MMTU-benchmark/MMTU huggingface dataset:
Task: List-to-table (source dataset: TEGRA)

You are given a table below, where each row corresponds to a record, with multiple columns/attributes. However, the column delimiters separating cell values belonging to different columns within the same row, are missing. 

Your task is to inspect the data below, identify column separators, and mark them clearly using "||" in each row. Please ensure that after your column separators "||" are inserted, each row should have the same number of columns. Also make sure that the output table preserves the order of the input rows, with each output row separated by a new line just like the input rows. 

No explanation and return the processed table string in JSON format: {"table": "<processed table string>"}

Input:
Net Sales 16,000 16,000 12,000 16,000 16,000
#x00A0;     
Cost of Goods Sold 50% of net sales 50% of net sales 50% of net sales 50% of net sales 50% of net sales
#x00A0;     
Gross Margin 8,000 8,000 6,000 8,000 8,000
#x00A0;     
Expenses     
Salaries 3,000 3,000 3,000 3,000 3,000
Advertising Expenses 800 800 600 300 300
Business Insurance 59 59 59 59 59
Telephone 170 170 170 170 170
Membership/Subscriptions 200 0 0 0 0
Rent/Utilities 700 700 700 700 700
Accountant 140 140 140 140 140
Travel, Entertainment, Gifts 100 100 100 100 100
Misc. Expenses 100 100 100 100 100
Donations 50 50 50 50 50
#x00A0;     
Total Operating Expenses 5,319 5,119 4,919 4,619 4,619
#x00A0;     
Net Operating Profit 2,681 2,881 1,081 3,381 3,381



TEGRA_table_txt_file: table_7014.txt
TEGRA_TABLE_TEXT: Net Sales 16,000 16,000 12,000 16,000 16,000
#x00A0;     
Cost of Goods Sold 50% of net sales 50% of net sales 50% of net sales 50% of net sales 50% of net sales
#x00A0;     
Gross Margin 8,000 8,000 6,000 8,000 8,000
#x00A0;     
Expenses     
Salaries 3,000 3,000 3,000 3,000 3,000
Advertising Expenses 800 800 600 300 300
Business Insurance 59 59 59 59 59
Telephone 170 170 170 170 170
Membership/Subscriptions 200 0 0 0 0
Rent/Utilities 700 700 700 700 700
Accountant 140 140 140 140 140
Travel, Entertainment, Gifts 100 100 100 100 100
Misc. Expenses 100 100 100 100 100
Donations 50 50 50 50 50
#x00A0;     
Total Operating Expenses 5,319 5,119 4,919 4,619 4,619
#x00A0;     
Net Operating Profit 2,681 2,881 1,081 3,381 3,381
label: Net Sales||16,000||16,000||12,000||16,000||16,000
#x00A0;||||||||||
Cost of Goods Sold||50% of net sales||50% of net sales||50% of net sales||50% of net sales||50% of net sales
#x00A0;||||||||||
Gross Margin||8,000||8,000||6,000||8,000||8,000
#x00A0;||||||||||
Expenses||||||||||
Salaries||3,000||3,000||3,000||3,000||3,000
Advertising Expenses||800||800||600||300||300
Business Insurance||59||59||59||59||59
Telephone||170||170||170||170||170
Membership/Subscriptions||200||0||0||0||0
Rent/Utilities||700||700||700||700||700
Accountant||140||140||140||140||140
Travel, Entertainment, Gifts||100||100||100||100||100
Misc. Expenses||100||100||100||100||100
Donations||50||50||50||50||50
#x00A0;||||||||||
Total Operating Expenses||5,319||5,119||4,919||4,619||4,619
#x00A0;||||||||||
Net Operating Profit||2,681||2,881||1,081||3,381||3,381
Task: List-to-table (source dataset: TEGRA)

You are given a table below, where each row corresponds to a record, with multiple columns/attributes. However, the column delimiters separating cell values belonging to different columns within the same row, are missing. 

Your task is to inspect the data below, identify column separators, and mark them clearly using "||" in each row. Please ensure that after your column separators "||" are inserted, each row should have the same number of columns. Also make sure that the output table preserves the order of the input rows, with each output row separated by a new line just like the input rows. 

No explanation and return the processed table string in JSON format: {"table": "<processed table string>"}

Input:
2614 Judson Ave For Sale $99,900 4 2 1,872
1905 Main St Not For Sale - - - -
1905 Main St Not For Sale - 3 2 1,346
1826 Main St Not For Sale - - 2 1,767
2518 Judson Ave Not For Sale - - - -
2621 Judson Ave Not For Sale - 2 1 1,100
1825 Main St Not For Sale - - - 2,090
2626 Judson Ave Not For Sale - 3 3 2,568
2511 Judson Ave Not For Sale - - - -
2612 Judson Ave Not For Sale - - - -



TEGRA_table_txt_file: table_3873.txt
TEGRA_TABLE_TEXT: 2614 Judson Ave For Sale $99,900 4 2 1,872
1905 Main St Not For Sale - - - -
1905 Main St Not For Sale - 3 2 1,346
1826 Main St Not For Sale - - 2 1,767
2518 Judson Ave Not For Sale - - - -
2621 Judson Ave Not For Sale - 2 1 1,100
1825 Main St Not For Sale - - - 2,090
2626 Judson Ave Not For Sale - 3 3 2,568
2511 Judson Ave Not For Sale - - - -
2612 Judson Ave Not For Sale - - - -
label: 2614 Judson Ave||For Sale||$99,900||4||2||1,872
1905 Main St||Not For Sale||-||-||-||-
1905 Main St||Not For Sale||-||3||2||1,346
1826 Main St||Not For Sale||-||-||2||1,767
2518 Judson Ave||Not For Sale||-||-||-||-
2621 Judson Ave||Not For Sale||-||2||1||1,100
1825 Main St||Not For Sale||-||-||-||2,090
2626 Judson Ave||Not For Sale||-||3||3||2,568
2511 Judson Ave||Not For Sale||-||-||-||-
2612 Judson Ave||Not For Sale||-||-||-||-
Task: List-to-table (source dataset: TEGRA)

You are given a table below, where each row corresponds to a record, with multiple columns/attributes. However, the column delimiters separating cell values belonging to different columns within the same row, are missing. 

Your task is to inspect the data below, identify column separators, and mark them clearly using "||" in each row. Please ensure that after your column separators "||" are inserted, each row should have the same number of columns. Also make sure that the output table preserves the order of the input rows, with each output row separated by a new line just like the input rows. 

No explanation and return the processed table string in JSON format: {"table": "<processed table string>"}

Input:
1996 $0 $0 $2,353 $2,353
1997 $3,969 $0 $4,556 $8,525
1998 $0 $0 $11,202 $11,202
1999 $0 $0 $19,841 $19,841
2000 $3,778 $3,606 $37,229 $44,613
2001 $0 $0 $18,999 $18,999
2002 $0 $9,500 $27,002 $36,502
2003 $0 $0 $47,485 $47,485
2004 $0 $0 $25,534 $25,534
2005 $0 $0 $17,701 $17,701
2006 $0 $0 $30,435 $30,435
2007 $0 $9,276 $10,839 $20,115
2008 $0 $107,617 $10,596 $118,213
2009 $0 $0 $79,280 $79,280
2010 $3,705 $0 $13,944 $17,649
2011 $0 $0 $57,216 $57,216
2012 $0 $0 $42,532 $42,532



TEGRA_table_txt_file: table_2745.txt
TEGRA_TABLE_TEXT: 1996 $0 $0 $2,353 $2,353
1997 $3,969 $0 $4,556 $8,525
1998 $0 $0 $11,202 $11,202
1999 $0 $0 $19,841 $19,841
2000 $3,778 $3,606 $37,229 $44,613
2001 $0 $0 $18,999 $18,999
2002 $0 $9,500 $27,002 $36,502
2003 $0 $0 $47,485 $47,485
2004 $0 $0 $25,534 $25,534
2005 $0 $0 $17,701 $17,701
2006 $0 $0 $30,435 $30,435
2007 $0 $9,276 $10,839 $20,115
2008 $0 $107,617 $10,596 $118,213
2009 $0 $0 $79,280 $79,280
2010 $3,705 $0 $13,944 $17,649
2011 $0 $0 $57,216 $57,216
2012 $0 $0 $42,532 $42,532
label: 1996||$0||$0||$2,353||$2,353
1997||$3,969||$0||$4,556||$8,525
1998||$0||$0||$11,202||$11,202
1999||$0||$0||$19,841||$19,841
2000||$3,778||$3,606||$37,229||$44,613
2001||$0||$0||$18,999||$18,999
2002||$0||$9,500||$27,002||$36,502
2003||$0||$0||$47,485||$47,485
2004||$0||$0||$25,534||$25,534
2005||$0||$0||$17,701||$17,701
2006||$0||$0||$30,435||$30,435
2007||$0||$9,276||$10,839||$20,115
2008||$0||$107,617||$10,596||$118,213
2009||$0||$0||$79,280||$79,280
2010||$3,705||$0||$13,944||$17,649
2011||$0||$0||$57,216||$57,216
2012||$0||$0||$42,532||$42,532
Task: List-to-table (source dataset: TEGRA)

You are given a table below, where each row corresponds to a record, with multiple columns/attributes. However, the column delimiters separating cell values belonging to different columns within the same row, are missing. 

Your task is to inspect the data below, identify column separators, and mark them clearly using "||" in each row. Please ensure that after your column separators "||" are inserted, each row should have the same number of columns. Also make sure that the output table preserves the order of the input rows, with each output row separated by a new line just like the input rows. 

No explanation and return the processed table string in JSON format: {"table": "<processed table string>"}

Input:
B 971.88 986.15
C 1,110.72 1,127.03
D 1,249.56 1,267.91
E 1,527.24 1,549.67
F 1,804.92 1,831.43
G 2,082.60 2,113.18



TEGRA_table_txt_file: table_7940.txt
TEGRA_TABLE_TEXT: B 971.88 986.15
C 1,110.72 1,127.03
D 1,249.56 1,267.91
E 1,527.24 1,549.67
F 1,804.92 1,831.43
G 2,082.60 2,113.18
label: B||971.88||986.15
C||1,110.72||1,127.03
D||1,249.56||1,267.91
E||1,527.24||1,549.67
F||1,804.92||1,831.43
G||2,082.60||2,113.18
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|    | A   | B   | C                                            | D        | E          | F             |
|---:|:----|:----|:---------------------------------------------|:---------|:-----------|:--------------|
|  1 |     |     |                                              |          |            |               |
|  2 |     |     |                                              |          |            |               |
|  3 |     |     |                                              |          |            |               |
|  4 |     | S/N | ITEM DESCRIPTION                             | QTY/Days | UNIT PRICE | TOTAL PRICE   |
|  5 |     | 1   | Logistics for Pre - installation Site Survey |          |            | [Target Cell] |
|  6 |     | 2   | Transport for 2 Engineer                     | 2        | 50000      | 100000        |
|  7 |     | 3   | Transport for 2 Technicians                  |          |            | 0             |
|  8 |     | 4   | Hotel Accomodation for 2 Engineer            | 21       | 16000      | 336000        |
|  9 |     | 5   | Hotel Accomodation for 2 Technicains         |          |            |               |
| 10 |     | 6   | Transport of Goods                           | 1        | 200000     | 200000        |
| 11 |     | 7   | Outstation for 2 Engineer                    | 21       | 4000       | 84000         |
| 12 |     | 8   | Outstation for 2 Technicians                 |          |            |               |
| 13 |     | 9   | Local Transport                              | 2        | 14700      | 29400         |
| 14 |     | 10  | Project Management                           | 1        | 50000      | 50000         |
| 15 |     |     | TOTAL                                        |          |            | 799400        |



min_row: 1
max_row: 15
min_col: 1
max_col: 6
target_cell_row_idx: 5
target_cell_col_idx: 6
target_cell_value: 0
formula: =D5*E5
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|    | A   | B   | C                                            | D        | E          | F             |
|---:|:----|:----|:---------------------------------------------|:---------|:-----------|:--------------|
|  1 |     |     |                                              |          |            |               |
|  2 |     |     |                                              |          |            |               |
|  3 |     |     |                                              |          |            |               |
|  4 |     | S/N | ITEM DESCRIPTION                             | QTY/Days | UNIT PRICE | TOTAL PRICE   |
|  5 |     | 1   | Logistics for Pre - installation Site Survey |          |            | 0             |
|  6 |     | 2   | Transport for 2 Engineer                     | 2        | 50000      | 100000        |
|  7 |     | 3   | Transport for 2 Technicians                  |          |            | 0             |
|  8 |     | 4   | Hotel Accomodation for 2 Engineer            | 21       | 16000      | 336000        |
|  9 |     | 5   | Hotel Accomodation for 2 Technicains         |          |            |               |
| 10 |     | 6   | Transport of Goods                           | 1        | 200000     | [Target Cell] |
| 11 |     | 7   | Outstation for 2 Engineer                    | 21       | 4000       | 84000         |
| 12 |     | 8   | Outstation for 2 Technicians                 |          |            |               |
| 13 |     | 9   | Local Transport                              | 2        | 14700      | 29400         |
| 14 |     | 10  | Project Management                           | 1        | 50000      | 50000         |
| 15 |     |     | TOTAL                                        |          |            | 799400        |



min_row: 1
max_row: 15
min_col: 1
max_col: 6
target_cell_row_idx: 10
target_cell_col_idx: 6
target_cell_value: 200000
formula: =D10*E10
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|    | A   | B   | C                                            | D        | E          | F             |
|---:|:----|:----|:---------------------------------------------|:---------|:-----------|:--------------|
|  3 |     |     |                                              |          |            |               |
|  4 |     | S/N | ITEM DESCRIPTION                             | QTY/Days | UNIT PRICE | TOTAL PRICE   |
|  5 |     | 1   | Logistics for Pre - installation Site Survey |          |            | 0             |
|  6 |     | 2   | Transport for 2 Engineer                     | 2        | 50000      | 100000        |
|  7 |     | 3   | Transport for 2 Technicians                  |          |            | 0             |
|  8 |     | 4   | Hotel Accomodation for 2 Engineer            | 21       | 16000      | 336000        |
|  9 |     | 5   | Hotel Accomodation for 2 Technicains         |          |            |               |
| 10 |     | 6   | Transport of Goods                           | 1        | 200000     | 200000        |
| 11 |     | 7   | Outstation for 2 Engineer                    | 21       | 4000       | 84000         |
| 12 |     | 8   | Outstation for 2 Technicians                 |          |            |               |
| 13 |     | 9   | Local Transport                              | 2        | 14700      | [Target Cell] |
| 14 |     | 10  | Project Management                           | 1        | 50000      | 50000         |
| 15 |     |     | TOTAL                                        |          |            | 799400        |



min_row: 3
max_row: 15
min_col: 1
max_col: 6
target_cell_row_idx: 13
target_cell_col_idx: 6
target_cell_value: 29400
formula: =D13*E13
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|    | A   | B   | C                                            | D        | E             | F           |
|---:|:----|:----|:---------------------------------------------|:---------|:--------------|:------------|
|  1 |     |     |                                              |          |               |             |
|  2 |     |     |                                              |          |               |             |
|  3 |     |     |                                              |          |               |             |
|  4 |     | S/N | ITEM DESCRIPTION                             | QTY/Days | UNIT PRICE    | TOTAL PRICE |
|  5 |     | 1   | Logistics for Pre - installation Site Survey |          |               | 0           |
|  6 |     | 2   | Transport for 2 Engineer                     | 2        | [Target Cell] | 100000      |
|  7 |     | 3   | Transport for 2 Technicians                  |          |               | 0           |
|  8 |     | 4   | Hotel Accomodation for 2 Engineer            | 21       | 16000         | 336000      |
|  9 |     | 5   | Hotel Accomodation for 2 Technicains         |          |               |             |
| 10 |     | 6   | Transport of Goods                           | 1        | 200000        | 200000      |
| 11 |     | 7   | Outstation for 2 Engineer                    | 21       | 4000          | 84000       |
| 12 |     | 8   | Outstation for 2 Technicians                 |          |               |             |
| 13 |     | 9   | Local Transport                              | 2        | 14700         | 29400       |
| 14 |     | 10  | Project Management                           | 1        | 50000         | 50000       |
| 15 |     |     | TOTAL                                        |          |               | 799400      |



min_row: 1
max_row: 15
min_col: 1
max_col: 6
target_cell_row_idx: 6
target_cell_col_idx: 5
target_cell_value: 50000
formula: =25000*2
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|    | A   | B   | C                                            | D        | E             | F           |
|---:|:----|:----|:---------------------------------------------|:---------|:--------------|:------------|
|  1 |     |     |                                              |          |               |             |
|  2 |     |     |                                              |          |               |             |
|  3 |     |     |                                              |          |               |             |
|  4 |     | S/N | ITEM DESCRIPTION                             | QTY/Days | UNIT PRICE    | TOTAL PRICE |
|  5 |     | 1   | Logistics for Pre - installation Site Survey |          |               | 0           |
|  6 |     | 2   | Transport for 2 Engineer                     | 2        | 50000         | 100000      |
|  7 |     | 3   | Transport for 2 Technicians                  |          |               | 0           |
|  8 |     | 4   | Hotel Accomodation for 2 Engineer            | 21       | [Target Cell] | 336000      |
|  9 |     | 5   | Hotel Accomodation for 2 Technicains         |          |               |             |
| 10 |     | 6   | Transport of Goods                           | 1        | 200000        | 200000      |
| 11 |     | 7   | Outstation for 2 Engineer                    | 21       | 4000          | 84000       |
| 12 |     | 8   | Outstation for 2 Technicians                 |          |               |             |
| 13 |     | 9   | Local Transport                              | 2        | 14700         | 29400       |
| 14 |     | 10  | Project Management                           | 1        | 50000         | 50000       |
| 15 |     |     | TOTAL                                        |          |               | 799400      |



min_row: 1
max_row: 15
min_col: 1
max_col: 6
target_cell_row_idx: 8
target_cell_col_idx: 5
target_cell_value: 16000
formula: =8000*2
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|    | A   | B   | C                                            | D        | E             | F           |
|---:|:----|:----|:---------------------------------------------|:---------|:--------------|:------------|
|  1 |     |     |                                              |          |               |             |
|  2 |     |     |                                              |          |               |             |
|  3 |     |     |                                              |          |               |             |
|  4 |     | S/N | ITEM DESCRIPTION                             | QTY/Days | UNIT PRICE    | TOTAL PRICE |
|  5 |     | 1   | Logistics for Pre - installation Site Survey |          |               | 0           |
|  6 |     | 2   | Transport for 2 Engineer                     | 2        | 50000         | 100000      |
|  7 |     | 3   | Transport for 2 Technicians                  |          |               | 0           |
|  8 |     | 4   | Hotel Accomodation for 2 Engineer            | 21       | 16000         | 336000      |
|  9 |     | 5   | Hotel Accomodation for 2 Technicains         |          |               |             |
| 10 |     | 6   | Transport of Goods                           | 1        | 200000        | 200000      |
| 11 |     | 7   | Outstation for 2 Engineer                    | 21       | [Target Cell] | 84000       |
| 12 |     | 8   | Outstation for 2 Technicians                 |          |               |             |
| 13 |     | 9   | Local Transport                              | 2        | 14700         | 29400       |
| 14 |     | 10  | Project Management                           | 1        | 50000         | 50000       |
| 15 |     |     | TOTAL                                        |          |               | 799400      |



min_row: 1
max_row: 15
min_col: 1
max_col: 6
target_cell_row_idx: 11
target_cell_col_idx: 5
target_cell_value: 4000
formula: =2000*2
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|     | A    | B                | C                     | D   | E   | F   |
|----:|:-----|:-----------------|:----------------------|:----|:----|:----|
| 150 | 38.0 | Who is next?     | VNFs on Openstack     |     |     |     |
| 151 |      |                  | VMware vCenter as VIM |     |     |     |
| 152 |      |                  | vCloud Dir vs vCenter |     |     |     |
| 153 |      |                  | VNFs on AWS           |     |     |     |
| 154 |      |                  |                       |     |     |     |
| 155 |      |                  |                       |     |     |     |
| 156 |      |                  |                       |     |     |     |
| 157 |      |                  |                       |     |     |     |
| 158 |      | Geo distribution |                       |     |     |     |
| 159 |      | APJ              | 10                    |     |     |     |
| 160 |      | US               | [Target Cell]         |     |     |     |
| 161 |      | EMEAR            | 13                    |     |     |     |
| 162 |      | China            | 3                     |     |     |     |
| 163 |      | Latam            | 2                     |     |     |     |
| 164 |      |                  | 35                    |     |     |     |
| 165 |      |                  |                       |     |     |     |
| 166 |      |                  |                       |     |     |     |
| 167 |      |                  |                       |     |     |     |
| 168 |      |                  |                       |     |     |     |
| 169 |      |                  |                       |     |     |     |
| 170 |      |                  |                       |     |     |     |



min_row: 150
max_row: 170
min_col: 1
max_col: 6
target_cell_row_idx: 160
target_cell_col_idx: 3
target_cell_value: 7
formula: =COUNTIF(E2:E153,"US")/4
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|    | A   | B    | C                                            | D    | E        | F             |
|---:|:----|:-----|:---------------------------------------------|:-----|:---------|:--------------|
|  5 |     | 1.0  | Logistics for Pre - installation Site Survey |      |          | 0             |
|  6 |     | 2.0  | Transport for 2 Engineer                     | 2.0  | 50000.0  | 100000        |
|  7 |     | 3.0  | Transport for 2 Technicians                  |      |          | 0             |
|  8 |     | 4.0  | Hotel Accomodation for 2 Engineer            | 21.0 | 16000.0  | 336000        |
|  9 |     | 5.0  | Hotel Accomodation for 2 Technicains         |      |          |               |
| 10 |     | 6.0  | Transport of Goods                           | 1.0  | 200000.0 | 200000        |
| 11 |     | 7.0  | Outstation for 2 Engineer                    | 21.0 | 4000.0   | 84000         |
| 12 |     | 8.0  | Outstation for 2 Technicians                 |      |          |               |
| 13 |     | 9.0  | Local Transport                              | 2.0  | 14700.0  | 29400         |
| 14 |     | 10.0 | Project Management                           | 1.0  | 50000.0  | 50000         |
| 15 |     |      | TOTAL                                        |      |          | [Target Cell] |



min_row: 5
max_row: 15
min_col: 1
max_col: 6
target_cell_row_idx: 15
target_cell_col_idx: 6
target_cell_value: 799400
formula: =SUM(F5:F14)
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|    | A   | B   | C                                            | D        | E             | F           |
|---:|:----|:----|:---------------------------------------------|:---------|:--------------|:------------|
|  3 |     |     |                                              |          |               |             |
|  4 |     | S/N | ITEM DESCRIPTION                             | QTY/Days | UNIT PRICE    | TOTAL PRICE |
|  5 |     | 1   | Logistics for Pre - installation Site Survey |          |               | 0           |
|  6 |     | 2   | Transport for 2 Engineer                     | 2        | 50000         | 100000      |
|  7 |     | 3   | Transport for 2 Technicians                  |          |               | 0           |
|  8 |     | 4   | Hotel Accomodation for 2 Engineer            | 21       | 16000         | 336000      |
|  9 |     | 5   | Hotel Accomodation for 2 Technicains         |          |               |             |
| 10 |     | 6   | Transport of Goods                           | 1        | 200000        | 200000      |
| 11 |     | 7   | Outstation for 2 Engineer                    | 21       | 4000          | 84000       |
| 12 |     | 8   | Outstation for 2 Technicians                 |          |               |             |
| 13 |     | 9   | Local Transport                              | 2        | [Target Cell] | 29400       |
| 14 |     | 10  | Project Management                           | 1        | 50000         | 50000       |
| 15 |     |     | TOTAL                                        |          |               | 799400      |



min_row: 3
max_row: 15
min_col: 1
max_col: 6
target_cell_row_idx: 13
target_cell_col_idx: 5
target_cell_value: 14700
formula: =700*21
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|     | A   | B                | C                     | D   | E   | F   |
|----:|:----|:-----------------|:----------------------|:----|:----|:----|
| 151 |     |                  | VMware vCenter as VIM |     |     |     |
| 152 |     |                  | vCloud Dir vs vCenter |     |     |     |
| 153 |     |                  | VNFs on AWS           |     |     |     |
| 154 |     |                  |                       |     |     |     |
| 155 |     |                  |                       |     |     |     |
| 156 |     |                  |                       |     |     |     |
| 157 |     |                  |                       |     |     |     |
| 158 |     | Geo distribution |                       |     |     |     |
| 159 |     | APJ              | 10                    |     |     |     |
| 160 |     | US               | 7                     |     |     |     |
| 161 |     | EMEAR            | [Target Cell]         |     |     |     |
| 162 |     | China            | 3                     |     |     |     |
| 163 |     | Latam            | 2                     |     |     |     |
| 164 |     |                  | 35                    |     |     |     |
| 165 |     |                  |                       |     |     |     |
| 166 |     |                  |                       |     |     |     |
| 167 |     |                  |                       |     |     |     |
| 168 |     |                  |                       |     |     |     |
| 169 |     |                  |                       |     |     |     |
| 170 |     |                  |                       |     |     |     |
| 171 |     |                  |                       |     |     |     |



min_row: 151
max_row: 171
min_col: 1
max_col: 6
target_cell_row_idx: 161
target_cell_col_idx: 3
target_cell_value: 13
formula: =COUNTIF(E2:E153,"EMEAR")/4
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|    | A   | B                     | C        | D             | E       | F        | G        | H       | I     | J      | K     | L    | M       | N       |
|---:|:----|:----------------------|:---------|:--------------|:--------|:---------|:---------|:--------|:------|:-------|:------|:-----|:--------|:--------|
|  1 |     |                       | 1        | [Target Cell] | 3       | 4        | 5        | 6       | 7     | 8      | 9     | 10   | 11      | 12      |
|  2 | #   | Question              | Softbank | KDDI          | DT      | Swisscom | NTT-East | NTT-Doc | Vz IT | Vz MNS | Spark | AT&T | Ooredoo | Telstra |
|  3 | 1   | VNFs on Openstack     | No       | Yes           | Yes     | Yes      | Yes      | Yes     | No    | Yes    | No    | Yes  | Yes     | Yes     |
|  4 | 2   | VMware vCenter as VIM | Yes      | No            | n/a     | Yes      | No       | No      | Yes   | Yes    | Yes   | No   | Yes     | Yes     |
|  5 | 3   | vCloud Dir vs vCenter | No       | n/a           | n/a     | Yes      | No       | No      | Dep   | Dep    | No    | No   | Yes     | No      |
|  6 | 4   | VNFs on AWS           | No       | Yes           | n/a     | No       | No       | No      | Yes   | Yes    | No    | Dep  | No      | Yes     |
|  7 |     |                       |          |               |         |          |          |         |       |        |       |      |         |         |
|  8 |     |                       |          |               |         |          |          |         |       |        |       |      |         |         |
|  9 | #   | Question              | Yes      | No            | Depends | n/a      | Checksum |         |       |        |       |      |         |         |
| 10 | 1   | VNFs on Openstack     | 27       | 4             | 2       | 0        | 33       |         |       |        |       |      |         |         |
| 11 | 2   | VMware vCenter as VIM | 17       | 14            | 1       | 1        | 33       |         |       |        |       |      |         |         |



min_row: 1
max_row: 11
min_col: 1
max_col: 14
target_cell_row_idx: 1
target_cell_col_idx: 4
target_cell_value: 2
formula: =C1+1
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|     | A    | B                | C                     | D   | E   | F   |
|----:|:-----|:-----------------|:----------------------|:----|:----|:----|
| 149 |      |                  | VNFs on AWS           |     |     |     |
| 150 | 38.0 | Who is next?     | VNFs on Openstack     |     |     |     |
| 151 |      |                  | VMware vCenter as VIM |     |     |     |
| 152 |      |                  | vCloud Dir vs vCenter |     |     |     |
| 153 |      |                  | VNFs on AWS           |     |     |     |
| 154 |      |                  |                       |     |     |     |
| 155 |      |                  |                       |     |     |     |
| 156 |      |                  |                       |     |     |     |
| 157 |      |                  |                       |     |     |     |
| 158 |      | Geo distribution |                       |     |     |     |
| 159 |      | APJ              | [Target Cell]         |     |     |     |
| 160 |      | US               | 6                     |     |     |     |
| 161 |      | EMEAR            | 11                    |     |     |     |
| 162 |      | China            | 3                     |     |     |     |
| 163 |      | Latam            | 2                     |     |     |     |
| 164 |      |                  |                       |     |     |     |
| 165 |      |                  |                       |     |     |     |
| 166 |      |                  |                       |     |     |     |
| 167 |      |                  |                       |     |     |     |
| 168 |      |                  |                       |     |     |     |
| 169 |      |                  |                       |     |     |     |



min_row: 149
max_row: 169
min_col: 1
max_col: 6
target_cell_row_idx: 159
target_cell_col_idx: 3
target_cell_value: 10
formula: =COUNTIF(E2:E129,"APJ")/4
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|     | A    | B                | C                     | D   | E   | F   |
|----:|:-----|:-----------------|:----------------------|:----|:----|:----|
| 150 | 38.0 | Who is next?     | VNFs on Openstack     |     |     |     |
| 151 |      |                  | VMware vCenter as VIM |     |     |     |
| 152 |      |                  | vCloud Dir vs vCenter |     |     |     |
| 153 |      |                  | VNFs on AWS           |     |     |     |
| 154 |      |                  |                       |     |     |     |
| 155 |      |                  |                       |     |     |     |
| 156 |      |                  |                       |     |     |     |
| 157 |      |                  |                       |     |     |     |
| 158 |      | Geo distribution |                       |     |     |     |
| 159 |      | APJ              | 10                    |     |     |     |
| 160 |      | US               | [Target Cell]         |     |     |     |
| 161 |      | EMEAR            | 11                    |     |     |     |
| 162 |      | China            | 3                     |     |     |     |
| 163 |      | Latam            | 2                     |     |     |     |
| 164 |      |                  |                       |     |     |     |
| 165 |      |                  |                       |     |     |     |
| 166 |      |                  |                       |     |     |     |
| 167 |      |                  |                       |     |     |     |
| 168 |      |                  |                       |     |     |     |
| 169 |      |                  |                       |     |     |     |
| 170 |      |                  |                       |     |     |     |



min_row: 150
max_row: 170
min_col: 1
max_col: 6
target_cell_row_idx: 160
target_cell_col_idx: 3
target_cell_value: 6
formula: =COUNTIF(E2:E129,"US")/4
Task: Formula-prediction-context (source dataset: cisco-random)

You are given an table region extracted from a real Excel spreadsheet, which is centered around a cell called "Target Cell" (labeled as "[Target Cell]" shown in the table below). The table region below includes up to 10 rows above/below the Target Cell, and 10 columns to the left/right of the Target Cell. 

Your task is to analyze the table context surrounding the Target Cell, and predict the most likely Excel formula that should go in the Target Cell. If multiple formulas are possible, choose the most reasonable or commonly used based on context.

1. Please follow the standard formula syntax in Excel, e.g. always begins with an equal sign (=).
2. Base your predicted formula on the surrounding table context, e.g., column headers, annotations, and data values in nearby cells.
3. No explanation — just output the formula itself in a JSON format: {"formula": "your_formula_here"}.

Input Excel Snippet in Markdown format:
|     | A   | B                | C                     | D   | E   | F   |
|----:|:----|:-----------------|:----------------------|:----|:----|:----|
| 151 |     |                  | VMware vCenter as VIM |     |     |     |
| 152 |     |                  | vCloud Dir vs vCenter |     |     |     |
| 153 |     |                  | VNFs on AWS           |     |     |     |
| 154 |     |                  |                       |     |     |     |
| 155 |     |                  |                       |     |     |     |
| 156 |     |                  |                       |     |     |     |
| 157 |     |                  |                       |     |     |     |
| 158 |     | Geo distribution |                       |     |     |     |
| 159 |     | APJ              | 10                    |     |     |     |
| 160 |     | US               | 6                     |     |     |     |
| 161 |     | EMEAR            | [Target Cell]         |     |     |     |
| 162 |     | China            | 3                     |     |     |     |
| 163 |     | Latam            | 2                     |     |     |     |
| 164 |     |                  |                       |     |     |     |
| 165 |     |                  |                       |     |     |     |
| 166 |     |                  |                       |     |     |     |
| 167 |     |                  |                       |     |     |     |
| 168 |     |                  |                       |     |     |     |
| 169 |     |                  |                       |     |     |     |
| 170 |     |                  |                       |     |     |     |
| 171 |     |                  |                       |     |     |     |



min_row: 151
max_row: 171
min_col: 1
max_col: 6
target_cell_row_idx: 161
target_cell_col_idx: 3
target_cell_value: 11
formula: =COUNTIF(E2:E129,"EMEAR")/4