SYMBOL INDEX (92 symbols across 8 files)

FILE: att.py
  class Attention (line 7) | class Attention(Layer):
    method __init__ (line 8) | def __init__(self, attention_size, **kwargs):
    method build (line 12) | def build(self, input_shape):
    method call (line 30) | def call(self, x, mask=None):
    method compute_mask (line 45) | def compute_mask(self, input, input_mask=None):
    method compute_output_shape (line 48) | def compute_output_shape(self, input_shape):
    method get_config (line 52) | def get_config(self):

FILE: bert/args.py
  class PoolingStrategy (line 5) | class PoolingStrategy(Enum):
    method __str__ (line 15) | def __str__(self):
    method from_string (line 19) | def from_string(s):

FILE: bert/extract_feature.py
  class InputExample (line 12) | class InputExample(object):
    method __init__ (line 14) | def __init__(self, unique_id, text_a, text_b):
  class InputFeatures (line 20) | class InputFeatures(object):
    method __init__ (line 23) | def __init__(self, unique_id, tokens, input_ids, input_mask, input_typ...
  class BertVector (line 31) | class BertVector:
    method __init__ (line 33) | def __init__(self, batch_size=32, pooling_strategy="REDUCE_MEAN", max_...
    method get_estimator (line 61) | def get_estimator(self):
    method predict_from_queue (line 90) | def predict_from_queue(self):
    method encode (line 95) | def encode(self, sentence):
    method queue_predict_input_fn (line 100) | def queue_predict_input_fn(self):
    method generate_from_queue (line 114) | def generate_from_queue(self):
    method input_fn_builder (line 124) | def input_fn_builder(self, features, seq_length):
    method model_fn_builder (line 171) | def model_fn_builder(self, bert_config, init_checkpoint, layer_indexes):
    method convert_examples_to_features (line 224) | def convert_examples_to_features(self, seq_length, tokenizer):
    method _truncate_seq_pair (line 299) | def _truncate_seq_pair(self, tokens_a, tokens_b, max_length):
    method _to_example (line 316) | def _to_example(sentences):

FILE: bert/graph.py
  function import_tf (line 14) | def import_tf(device_id=-1, verbose=False):
  function set_logger (line 22) | def set_logger(context, verbose=False):
  function optimize_graph (line 36) | def optimize_graph(logger=None, verbose=False, pooling_strategy=PoolingS...

FILE: bert/modeling.py
  class BertConfig (line 30) | class BertConfig(object):
    method __init__ (line 33) | def __init__(self,
    method from_dict (line 82) | def from_dict(cls, json_object):
    method from_json_file (line 90) | def from_json_file(cls, json_file):
    method to_dict (line 96) | def to_dict(self):
    method to_json_string (line 101) | def to_json_string(self):
  class BertModel (line 106) | class BertModel(object):
    method __init__ (line 130) | def __init__(self,
    method get_pooled_output (line 235) | def get_pooled_output(self):
    method get_sequence_output (line 238) | def get_sequence_output(self):
    method get_all_encoder_layers (line 247) | def get_all_encoder_layers(self):
    method get_embedding_output (line 250) | def get_embedding_output(self):
    method get_embedding_table (line 261) | def get_embedding_table(self):
  function gelu (line 265) | def gelu(input_tensor):
  function get_activation (line 281) | def get_activation(activation_string):
  function get_assignment_map_from_checkpoint (line 318) | def get_assignment_map_from_checkpoint(tvars, init_checkpoint):
  function dropout (line 345) | def dropout(input_tensor, dropout_prob):
  function layer_norm (line 363) | def layer_norm(input_tensor, name=None):
  function layer_norm_and_dropout (line 369) | def layer_norm_and_dropout(input_tensor, dropout_prob, name=None):
  function create_initializer (line 376) | def create_initializer(initializer_range=0.02):
  function embedding_lookup (line 381) | def embedding_lookup(input_ids,
  function embedding_postprocessor (line 430) | def embedding_postprocessor(input_tensor,
  function create_attention_mask_from_input_mask (line 526) | def create_attention_mask_from_input_mask(from_tensor, to_mask):
  function attention_layer (line 560) | def attention_layer(from_tensor,
  function transformer_model (line 756) | def transformer_model(input_tensor,
  function get_shape_list (line 897) | def get_shape_list(tensor, expected_rank=None, name=None):
  function reshape_to_matrix (line 934) | def reshape_to_matrix(input_tensor):
  function reshape_from_matrix (line 948) | def reshape_from_matrix(output_tensor, orig_shape_list):
  function assert_rank (line 961) | def assert_rank(tensor, expected_rank, name=None):

FILE: bert/optimization.py
  function create_optimizer (line 25) | def create_optimizer(loss, init_lr, num_train_steps, num_warmup_steps, u...
  class AdamWeightDecayOptimizer (line 84) | class AdamWeightDecayOptimizer(tf.train.Optimizer):
    method __init__ (line 87) | def __init__(self,
    method apply_gradients (line 105) | def apply_gradients(self, grads_and_vars, global_step=None, name=None):
    method _do_use_weight_decay (line 156) | def _do_use_weight_decay(self, param_name):
    method _get_variable_name (line 166) | def _get_variable_name(self, param_name):

FILE: bert/tokenization.py
  function convert_to_unicode (line 27) | def convert_to_unicode(text):
  function printable_text (line 47) | def printable_text(text):
  function load_vocab (line 70) | def load_vocab(vocab_file):
  function convert_by_vocab (line 85) | def convert_by_vocab(vocab, items):
  function convert_tokens_to_ids (line 93) | def convert_tokens_to_ids(vocab, tokens):
  function convert_ids_to_tokens (line 97) | def convert_ids_to_tokens(inv_vocab, ids):
  function whitespace_tokenize (line 101) | def whitespace_tokenize(text):
  class FullTokenizer (line 110) | class FullTokenizer(object):
    method __init__ (line 113) | def __init__(self, vocab_file, do_lower_case=True):
    method tokenize (line 119) | def tokenize(self, text):
    method convert_tokens_to_ids (line 127) | def convert_tokens_to_ids(self, tokens):
    method convert_ids_to_tokens (line 130) | def convert_ids_to_tokens(self, ids):
  class BasicTokenizer (line 134) | class BasicTokenizer(object):
    method __init__ (line 137) | def __init__(self, do_lower_case=True):
    method tokenize (line 145) | def tokenize(self, text):
    method _run_strip_accents (line 169) | def _run_strip_accents(self, text):
    method _run_split_on_punc (line 180) | def _run_split_on_punc(self, text):
    method _tokenize_chinese_chars (line 200) | def _tokenize_chinese_chars(self, text):
    method _is_chinese_char (line 213) | def _is_chinese_char(self, cp):
    method _clean_text (line 235) | def _clean_text(self, text):
  class WordpieceTokenizer (line 249) | class WordpieceTokenizer(object):
    method __init__ (line 252) | def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=...
    method tokenize (line 257) | def tokenize(self, text):
  function _is_whitespace (line 311) | def _is_whitespace(char):
  function _is_control (line 323) | def _is_control(char):
  function _is_punctuation (line 335) | def _is_punctuation(char):

FILE: load_data.py
  function read_txt_file (line 6) | def read_txt_file(file_path):
  function get_train_test_pd (line 21) | def get_train_test_pd():