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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
{
"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:
#### 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"
}
}
``` |