AMALIA LLM 0626 Post-Train Data
Collection
Datasets used to perform the Supervised Fine-Tuning post-training stage of the model. • 29 items • Updated • 2
Datasets:
messages listlengths 2 2 | chat_template_kwargs dict | source stringclasses 1
value |
|---|---|---|
[
{
"from": "user",
"value": "Task: Kindly complete the input table by providing the value for the missing entry, indicated by '[MISSING]'. Only the filled-in value is required, not the entire table. Return the final result as JSON in the format {\"value\": \"<value filled in>\"}.\n\nIn:\n|Pos|Driver|DQSF|IND... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Please determine the matching columns between Table A and Table B. State the corresponding columns in Table B for each of the columns in Table A. If a column in Table A has no counterpart in Table B, you can map it to None. Represent the mappings using a list of column head... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Take a look at the table of inputs and outputs in two columns. Analyze the patterns based on the initial rows and compute the output value for the last row labeled as '[Output Value].' Provide only the output value and do not include any other data. Return the final result ... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Locate the corresponding columns in Table B that match with those in Table A. For each column in Table A, provide the corresponding column in Table B. If a column in A doesn't have a corresponding column in Table B, you can map it to None. Represent the mappings using pairs... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: Could you establish if Restaurant (1) and Restaurant (2) are referring to the same entity or not? Provide your ultimate answer as 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show you... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: I would like you to ascertain whether Book 1 and Book 2 are referring to the same entity or not. Your final response should be 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reasonin... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Discover the columns in Table B that match with those in Table A. For each column present in Table A, specify the corresponding column present in Table B. If a column in Table A doesn't have a corresponding column in Table B, you can represent it as None. Use pairs of ... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: Kindly analyze the input table and inform me about any cell or cells that contain errors. If there are multiple cells containing errors, list them. If no cells contain errors, state 'None'. Only provide the cells you have a high degree of confidence in identifying as... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Use the table given with inputs and outputs in two columns to identify patterns based on the first few rows. Afterward, predict the value for the last row denoted as '[Output Value].' Please only return the output value and exclude all other details. Return the final r... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: Your job is to write an SQL query while considering the input table and question. Use 'table' as the table name if it helps. Return the final result as JSON in the format {\"SQL\": \"<SQL code>\"}.\n\nInput:\n**Input table:**\n|Song title|Artist|Decade|Genre|Single /... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: I would like you to verify whether Book A and Book B are referring to the same entity or not. Your final response should be 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reas... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: Take the list provided and transform it into a table with several columns. The table should be in plain text format, with vertical bars (|) as column dividers and a new line for each row. Return the final result as JSON in the format {\"table\": \"<table transformed ... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Find the corresponding columns between Table A and Table B. Specify the related columns in Table B for each column listed in Table A. If a column in Table A has no corresponding match in Table B, you can represent it as None. Utilize pairs of column headers within a li... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Instruction: I'd appreciate it if you could move the column \"Lizard lick Towing(Season 1)\" in the table to the leftmost position. Share the table after the move.\n\n[Q]:\n|Cast member|Lizard lick Towing(Season 1)|Lizard lick Towing(Season 2)|Lizard lick Towing(Season 3)|Lizard ... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: Kindly arrange the table by sorting it based on column \"Type\" in alphabetical ascending order. After the sorting, please provide the updated table.\n\n[Q]:\n|Year|Type|Work|First appeared in|\n|---|---|---|---|\n|1939|Novella|Komalathin Kobam|Kudi arasu|\n|1939|Nov... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: We need to ascertain if Movie 1 and Movie 2 denote the same entity. Your final response should be 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reasoning before showing the f... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Instruction: Create a new additional column for the input table and append it to the right. Share the modified table, which includes the new column.\n\nQuestion:\n|Week #|Judges' score(Inaba)|Judges' score(Hough)|Judges' score(Tonioli)|\n|---|---|---|---|\n|1|8|7|7|\n|2|8 7|7 7|8... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: I request an examination of the input table to identify any cell or cells with errors. Should multiple cells contain errors, compile them into a list. If no cells contain errors, reply with 'None'. Share only those cells for which you have a high level of confidence ... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: You are tasked with supplying the missing data in the input table, denoted by '[MISSING]'. Provide solely the value you inserted, not the whole table. Return the final result as JSON in the format {\"value\": \"<value filled in>\"}.\n\n[Q]:\n|Director|Opening|Genre|\... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Generate a fresh row for the input table and add it at the end. Show the updated table with the newly created row.\n\n## Input:\n|Year|Name|Status|\n|---|---|---|\n|1939|Hattie McDaniel|Won|\n|1949|Ethel Waters|Nominated|\n|1959|Juanita Moore|Nominated|\n|1967|Beah Richards... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Discover the columns in Table B that match with those in Table A. For each column present in Table A, specify the corresponding column present in Table B. If a column in Table A doesn't have a corresponding column in Table B, you can represent it as None. Use pairs of colum... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Instruction: Please write the SQL query given the input table and question. Use 'table' as the table name if needed. Return the final result as JSON in the format {\"SQL\": \"<SQL code>\"}.\n\nInput:\n**Input table:**\n|Date|Venue|Score|Result|Competition|\n|---|---|---|---|---|\... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: I request you to establish if the First Citation and the Second Citation denote the same entity. State your final answer as 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reasoning b... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Develop an SQL query with the input table and question in mind. Don't forget to consider using 'table' as the table name if applicable. Return the final result as JSON in the format {\"SQL\": \"<SQL code>\"}.\n\nQ:\n**Input table:**\n|Year|Matches|Wins|Losses|No Result... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: You are accountable for providing the missing value in the input table, identified as '[MISSING]'. Share only the value you filled in, not the rest of the table. Return the final result as JSON in the format {\"value\": \"<value filled in>\"}.\n\nQ:\n|Year|Player|Natio... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Could you select the row or rows with the value of column \"P\" being \"22\" in the table? Once done, share the table with the selected rows.\n\nIn:\n|Team|P|CL|CL3|T|A|C|Pen|Pts|CW|CW2|WD4|WD2|WD|LD4|LD2|LD0|\n|---|---|---|---|---|---|---|---|---|---|---|---|---|---|-... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Could you spare a moment to summarize the input table's key findings? Return the final result as JSON in the format {\"summary\": \"<summary of table>\"}.\n\nInput:\n|Country|TV Network|Language|Qualifying|Race 1|Race 2|Race 3|\n|---|---|---|---|---|---|---|\n|United K... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: With the input table data and the list of potential headers, identify the best-fitting column header for each table column using only the candidate headers. Provide the optimal column header for each column, presenting them in a list. Return the final result a... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: After examining the table, could you provide a brief summary of its main points? Return the final result as JSON in the format {\"summary\": \"<summary of table>\"}.\n\nQ:\n|Number BR|Number SR|Name|Builder|BOB or WC|Shape|Current Location|\n|---|---|---|---|---|---|--... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Instruction: Please identify the columns in Table B that correspond to those in Table A. Indicate the associated columns in Table B for each column present in Table A. If a column from Table A doesn't have a corresponding column in Table B, you can denote it as None. Use pairs of... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Kindly arrange the table by sorting it based on column \"Population\" in alphabetical ascending order. After the sorting, please provide the updated table.\n\nQ:\n|Denominations|Population|% of total|\n|---|---|---|\n|Lutherans|215,093|2.2|\n|No religion|1,806,409|18.2... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Instruction: Could you choose the row or rows with the value of column \"Year\" equal to \"2002\" in the table? After the selection, kindly return the table.\n\n## Input:\n|Year|Host(s)|Final venue|Result(Runner-up)|\n|---|---|---|---|\n|2002|West Indies|to be determined|nan|\n|2... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Please generate an SQL query by referring to the input table and question provided. You may use 'table' as the table name if necessary. Return the final result as JSON in the format {\"SQL\": \"<SQL code>\"}.\n\n[Q]:\n**Input table:**\n|Rider|Bike|Laps|Time|Grid|\n|---|---|... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: After reviewing the input table, could you provide a brief summary of its main points? Return the final result as JSON in the format {\"summary\": \"<summary of table>\"}.\n\nInput:\n|#|Contestants|Episodes(9)|Episodes(10)|Episodes(1)|Episodes(2)|Episodes(3)|Episodes(4... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: Take a moment to examine the input table and let me know which cell or cells contain errors. If there are multiple erroneous cells, gather them in a list. If no cells are erroneous, indicate 'None'. Please only provide the erroneous cell or cells that you are ... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: You've been provided with a table containing input-output pairs in two columns. Analyze the patterns between inputs and outputs from the initial rows and predict the value for the last row labeled as '[Output Value].' Please exclude any other information and provide ... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Utilizing the input table data and the roster of feasible headers, ascertain the most appropriate column header for every table column. Select column headers exclusively from the list of candidates, and furnish the chosen ones in a list format. Return the final result ... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: It is essential to ascertain if Book 1 and Book 2 refer to the same entity. Your final response should be 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reasoning before showing the fi... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Could you mark the third, fifth rows in the table for selection? Afterward, share the table with the chosen row(s).\n\n[Q]:\n|Second Round|Third Round|Fourth Round|Semi Finals|Final|\n|---|---|---|---|---|\n|14.0|8.0|nan|nan|nan|\n|32.0|32.0|nan|nan|nan|\n|8.0|nan|23.0|nan|... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: I request you to confirm whether Movie A and Movie B represent the same entity or not. Please respond with 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reasoning before showing the... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Please ascertain the matching pairs of columns between Table A and Table B. State the corresponding columns in Table B for every column listed in Table A. If a column in Table A has no corresponding match in Table B, you can denote it as None. Use a list structure with... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: It falls under your responsibility to complete the missing value in the input table, denoted as '[MISSING]'. Provide just the filled-in value; the rest of the table is not needed. Return the final result as JSON in the format {\"value\": \"<value filled in>\"}... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: I request you to change the list below into a table with multiple columns. Ensure the table is in plain text, using vertical bars (|) as column separators and a new line for each row. Return the final result as JSON in the format {\"table\": \"<table transformed from t... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: Extend the input table by generating a new supplementary column and placing it on the right-hand side. Present the modified table with the added column.\n\nInput:\n|Year|Music video|Length|\n|---|---|---|\n|2010|\"Mazeltov\"|3:34|\n|2010|\"All Day Long\"|4:01|\n|2010... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Locate the corresponding columns in Table B that match with those in Table A. For each column in Table A, provide the corresponding column in Table B. If a column in A doesn't have a corresponding column in Table B, you can map it to None. Represent the mappings using pairs... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: Given the input table data and the list of headers that are potential candidates, your role is to choose the most suitable header for each column in the table. Choose exclusively from the candidate headers list and provide the selected column headers as a list... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: Change the list provided into a table with several columns. The table should be in plain text, with vertical bars (|) as column separators and a new line for each row. Return the final result as JSON in the format {\"table\": \"<table transformed from the list>\"}.\n... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Please select the row or rows with the value of column \"Aspect ratio\" equal to \"16:9\". Please return the table with the selected rows.\n\n## Input:\n|DVD title|Series(s)|Aspect ratio|Episode count|Total running time|Release date(s)|\n|---|---|---|---|---|---|\n|The Soot... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: Please confirm whether Anime I and Anime II are denoting the same entity or not. Please respond with 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reasoning before showing th... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Instruction: Please change the list below into a table with multiple columns. The table should be in plain text, utilizing vertical bars (|) as column separators and a new line for each row. Return the final result as JSON in the format {\"table\": \"<table transformed from the l... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Change the list provided into a table with several columns. The table should be in plain text, with vertical bars (|) as column separators and a new line for each row. Return the final result as JSON in the format {\"table\": \"<table transformed from the list>\"}.\n\n... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: I request you to establish whether Restaurant A and Restaurant B represent the same entity or not. Indicate your final response as either 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and ... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Could you establish if Cosmetic (1) and Cosmetic (2) are referring to the same entity or not? Provide your ultimate answer as 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reasoning before... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: Could you establish if Restaurant A and Restaurant B are referring to the same entity or not? Provide your ultimate answer as 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your re... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: Take a look at the table of inputs and outputs in two columns. Analyze the patterns based on the initial rows and compute the output value for the last row labeled as '[Output Value].' Provide only the output value and do not include any other data. Return the... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Description: Based on the input table data and the selection of potential headers, opt for the most suitable column header for each table column. Restrict your choices to the provided candidates, and organize the selected column headers into a list. Return the final result as JSO... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: We need to establish if Baby_product A and Baby_product B represent the same entity. Provide your ultimate answer as 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reasoning b... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Summarize the table and its key details for easy understanding. Return the final result as JSON in the format {\"summary\": \"<summary of table>\"}.\n\nInput:\n|Club|Coach|Captain|Kit Supplier|Stadium|Capacity|\n|---|---|---|---|---|---|\n|Bath|Steve Meehan|Luke Watson... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Examine the provided input table data along with the list of potential headers. Your goal is to identify the most fitting header for each column within the table. Only consider column headers from the candidate list, and present the chosen headers in the shape of a list. Re... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Given the input table, can you provide a summary that captures its main data? Return the final result as JSON in the format {\"summary\": \"<summary of table>\"}.\n\nIn:\n|System|Creator|Last Updated|Android Tablet Application|iOS Tablet Application|Mobile Platforms|Offline... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: Please identify the columns in Table B that correspond to those in Table A. Indicate the associated columns in Table B for each column present in Table A. If a column from Table A doesn't have a corresponding column in Table B, you can denote it as None. Use p... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: Kindly assess the input table and inform me about any cell or cells that are flawed. If there are multiple flawed cells, list them. If no cells are flawed, state 'None'. Share only the cells that you are highly confident are flawed. Return the final result as JSON in the fo... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Objective: Your task is to give a summary of the input table's main information. Return the final result as JSON in the format {\"summary\": \"<summary of table>\"}.\n\nQ:\n|Division|Team|MLB Affiliation|City|Stadium|Capacity|\n|---|---|---|---|---|---|\n|Northern|Billings Mustan... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Instruction: It is essential to determine if Book (1) and Book (2) refer to the same entity. Indicate your conclusive answer as either 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reasoning bef... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "Task: I'd be grateful if you could mark the row or rows with the value of column \"Runner-up\" equal to \"Karachi C\" in the table for selection. Provide the table with the chosen rows.\n\n[Q]:\n|Year|Winning team|Runner-up|Number of teams|Regional|Departmental|Number of matches|... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: Let's verify if Bike (1) and Bike (2) pertain to the same entity or not. Your ultimate answer should be 'Yes' or 'No'. Return the final result as JSON in the format {\"answer\": \"<Yes or No>\"}. Let's think step by step and show your reasoning before showing... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: Your responsibility is to supply the missing value in the input table marked as '[MISSING]'. Provide only the value filled in, not the entire table content. Return the final result as JSON in the format {\"value\": \"<value filled in>\"}.\n\nQuestion:\n|Date|C... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
[
{
"from": "user",
"value": "# Task Description: Please create a new additional column for the input table and append it to the right. Share the resulting table with the added column.\n\n[Q]:\n|Place|Province|White population|\n|---|---|---|\n|Pretoria|Gauteng|389,022|\n|Johannesburg|Gauteng|133,379|\n|Cape ... | {
"custom_instructions": "",
"enable_thinking": false,
"python_tools": [],
"xml_tools": []
} | table-gpt |
End of preview. Expand in Data Studio
AMALIA smoltalk2
Version of the table_gpt_no_think subset of HuggingFaceTB/smoltalk2 dataset used in the AMALIA's Supervised Fine-Tuning stage.
Original Dataset: https://huggingface.co/datasets/HuggingFaceTB/smoltalk2
This dataset is provided as part of the AMALIA project and is included in the data mix used to post-train the AMALIA model.
Citation
If you use this dataset or AMALIA in your work, please cite:
@inproceedings{simplicio-etal-2026-amalia,
title = "{AMALIA}: A Fully Open Large Language Model for {E}uropean {P}ortuguese",
author = "Simpl{{\'i}}cio, Afonso and Vinagre, Gon{{\c{{c}}}}alo and Ramos, Miguel Moura and Tavares, Diogo and Ferreira, Rafael and Attanasio, Giuseppe and Alves, Duarte M. and Calvo, In{{\^e}}s and Vieira, In{{\^e}}s and Guerra, Rui and Furtado, James and Canaverde, Beatriz and Paulo, Iago and Ramos, Vasco and Gl{{\'o}}ria-Silva, Diogo and Faria, Miguel and Treviso, Marcos and Gomes, Daniel and Gomes, Pedro and Semedo, David and Martins, Andr{{\'e}} and Magalh{{\~a}}es, Jo{{\~a}}o",
booktitle = "Proceedings of the 17th International Conference on Computational Processing of {{P}}ortuguese ({{PROPOR}} 2026) - Vol. 1",
month = apr,
year = "2026",
address = "Salvador, Brazil",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.propor-1.38/",
pages = "380--391",
isbn = "979-8-89176-387-6"
}
@misc{simplicio2026amaliatechnicalreportfully,
title = {AMALIA Technical Report: A Fully Open Source Large Language Model for European Portuguese},
author = {Afonso Simplício and Gonçalo Vinagre and Miguel Moura Ramos and Diogo Tavares and Rafael Ferreira and Giuseppe Attanasio and Duarte M. Alves and Inês Calvo and Inês Vieira and Rui Guerra and James Furtado and Beatriz Canaverde and Iago Paulo and Vasco Ramos and Diogo Glória-Silva and Miguel Faria and Marcos Treviso and Daniel Gomes and Pedro Gomes and David Semedo and André Martins and João Magalhães},
year = {2026},
eprint = {2603.26511},
archivePrefix = {arXiv},
primaryClass = {cs.CL},
url = {https://arxiv.org/abs/2603.26511}
}
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