run_ner.py
def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer, mode="normal"):
    """Convert a set of `InputExample`s to a list of `InputFeatures`."""

    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))

        feature = convert_single_example(ex_index, example, label_list,
                                         max_seq_length, tokenizer, mode=mode)

        features.append(feature)
    return features
Convert a set of `InputExample`s to a list of `InputFeatures`.
Convert a set of `InputExample`s to a list of `InputFeatures`.
Convert a set of ` InputExample ` s to a list of ` InputFeatures ` .
def convert_examples_to_features(examples, label_list, max_seq_length,
                                 tokenizer, mode="normal"):
    features = []
    for (ex_index, example) in enumerate(examples):
        if ex_index % 10000 == 0:
            tf.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
        feature = convert_single_example(ex_index, example, label_list,
                                         max_seq_length, tokenizer, mode=mode)
        features.append(feature)
    return features
def convert_examples_to_features ( examples , label_list , max_seq_length , tokenizer , mode = "normal" ) : features = [ ] for ( ex_index , example ) in enumerate ( examples ) : if ex_index % 10000 == 0 : tf . logging . info ( "Writing example %d of %d" % ( ex_index , len ( examples ) ) ) feature = convert_single_example ( ex_index , example , label_list , max_seq_length , tokenizer , mode = mode ) features . append ( feature ) return features
Convert a set of `InputExample`s to a list of `InputFeatures`.
Convert a set of ` InputExample ` s to a list of ` InputFeatures ` .
"""Convert a set of `InputExample`s to a list of `InputFeatures`."""
examples
label_list
max_seq_length
tokenizer
mode