text: 
  def config_JointEmbeder():   
      conf = {
          # data_params
          'dataset_name':'CodeSearchDataset', # name of dataset to specify a data loader
              #training data
              'train_name':'train.name.h5',
              'train_api':'train.apiseq.h5',
              'train_tokens':'train.tokens.h5',
              'train_desc':'train.desc.h5',
              #test data
              'valid_name':'valid.name.h5',
              'valid_api':'valid.apiseq.h5',
              'valid_tokens':'valid.tokens.h5',
              'valid_desc':'valid.desc.h5',
              #use data (computing code vectors)
              'use_codebase':'use.rawcode.txt',#'use.rawcode.h5'
              'use_names':'use.methname.h5',
              'use_apis':'use.apiseq.h5',
              'use_tokens':'use.tokens.h5',     
              #results data(code vectors)            
              'use_codevecs':'use.codevecs.h5',        
                     
              #parameters
              'name_len': 6,
              'api_len':30,
              'tokens_len':50,
              'desc_len': 30,
              'n_words': 10000, # len(vocabulary) + 1
              #vocabulary info
              'vocab_name':'vocab.name.json',
              'vocab_api':'vocab.apiseq.json',
              'vocab_tokens':'vocab.tokens.json',
              'vocab_desc':'vocab.desc.json',
                      
          #training_params            
              'batch_size': 64,
              'chunk_size':200000,
              'nb_epoch': 15,
              #'optimizer': 'adam',
              'learning_rate':2.08e-4,
              'adam_epsilon':1e-8,
              'warmup_steps':5000,
              'fp16': False,
              'fp16_opt_level': 'O1', #For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3'].
                              #"See details at https://nvidia.github.io/apex/amp.html"
  
          # model_params
              'emb_size': 512,
              'n_hidden': 512,#number of hidden dimension of code/desc representation
              # recurrent
              'lstm_dims': 256, # * 2          
              'margin': 0.3986,
              'sim_measure':'cos',#similarity measure: cos, poly, sigmoid, euc, gesd, aesd. see https://arxiv.org/pdf/1508.01585.pdf
                           #cos, poly and sigmoid are fast with simple dot, while euc, gesd and aesd are slow with vector normalization.
      }
      return conf
  
  
  
  