How do I fine-tune google bert of text multi class classifier

Im currently working on google bert classifier extracted from here:

and Im using it to classify Stack Overflow questions and tags
https://storage.googleapis.com/tensorflow-workshop-examples/stack-overflow-data.csv
I get very bad prediction and poor accuracy and Im wondering which parameters should I fine tune in order to get better results.
I attached all of code
The main file that I run, contains the parameters +run_bert()

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

import collections
import csv
import os
import src_bert.modeling as modeling
import src_bert.tokenization as tokenization
import tensorflow as tf
import numpy as np
from sklearn.model_selection import train_test_split
from src_bert.bert_model import *

DO_LOWER_CASE = False
BERT_INIT_CHKPNT = "./src_bert/cased_L-12_H-768_A-12/bert_model.ckpt"
BERT_CONFIG = './src_bert/cased_L-12_H-768_A-12/bert_config.json'
BERT_VOCAB = "./src_bert/cased_L-12_H-768_A-12/vocab.txt"
MAX_SEQ_LENGTH = 128
OUTPUT_DIR = "./working/output"
TRAIN_BATCH_SIZE = 32
PREDICT_BATCH_SIZE = EVAL_BATCH_SIZE = 8
LEARNING_RATE = 2e-5
NUM_TRAIN_EPOCHS = 1.0
# Warmup is a period of time where hte learning rate
# is small and gradually increases--usually helps training.
WARMUP_PROPORTION = 0.1
# Model configs
SAVE_CHECKPOINTS_STEPS = 10000
SAVE_SUMMARY_STEPS = 500
TRAIN_VAL_RATIO = 0.9

def run_bert(x_train_val, x_test, y_train_val, y_test, label_list):
    tf.logging.set_verbosity(tf.logging.INFO)
    os.makedirs(OUTPUT_DIR, exist_ok=True)
    LEN = x_train_val.shape[0]
    SIZE_TRAIN = int(TRAIN_VAL_RATIO * LEN)

    x_val = x_train_val[SIZE_TRAIN:]
    y_val = y_train_val[SIZE_TRAIN:]
    x_train = x_train_val[:SIZE_TRAIN]
    y_train = y_train_val[:SIZE_TRAIN]

    tokenization.validate_case_matches_checkpoint(DO_LOWER_CASE,
                                                BERT_INIT_CHKPNT)


    bert_config = modeling.BertConfig.from_json_file(BERT_CONFIG)

    if 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" %
            (MAX_SEQ_LENGTH, bert_config.max_position_embeddings))

    tf.gfile.MakeDirs(OUTPUT_DIR)

    tokenizer = tokenization.FullTokenizer(
        vocab_file=BERT_VOCAB, do_lower_case=DO_LOWER_CASE)


    run_config = tf.estimator.RunConfig(
        model_dir=OUTPUT_DIR,
        save_summary_steps=SAVE_SUMMARY_STEPS,
        keep_checkpoint_max=1,
        save_checkpoints_steps=SAVE_CHECKPOINTS_STEPS)

    train_examples = None
    num_train_steps = None
    num_warmup_steps = None
    # Train
    train_examples = create_examples(x_train, y_train)
    num_train_steps = int(
        len(train_examples) / TRAIN_BATCH_SIZE * NUM_TRAIN_EPOCHS)
    num_warmup_steps = int(num_train_steps * WARMUP_PROPORTION)

    model_fn = model_fn_builder(
        bert_config=bert_config,
        num_labels=len(label_list),
        init_checkpoint=BERT_INIT_CHKPNT,
        learning_rate=LEARNING_RATE,
        num_train_steps=num_train_steps,
        num_warmup_steps=num_warmup_steps,
        use_tpu=False,
        use_one_hot_embeddings=False)

  # If TPU is not available, this will fall back to normal Estimator on CPU
  # or GPU.

    estimator = tf.estimator.Estimator(
        model_fn=model_fn,
        config=run_config,
        params={"batch_size": TRAIN_BATCH_SIZE})

    train_file = os.path.join(OUTPUT_DIR, "train.tf_record")
    file_based_convert_examples_to_features(
        train_examples, label_list, MAX_SEQ_LENGTH, tokenizer, train_file)

    train_input_fn = file_based_input_fn_builder(
        input_file=train_file,
        seq_length=MAX_SEQ_LENGTH,
        is_training=True,
        drop_remainder=True)
    estimator.train(input_fn=train_input_fn, max_steps=num_train_steps)

    #Evalution
    eval_examples = create_examples(x_val, y_val)
    num_actual_eval_examples = len(eval_examples)

    eval_file = os.path.join(OUTPUT_DIR, "eval.tf_record")
    file_based_convert_examples_to_features(
        eval_examples, label_list, MAX_SEQ_LENGTH, tokenizer, eval_file)

    tf.logging.info("***** Running evaluation *****")
    # This tells the estimator to run through the entire set.
    eval_steps = None
    # However, if running eval on the TPU, you will need to specify the
    # number of steps.

    eval_drop_remainder = False
    eval_input_fn = file_based_input_fn_builder(
        input_file=eval_file,
        seq_length=MAX_SEQ_LENGTH,
        is_training=False,
        drop_remainder=eval_drop_remainder)

    result = estimator.evaluate(input_fn=eval_input_fn, steps=eval_steps)

    output_eval_file = os.path.join(OUTPUT_DIR, "eval_results.txt")
    with tf.gfile.GFile(output_eval_file, "w") as writer:
      tf.logging.info("***** Eval results *****")
      for key in sorted(result.keys()):
        tf.logging.info("  %s = %s", key, str(result[key]))
        writer.write("%s = %s\n" % (key, str(result[key])))
    #Testing
    predict_examples = create_examples(x_test, y_test, False)

    predict_file = os.path.join(OUTPUT_DIR, "predict.tf_record")
    file_based_convert_examples_to_features(predict_examples, label_list,
                                            MAX_SEQ_LENGTH, tokenizer,
                                            predict_file)

    tf.logging.info("***** Running prediction*****")
    predict_drop_remainder = False
    predict_input_fn = file_based_input_fn_builder(
        input_file=predict_file,
        seq_length=MAX_SEQ_LENGTH,
        is_training=False,
        drop_remainder=predict_drop_remainder)
    result = estimator.predict(input_fn=predict_input_fn)

    tf.logging.info("***** Predict results *****")
    correct_labeling = []
    accuracy = 0
    for x, prediction, correct_label in zip(x_test,result,y_test):
        probabilities = prediction["probabilities"]
        max_index_label = np.argmax(probabilities)
        tf.logging.info("{0} orig label {1} predicted {2}".format(x,correct_label, label_list[max_index_label]))
        if label_list[max_index_label]==correct_label:
            accuracy+=1
        correct_labeling.append(label_list[max_index_label]==correct_label)
    print("test accuracy is {0}".format(100*(accuracy/len(x_test)))) 
    return correct_labeling


if __name__ == "__main__":
    input_csv = pd.read_csv('stack-overflow-data.csv')
    input_csv = input_csv.dropna()
    tweets = input_csv['post']
    labels = []
    print("loading DB")
    map_index_to_label = list(set(input_csv['tags']))
    map_label_to_index = {l:i for i,l in enumerate(map_index_to_label)}
    for label in input_csv['tags']:
        labels.append(map_label_to_index[label])
    num_labels = len(map_index_to_label)
    print("splitting")
    X_train, X_test, y_train, y_test = train_test_split(tweets, labels, test_size=0.33, random_state=42)
    bert_test_res = run_bert(X_train, X_test, y_train, y_test, list(set(labels)))

and helper bert model code, code which taken from google bert:

from  **future**  import absolute_import
from  **future**  import division
from  **future**  import print_function
import collections
import csv
import os
import src_bert.modeling as modeling
import src_bert.optimization as optimization
import src_bert.tokenization as tokenization
import tensorflow as tf
import pandas as pd
from sklearn.model_selection import train_test_split

class InputExample(object):
  """A single training/test example for simple sequence classification."""

  def __init__(self, text_a, text_b=None, label=None):
    """Constructs a InputExample.
    Args:
      guid: Unique id for the example.
      text_a: string. The untokenized text of the first sequence. For single
        sequence tasks, only this sequence must be specified.
      text_b: (Optional) string. The untokenized text of the second sequence.
        Only must be specified for sequence pair tasks.
      label: (Optional) string. The label of the example. This should be
        specified for train and dev examples, but not for test examples.
    """
   self.text_a = text_a
    self.text_b = text_b
    self.label = label


def create_examples(x_values, y_values, labels_available=True):
    """Creates examples for the training and dev sets."""
    examples = []
    for x, y in zip(x_values,y_values):
        if labels_available:
            label = y
        else:
            label = None
        examples.append(
            InputExample(text_a=x, label=label))
    return examples


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_id,
               is_real_example=True):
    self.input_ids = input_ids
    self.input_mask = input_mask
    self.segment_ids = segment_ids
    self.label_id = label_id
    self.is_real_example = is_real_example


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_id=0,
        is_real_example=False)

  label_map = {}
  for (i, label) in enumerate(label_list):
    label_map[label] = i+1

  tokens_a = tokenizer.tokenize(example.text_a)
  tokens_b = None
  if example.text_b:
    tokens_b = tokenizer.tokenize(example.text_b)

  if tokens_b:
    # Modifies `tokens_a` and `tokens_b` in place so that the total
    # length is less than the specified length.
    # Account for [CLS], [SEP], [SEP] with "- 3"
    _truncate_seq_pair(tokens_a, tokens_b, max_seq_length - 3)
  else:
    # Account for [CLS] and [SEP] with "- 2"
    if len(tokens_a) > max_seq_length - 2:
      tokens_a = tokens_a[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.
  tokens = []
  segment_ids = []
  tokens.append("[CLS]")
  segment_ids.append(0)
  for token in tokens_a:
    tokens.append(token)
    segment_ids.append(0)
  tokens.append("[SEP]")
  segment_ids.append(0)

  if tokens_b:
    for token in tokens_b:
      tokens.append(token)
      segment_ids.append(1)
    tokens.append("[SEP]")
    segment_ids.append(1)

  input_ids = tokenizer.convert_tokens_to_ids(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)

  assert len(input_ids) == max_seq_length
  assert len(input_mask) == max_seq_length
  assert len(segment_ids) == max_seq_length

  if example.label != None:
      label_id = label_map[example.label]
  else:
      label_id = 0
  if ex_index < 5:
    tf.logging.info("*** Example ***")
    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: %s (id = %d)" % (example.label, label_id))

  feature = InputFeatures(
      input_ids=input_ids,
      input_mask=input_mask,
      segment_ids=segment_ids,
      label_id=label_id,
      is_real_example=True)
  return feature

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."""

  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)

    def create_int_feature(values):
      f = tf.train.Feature(int64_list=tf.train.Int64List(value=list(values)))
      return f

    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_id])
    features["is_real_example"] = create_int_feature(
        [int(feature.is_real_example)])

    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([], tf.int64),
      "is_real_example": tf.FixedLenFeature([], 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)

    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 _truncate_seq_pair(tokens_a, tokens_b, max_length):
  """Truncates a sequence pair in place to the maximum length."""

  # This is a simple heuristic which will always truncate the longer sequence
  # one token at a time. This makes more sense than truncating an equal percent
  # of tokens from each, since if one sequence is very short then each token
  # that's truncated likely contains more information than a longer sequence.
  while True:
    total_length = len(tokens_a) + len(tokens_b)
    if total_length <= max_length:
      break
    if len(tokens_a) > len(tokens_b):
      tokens_a.pop()
    else:
      tokens_b.pop()

def create_model(bert_config, is_training, input_ids, input_mask, segment_ids,
                 labels, num_labels, use_one_hot_embeddings):
  """Creates a classification model."""
  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_one_hot_embeddings)

  # In the demo, we are doing a simple classification task on the entire
  # segment.
  #
  # If you want to use the token-level output, use model.get_sequence_output()
  # instead.
  output_layer = model.get_pooled_output()

  hidden_size = output_layer.shape[-1].value

  output_weights = tf.get_variable(
      "output_weights", [num_labels, hidden_size],
      initializer=tf.truncated_normal_initializer(stddev=0.02))

  output_bias = tf.get_variable(
      "output_bias", [num_labels], initializer=tf.zeros_initializer())

  with tf.variable_scope("loss"):
    if is_training:
      # I.e., 0.1 dropout
      output_layer = tf.nn.dropout(output_layer, keep_prob=0.9)

    logits = tf.matmul(output_layer, output_weights, transpose_b=True)
    logits = tf.nn.bias_add(logits, output_bias)
    probabilities = tf.nn.softmax(logits, axis=-1)
    log_probs = tf.nn.log_softmax(logits, axis=-1)

    one_hot_labels = tf.one_hot(labels, depth=num_labels, dtype=tf.float32)

    per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
    loss = tf.reduce_mean(per_example_loss)

    return (loss, per_example_loss, logits, probabilities)

def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate,
                     num_train_steps, num_warmup_steps, use_tpu,
                     use_one_hot_embeddings):
  """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"]
    is_real_example = None
    if "is_real_example" in features:
        is_real_example = tf.cast(features["is_real_example"], dtype=tf.float32)
    else:
        is_real_example = tf.ones(tf.shape(label_ids), dtype=tf.float32)

    is_training = (mode == tf.estimator.ModeKeys.TRAIN)
    (total_loss, per_example_loss, logits, probabilities) = create_model(
        bert_config, is_training, input_ids, input_mask, segment_ids, label_ids,
        num_labels, use_one_hot_embeddings)

    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(
          total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)

      output_spec = tf.estimator.EstimatorSpec(
          mode=mode,
          loss=total_loss,
          train_op=train_op,
          scaffold=scaffold_fn)
    elif mode == tf.estimator.ModeKeys.EVAL:
      def metric_fn(per_example_loss, label_ids, logits, is_real_example):
        predictions = tf.argmax(logits, axis=-1, output_type=tf.int32)
        accuracy = tf.metrics.accuracy(
        labels=label_ids, predictions=predictions, weights=is_real_example)
        loss = tf.metrics.mean(values=per_example_loss, weights=is_real_example)
        return {
          "eval_accuracy": accuracy,
          "eval_loss": loss
        }

      eval_metrics = metric_fn(per_example_loss, label_ids, logits, is_real_example)

      output_spec = tf.estimator.EstimatorSpec(
          mode=mode,
          loss=total_loss,
          eval_metric_ops=eval_metrics,
          scaffold=scaffold_fn)
    else:
      output_spec = tf.estimator.EstimatorSpec(
          mode=mode,
          predictions={"probabilities": probabilities},
          scaffold=scaffold_fn)
    return output_spec

  return model_fn

other files are taken straight from:

Thanks.