README.md

---
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
{
  "date": {
    "0": "1st",
    "1": "3rd",
    "2": "4th",
    "3": "11th",
    "4": "17th",
    "5": "24th",
    "6": "25th"
  },
  "opponent": {
    "0": "bracknell bees",
    "1": "slough jets",
    "2": "slough jets",
    "3": "wightlink raiders",
    "4": "romford raiders",
    "5": "swindon wildcats",
    "6": "swindon wildcats"
  },
  "venue": {
    "0": "home",
    "1": "away",
    "2": "home",
    "3": "home",
    "4": "home",
    "5": "away",
    "6": "home"
  },
  "result": {
    "0": "won 4 - 1",
    "1": "won 7 - 3",
    "2": "lost 5 - 3",
    "3": "won 7 - 2",
    "4": "lost 3 - 4",
    "5": "lost 2 - 4",
    "6": "won 8 - 2"
  },
  "attendance": {
    "0": 1753,
    "1": 751,
    "2": 1421,
    "3": 1552,
    "4": 1535,
    "5": 902,
    "6": 2124
  },
  "competition": {
    "0": "league",
    "1": "league",
    "2": "league",
    "3": "league",
    "4": "league",
    "5": "league",
    "6": "league"
  },
  "man of the match": {
    "0": "martin bouz",
    "1": "joe watkins",
    "2": "nick cross",
    "3": "neil liddiard",
    "4": "stuart potts",
    "5": "lukas smital",
    "6": "vaclav zavoral"
  }
}

```

### 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"
  }
}
```