db_id string | context string | question string | query string | answer string | answer_prefix string | max_new_tokens int64 |
|---|---|---|---|---|---|---|
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT count(*) FROM singer | pd.DataFrame({'count(*)': ['6']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT count(*) FROM singer | pd.DataFrame({'count(*)': ['6']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT name , country , age FROM singer ORDER BY age DESC | pd.DataFrame({'Name': ['Joe Sharp', 'John Nizinik', 'Rose White', 'Timbaland', 'Justin Brown', 'Tribal King'], 'Country': ['Netherlands', 'France', 'France', 'United States', 'France', 'France'], 'Age': ['52', '43', '41', '32', '29', '25']}, index=[0, 1, 2, 3, 4, 5]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT name , country , age FROM singer ORDER BY age DESC | pd.DataFrame({'Name': ['Joe Sharp', 'John Nizinik', 'Rose White', 'Timbaland', 'Justin Brown', 'Tribal King'], 'Country': ['Netherlands', 'France', 'France', 'United States', 'France', 'France'], 'Age': ['52', '43', '41', '32', '29', '25']}, index=[0, 1, 2, 3, 4, 5]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT avg(age) , min(age) , max(age) FROM singer WHERE country = 'France' | pd.DataFrame({'avg(age)': ['34.5'], 'min(age)': ['25'], 'max(age)': ['43']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT avg(age) , min(age) , max(age) FROM singer WHERE country = 'France' | pd.DataFrame({'avg(age)': ['34.5'], 'min(age)': ['25'], 'max(age)': ['43']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT song_name , song_release_year FROM singer ORDER BY age LIMIT 1 | pd.DataFrame({'Song_Name': ['Love'], 'Song_release_year': ['2016']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT song_name , song_release_year FROM singer ORDER BY age LIMIT 1 | pd.DataFrame({'Song_Name': ['Love'], 'Song_release_year': ['2016']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT DISTINCT country FROM singer WHERE age > 20 | pd.DataFrame({'Country': ['Netherlands', 'United States', 'France']}, index=[0, 1, 2]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT DISTINCT country FROM singer WHERE age > 20 | pd.DataFrame({'Country': ['Netherlands', 'United States', 'France']}, index=[0, 1, 2]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT country , count(*) FROM singer GROUP BY country | pd.DataFrame({'Country': ['France', 'Netherlands', 'United States'], 'count(*)': ['4', '1', '1']}, index=[0, 1, 2]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT country , count(*) FROM singer GROUP BY country | pd.DataFrame({'Country': ['France', 'Netherlands', 'United States'], 'count(*)': ['4', '1', '1']}, index=[0, 1, 2]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT song_name FROM singer WHERE age > (SELECT avg(age) FROM singer) | pd.DataFrame({'Song_Name': ['You', 'Sun', 'Gentleman']}, index=[0, 1, 2]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT song_name FROM singer WHERE age > (SELECT avg(age) FROM singer) | pd.DataFrame({'Song_Name': ['You', 'Sun', 'Gentleman']}, index=[0, 1, 2]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT LOCATION , name FROM stadium WHERE capacity BETWEEN 5000 AND 10000 | pd.DataFrame({'Location': [], 'Name': []}, index=[]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT LOCATION , name FROM stadium WHERE capacity BETWEEN 5000 AND 10000 | pd.DataFrame({'Location': [], 'Name': []}, index=[]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | select max(capacity), average from stadium | pd.DataFrame({'max(capacity)': ['52500'], 'Average': ['730']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | select avg(capacity) , max(capacity) from stadium | pd.DataFrame({'avg(capacity)': ['10621.666666666666'], 'max(capacity)': ['52500']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT name , capacity FROM stadium ORDER BY average DESC LIMIT 1 | pd.DataFrame({'Name': ["Stark's Park"], 'Capacity': ['10104']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT name , capacity FROM stadium ORDER BY average DESC LIMIT 1 | pd.DataFrame({'Name': ["Stark's Park"], 'Capacity': ['10104']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT count(*) FROM concert WHERE YEAR = 2014 OR YEAR = 2015 | pd.DataFrame({'count(*)': ['6']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT count(*) FROM concert WHERE YEAR = 2014 OR YEAR = 2015 | pd.DataFrame({'count(*)': ['6']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT T2.name , count(*) FROM concert AS T1 JOIN stadium AS T2 ON T1.stadium_id = T2.stadium_id GROUP BY T1.stadium_id | pd.DataFrame({'Name': ["Stark's Park", 'Glebe Park', 'Somerset Park', 'Recreation Park', 'Balmoor'], 'count(*)': ['1', '1', '2', '1', '1']}, index=[0, 1, 2, 3, 4]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT T2.name , count(*) FROM concert AS T1 JOIN stadium AS T2 ON T1.stadium_id = T2.stadium_id GROUP BY T1.stadium_id | pd.DataFrame({'Name': ["Stark's Park", 'Glebe Park', 'Somerset Park', 'Recreation Park', 'Balmoor'], 'count(*)': ['1', '1', '2', '1', '1']}, index=[0, 1, 2, 3, 4]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT T2.name , T2.capacity FROM concert AS T1 JOIN stadium AS T2 ON T1.stadium_id = T2.stadium_id WHERE T1.year >= 2014 GROUP BY T2.stadium_id ORDER BY count(*) DESC LIMIT 1 | pd.DataFrame({'Name': ['Somerset Park'], 'Capacity': ['11998']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | select t2.name , t2.capacity from concert as t1 join stadium as t2 on t1.stadium_id = t2.stadium_id where t1.year > 2013 group by t2.stadium_id order by count(*) desc limit 1 | pd.DataFrame({'Name': ['Somerset Park'], 'Capacity': ['11998']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT YEAR FROM concert GROUP BY YEAR ORDER BY count(*) DESC LIMIT 1 | pd.DataFrame({'Year': ['2015']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT YEAR FROM concert GROUP BY YEAR ORDER BY count(*) DESC LIMIT 1 | pd.DataFrame({'Year': ['2015']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT name FROM stadium WHERE stadium_id NOT IN (SELECT stadium_id FROM concert) | pd.DataFrame({'Name': ['Bayview Stadium', 'Hampden Park', 'Forthbank Stadium', 'Gayfield Park']}, index=[0, 1, 2, 3]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT name FROM stadium WHERE stadium_id NOT IN (SELECT stadium_id FROM concert) | pd.DataFrame({'Name': ['Bayview Stadium', 'Hampden Park', 'Forthbank Stadium', 'Gayfield Park']}, index=[0, 1, 2, 3]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT country FROM singer WHERE age > 40 INTERSECT SELECT country FROM singer WHERE age < 30 | pd.DataFrame({'Country': ['France']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT name FROM stadium EXCEPT SELECT T2.name FROM concert AS T1 JOIN stadium AS T2 ON T1.stadium_id = T2.stadium_id WHERE T1.year = 2014 | pd.DataFrame({'Name': ['Balmoor', 'Bayview Stadium', 'Forthbank Stadium', 'Gayfield Park', 'Hampden Park', 'Recreation Park']}, index=[0, 1, 2, 3, 4, 5]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT name FROM stadium EXCEPT SELECT T2.name FROM concert AS T1 JOIN stadium AS T2 ON T1.stadium_id = T2.stadium_id WHERE T1.year = 2014 | pd.DataFrame({'Name': ['Balmoor', 'Bayview Stadium', 'Forthbank Stadium', 'Gayfield Park', 'Hampden Park', 'Recreation Park']}, index=[0, 1, 2, 3, 4, 5]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT T2.concert_name , T2.theme , count(*) FROM singer_in_concert AS T1 JOIN concert AS T2 ON T1.concert_id = T2.concert_id GROUP BY T2.concert_id | pd.DataFrame({'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'count(*)': ['3', '2', '1', '1', '2', '1']}, index=[0, 1, 2, 3, 4, 5]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | select t2.concert_name , t2.theme , count(*) from singer_in_concert as t1 join concert as t2 on t1.concert_id = t2.concert_id group by t2.concert_id | pd.DataFrame({'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'count(*)': ['3', '2', '1', '1', '2', '1']}, index=[0, 1, 2, 3, 4, 5]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT T2.name , count(*) FROM singer_in_concert AS T1 JOIN singer AS T2 ON T1.singer_id = T2.singer_id GROUP BY T2.singer_id | pd.DataFrame({'Name': ['Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'count(*)': ['2', '3', '1', '2', '2']}, index=[0, 1, 2, 3, 4]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT T2.name , count(*) FROM singer_in_concert AS T1 JOIN singer AS T2 ON T1.singer_id = T2.singer_id GROUP BY T2.singer_id | pd.DataFrame({'Name': ['Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'count(*)': ['2', '3', '1', '2', '2']}, index=[0, 1, 2, 3, 4]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT T2.name FROM singer_in_concert AS T1 JOIN singer AS T2 ON T1.singer_id = T2.singer_id JOIN concert AS T3 ON T1.concert_id = T3.concert_id WHERE T3.year = 2014 | pd.DataFrame({'Name': ['Timbaland', 'Justin Brown', 'John Nizinik', 'Justin Brown', 'Tribal King', 'Rose White']}, index=[0, 1, 2, 3, 4, 5]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'concert_ID': ['1', '2', '3', '4', '5', '6'], 'concert_Name': ['Auditions', 'Super bootcamp', 'Home Visits', 'Week 1', 'Week 1', 'Week 2'], 'Theme': ['Free choice', 'Free choice 2', 'Bleeding Love', 'Wide Awake', 'Happy Tonight', 'Party All Night'], 'Stadium_ID': ['1', '2', '2', '10', '9', '7'], 'Year': [... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT T2.name FROM singer_in_concert AS T1 JOIN singer AS T2 ON T1.singer_id = T2.singer_id JOIN concert AS T3 ON T1.concert_id = T3.concert_id WHERE T3.year = 2014 | pd.DataFrame({'Name': ['Timbaland', 'Justin Brown', 'John Nizinik', 'Justin Brown', 'Tribal King', 'Rose White']}, index=[0, 1, 2, 3, 4, 5]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT name , country FROM singer WHERE song_name LIKE '%Hey%' | pd.DataFrame({'Name': ['Justin Brown'], 'Country': ['France']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Singer_ID': ['1', '2', '3', '4', '5', '6'], 'Name': ['Joe Sharp', 'Timbaland', 'Justin Brown', 'Rose White', 'John Nizinik', 'Tribal King'], 'Country': ['Netherlands', 'United States', 'France', 'France', 'France', 'France'], 'Song_Name': ['You', 'Dangerous', 'Hey Oh', 'Sun', 'Gentleman', 'Love'], 'Song_... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT name , country FROM singer WHERE song_name LIKE '%Hey%' | pd.DataFrame({'Name': ['Justin Brown'], 'Country': ['France']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT T2.name , T2.location FROM concert AS T1 JOIN stadium AS T2 ON T1.stadium_id = T2.stadium_id WHERE T1.Year = 2014 INTERSECT SELECT T2.name , T2.location FROM concert AS T1 JOIN stadium AS T2 ON T1.stadium_id = T2.stadium_id WHERE T1.Year = 2015 | pd.DataFrame({'Name': ['Somerset Park'], 'Location': ['Ayr United']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | SELECT T2.name , T2.location FROM concert AS T1 JOIN stadium AS T2 ON T1.stadium_id = T2.stadium_id WHERE T1.Year = 2014 INTERSECT SELECT T2.name , T2.location FROM concert AS T1 JOIN stadium AS T2 ON T1.stadium_id = T2.stadium_id WHERE T1.Year = 2015 | pd.DataFrame({'Name': ['Somerset Park'], 'Location': ['Ayr United']}, index=[0]) | Answer: | 274,316 |
concert_singer | pd.DataFrame({'Stadium_ID': ['1', '2', '3', '4', '5', '6', '7', '9', '10'], 'Location': ['Raith Rovers', 'Ayr United', 'East Fife', "Queen's Park", 'Stirling Albion', 'Arbroath', 'Alloa Athletic', 'Peterhead', 'Brechin City'], 'Name': ["Stark's Park", 'Somerset Park', 'Bayview Stadium', 'Hampden Park', 'Forthbank Stadi... | You are a question-answering model specialized in tabular data.
I will provide a table as a string in the format of a pandas DataFrame constructor (i.e., pd.DataFrame({...})). This string contains column names, row values, and an index. You must return the exact table cells that directly answer the provided natural lan... | select count(*) from concert where stadium_id = (select stadium_id from stadium order by capacity desc limit 1) | pd.DataFrame({'count(*)': ['0']}, index=[0]) | Answer: | 274,316 |
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