Effyis/Table-Extraction
收藏资源简介：
该数据集旨在评估大型语言模型（LLMs）从文本中提取表格的能力。它提供了一系列包含表格的文本片段及其对应的JSON格式的结构化表示。数据集基于Table Fact Dataset（也称为TabFact），该数据集包含从维基百科提取的16,573个表格。每个数据点由两部分组成：包含嵌入表格的文本片段（context）和表示提取的表格结构的JSON对象（answer）。JSON对象的格式详细说明了每个列标题及其对应的行数据。
This dataset is designed to evaluate the table extraction capability of Large Language Models (LLMs) from natural language text. It provides a set of text snippets containing embedded tables, paired with their structured representations in JSON format. This dataset is based on the Table Fact Dataset (also known as TabFact), which contains 16,573 tables extracted from Wikipedia. Each data point includes two components: a text snippet embedding the target table (context) and a JSON object that delineates the structure of the extracted table (answer). The JSON object format specifies each column header and its corresponding row data.
Table Extract Dataset
概述
该数据集旨在评估大型语言模型（LLMs）从文本中提取表格的能力。它提供了一系列包含表格的文本片段及其对应的JSON格式结构化表示。
来源
数据集基于Table Fact Dataset（也称为TabFact），包含从维基百科提取的16,573个表格。
结构
每个数据点包含两个元素：
- context：包含嵌入表格的文本片段。
- answer：表示提取的表格结构的JSON对象。
JSON对象格式如下： json { "column_1": { "row_id": "val1", "row_id": "val2", ... }, "column_2": { "row_id": "val1", "row_id": "val2", ... }, ... }
每个键代表一个列标题，对应的值是包含每列中各行键值对的对象。
示例
示例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" } }
示例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" } }
