readme: fintuned the kykim/bert-kor-base model as a dense passage retrieval context encoder by KLUE dataset
  this link is experiment result. https://wandb.ai/thingsu/DenseRetrieval
  
  Corpus : Korean Wikipedia Corpus 
  
  Trained Strategy : 
   - Pretrained Model : kykim/bert-kor-base 
   - Inverse Cloze Task : 16 Epoch, by korquad v 1.0, KLUE MRC dataset
   - In-batch Negatives : 12 Epoch, by KLUE MRC dataset, random sampling between Sparse Retrieval(TF-IDF) top 100 passage per each query
   
  You must need to use Korean wikipedia corpus
  
  <pre>
  <code>
  from Transformers import AutoTokenizer, BertPreTrainedModel, BertModel
  
  class BertEncoder(BertPreTrainedModel):
      def __init__(self, config):
          super(BertEncoder, self).__init__(config)
          
          self.bert = BertModel(config)
          self.init_weights()
  
  
      def forward(self, input_ids, attention_mask=None, token_type_ids=None):
          outputs = self.bert(input_ids, attention_mask, token_type_ids)
          pooled_output = outputs[1]
          return pooled_output
          
  model_name = 'kykim/bert-kor-base' 
  tokenizer = AutoTokenizer.from_pretrained(model_name)
  
  q_encoder = BertEncoder.from_pretrained("thingsu/koDPR_question")
  p_encoder = BertEncoder.from_pretrained("thingsu/koDPR_context")
  
  </code>
  </pre>