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.gitattributes

@@ -53,3 +53,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.jpg filter=lfs diff=lfs merge=lfs -text
 *.jpeg filter=lfs diff=lfs merge=lfs -text
 *.webp filter=lfs diff=lfs merge=lfs -text
+table_extract.csv filter=lfs diff=lfs merge=lfs -text

DataPreparation.ipynb

@@ -0,0 +1,64 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "SFEUqifXS0At"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "Processing CSV files: 100%|██████████| 16573/16573 [01:14<00:00, 222.34it/s]\n"
+          ]
+        }
+      ],
+      "source": [
+        "import os\n",
+        "import pandas as pd\n",
+        "from tqdm import tqdm\n",
+        "\n",
+        "# Create an empty DataFrame\n",
+        "df = pd.DataFrame()\n",
+        "df['context'] = None\n",
+        "df['answer'] = None\n",
+        "\n",
+        "# Read all CSV files from the folder 'all_csv'\n",
+        "folder_path = 'all_csv'  # Path to the folder containing CSV files\n",
+        "paths = [os.path.join(folder_path, filename) for filename in os.listdir(folder_path) if filename.endswith('.csv')]\n",
+        "for i, path in enumerate(tqdm(paths, desc=\"Processing CSV files\")):\n",
+        "    data = pd.read_csv(path, sep='#')\n",
+        "    df.loc[i, 'context'] = data.to_string()\n",
+        "    df.loc[i, 'answer'] = data.to_json(force_ascii=False)\n",
+        "\n",
+        "# Write the DataFrame to a CSV file\n",
+        "df.to_csv('table_extract.csv', index=False)"
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.10.4"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

README.md

@@ -1,3 +1,129 @@
----
 license: apache-2.0
+language:
+- en
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: table_extract.csv
+tags:
+- TABLES
 ---
+
+# 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"
+  }
+}
+```
+
+
+

table_extract.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:eccb38d12364c313b49a70750e7f246a2f9d91ef25779efb75beefc1ba4fd135
+size 56920386