Task: NL2SQL (source dataset: Spider)

Natural language to SQL, also known as NL2SQL, is the problem of generating a SQL query that can be executed on a input table, for a given natural language question.

You will be given an input table called "table" in the markdown format, as well as a question. Your task is to generate an SQL query that can be executed on the input table to answer the question.

Please ensure that the generated SQL only return relevant columns being specifically asked in the question (e.g., in SQL please don't return more than one columns in SELECT if only one column is needed to answer the given question, and similarly please refrain from using SELECT * unless the entire row is needed to answer the question).
 
When generating SQL code, please use the SQLite syntax to ensure that your SQL is executable on SQLite3. Use only columns that are present in the table, always use quotes around your column and table names (some column names have punctuation or special characters), and refer to the columns using the column-names exactly as they appear in the table, do not add or remove punctuation (underscore, slash, etc.)

No explanation, return the code only with the following markdown codeblock format : ```sql<SQL CODE>```. For example, ```sql
SELECT * FROM "table"
``` is a valid format.

Database:
car_racing

Tables:

Table: country
|   Country_Id | Country     | Capital    | Official_native_language   | Regoin        |
|-------------:|:------------|:-----------|:---------------------------|:--------------|
|            1 | Japan       | Tokyo      | Japanese                   | Asia          |
|            2 | USA         | Washington | English                    | North America |
|            6 | Britain     | London     | British English            | Europe        |
|            3 | China       | Beijing    | Chinese                    | Asia          |
|            4 | Ireland     | Dublin     | Irish English              | Europe        |
|            5 | Isle of Man | Douglas    | English Manx               | Europe        |


Table: team
|   Team_ID | Team                  | Make                  | Manager         | Sponsor               | Car_Owner       |
|----------:|:----------------------|:----------------------|:----------------|:----------------------|:----------------|
|         1 | Arrington Racing      | Chrysler Imperial     | Buddy Arrington | Arrington Racing      | Buddy Arrington |
|         2 | Benfield Racing       | Buick Regal           | Joe Ruttman     | Levi Garrett          | Ron Benfield    |
|         3 | Blue Max Racing       | Pontiac Grand Prix    | Tim Richmond    | Old Milwaukee         | Raymond Beadle  |
|         4 | Bobby Hawkins Racing  | Chevrolet Monte Carlo | David Pearson   | Chattanooga Chew      | Bobby Hawkins   |
|         5 | Bud Moore Engineering | Ford Thunderbird      | Dale Earnhardt  | Wrangler Jeans        | Bud Moore       |
|         6 | Cliff Stewart Racing  | Pontiac Grand Prix    | Geoff Bodine    | Gatorade              | Cliff Stewart   |
|         7 | DiGard Motorsports    | Buick Regal           | Bobby Allison   | Miller American       | Bill Gardner    |
|         8 | Donlavey Racing       | Ford Thunderbird      | Dick Brooks     | Chameleon Sunglasses  | Junie Donlavey  |
|         9 | Ellington Racing      | Chevrolet Monte Carlo | Lake Speed      | UNO / Bull Frog Knits | Hoss Ellington  |
|        10 | Hagan Racing          | Chevrolet Monte Carlo | Terry Labonte   | Budweiser             | Billy Hagan     |


Table: driver
|   Driver_ID | Driver         |   Country |   Age |   Car_# | Make      |   Points |   Laps | Winnings   |
|------------:|:---------------|----------:|------:|--------:|:----------|---------:|-------:|:-----------|
|           1 | Kasey Kahne    |         2 |    23 |       9 | Dodge     |      185 |    334 | $530,164   |
|           2 | Matt Kenseth   |         2 |    21 |      17 | Ford      |      175 |    334 | $362,491   |
|           3 | Tony Stewart   |         2 |    19 |      20 | Chevrolet |      175 |    334 | $286,386   |
|           4 | Denny Hamlin * |         2 |    25 |      11 | Chevrolet |      165 |    334 | $208,500   |
|           5 | Kevin Li       |         3 |    23 |      29 | Chevrolet |      160 |    334 | $204,511   |
|           6 | Jeff Chen      |         3 |    25 |      31 | Chevrolet |      150 |    334 | $172,220   |
|           7 | Scott Riggs    |         1 |    22 |      10 | Dodge     |      146 |    334 | $133,850   |
|           8 | Mali Lu Jr. *  |         3 |    29 |       1 | Chevrolet |      147 |    334 | $156,608   |
|           9 | Mark Martin    |         5 |    21 |       6 | Ford      |      143 |    334 | $151,850   |
|          10 | Bobby Labonte  |         4 |    25 |      43 | Dodge     |      134 |    334 | $164,211   |


Table: team_driver
|   Team_ID |   Driver_ID |
|----------:|------------:|
|         1 |           1 |
|         2 |           3 |
|         1 |           5 |
|         5 |           4 |
|         1 |           9 |
|         1 |          10 |
|        10 |           7 |
|         9 |           6 |
|         1 |           2 |
|         1 |           8 |


Question: Find the number of drivers whose points are greater than 150 for each make.

db_id: car_racing
tables: name: country
data: country.csv
name: team
data: team.csv
name: driver
data: driver.csv
name: team_driver
data: team_driver.csv
label: SELECT make ,  count(*) FROM driver WHERE points  >  150 GROUP BY make
sqlite_path: $MMTU_HOME/data_sqlite/spider/spider_data/test_database/car_racing/car_racing.sqlite