Update README.md

README.md

@@ -10,29 +10,50 @@ tags:
 
 # T5 large LM Adapt for Text to SQL
 
-This model is fine-tuned from the [t5-large-LM-adapt](https://huggingface.co/google/t5-large-lm-adapt) checkpoint. 
-While training the model on Text2SQL task, the model learns how to generate a SQL query based on the question posed in natural language, however in some cases the SQL query contains unknown columns etc. and altogether does not take the schema of the specific database into account. That is where our approach comes in. We incorporated the database schema into the input question while training, to specify which columns and relations are available to generate an applicable SQL query.
+### Tl;dr
+
+This model is purposed to generate structured SQL queries from the natural-language prompts.
+
+### Intro
+
+In the Text2SQL task, the model learns how to generate a SQL query based on the question posed in natural language. 
+However, in some cases, the SQL query contains unknown columns etc., and altogether does not take the schema of the specific database into account. 
+
+That is where our approach comes in. 
+We incorporated the database schema into the input question while training to specify which columns and relations are available to generate an applicable SQL query.
+
+The exposition of database schema, together with the prompt, allows the model to learn the mapping of the schema to the expected output.
+This allows the model to better generalize to the schemas that were not present in the training data.
+
+### Base model
+
+We fine-tune this model from the [t5-large-LM-adapt](https://huggingface.co/google/t5-large-lm-adapt) checkpoint.
 
 ## Spider and Spider-Syn dataset
 
 The model was fine-tuned on the training splits of [Spider](https://yale-lily.github.io/spider) and [Spider-Syn](https://github.com/ygan/Spider-Syn/tree/main/Spider-Syn) datasets. Instead of using only the questions, we added the database schema to the question, as we wanted the model to generate a question over a given database
 
-_input_:
+_Input prompt_:
 
-```
+```python
 Question:  What is the average, minimum, and maximum age for all French musicians?
-Schema: "stadium" "Stadium_ID" int , "Location" text , "Name" text , "Capacity" int , "Highest" int , "Lowest" int , "Average" int , foreign_key:  primary key: "Stadium_ID" [SEP] "singer" "Singer_ID" int , "Name" text , "Country" text , "Song_Name" text , "Song_release_year" text , "Age" int , "Is_male" bool , foreign_key:  primary key: "Singer_ID" [SEP] "concert" "concert_ID" int , "concert_Name" text , "Theme" text , "Year" text , foreign_key: "Stadium_ID" text from "stadium" "Stadium_ID" , primary key: "concert_ID" [SEP] "singer_in_concert"  foreign_key: "concert_ID" int from "concert" "concert_ID" , "Singer_ID" text from "singer" "Singer_ID" , primary key: "concert_ID" "Singer_ID"
+Schema: "stadium" "Stadium_ID" int , "Location" text , "Name" text , "Capacity" int , "Highest" int , "Lowest" int ,
+        "Average" int , foreign_key:  primary key: "Stadium_ID" [SEP] "singer" "Singer_ID" int , "Name" text , "Country" text ,
+        "Song_Name" text , "Song_release_year" text , "Age" int , "Is_male" bool ,
+        foreign_key:  primary key: "Singer_ID" [SEP],
+        "concert" "concert_ID" int , "concert_Name" text , "Theme" text , "Year" text , foreign_key: "Stadium_ID" text from "stadium",
+        "Stadium_ID" , primary key: "concert_ID" [SEP] "singer_in_concert",
+        foreign_key: "concert_ID" int from "concert",
+        "concert_ID" , "Singer_ID" text from "singer" "Singer_ID" , primary key: "concert_ID" "Singer_ID"
 ```
 
-=> _target_:
+_Expected output_:
 
-```
+```sql
 SELECT avg(age), min(age), max(age) FROM singer WHERE country = 'France'
 ```
 
-When evaluating we query the sqlite database
-=> _query result_:
-
+When evaluating the output, we query the _SQLite_ database and get:
 ```
 [[34.5, 25, 43]]
 ```
@@ -69,10 +90,9 @@ output_text = tokenizer.decode(outputs, skip_special_tokens=True)
 
 print("SQL Query:")
 print(output_text)
-
 ```
-returns:
-```bash
+outputs:
+```sql
 SQL Query:
 SELECT avg(age), min(age), max(age) FROM singer WHERE country = 'France'
 ```