SYMBOL INDEX (173 symbols across 24 files)

FILE: bert-chinese-web/predict.py
  class Bert_summary_model (line 10) | class Bert_summary_model(object):
    method __init__ (line 11) | def __init__(self, device=torch.device("cuda:0" if (torch.cuda.is_avai...
    method load_model (line 17) | def load_model(self, load_from):
    method save (line 25) | def save(self):
    method long_predict (line 37) | def long_predict(self, document: str, max_summary_size=max_summary_siz...
    method predict (line 80) | def predict(self, document: str, max_summary_size=max_summary_size, mi...

FILE: bert-chinese-web/src/models/encoder.py
  class Classifier (line 10) | class Classifier(nn.Module):
    method __init__ (line 11) | def __init__(self, hidden_size):
    method forward (line 16) | def forward(self, sents_vec, mask_cls):
  class PositionalEncoding (line 22) | class PositionalEncoding(nn.Module):
    method __init__ (line 24) | def __init__(self, dropout, dim, max_len=5000):
    method forward (line 37) | def forward(self, emb, step=None):
    method get_emb (line 47) | def get_emb(self, emb):
  class TransformerEncoderLayer (line 51) | class TransformerEncoderLayer(nn.Module):
    method __init__ (line 52) | def __init__(self, d_model, heads, d_ff, dropout):
    method forward (line 61) | def forward(self, iter, query, inputs, mask):
  class TransformerInterEncoder (line 74) | class TransformerInterEncoder(nn.Module):
    method __init__ (line 75) | def __init__(self, d_model, d_ff, heads, dropout, num_inter_layers=0):
    method forward (line 88) | def forward(self, top_vecs, mask):
  class RNNEncoder (line 106) | class RNNEncoder(nn.Module):
    method __init__ (line 108) | def __init__(self, bidirectional, num_layers, input_size,
    method forward (line 125) | def forward(self, x, mask):

FILE: bert-chinese-web/src/models/model_builder_LAI.py
  class Bert (line 8) | class Bert(nn.Module):
    method __init__ (line 9) | def __init__(self, bert_config):
    method forward (line 13) | def forward(self, x, segs, mask):
  class Summarizer (line 19) | class Summarizer(nn.Module):
    method __init__ (line 20) | def __init__(self, device, bert_config_path=None):
    method load_cp (line 28) | def load_cp(self, pt):
    method forward (line 31) | def forward(self, x, segs, clss, mask, mask_cls, sentence_range=None):

FILE: bert-chinese-web/src/models/neural.py
  function gelu (line 7) | def gelu(x):
  class PositionwiseFeedForward (line 11) | class PositionwiseFeedForward(nn.Module):
    method __init__ (line 21) | def __init__(self, d_model, d_ff, dropout=0.1):
    method forward (line 30) | def forward(self, x):
  class MultiHeadedAttention (line 36) | class MultiHeadedAttention(nn.Module):
    method __init__ (line 78) | def __init__(self, head_count, model_dim, dropout=0.1, use_final_linea...
    method forward (line 98) | def forward(self, key, value, query, mask=None,

FILE: bert-chinese-web/src/models/optimizers.py
  function use_gpu (line 8) | def use_gpu(opt):
  function build_optim (line 16) | def build_optim(model, opt, checkpoint):
  class MultipleOptimizer (line 77) | class MultipleOptimizer(object):
    method __init__ (line 80) | def __init__(self, op):
    method zero_grad (line 84) | def zero_grad(self):
    method step (line 89) | def step(self):
    method state (line 95) | def state(self):
    method state_dict (line 99) | def state_dict(self):
    method load_state_dict (line 103) | def load_state_dict(self, state_dicts):
  class Optimizer (line 110) | class Optimizer(object):
    method __init__ (line 140) | def __init__(self, method, learning_rate, max_grad_norm,
    method set_parameters (line 162) | def set_parameters(self, params):
    method _set_rate (line 194) | def _set_rate(self, learning_rate):
    method step (line 202) | def step(self):

FILE: bert-chinese-web/src/models/rnn.py
  class LayerNormLSTMCell (line 6) | class LayerNormLSTMCell(nn.LSTMCell):
    method __init__ (line 8) | def __init__(self, input_size, hidden_size, bias=True):
    method forward (line 15) | def forward(self, input, hidden=None):
  class LayerNormLSTM (line 35) | class LayerNormLSTM(nn.Module):
    method __init__ (line 37) | def __init__(self, input_size, hidden_size, num_layers=1, bias=True, b...
    method forward (line 58) | def forward(self, input, hidden=None):

FILE: bert-chinese-web/src/others/utils.py
  function doc_split (line 5) | def doc_split(doc: str):
  function sent_token_split (line 17) | def sent_token_split(doc):
  function filter_chinese_space (line 23) | def filter_chinese_space(text: str) -> int:
  function filter (line 40) | def filter(x: str):
  function str2bool (line 55) | def str2bool(v):
  function int_arr_to_str (line 64) | def int_arr_to_str(arr: list):
  function label_to_idx (line 69) | def label_to_idx(label_arr: list):

FILE: bert-chinese-web/src/prepro/data_builder.py
  class BatchExample (line 8) | class BatchExample(object):
    method _pad (line 9) | def _pad(self, data, pad_id, width=-1):
    method __init__ (line 15) | def __init__(self, batch_example=None, device=None):
    method __len__ (line 37) | def __len__(self):
  class Example (line 41) | class Example(object):
    method __init__ (line 42) | def __init__(self, data: list, device=None):
  class BertData (line 59) | class BertData(object):
    method __init__ (line 60) | def __init__(self, vocab_path, device='cpu'):
    method split_long_doc (line 67) | def split_long_doc(self, document: str, max_num=510):
    method preprocess (line 85) | def preprocess(self, document: str, min_sent_num=3):

FILE: bert-chinese-web/web_main.py
  function index (line 10) | def index():
  function api_summary (line 15) | def api_summary():

FILE: bert-sum-dataprocess/main.py
  function get_input_data_iter (line 6) | def get_input_data_iter():

FILE: bert-sum-dataprocess/src/utils.py
  function filter (line 4) | def filter(x: str):
  function have_dirty_key (line 15) | def have_dirty_key(doc):
  function paser_out_label (line 25) | def paser_out_label(doc_sents: list, key_sents: list):
  function sent_token_split (line 50) | def sent_token_split(doc: str):
  function doc_split (line 55) | def doc_split(doc: str):
  function format_to_json (line 63) | def format_to_json(doc_sents_arr, idx_arr):
  function save_data_arr_to_json (line 70) | def save_data_arr_to_json(data_arr_iter, chunk_size=2000, file_name='dat...

FILE: bertsum-chinese/preprocess_LAI.py
  function do_format_to_bert (line 8) | def do_format_to_bert(args):

FILE: bertsum-chinese/src/models/data_loader.py
  class Batch (line 9) | class Batch(object):
    method _pad (line 10) | def _pad(self, data, pad_id, width=-1):
    method __init__ (line 16) | def __init__(self, minibatch=None, device=None, is_test=False):
    method __len__ (line 49) | def __len__(self):
  function batch (line 53) | def batch(data, batch_size):
  function load_dataset (line 69) | def load_dataset(args, corpus_type, shuffle):
  function simple_batch_size_fn (line 95) | def simple_batch_size_fn(new, count):
  class Dataloader (line 109) | class Dataloader(object):
    method __init__ (line 110) | def __init__(self, args, datasets, batch_size,
    method __iter__ (line 123) | def __iter__(self):
    method _next_dataset_iterator (line 132) | def _next_dataset_iterator(self, dataset_iter):
  class DataIterator (line 150) | class DataIterator(object):
    method __init__ (line 151) | def __init__(self, args, dataset, batch_size, device=None, is_test=False,
    method preprocess (line 164) | def preprocess(self, a_example, is_test):
    method batch_buffer (line 183) | def batch_buffer(self, data, batch_size):
    method create_batches (line 206) | def create_batches(self):
    method __iter__ (line 223) | def __iter__(self):

FILE: bertsum-chinese/src/models/encoder.py
  class Classifier (line 13) | class Classifier(nn.Module):
    method __init__ (line 14) | def __init__(self, hidden_size):
    method forward (line 19) | def forward(self, sents_vec, mask_cls):
  class PositionalEncoding (line 25) | class PositionalEncoding(nn.Module):
    method __init__ (line 27) | def __init__(self, dropout, dim, max_len=5000):
    method forward (line 40) | def forward(self, emb, step=None):
    method get_emb (line 50) | def get_emb(self, emb):
  class TransformerEncoderLayer (line 54) | class TransformerEncoderLayer(nn.Module):
    method __init__ (line 55) | def __init__(self, d_model, heads, d_ff, dropout):
    method forward (line 64) | def forward(self, iter, query, inputs, mask):
  class TransformerInterEncoder (line 77) | class TransformerInterEncoder(nn.Module):
    method __init__ (line 78) | def __init__(self, d_model, d_ff, heads, dropout, num_inter_layers=0):
    method forward (line 91) | def forward(self, top_vecs, mask):
  class RNNEncoder (line 109) | class RNNEncoder(nn.Module):
    method __init__ (line 111) | def __init__(self, bidirectional, num_layers, input_size,
    method forward (line 128) | def forward(self, x, mask):

FILE: bertsum-chinese/src/models/model_builder_LAI.py
  function build_optim (line 14) | def build_optim(args, model, checkpoint):
  class Bert (line 45) | class Bert(nn.Module):
    method __init__ (line 46) | def __init__(self, mode_path, load_pretrained_bert, bert_config):
    method forward (line 54) | def forward(self, x, segs, mask):
  class Summarizer (line 63) | class Summarizer(nn.Module):
    method __init__ (line 64) | def __init__(self, args, device, load_pretrained_bert=False, bert_conf...
    method load_cp (line 93) | def load_cp(self, pt):
    method forward (line 96) | def forward(self, x, segs, clss, mask, mask_cls, sentence_range=None):

FILE: bertsum-chinese/src/models/neural.py
  function gelu (line 7) | def gelu(x):
  class PositionwiseFeedForward (line 11) | class PositionwiseFeedForward(nn.Module):
    method __init__ (line 21) | def __init__(self, d_model, d_ff, dropout=0.1):
    method forward (line 30) | def forward(self, x):
  class MultiHeadedAttention (line 36) | class MultiHeadedAttention(nn.Module):
    method __init__ (line 78) | def __init__(self, head_count, model_dim, dropout=0.1, use_final_linea...
    method forward (line 98) | def forward(self, key, value, query, mask=None,

FILE: bertsum-chinese/src/models/optimizers.py
  function use_gpu (line 10) | def use_gpu(opt):
  function build_optim (line 18) | def build_optim(model, opt, checkpoint):
  class MultipleOptimizer (line 79) | class MultipleOptimizer(object):
    method __init__ (line 82) | def __init__(self, op):
    method zero_grad (line 86) | def zero_grad(self):
    method step (line 91) | def step(self):
    method state (line 97) | def state(self):
    method state_dict (line 101) | def state_dict(self):
    method load_state_dict (line 105) | def load_state_dict(self, state_dicts):
  class Optimizer (line 112) | class Optimizer(object):
    method __init__ (line 142) | def __init__(self, method, learning_rate, max_grad_norm,
    method set_parameters (line 164) | def set_parameters(self, params):
    method _set_rate (line 196) | def _set_rate(self, learning_rate):
    method step (line 204) | def step(self):

FILE: bertsum-chinese/src/models/rnn.py
  class LayerNormLSTMCell (line 8) | class LayerNormLSTMCell(nn.LSTMCell):
    method __init__ (line 10) | def __init__(self, input_size, hidden_size, bias=True):
    method forward (line 17) | def forward(self, input, hidden=None):
  class LayerNormLSTM (line 37) | class LayerNormLSTM(nn.Module):
    method __init__ (line 39) | def __init__(self, input_size, hidden_size, num_layers=1, bias=True, b...
    method forward (line 60) | def forward(self, input, hidden=None):

FILE: bertsum-chinese/src/models/trainer.py
  function build_trainer (line 10) | def build_trainer(args, model, optim):
  class Trainer (line 19) | class Trainer(object):
    method __init__ (line 20) | def __init__(self, args, model, optim, grad_accum_count=1):
    method train (line 31) | def train(self, train_iter_fct, train_steps):
    method test (line 61) | def test(self, test_iter, step):
    method _gradient_accumulation (line 104) | def _gradient_accumulation(self, true_batchs):
    method _save (line 129) | def _save(self, step):

FILE: bertsum-chinese/src/others/logging.py
  function init_logger (line 9) | def init_logger(log_file=None, log_file_level=logging.NOTSET):

FILE: bertsum-chinese/src/others/statistical.py
  function apply_statis (line 7) | def apply_statis(x: pd.Series):
  function sent_sount_stas (line 23) | def sent_sount_stas():

FILE: bertsum-chinese/src/others/utils.py
  function str2bool (line 6) | def str2bool(v):
  function int_arr_to_str (line 15) | def int_arr_to_str(arr: list):
  function label_to_idx (line 20) | def label_to_idx(label_arr: list):
  function tally_parameters (line 25) | def tally_parameters(model):

FILE: bertsum-chinese/src/prepro/data_builder_LAI.py
  class BertData (line 14) | class BertData():
    method __init__ (line 15) | def __init__(self, args):
    method preprocess (line 23) | def preprocess(self, src: str, key_sents_ids: list) -> tuple:
  function _format_to_bert (line 69) | def _format_to_bert(params) -> None:
  function format_to_bert (line 97) | def format_to_bert(args) -> None:

FILE: bertsum-chinese/train_LAI.py
  function test (line 19) | def test(args, test_from, step):
  function train (line 43) | def train(args, device_id):