diff --git "a/Benchmark2/benchmark-2-openchat.ipynb" "b/Benchmark2/benchmark-2-openchat.ipynb"
new file mode 100644--- /dev/null
+++ "b/Benchmark2/benchmark-2-openchat.ipynb"
@@ -0,0 +1,6377 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b6920725",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011813,
+     "end_time": "2024-05-31T18:42:32.343085",
+     "exception": false,
+     "start_time": "2024-05-31T18:42:32.331272",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<h2><center><font color=#D40004><u>Benchmark 2: Openchat</u></font></center></h2>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "de1daec0",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011375,
+     "end_time": "2024-05-31T18:42:32.366254",
+     "exception": false,
+     "start_time": "2024-05-31T18:42:32.354879",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<div style=\"padding: 40px; background: linear-gradient(135deg, #f5f7fa, #cdd2d8); border: 3px groove #d1d8e0; border-radius: 30px; box-shadow: 0 10px 25px rgba(0,0,0,0.1); font-size: 120%; line-height: 1.9; color: #333; font-family: 'Georgia', serif; text-align: justify; position: relative;\">\n",
+    "    <h2 style=\"color: #2c3e50; font-size: 150%; border-bottom: 3px solid #3498db; display: inline-block; padding-bottom: 10px; margin-bottom: 20px;\">\n",
+    "        Notebook Gool\n",
+    "    </h2>\n",
+    "    <p style=\"font-size: 140%; color: #34495e; letter-spacing: 1px;\">\n",
+    "The objective of this notebook is to evaluate the performance of openchat using the Table-extract Benchmark dataset available at <a href=\"https://huggingface.co/datasets/Effyis/Table-Extraction\" style=\"color: #fffff; text-decoration: none;\">Hugging Face.</a></p>\n",
+    "</div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd1a5e0e",
+   "metadata": {
+    "papermill": {
+     "duration": 0.010999,
+     "end_time": "2024-05-31T18:42:32.388876",
+     "exception": false,
+     "start_time": "2024-05-31T18:42:32.377877",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:center; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'></span></b> <b>Table of Content</b></div>\n",
+    "\n",
+    "* [I. Loading and Importing Libraries](#1)\n",
+    "* [II. Definition and Implementation of Metrics](#2)\n",
+    "* [III. Clean Response Obtained by LLM](#3)\n",
+    "* [IV. Data Preparation](#5)\n",
+    "* [V. Benchmark](#6)\n",
+    "  * [Prompt](#61)\n",
+    "  * [OpenChat](#62)\n",
+    "  * [Gemini-Pro](#63)\n",
+    "  * [OpenHermes-Mistral](#64)\n",
+    "  * [ Mistral-7B-Instruct-v0.2](#65)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4005591",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011603,
+     "end_time": "2024-05-31T18:42:32.411521",
+     "exception": false,
+     "start_time": "2024-05-31T18:42:32.399918",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='1'></a>\n",
+    "# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:left; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'>I |</span></b> <b>Loading and Importing Libraries</b></div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "49687d79",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:42:32.435657Z",
+     "iopub.status.busy": "2024-05-31T18:42:32.434871Z",
+     "iopub.status.idle": "2024-05-31T18:43:39.462522Z",
+     "shell.execute_reply": "2024-05-31T18:43:39.461516Z"
+    },
+    "papermill": {
+     "duration": 67.042525,
+     "end_time": "2024-05-31T18:43:39.465027",
+     "exception": false,
+     "start_time": "2024-05-31T18:42:32.422502",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "!pip install google-generativeai\n",
+    "!pip install --upgrade pip\n",
+    "!pip install bitsandbytes\n",
+    "!pip install transformers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e33c869d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:39.490175Z",
+     "iopub.status.busy": "2024-05-31T18:43:39.489848Z",
+     "iopub.status.idle": "2024-05-31T18:43:45.419675Z",
+     "shell.execute_reply": "2024-05-31T18:43:45.418870Z"
+    },
+    "papermill": {
+     "duration": 5.945366,
+     "end_time": "2024-05-31T18:43:45.422072",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:39.476706",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import re\n",
+    "import json\n",
+    "from tqdm import tqdm\n",
+    "import pandas as pd\n",
+    "from datasets import load_dataset, Dataset\n",
+    "from wand.image import Image as WImage\n",
+    "import torch\n",
+    "import pandas as pd\n",
+    "from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig\n",
+    "import time \n",
+    "import random\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9a5a0def",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:45.446829Z",
+     "iopub.status.busy": "2024-05-31T18:43:45.445980Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.064310Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.063503Z"
+    },
+    "papermill": {
+     "duration": 0.632813,
+     "end_time": "2024-05-31T18:43:46.066514",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:45.433701",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import google.generativeai as genai\n",
+    "import time \n",
+    "genai.configure(api_key=\"AIzaSyAhz9UBzkEIYI886zZRm40qqB1Kd_9Y4-0\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "b4178da4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.092857Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.091786Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.099836Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.099131Z"
+    },
+    "papermill": {
+     "duration": 0.023475,
+     "end_time": "2024-05-31T18:43:46.101850",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.078375",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Set random seed for reproducibility\n",
+    "random.seed(42)\n",
+    "np.random.seed(42)\n",
+    "torch.manual_seed(42)\n",
+    "torch.cuda.manual_seed_all(42)\n",
+    "torch.backends.cudnn.deterministic = True\n",
+    "torch.backends.cudnn.benchmark = False"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b88ffeeb",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011136,
+     "end_time": "2024-05-31T18:43:46.125097",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.113961",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='2'></a>\n",
+    "# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:left; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'>II |</span></b> <b>Definition and Implementation of Metrics</b></div>\n",
+    "So, let's begin by providing an example of the example output."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "0d55fbfd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.148246Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.147962Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.154390Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.153555Z"
+    },
+    "papermill": {
+     "duration": 0.020108,
+     "end_time": "2024-05-31T18:43:46.156203",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.136095",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "desired_output = [{'aircraft': 'robinson r - 22',\n",
+    "  'description': 'light utility helicopter',\n",
+    "  'max gross weight': '1370 lb (635 kg)',\n",
+    "  'total disk area': '497 ft square (46.2 m square)',\n",
+    "  'max disk loading': '2.6 lb / ft square (14 kg / m square)'},\n",
+    " {'aircraft': 'bell 206b3 jetranger',\n",
+    "  'description': 'turboshaft utility helicopter',\n",
+    "  'max gross weight': '3200 lb (1451 kg)',\n",
+    "  'total disk area': '872 ft square (81.1 m square)',\n",
+    "  'max disk loading': '3.7 lb / ft square (18 kg / m square)'},\n",
+    " {'aircraft': 'ch - 47d chinook',\n",
+    "  'description': 'tandem rotor helicopter',\n",
+    "  'max gross weight': '50000 lb (22680 kg)',\n",
+    "  'total disk area': '5655 ft square (526 m square)',\n",
+    "  'max disk loading': '8.8 lb / ft square (43 kg / m square)'},\n",
+    " {'aircraft': 'mil mi - 26',\n",
+    "  'description': 'heavy - lift helicopter',\n",
+    "  'max gross weight': '123500 lb (56000 kg)',\n",
+    "  'total disk area': '8495 ft square (789 m square)',\n",
+    "  'max disk loading': '14.5 lb / ft square (71 kg / m square)'},\n",
+    " {'aircraft': 'ch - 53e super stallion',\n",
+    "  'description': 'heavy - lift helicopter',\n",
+    "  'max gross weight': '73500 lb (33300 kg)',\n",
+    "  'total disk area': '4900 ft square (460 m square)',\n",
+    "  'max disk loading': '15 lb / ft square (72 kg / m square)'}]\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7eab15f8",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011226,
+     "end_time": "2024-05-31T18:43:46.178915",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.167689",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "To compare between the expected list of records and the predicted list of records, we first need to verify the percentage of predicted keys relative to the desired keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "aa514ea1",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011529,
+     "end_time": "2024-05-31T18:43:46.202339",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.190810",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    ">##  Percentage of predicted keys"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92f9cc82",
+   "metadata": {
+    "papermill": {
+     "duration": 0.01121,
+     "end_time": "2024-05-31T18:43:46.225678",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.214468",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Let's begin by defining a function to retrieve all keys of record"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "144501e7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.248870Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.248595Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.254946Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.254035Z"
+    },
+    "papermill": {
+     "duration": 0.02004,
+     "end_time": "2024-05-31T18:43:46.256806",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.236766",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def get_keys(d):\n",
+    "    # Iterate over each key-value pair in the dictionary\n",
+    "    for k, v in d.items():\n",
+    "        # Append the key to the list of all_keys\n",
+    "        all_keys.append(k)\n",
+    "        # If the value is a dictionary, recursively call get_keys\n",
+    "        if isinstance(v, dict):\n",
+    "            get_keys(v)\n",
+    "        # If the value is a list, iterate over each item\n",
+    "        elif isinstance(v, list):\n",
+    "            for item in v:\n",
+    "                # If the item is a dictionary, recursively call get_keys\n",
+    "                if isinstance(item, dict):\n",
+    "                    get_keys(item)\n",
+    "# Define a function to retrieve all unique keys from a nested dictionary\n",
+    "def get_all_keys(d):\n",
+    "    # Declare all_keys as a global variable\n",
+    "    global all_keys\n",
+    "    # Initialize all_keys as an empty list\n",
+    "    all_keys = []\n",
+    "    # Call the helper function get_keys to populate all_keys\n",
+    "    get_keys(d)\n",
+    "    # Return a list containing the unique keys by converting all_keys to a set and then back to a list\n",
+    "    return list(set(all_keys))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e3bb288e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.280499Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.280236Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.286509Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.285865Z"
+    },
+    "papermill": {
+     "duration": 0.020532,
+     "end_time": "2024-05-31T18:43:46.288468",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.267936",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['max disk loading',\n",
+       " 'aircraft',\n",
+       " 'max gross weight',\n",
+       " 'total disk area',\n",
+       " 'description']"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Testing our function\n",
+    "get_all_keys(desired_output[0])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ce46b02",
+   "metadata": {
+    "papermill": {
+     "duration": 0.01119,
+     "end_time": "2024-05-31T18:43:46.311091",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.299901",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Now, we define the percentage of predicted keys as follows:\n",
+    "\n",
+    "$$\\Large \\text{Percentage of predicted keys} = \\frac{\\text{Number of correctly predicted keys}}{\\text{Total number of true keys}}$$\n",
+    "This percentage is calculated for every record in the list, then summed and divided by the number of records in the list."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "38e453a1",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.334813Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.334510Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.340513Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.339627Z"
+    },
+    "papermill": {
+     "duration": 0.019887,
+     "end_time": "2024-05-31T18:43:46.342328",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.322441",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def process_dict(data):\n",
+    "    if isinstance(data, dict):\n",
+    "        for key, value in data.items():\n",
+    "            if isinstance(value, str):\n",
+    "                data[key] = value.strip().lower()\n",
+    "            elif isinstance(value, list):\n",
+    "                data[key] = [process_dict(item) for item in value]\n",
+    "            elif isinstance(value, dict):\n",
+    "                data[key] = process_dict(value)\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f3476fb5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.366091Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.365417Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.371401Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.370549Z"
+    },
+    "papermill": {
+     "duration": 0.019948,
+     "end_time": "2024-05-31T18:43:46.373463",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.353515",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def percentage_of_predicted_keys(true_dic, pred_dic):\n",
+    "    true_dic=process_dict(true_dic)\n",
+    "    pred_dic=process_dict(pred_dic)\n",
+    "    # Get all keys of the true dictionary\n",
+    "    all_keys_of_true_dic = get_all_keys(true_dic)\n",
+    "    # Get all keys of the predicted dictionary\n",
+    "    all_keys_of_pred_dic = get_all_keys(pred_dic)\n",
+    "    \n",
+    "    # Check if there are no keys in the true dictionary to avoid division by zero\n",
+    "    if len(all_keys_of_true_dic) == 0:\n",
+    "        return 0  # Avoid division by zero\n",
+    "    \n",
+    "    # Initialize count of predicted keys\n",
+    "    p_keys = 0\n",
+    "    # Iterate through all keys in the predicted dictionary\n",
+    "    for key in all_keys_of_pred_dic:\n",
+    "        # Check if the key is also present in the true dictionary\n",
+    "        if key in all_keys_of_true_dic:\n",
+    "            # Increment count if the key is found in both dictionaries\n",
+    "            p_keys += 1\n",
+    "    \n",
+    "    # Calculate the percentage of predicted keys compared to true keys\n",
+    "    p_keys /= len(all_keys_of_true_dic)\n",
+    "    # Return the percentage of predicted keys\n",
+    "    return p_keys"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "7ecfcbc7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.397542Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.396938Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.401826Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.400960Z"
+    },
+    "papermill": {
+     "duration": 0.018768,
+     "end_time": "2024-05-31T18:43:46.403757",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.384989",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def average_percentage_key(true_list, pred_list):\n",
+    "    min_length = min(len(true_list), len(pred_list))  # Find the minimum length of the two lists\n",
+    "    score = 0\n",
+    "    for i in range(min_length):\n",
+    "        score += percentage_of_predicted_keys(true_list[i], pred_list[i])\n",
+    "    return score / len(true_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "80d9f735",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.428184Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.427524Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.433961Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.433116Z"
+    },
+    "papermill": {
+     "duration": 0.020494,
+     "end_time": "2024-05-31T18:43:46.436009",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.415515",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average percentage of keys: 1.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example true and predicted lists\n",
+    "true_list = [{'key1': 1, 'key2': 2, 'key3': 3}, {'key1': 4, 'key2': 5, 'key3': 6}, {'key1': 7, 'key2': 8, 'key3': 9}]\n",
+    "pred_list = [{'key1': 1, 'key2': 2, 'key3': 3}, {'key1': 4, 'key2': 5, 'key3': 7}, {'key1': 7, 'key2': 8, 'key3': 9}]\n",
+    "\n",
+    "# Test the function\n",
+    "result = average_percentage_key(true_list, pred_list)\n",
+    "print(\"Average percentage of keys:\", result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3347362f",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011022,
+     "end_time": "2024-05-31T18:43:46.458274",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.447252",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "Now we will define the principal metrics used to compare the values of two list recods."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7c2c9671",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011035,
+     "end_time": "2024-05-31T18:43:46.480486",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.469451",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    ">## Percentage of predicted values\n",
+    "\n",
+    "The function calculates the percentage of correctly predicted values compared to the total number of true values across different types of data structures.\n",
+    "\n",
+    "The formula for calculating the percentage of values is as follows:\n",
+    "\n",
+    "$$\n",
+    "\\text{Average percentage of values} = \\frac{\\sum_{i=1}^{\\text{Total number of records}} p_i }{Total number of records}\n",
+    "$$\n",
+    "\n",
+    "Here, $p_i$ represents the percentage of correctly predicted values for each key. It's calculated as:\n",
+    "\n",
+    "$$p_i = \\frac{\\text{Number of correctly predicted values of item i}}{\\text{Total number of true values of item i}}$$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "da12189d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.504433Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.504056Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.510074Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.509209Z"
+    },
+    "papermill": {
+     "duration": 0.020467,
+     "end_time": "2024-05-31T18:43:46.512170",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.491703",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def calculate_percentage_of_values(true_dic, pred_dic):\n",
+    "    total_percentage = 0  # Initialize total percentage\n",
+    "     # Type 1: Single string values\n",
+    "    for key, true_value in true_dic.items():  # Loop through key-value pairs in true_dic\n",
+    "        \n",
+    "            # Check if the key exists in pred_dic, if its value is a string and if it matches the true value\n",
+    "            if key in pred_dic and str(pred_dic[key]) == str(true_value):\n",
+    "                match = 1  # Assign perfect match\n",
+    "            else:\n",
+    "                match = 0  # Assign no match\n",
+    "            total_percentage += match\n",
+    "    return total_percentage / len(true_dic)  # Calculate and return the average percentage"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "520856dd",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.537035Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.536699Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.543001Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.541938Z"
+    },
+    "papermill": {
+     "duration": 0.021493,
+     "end_time": "2024-05-31T18:43:46.545462",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.523969",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def average_percentage_value(true_list, pred_list):\n",
+    "    min_length = min(len(true_list), len(pred_list))  # Find the minimum length of the two lists\n",
+    "    score = 0\n",
+    "    for i in range(min_length):\n",
+    "        score += calculate_percentage_of_values(true_list[i], pred_list[i])\n",
+    "    return score / len(true_list)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "3295e756",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.572677Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.572322Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.578952Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.577947Z"
+    },
+    "papermill": {
+     "duration": 0.020978,
+     "end_time": "2024-05-31T18:43:46.580874",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.559896",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average percentage of keys: 0.8888888888888888\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Example true and predicted lists\n",
+    "true_list = [{'key1': 1, 'key2': 2, 'key3': 3}, {'key1': 4, 'key2': 5, 'key3': 6}, {'key1': 7, 'key2': 8, 'key3': 9}]\n",
+    "pred_list = [{'key1': 1, 'key2': 2, 'key3': 3}, {'key1': 4, 'key2': 5, 'key3': 7}, {'key1': 7, 'key2': 8, 'key3': 9}]\n",
+    "\n",
+    "# Test the function\n",
+    "result = average_percentage_value(true_list, pred_list)\n",
+    "print(\"Average percentage of keys:\", result)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6d7781eb",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011,
+     "end_time": "2024-05-31T18:43:46.603194",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.592194",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='3'></a>\n",
+    "# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:left; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'>III |</span></b> <b>Clean Response Obtained by LLM</b></div>\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "74d01368",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.626862Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.626490Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.632581Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.631758Z"
+    },
+    "papermill": {
+     "duration": 0.020219,
+     "end_time": "2024-05-31T18:43:46.634511",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.614292",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "\n",
+    "def parse_json(data_str):\n",
+    "    # Remove leading/trailing whitespace and newlines\n",
+    "    data_str = data_str.strip()\n",
+    "\n",
+    "    # Check if the string is enclosed within triple backticks (\"```json\" and \"```\")\n",
+    "    if data_str.startswith(\"```json\") and data_str.endswith(\"```\"):\n",
+    "        # Remove the leading/trailing \"```json\" and \"```\"\n",
+    "        data_str = data_str[len(\"```json\"): -len(\"```\")]\n",
+    "\n",
+    "    try:\n",
+    "        # Parse JSON\n",
+    "        data = json.loads(data_str)\n",
+    "        return data\n",
+    "    except json.JSONDecodeError as e:\n",
+    "        print(\"JSON parsing error:\", e)\n",
+    "        return None"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "5ca3e906",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.659623Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.658864Z",
+     "iopub.status.idle": "2024-05-31T18:43:46.666973Z",
+     "shell.execute_reply": "2024-05-31T18:43:46.666195Z"
+    },
+    "papermill": {
+     "duration": 0.022205,
+     "end_time": "2024-05-31T18:43:46.668839",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.646634",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'aircraft': 'robinson r - 22',\n",
+       "  'description': 'light utility helicopter',\n",
+       "  'max gross weight': '1370 lb (635 kg)',\n",
+       "  'total disk area': '497 ft square (46.2 m square)',\n",
+       "  'max disk loading': '2.6 lb / ft square (14 kg / m square)'},\n",
+       " {'aircraft': 'bell 206b3 jetranger',\n",
+       "  'description': 'turboshaft utility helicopter',\n",
+       "  'max gross weight': '3200 lb (1451 kg)',\n",
+       "  'total disk area': '872 ft square (81.1 m square)',\n",
+       "  'max disk loading': '3.7 lb / ft square (18 kg / m square)'},\n",
+       " {'aircraft': 'ch - 47d chinook',\n",
+       "  'description': 'tandem rotor helicopter',\n",
+       "  'max gross weight': '50000 lb (22680 kg)',\n",
+       "  'total disk area': '5655 ft square (526 m square)',\n",
+       "  'max disk loading': '8.8 lb / ft square (43 kg / m square)'},\n",
+       " {'aircraft': 'mil mi - 26',\n",
+       "  'description': 'heavy - lift helicopter',\n",
+       "  'max gross weight': '123500 lb (56000 kg)',\n",
+       "  'total disk area': '8495 ft square (789 m square)',\n",
+       "  'max disk loading': '14.5 lb / ft square (71 kg / m square)'},\n",
+       " {'aircraft': 'ch - 53e super stallion',\n",
+       "  'description': 'heavy - lift helicopter',\n",
+       "  'max gross weight': '73500 lb (33300 kg)',\n",
+       "  'total disk area': '4900 ft square (460 m square)',\n",
+       "  'max disk loading': '15 lb / ft square (72 kg / m square)'}]"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "response_str = \"\"\"[{\"aircraft\": \"robinson r - 22\",\n",
+    " \"description\": \"light utility helicopter\",\n",
+    " \"max gross weight\": \"1370 lb (635 kg)\",\n",
+    " \"total disk area\": \"497 ft square (46.2 m square)\",\n",
+    " \"max disk loading\": \"2.6 lb / ft square (14 kg / m square)\"},\n",
+    "{\"aircraft\": \"bell 206b3 jetranger\",\n",
+    " \"description\": \"turboshaft utility helicopter\",\n",
+    " \"max gross weight\": \"3200 lb (1451 kg)\",\n",
+    " \"total disk area\": \"872 ft square (81.1 m square)\",\n",
+    " \"max disk loading\": \"3.7 lb / ft square (18 kg / m square)\"},\n",
+    "{\"aircraft\": \"ch - 47d chinook\",\n",
+    " \"description\": \"tandem rotor helicopter\",\n",
+    " \"max gross weight\": \"50000 lb (22680 kg)\",\n",
+    " \"total disk area\": \"5655 ft square (526 m square)\",\n",
+    " \"max disk loading\": \"8.8 lb / ft square (43 kg / m square)\"},\n",
+    "{\"aircraft\": \"mil mi - 26\",\n",
+    " \"description\": \"heavy - lift helicopter\",\n",
+    " \"max gross weight\": \"123500 lb (56000 kg)\",\n",
+    " \"total disk area\": \"8495 ft square (789 m square)\",\n",
+    " \"max disk loading\": \"14.5 lb / ft square (71 kg / m square)\"},\n",
+    "{\"aircraft\": \"ch - 53e super stallion\",\n",
+    " \"description\": \"heavy - lift helicopter\",\n",
+    " \"max gross weight\": \"73500 lb (33300 kg)\",\n",
+    " \"total disk area\": \"4900 ft square (460 m square)\",\n",
+    " \"max disk loading\": \"15 lb / ft square (72 kg / m square)\"}]\"\"\"\n",
+    "\n",
+    "# Convert the string representation to a list of dictionaries\n",
+    "parse_json(response_str)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "daecb5c0",
+   "metadata": {
+    "papermill": {
+     "duration": 0.013403,
+     "end_time": "2024-05-31T18:43:46.693812",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.680409",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='5'></a>\n",
+    "# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:left; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'>IV |</span></b> <b> Data Preparation</b></div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40c089cd",
+   "metadata": {
+    "papermill": {
+     "duration": 0.014721,
+     "end_time": "2024-05-31T18:43:46.724815",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.710094",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "I'll extract a sample of 100 records from the dataset excluding those with Arabic names, and then simplify the output to enhance performance."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "937a9d87",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:46.756186Z",
+     "iopub.status.busy": "2024-05-31T18:43:46.755526Z",
+     "iopub.status.idle": "2024-05-31T18:43:47.978038Z",
+     "shell.execute_reply": "2024-05-31T18:43:47.977150Z"
+    },
+    "papermill": {
+     "duration": 1.240895,
+     "end_time": "2024-05-31T18:43:47.980091",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:46.739196",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>context</th>\n",
+       "      <th>answer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>aircraft                    ...</td>\n",
+       "      <td>{\"aircraft\":{\"0\":\"robinson r - 22\",\"1\":\"bell 2...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>order year manufacturer                  mod...</td>\n",
+       "      <td>{\"order year\":{\"0\":\"1992 - 93\",\"1\":\"1996\",\"2\":...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>player       no    nationality  ...</td>\n",
+       "      <td>{\"player\":{\"0\":\"quincy acy\",\"1\":\"hassan adams\"...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>player  no              nationali...</td>\n",
+       "      <td>{\"player\":{\"0\":\"patrick o'bryant\",\"1\":\"jermain...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>player       no    nationality  ...</td>\n",
+       "      <td>{\"player\":{\"0\":\"mark baker\",\"1\":\"marcus banks\"...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             context  \\\n",
+       "0                    aircraft                    ...   \n",
+       "1    order year manufacturer                  mod...   \n",
+       "2                player       no    nationality  ...   \n",
+       "3               player  no              nationali...   \n",
+       "4                player       no    nationality  ...   \n",
+       "\n",
+       "                                              answer  \n",
+       "0  {\"aircraft\":{\"0\":\"robinson r - 22\",\"1\":\"bell 2...  \n",
+       "1  {\"order year\":{\"0\":\"1992 - 93\",\"1\":\"1996\",\"2\":...  \n",
+       "2  {\"player\":{\"0\":\"quincy acy\",\"1\":\"hassan adams\"...  \n",
+       "3  {\"player\":{\"0\":\"patrick o'bryant\",\"1\":\"jermain...  \n",
+       "4  {\"player\":{\"0\":\"mark baker\",\"1\":\"marcus banks\"...  "
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df =  pd.read_csv(\"/kaggle/input/table-extraction/table_extract.csv\")\n",
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "c70acdfc",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:48.005676Z",
+     "iopub.status.busy": "2024-05-31T18:43:48.005166Z",
+     "iopub.status.idle": "2024-05-31T18:43:48.009839Z",
+     "shell.execute_reply": "2024-05-31T18:43:48.009009Z"
+    },
+    "papermill": {
+     "duration": 0.019566,
+     "end_time": "2024-05-31T18:43:48.011735",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:47.992169",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def is_arabic_name(name):\n",
+    "      \"\"\"\n",
+    "      Checks if a name contains Arabic characters.\n",
+    "\n",
+    "      Args:\n",
+    "        name: The name string to check.\n",
+    "\n",
+    "      Returns:\n",
+    "        True if Arabic characters are found, False otherwise.\n",
+    "      \"\"\"\n",
+    "      # Regular expression to match Arabic characters\n",
+    "      arabic_pattern = re.compile(\"[\\u0600-\\u06FF]+\")\n",
+    "\n",
+    "      # Search for Arabic characters in the name\n",
+    "      match = arabic_pattern.search(name)\n",
+    "\n",
+    "      # Return True if a match is found, False otherwise\n",
+    "      return bool(match)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "b2cd7f4f",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:48.036321Z",
+     "iopub.status.busy": "2024-05-31T18:43:48.035722Z",
+     "iopub.status.idle": "2024-05-31T18:43:48.878798Z",
+     "shell.execute_reply": "2024-05-31T18:43:48.877862Z"
+    },
+    "papermill": {
+     "duration": 0.857601,
+     "end_time": "2024-05-31T18:43:48.880980",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:48.023379",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df = df[~df['context'].apply(lambda x: is_arabic_name(x))]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "5ff7b30d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:48.907676Z",
+     "iopub.status.busy": "2024-05-31T18:43:48.907365Z",
+     "iopub.status.idle": "2024-05-31T18:43:48.912778Z",
+     "shell.execute_reply": "2024-05-31T18:43:48.911977Z"
+    },
+    "papermill": {
+     "duration": 0.019861,
+     "end_time": "2024-05-31T18:43:48.914742",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:48.894881",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df_sample =df.loc[:100]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "36e74deb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:48.939496Z",
+     "iopub.status.busy": "2024-05-31T18:43:48.938769Z",
+     "iopub.status.idle": "2024-05-31T18:43:48.944817Z",
+     "shell.execute_reply": "2024-05-31T18:43:48.944090Z"
+    },
+    "papermill": {
+     "duration": 0.020103,
+     "end_time": "2024-05-31T18:43:48.946542",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:48.926439",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def transform_json_to_records(json_data):\n",
+    "    \"\"\"\n",
+    "    Transforms a structured JSON object into a list of records.\n",
+    "\n",
+    "    The function assumes the structure of the JSON object is a dictionary of dictionaries,\n",
+    "    where each top-level key is a field name, and its value is a dictionary mapping indices\n",
+    "    to field values. All sub-dictionaries must have the same keys.\n",
+    "\n",
+    "    Parameters:\n",
+    "    - json_data: A dictionary representing the structured JSON object to transform.\n",
+    "\n",
+    "    Returns:\n",
+    "    - A list of dictionaries, where each dictionary represents a record with fields and values\n",
+    "      derived from the input JSON.\n",
+    "    \"\"\"\n",
+    "    json_data = json.loads(json_data)\n",
+    "    # Extract keys from the first dictionary item to use as indices\n",
+    "    indices = list(next(iter(json_data.values())).keys())\n",
+    "    # Initialize the list to store transformed records\n",
+    "    records = []\n",
+    "\n",
+    "    # Loop over each index to create a record\n",
+    "    for index in indices:\n",
+    "        record = {field: values[index] for field, values in json_data.items()}\n",
+    "        records.append(record)\n",
+    "\n",
+    "    return records"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "ffd53e33",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:48.971043Z",
+     "iopub.status.busy": "2024-05-31T18:43:48.970775Z",
+     "iopub.status.idle": "2024-05-31T18:43:48.982528Z",
+     "shell.execute_reply": "2024-05-31T18:43:48.981700Z"
+    },
+    "papermill": {
+     "duration": 0.026522,
+     "end_time": "2024-05-31T18:43:48.984677",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:48.958155",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df_sample.loc[:, 'answer'] = df_sample['answer'].map(transform_json_to_records)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "40483cb0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:49.010744Z",
+     "iopub.status.busy": "2024-05-31T18:43:49.010286Z",
+     "iopub.status.idle": "2024-05-31T18:43:49.042579Z",
+     "shell.execute_reply": "2024-05-31T18:43:49.041778Z"
+    },
+    "papermill": {
+     "duration": 0.047173,
+     "end_time": "2024-05-31T18:43:49.044758",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:48.997585",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>context</th>\n",
+       "      <th>answer</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>aircraft                    ...</td>\n",
+       "      <td>[{'aircraft': 'robinson r - 22', 'description'...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>order year manufacturer                  mod...</td>\n",
+       "      <td>[{'order year': '1992 - 93', 'manufacturer': '...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>player       no    nationality  ...</td>\n",
+       "      <td>[{'player': 'quincy acy', 'no': '4', 'national...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>player  no              nationali...</td>\n",
+       "      <td>[{'player': 'patrick o'bryant', 'no': 13, 'nat...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>player       no    nationality  ...</td>\n",
+       "      <td>[{'player': 'mark baker', 'no': '3', 'national...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                             context  \\\n",
+       "0                    aircraft                    ...   \n",
+       "1    order year manufacturer                  mod...   \n",
+       "2                player       no    nationality  ...   \n",
+       "3               player  no              nationali...   \n",
+       "4                player       no    nationality  ...   \n",
+       "\n",
+       "                                              answer  \n",
+       "0  [{'aircraft': 'robinson r - 22', 'description'...  \n",
+       "1  [{'order year': '1992 - 93', 'manufacturer': '...  \n",
+       "2  [{'player': 'quincy acy', 'no': '4', 'national...  \n",
+       "3  [{'player': 'patrick o'bryant', 'no': 13, 'nat...  \n",
+       "4  [{'player': 'mark baker', 'no': '3', 'national...  "
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sample.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43c4fc4d",
+   "metadata": {
+    "papermill": {
+     "duration": 0.056482,
+     "end_time": "2024-05-31T18:43:49.113347",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:49.056865",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='6'></a>\n",
+    "# <div style=\"padding: 30px; color:white; margin:10; font-size:75%; text-align:left; display:fill; border-radius:10px; background-color:#3b3745\"><b><span style='color:#F1A424'>V |</span></b> <b>Benchmark</b></div>\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f191a75f",
+   "metadata": {
+    "papermill": {
+     "duration": 0.011795,
+     "end_time": "2024-05-31T18:43:49.137322",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:49.125527",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='61'></a>\n",
+    ">## Prompt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "ade0a612",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:49.163630Z",
+     "iopub.status.busy": "2024-05-31T18:43:49.162872Z",
+     "iopub.status.idle": "2024-05-31T18:43:49.169637Z",
+     "shell.execute_reply": "2024-05-31T18:43:49.168786Z"
+    },
+    "papermill": {
+     "duration": 0.021972,
+     "end_time": "2024-05-31T18:43:49.171493",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:49.149521",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "prompt = \"\"\"Your task is to extract relevant information from the provided context and format it into a  list of records, following the template below.\n",
+    " A JSON object representing the extracted table structure. The  list of records follows this format: \n",
+    " [ { \"column_1\": \"val1\",\"column_2\": \"val1\",\"column_3\": \"val1\",...},\n",
+    "   { \"column_1\": \"val2\",\"column_2\": \"val2\",\"column_3\": \"val3\",...},\n",
+    "   ...\n",
+    " ]\n",
+    " Each key in the records represents a column header, and the corresponding value is another object containing key-value pairs for each row in that column.\n",
+    "\n",
+    "INPUT example:\n",
+    "# do not use the data from the examples & template; they are just for reference only. The following data contains actual information. If a value is not found, leave it empty. \n",
+    "\n",
+    "                  aircraft                    description      max gross weight                total disk area                        max disk loading\n",
+    "0          robinson r - 22       light utility helicopter      1370 lb (635 kg)  497 ft square (46.2 m square)   2.6 lb / ft square (14 kg / m square)\n",
+    "1     bell 206b3 jetranger  turboshaft utility helicopter     3200 lb (1451 kg)  872 ft square (81.1 m square)   3.7 lb / ft square (18 kg / m square)\n",
+    "2         ch - 47d chinook        tandem rotor helicopter   50000 lb (22680 kg)  5655 ft square (526 m square)   8.8 lb / ft square (43 kg / m square)\n",
+    "3              mil mi - 26        heavy - lift helicopter  123500 lb (56000 kg)  8495 ft square (789 m square)  14.5 lb / ft square (71 kg / m square)\n",
+    "4  ch - 53e super stallion        heavy - lift helicopter   73500 lb (33300 kg)  4900 ft square (460 m square)    15 lb / ft square (72 kg / m square)\n",
+    "\n",
+    "OUTPUT example:\n",
+    "# do not use the data from the examples & template; they are just for reference only. The following data contains actual information. If a value is not found, leave it empty. \n",
+    "[{\"aircraft\": \"robinson r - 22\",\n",
+    " \"description\": \"light utility helicopter\",\n",
+    " \"max gross weight\": \"1370 lb (635 kg)\",\n",
+    " \"total disk area\": \"497 ft square (46.2 m square)\",\n",
+    " \"max disk loading\": \"2.6 lb / ft square (14 kg / m square)\"},\n",
+    "{\"aircraft\": \"bell 206b3 jetranger\",\n",
+    " \"description\": \"turboshaft utility helicopter\",\n",
+    " \"max gross weight\": \"3200 lb (1451 kg)\",\n",
+    " \"total disk area\": \"872 ft square (81.1 m square)\",\n",
+    " \"max disk loading\": \"3.7 lb / ft square (18 kg / m square)\"},\n",
+    "{\"aircraft\": \"ch - 47d chinook\",\n",
+    " \"description\": \"tandem rotor helicopter\",\n",
+    " \"max gross weight\": \"50000 lb (22680 kg)\",\n",
+    " \"total disk area\": \"5655 ft square (526 m square)\",\n",
+    " \"max disk loading\": \"8.8 lb / ft square (43 kg / m square)\"},\n",
+    "{\"aircraft\": \"mil mi - 26\",\n",
+    " \"description\": \"heavy - lift helicopter\",\n",
+    " \"max gross weight\": \"123500 lb (56000 kg)\",\n",
+    " \"total disk area\": \"8495 ft square (789 m square)\",\n",
+    " \"max disk loading\": \"14.5 lb / ft square (71 kg / m square)\"},\n",
+    "{\"aircraft\": \"ch - 53e super stallion\",\n",
+    " \"description\": \"heavy - lift helicopter\",\n",
+    " \"max gross weight\": \"73500 lb (33300 kg)\",\n",
+    " \"total disk area\": \"4900 ft square (460 m square)\",\n",
+    " \"max disk loading\": \"15 lb / ft square (72 kg / m square)\"}]\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abc85dbc",
+   "metadata": {
+    "papermill": {
+     "duration": 0.012075,
+     "end_time": "2024-05-31T18:43:49.195733",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:49.183658",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "source": [
+    "<a id='62'></a>\n",
+    ">## OpenChat"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "4d7641e4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:49.222862Z",
+     "iopub.status.busy": "2024-05-31T18:43:49.222025Z",
+     "iopub.status.idle": "2024-05-31T18:43:49.226803Z",
+     "shell.execute_reply": "2024-05-31T18:43:49.225878Z"
+    },
+    "papermill": {
+     "duration": 0.020487,
+     "end_time": "2024-05-31T18:43:49.228685",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:49.208198",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "df_sample =df.loc[:50]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "af6ad0b5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:43:49.254939Z",
+     "iopub.status.busy": "2024-05-31T18:43:49.254611Z",
+     "iopub.status.idle": "2024-05-31T18:45:21.314757Z",
+     "shell.execute_reply": "2024-05-31T18:45:21.313701Z"
+    },
+    "papermill": {
+     "duration": 92.075473,
+     "end_time": "2024-05-31T18:45:21.316713",
+     "exception": false,
+     "start_time": "2024-05-31T18:43:49.241240",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "802f3f2be634485785f3d334448f2cfc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "config.json:   0%|          | 0.00/623 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2fcaf457b45846f5bbc6ffa4fa405243",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Downloading shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00001-of-00003.safetensors:   0%|          | 0.00/4.94G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5631bac1a7414ae3884420e990fe848c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00002-of-00003.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e6b8f270ba1c456781c2dc73c5ba707d",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00003-of-00003.safetensors:   0%|          | 0.00/4.54G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d3f5b442705c4ed3a72e0a922c1fa874",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/3 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "68da855e379b41ed95c4bb898d87f691",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/179 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.5` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`. This was detected when initializing the generation config instance, which means the corresponding file may hold incorrect parameterization and should be fixed.\n",
+      "  warnings.warn(\n",
+      "/opt/conda/lib/python3.10/site-packages/transformers/generation/configuration_utils.py:515: UserWarning: `do_sample` is set to `False`. However, `temperature` is set to `0.5` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `temperature`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "52a819ee01b1460bbe23d57825430b6c",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/1.62k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "09a08852fce1482ea63d1809d9fd85e0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.model:   0%|          | 0.00/493k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
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+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/1.80M [00:00<?, ?B/s]"
+      ]
+     },
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+     "output_type": "display_data"
+    },
+    {
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+       "version_major": 2,
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+      },
+      "text/plain": [
+       "added_tokens.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
+      ]
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+    },
+    {
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+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/502 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
+     ]
+    }
+   ],
+   "source": [
+    "base_model_id = \"openchat/openchat-3.5-1210\"\n",
+    "bnb_config = BitsAndBytesConfig(\n",
+    "    load_in_4bit=True,\n",
+    "    bnb_4bit_use_double_quant=True,\n",
+    "    bnb_4bit_quant_type=\"nf4\",\n",
+    "    bnb_4bit_compute_dtype=torch.bfloat16\n",
+    ")\n",
+    "\n",
+    "model = AutoModelForCausalLM.from_pretrained(base_model_id, quantization_config=bnb_config, device_map=\"auto\")\n",
+    "tokenizer = AutoTokenizer.from_pretrained(base_model_id)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "bc6f67c7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T18:45:21.347069Z",
+     "iopub.status.busy": "2024-05-31T18:45:21.346776Z",
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+     "shell.execute_reply": "2024-05-31T18:45:21.352587Z"
+    },
+    "papermill": {
+     "duration": 0.023834,
+     "end_time": "2024-05-31T18:45:21.355237",
+     "exception": false,
+     "start_time": "2024-05-31T18:45:21.331403",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def prediction(context):\n",
+    "    template = f\"Instruction:\\n{prompt}\\nINPUTDATA:{context}\\nResponse:\\n\"\n",
+    "    messages = [\n",
+    "        {\"role\": \"user\", \"content\": template}\n",
+    "    ]\n",
+    "\n",
+    "    input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt').to('cuda')\n",
+    "    \n",
+    "    model.eval()\n",
+    "    with torch.no_grad():\n",
+    "        output_ids = model.generate(input_ids,max_new_tokens=6000,do_sample=True,temperature=0.000000005,top_k=100,penalty_alpha=0.2,epsilon_cutoff=3e-4,eta_cutoff=3e-4,use_cache=True)\n",
+    "        response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True) \n",
+    "    return response"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "0c922768",
+   "metadata": {
+    "execution": {
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+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Generating Predictions:   0%|          | 0/50 [00:00<?, ?it/s]2024-05-31 18:45:31.446316: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+      "2024-05-31 18:45:31.446496: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+      "2024-05-31 18:45:31.577046: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+      "Generating Predictions: 100%|██████████| 50/50 [1:48:52<00:00, 130.65s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create a copy of the DataFrame\n",
+    "df_copy3 = df_sample.copy()\n",
+    "df_copy3['pred_response'] = None\n",
+    "\n",
+    "# Iterate through each row in the DataFrame with tqdm for progress visualization\n",
+    "for i in tqdm(df_copy3.index, desc=\"Generating Predictions\", total=len(df_copy3)):\n",
+    "    \n",
+    "    context = df_copy3.loc[i,'context']\n",
+    "    res = prediction(context)\n",
+    "    # Update the 'pred_response' column with the generated prediction\n",
+    "    df_copy3.loc[i,'pred_response'] = res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "a10a701d",
+   "metadata": {
+    "execution": {
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+     "iopub.status.busy": "2024-05-31T20:34:14.055269Z",
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+    },
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+     "exception": false,
+     "start_time": "2024-05-31T20:34:14.034756",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "JSON parsing error: Extra data: line 51 column 128 (char 6796)\n",
+      "JSON parsing error: Expecting ':' delimiter: line 9 column 73 (char 1697)\n",
+      "JSON parsing error: Unterminated string starting at: line 486 column 42 (char 18976)\n",
+      "JSON parsing error: Expecting value: line 91 column 179 (char 16683)\n",
+      "JSON parsing error: Expecting ',' delimiter: line 16 column 13 (char 2802)\n",
+      "JSON parsing error: Expecting ',' delimiter: line 13 column 13 (char 2211)\n"
+     ]
+    }
+   ],
+   "source": [
+    "sum_key=0\n",
+    "sum_val = 0\n",
+    "count_errors = 0\n",
+    "for i in df_copy3.index:\n",
+    "\n",
+    "    pred_records = parse_json(df_copy3.loc[i,'pred_response'])\n",
+    "    if pred_records==None:\n",
+    "        count_errors +=1\n",
+    "        continue\n",
+    "    true_records = df_copy3.loc[i,'answer']\n",
+    "    \n",
+    "    sum_key += average_percentage_key(true_records,pred_records)\n",
+    "    \n",
+    "    sum_val += average_percentage_value(true_records,pred_records)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "7e057874",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-05-31T20:34:14.119527Z",
+     "iopub.status.busy": "2024-05-31T20:34:14.119231Z",
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+    },
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+     "start_time": "2024-05-31T20:34:14.099269",
+     "status": "completed"
+    },
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Average Percentage of Predicted Keys: 0.9125238634471589\n",
+      "Average Percentage of Predicted values: 0.8812053520808004\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Average Percentage of Predicted Keys:\", sum_key/(len(df_copy3)-count_errors))\n",
+    "print(\"Average Percentage of Predicted values:\", sum_val/(len(df_copy3)-count_errors))"
+   ]
+  }
+ ],
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