run_ner.py

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import collections
import csv
import json
import os
import time

import numpy as np
import tensorflow as tf

from bert import modeling
from bert import optimization
from bert import tokenization

MIN_FLOAT = -1e30

flags = tf.flags
FLAGS = flags.FLAGS

flags.DEFINE_string("bert_config_file", None, "The config json file corresponding to the pre-trained BERT model.")
flags.DEFINE_string("vocab_file", None, "The vocabulary file that the BERT model was trained on.")
flags.DEFINE_string("data_dir", None, "The input data dir. Should contain the .json files for the task.")
flags.DEFINE_string("task_name", None, "The name of the task to train.")
flags.DEFINE_string("output_dir", None, "The output directory where the model checkpoints will be written.")
flags.DEFINE_string("export_dir", None, "The export directory where the saved model will be written.")


flags.DEFINE_integer("random_seed", 100, "Random seed for weight initialzation.")
flags.DEFINE_string("predict_tag", None, "Predict tag for predict result tracking.")

flags.DEFINE_string("init_checkpoint", None, "Initial checkpoint (usually from a pre-trained BERT model).")
flags.DEFINE_bool("do_lower_case", True, "Whether to lower case the input text. True for uncased models and False for cased models.")

flags.DEFINE_integer(
    "max_seq_length", 128,
    "The maximum total input sequence length after WordPiece tokenization. "
    "Sequences longer than this will be truncated, and sequences shorter than this will be padded.")

flags.DEFINE_bool("do_train", False, "Whether to run training.")
flags.DEFINE_bool("do_eval", False, "Whether to run evaluation.")
flags.DEFINE_bool("do_predict", False, "Whether to run prediction.")
flags.DEFINE_bool("do_export", False, "Whether to run exporting.")

flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")
flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")
flags.DEFINE_integer("predict_batch_size", 8, "Total batch size for predict.")

flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
flags.DEFINE_float("num_train_epochs", 3.0, "Total number of training epochs to perform.")
flags.DEFINE_float("warmup_proportion", 0.1, "Proportion of training to perform linear learning rate warmup for.")

flags.DEFINE_integer("save_checkpoints_steps", 1000, "How often to save the model checkpoint.")
flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.")

flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
flags.DEFINE_integer("num_tpu_cores", 8,"Only used if `use_tpu` is True. Total number of TPU cores to use.")
flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")

flags.DEFINE_string(
    "tpu_name", None,
    "The Cloud TPU to use for training. This should be either the name "
    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 url.")

flags.DEFINE_string(
    "tpu_zone", None,
    "[Optional] GCE zone where the Cloud TPU is located in. If not "
    "specified, we will attempt to automatically detect the GCE project from metadata.")

flags.DEFINE_string(
    "gcp_project", None,
    "[Optional] Project name for the Cloud TPU-enabled project. If not "
    "specified, we will attempt to automatically detect the GCE project from metadata.")

class InputExample(object):
    """A single training/test example for simple sequence classification."""
    def __init__(self,
                 guid,
                 text,
                 label=None):
        """Constructs a InputExample.

        Args:
          guid: Unique id for the example.
          text: string. The untokenized text of the first sequence. For single
            sequence tasks, only this sequence must be specified.
          label: (Optional) string. The label of the example. This should be
            specified for train and dev examples, but not for test examples.
        """
        self.guid = guid
        self.text = text
        self.label = label

class PaddingInputExample(object):
    """Fake example so the num input examples is a multiple of the batch size.
    
    When running eval/predict on the TPU, we need to pad the number of examples
    to be a multiple of the batch size, because the TPU requires a fixed batch
    size. The alternative is to drop the last batch, which is bad because it means
    the entire output data won't be generated.
    
    We use this class instead of `None` because treating `None` as padding
    battches could cause silent errors.
    """

class InputFeatures(object):
    """A single set of features of data."""
    def __init__(self,
                 input_ids,
                 input_mask,
                 segment_ids,
                 label_ids):
        self.input_ids = input_ids
        self.input_mask = input_mask
        self.segment_ids = segment_ids
        self.label_ids = label_ids     

class NerProcessor(object):
    """Processor for the NER data set."""
    def __init__(self,
                 data_dir,
                 task_name):
        self.data_dir = data_dir
        self.task_name = task_name
    
    def get_train_examples(self):
        """Gets a collection of `InputExample`s for the train set."""
        data_path = os.path.join(self.data_dir, "train-{0}".format(self.task_name), "train-{0}.json".format(self.task_name))
        data_list = self._read_json(data_path)
        example_list = self._get_example(data_list)
        return example_list
    
    def get_dev_examples(self):
        """Gets a collection of `InputExample`s for the dev set."""
        data_path = os.path.join(self.data_dir, "dev-{0}".format(self.task_name), "dev-{0}.json".format(self.task_name))
        data_list = self._read_json(data_path)
        example_list = self._get_example(data_list)
        return example_list
    
    def get_test_examples(self):
        """Gets a collection of `InputExample`s for the test set."""
        data_path = os.path.join(self.data_dir, "test-{0}".format(self.task_name), "test-{0}.json".format(self.task_name))
        data_list = self._read_json(data_path)
        example_list = self._get_example(data_list)
        return example_list
    
    def get_labels(self):
        """Gets the list of labels for this data set."""
        data_path = os.path.join(self.data_dir, "resource", "label.vocab")
        label_list = self._read_text(data_path)
        return label_list
    
    def _read_text(self,
                   data_path):
        if os.path.exists(data_path):
            with open(data_path, "rb") as file:
                data_list = []
                for line in file:
                    data_list.append(line.decode("utf-8").strip())

                return data_list
        else:
            raise FileNotFoundError("data path not found")
    
    def _read_json(self,
                   data_path):
        if os.path.exists(data_path):
            with open(data_path, "r") as file:
                data_list = json.load(file)
                return data_list
        else:
            raise FileNotFoundError("data path not found")
    
    def _get_example(self,
                     data_list):
        example_list = []
        for data in data_list:
            guid = data["id"]
            text = tokenization.convert_to_unicode(data["text"])
            label = tokenization.convert_to_unicode(data["label"])
            example = InputExample(guid=guid, text=text, label=label)
            example_list.append(example)
        
        return example_list

def convert_single_example(ex_index,
                           example,
                           label_list,
                           max_seq_length,
                           tokenizer):
    """Converts a single `InputExample` into a single `InputFeatures`."""
    if isinstance(example, PaddingInputExample):
        return InputFeatures(
            input_ids=[0] * max_seq_length,
            input_mask=[0] * max_seq_length,
            segment_ids=[0] * max_seq_length,
            label_ids=[0] * max_seq_length)

    label_map = {}
    for (i, label) in enumerate(label_list):
        label_map[label] = i
    
    text_tokens = example.text.split(" ")
    label_tokens = example.label.split(" ")
    
    tokens = []
    labels = []
    for text_token, label_token in zip(text_tokens, label_tokens):
        text_sub_tokens = tokenizer.tokenize(text_token)
        label_sub_tokens = [label_token] + ["X"] * (len(text_sub_tokens) - 1)
        tokens.extend(text_sub_tokens)
        labels.extend(label_sub_tokens)
    
    if len(tokens) > max_seq_length - 2:
        tokens = tokens[0:(max_seq_length - 2)]
    
    if len(labels) > max_seq_length - 2:
        labels = labels[0:(max_seq_length - 2)]
    
    # The convention in BERT is:
    # (a) For sequence pairs:
    #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]
    #  type_ids: 0     0  0    0    0     0       0 0     1  1  1  1   1 1
    # (b) For single sequences:
    #  tokens:   [CLS] the dog is hairy . [SEP]
    #  type_ids: 0     0   0   0  0     0 0
    #
    # Where "type_ids" are used to indicate whether this is the first
    # sequence or the second sequence. The embedding vectors for `type=0` and
    # `type=1` were learned during pre-training and are added to the wordpiece
    # embedding vector (and position vector). This is not *strictly* necessary
    # since the [SEP] token unambiguously separates the sequences, but it makes
    # it easier for the model to learn the concept of sequences.
    #
    # For classification tasks, the first vector (corresponding to [CLS]) is
    # used as the "sentence vector". Note that this only makes sense because
    # the entire model is fine-tuned.
    
    input_tokens = []
    segment_ids = []
    label_ids = []
    
    input_tokens.append("[CLS]")
    segment_ids.append(0)
    label_ids.append(label_map["[CLS]"])
    
    for i, token in enumerate(tokens):
        input_tokens.append(token)
        segment_ids.append(0)
        label_ids.append(label_map[labels[i]])
    
    input_tokens.append("[SEP]")
    segment_ids.append(0)
    label_ids.append(label_map["[SEP]"])
    
    input_ids = tokenizer.convert_tokens_to_ids(input_tokens)
    
    # The mask has 1 for real tokens and 0 for padding tokens. Only real tokens are attended to.
    input_mask = [1] * len(input_ids)
    
    # Zero-pad up to the sequence length.
    while len(input_ids) < max_seq_length:
        input_ids.append(0)
        input_mask.append(0)
        segment_ids.append(0)
        label_ids.append(0)

    assert len(input_ids) == max_seq_length
    assert len(input_mask) == max_seq_length
    assert len(segment_ids) == max_seq_length
    assert len(label_ids) == max_seq_length
    
    if ex_index < 5:
        tf.logging.info("*** Example ***")
        tf.logging.info("guid: %s" % (example.guid))
        tf.logging.info("tokens: %s" % " ".join([tokenization.printable_text(x) for x in tokens]))
        tf.logging.info("input_ids: %s" % " ".join([str(x) for x in input_ids]))
        tf.logging.info("input_mask: %s" % " ".join([str(x) for x in input_mask]))
        tf.logging.info("segment_ids: %s" % " ".join([str(x) for x in segment_ids]))
        tf.logging.info("label_ids: %s" % " ".join([str(x) for x in label_ids]))

    feature = InputFeatures(
        input_ids=input_ids,
        input_mask=input_mask,
        segment_ids=segment_ids,
        label_ids=label_ids)
    return feature

def convert_examples_to_features(examples,
                                 label_list,
                                 max_seq_length,
                                 tokenizer):
    """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)
        features.append(feature)
    
    return features

def input_fn_builder(features,
                     seq_length,
                     is_training,
                     drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""
    all_input_ids = []
    all_input_mask = []
    all_segment_ids = []
    all_label_ids = []
    
    for feature in features:
        all_input_ids.append(feature.input_ids)
        all_input_mask.append(feature.input_mask)
        all_segment_ids.append(feature.segment_ids)
        all_label_ids.append(feature.label_ids)
    
    def input_fn(params):
        batch_size = params["batch_size"]
        num_examples = len(features)

        # This is for demo purposes and does NOT scale to large data sets. We do
        # not use Dataset.from_generator() because that uses tf.py_func which is
        # not TPU compatible. The right way to load data is with TFRecordReader.
        d = tf.data.Dataset.from_tensor_slices({
            "input_ids": tf.constant(all_input_ids, shape=[num_examples, seq_length], dtype=tf.int32),
            "input_mask": tf.constant(all_input_mask, shape=[num_examples, seq_length], dtype=tf.int32),
            "segment_ids": tf.constant(all_segment_ids, shape=[num_examples, seq_length], dtype=tf.int32),
            "label_ids": tf.constant(all_label_ids, shape=[num_examples, seq_length], dtype=tf.int32),
        })
        
        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100, seed=np.random.randint(10000))
        
        d = d.batch(batch_size=batch_size, drop_remainder=drop_remainder)
        return d
    
    return input_fn

def file_based_convert_examples_to_features(examples,
                                            label_list,
                                            max_seq_length,
                                            tokenizer,
                                            output_file):
    """Convert a set of `InputExample`s to a TFRecord file."""
    def create_int_feature(values):
        return tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
    
    writer = tf.python_io.TFRecordWriter(output_file)
    
    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)
        
        features = collections.OrderedDict()
        features["input_ids"] = create_int_feature(feature.input_ids)
        features["input_mask"] = create_int_feature(feature.input_mask)
        features["segment_ids"] = create_int_feature(feature.segment_ids)
        features["label_ids"] = create_int_feature([feature.label_ids])
        
        tf_example = tf.train.Example(features=tf.train.Features(feature=features))
        
        writer.write(tf_example.SerializeToString())
    
    writer.close()

def file_based_input_fn_builder(input_file,
                                seq_length,
                                is_training,
                                drop_remainder):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""
    name_to_features = {
        "input_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "input_mask": tf.FixedLenFeature([seq_length], tf.int64),
        "segment_ids": tf.FixedLenFeature([seq_length], tf.int64),
        "label_ids": tf.FixedLenFeature([seq_length], tf.int64),
    }
    
    def _decode_record(record,
                       name_to_features):
        """Decodes a record to a TensorFlow example."""
        example = tf.parse_single_example(record, name_to_features)
        
        # tf.Example only supports tf.int64, but the TPU only supports tf.int32. So cast all int64 to int32.
        for name in list(example.keys()):
            t = example[name]
            if t.dtype == tf.int64:
                t = tf.to_int32(t)
            example[name] = t
        
        return example
    
    def input_fn(params):
        """The actual input function."""
        batch_size = params["batch_size"]
        
        # For training, we want a lot of parallel reading and shuffling.
        # For eval, we want no shuffling and parallel reading doesn't matter.
        d = tf.data.TFRecordDataset(input_file)
        
        if is_training:
            d = d.repeat()
            d = d.shuffle(buffer_size=100, seed=np.random.randint(10000))
        
        d = d.apply(tf.contrib.data.map_and_batch(
            lambda record: _decode_record(record, name_to_features),
            batch_size=batch_size,
            drop_remainder=drop_remainder))
        
        return d
    
    return input_fn

def create_model(bert_config,
                 input_ids,
                 input_mask,
                 segment_ids,
                 label_ids,
                 label_list,
                 mode,
                 use_tpu):
    """Creates a NER model."""
    is_training = (mode == tf.estimator.ModeKeys.TRAIN)
    model = modeling.BertModel(
        config=bert_config,
        is_training=is_training,
        input_ids=input_ids,
        input_mask=input_mask,
        token_type_ids=segment_ids,
        use_one_hot_embeddings=use_tpu)
    
    # If you want to use sentence-level output, use model.get_pooled_output()
    # If you want to use token-level output, use model.get_sequence_output()
    with tf.variable_scope("ner", reuse=tf.AUTO_REUSE):
        result = model.get_sequence_output()
        result_mask = tf.cast(tf.expand_dims(input_mask, axis=-1), dtype=tf.float32)
        
        kernel_initializer = tf.glorot_uniform_initializer(seed=np.random.randint(10000), dtype=tf.float32)
        bias_initializer = tf.zeros_initializer
        dense_layer = tf.keras.layers.Dense(units=len(label_list), activation=None, use_bias=True,
            kernel_initializer=kernel_initializer, bias_initializer=bias_initializer,
            kernel_regularizer=None, bias_regularizer=None, trainable=True)
        
        dropout_layer = tf.keras.layers.Dropout(rate=0.1, seed=np.random.randint(10000))
        
        result = dense_layer(result)
        if mode == tf.estimator.ModeKeys.TRAIN:
            result = dropout_layer(result)
        
        masked_result = result * result_mask + MIN_FLOAT * (1 - result_mask)
        predicts = tf.cast(tf.argmax(tf.nn.softmax(masked_result, axis=-1), axis=-1), dtype=tf.int32)
    
    loss = tf.constant(0.0, dtype=tf.float32)
    if mode in [tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL] and label_ids is not None:
        with tf.variable_scope("loss", reuse=tf.AUTO_REUSE):
            label = tf.cast(label_ids, dtype=tf.float32)
            label_mask = tf.cast(input_mask, dtype=tf.float32)
            
            masked_label = tf.cast(label * label_mask, dtype=tf.int32)
            
            cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=masked_label, logits=masked_result)
            loss = tf.reduce_sum(cross_entropy * label_mask) / tf.reduce_sum(tf.reduce_max(label_mask, axis=-1))
    
    return loss, predicts

def model_fn_builder(bert_config,
                     label_list,
                     init_checkpoint,
                     learning_rate,
                     num_train_steps,
                     num_warmup_steps,
                     use_tpu):
    """Returns `model_fn` closure for TPUEstimator."""
    def model_fn(features,
                 labels,
                 mode,
                 params):  # pylint: disable=unused-argument
        """The `model_fn` for TPUEstimator."""
        tf.logging.info("*** Features ***")
        for name in sorted(features.keys()):
            tf.logging.info("  name = %s, shape = %s" % (name, features[name].shape))

        input_ids = features["input_ids"]
        input_mask = features["input_mask"]
        segment_ids = features["segment_ids"]
        label_ids = features["label_ids"] if mode in [tf.estimator.ModeKeys.TRAIN, tf.estimator.ModeKeys.EVAL] else None
        
        loss, predicts = create_model(bert_config, input_ids, input_mask, segment_ids, label_ids, label_list, mode, use_tpu)
        
        tvars = tf.trainable_variables()
        initialized_variable_names = {}
        scaffold_fn = None
        
        if init_checkpoint:
            assignment_map, initialized_variable_names = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
        
        if use_tpu:
            def tpu_scaffold():
                tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
                return tf.train.Scaffold()
            
            scaffold_fn = tpu_scaffold
        else:
            tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
        
        tf.logging.info("**** Trainable Variables ****")
        for var in tvars:
            init_string = ""
            if var.name in initialized_variable_names:
                init_string = ", *INIT_FROM_CKPT*"
            
            tf.logging.info("  name = %s, shape = %s%s", var.name, var.shape, init_string)
        
        output_spec = None        
        if mode == tf.estimator.ModeKeys.TRAIN:
            train_op = optimization.create_optimizer(loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=loss,
                train_op=train_op,
                scaffold_fn=scaffold_fn)
        elif mode == tf.estimator.ModeKeys.EVAL:
            def metric_fn(labels,
                          predicts,
                          label_list):
                label_map = {}
                for (i, label) in enumerate(label_list):
                    label_map[label] = i
                
                pad_id = tf.constant(label_map["[PAD]"], shape=[], dtype=tf.int32)
                out_id = tf.constant(label_map["O"], shape=[], dtype=tf.int32)
                x_id = tf.constant(label_map["X"], shape=[], dtype=tf.int32)
                cls_id = tf.constant(label_map["[CLS]"], shape=[], dtype=tf.int32)
                sep_id = tf.constant(label_map["[SEP]"], shape=[], dtype=tf.int32)
                
                masked_labels = (tf.cast(tf.not_equal(labels, pad_id), dtype=tf.int32) *
                    tf.cast(tf.not_equal(labels, out_id), dtype=tf.int32) *
                    tf.cast(tf.not_equal(labels, x_id), dtype=tf.int32) *
                    tf.cast(tf.not_equal(labels, cls_id), dtype=tf.int32) *
                    tf.cast(tf.not_equal(labels, sep_id), dtype=tf.int32))
                
                masked_predicts = (tf.cast(tf.not_equal(predicts, pad_id), dtype=tf.int32) *
                    tf.cast(tf.not_equal(predicts, out_id), dtype=tf.int32) *
                    tf.cast(tf.not_equal(predicts, x_id), dtype=tf.int32) *
                    tf.cast(tf.not_equal(predicts, cls_id), dtype=tf.int32) *
                    tf.cast(tf.not_equal(predicts, sep_id), dtype=tf.int32))
                
                precision = tf.metrics.precision(labels=masked_labels, predictions=masked_predicts)
                recall = tf.metrics.recall(labels=masked_labels, predictions=masked_predicts)
                
                metric = {
                    "precision": precision,
                    "recall": recall
                }
                
                return metric
            
            eval_metrics = (metric_fn, [label_ids, predicts, label_list])
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=loss,
                eval_metrics=eval_metrics,
                scaffold_fn=scaffold_fn)
        else:
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                predictions={ "predicts": predicts },
                scaffold_fn=scaffold_fn)
        
        return output_spec
    
    return model_fn

def serving_input_fn():
    with tf.variable_scope("export"):
        features = {
            'input_ids': tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_ids'),
            'input_mask': tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='input_mask'),
            'segment_ids': tf.placeholder(tf.int32, [None, FLAGS.max_seq_length], name='segment_ids')
        }
        
        return tf.estimator.export.build_raw_serving_input_receiver_fn(features)()

def decode_predicts(predicts,
                    label_list,
                    max_seq_length,
                    tokenizer):
    predict_decodings = []
    for predict in predicts:
        input_tokens = tokenizer.convert_ids_to_tokens(predict["input_ids"])
        input_mask = predict["input_mask"]
        expected_labels = [label_list[idx] for idx in predict["label_ids"]]
        predict_labels = [label_list[idx] for idx in predict["predict_ids"]]
        
        decoded_tokens = []
        decoded_labels = []
        decoded_predicts = []
        for token, mask, expected_label, predict_label in zip(input_tokens, input_mask, expected_labels, predict_labels):
            if mask == 0:
                break
            
            if token in ["[CLS]", "[SEP]"]:
                continue
            
            if token[:2] == "##":
                decoded_tokens[-1] = decoded_tokens[-1] + token[2:]
                continue
            elif expected_label == "X":
                decoded_tokens[-1] = decoded_tokens[-1] + token
                continue
            
            if predict_label in ["[PAD]", "[CLS]", "[SEP]", "X"]:
                predict_label = "O"
            
            decoded_tokens.append(token)
            decoded_labels.append(expected_label)
            decoded_predicts.append(predict_label)
        
        predict_decoding = {
            "text": " ".join(decoded_tokens),
            "label": " ".join(decoded_labels),
            "predict": " ".join(decoded_predicts)
        }
        
        predict_decodings.append(predict_decoding)
    
    return predict_decodings

def write_to_json(data_list,
                  data_path):
    data_folder = os.path.dirname(data_path)
    if not os.path.exists(data_folder):
        os.mkdir(data_folder)
    
    with open(data_path, "w") as file:  
        json.dump(data_list, file, indent=4)

def write_to_text(data_list,
                  data_path):
    data_folder = os.path.dirname(data_path)
    if not os.path.exists(data_folder):
        os.mkdir(data_folder)
    
    with open(data_path, "w") as file:
        for data in data_list:
            file.write("{0}\n".format(data))

def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)
    
    np.random.seed(FLAGS.random_seed)
    
    bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
    
    if FLAGS.max_seq_length > bert_config.max_position_embeddings:
        raise ValueError("Cannot use sequence length %d because the BERT model was only trained up to sequence length %d" %
            (FLAGS.max_seq_length, bert_config.max_position_embeddings))
    
    tf.gfile.MakeDirs(FLAGS.output_dir)
    
    tokenization.validate_case_matches_checkpoint(FLAGS.do_lower_case, FLAGS.init_checkpoint)
    tokenizer = tokenization.FullTokenizer(vocab_file=FLAGS.vocab_file, do_lower_case=FLAGS.do_lower_case)
    
    data_dir = FLAGS.data_dir
    task_name = FLAGS.task_name.lower()
    processor = NerProcessor(data_dir, task_name)
    label_list = processor.get_labels()
    
    train_examples = None
    num_train_steps = None
    num_warmup_steps = None
    if FLAGS.do_train:
        train_examples = processor.get_train_examples()
        num_train_steps = int(len(train_examples) / FLAGS.train_batch_size * FLAGS.num_train_epochs)
        num_warmup_steps = int(num_train_steps * FLAGS.warmup_proportion)
    
    tpu_cluster_resolver = None
    if FLAGS.use_tpu and FLAGS.tpu_name:
        tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(
            FLAGS.tpu_name, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project)
    
    is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
    run_config = tf.contrib.tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=FLAGS.master,
        model_dir=FLAGS.output_dir,
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,
        tpu_config=tf.contrib.tpu.TPUConfig(
            iterations_per_loop=FLAGS.iterations_per_loop,
            num_shards=FLAGS.num_tpu_cores,
            per_host_input_for_training=is_per_host))
    
    model_fn = model_fn_builder(
        bert_config=bert_config,
        label_list=label_list,
        init_checkpoint=FLAGS.init_checkpoint,
        learning_rate=FLAGS.learning_rate,
        num_train_steps=num_train_steps,
        num_warmup_steps=num_warmup_steps,
        use_tpu=FLAGS.use_tpu)
    
    # If TPU is not available, this will fall back to normal Estimator on CPU or GPU.
    estimator = tf.contrib.tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,
        model_fn=model_fn,
        config=run_config,
        export_to_tpu=FLAGS.use_tpu,
        train_batch_size=FLAGS.train_batch_size,
        eval_batch_size=FLAGS.eval_batch_size,
        predict_batch_size=FLAGS.predict_batch_size)
    
    if FLAGS.do_train:
        tf.logging.info("***** Run training *****")
        tf.logging.info("  Num examples = %d", len(train_examples))
        tf.logging.info("  Batch size = %d", FLAGS.train_batch_size)
        tf.logging.info("  Num steps = %d", num_train_steps)
        
        train_features = convert_examples_to_features(
            examples=train_examples,
            label_list=label_list,
            max_seq_length=FLAGS.max_seq_length,
            tokenizer=tokenizer)

        train_input_fn = input_fn_builder(
            features=train_features,
            seq_length=FLAGS.max_seq_length,
            is_training=True,
            drop_remainder=True)
        
        estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)
    
    if FLAGS.do_eval:
        eval_examples = processor.get_dev_examples()
        tf.logging.info("***** Run evaluation *****")
        tf.logging.info("  Num examples = %d", len(eval_examples))
        tf.logging.info("  Batch size = %d", FLAGS.eval_batch_size)
        
        eval_features = convert_examples_to_features(
            examples=eval_examples,
            label_list=label_list,
            max_seq_length=FLAGS.max_seq_length,
            tokenizer=tokenizer)

        eval_input_fn = input_fn_builder(
            features=eval_features,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=False)
        
        result = estimator.evaluate(input_fn=eval_input_fn)
        precision = result["precision"]
        recall = result["recall"]
        f1_score = 2.0 * precision * recall / (precision + recall)
        
        tf.logging.info("***** Evaluation result *****")
        tf.logging.info("  Precision = %s", str(precision))
        tf.logging.info("  Recall = %s", str(recall))
        tf.logging.info("  F1 score = %s", str(f1_score))
    
    if FLAGS.do_predict:
        predict_examples = processor.get_test_examples()
        tf.logging.info("***** Run prediction *****")
        tf.logging.info("  Num examples = %d", len(predict_examples))
        tf.logging.info("  Batch size = %d", FLAGS.predict_batch_size)
        
        predict_features = convert_examples_to_features(
            examples=predict_examples,
            label_list=label_list,
            max_seq_length=FLAGS.max_seq_length,
            tokenizer=tokenizer)

        predict_input_fn = input_fn_builder(
            features=predict_features,
            seq_length=FLAGS.max_seq_length,
            is_training=False,
            drop_remainder=False)
        
        result = estimator.predict(input_fn=predict_input_fn)
        
        predicts = [{
            "input_ids": feature.input_ids,
            "input_mask": feature.input_mask,
            "label_ids": feature.label_ids,
            "predict_ids": predict["predicts"].tolist()
        } for feature, predict in zip(predict_features, result)]
        
        predict_decodings = decode_predicts(
            predicts=predicts,
            label_list=label_list,
            max_seq_length=FLAGS.max_seq_length,
            tokenizer=tokenizer)
        
        predict_tag = FLAGS.predict_tag if FLAGS.predict_tag else str(time.time())
        output_path = os.path.join(FLAGS.output_dir, "predict.{0}.json".format(predict_tag))
        write_to_json(predict_decodings, output_path)
    
    if FLAGS.do_export:
        tf.logging.info("***** Running exporting *****")
        tf.gfile.MakeDirs(FLAGS.export_dir)
        estimator.export_savedmodel(FLAGS.export_dir, serving_input_fn, as_text=False)

if __name__ == "__main__":
    flags.mark_flag_as_required("bert_config_file")
    flags.mark_flag_as_required("vocab_file")
    flags.mark_flag_as_required("init_checkpoint")
    flags.mark_flag_as_required("data_dir")
    flags.mark_flag_as_required("output_dir")
    flags.mark_flag_as_required("export_dir")
    tf.app.run()