Equity_Research_Intent_Detection.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPOb4u45HEyc41AiHEYVmh/",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/aasthameh/Android-App-Market-on-Google-Play/blob/main/Equity_Research_Intent_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzIsDw9jUVPh",
        "outputId": "d4996782-23aa-4b2a-c2d7-f98dbdc2c585"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (2.2.2)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.5.2)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.44.2)\n",
            "Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.5.0+cu121)\n",
            "Requirement already satisfied: numpy>=1.22.4 in /usr/local/lib/python3.10/dist-packages (from pandas) (1.26.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas) (2024.2)\n",
            "Requirement already satisfied: scipy>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.13.1)\n",
            "Requirement already satisfied: joblib>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.4.2)\n",
            "Requirement already satisfied: threadpoolctl>=3.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.5.0)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.24.7)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.5)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.4.2)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.6.1)\n",
            "Requirement already satisfied: sympy==1.13.1 in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n",
            "Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy==1.13.1->torch) (1.3.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (3.0.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "from google.colab import files\n",
        "from sklearn.model_selection import train_test_split\n",
        "pd.set_option('display.max_colwidth', None)\n",
        "\n",
        "!pip install pandas scikit-learn transformers torch\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "* **Dataset**- It contains questions covering topics relevant to financial analysis, company operations, and strategic insights, making it a valuable resource for researchers, data scientists, and finance professionals. (The dataset has been taken from Kaggle (https://www.kaggle.com/datasets/yousefsaeedian/financial-q-and-a-10k))\n",
        "\n",
        "\n",
        "* **Methodolology -** I observed the questions(/queries) in the dataset, picked some keywords which will likely cover all the intents. I filtered the dataset to keep a total of random 60 sentences.\n",
        "\n",
        "\n",
        "* **Data labelling -** Data was labelled using ChatGPT first and then it was checked manually.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "Uw1k0a4f5i2f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# loading and observing the dataset\n",
        "df = pd.read_csv(\"/content/Financial-QA-10k.csv\")\n",
        "df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_oCZi383UnBS",
        "outputId": "3e8f6f51-bff1-44a2-dd70-d959303ce20b"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                              question  \\\n",
              "0  What area did NVIDIA initially focus on before expanding to other computationally intensive fields?   \n",
              "1        What are some of the recent applications of GPU-powered deep learning as mentioned by NVIDIA?   \n",
              "2                                                What significant invention did NVIDIA create in 1999?   \n",
              "3                             How does NVIDIA's platform strategy contribute to the markets it serves?   \n",
              "4                                                    What does NVIDIA's CUDA programming model enable?   \n",
              "5                                       What industries use NVIDIA's GPUs and software for automation?   \n",
              "6      Why did NVIDIA and SoftBank terminate their Share Purchase Agreement for acquiring Arm Limited?   \n",
              "7                What amount did NVIDIA record as an acquisition termination cost in fiscal year 2023?   \n",
              "8                                       What does the NVIDIA computing platform focus on accelerating?   \n",
              "9                                        What are the key components of the NVIDIA computing platform?   \n",
              "\n",
              "                                                                                                                                                                                                                           answer  \\\n",
              "0                                                                                                                                                                                        NVIDIA initially focused on PC graphics.   \n",
              "1                                                                                                      Recent applications of GPU-powered deep learning include recommendation systems, large language models, and generative AI.   \n",
              "2                                                                                                                                                                                                NVIDIA invented the GPU in 1999.   \n",
              "3                                                                                             NVIDIA's platform strategy brings together hardware, systems, software, algorithms, libraries, and services to create unique value.   \n",
              "4                                                                                                              NVIDIA's CUDA programming model opened the parallel processing capabilities of GPUs for general purpose computing.   \n",
              "5                                   NVIDIA's GPUs and software are used for automation in various industries including transportation, healthcare, financial services, professional design, movies, and building products design.   \n",
              "6                                                                        NVIDIA and SoftBank terminated their Share Purchase Agreement due to significant regulatory challenges that prevented the completion of the transaction.   \n",
              "7                                                                                                                                           NVIDIA recorded an acquisition termination cost of $1.35 billion in fiscal year 2023.   \n",
              "8                                The NVIDIA computing platform focuses on accelerating the most compute-intensive workloads, such as AI, data analytics, graphics and scientific computing, across various types of data centers.   \n",
              "9  The NVIDIA computing platform includes energy-efficient GPUs, data processing units (DPUs), interconnects, systems, the CUDA programming model, and a suite of software libraries, SDKs, application frameworks, and services.   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       context  \\\n",
              "0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.   \n",
              "1    Some of the most recent applications of GPU-powered deep learning include recommendation systems, which are AI algorithms trained to understand the preferences, previous decisions, and characteristics of people and products using data gathered about their interactions, large language models, which can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets, and generative AI, which uses algorithms that create new content, including audio, code, images, text, simulations, and videos, based on the data they have been trained on.   \n",
              "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                 Our invention of the GPU in 1999 defined modern computer graphics and established NVIDIA as the leader in computer graphics.   \n",
              "3                                                                                                                                                                                                                                                                                                                                                                                                                                                          NVIDIA has a platform strategy, bringing together hardware, systems, software, algorithms, libraries, and services to create unique value for the markets we serve.   \n",
              "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                        With our introduction of the CUDA programming model in 2006, we opened the parallel processing capabilities of our GPU for general purpose computing.   \n",
              "5  A rapidly growing number of enterprises and startups across a broad range of industries use our GPUs and software to bring automation to the products and services they build. The transportation industry is turning to our platforms for autonomous driving; the healthcare industry is leveraging them for enhanced medical imaging and acceleration of drug discovery; and the financial services industry is using them for fraud detection. Professional designers use our GPUs and software to create visual effects in movies and to design buildings and products ranging from cell phones to commercial aircraft.   \n",
              "6                                                                                                                                                                                                                                                  Termination of the Arm Share Purchase Agreement In February 2022, NVIDIA and SoftBank Group Corp., or SoftBank, announced the termination of the Share Purchase Agreement whereby NVIDIA would have acquired Arm Limited, or Arm, from SoftBank. The parties agreed to terminate because of significant regulatory challenges preventing the completion of the transaction.   \n",
              "7                                                                                                                                                                                                                                                                                                                                                                                                                                                                             We recorded an acquisition termination cost of $1.35 billion in fiscal year 2023 reflecting the write-off of the prepayment provided at signing.   \n",
              "8                      Data Center The NVIDIA computing platform is focused on accelerating the most compute-intensive workloads, such as AI, data analytics, graphics and scientific computing, across hyperscale, cloud, enterprise, public sector, and edge data centers. The platform consists of our energy efficient GPUs, data processing units, or DPUs, interconnects and systems, our CUDA programming model, and a growing body of software libraries, software development kits, or SDKs, application frameworks and services, which are either available as part of the platform or packaged and sold separately.   \n",
              "9                      Data Center The NVIDIA computing platform is focused on accelerating the most compute-intensive workloads, such as AI, data analytics, graphics and scientific computing, across hyperscale, cloud, enterprise, public sector, and edge data centers. The platform consists of our energy efficient GPUs, data processing units, or DPUs, interconnects and systems, our CUDA programming model, and a growing body of software libraries, software development kits, or SDKs, application frameworks and services, which are either available as part of the platform or packaged and sold separately.   \n",
              "\n",
              "  ticker    filing  \n",
              "0   NVDA  2023_10K  \n",
              "1   NVDA  2023_10K  \n",
              "2   NVDA  2023_10K  \n",
              "3   NVDA  2023_10K  \n",
              "4   NVDA  2023_10K  \n",
              "5   NVDA  2023_10K  \n",
              "6   NVDA  2023_10K  \n",
              "7   NVDA  2023_10K  \n",
              "8   NVDA  2023_10K  \n",
              "9   NVDA  2023_10K  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4e79064e-0d96-4df5-8e40-4232702c6783\" class=\"colab-df-container\">\n",
              "    <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>question</th>\n",
              "      <th>answer</th>\n",
              "      <th>context</th>\n",
              "      <th>ticker</th>\n",
              "      <th>filing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>What area did NVIDIA initially focus on before expanding to other computationally intensive fields?</td>\n",
              "      <td>NVIDIA initially focused on PC graphics.</td>\n",
              "      <td>Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>What are some of the recent applications of GPU-powered deep learning as mentioned by NVIDIA?</td>\n",
              "      <td>Recent applications of GPU-powered deep learning include recommendation systems, large language models, and generative AI.</td>\n",
              "      <td>Some of the most recent applications of GPU-powered deep learning include recommendation systems, which are AI algorithms trained to understand the preferences, previous decisions, and characteristics of people and products using data gathered about their interactions, large language models, which can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets, and generative AI, which uses algorithms that create new content, including audio, code, images, text, simulations, and videos, based on the data they have been trained on.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>What significant invention did NVIDIA create in 1999?</td>\n",
              "      <td>NVIDIA invented the GPU in 1999.</td>\n",
              "      <td>Our invention of the GPU in 1999 defined modern computer graphics and established NVIDIA as the leader in computer graphics.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>How does NVIDIA's platform strategy contribute to the markets it serves?</td>\n",
              "      <td>NVIDIA's platform strategy brings together hardware, systems, software, algorithms, libraries, and services to create unique value.</td>\n",
              "      <td>NVIDIA has a platform strategy, bringing together hardware, systems, software, algorithms, libraries, and services to create unique value for the markets we serve.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>What does NVIDIA's CUDA programming model enable?</td>\n",
              "      <td>NVIDIA's CUDA programming model opened the parallel processing capabilities of GPUs for general purpose computing.</td>\n",
              "      <td>With our introduction of the CUDA programming model in 2006, we opened the parallel processing capabilities of our GPU for general purpose computing.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>What industries use NVIDIA's GPUs and software for automation?</td>\n",
              "      <td>NVIDIA's GPUs and software are used for automation in various industries including transportation, healthcare, financial services, professional design, movies, and building products design.</td>\n",
              "      <td>A rapidly growing number of enterprises and startups across a broad range of industries use our GPUs and software to bring automation to the products and services they build. The transportation industry is turning to our platforms for autonomous driving; the healthcare industry is leveraging them for enhanced medical imaging and acceleration of drug discovery; and the financial services industry is using them for fraud detection. Professional designers use our GPUs and software to create visual effects in movies and to design buildings and products ranging from cell phones to commercial aircraft.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Why did NVIDIA and SoftBank terminate their Share Purchase Agreement for acquiring Arm Limited?</td>\n",
              "      <td>NVIDIA and SoftBank terminated their Share Purchase Agreement due to significant regulatory challenges that prevented the completion of the transaction.</td>\n",
              "      <td>Termination of the Arm Share Purchase Agreement In February 2022, NVIDIA and SoftBank Group Corp., or SoftBank, announced the termination of the Share Purchase Agreement whereby NVIDIA would have acquired Arm Limited, or Arm, from SoftBank. The parties agreed to terminate because of significant regulatory challenges preventing the completion of the transaction.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>What amount did NVIDIA record as an acquisition termination cost in fiscal year 2023?</td>\n",
              "      <td>NVIDIA recorded an acquisition termination cost of $1.35 billion in fiscal year 2023.</td>\n",
              "      <td>We recorded an acquisition termination cost of $1.35 billion in fiscal year 2023 reflecting the write-off of the prepayment provided at signing.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>What does the NVIDIA computing platform focus on accelerating?</td>\n",
              "      <td>The NVIDIA computing platform focuses on accelerating the most compute-intensive workloads, such as AI, data analytics, graphics and scientific computing, across various types of data centers.</td>\n",
              "      <td>Data Center The NVIDIA computing platform is focused on accelerating the most compute-intensive workloads, such as AI, data analytics, graphics and scientific computing, across hyperscale, cloud, enterprise, public sector, and edge data centers. The platform consists of our energy efficient GPUs, data processing units, or DPUs, interconnects and systems, our CUDA programming model, and a growing body of software libraries, software development kits, or SDKs, application frameworks and services, which are either available as part of the platform or packaged and sold separately.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>What are the key components of the NVIDIA computing platform?</td>\n",
              "      <td>The NVIDIA computing platform includes energy-efficient GPUs, data processing units (DPUs), interconnects, systems, the CUDA programming model, and a suite of software libraries, SDKs, application frameworks, and services.</td>\n",
              "      <td>Data Center The NVIDIA computing platform is focused on accelerating the most compute-intensive workloads, such as AI, data analytics, graphics and scientific computing, across hyperscale, cloud, enterprise, public sector, and edge data centers. The platform consists of our energy efficient GPUs, data processing units, or DPUs, interconnects and systems, our CUDA programming model, and a growing body of software libraries, software development kits, or SDKs, application frameworks and services, which are either available as part of the platform or packaged and sold separately.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4e79064e-0d96-4df5-8e40-4232702c6783')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-4e79064e-0d96-4df5-8e40-4232702c6783 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-4e79064e-0d96-4df5-8e40-4232702c6783');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8d2fc234-b153-4936-95a1-00a1946cc2ae\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8d2fc234-b153-4936-95a1-00a1946cc2ae')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8d2fc234-b153-4936-95a1-00a1946cc2ae button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7000,\n  \"fields\": [\n    {\n      \"column\": \"question\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6932,\n        \"samples\": [\n          \"What is discussed in Item 8 of a financial document?\",\n          \"What are the net lease obligations for operating and finance leases as of 2024 according to the disclosed financial data?\",\n          \"What was the net fair value of the company's outstanding derivatives as of January 28, 2024?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6528,\n        \"samples\": [\n          \"$52,124\",\n          \"Amortization of film and TV production and acquisition costs, residuals and participations expenses, and distribution expenses\",\n          \"From 2022 to 2023, lululemon's net revenue increased by 19% to $9.6 billion, and comparable sales increased by 13% overall.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6756,\n        \"samples\": [\n          \"Item 8, which requires the inclusion of financial statements and supplementary data, directs readers to Item 15(a) for this information.\",\n          \"Our largest vendors are Sony, Nintendo, and Microsoft, which collectively accounted for a majority of our new product purchases in fiscal 2022.\",\n          \"Because the hours of operation are shorter than many other retailers, and due to other efficiencies inherent in a warehouse-type operation, labor costs are lower relative to the volume of sales. Merchandise is generally stored on racks above the sales floor and displayed on pallets containing large quantities, reducing labor required.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ticker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 69,\n        \"samples\": [\n          \"CRM\",\n          \"NVDA\",\n          \"EFX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"filing\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2023_10K\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = df.rename(columns = {'question':'query'})\n",
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "GRA9Qs4AYLyA",
        "outputId": "d466feb6-7e2d-4257-f4f6-a0123295d008"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                                                          query  \\\n",
              "0                                                           What area did NVIDIA initially focus on before expanding to other computationally intensive fields?   \n",
              "1                                                                 What are some of the recent applications of GPU-powered deep learning as mentioned by NVIDIA?   \n",
              "2                                                                                                         What significant invention did NVIDIA create in 1999?   \n",
              "3                                                                                      How does NVIDIA's platform strategy contribute to the markets it serves?   \n",
              "4                                                                                                             What does NVIDIA's CUDA programming model enable?   \n",
              "...                                                                                                                                                         ...   \n",
              "6995  What was the interest rate for the 5.400% Senior Notes due in 2028 after accounting for unamortized original issue discount and deferred financing costs?   \n",
              "6996                               What changes were made to the LVSC Revolving Credit Agreement according to the Fourth Amendment enacted on January 30, 2023?   \n",
              "6997       What was the increase in interest expense for Las Vegas Sands Corp. and subsidiaries due to the downgrades in the years 2023 and 2022, respectively?   \n",
              "6998                    What are the new leverage and interest coverage ratios set by SCL under the amended and restated facility agreement as of January 2024?   \n",
              "6999                                    What was the total amount available to be drawn under the Singapore Delayed Draw Term Facility as of December 31, 2023?   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                         answer  \\\n",
              "0                                                                                                                                                                                                                                                                                                                                                                                      NVIDIA initially focused on PC graphics.   \n",
              "1                                                                                                                                                                                                                                                                                                    Recent applications of GPU-powered deep learning include recommendation systems, large language models, and generative AI.   \n",
              "2                                                                                                                                                                                                                                                                                                                                                                                              NVIDIA invented the GPU in 1999.   \n",
              "3                                                                                                                                                                                                                                                                                           NVIDIA's platform strategy brings together hardware, systems, software, algorithms, libraries, and services to create unique value.   \n",
              "4                                                                                                                                                                                                                                                                                                            NVIDIA's CUDA programming model opened the parallel processing capabilities of GPUs for general purpose computing.   \n",
              "...                                                                                                                                                                                                                                                                                                                                                                                                                         ...   \n",
              "6995                                                                                                                                                                                                                                                                                                                                                                                                                     5.400%   \n",
              "6996  The Fourth Amendment to the LVSC Revolving Credit Agreement on January 30, 2023, included changes such as determining consolidated adjusted EBITDA on a year-to-date annualized basis, extending the minimum liquidity requirement to December 31, 2023, and extending restrictions on dividends and distributions unless liquidity exceeds $1.0 billion on a pro forma basis after such distributions, to the same date.   \n",
              "6997                                                                                                                                                                                                                                                                                            The interest expense increased by $30 million and $16 million for the years 2023 and 2022, respectively, due to the downgrades.   \n",
              "6998                                                                                                                                                                                                                                                                                           As of January 2024, the new leverage ratio should not exceed 4.0x, and the interest coverage ratio should not be less than 2.5x.   \n",
              "6999                                                                                                                                                                                                                                                                                                                                                                                                              $2.79 billion   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     context  \\\n",
              "0                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.   \n",
              "1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  Some of the most recent applications of GPU-powered deep learning include recommendation systems, which are AI algorithms trained to understand the preferences, previous decisions, and characteristics of people and products using data gathered about their interactions, large language models, which can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets, and generative AI, which uses algorithms that create new content, including audio, code, images, text, simulations, and videos, based on the data they have been trained on.   \n",
              "2                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               Our invention of the GPU in 1999 defined modern computer graphics and established NVIDIA as the leader in computer graphics.   \n",
              "3                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                        NVIDIA has a platform strategy, bringing together hardware, systems, software, algorithms, libraries, and services to create unique value for the markets we serve.   \n",
              "4                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      With our introduction of the CUDA programming model in 2006, we opened the parallel processing capabilities of our GPU for general purpose computing.   \n",
              "...                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                      ...   \n",
              "6995                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                              The 5.400% Senior Notes due in 2028 have an interest rate of 5.400%, net of unamortized original issue discount and deferred financing costs of $11 and $13, respectively.   \n",
              "6996  On January 30, 2023, LVSC entered into amendment No. 4 (the “Fourth Amendment”) with lenders to the LVSC Revolving Credit Agreement. Pursuant to the Fourth Amendment, the existing LVSC Revolving Credit Agreement was amended to (a) determine consolidated adjusted EBITDA on a year-to-date annualized basis during the period commencing on the effective date and ending on and including December 31, 2023, as follows: (i) for the fiscal quarter ended March 31, 2023, consolidated adjusted EBITDA for such fiscal quarter multiplied by four, (ii) for the fiscal quarter ended June 30, 2023, consolidated adjusted EBITDA for such fiscal quarter and the immediately preceding fiscal quarter multiplied by two, and (iii) for the fiscal quarter ended September 30, 2023, consolidated adjusted EBITDA for such fiscal quarter and the two immediately preceding fiscal quarters, multiplied by four-thirds; (b) extend the period during which LVSC is required to maintain a specified amount of minimum liquidity as of the last day of each month to December 31, 2023; and (c) extend the period during which LVSC is unable to declare or pay any dividend or other distribution, unless liquidity is greater than $1.0 billion on a pro forma basis after giving effect to such dividend or distribution, to December 31, 2023.   \n",
              "6997                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           Following the downgrades, each series of the outstanding SCL Senior Notes had its coupon increased, resulting in an increase of $30 million and $16 million in interest expense for the years ended December 31, 2023 and 2022, respectively.   \n",
              "6998                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                       The amended and restated facility agreement with SCL extended the waiver period to January 1, 2024, during which SCL is required to comply with the new financial requirements of maintaining a consolidated leverage ratio not exceeding 4.0x and an interest coverage ratio not less than 2.5x.   \n",
              "6999                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                             As of December 31, 2023, SGD 3.69 billion (approximately $2.79 billion at exchange rates in effect on December 31, 2023) remains available to be drawn under the Singapore Delayed Draw Term Facility once the construction cost estimate and construction schedule for the MBS Expansion Project are delivered to lenders.   \n",
              "\n",
              "     ticker    filing  \n",
              "0      NVDA  2023_10K  \n",
              "1      NVDA  2023_10K  \n",
              "2      NVDA  2023_10K  \n",
              "3      NVDA  2023_10K  \n",
              "4      NVDA  2023_10K  \n",
              "...     ...       ...  \n",
              "6995    LVS  2023_10K  \n",
              "6996    LVS  2023_10K  \n",
              "6997    LVS  2023_10K  \n",
              "6998    LVS  2023_10K  \n",
              "6999    LVS  2023_10K  \n",
              "\n",
              "[7000 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-93033d55-3685-45d8-a86c-068e8d55b122\" class=\"colab-df-container\">\n",
              "    <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>query</th>\n",
              "      <th>answer</th>\n",
              "      <th>context</th>\n",
              "      <th>ticker</th>\n",
              "      <th>filing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>What area did NVIDIA initially focus on before expanding to other computationally intensive fields?</td>\n",
              "      <td>NVIDIA initially focused on PC graphics.</td>\n",
              "      <td>Since our original focus on PC graphics, we have expanded to several other large and important computationally intensive fields.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>What are some of the recent applications of GPU-powered deep learning as mentioned by NVIDIA?</td>\n",
              "      <td>Recent applications of GPU-powered deep learning include recommendation systems, large language models, and generative AI.</td>\n",
              "      <td>Some of the most recent applications of GPU-powered deep learning include recommendation systems, which are AI algorithms trained to understand the preferences, previous decisions, and characteristics of people and products using data gathered about their interactions, large language models, which can recognize, summarize, translate, predict and generate text and other content based on knowledge gained from massive datasets, and generative AI, which uses algorithms that create new content, including audio, code, images, text, simulations, and videos, based on the data they have been trained on.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>What significant invention did NVIDIA create in 1999?</td>\n",
              "      <td>NVIDIA invented the GPU in 1999.</td>\n",
              "      <td>Our invention of the GPU in 1999 defined modern computer graphics and established NVIDIA as the leader in computer graphics.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>How does NVIDIA's platform strategy contribute to the markets it serves?</td>\n",
              "      <td>NVIDIA's platform strategy brings together hardware, systems, software, algorithms, libraries, and services to create unique value.</td>\n",
              "      <td>NVIDIA has a platform strategy, bringing together hardware, systems, software, algorithms, libraries, and services to create unique value for the markets we serve.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>What does NVIDIA's CUDA programming model enable?</td>\n",
              "      <td>NVIDIA's CUDA programming model opened the parallel processing capabilities of GPUs for general purpose computing.</td>\n",
              "      <td>With our introduction of the CUDA programming model in 2006, we opened the parallel processing capabilities of our GPU for general purpose computing.</td>\n",
              "      <td>NVDA</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6995</th>\n",
              "      <td>What was the interest rate for the 5.400% Senior Notes due in 2028 after accounting for unamortized original issue discount and deferred financing costs?</td>\n",
              "      <td>5.400%</td>\n",
              "      <td>The 5.400% Senior Notes due in 2028 have an interest rate of 5.400%, net of unamortized original issue discount and deferred financing costs of $11 and $13, respectively.</td>\n",
              "      <td>LVS</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6996</th>\n",
              "      <td>What changes were made to the LVSC Revolving Credit Agreement according to the Fourth Amendment enacted on January 30, 2023?</td>\n",
              "      <td>The Fourth Amendment to the LVSC Revolving Credit Agreement on January 30, 2023, included changes such as determining consolidated adjusted EBITDA on a year-to-date annualized basis, extending the minimum liquidity requirement to December 31, 2023, and extending restrictions on dividends and distributions unless liquidity exceeds $1.0 billion on a pro forma basis after such distributions, to the same date.</td>\n",
              "      <td>On January 30, 2023, LVSC entered into amendment No. 4 (the “Fourth Amendment”) with lenders to the LVSC Revolving Credit Agreement. Pursuant to the Fourth Amendment, the existing LVSC Revolving Credit Agreement was amended to (a) determine consolidated adjusted EBITDA on a year-to-date annualized basis during the period commencing on the effective date and ending on and including December 31, 2023, as follows: (i) for the fiscal quarter ended March 31, 2023, consolidated adjusted EBITDA for such fiscal quarter multiplied by four, (ii) for the fiscal quarter ended June 30, 2023, consolidated adjusted EBITDA for such fiscal quarter and the immediately preceding fiscal quarter multiplied by two, and (iii) for the fiscal quarter ended September 30, 2023, consolidated adjusted EBITDA for such fiscal quarter and the two immediately preceding fiscal quarters, multiplied by four-thirds; (b) extend the period during which LVSC is required to maintain a specified amount of minimum liquidity as of the last day of each month to December 31, 2023; and (c) extend the period during which LVSC is unable to declare or pay any dividend or other distribution, unless liquidity is greater than $1.0 billion on a pro forma basis after giving effect to such dividend or distribution, to December 31, 2023.</td>\n",
              "      <td>LVS</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6997</th>\n",
              "      <td>What was the increase in interest expense for Las Vegas Sands Corp. and subsidiaries due to the downgrades in the years 2023 and 2022, respectively?</td>\n",
              "      <td>The interest expense increased by $30 million and $16 million for the years 2023 and 2022, respectively, due to the downgrades.</td>\n",
              "      <td>Following the downgrades, each series of the outstanding SCL Senior Notes had its coupon increased, resulting in an increase of $30 million and $16 million in interest expense for the years ended December 31, 2023 and 2022, respectively.</td>\n",
              "      <td>LVS</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6998</th>\n",
              "      <td>What are the new leverage and interest coverage ratios set by SCL under the amended and restated facility agreement as of January 2024?</td>\n",
              "      <td>As of January 2024, the new leverage ratio should not exceed 4.0x, and the interest coverage ratio should not be less than 2.5x.</td>\n",
              "      <td>The amended and restated facility agreement with SCL extended the waiver period to January 1, 2024, during which SCL is required to comply with the new financial requirements of maintaining a consolidated leverage ratio not exceeding 4.0x and an interest coverage ratio not less than 2.5x.</td>\n",
              "      <td>LVS</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6999</th>\n",
              "      <td>What was the total amount available to be drawn under the Singapore Delayed Draw Term Facility as of December 31, 2023?</td>\n",
              "      <td>$2.79 billion</td>\n",
              "      <td>As of December 31, 2023, SGD 3.69 billion (approximately $2.79 billion at exchange rates in effect on December 31, 2023) remains available to be drawn under the Singapore Delayed Draw Term Facility once the construction cost estimate and construction schedule for the MBS Expansion Project are delivered to lenders.</td>\n",
              "      <td>LVS</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7000 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-93033d55-3685-45d8-a86c-068e8d55b122')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-93033d55-3685-45d8-a86c-068e8d55b122 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-93033d55-3685-45d8-a86c-068e8d55b122');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-348ec114-a19e-433a-bb46-a1d4aa40ecb8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-348ec114-a19e-433a-bb46-a1d4aa40ecb8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-348ec114-a19e-433a-bb46-a1d4aa40ecb8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_eac930d5-dd5d-4015-b7aa-e55646c0ab1e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_eac930d5-dd5d-4015-b7aa-e55646c0ab1e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 7000,\n  \"fields\": [\n    {\n      \"column\": \"query\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6932,\n        \"samples\": [\n          \"What is discussed in Item 8 of a financial document?\",\n          \"What are the net lease obligations for operating and finance leases as of 2024 according to the disclosed financial data?\",\n          \"What was the net fair value of the company's outstanding derivatives as of January 28, 2024?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6528,\n        \"samples\": [\n          \"$52,124\",\n          \"Amortization of film and TV production and acquisition costs, residuals and participations expenses, and distribution expenses\",\n          \"From 2022 to 2023, lululemon's net revenue increased by 19% to $9.6 billion, and comparable sales increased by 13% overall.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6756,\n        \"samples\": [\n          \"Item 8, which requires the inclusion of financial statements and supplementary data, directs readers to Item 15(a) for this information.\",\n          \"Our largest vendors are Sony, Nintendo, and Microsoft, which collectively accounted for a majority of our new product purchases in fiscal 2022.\",\n          \"Because the hours of operation are shorter than many other retailers, and due to other efficiencies inherent in a warehouse-type operation, labor costs are lower relative to the volume of sales. Merchandise is generally stored on racks above the sales floor and displayed on pallets containing large quantities, reducing labor required.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ticker\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 69,\n        \"samples\": [\n          \"CRM\",\n          \"NVDA\",\n          \"EFX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"filing\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"2023_10K\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# I observed a few keyword which may likely cover queries for all intents\n",
        "keywords = ['market', 'cost', 'amount', 'fiscal', 'shareholder', 'dividend', 'revenue', 'fund', 'compensat', 'repurchase', 'stock', 'growth', 'compare', 'increase', 'decrease']"
      ],
      "metadata": {
        "id": "rjaNKHgbU982"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter rows containing any of the keywords\n",
        "pattern = '|'.join(keywords)\n",
        "\n",
        "filtered_df = df[df['query'].str.contains(pattern, case=False, na=False)]\n",
        "\n",
        "\n",
        "df_no_duplicates = filtered_df.drop_duplicates(subset=['query'], keep='first')\n",
        "df_no_duplicates.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 227
        },
        "id": "Q5_QrGKpNoJG",
        "outputId": "3fb7919b-b3c2-442c-bdda-bc782f210ea5"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                           query  \\\n",
              "count                                                                       2359   \n",
              "unique                                                                      2359   \n",
              "top     How does NVIDIA's platform strategy contribute to the markets it serves?   \n",
              "freq                                                                           1   \n",
              "\n",
              "                      answer  \\\n",
              "count                   2358   \n",
              "unique                  2223   \n",
              "top     Pages 44 through 121   \n",
              "freq                      12   \n",
              "\n",
              "                                                                                                                                                                                                                                                                                                                                                                       context  \\\n",
              "count                                                                                                                                                                                                                                                                                                                                                                     2359   \n",
              "unique                                                                                                                                                                                                                                                                                                                                                                    2331   \n",
              "top     ITEM 11. EXECUTIVE COMPENSATION Information regarding our executive compensation required by this item will be contained in our 2023 Proxy Statement under the captions “Executive Compensation”, “Compensation Committee Interlocks and Insider Participation”, “Director Compensation” and “Compensation Committee Report,” and is hereby incorporated by reference.   \n",
              "freq                                                                                                                                                                                                                                                                                                                                                                         4   \n",
              "\n",
              "       ticker    filing  \n",
              "count    2359      2359  \n",
              "unique     68         1  \n",
              "top       JNJ  2023_10K  \n",
              "freq       83      2359  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2d1d74a7-542d-416f-bacc-c2d348b40673\" class=\"colab-df-container\">\n",
              "    <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>query</th>\n",
              "      <th>answer</th>\n",
              "      <th>context</th>\n",
              "      <th>ticker</th>\n",
              "      <th>filing</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2359</td>\n",
              "      <td>2358</td>\n",
              "      <td>2359</td>\n",
              "      <td>2359</td>\n",
              "      <td>2359</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>2359</td>\n",
              "      <td>2223</td>\n",
              "      <td>2331</td>\n",
              "      <td>68</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>How does NVIDIA's platform strategy contribute to the markets it serves?</td>\n",
              "      <td>Pages 44 through 121</td>\n",
              "      <td>ITEM 11. EXECUTIVE COMPENSATION Information regarding our executive compensation required by this item will be contained in our 2023 Proxy Statement under the captions “Executive Compensation”, “Compensation Committee Interlocks and Insider Participation”, “Director Compensation” and “Compensation Committee Report,” and is hereby incorporated by reference.</td>\n",
              "      <td>JNJ</td>\n",
              "      <td>2023_10K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>12</td>\n",
              "      <td>4</td>\n",
              "      <td>83</td>\n",
              "      <td>2359</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2d1d74a7-542d-416f-bacc-c2d348b40673')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2d1d74a7-542d-416f-bacc-c2d348b40673 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2d1d74a7-542d-416f-bacc-c2d348b40673');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-9a57ff82-f7f4-459f-8fb7-d4903e1ec61d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9a57ff82-f7f4-459f-8fb7-d4903e1ec61d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9a57ff82-f7f4-459f-8fb7-d4903e1ec61d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_no_duplicates\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"query\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"2359\",\n          \"How does NVIDIA's platform strategy contribute to the markets it serves?\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"answer\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2223,\n          \"12\",\n          \"2358\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"context\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2331,\n          \"4\",\n          \"2359\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ticker\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          68,\n          \"83\",\n          \"2359\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"filing\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"2359\",\n          1,\n          \"2023_10K\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# select 60 sentences in random order\n",
        "random_rows = df['query'].sample(n=60, random_state=1)  # random_state for reproducibility\n",
        "\n",
        "# Create a new DataFrame from the selected rows\n",
        "filtered_df = pd.DataFrame(random_rows)"
      ],
      "metadata": {
        "id": "o3v95156Odhw"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "*I used ChatGPT to generate labels in a batch of 10 or more sentences- I noticed it is simply giving me a same repeated pattern (some labels were right too) so I used it to create a new column and later manually corrected it*"
      ],
      "metadata": {
        "id": "D0b-64Gf8kXK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Labels:**\n",
        "\n",
        "**Metric enquiry**\n",
        "\n",
        "**Historical Data**\n",
        "\n",
        "**Forecasting & Predictions**\n",
        "\n",
        "**Fundamental Data Request**\n",
        "\n",
        "**Comparative Analysis**\n",
        "\n",
        "**News Impact**"
      ],
      "metadata": {
        "id": "7twJJbdUWmZN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# SOME IMP POINTERS ON DATA LABELLING\n",
        "# Overlapping labels\n",
        "\n",
        "Many sentences had a couple or more suitable labels,  for example -\n",
        "1. How much did Resource Industries' profit increase in 2023 and what were the main contributing factors?\n",
        "[HISTORICAL DATA, COMPARATIVE ANALYSIS]\n",
        "\n",
        "2. What was the net cash used in investing activities for the year ended December 31, 2023, and what were the primary components of this expenditure?\n",
        "[HISTORICAL DATA, METRIC INQUIRY]\n",
        "\n",
        "In case of overlapping labels, I tried to use the label that has lesser frequency (It has not been strictly followed)\n",
        "\n",
        "#'Other' label\n",
        "\n",
        "A suggestion is that a new label 'Other' can be incorporated, for queries like the following-\n",
        "\n",
        "1. How is the Financial Statements and Supplementary Data section numbered in some documents?\n",
        "\n",
        "It does not neately fall under 'Fundamental Data Request' but has been labelled as that because it is asking for something related to a financial document. However, a new category 'Other' may help as-\n",
        "\n",
        "      * It is more suitable\n",
        "      * It will divide the frequency count for 'Fundamental Data Request' which is currently the majority, making the data more balanced.\n",
        "\n",
        " # Noise in the data\n",
        "\n",
        " Some queries were not fitting into any labels and are seen as noise in the data."
      ],
      "metadata": {
        "id": "gm8hZZINR2X2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "labels = [\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "     \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "     \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "     \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "     \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "     \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "     \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "     \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "     \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "     \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "    \"News Impact\", \"Metric Inquiry\", \"Fundamental Data Request\",\n",
        "\n",
        "]\n",
        "len(labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "04pMP2OT-aRB",
        "outputId": "4fed934a-74a3-48d3-9bf3-9036a6b6c528"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "60"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# add labels as a new col\n",
        "filtered_df['intent'] = labels\n",
        "\n",
        "final_df = filtered_df[['query', 'intent']]\n",
        "final_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "GVaRejsc5ZUB",
        "outputId": "6dad4dd5-1aa5-48fc-9e84-6d4a4bcbe8d4"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                                                                                   query  \\\n",
              "2305                                                                                                                    In how many countries are Eli Lilly and Company's products sold?   \n",
              "4388                                                                                                   What was the amount of unrecognized tax benefits recorded as of January 28, 2023?   \n",
              "1686                                                               What section of the Annual Report on Form 10-K contains the consolidated financial statements and accompanying notes?   \n",
              "4945                                                                                                      What are the expected impacts of CORSIA and ETS on FedEx Express's operations?   \n",
              "4197                                                                                                                          What does Part IV Item 15 include in a financial document?   \n",
              "1651                                                                    Where are the consolidated financial statements and accompanying notes listed in the Annual Report on Form 10-K?   \n",
              "1528                                                                                     In which item and part of a financial document would you find information on legal proceedings?   \n",
              "6346                                                                                              What are the consequences of the company failing to protect its intellectual property?   \n",
              "6189                                                                                    How are expected credit losses estimated for securities according to their accounting treatment?   \n",
              "5389                                                                                                                       What was the adjustment for Cadillac dealer strategy in 2023?   \n",
              "2575                                                          What were the unrealized losses on U.S. government and agency obligations for more than 12 months as of December 31, 2023?   \n",
              "4474                                                                              Where are the consolidated financial statements and accompanying notes located as described in Item 8?   \n",
              "2403                                                                                                         How does the company's infrastructure software solutions support customers?   \n",
              "1070                                                                                                                          How does American Express manage and mitigate market risk?   \n",
              "3216                                                                                                                        What challenges does the company face regarding water usage?   \n",
              "5856                                                                                               As of October 31, 2023, what was the goodwill amount for the HPC & AI reporting unit?   \n",
              "3534                                                                              How much did Resource Industries' profit increase in 2023 and what were the main contributing factors?   \n",
              "1989                                                                                              What were the basic earnings per share for Visa's Class A common stock in fiscal 2023?   \n",
              "4873                                  What was the net cash used in investing activities for the year ended December 31, 2023, and what were the primary components of this expenditure?   \n",
              "1555                                                                               What was the primary reason for the increase in shipping costs in 2023 compared to the previous year?   \n",
              "413                                                                                                     What overall context does Item 3. Legal Proceedings offer regarding the company?   \n",
              "2918                                                                                               What architecture is the AMD Radeon RX 7000 series built on and what does it provide?   \n",
              "231                                                                                                                                       What is the primary goal of Tesla's existence?   \n",
              "2452                                                                                                    Why did the current portion of long-term debt increase in 2023 compared to 2022?   \n",
              "4776                                                                      What steps will be taken to monitor the Asia Pacific reporting unit's performance against possible impairment?   \n",
              "6690                                                                              What event took place in September 2021 involving the Company and the counsel representing plaintiffs?   \n",
              "6424                                                                                                                               How does Intuit's ProSeries software serve its users?   \n",
              "5730                                                                                                                                   What drove the increase in SM&A expenses in 2023?   \n",
              "4285                                                                           How many aircraft did Delta Air Lines lease as of December 31, 2023, and how are these leases classified?   \n",
              "2520                                                                                                                            What was the earnings from operations for Optum in 2023?   \n",
              "3251  What is the impact of structural changes on the unit case volume and concentrate sales volume of the company on a consolidated basis or at the geographic operating segment level?   \n",
              "3708                                                                                       What program offers Chipotle employees access to tuition-free degrees at select universities?   \n",
              "248                                                                                                                               What information is provided under ITEM 6 in the text?   \n",
              "5958                                                                         How did the expenses from comparable owned and leased hotels change and what were the contributing factors?   \n",
              "4518                                                                                     How much did Electronic Arts' live services and other net revenue increase in fiscal year 2023?   \n",
              "2196                                                    What is the responsibility of AMC Entertainment Holdings, Inc.'s management regarding internal control over financial reporting?   \n",
              "5248                                                                                                                           What section from item 8 addresses financial information?   \n",
              "503                                                                                                            How many warehouses did Costco operate worldwide as of September 3, 2023?   \n",
              "695                                                                                What was the weighted-average grant-date fair value of stock options granted in 2021, 2022, and 2023?   \n",
              "941                                                 What are some of the potential consequences for Meta Platforms, Inc. from inquiries or investigations as noted in the provided text?   \n",
              "1144                                                                                What led to the significant decrease in supplier settlements in the fiscal year ended June 30, 2023?   \n",
              "2868                                                                                                               What is the projected range for capital expenditures for fiscal 2024?   \n",
              "4029                                                                                                          What potential impacts of the Rebate Rule have led PCMA to file a lawsuit?   \n",
              "6365                                                                                            By what percentage did the closing solutions revenues decrease in 2023 compared to 2022?   \n",
              "5883                                                                                                                   What was the increase in non-GAAP gross profit from 2022 to 2023?   \n",
              "2252                                                                                                   What is the relationship between the ESG goals and the long-term growth strategy?   \n",
              "1885                                                                                                  How could changes in input estimates impact the financial position of the company?   \n",
              "5212                                                                                          How is the Financial Statements and Supplementary Data section numbered in some documents?   \n",
              "2866                                                              What was the increase in net cash used from financing activities for fiscal 2022 compared to the previous fiscal year?   \n",
              "2103                                                                                                            What was AMC's minimum liquidity requirement under the Credit Agreement?   \n",
              "3300                                                                                                                       When was AT&T Inc. incorporated and under which state's laws?   \n",
              "5033                                                                                                              How are changes in operating expenses primarily driven at Ford Credit?   \n",
              "1546                                                                                                 What uncertainties exist regarding projections of future cash needs and cash flows?   \n",
              "4385                                                       How are fixed payments for common area maintenance, real estate taxes, and insurance treated in real estate lease agreements?   \n",
              "3230                                                                   What type of legal action did the company seek in the initial lawsuit filed against Aqua-Chem in Georgia in 2002?   \n",
              "3724                                                                                                                What was the comprehensive income of Chipotle Mexican Grill in 2023?   \n",
              "4661                          What was the total amount spent by Enphase Energy, Inc. on repurchasing shares under the 2023 Repurchase Program during the year ending December 31, 2023?   \n",
              "5376                                                                                  What is the effect of a 10% increase in the cost of sales incentives on General Motors’ liability?   \n",
              "4962                                                   What financial impact did the actuarial adjustments of pension and postretirement healthcare plans have in 2023 compared to 2022?   \n",
              "3023                                                                                                           What are exploratory wells and how do they differ from development wells?   \n",
              "\n",
              "                        intent  \n",
              "2305               News Impact  \n",
              "4388            Metric Inquiry  \n",
              "1686  Fundamental Data Request  \n",
              "4945               News Impact  \n",
              "4197            Metric Inquiry  \n",
              "1651  Fundamental Data Request  \n",
              "1528               News Impact  \n",
              "6346            Metric Inquiry  \n",
              "6189  Fundamental Data Request  \n",
              "5389               News Impact  \n",
              "2575            Metric Inquiry  \n",
              "4474  Fundamental Data Request  \n",
              "2403               News Impact  \n",
              "1070            Metric Inquiry  \n",
              "3216  Fundamental Data Request  \n",
              "5856               News Impact  \n",
              "3534            Metric Inquiry  \n",
              "1989  Fundamental Data Request  \n",
              "4873               News Impact  \n",
              "1555            Metric Inquiry  \n",
              "413   Fundamental Data Request  \n",
              "2918               News Impact  \n",
              "231             Metric Inquiry  \n",
              "2452  Fundamental Data Request  \n",
              "4776               News Impact  \n",
              "6690            Metric Inquiry  \n",
              "6424  Fundamental Data Request  \n",
              "5730               News Impact  \n",
              "4285            Metric Inquiry  \n",
              "2520  Fundamental Data Request  \n",
              "3251               News Impact  \n",
              "3708            Metric Inquiry  \n",
              "248   Fundamental Data Request  \n",
              "5958               News Impact  \n",
              "4518            Metric Inquiry  \n",
              "2196  Fundamental Data Request  \n",
              "5248               News Impact  \n",
              "503             Metric Inquiry  \n",
              "695   Fundamental Data Request  \n",
              "941                News Impact  \n",
              "1144            Metric Inquiry  \n",
              "2868  Fundamental Data Request  \n",
              "4029               News Impact  \n",
              "6365            Metric Inquiry  \n",
              "5883  Fundamental Data Request  \n",
              "2252               News Impact  \n",
              "1885            Metric Inquiry  \n",
              "5212  Fundamental Data Request  \n",
              "2866               News Impact  \n",
              "2103            Metric Inquiry  \n",
              "3300  Fundamental Data Request  \n",
              "5033               News Impact  \n",
              "1546            Metric Inquiry  \n",
              "4385  Fundamental Data Request  \n",
              "3230               News Impact  \n",
              "3724            Metric Inquiry  \n",
              "4661  Fundamental Data Request  \n",
              "5376               News Impact  \n",
              "4962            Metric Inquiry  \n",
              "3023  Fundamental Data Request  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-79da605c-2f0f-403f-967b-e6105d9af6d3\" class=\"colab-df-container\">\n",
              "    <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>query</th>\n",
              "      <th>intent</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2305</th>\n",
              "      <td>In how many countries are Eli Lilly and Company's products sold?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4388</th>\n",
              "      <td>What was the amount of unrecognized tax benefits recorded as of January 28, 2023?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1686</th>\n",
              "      <td>What section of the Annual Report on Form 10-K contains the consolidated financial statements and accompanying notes?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4945</th>\n",
              "      <td>What are the expected impacts of CORSIA and ETS on FedEx Express's operations?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4197</th>\n",
              "      <td>What does Part IV Item 15 include in a financial document?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1651</th>\n",
              "      <td>Where are the consolidated financial statements and accompanying notes listed in the Annual Report on Form 10-K?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1528</th>\n",
              "      <td>In which item and part of a financial document would you find information on legal proceedings?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6346</th>\n",
              "      <td>What are the consequences of the company failing to protect its intellectual property?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6189</th>\n",
              "      <td>How are expected credit losses estimated for securities according to their accounting treatment?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5389</th>\n",
              "      <td>What was the adjustment for Cadillac dealer strategy in 2023?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2575</th>\n",
              "      <td>What were the unrealized losses on U.S. government and agency obligations for more than 12 months as of December 31, 2023?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4474</th>\n",
              "      <td>Where are the consolidated financial statements and accompanying notes located as described in Item 8?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2403</th>\n",
              "      <td>How does the company's infrastructure software solutions support customers?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1070</th>\n",
              "      <td>How does American Express manage and mitigate market risk?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3216</th>\n",
              "      <td>What challenges does the company face regarding water usage?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5856</th>\n",
              "      <td>As of October 31, 2023, what was the goodwill amount for the HPC &amp; AI reporting unit?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3534</th>\n",
              "      <td>How much did Resource Industries' profit increase in 2023 and what were the main contributing factors?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1989</th>\n",
              "      <td>What were the basic earnings per share for Visa's Class A common stock in fiscal 2023?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4873</th>\n",
              "      <td>What was the net cash used in investing activities for the year ended December 31, 2023, and what were the primary components of this expenditure?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1555</th>\n",
              "      <td>What was the primary reason for the increase in shipping costs in 2023 compared to the previous year?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>413</th>\n",
              "      <td>What overall context does Item 3. Legal Proceedings offer regarding the company?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2918</th>\n",
              "      <td>What architecture is the AMD Radeon RX 7000 series built on and what does it provide?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>What is the primary goal of Tesla's existence?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2452</th>\n",
              "      <td>Why did the current portion of long-term debt increase in 2023 compared to 2022?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4776</th>\n",
              "      <td>What steps will be taken to monitor the Asia Pacific reporting unit's performance against possible impairment?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6690</th>\n",
              "      <td>What event took place in September 2021 involving the Company and the counsel representing plaintiffs?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6424</th>\n",
              "      <td>How does Intuit's ProSeries software serve its users?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5730</th>\n",
              "      <td>What drove the increase in SM&amp;A expenses in 2023?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4285</th>\n",
              "      <td>How many aircraft did Delta Air Lines lease as of December 31, 2023, and how are these leases classified?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2520</th>\n",
              "      <td>What was the earnings from operations for Optum in 2023?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3251</th>\n",
              "      <td>What is the impact of structural changes on the unit case volume and concentrate sales volume of the company on a consolidated basis or at the geographic operating segment level?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3708</th>\n",
              "      <td>What program offers Chipotle employees access to tuition-free degrees at select universities?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>What information is provided under ITEM 6 in the text?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5958</th>\n",
              "      <td>How did the expenses from comparable owned and leased hotels change and what were the contributing factors?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4518</th>\n",
              "      <td>How much did Electronic Arts' live services and other net revenue increase in fiscal year 2023?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2196</th>\n",
              "      <td>What is the responsibility of AMC Entertainment Holdings, Inc.'s management regarding internal control over financial reporting?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5248</th>\n",
              "      <td>What section from item 8 addresses financial information?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>503</th>\n",
              "      <td>How many warehouses did Costco operate worldwide as of September 3, 2023?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>695</th>\n",
              "      <td>What was the weighted-average grant-date fair value of stock options granted in 2021, 2022, and 2023?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>941</th>\n",
              "      <td>What are some of the potential consequences for Meta Platforms, Inc. from inquiries or investigations as noted in the provided text?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1144</th>\n",
              "      <td>What led to the significant decrease in supplier settlements in the fiscal year ended June 30, 2023?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2868</th>\n",
              "      <td>What is the projected range for capital expenditures for fiscal 2024?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4029</th>\n",
              "      <td>What potential impacts of the Rebate Rule have led PCMA to file a lawsuit?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6365</th>\n",
              "      <td>By what percentage did the closing solutions revenues decrease in 2023 compared to 2022?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5883</th>\n",
              "      <td>What was the increase in non-GAAP gross profit from 2022 to 2023?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2252</th>\n",
              "      <td>What is the relationship between the ESG goals and the long-term growth strategy?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1885</th>\n",
              "      <td>How could changes in input estimates impact the financial position of the company?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5212</th>\n",
              "      <td>How is the Financial Statements and Supplementary Data section numbered in some documents?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2866</th>\n",
              "      <td>What was the increase in net cash used from financing activities for fiscal 2022 compared to the previous fiscal year?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2103</th>\n",
              "      <td>What was AMC's minimum liquidity requirement under the Credit Agreement?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3300</th>\n",
              "      <td>When was AT&amp;T Inc. incorporated and under which state's laws?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5033</th>\n",
              "      <td>How are changes in operating expenses primarily driven at Ford Credit?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1546</th>\n",
              "      <td>What uncertainties exist regarding projections of future cash needs and cash flows?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4385</th>\n",
              "      <td>How are fixed payments for common area maintenance, real estate taxes, and insurance treated in real estate lease agreements?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3230</th>\n",
              "      <td>What type of legal action did the company seek in the initial lawsuit filed against Aqua-Chem in Georgia in 2002?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3724</th>\n",
              "      <td>What was the comprehensive income of Chipotle Mexican Grill in 2023?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4661</th>\n",
              "      <td>What was the total amount spent by Enphase Energy, Inc. on repurchasing shares under the 2023 Repurchase Program during the year ending December 31, 2023?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5376</th>\n",
              "      <td>What is the effect of a 10% increase in the cost of sales incentives on General Motors’ liability?</td>\n",
              "      <td>News Impact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4962</th>\n",
              "      <td>What financial impact did the actuarial adjustments of pension and postretirement healthcare plans have in 2023 compared to 2022?</td>\n",
              "      <td>Metric Inquiry</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3023</th>\n",
              "      <td>What are exploratory wells and how do they differ from development wells?</td>\n",
              "      <td>Fundamental Data Request</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-79da605c-2f0f-403f-967b-e6105d9af6d3')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-79da605c-2f0f-403f-967b-e6105d9af6d3 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-79da605c-2f0f-403f-967b-e6105d9af6d3');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-55816cd5-c4b2-4dab-b7bd-ba10f611fb9e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-55816cd5-c4b2-4dab-b7bd-ba10f611fb9e')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-55816cd5-c4b2-4dab-b7bd-ba10f611fb9e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_ea4fec76-c92d-4e17-959c-e1a0ad069cb5\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('final_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_ea4fec76-c92d-4e17-959c-e1a0ad069cb5 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('final_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_df",
              "summary": "{\n  \"name\": \"final_df\",\n  \"rows\": 60,\n  \"fields\": [\n    {\n      \"column\": \"query\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 60,\n        \"samples\": [\n          \"In how many countries are Eli Lilly and Company's products sold?\",\n          \"Where are the consolidated financial statements and accompanying notes listed in the Annual Report on Form 10-K?\",\n          \"What section from item 8 addresses financial information?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"intent\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"News Impact\",\n          \"Metric Inquiry\",\n          \"Fundamental Data Request\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# downloading the file for a manual check and edit\n",
        "final_df.to_csv('final_df.csv', index=False)\n",
        "files.download(\"final_df.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "FCTQRZsWAZov",
        "outputId": "ceb55c27-f6db-481b-acc8-8fba638d82ff"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_afd4bc55-493b-473f-b22d-ff9fd4a57bda\", \"final_df.csv\", 6521)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# the final dataset with corrected labels\n",
        "updated_dataset = pd.read_csv('/content/final_df.csv')"
      ],
      "metadata": {
        "id": "ts9vV6wHZt-B"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# frequency count for each label\n",
        "label_counts = updated_dataset['intent'].value_counts()\n",
        "\n",
        "plt.figure(figsize=(5, 5))\n",
        "plt.pie(label_counts, labels=label_counts.index, autopct='%1.1f%%', startangle=140)\n",
        "plt.title('Frequency Count of Each Label')\n",
        "plt.axis('equal')  # Equal aspect ratio ensures that pie chart is a circle.\n",
        "plt.show()\n",
        "\n",
        "print(\"\\nThere is a class imbalance\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "DGWdhfeZBYM2",
        "outputId": "59622865-8dc8-4e66-ff27-91640397a501"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "There is a class imbalance\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = updated_dataset['query']  # Features\n",
        "y = updated_dataset['intent']  #labels\n",
        "\n",
        "# Split the data into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
      ],
      "metadata": {
        "id": "Bd7HD-VPaPoM"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training feature set shape:\", X_train.shape)\n",
        "print(\"Testing feature set shape:\", X_test.shape)\n",
        "print(\"Training target set shape:\", y_train.shape)\n",
        "print(\"Testing target set shape:\", y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SGq1HEzVa5N2",
        "outputId": "119f1f16-5c2c-4b2b-bba8-e898c8959884"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training feature set shape: (48,)\n",
            "Testing feature set shape: (12,)\n",
            "Training target set shape: (48,)\n",
            "Testing target set shape: (12,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vectorizer = TfidfVectorizer()\n",
        "label_encoder = LabelEncoder()\n",
        "X_train_tfidf = vectorizer.fit_transform(X_train)\n",
        "X_test_tfidf = vectorizer.transform(X_test)"
      ],
      "metadata": {
        "id": "YSPAiauZmx3k"
      },
      "execution_count": 86,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit and transform the labels\n",
        "y_train = label_encoder.fit_transform(y_train)\n",
        "y_test = label_encoder.transform(y_test)  # Important: only transform test labels\n"
      ],
      "metadata": {
        "id": "c_Nt9WxKo-zz"
      },
      "execution_count": 87,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train the model\n",
        "model = LogisticRegression()\n",
        "model.fit(X_train_tfidf, y_train)\n",
        "# Make predictions\n",
        "y_pred = model.predict(X_test_tfidf)"
      ],
      "metadata": {
        "id": "RG6cpFyumyO_"
      },
      "execution_count": 88,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Performance and enhancement\n",
        "* The model shows strong precision for \"Forecasting & Predictions\" (100%) but fails to detect many of these instances (25% recall). In contrast, it has good recall for \"Comparative Analysis\" (100%) but low precision (45%).\n",
        "\n",
        "* The overall metrics indicate some imbalance in class performance, with one class being identified perfectly while the other struggles.\n",
        "\n",
        "**Enhancement** - *The dataset is imbalanced and the minority class can be augmented for improvement.*"
      ],
      "metadata": {
        "id": "JNUUI-78EgA8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model\n",
        "print(classification_report(y_test, y_pred, target_names=label_encoder.classes_, labels=np.unique(y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W_0P9gUknjq5",
        "outputId": "fff73cc6-e27f-442e-daee-70b57a80211b"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                           precision    recall  f1-score   support\n",
            "\n",
            "     Comparative Analysis       0.45      1.00      0.62         5\n",
            "Forecasting & Predictions       1.00      0.25      0.40         4\n",
            "\n",
            "                micro avg       0.50      0.67      0.57         9\n",
            "                macro avg       0.73      0.62      0.51         9\n",
            "             weighted avg       0.70      0.67      0.52         9\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/metrics/_classification.py:2642: UserWarning: labels size, 2, does not match size of target_names, 6\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    }
  ]
}