Update README.md

README.md

@@ -111,40 +111,40 @@ First we need to extract all the information from the above data such as QA, tab
 This data is then expressed in natural language, e.g:
 
 ```
-{ "instruction": "concert_singer contains tables such as stadium, singer, concert, singer_in_concert. Table stadium has columns such as stadium_id. location, name, capacity, highest, lowest, average. table stadium has columns such as stadium_id, location, name, capacity, highest, lowest, average. stadium_id is the primary key. table singer has columns such as singer_id, name, country, song_name, song_release_year name, song_release_year, age, is_male. singer_id is the primary key. table concert has columns such as concert_id, concert_name, theme, stadium_id. year. Table singer_in_concert has columns such as concert_id, singer_id. concert_id is the primary key. The year of concert is the foreign key of location of stadium. The stadium_id of singer_in_concert is the foreign key of name of singer. concert is the foreign key of concert_name of concert.". 
-"input": "How many singers do we have?". 
-"response": "concert_singer | select count(*) from singer"}
+{"instruction": "department_management contains tables such as department, head, management. Table department has columns such as department_id, name, creation, ranking, budget_in_billions, num_employees. department_id is the primary key. Table head has columns such as head_id, name, born_state, age. head_id is the primary key. Table management has columns such as department_id, head_id, temporary_acting. department_id is the primary key. The head_id of management is the foreign key of head_id of head. The department_id of management is the foreign key of department_id of department.",
+"input": "How many heads of the departments are older than 56 ?",
+"output": "select count(*) from head where age > 56"}
 ```
 
 The code implementation of the above data pre-processing section is as follows:
 
 ```bash
-## 生成train数据
+## Generate train data
 python dbgpt_hub/utils/sql_data_process.py 
 
-## 生成dev数据
+## Generate dev data
 python dbgpt_hub/utils/sql_data_process.py \
     --data_filepaths data/spider/dev.json \
     --output_file dev_sql.json \
-
 ```
 
 When fine-tuning the model, we also customize the prompt dict to optimize the input: 
 
 ``` python
 SQL_PROMPT_DICT = {
     "prompt_input": (
-        "I want you to act as a SQL terminal in front of an example database. "
-        "Below is an instruction that describes a task, Write a response that appropriately completes the request.\n\n"
-        "##Instruction:\n{instruction}\n\n###Input:\n{input}\n\n###Response: "
-    ).
+        "I want you to act as a SQL terminal in front of an example database, \
+         you need only to return the sql command to me.Below is an instruction that describes a task, \
+         Write a response that appropriately completes the request.\n\n  \
+         The instruction is {instruction}, So please tell me {input}, ###Response:"
+    ),
     "prompt_no_input": (
-        "I want you to act as a SQL terminal in front of an example database. "
-        "Below is an instruction that describes a task, Write a response that appropriately completes the request.\n\n"
-        "####Instruction:\n{instruction}\n\n### Response: "
-    ).
+        "I want you to act as a SQL terminal in front of an example database, \
+        you need only to return the sql command to me.Below is an instruction that describes a task, \
+        Write a response that appropriately completes the request.\n\n  \
+        The instruction is {instruction}, ###Response:"
+    ),
 }
-
 ```
 
 ### 3.3. Model fine-tuning
@@ -181,8 +181,7 @@ To evaluate model performance on the dataset, default is spider dataset.
 Run the following command:
 
 ```bash
-cd eval
-python evaluation.py --plug_value --input  Your_model_pred_file
+python eval/evaluation.py --plug_value --input
 ```
 
 ## 4. RoadMap