Update README.md

add more details after competition

README.md

@@ -1 +1,36 @@
-fintuned the kykim/bert-kor-base model as a dense passage retrieval context encoder by KLUE datasets
+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
+ 
+I'm not confident about this model will work in other dataset or corpus. 
+'''
+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")
+
+'''
+        
+