Effyis/Table-Extraction

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, 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:

{
  "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:

{
  "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"
  }
}