Update README.md

README.md

@@ -17,18 +17,30 @@ This model is a fine-tuned version of [KT-AI/midm-bitext-S-7B-inst-v1](https://h
 
 ## Model description
 
-More information needed
+Midm은 KT가 개발한 사전학습 한국어-영어 언어모델 입니다. 문자열을 입력으로 하며, 문자열을 생성합니다.
+해당 모델(KT-AI/midm-bitext-S-7B-inst-v1)을 베이스 모델로 하여 미세튜닝을 진행하였습니다. 
+
+Midm is a pre-trained Korean-English language model developed by KT. It takes text as input and creates text.
+We fine-tuned the model based on KT-AI/midm-bitext-S-7B-inst-v1.
 
 ## Intended uses & limitations
 
-More information needed
+nsmc 데이터셋의 사용자가 입력한 리뷰 문장을 분류하는 에이전트이다. 사용자 리뷰 문장으로부터 '긍정' 또는 '부정'을 판단합니다.
+
+This is an agent that classifies user-input review sentences from NSMC dataset. 
+It determines whether the user review sentences are 'positive' or 'negative'.
+
+## Training and test data
 
-## Training and evaluation data
+Training 및 test 데이터는 nsmc 데이터 셋에서 로딩해 사용합니다. (elvaluation 데이터는 사용하지 않습니다.)
 
-More information needed
+We load and use training and test data from the NSMC dataset. (We do not use an evaluation data.)
 
 ## Training procedure
 
+사용자의 영화 리뷰 문장을 입력으로 받아 문장을 '긍정(1)' 또는 '부정(0)'으로 분류합니다. 
+Accepts movie review sentences from the user as input and classifies the sentences as 'Positive (1)' or 'Negative (0)'.
+
 ### Training hyperparameters
 
 The following hyperparameters were used during training:
@@ -46,7 +58,23 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-
+- The following are the results considering incorrectly generated words(e.g., **정**, **' '**).
+  - **Binary Confusion Matrix**
+    |          | TP | TN |
+    |----------|--------------------|--------------------|
+    | PP |        443         |         49         |
+    | PN |         57         |        451         |
+
+  - **Accuracy**: 0.894
+
+- The following are the results without considering incorrectly generated words as wrong(e.g., **정**, **' '**).
+  - **Binary Confusion Matrix**
+    |          | TP | TN |
+    |----------|--------------------|--------------------|
+    | PP |        443         |         38         |
+    | PN |         44         |        451         |
+     
+  - **Accuracy**: 0.916
 
 ### Framework versions