(input_files, tokenizer, max_seq_length,
                              dupe_factor, short_seq_prob, masked_lm_prob,
                              max_predictions_per_seq, rng)
return instances
Create `TrainingInstance`s from raw text.
Create `TrainingInstance`s from raw text.
Create TrainingInstance s from raw text .
def create_training_instances(input_files, tokenizer, max_seq_length,
                              dupe_factor, short_seq_prob, masked_lm_prob,
                              max_predictions_per_seq, rng):
    """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.
    print("create_training_instances.started...")
    for input_file in input_files:
        with tf.gfile.GFile(input_file, "r") as reader:
            while True:
                line = tokenization.convert_to_unicode(reader.readline().replace("<eop>",""))# .replace("”","")) # 将<eop>、”替换掉。
                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))

    rng.shuffle(instances)
    print("create_training_instances.ended...")

    return instances
def create_training_instances ( input_files , tokenizer , max_seq_length , dupe_factor , short_seq_prob , masked_lm_prob , max_predictions_per_seq , rng ) : 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. print ( "create_training_instances.started..." ) for input_file in input_files : with tf . gfile . GFile ( input_file , "r" ) as reader : while True : line = tokenization . convert_to_unicode ( reader . readline ( ) . replace ( "<eop>" , "" ) ) # .replace("”","")) # 将<eop>、”替换掉。 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 ) ) rng . shuffle ( instances ) print ( "create_training_instances.ended..." ) return instances