Datasets:
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---
license: apache-2.0
task_categories:
- feature-extraction
language:
- en
- ar
configs:
- config_name: default
data_files:
- split: train
path: table_extract.csv
tags:
- finance
---
# Table Extract Dataset
This dataset is designed to evaluate the ability of large language models (LLMs) to extract tables from text. It provides a collection of text snippets containing tables and their corresponding structured representations in JSON format.
## Source
The dataset is based on the [Table Fact Dataset](https://github.com/wenhuchen/Table-Fact-Checking/tree/master?tab=readme-ov-file), also known as TabFact, which contains 16,573 tables extracted from Wikipedia.
## Schema:
Each data point in the dataset consists of two elements:
* context: A string containing the text snippet with the embedded table.
* answer: A JSON object representing the extracted table structure.
The JSON object follows this format:
{
"column_1": { "row_id": "val1", "row_id": "val2", ... },
"column_2": { "row_id": "val1", "row_id": "val2", ... },
...
}
Each key in the JSON object represents a column header, and the corresponding value is another object containing key-value pairs for each row in that column.
## Examples:
### Example 1:
#### Context:
#### Answer:
```json
{
"aircraft": {
"0": "robinson r - 22",
"1": "bell 206b3 jetranger",
"2": "ch - 47d chinook",
"3": "mil mi - 26",
"4": "ch - 53e super stallion"
},
"description": {
"0": "light utility helicopter",
"1": "turboshaft utility helicopter",
"2": "tandem rotor helicopter",
"3": "heavy - lift helicopter",
"4": "heavy - lift helicopter"
},
"max gross weight": {
"0": "1370 lb (635 kg)",
"1": "3200 lb (1451 kg)",
"2": "50000 lb (22680 kg)",
"3": "123500 lb (56000 kg)",
"4": "73500 lb (33300 kg)"
},
"total disk area": {
"0": "497 ft square (46.2 m square)",
"1": "872 ft square (81.1 m square)",
"2": "5655 ft square (526 m square)",
"3": "8495 ft square (789 m square)",
"4": "4900 ft square (460 m square)"
},
"max disk loading": {
"0": "2.6 lb / ft square (14 kg / m square)",
"1": "3.7 lb / ft square (18 kg / m square)",
"2": "8.8 lb / ft square (43 kg / m square)",
"3": "14.5 lb / ft square (71 kg / m square)",
"4": "15 lb / ft square (72 kg / m square)"
}
}
```
### Example 2:
#### Context:
#### Answer:
```json
{
"country": {
"exonym": {
"0": "iceland",
"1": "indonesia",
"2": "iran",
"3": "iraq",
"4": "ireland",
"5": "isle of man"
},
"endonym": {
"0": "ísland",
"1": "indonesia",
"2": "īrān ایران",
"3": "al - 'iraq العراق îraq",
"4": "éire ireland",
"5": "isle of man ellan vannin"
}
},
"capital": {
"exonym": {
"0": "reykjavík",
"1": "jakarta",
"2": "tehran",
"3": "baghdad",
"4": "dublin",
"5": "douglas"
},
"endonym": {
"0": "reykjavík",
"1": "jakarta",
"2": "tehrān تهران",
"3": "baghdad بغداد bexda",
"4": "baile átha cliath dublin",
"5": "douglas doolish"
}
},
"official or native language(s) (alphabet/script)": {
"0": "icelandic",
"1": "bahasa indonesia",
"2": "persian ( arabic script )",
"3": "arabic ( arabic script ) kurdish",
"4": "irish english",
"5": "english manx"
}
}
``` |