---
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:
![example1](example1.png)
#### 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:
![example2](example2.png)
#### 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"
  }
}
```