if tokens:
            all_documents[-1].append(tokens)
  
    # Remove empty documents
    all_documents = [x for x in all_documents if x]
    rng.shuffle(all_documents)
  
    vocab_words = list(tokenizer.vocab.keys())
    instances = []
    for _ in range(dupe_factor):
      for document_index in range(len(all_documents)):
        instances.extend(
            create_instances_from_document(
                all_documents, document_index, max_seq_length, short_seq_prob,
                masked_lm_prob, max_predictions_per_seq, vocab_words, rng, must_mask))
  
    rng.shuffle(instances)
    return instances
function_tokens: def create_training_instances ( input_files , tokenizer , max_seq_length , dupe_factor , short_seq_prob , masked_lm_prob , max_predictions_per_seq , rng , must_mask ) : all_documents = [ [ ] ] # Input file format: # (1) One sentence per line. These should ideally be actual sentences, not # entire paragraphs or arbitrary spans of text. (Because we use the # sentence boundaries for the "next sentence prediction" task). # (2) Blank lines between documents. Document boundaries are needed so # that the "next sentence prediction" task doesn't span between documents. for input_file in input_files : with tf . gfile . GFile ( input_file , "r" ) as reader : while True : line = tokenization . convert_to_unicode ( reader . readline ( ) ) if not line : break line = line . strip ( ) # Empty lines are used as document delimiters if not line : all_documents . append ( [ ] ) tokens = tokenizer . tokenize ( line ) if tokens : all_documents [ - 1 ] . append ( tokens ) # Remove empty documents all_documents = [ x for x in all_documents if x ] rng . shuffle ( all_documents ) vocab_words = list ( tokenizer . vocab . keys ( ) ) instances = [ ] for _ in range ( dupe_factor ) : for document_index in range ( len ( all_documents ) ) : instances . extend ( create_instances_from_document ( all_documents , document_index , max_seq_length , short_seq_prob , masked_lm_prob , max_predictions_per_seq , vocab_words , rng , must_mask ) ) rng . shuffle ( instances ) return instances