{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "**Install All the Required Libraries**"
      ],
      "metadata": {
        "id": "J-_xGH7WddfC"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9P4RR3jOYP9X",
        "outputId": "d3409611-0f95-496e-ee05-f6ea398a7709"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/542.0 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m317.4/542.0 kB\u001b[0m \u001b[31m9.4 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.0/542.0 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m171.5/171.5 kB\u001b[0m \u001b[31m12.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.5/85.5 MB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m15.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m25.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.2/401.2 kB\u001b[0m \u001b[31m34.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "# Install the 'datasets' library to load data from Hugging Face into the Google Colab Notebook.\n",
        "# Install the 'transformers' library to utilize the AutoTokenizer for converting raw text into tokens.\n",
        "# Install the 'sentence_transformers' library to acquire the embedding model.\n",
        "# Install 'faiss-gpu' for efficient similarity searches with dense vectors.\n",
        "!pip install -q datasets transformers sentence_transformers faiss-gpu\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Set the Environment to Use Hugging Face Token:** Ensure that the environment is configured to authenticate using your Hugging Face API token."
      ],
      "metadata": {
        "id": "B15TmeUZdooj"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9fz0LPTnZEHu",
        "outputId": "b81fca08-dfe1-4d6f-859b-babc77be3123"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Token successfully set.\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "from IPython.display import clear_output\n",
        "from getpass import getpass\n",
        "\n",
        "# Prompt for the Hugging Face token securely\n",
        "hf_token = getpass(\"Please enter your Hugging Face token: \")\n",
        "os.environ[\"HF_TOKEN\"] = hf_token\n",
        "clear_output(wait=True)\n",
        "print(\"Token successfully set.\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Load the Dataset**"
      ],
      "metadata": {
        "id": "JQgXmGCldv7q"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341,
          "referenced_widgets": [
            "2847e9b5bf324ca0820f00da03eaeb11",
            "9f34117172f641bdae2c9eb822d1219a",
            "1b2d3141feb84005bcdf8bcb8329655d",
            "c8611415d5124c10b9537abfed7363db",
            "bd11c2d8bea8452d8e9e7dbaa385461d",
            "df5eb7f3eda9416086f2366748597cee",
            "73f4c2c2cea849f7a36df0d86b80debf",
            "2451c446566d46ce9b39afd89b62c44a",
            "921eb9a8c82d4a868488478e651faafb",
            "6347af2fb7bc40ce865f5b0e1b577ccb",
            "cae40c1dfb3441c5930214eb1548a746",
            "7cd7bfcb470440bc9458e0fb1807d1cd",
            "359e70118ebc4440ae4bca80aed91a4a",
            "f986aa1f103441a1af452b0226a7f52a",
            "9b4fc15b935f46dcb635feb978e74d4f",
            "f123b70067d8430585986b3681c48390",
            "be78b05a31f640c6b056448e5d9766de",
            "70626c5befbb463a9bbda3da59978df9",
            "a115a266b66043839a9ecf9420d4c5b2",
            "596f52bad5804282a3bd4fe3a168a797",
            "60d36f5c5f4541e0a110a9a9465ef597",
            "e6ee92b1b42b4ab48364c2412122ba1a",
            "589e85335fa44345a7b3dd751344465e",
            "fd2d0b59b37d42f1b35c42d8926fc5df",
            "01f9d4a160874f5ba9d69ce784e39fa9",
            "e550745f71c044d89f0c05f75a11a5e2",
            "4b557092255241b999c9983f64dd42ba",
            "731c30a7520045a2ab0c5889d651ef89",
            "ab0084b13d3d40a68f4b3510c3038a7f",
            "4a99065cea7b462180963a38695fc00f",
            "eb943879e1e841b1bfb16b1cb499e9df",
            "ed5e59b629aa4082ba6875c3882e6ba1",
            "29298a48c590451d8e10fb45deb6b853"
          ]
        },
        "id": "u_ma7FfGZMBC",
        "outputId": "889f08e3-74ba-491c-ded9-fbf14859a3af"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/5.34k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2847e9b5bf324ca0820f00da03eaeb11"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/15.6M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7cd7bfcb470440bc9458e0fb1807d1cd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/24926 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "589e85335fa44345a7b3dd751344465e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input', 'output', 'instruction', 'data_source'],\n",
              "        num_rows: 24926\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "dataset = load_dataset(\"garage-bAInd/Open-Platypus\")\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BrLEFI8CZMlx",
        "outputId": "8d79d0b3-4d9d-4a56-d5d7-0f3de4fde4ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['input', 'output', 'instruction', 'data_source'],\n",
              "    num_rows: 24926\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "dataset[\"train\"]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MVuiD_w5ZOiQ",
        "outputId": "70ef8d32-008f-4471-f9bf-691e24687d9e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      input                                             output  \\\n",
              "0            To find the probability of the spinner landing...   \n",
              "1            I need to choose 6 people out of 14, and the o...   \n",
              "2            First we count the number of all 4-letter word...   \n",
              "3            She can do this if and only if at least one of...   \n",
              "4            Think of the problem as a sequence of H's and ...   \n",
              "...     ...                                                ...   \n",
              "24921        There are several formulas that generate all p...   \n",
              "24922        Sure, here are some of the best universities:\\...   \n",
              "24923        [Enter stage left]\\n\\nFirst Citizen: Good morr...   \n",
              "24924        That's a very thoughtful idea! Here are some s...   \n",
              "24925        Certainly! Here's an implementation of the Tim...   \n",
              "\n",
              "                                             instruction    data_source  \n",
              "0      A board game spinner is divided into three par...  MATH/PRM-800K  \n",
              "1      My school's math club has 6 boys and 8 girls. ...  MATH/PRM-800K  \n",
              "2      How many 4-letter words with at least one cons...  MATH/PRM-800K  \n",
              "3      Melinda will roll two standard six-sided dice ...  MATH/PRM-800K  \n",
              "4      Let $p$ be the probability that, in the proces...  MATH/PRM-800K  \n",
              "...                                                  ...            ...  \n",
              "24921  Can we find a formula to generate all prime nu...        guanaco  \n",
              "24922  What are some of the best university's for stu...        guanaco  \n",
              "24923  Write me a SPL (Shakespeare Programming Langua...        guanaco  \n",
              "24924  Hi. I want to make an embossed picture for my ...        guanaco  \n",
              "24925  Please implement the Timsort algorithm on Lean...        guanaco  \n",
              "\n",
              "[24926 rows x 4 columns]"
            ],
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              "      <td>To find the probability of the spinner landing...</td>\n",
              "      <td>A board game spinner is divided into three par...</td>\n",
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              "      <td>I need to choose 6 people out of 14, and the o...</td>\n",
              "      <td>My school's math club has 6 boys and 8 girls. ...</td>\n",
              "      <td>MATH/PRM-800K</td>\n",
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              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>First we count the number of all 4-letter word...</td>\n",
              "      <td>How many 4-letter words with at least one cons...</td>\n",
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              "      <td>Melinda will roll two standard six-sided dice ...</td>\n",
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              "      <td>MATH/PRM-800K</td>\n",
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              "      <td>That's a very thoughtful idea! Here are some s...</td>\n",
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              "      <td>Please implement the Timsort algorithm on Lean...</td>\n",
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              "          + ' 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-ebd63db8-65ec-432b-af5a-7a02a7ef06de\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ebd63db8-65ec-432b-af5a-7a02a7ef06de')\"\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-ebd63db8-65ec-432b-af5a-7a02a7ef06de 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\": \"dataset[\\\"train\\\"]\",\n  \"rows\": 24926,\n  \"fields\": [\n    {\n      \"column\": \"input\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"\",\n          \"Choose A, B, C or D as your solution.\",\n          \"When you review a fellow student's writing, try to make your feedback clear and specific. You can use questions such as the following to guide your feedback:\\nIdeas and development: Does the writer express a clear main idea and develop it with evidence, examples, and analysis?\\nOrganization: Does the writer order ideas in a clear, logical way so that the ideas build on one another and are easy to follow?\\nSentence fluency: Do the writer's sentences vary in structure and length, or does the writing sound choppy, rambling, or repetitive?\\nWord choice: Does the writer use words accurately and precisely, avoiding excessive repetition or inappropriate language to create clear, effective, and engaging writing?\\nGrammar and mechanics: Does the writer use accurate spelling, punctuation, and grammar to create writing that is correct and easy to read?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19530,\n        \"samples\": [\n          \"The first answer choice contains a vague pronoun reference. The pronoun he could refer to Steven or Jim.\\nThe second answer choice shows a possible correction for the vague pronoun reference. He has been replaced with Steven.\\nSteven's brother Jim wondered whether Steven ran fast enough to qualify for the Boston Marathon.\",\n          \"Once upon a midnight dreary, while I pondered weak and weary,\\nOver many a faint and forgotten tale of technology's lore\\u2014\\n    While I nodded, nearly napping, suddenly there came a tapping,\\nAs of someone gently rapping, rapping at my chamber door.\\n\\\"'Tis some visitor,\\\" I muttered, \\\"tapping at my chamber door\\u2014\\n            Only this and nothing more.\\\"\\n\\nAh, distinctly I remember it was in the bleak December;\\nAnd each separate dying ember writhed upon the floor.\\nEagerly I wished the morrow;\\u2014vainly I had sought to borrow\\nFrom my books surcease of sorrow\\u2014sorrow for the lost Lenore\\u2014\\nFor the rare and radiant maiden whom the angels name Lenore\\u2014\\n            Nameless here for evermore.\\n\\nDeep into that darkness peering, long I stood there wondering, fearing,\\nDoubting, dreaming dreams no mortals ever dared to dream before;\\nBut the silence was unbroken, and the stillness gave no token,\\nAnd the only words there spoken were the whispered words, \\\"Lenore!\\\"\\nThis I whispered, and an echo murmured back the word, \\\"Lenore!\\\"\\u2014\\n            Merely this and nothing more.\\n\\nBack into the chamber turning, all my soul within me burning,\\nSoon again I heard a tapping somewhat louder than before.\\n\\\"Surely,\\\" said I, \\\"surely that is something at my window lattice;\\n      Let me see, then, what thereat is, and this mystery explore\\u2014\\nLet my heart be still a moment and this mystery explore;\\u2014\\n            'Tis the wind and nothing more!\\\"\\n\\nOpen here I flung the shutter, when, with many a flirt and flutter,\\nIn there stepped a stately Raven of the saintly days of yore;\\nNot the least obeisance made he; not a moment stopped or stayed he;\\nBut, with mien of lord or lady, perched above my chamber door\\u2014\\n            Perched upon a bust of Pallas just above my chamber door\\u2014\\n            Perched, and sat, and nothing more.\\n\\nThen this ebony bird beguiling my sad fancy into smiling,\\nBy the grave and stern decorum of the countenance it wore,\\n\\\"Though thy crest be shorn and shaven, thou,\\\" I said, \\\"art sure no craven,\\nGhastly grim and ancient Raven wandering from the Nightly shore\\u2014\\nTell me what thy lordly name is on the Night's Plutonian shore!\\\"\\n            Quoth the Raven \\\"Nevermore.\\\"\\n\\nMuch I marvelled this ungainly fowl to hear discourse so plainly,\\nThough its answer little meaning\\u2014little relevancy bore;\\nFor we cannot help agreeing that no living human being\\nEver yet was blessed with seeing bird above his chamber door\\u2014\\nBird or beast upon the sculptured bust above his chamber door,\\n            With such name as \\\"Nevermore.\\\"\\n\\nThus I sat engaged in guessing, but no syllable expressing\\nTo the fowl whose fiery eyes now burned into my bosom's core;\\nThis and more I sat divining, with my head at ease reclining\\nOn the cushion's velvet lining that the lamplight gloated o'er,\\nBut whose velvet-violet lining with the lamplight gloating o'er,\\n            She shall press, ah, nevermore!\\n\\nAnd the Raven, never flitting, still is sitting, still is sitting\\nOn the pallid bust of Pallas just above my chamber door;\\nAnd his eyes have all the seeming of a demon's that is dreaming,\\nAnd the lamplight o'er him streaming throws his shadow on the floor;\\nAnd my soul from out that shadow that lies floating on the floor\\n            Shall be lifted\\u2014nevermore!\",\n          \"The graphs labelled (3), (4), and (5) are all invertible since no horizontal line intersects the graph in more than one place. In other words, for each real number $y$ there is at most one real number $x$ with $f(x)=y$. The first graph does not satisfy this condition. Thus the product of the labels corresponding to invertible functions is $3\\\\times 4\\\\times 5=60$.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"instruction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24926,\n        \"samples\": [\n          \"Under which of the following circumstances would the named defendant be least likely to be convicted of the charged offense?\\nA. Walter, a chemical engineer, painstakingly constructs an explosive device from readily available materials and secretes it beneath the house where his ex-wife and her boyfriend are living. He waits until he is sure both have had time to get home from their jobs, and then detonates the device by remote control, totally demolishing the house. Unknown to Walter, his ex-wife and her boyfriend had impulsively decided to spend the evening at a motel downtown. Walter is charged with attempted murder.\\nB. Yvette attempts to charge an expensive leather coat using a credit card that her boyfriend obtained when he stole a woman's purse. The store's electronic credit reporting system indicates the account is defunct, and the store clerk refuses to complete the transaction. Yvette is charged with attempting to obtain property by false pretenses.\\nC. Hazel, seeing a well-heeled couple cross the park, decides to rob them and use the money for groceries. Hazel approaches the couple, pulls out a gun, and tells them to hand over all of their money and valuables. The couple was having an unusually bad day: they had just been mugged five minutes before and were on their way to notify the police when Hazel approached them. Consequently, they had no money or valuables. Hazel is charged with attempted robbery.\\nD. Farley, a married man who believes adultery to be a felony, begins a torrid affair with Edna, his best friend's wife. Unknown to either of them, the jurisdiction they reside in has a statute that expressly makes noncriminal any sexual act between consenting adults in private. Farley is charged with attempted adultery.\",\n          \"What is the smallest positive integer $n$ such that all the roots of $z^4 - z^2 + 1 = 0$ are $n^{\\\\text{th}}$ roots of unity?\",\n          \"Summarize the process by which plants communicate with each other through chemical signals.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"data_source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"airoboros\",\n          \"ARB\",\n          \"reclor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "# Read the Dataset as the Pandas Dataset\n",
        "dataset[\"train\"].to_pandas()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Analyzing the Dataset**"
      ],
      "metadata": {
        "id": "KdVXok7yd2Oa"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 200,
          "referenced_widgets": [
            "64324065ca4943e1a47e14f0367787b5",
            "9c81d0f2399147c5be1733787d86a429",
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            "881b790368584d39bd1e452d1157fea9",
            "a6183ba03725462a8666a815453aec44",
            "31e70f11c5dc49568f03f134be8a09ea",
            "30a7181030924f3ea2405d20e4108a32",
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        "id": "0S-z5uKKZQmK",
        "outputId": "2bf6d298-52e9-49ff-b822-c4e5c4a3e941"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/746 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "64324065ca4943e1a47e14f0367787b5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9661ba504f0b4f3193e3570bf847b65f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a4d3da88c004435cb2eecf1d15274a0a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/435 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3343aa10f6a74111a5fdf1bc366a9c75"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Import AutoTokenizer from transformers to tokenize text for model processing\n",
        "from transformers import AutoTokenizer\n",
        "# Import matplotlib for creating static, interactive, and animated visualizations in Python\n",
        "import matplotlib.pyplot as plt\n",
        "# Import seaborn for making statistical graphics in Python\n",
        "import seaborn as sns\n",
        "\n",
        "# Load the tokenizer for Llama 2 from Nous Research, this tokenizer prepares text inputs for the Llama 2 model\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"NousResearch/Llama-2-7b-hf\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0N1Ky-hcZS6X",
        "outputId": "4251f6e2-0852-4288-ef31-8de44452ebcd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Instruction Tokens Count [85, 53, 75, 86, 82, 30, 46, 36, 101, 41, 19, 102, 34, 20, 54, 132, 104, 37, 171, 62, 23, 52, 43, 149, 184, 124, 47, 112, 60, 55, 95, 114, 59, 14, 52, 51, 153, 23, 26, 44, 21, 83, 19, 62, 55, 11, 65, 37, 100, 44, 61, 154, 88, 12, 37, 11, 32, 191, 148, 77, 30, 25, 69, 27, 22, 115, 53, 31, 35, 86, 72, 119, 17, 130, 25, 10, 25, 61, 33, 26, 37, 93, 30, 193, 21, 11, 88, 61, 22, 19, 305, 81, 87, 73, 51, 111, 64, 66, 25, 29, 73, 349, 126, 60, 117, 21, 40, 54, 63, 30, 74, 68, 38, 24, 65, 75, 67, 79, 110, 74, 171, 84, 372, 79, 18, 31, 22, 27, 13, 68, 120, 43, 41, 48, 665, 32, 35, 47, 97, 110, 57, 51, 17, 42, 25, 247, 45, 56, 100, 10, 10, 335, 72, 42, 27, 13, 58, 173, 10, 239, 55, 54, 122, 25, 72, 56, 10, 14, 35, 328, 41, 54, 64, 102, 57, 228, 232, 40, 57, 33, 30, 55, 38, 136, 108, 53, 66, 89, 66, 23, 128, 123, 77, 31, 14, 107, 37, 151, 44, 144, 46, 37, 43, 195, 83, 82, 23, 23, 322, 170, 15, 46, 78, 44, 24, 34, 203, 86, 152, 40, 49, 53, 23, 54, 33, 42, 61, 115, 55, 107, 265, 58, 18, 275, 184, 36, 66, 59, 82, 31, 88, 73, 40, 48, 37, 73, 61, 20, 108, 26, 23, 80, 53, 53, 39, 134, 37, 13, 105, 40, 26, 39, 99, 35, 172, 38, 54, 63, 17, 103, 54, 93, 65, 21, 27, 15, 33, 54, 32, 16, 31, 47, 27, 29, 20, 37, 44, 20, 60, 26, 24, 63, 282, 59, 47, 24, 43, 55, 33, 27, 40, 41, 30, 49, 58, 208, 15, 41, 30, 10, 34, 90, 100, 211, 45, 11, 66, 84, 48, 90, 36, 94, 328, 15, 29, 175, 38, 65, 72, 67, 10, 38, 148, 71, 178, 31, 27, 277, 103, 133, 98, 58, 27, 66, 56, 19, 93, 99, 32, 49, 53, 18, 79, 52, 27, 31, 111, 56, 52, 38, 47, 16, 15, 49, 38, 79, 58, 96, 29, 22, 62, 146, 55, 52, 79, 115, 69, 218, 143, 42, 32, 60, 58, 124, 127, 39, 22, 19, 24, 30, 396, 39, 22, 128, 32, 54, 18, 56, 290, 60, 81, 195, 54, 46, 49, 65, 220, 36, 45, 106, 56, 615, 40, 98, 49, 42, 72, 46, 12, 22, 166, 79, 21, 36, 113, 26, 27, 189, 37, 36, 114, 225, 48, 27, 45, 77, 87, 100, 69, 53, 35, 33, 64, 303, 42, 38, 63, 67, 31, 113, 96, 266, 60, 222, 149, 27, 53, 176, 26, 90, 132, 21, 57, 84, 89, 53, 489, 71, 98, 22, 56, 250, 30, 19, 200, 397, 48, 88, 27, 191, 82, 58, 264, 52, 22, 43, 52, 53, 26, 78, 40, 124, 116, 64, 169, 123, 47, 73, 73, 978, 38, 34, 52, 149, 65, 20, 69, 56, 70, 85, 22, 20, 229, 50, 54, 66, 10, 62, 55, 11, 21, 38, 190, 44, 42, 24, 93, 204, 102, 98, 34, 19, 128, 68, 57, 55, 77, 36, 124, 62, 142, 24, 301, 72, 15, 38, 21, 54, 585, 69, 306, 46, 39, 52, 69, 117, 65, 77, 106, 97, 90, 50, 53, 42, 50, 24, 24, 74, 26, 66, 71, 41, 102, 59, 35, 149, 29, 42, 82, 60, 96, 50, 18, 33, 53, 14, 39, 53, 30, 32, 159, 46, 207, 108, 60, 81, 129, 53, 46, 35, 30, 117, 50, 86, 23, 475, 104, 40, 37, 38, 14, 129, 60, 47, 145, 29, 14, 51, 173, 32, 70, 54, 110, 85, 49, 158, 19, 35, 26, 39, 362, 69, 124, 108, 190, 148, 31, 46, 27, 26, 153, 138, 233, 27, 25, 74, 148, 52, 24, 139, 97, 54, 131, 27, 102, 39, 11, 23, 22, 90, 28, 372, 146, 71, 24, 289, 27, 38, 19, 80, 114, 58, 53, 39, 30, 65, 99, 72, 82, 18, 31, 75, 19, 51, 30, 71, 106, 67, 124, 110, 108, 63, 65, 40, 66, 41, 29, 37, 93, 52, 104, 201, 58, 15, 44, 64, 84, 69, 35, 69, 18, 26, 126, 157, 44, 47, 43, 181, 11, 76, 67, 52, 50, 81, 19, 40, 111, 61, 80, 86, 12, 55, 62, 206, 127, 33, 89, 58, 129, 45, 40, 73, 36, 43, 83, 36, 121, 60, 56, 83, 40, 88, 39, 33, 38, 59, 281, 78, 54, 186, 47, 33, 89, 54, 52, 40, 57, 232, 110, 46, 58, 233, 42, 34, 73, 39, 99, 58, 53, 54, 51, 75, 56, 288, 45, 34, 23, 19, 47, 78, 985, 123, 241, 39, 28, 87, 20, 16, 42, 76, 143, 42, 31, 33, 62, 94, 86, 71, 55, 54, 27, 51, 26, 65, 56, 31, 47, 64, 111, 20, 77, 34, 53, 44, 89, 101, 79, 61, 126, 42, 219, 80, 34, 68, 73, 29, 74, 34, 95, 58, 46, 91, 108, 37, 56, 88, 90, 46, 96, 111, 60, 58, 58, 39, 90, 58, 38, 201, 46, 56, 58, 31, 56, 47, 52, 55, 86, 179, 60, 55, 44, 68, 70, 55, 76, 39, 21, 55, 24, 121, 76, 49, 60, 33, 35, 61, 107, 36, 92, 29, 45, 1066, 47, 45, 113, 40, 70, 37, 46, 100, 52, 55, 34, 44, 95, 44, 49, 47, 44, 101, 75, 36, 86, 62, 67, 81, 55, 88, 37, 65, 32, 23, 98, 144, 86, 126, 51, 53, 31, 55, 116, 46, 56, 24, 34, 54, 23, 61, 32, 51, 46, 67, 44, 101, 107, 68, 54, 59, 104, 75, 47, 65, 80, 81, 97, 28, 48, 22, 237, 57, 66, 107, 32, 52, 40, 94, 92, 52, 322, 58, 50, 43, 63, 72, 32, 42, 57, 86, 125, 66, 62, 103, 37, 47, 50, 48, 72, 86, 45, 70, 1062, 46, 38, 53, 76, 47, 76, 56, 67, 22, 60, 79, 58, 161, 113, 46, 103, 61, 38, 34, 24, 101, 41, 37, 43, 27, 40, 35, 47, 64, 104, 34, 73, 78, 62, 192, 20, 188, 70, 49, 65, 81, 19, 64, 33, 123, 67, 51, 46, 61, 84, 38, 73, 28, 71, 86, 50, 41, 111, 59, 26, 61, 29, 99, 34, 210, 34, 26, 85, 103, 74, 55, 56, 55, 28, 44, 97, 43, 94, 69, 23, 65, 48, 96, 25, 103, 22, 177, 45, 130, 54, 39, 110, 179, 38, 52, 82, 34, 37, 33, 46, 79, 52, 113, 61, 85, 197, 40, 49, 25, 999, 53, 112, 46, 46, 55, 66, 51, 31, 68, 97, 59, 168, 49, 322, 55, 36, 112, 51, 29, 26, 70, 17, 85, 47, 30, 97, 100, 53, 107, 25, 35, 26, 245, 43, 39, 72, 37, 83, 36, 44, 92, 80, 269, 77, 60, 48, 94, 151, 92, 56, 98, 33, 56, 88, 34, 81, 29, 34, 947, 32, 96, 103, 24, 66, 42, 49, 86, 26, 64, 64, 40, 276, 90, 64, 46, 101, 90, 87, 106, 38, 316, 70, 117, 52, 45, 108, 85, 113, 38, 37, 93, 41, 33, 68, 51, 60, 25, 41, 63, 95, 183, 55, 54, 45, 35, 48, 68, 60, 69, 34, 48, 54, 45, 71, 81, 66, 81, 50, 56, 36, 101, 25, 111, 35, 37, 43, 51, 25, 33, 40, 47, 53, 167, 35, 103, 63, 24, 57, 59, 52, 35, 65, 47, 45, 44, 58, 200, 49, 75, 51, 48, 64, 67, 23, 53, 22, 40, 46, 54, 44, 54, 84, 64, 59, 25, 78, 21, 50, 97, 47, 63, 11, 45, 254, 43, 111, 101, 64, 37, 50, 71, 87, 70, 22, 50, 197, 38, 294, 71, 28, 20, 100, 41, 41, 86, 82, 35, 25, 47, 30, 24, 84, 69, 52, 148, 51, 38, 48, 49, 101, 110, 38, 132, 30, 57, 60, 86, 479, 40, 207, 29, 79, 79, 190, 63, 66, 38, 101, 92, 51, 110, 40, 87, 68, 37, 87, 81, 64, 56, 65, 131, 55, 42, 48, 40, 114, 39, 48, 19, 81, 63, 94, 28, 50, 74, 59, 57, 43, 43, 46, 57, 113, 96, 35, 59, 270, 70, 67, 49, 31, 41, 44, 48, 70, 78, 303, 66, 70, 161, 31, 45, 72, 280, 36, 219, 77, 81, 61, 32, 50, 54, 42, 43, 189, 57, 36, 25, 58, 53, 121, 24, 33, 60, 76, 25, 37, 57, 84, 40, 22, 53, 76, 94, 60, 26, 163, 72, 35, 72, 38, 35, 39, 55, 119, 39, 75, 62, 39, 43, 41, 46, 38, 56, 88, 122, 53, 62, 34, 54, 131, 78, 82, 62, 24, 91, 53, 88, 60, 34, 30, 149, 80, 44, 30, 54, 38, 88, 77, 69, 55, 49, 48, 30, 50, 34, 72, 153, 63, 54, 105, 33, 26, 49, 36, 34, 81, 68, 689, 32, 58, 295, 71, 107, 96, 49, 203, 62, 23, 69, 22, 50, 35, 51, 59, 49, 36, 91, 90, 26, 61, 98, 95, 59, 89, 44, 98, 45, 42, 62, 60, 38, 98, 40, 64, 40, 65, 49, 37, 77, 40, 56, 71, 439, 47, 141, 23, 137, 33, 58, 78, 62, 64, 53, 59, 1020, 388, 37, 118, 1020, 51, 74, 115, 242, 57, 100, 42, 52, 90, 32, 50, 116, 47, 90, 98, 73, 73, 33, 202, 85, 89, 30, 177, 45, 67, 47, 51, 38, 60, 56, 62, 69, 48, 73, 35, 44, 53, 42, 78, 114, 42, 43, 63, 67, 43, 145, 54, 103, 156, 62, 50, 84, 94, 63, 54, 53, 47, 171, 105, 62, 97, 41, 49, 147, 27, 27, 39, 47, 48, 69, 671, 58, 104, 62, 85, 60, 65, 70, 69, 106, 81, 34, 259, 248, 82, 59, 41, 42, 26, 29, 49, 91, 51, 41, 161, 84, 121, 47, 41, 154, 50, 32, 43, 33, 57, 89, 62, 55, 67, 57, 48, 28, 43, 49, 118, 9, 50, 63, 52, 53, 76, 55, 93, 34, 39, 50, 153, 34, 73, 42, 45, 41, 21, 83, 66, 97, 120, 48, 104, 39, 49, 31, 57, 44, 47, 42, 73, 39, 58, 76, 93, 40, 54, 89, 78, 30, 40, 121, 33, 45, 94, 29, 358, 982, 86, 62, 39, 64, 36, 498, 53, 421, 93, 53, 36, 37, 99, 35, 111, 198, 145, 25, 53, 31, 68, 76, 80, 83, 26, 80, 159, 109, 132, 40, 66, 63, 33, 45, 52, 102, 75, 52, 27, 85, 106, 22, 60, 57, 108, 14, 26, 42, 25, 109, 46, 48, 132, 93, 42, 106, 45, 32, 62, 148, 62, 56, 16, 101, 34, 42, 78, 22, 40, 97, 85, 95, 86, 86, 72, 129, 48, 91, 98, 394, 37, 53, 57, 41, 135, 67, 45, 20, 27, 41, 94, 62, 37, 60, 34, 78, 77, 38, 157, 29, 75, 110, 45, 55, 41, 65, 108, 49, 34, 35, 105, 39, 32, 117, 679, 38, 71, 37, 31, 92, 81, 62, 60, 69, 37, 46, 67, 39, 36, 48, 60, 39, 55, 47, 48, 50, 48, 36, 119, 107, 59, 52, 84, 59, 68, 37, 59, 40, 17, 50, 91, 20, 198, 64, 56, 25, 36, 52, 83, 44, 125, 116, 51, 75, 64, 47, 32, 132, 88, 39, 52, 55, 73, 80, 69, 97, 81, 58, 49, 317, 103, 70, 124, 47, 40, 32, 64, 37, 48, 48, 28, 27, 47, 61, 63, 26, 40, 137, 131, 65, 59, 23, 77, 73, 33, 139, 27, 41, 72, 18, 178, 136, 31, 36, 48, 33, 47, 65, 61, 51, 15, 66, 48, 94, 31, 68, 49, 124, 43, 44, 71, 33, 38, 59, 51, 67, 46, 61, 94, 71, 146, 45, 51, 35, 27, 29, 26, 217, 32, 27, 72, 35, 76, 74, 82, 83, 82, 28, 55, 114, 33, 51, 48, 79, 61, 76, 45, 49, 60, 83, 153, 76, 42, 45, 150, 95, 44, 59, 55, 53, 98, 78, 66, 109, 62, 52, 133, 57, 35, 55, 33, 80, 51, 68, 72, 100, 28, 65, 109, 55, 37, 62, 88, 374, 49, 38, 125, 83, 52, 23, 53, 46, 59, 49, 36, 81, 72, 73, 65, 98, 55, 70, 83, 54, 75, 110, 52, 49, 58, 107, 155, 64, 139, 76, 141, 30, 19, 17, 77, 53, 19, 36, 61, 29, 60, 33, 28, 117, 43, 23, 41, 7, 19, 76, 98, 27, 36, 55, 26, 30, 57, 49, 23, 30, 13, 23, 43, 58, 112, 45, 20, 30, 61, 46, 66, 27, 15, 38, 12, 41, 32, 31, 76, 33, 30, 34, 42, 80, 60, 28, 60, 24, 14, 63, 48, 78, 80, 12, 69, 57, 20, 92, 15, 45, 82, 22, 101, 40, 57, 31, 103, 146, 31, 57, 35, 35, 44, 50, 52, 155, 52, 42, 49, 32, 21, 55, 15, 34, 33, 25, 38, 21, 17, 25, 20, 30, 19, 32, 78, 27, 38, 34, 27, 56, 41, 264, 109, 32, 38, 23, 79, 12, 62, 46, 65, 67, 118, 23, 55, 141, 57, 64, 42, 86, 19, 52, 41, 22, 43, 29, 190, 28, 17, 18, 50, 44, 61, 46, 52, 86, 39, 21, 22, 35, 40, 27, 77, 125, 85, 62, 75, 42, 35, 14, 21, 182, 25, 19, 57, 56, 47, 21, 113, 29, 43, 94, 70, 99, 21, 33, 37, 57, 52, 20, 32, 92, 44, 29, 35, 63, 17, 93, 24, 42, 121, 33, 123, 21, 42, 41, 16, 179, 22, 35, 17, 44, 35, 65, 44, 29, 17, 111, 207, 39, 34, 11, 27, 124, 30, 36, 72, 12, 23, 23, 29, 71, 62, 36, 17, 14, 16, 31, 51, 148, 87, 61, 115, 78, 44, 35, 17, 31, 31, 49, 94, 24, 64, 17, 18, 47, 21, 35, 148, 67, 62, 24, 39, 23, 49, 55, 29, 89, 57, 18, 36, 13, 18, 44, 35, 41, 57, 38, 20, 40, 62, 63, 16, 41, 195, 31, 25, 50, 87, 32, 30, 47, 97, 71, 34, 31, 22, 19, 64, 13, 48, 29, 27, 24, 56, 43, 177, 60, 20, 16, 57, 106, 13, 41, 71, 36, 104, 25, 154, 76, 46, 38, 34, 13, 49, 34, 17, 14, 42, 188, 118, 73, 51, 75, 24, 86, 79, 38, 17, 33, 27, 26, 66, 35, 40, 39, 24, 113, 41, 71, 39, 47, 17, 16, 15, 45, 13, 24, 40, 147, 13, 39, 13, 42, 26, 17, 43, 21, 37, 30, 31, 82, 45, 15, 32, 93, 20, 28, 21, 20, 43, 92, 39, 51, 36, 61, 24, 54, 26, 18, 73, 30, 28, 61, 28, 44, 30, 76, 71, 27, 44, 34, 28, 49, 55, 48, 47, 28, 32, 27, 18, 29, 70, 29, 37, 30, 18, 50, 70, 26, 29, 31, 37, 88, 18, 99, 83, 22, 44, 18, 13, 43, 25, 22, 27, 212, 65, 26, 72, 52, 28, 13, 51, 41, 28, 22, 50, 22, 57, 81, 24, 54, 37, 34, 48, 38, 16, 23, 20, 118, 33, 72, 37, 29, 29, 25, 62, 59, 84, 72, 19, 26, 17, 19, 139, 133, 41, 40, 141, 15, 79, 101, 28, 56, 160, 191, 22, 83, 15, 16, 135, 17, 146, 23, 18, 78, 34, 68, 34, 42, 22, 112, 37, 30, 97, 48, 26, 35, 17, 36, 36, 17, 28, 64, 23, 39, 65, 53, 65, 98, 31, 35, 21, 69, 32, 16, 16, 16, 28, 32, 144, 50, 13, 117, 79, 42, 66, 42, 15, 18, 32, 22, 110, 17, 76, 25, 57, 49, 41, 29, 33, 25, 40, 11, 30, 85, 29, 49, 22, 42, 18, 42, 24, 60, 38, 61, 40, 30, 23, 39, 55, 129, 20, 34, 47, 96, 21, 59, 101, 26, 21, 76, 27, 56, 66, 43, 27, 42, 16, 82, 28, 80, 39, 66, 36, 43, 41, 73, 35, 58, 31, 49, 36, 53, 38, 37, 121, 108, 110, 24, 21, 24, 31, 69, 17, 93, 15, 62, 68, 57, 40, 33, 56, 25, 21, 42, 37, 16, 70, 25, 21, 60, 24, 26, 82, 23, 33, 35, 48, 40, 56, 40, 188, 59, 33, 78, 58, 57, 23, 88, 13, 41, 30, 140, 105, 50, 43, 14, 16, 98, 90, 70, 246, 18, 23, 35, 20, 50, 54, 36, 48, 77, 84, 87, 37, 49, 23, 49, 74, 45, 123, 64, 33, 66, 19, 71, 25, 34, 30, 24, 106, 62, 52, 19, 40, 38, 130, 44, 42, 93, 26, 58, 24, 49, 92, 10, 47, 51, 40, 40, 37, 37, 58, 119, 19, 14, 48, 33, 100, 49, 229, 23, 22, 45, 19, 42, 30, 77, 15, 21, 24, 21, 25, 18, 21, 27, 26, 34, 34, 83, 55, 42, 22, 18, 30, 74, 98, 17, 109, 15, 36, 31, 35, 30, 34, 109, 17, 15, 179, 116, 15, 16, 79, 46, 121, 20, 17, 39, 15, 65, 50, 24, 26, 13, 113, 16, 99, 88, 121, 76, 19, 62, 32, 85, 34, 40, 81, 105, 23, 43, 146, 80, 31, 37, 18, 38, 28, 91, 25, 142, 105, 86, 19, 22, 54, 65, 37, 22, 40, 15, 28, 50, 22, 58, 30, 33, 19, 41, 50, 80, 20, 27, 52, 18, 16, 23, 19, 24, 19, 27, 91, 19, 42, 42, 32, 16, 65, 42, 14, 17, 20, 28, 38, 51, 25, 146, 42, 28, 55, 28, 25, 49, 51, 47, 85, 45, 31, 27, 26, 62, 21, 46, 12, 95, 39, 39, 32, 59, 79, 234, 14, 258, 65, 124, 37, 274, 30, 43, 21, 262, 71, 90, 30, 50, 18, 15, 253, 66, 62, 99, 27, 27, 36, 29, 56, 43, 342, 92, 60, 46, 50, 55, 75, 44, 57, 42, 214, 72, 81, 119, 349, 62, 44, 86, 21, 105, 32, 72, 65, 48, 92, 122, 25, 52, 31, 79, 123, 48, 28, 83, 20, 54, 52, 84, 49, 60, 36, 83, 82, 37, 41, 354, 34, 118, 29, 17, 285, 30, 59, 25, 28, 56, 62, 44, 83, 28, 29, 63, 54, 51, 58, 70, 44, 71, 35, 87, 18, 59, 308, 179, 92, 144, 45, 88, 13, 88, 44, 258, 10, 20, 138, 40, 29, 83, 13, 67, 40, 344, 82, 119, 76, 36, 33, 30, 313, 53, 67, 98, 97, 134, 16, 79, 82, 20, 125, 94, 56, 318, 32, 62, 92, 180, 15, 77, 28, 39, 58, 95, 45, 23, 123, 126, 43, 65, 49, 103, 76, 74, 95, 26, 66, 45, 131, 35, 117, 47, 10, 52, 24, 86, 114, 81, 79, 113, 179, 51, 270, 213, 18, 59, 126, 70, 84, 188, 43, 44, 110, 65, 176, 69, 29, 234, 68, 108, 45, 75, 66, 260, 31, 123, 100, 24, 95, 89, 55, 42, 49, 88, 75, 16, 47, 77, 37, 79, 74, 51, 65, 84, 96, 75, 99, 43, 27, 11, 253, 61, 35, 42, 64, 38, 67, 34, 34, 77, 153, 95, 66, 51, 122, 65, 53, 66, 70, 114, 48, 97, 69, 113, 54, 60, 64, 111, 24, 52, 121, 37, 76, 36, 71, 29, 84, 105, 73, 25, 418, 65, 76, 90, 104, 31, 66, 89, 125, 51, 40, 280, 352, 113, 65, 59, 27, 117, 74, 97, 27, 91, 41, 327, 48, 413, 29, 362, 51, 53, 205, 58, 134, 434, 72, 81, 119, 98, 107, 126, 66, 91, 46, 110, 51, 86, 93, 40, 57, 50, 50, 57, 32, 70, 24, 13, 180, 43, 91, 90, 53, 53, 72, 56, 99, 85, 78, 24, 57, 65, 34, 32, 62, 57, 38, 10, 48, 74, 274, 84, 59, 35, 33, 76, 47, 69, 71, 43, 91, 76, 94, 46, 50, 93, 34, 24, 54, 42, 23, 19, 53, 52, 32, 36, 77, 22, 70, 100, 50, 86, 69, 134, 122, 25, 66, 51, 39, 46, 72, 73, 274, 37, 99, 40, 40, 111, 77, 35, 37, 47, 46, 538, 97, 83, 86, 29, 66, 55, 140, 40, 500, 84, 99, 25, 96, 33, 77, 18, 95, 104, 71, 52, 179, 100, 78, 79, 30, 66, 64, 101, 31, 310, 33, 240, 146, 57, 60, 47, 22, 56, 10, 105, 24, 186, 68, 31, 56, 76, 60, 60, 76, 54, 34, 44, 53, 21, 80, 37, 118, 73, 37, 34, 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35, 128, 56, 45, 9, 14, 63, 18, 136, 7, 57, 27, 23, 27, 6, 37, 12, 174, 93, 28, 123, 89, 19, 338, 18, 17, 90, 171, 18, 14, 6, 71, 749, 25, 58, 18, 33, 470, 399, 26, 128, 48, 20, 445, 66, 9, 9, 68, 16, 57, 272, 23, 24, 9, 12, 93, 14, 7, 42, 17, 19, 9, 68, 25, 18, 20, 18, 26, 256, 34, 12, 25, 50, 66, 14, 10, 77, 49, 26, 35, 15, 57, 36, 104, 15, 9, 15, 21, 17, 14, 10, 9, 39, 13, 23, 59, 89, 86, 13, 41, 65, 148, 98, 10, 67, 131, 18, 12, 52, 18, 7, 12, 25, 32, 21, 59, 22, 102, 27, 17, 26, 41, 116, 14, 1345, 31, 34, 8, 20, 14, 87, 19, 62, 56, 12, 23, 69, 14, 52, 42, 13, 15, 15, 28, 75, 40, 28, 52, 116, 75, 5, 11, 20, 9, 8, 51, 19, 32, 12, 21, 26, 32, 16, 39, 30, 47, 33, 38, 60, 76, 708, 14, 20, 10, 19, 23, 58, 20, 42, 39, 61, 74, 18, 17, 28, 37, 59, 26, 30, 35, 14, 8, 15, 36, 17, 44, 51, 498, 34, 143, 20, 21, 18, 118, 40, 23, 66, 69, 16, 30, 14, 51, 68, 26, 59, 15, 32, 35, 17, 40, 11, 11, 16, 36, 32, 22, 14, 18, 22, 106, 16, 72, 90, 34, 19, 19, 21, 19, 31, 15, 21, 117, 19, 15, 66, 77, 25, 11, 28, 40, 27, 25, 20, 20, 25, 234, 32, 15, 69, 63, 20, 6, 96, 22, 8, 74, 23, 29, 17, 26, 33, 88, 22, 29, 21, 28, 20, 15, 17, 32, 41, 53, 32, 40, 67, 82, 32, 327, 30, 19, 107, 46, 74, 65, 30, 59, 9, 60, 23, 48, 46, 25, 43, 59, 38, 122, 23, 24, 49, 12, 8, 28, 16, 20, 22, 65, 2445, 4, 1062, 34, 29, 25, 380, 42, 34, 19, 75, 60, 30, 38, 16, 18, 34, 16, 9, 22, 20, 41, 482, 7, 75, 47, 135, 134, 184, 21, 22, 43, 46, 24, 20, 79, 15, 11, 14, 28, 8, 200, 13, 553, 64, 16, 33, 27, 19, 56, 15, 654, 28, 346, 31, 83, 44, 371, 11, 29, 141, 108, 77, 451, 19, 60, 70, 205, 161, 52, 41, 877, 120, 1109, 67, 62, 19, 36, 127, 50, 77, 8, 17, 14, 48, 951, 32, 315, 14, 49, 732, 151, 19, 11, 137, 708, 48, 16, 12, 113, 12, 28, 9, 35, 22, 21, 32, 31, 14, 28, 15, 52, 17, 24, 279, 46, 46, 16, 223, 61, 23, 39, 27, 48, 24, 54, 54, 79, 646, 54, 21, 24, 16, 12, 18, 20, 19, 44, 15, 43, 23, 16, 72, 21, 8, 87, 21, 24, 1298, 26, 31, 28, 9, 54, 12, 139, 39, 19, 23, 8, 26, 7, 69, 37, 59, 40, 63, 29, 30, 23, 28, 64, 7, 17, 25, 24, 31, 13, 39, 15, 16, 11, 85, 137, 17, 30, 17, 15, 14, 16, 25, 15, 17, 18, 41, 11, 7, 39, 37, 29, 111, 39, 14, 16, 31, 72, 15, 13, 9, 14, 246, 5, 15, 18, 16, 23, 7, 63, 49, 16, 48, 16, 26, 59, 38, 25, 104, 18, 37, 49, 51, 15, 64, 17, 12, 24, 31, 29, 278, 14, 47, 105, 23, 267, 12, 18, 18, 343, 16, 16, 20, 255, 22, 10, 56, 87, 52, 39, 7, 12, 59, 12, 18, 14, 50, 24, 62, 13, 14, 69, 19, 44, 11, 72, 105, 84, 9, 573, 16, 30, 8, 103, 116, 20, 145, 11, 86, 44, 425, 87, 38, 39, 62, 14, 276, 17, 9, 63, 49, 19, 21, 14, 31, 69, 15]\n",
            "Length of Instruction Tokens Count 24926\n"
          ]
        }
      ],
      "source": [
        "# Import the tokenizer from the transformers library.\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "# Initialize the tokenizer with the specific model from Nous Research. This tokenizer is used to convert text into tokens.\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"NousResearch/Llama-2-7b-hf\")\n",
        "\n",
        "# The dataset variable should contain your data. Assuming it's loaded with datasets library and has a 'train' split.\n",
        "# This section assumes the dataset is loaded and has an \"instruction\" column that we want to tokenize.\n",
        "\n",
        "# Tokenize each instruction in the dataset's training split. This loop goes through each example in the training set,\n",
        "# tokenizes the text found in the \"instruction\" column, and calculates the number of tokens in each instruction.\n",
        "instruction_tokens_count = [len(tokenizer.tokenize(example[\"instruction\"])) for example in dataset[\"train\"]]\n",
        "\n",
        "# Print the total count of tokens in each instruction. This will output a list where each entry is the token count\n",
        "# for a corresponding instruction in the dataset.\n",
        "print(\"Instruction Tokens Count\", instruction_tokens_count)\n",
        "\n",
        "# Print the total number of instructions processed. This tells us how many instructions have been tokenized\n",
        "# and counted in the dataset.\n",
        "print(\"Length of Instruction Tokens Count\", len(instruction_tokens_count))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZVikDv9TZU_3",
        "outputId": "4e1e3cab-8605-43f5-e650-fbb1a075bde4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Output Tokens Count [223, 105, 193, 96, 440, 217, 324, 80, 267, 267, 135, 194, 251, 194, 130, 958, 158, 76, 137, 201, 256, 206, 384, 59, 306, 558, 634, 98, 122, 423, 122, 142, 178, 210, 215, 306, 124, 306, 209, 216, 40, 513, 239, 278, 130, 505, 228, 315, 112, 169, 625, 326, 544, 186, 375, 286, 125, 421, 374, 87, 96, 141, 149, 104, 32, 227, 195, 133, 181, 432, 78, 553, 609, 437, 428, 167, 355, 413, 143, 322, 165, 317, 108, 414, 197, 20, 101, 483, 426, 356, 805, 277, 293, 312, 355, 189, 74, 58, 76, 124, 270, 245, 301, 183, 195, 236, 222, 170, 143, 339, 278, 246, 190, 151, 150, 81, 321, 78, 244, 212, 426, 273, 338, 212, 113, 258, 180, 206, 117, 153, 108, 241, 194, 161, 307, 188, 244, 214, 220, 230, 515, 202, 246, 123, 226, 53, 202, 104, 272, 243, 103, 66, 213, 74, 110, 199, 347, 112, 226, 858, 275, 102, 318, 99, 277, 263, 229, 569, 78, 187, 175, 63, 200, 372, 88, 330, 105, 226, 166, 151, 259, 627, 200, 432, 349, 121, 285, 327, 384, 67, 569, 152, 364, 53, 158, 294, 212, 108, 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189, 6, 466, 202, 70, 255, 659, 185, 454, 275, 157, 122, 168, 565, 745, 448, 1164, 246, 548, 169, 794, 670, 1015, 460, 1855, 303, 233, 470, 89, 868, 1376, 348, 609, 304, 1083, 594, 1699, 1047, 869, 142, 152, 337, 5, 855, 491, 406, 110, 1203, 194, 583, 282, 5, 563, 371, 3, 837, 19, 369, 140, 450, 309, 1057, 466, 276, 127, 55, 525, 4, 246, 611, 391, 277, 364, 529, 5, 552, 558, 671, 131, 361, 794, 617, 249, 137, 200, 145, 175, 474, 325, 152, 499, 624, 463, 171, 313, 350, 1215, 401, 113, 460, 342, 296, 132, 401, 39, 5, 180, 690, 800, 936, 757, 592, 1676, 415, 4, 971, 713, 322, 444, 295, 1079, 1061, 543, 746, 1, 626, 210, 857, 734, 313, 81, 1092, 6, 281, 1099, 158, 86, 173, 3, 3, 645, 967, 272, 157, 474, 506, 324, 496, 880, 377, 278, 204, 179, 79, 143, 282, 890, 415, 1053, 224, 143, 265, 816, 44, 266, 154, 639, 258, 1020, 566, 38, 114, 93, 367, 412, 371, 227, 766, 205, 809, 1062, 380, 228, 643, 17, 192, 310, 715, 1609, 267, 932, 1228, 194, 230, 210, 314, 311, 494, 322, 645, 822, 286, 254, 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159, 167, 185, 634, 432, 463, 470, 302, 82, 141, 51, 235, 450, 105, 321, 518, 789, 177, 748, 323, 179, 187, 242, 850, 88, 1002, 790, 1435, 169, 289, 908, 588, 502, 215, 171, 59, 660, 411, 360, 592, 172, 739, 231, 145, 1049, 678, 223, 1316, 313, 630, 141, 114, 662, 347, 592, 1237, 182, 963, 753, 1252, 169, 707, 536, 184, 642, 139, 1155, 70, 976, 301, 1486, 1035, 86, 344, 962, 30, 1353, 849, 109, 305, 382, 1044, 429, 559, 10, 3, 22, 27, 433, 262, 140, 1031, 438, 202, 43, 250, 522, 8, 225, 392, 450, 332, 125, 5, 269, 2, 108, 40, 188, 693, 530, 520, 355, 12, 171, 312, 653, 984, 314, 675, 52, 850, 96, 14, 864, 968, 254, 354, 134, 427, 86, 312, 671, 7, 367, 729, 401, 534, 199, 345, 605, 164, 649, 18, 5, 275, 429, 254, 255, 217, 5, 375, 750, 312, 130, 738, 546, 719, 56, 245, 73, 393, 307, 103, 132, 213, 644, 393, 86, 27, 240, 17, 460, 230, 512, 625, 201, 52, 918, 813, 334, 93, 639, 723, 550, 34, 882, 133, 880, 483, 77, 227, 274, 244, 204, 246, 283, 409, 174, 239, 810, 51, 468, 5, 870, 281, 45, 269, 264, 157, 260, 755, 299, 49, 200, 45, 383, 530, 472, 144, 39, 777, 348, 552, 142, 246, 284, 382, 1440, 845, 152, 302, 775, 1220, 211, 824, 627, 531, 558, 185, 113, 51, 597, 208, 51, 237, 5, 255, 102, 114, 2078, 614, 246, 826, 2, 695, 195, 411, 774, 780, 860, 521, 7, 913, 236, 99, 300, 866, 676, 844, 185, 894, 1007, 241, 288, 267, 871, 952, 128, 417, 127, 90, 223, 832, 567, 72, 185, 540, 323, 202, 236, 97, 627, 2, 161, 968, 101, 351, 716, 176, 199, 24, 346, 64, 276, 506, 386, 74, 615, 782, 1245, 256, 573, 1128, 257, 4, 371, 615, 321, 398, 338, 640, 381, 60, 880, 298, 311, 203, 197, 96, 137, 718, 37, 607, 747, 316, 434, 147, 777, 817, 346, 237, 313, 738, 320, 463, 1143, 245, 491, 305, 794, 436, 295, 110, 718, 549, 368, 357, 641, 216, 369, 420, 458, 406, 421, 212, 351, 720, 138, 321, 211, 749, 324, 21, 282, 327, 65, 277, 659, 373, 329, 394, 127, 431, 153, 107, 277, 964, 466, 87, 251, 987, 392, 168, 305, 204, 134, 478, 358, 503, 461, 140, 410, 246, 105, 126, 623, 525, 69, 380, 460, 79, 113, 518, 474, 70, 64, 367, 30, 246, 490, 285, 480, 714, 593, 267, 22, 315, 437, 251, 50, 437, 335, 404, 290, 289, 236, 208, 355, 556, 447, 438, 187, 522, 380, 358, 258, 335, 395, 702, 492, 1006, 647, 389, 96, 492, 1032, 89, 181, 228, 526, 455, 463, 451, 654, 514, 688, 585, 57, 233, 572, 836, 440, 400, 447, 377, 53, 39, 444, 490, 258, 1058, 691, 695, 920, 555, 872, 842, 545, 68, 93, 471, 385, 482, 426, 513, 779, 412, 129, 525, 495, 546, 1027, 379, 986, 338, 265, 2642, 336, 130, 308, 433, 825, 1009, 222, 1042, 471, 1390, 115, 512, 178, 743, 655, 1562, 88, 755, 269, 697, 780, 930, 526, 465, 351, 349, 718, 554, 324, 656, 525, 765, 371, 219, 357, 262, 1136, 803, 726, 1189, 688, 703, 1624, 376, 395, 104, 289, 340, 403, 400, 126, 273, 377, 502, 393, 133, 450, 1118, 217, 229, 800, 425, 448, 371, 350, 153, 668, 147, 352, 290, 405, 1098, 54, 636, 533, 294, 272, 741, 723, 61, 247, 181, 448, 955, 144, 1346, 920, 1401, 523, 651, 165, 425, 153, 105, 271, 187, 106, 1452, 625, 147, 329, 164, 295, 27, 338, 234, 522, 232, 365, 420, 347, 54, 672, 107, 316, 880, 501, 491, 798, 542, 130, 308, 1263, 653, 208, 837, 374, 404, 958, 261, 651, 448, 366, 443, 95, 158, 363, 134, 678, 151, 482, 393, 363, 153, 352, 561, 283, 483, 561, 186, 361, 717, 46, 155, 556, 292, 412, 537, 1047, 140, 510, 322, 384, 278, 266, 425, 464, 680, 513, 454, 885, 549, 1079, 235, 875, 492, 875, 351, 562, 347, 298, 207, 318, 307, 293, 1224, 591, 987, 89, 540, 290, 1093, 176, 585, 439, 690, 126, 125, 506, 775, 830, 193, 1066, 664, 393, 662, 454, 436, 387, 462, 142, 781, 39, 272, 652, 480, 413, 233, 311, 611, 993, 890, 567, 636, 252, 621, 807, 594, 145, 121, 463, 163, 656, 275, 386, 1422, 262, 280, 334, 1200, 159, 63, 232, 361, 228, 382, 362, 231, 236, 37, 501, 422, 118, 301, 444, 336, 151, 338, 708, 216, 79, 831, 285, 249, 256, 179, 141, 217, 43, 212, 325, 120, 608, 210, 585, 30, 435, 172, 451, 67, 805, 552, 767, 457, 338, 109, 583, 154, 508, 318, 557, 342, 117, 554, 63, 941, 270, 1033, 268, 288, 660, 564, 434, 457, 530, 136, 504, 321, 300, 560, 458, 491, 40, 249, 719, 712, 554, 437, 561, 126, 23, 803, 365, 778, 184, 292, 197, 405, 466, 192, 383, 783, 792, 349, 392, 254, 541, 718, 799, 926, 170, 709, 96, 617, 1046, 607, 1936, 274, 578, 538, 394, 511, 747, 901, 524, 239, 986, 756, 514, 147, 95, 1482, 688, 207, 65, 262, 435, 677, 87, 235, 780, 264, 24, 314, 25, 751, 772, 982, 2023, 896, 419, 442, 383, 1314, 252, 389, 1073, 836, 773, 57, 922, 3933, 520, 710, 794, 587, 399, 538, 593, 657, 548, 210, 1010, 11, 814, 1728, 306, 388, 551, 43, 605, 61, 871, 807, 600, 196, 630, 553, 199, 460, 77, 164, 514, 358, 245, 325, 446, 351, 225, 274, 209, 169, 337, 62, 66, 81, 253, 271, 136, 509, 116, 466, 203, 964, 566, 570, 1110, 691, 280, 219, 618, 183, 768, 366, 347, 405, 352, 434, 137, 254, 1509, 283, 991, 645, 122, 276, 259, 178, 284, 878, 209, 220, 113, 278, 99, 80, 89, 175, 700, 220, 386, 379, 183, 192, 357, 153, 355, 229, 498, 930, 2511, 579, 378, 270, 341, 556, 1187, 222, 143, 1142, 273, 908, 702, 756, 302, 205, 461, 368, 653, 225, 145, 340, 435, 164, 366, 440, 417, 420, 395, 927, 134, 162, 380, 528, 414, 357, 582, 1299, 623, 217, 629, 474, 102, 135, 223, 30, 986, 162, 91, 248, 593, 690, 701, 707, 387, 143, 411, 354, 111, 320, 136, 352, 300, 582, 355, 678, 579, 881, 129, 444, 174, 264, 205, 297, 323, 155, 387, 810, 441, 235, 122, 321, 253, 334, 298, 537, 621, 183, 290, 611, 75, 456, 267, 557, 87, 754, 296, 148, 774, 537, 615, 386, 161, 1257, 95, 771, 1792, 183, 507, 182, 2327, 490, 458, 712, 97, 75, 392, 31, 295, 376, 983, 479, 726, 431, 342, 360, 1044, 233, 142, 519, 213, 628, 796]\n",
            "Length of the Output Tokens Count 24926\n"
          ]
        }
      ],
      "source": [
        "# Tokenize each \"output\" entry in the training dataset. We perform a loop over each example in 'train'.\n",
        "# For each example, we extract the text in the \"output\" column, tokenize this text, and then calculate\n",
        "# the number of tokens. The result is a list of token counts, where each count corresponds to an example.\n",
        "output_tokens_count = [len(tokenizer.tokenize(example[\"output\"])) for example in dataset[\"train\"]]\n",
        "\n",
        "# Print the list of token counts for each output in the training dataset. This provides a visualization\n",
        "# of the number of tokens per output text, useful for understanding the data's complexity and variability.\n",
        "print(\"Output Tokens Count\", output_tokens_count)\n",
        "\n",
        "# Print the total number of outputs processed, which is the length of the 'output_tokens_count' list.\n",
        "# This number tells us how many output texts have been tokenized and counted in the dataset.\n",
        "print(\"Length of the Output Tokens Count\", len(output_tokens_count))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DBk9PUCOZxIK",
        "outputId": "d709d7b5-951e-4c84-8405-1d11a071581a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Combine Tokens Count [308, 158, 268, 182, 522, 247, 370, 116, 368, 308, 154, 296, 285, 214, 184, 1090, 262, 113, 308, 263, 279, 258, 427, 208, 490, 682, 681, 210, 182, 478, 217, 256, 237, 224, 267, 357, 277, 329, 235, 260, 61, 596, 258, 340, 185, 516, 293, 352, 212, 213, 686, 480, 632, 198, 412, 297, 157, 612, 522, 164, 126, 166, 218, 131, 54, 342, 248, 164, 216, 518, 150, 672, 626, 567, 453, 177, 380, 474, 176, 348, 202, 410, 138, 607, 218, 31, 189, 544, 448, 375, 1110, 358, 380, 385, 406, 300, 138, 124, 101, 153, 343, 594, 427, 243, 312, 257, 262, 224, 206, 369, 352, 314, 228, 175, 215, 156, 388, 157, 354, 286, 597, 357, 710, 291, 131, 289, 202, 233, 130, 221, 228, 284, 235, 209, 972, 220, 279, 261, 317, 340, 572, 253, 263, 165, 251, 300, 247, 160, 372, 253, 113, 401, 285, 116, 137, 212, 405, 285, 236, 1097, 330, 156, 440, 124, 349, 319, 239, 583, 113, 515, 216, 117, 264, 474, 145, 558, 337, 266, 223, 184, 289, 682, 238, 568, 457, 174, 351, 416, 450, 90, 697, 275, 441, 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881, 1560, 218, 314, 1008, 634, 553, 1556, 305, 169, 806, 438, 1496, 719, 233, 802, 993, 189, 1067, 700, 1413, 1394, 1501, 675, 976, 954, 702, 368, 632, 1359, 927, 1112, 841, 1293, 201, 743, 594, 217, 774, 182, 1252, 746, 1026, 360, 1567, 1098, 99, 464, 1003, 169, 1528, 895, 1471, 363, 426, 1101, 447, 583, 85, 29, 1353, 63, 452, 335, 1194, 1080, 480, 940, 71, 290, 644, 49, 1227, 435, 491, 381, 1197, 52, 293, 23, 1266, 197, 1001, 727, 614, 627, 1386, 48, 216, 433, 715, 1140, 1266, 723, 167, 910, 1289, 24, 975, 988, 310, 425, 806, 587, 538, 1199, 694, 38, 1043, 755, 455, 554, 1714, 1062, 628, 1379, 734, 1205, 56, 333, 471, 315, 301, 295, 27, 444, 849, 359, 258, 786, 559, 761, 159, 281, 210, 421, 376, 740, 1704, 254, 664, 412, 110, 68, 294, 28, 572, 252, 546, 693, 263, 103, 969, 832, 1210, 736, 744, 859, 571, 55, 972, 172, 909, 598, 1268, 255, 1161, 321, 243, 982, 1842, 1254, 1026, 282, 961, 101, 531, 35, 914, 1264, 66, 387, 293, 179, 975, 822, 336, 1086, 217, 66, 1485, 560, 528, 615, 151, 854, 434, 571, 1376, 287, 341, 412, 1492, 873, 194, 375, 870, 1245, 222, 894, 666, 577, 681, 711, 142, 623, 623, 252, 166, 1034, 42, 286, 154, 150, 2133, 663, 267, 850, 13, 802, 1075, 459, 802, 860, 988, 570, 41, 1014, 255, 117, 1126, 906, 704, 925, 231, 1040, 1036, 264, 304, 306, 921, 1042, 152, 502, 1179, 1311, 729, 873, 670, 846, 1815, 578, 388, 224, 937, 118, 652, 28, 233, 991, 1951, 419, 745, 202, 245, 795, 423, 893, 320, 541, 1002, 985, 650, 907, 1305, 963, 600, 1168, 431, 17, 394, 636, 345, 436, 374, 660, 1595, 899, 929, 359, 351, 236, 873, 199, 1051, 753, 531, 655, 768, 385, 458, 188, 809, 1181, 376, 270, 371, 756, 333, 489, 1162, 287, 515, 444, 819, 450, 312, 199, 728, 582, 391, 381, 706, 230, 380, 441, 477, 468, 502, 226, 374, 734, 152, 382, 256, 803, 391, 528, 383, 337, 80, 294, 708, 388, 347, 405, 180, 457, 160, 119, 315, 999, 489, 162, 263, 1023, 404, 180, 324, 239, 174, 508, 380, 517, 485, 163, 434, 359, 139, 142, 671, 561, 79, 409, 1378, 92, 131, 585, 495, 129, 107, 406, 1169, 266, 505, 353, 529, 732, 635, 297, 37, 335, 452, 336, 71, 564, 357, 526, 347, 297, 242, 249, 401, 639, 485, 565, 205, 665, 443, 401, 432, 416, 431, 729, 518, 1026, 664, 400, 156, 544, 1072, 112, 275, 239, 576, 548, 502, 500, 942, 529, 764, 641, 102, 257, 581, 846, 609, 414, 673, 448, 68, 49, 879, 502, 268, 1082, 743, 917, 1067, 569, 890, 883, 665, 207, 104, 485, 396, 499, 664, 592, 795, 476, 140, 614, 514, 562, 1353, 690, 1000, 347, 283, 2793, 354, 485, 343, 561, 881, 1054, 231, 1056, 534, 1408, 251, 519, 235, 770, 678, 1589, 94, 792, 281, 871, 873, 958, 649, 554, 370, 687, 736, 571, 414, 827, 543, 779, 377, 290, 1106, 287, 1194, 821, 759, 1659, 1087, 729, 1752, 424, 415, 549, 355, 349, 412, 468, 142, 330, 649, 525, 417, 142, 462, 1211, 231, 236, 842, 442, 467, 380, 418, 178, 686, 167, 370, 316, 661, 1132, 66, 661, 583, 360, 286, 751, 800, 110, 273, 216, 463, 1012, 180, 1450, 935, 1410, 538, 672, 182, 439, 163, 114, 310, 200, 129, 1511, 714, 233, 342, 205, 360, 175, 436, 244, 589, 363, 383, 432, 399, 72, 679, 119, 341, 912, 522, 550, 820, 644, 157, 325, 1289, 694, 324, 851, 1719, 435, 992, 269, 671, 462, 453, 462, 157, 214, 375, 157, 747, 165, 534, 435, 376, 168, 367, 589, 358, 523, 589, 238, 477, 792, 51, 166, 576, 301, 420, 588, 1066, 172, 522, 343, 410, 310, 282, 464, 494, 727, 546, 492, 945, 625, 1787, 249, 895, 502, 894, 374, 620, 367, 340, 246, 379, 381, 311, 1241, 619, 1024, 148, 566, 320, 1128, 190, 593, 454, 726, 143, 169, 557, 1273, 864, 336, 1086, 685, 411, 780, 494, 459, 453, 531, 158, 811, 53, 323, 720, 506, 472, 248, 343, 646, 1010, 930, 578, 647, 268, 657, 839, 616, 159, 139, 485, 269, 672, 347, 476, 1456, 281, 299, 355, 1219, 190, 78, 253, 478, 247, 397, 428, 308, 261, 48, 529, 462, 145, 326, 464, 356, 176, 572, 740, 231, 148, 894, 305, 255, 352, 201, 149, 291, 66, 241, 342, 146, 641, 298, 607, 59, 456, 200, 471, 82, 822, 584, 808, 510, 370, 149, 650, 236, 540, 645, 587, 361, 224, 600, 137, 1006, 300, 1092, 277, 348, 683, 612, 480, 482, 573, 195, 542, 443, 323, 584, 507, 503, 48, 277, 735, 732, 576, 502, 3006, 130, 1085, 837, 394, 803, 564, 334, 231, 424, 541, 252, 413, 821, 808, 367, 426, 270, 550, 740, 819, 967, 652, 716, 171, 664, 1181, 741, 2120, 295, 600, 581, 440, 535, 767, 980, 539, 250, 1000, 784, 522, 347, 108, 2035, 752, 223, 98, 289, 454, 733, 102, 889, 808, 610, 55, 397, 69, 1122, 783, 1011, 2164, 1004, 496, 893, 402, 1374, 322, 594, 1234, 888, 814, 934, 1042, 5042, 587, 772, 813, 623, 526, 588, 670, 665, 565, 224, 1058, 962, 846, 2043, 320, 437, 1283, 194, 624, 72, 1008, 1515, 648, 212, 642, 666, 211, 488, 86, 199, 536, 379, 277, 356, 460, 379, 240, 326, 226, 193, 616, 108, 112, 97, 476, 332, 159, 548, 143, 514, 227, 1018, 620, 649, 1756, 745, 301, 243, 634, 195, 786, 386, 366, 449, 367, 477, 160, 270, 1581, 304, 999, 732, 143, 300, 1557, 204, 315, 906, 218, 274, 125, 417, 138, 99, 112, 183, 726, 227, 455, 416, 242, 232, 420, 182, 385, 252, 526, 994, 2518, 596, 403, 294, 372, 569, 1226, 237, 159, 1153, 358, 1045, 719, 786, 319, 220, 475, 384, 678, 240, 162, 358, 476, 175, 373, 479, 454, 449, 506, 966, 148, 178, 411, 600, 429, 370, 591, 1313, 869, 222, 644, 492, 118, 158, 230, 93, 1035, 178, 139, 264, 619, 749, 739, 732, 491, 161, 448, 403, 162, 335, 200, 369, 312, 606, 386, 707, 857, 895, 176, 549, 197, 531, 217, 315, 341, 498, 403, 826, 461, 490, 144, 331, 309, 421, 350, 576, 628, 195, 349, 623, 93, 470, 317, 581, 149, 767, 310, 217, 793, 581, 626, 458, 266, 1341, 104, 1344, 1808, 213, 515, 285, 2443, 510, 603, 723, 183, 119, 817, 118, 333, 415, 1045, 493, 1002, 448, 351, 423, 1093, 252, 163, 533, 244, 697, 811]\n",
            "Length of the Combine Tokens Count 24926\n"
          ]
        }
      ],
      "source": [
        "# Initialize an empty list to store the combined token counts.\n",
        "combine_tokens_count = []\n",
        "\n",
        "# Iterate through each pair of counts in the two lists.\n",
        "for instruction, output in zip(instruction_tokens_count, output_tokens_count):\n",
        "    # Add the sum of the current pair of counts to the combine_tokens_count list.\n",
        "    combine_tokens_count.append(instruction + output)\n",
        "\n",
        "# Print the combined tokens count.\n",
        "print(\"Combine Tokens Count\", combine_tokens_count)\n",
        "\n",
        "# Print the length of the combined tokens count list.\n",
        "print(\"Length of the Combine Tokens Count\", len(combine_tokens_count))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Xi1imVWSZ-S4",
        "outputId": "af82ac1f-0b7c-460c-fc1e-4709e21c90c4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Define a function to plot the distribution of token counts.\n",
        "def plot_distribution(tokens_count, title):\n",
        "    # Set the style of the plot.\n",
        "    sns.set_style(\"whitegrid\")\n",
        "\n",
        "    # Create a new figure with a specified size.\n",
        "    plt.figure(figsize=(15, 6))\n",
        "\n",
        "    # Create a histogram of token counts with specified bins, color, and edge color.\n",
        "    plt.hist(tokens_count, bins=50, color='#3498db', edgecolor='black')\n",
        "\n",
        "    # Set the title of the plot.\n",
        "    plt.title(title, fontsize=16)\n",
        "\n",
        "    # Set labels for the x and y axes.\n",
        "    plt.xlabel(\"Number of Tokens\", fontsize=14)\n",
        "    plt.ylabel(\"Number of Examples\", fontsize=14)\n",
        "\n",
        "    # Set the font size for x and y tick labels.\n",
        "    plt.xticks(fontsize=12)\n",
        "    plt.yticks(fontsize=12)\n",
        "\n",
        "    # Adjust layout to prevent clipping of titles and labels.\n",
        "    plt.tight_layout()\n",
        "\n",
        "    # Display the plot.\n",
        "    plt.show()\n",
        "\n",
        "# Plot the distribution of token counts for the instruction column.\n",
        "plot_distribution(instruction_tokens_count, \"Distribution of the tokens count for Instruction Column\")\n",
        "\n",
        "# Plot the distribution of token counts for the output column.\n",
        "plot_distribution(output_tokens_count, \"Distribution of the tokens count for Output Column\")\n",
        "\n",
        "# Plot the distribution of token counts for the combined instruction + output column.\n",
        "plot_distribution(combine_tokens_count, \"Distribution of the tokens count for combined Instruction + Output Column\")\n",
        "\n",
        "# The mean is around 500 tokens, but there is a long tail distribution which goes up to 5000 tokens.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Remove rows where the combined token count (from both the Instruction and Output columns) exceeds 2048 tokens.**"
      ],
      "metadata": {
        "id": "exTuZBdKeLWR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The default maximum context size for the Llama 2 model is 4096 tokens"
      ],
      "metadata": {
        "id": "T8x_8yMJeQN4"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "DlSEzB7FaWxd",
        "outputId": "d39b2a2b-0686-4984-915a-b9c2e1888dbb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of Valid Rows: 24895\n",
            "Removing: 31 rows....\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Define the maximum token count threshold.\n",
        "max_token_threshold = 2048\n",
        "\n",
        "# Filter rows with less than or equal to 2048 tokens.\n",
        "valid_indices = [i for i, count in enumerate(combine_tokens_count) if count <= max_token_threshold]\n",
        "\n",
        "# Print the number of valid rows.\n",
        "print(f\"Number of Valid Rows: {len(valid_indices)}\")\n",
        "\n",
        "# Calculate the number of rows to remove.\n",
        "rows_to_remove = len(dataset['train']) - len(valid_indices)\n",
        "\n",
        "# Print the number of rows to be removed.\n",
        "print(f\"Removing: {rows_to_remove} rows....\")\n",
        "\n",
        "# Extract valid rows based on indices.\n",
        "dataset['train'] = dataset['train'].select(valid_indices)\n",
        "\n",
        "# Get token counts for valid rows.\n",
        "token_counts = [combine_tokens_count[i] for i in valid_indices]\n",
        "\n",
        "# Plot the new distribution of combined token counts for the remaining valid rows.\n",
        "plot_distribution(token_counts, \"New distribution of Combine Total Count (Instruction + Output Columns)\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o8Orx5FDacwO",
        "outputId": "a537712e-8d4d-47a6-c3d4-4f55ad8bca0b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input', 'output', 'instruction', 'data_source'],\n",
              "        num_rows: 24895\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "xEF601Lpafwh",
        "outputId": "7855e72d-f728-495e-83d7-c4451c316cd8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      input                                             output  \\\n",
              "0            To find the probability of the spinner landing...   \n",
              "1            I need to choose 6 people out of 14, and the o...   \n",
              "2            First we count the number of all 4-letter word...   \n",
              "3            She can do this if and only if at least one of...   \n",
              "4            Think of the problem as a sequence of H's and ...   \n",
              "...     ...                                                ...   \n",
              "24890        There are several formulas that generate all p...   \n",
              "24891        Sure, here are some of the best universities:\\...   \n",
              "24892        [Enter stage left]\\n\\nFirst Citizen: Good morr...   \n",
              "24893        That's a very thoughtful idea! Here are some s...   \n",
              "24894        Certainly! Here's an implementation of the Tim...   \n",
              "\n",
              "                                             instruction    data_source  \n",
              "0      A board game spinner is divided into three par...  MATH/PRM-800K  \n",
              "1      My school's math club has 6 boys and 8 girls. ...  MATH/PRM-800K  \n",
              "2      How many 4-letter words with at least one cons...  MATH/PRM-800K  \n",
              "3      Melinda will roll two standard six-sided dice ...  MATH/PRM-800K  \n",
              "4      Let $p$ be the probability that, in the proces...  MATH/PRM-800K  \n",
              "...                                                  ...            ...  \n",
              "24890  Can we find a formula to generate all prime nu...        guanaco  \n",
              "24891  What are some of the best university's for stu...        guanaco  \n",
              "24892  Write me a SPL (Shakespeare Programming Langua...        guanaco  \n",
              "24893  Hi. I want to make an embossed picture for my ...        guanaco  \n",
              "24894  Please implement the Timsort algorithm on Lean...        guanaco  \n",
              "\n",
              "[24895 rows x 4 columns]"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>input</th>\n",
              "      <th>output</th>\n",
              "      <th>instruction</th>\n",
              "      <th>data_source</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td></td>\n",
              "      <td>To find the probability of the spinner landing...</td>\n",
              "      <td>A board game spinner is divided into three par...</td>\n",
              "      <td>MATH/PRM-800K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td></td>\n",
              "      <td>I need to choose 6 people out of 14, and the o...</td>\n",
              "      <td>My school's math club has 6 boys and 8 girls. ...</td>\n",
              "      <td>MATH/PRM-800K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td></td>\n",
              "      <td>First we count the number of all 4-letter word...</td>\n",
              "      <td>How many 4-letter words with at least one cons...</td>\n",
              "      <td>MATH/PRM-800K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td></td>\n",
              "      <td>She can do this if and only if at least one of...</td>\n",
              "      <td>Melinda will roll two standard six-sided dice ...</td>\n",
              "      <td>MATH/PRM-800K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td></td>\n",
              "      <td>Think of the problem as a sequence of H's and ...</td>\n",
              "      <td>Let $p$ be the probability that, in the proces...</td>\n",
              "      <td>MATH/PRM-800K</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24890</th>\n",
              "      <td></td>\n",
              "      <td>There are several formulas that generate all p...</td>\n",
              "      <td>Can we find a formula to generate all prime nu...</td>\n",
              "      <td>guanaco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24891</th>\n",
              "      <td></td>\n",
              "      <td>Sure, here are some of the best universities:\\...</td>\n",
              "      <td>What are some of the best university's for stu...</td>\n",
              "      <td>guanaco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24892</th>\n",
              "      <td></td>\n",
              "      <td>[Enter stage left]\\n\\nFirst Citizen: Good morr...</td>\n",
              "      <td>Write me a SPL (Shakespeare Programming Langua...</td>\n",
              "      <td>guanaco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24893</th>\n",
              "      <td></td>\n",
              "      <td>That's a very thoughtful idea! Here are some s...</td>\n",
              "      <td>Hi. I want to make an embossed picture for my ...</td>\n",
              "      <td>guanaco</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24894</th>\n",
              "      <td></td>\n",
              "      <td>Certainly! Here's an implementation of the Tim...</td>\n",
              "      <td>Please implement the Timsort algorithm on Lean...</td>\n",
              "      <td>guanaco</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>24895 rows × 4 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-5f60ec43-accb-4ec1-97d2-bda79c176f6a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  <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",
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              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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",
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              "        document.querySelector('#df-5f60ec43-accb-4ec1-97d2-bda79c176f6a 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-5f60ec43-accb-4ec1-97d2-bda79c176f6a');\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",
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              "  .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",
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              "\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",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "  }\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",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-d587b96d-ef6b-4499-aa56-cafbdf87413f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
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              "type": "dataframe",
              "summary": "{\n  \"name\": \"dataset[\\\"train\\\"]\",\n  \"rows\": 24895,\n  \"fields\": [\n    {\n      \"column\": \"input\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"\",\n          \"Choose A, B, C or D as your solution.\",\n          \"When you review a fellow student's writing, try to make your feedback clear and specific. You can use questions such as the following to guide your feedback:\\nIdeas and development: Does the writer express a clear main idea and develop it with evidence, examples, and analysis?\\nOrganization: Does the writer order ideas in a clear, logical way so that the ideas build on one another and are easy to follow?\\nSentence fluency: Do the writer's sentences vary in structure and length, or does the writing sound choppy, rambling, or repetitive?\\nWord choice: Does the writer use words accurately and precisely, avoiding excessive repetition or inappropriate language to create clear, effective, and engaging writing?\\nGrammar and mechanics: Does the writer use accurate spelling, punctuation, and grammar to create writing that is correct and easy to read?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 19499,\n        \"samples\": [\n          \"Mark: \\\"Hey guys, I was just going through my photos from our last trip to Iceland, and it reminded me how incredible the natural world can be.\\\"\\n\\nOlivia: \\\"Oh, absolutely! I still can't get over those stunning waterfalls and volcanic landscapes we saw.\\\"\\n\\nDavid: \\\"It's amazing how such beautiful places have been shaped by millions of years of geological processes.\\\"\\n\\nSarah: \\\"And don't forget the diverse ecosystems that exist within these environments. It's truly fascinating!\\\"\\n</s>\\nUSER: Speaking of breathtaking wonders, what other places would you all like to visit or explore?\\nASSISTANT: Mark: \\\"I've always wanted to see the Great Barrier Reef in Australia. The underwater life there must be mind-blowing.\\\"\\n\\nOlivia: \\\"For me, it's definitely the Amazon Rainforest. With its rich biodiversity and unique species, it's a treasure trove for any nature lover.\\\"\\n\\nDavid: \\\"I'd love to go to Machu Picchu in Peru. Not only does it offer stunning views, but it also has a rich historical background.\\\"\\n\\nSarah: \\\"The Gal\\u00e1pagos Islands have been on my bucket list for ages. The unique wildlife found there played a huge role in shaping our understanding of evolution.\\\"\\n\\n*Emily nods in agreement*\\n</s>\\nUSER: Those all sound amazing. What do you think makes these places so special and awe-inspiring?\\nASSISTANT: Olivia: \\\"I think one factor is their sheer beauty. These places have landscapes and features that are unlike anything else on Earth.\\\"\\n\\nMark: \\\"And they often showcase the incredible power of nature, like massive waterfalls or active volcanoes.\\\"\\n\\nDavid: \\\"The historical significance of some locations also adds to their allure. It's fascinating to think about how ancient civilizations interacted with these natural wonders.\\\"\\n\\nSarah: \\\"For me, it's the unique ecosystems and biodiversity found in these places. They're home to species that can't be found anywhere else in the world.\\\"\\n\\n*Emily smiles*\\n</s>\\nUSER: I completely agree with all of you. How do you think we can help preserve these wonders for future generations?\\nASSISTANT: Sarah: \\\"One important way is through education and raising awareness about the threats facing these environments, such as climate change, pollution, and habitat destruction.\\\"\\n\\nOlivia: \\\"Supporting conservation efforts and organizations working to protect these areas is also crucial.\\\"\\n\\nMark: \\\"When visiting these places, we should practice responsible tourism by minimizing our impact on the environment and respecting local cultures.\\\"\\n\\nDavid: \\\"We can also advocate for policies and regulations that prioritize environmental protection and sustainable development.\\\"\\n\\n*Emily nods thoughtfully*\\n</s>\\nUSER: Great points! Let's make sure we do our part in preserving these amazing places. What other breathtaking wonders would you recommend people visit at least once in their lifetime?\\nASSISTANT: Mark: \\\"I'd say the Northern Lights, or Aurora Borealis. Seeing those colorful lights dancing across the sky is a truly magical experience.\\\"\\n\\nOlivia: \\\"The Grand Canyon in Arizona is another must-see. The sheer size and beauty of it will leave you speechless.\\\"\\n\\nDavid: \\\"I'd recommend visiting Angkor Wat in Cambodia. The intricate architecture combined with its stunning location makes it an unforgettable experience.\\\"\\n\\nSarah: \\\"Definitely the Great Blue Hole in Belize. As a marine biologist, I find this underwater sinkhole absolutely fascinating.\\\"\\n\\n*Emily smiles*\\n</s>\\nUSER: Thank you all for sharing your recommendations. I can't wait to plan our next adventure together!\\nASSISTANT: Sarah: \\\"Me too! It's always such a pleasure exploring these wonders with friends who share the same passion for nature and discovery.\\\"\\n\\nMark: \\\"Absolutely, it makes the experience even more memorable and enjoyable.\\\"\\n\\nOlivia: \\\"I'm already looking forward to our next trip. Let's start brainstorming ideas soon!\\\"\\n\\nDavid: \\\"Agreed! There's so much of this beautiful planet left to explore, and I can't think of better people to do it with.\\\"\\n\\n*Everyone shares a group hug and continues discussing their travel plans*\",\n          \"The center of the image circle is simply the center of the original circle reflected over the line $y=x$. When reflecting over this line, we swap the $x$ and $y$ coordinates. Thus, the image center is the point $(-5, 6)$.\",\n          \"A redwood tree is a plant. It has small leaves.\\nRedwood trees have fuzzy reddish-brown bark. Their bark helps protect redwood trees from fires.\\nA bald eagle is an animal. It walks and flies.\\nBald eagles live in trees near water. They eat mostly fish.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"instruction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 24895,\n        \"samples\": [\n          \"Simplify\\n\\\\[4 \\\\sin x \\\\sin (60^\\\\circ - x) \\\\sin (60^\\\\circ + x).\\\\]The answer will be a trigonometric function of some simple function of $x,$ like \\\"$\\\\cos (2x)$\\\" or \\\"$\\\\sin (x^3)$\\\".\",\n          \"What is the probability of spinning the spinner pictured and getting a prime number? Express your answer as a common fraction. [asy]\\nimport olympiad; defaultpen(linewidth(0.8)); size(100); dotfactor=4;\\ndraw(Circle((0,0),1));\\nstring[] labels = {\\\"3\\\",\\\"6\\\",\\\"1\\\",\\\"4\\\",\\\"5\\\",\\\"2\\\"};\\nfor(int i = 0; i < 6; ++i){\\n\\nlabel(labels[i],0.7*dir(60*i));\\n\\ndraw((0,0)--dir(60*i - 30));\\n}\\ndraw((0,0)--0.7*dir(130),linewidth(2.0),EndArrow(size=6));\\n[/asy]\",\n          \"Five points $A$, $B$, $C$, $D$, and $O$ lie on a flat field.  $A$ is directly north of $O$, $B$ is directly west of $O$, $C$ is directly south of $O$, and $D$ is directly east of $O$. The  distance between $C$ and $D$ is 140 m.  A hot-air balloon is positioned in the air at $H$ directly above $O$. The balloon is held in place by four ropes $HA$, $HB$, $HC$, and $HD$.  Rope $HC$ has length 150 m and rope $HD$ has length 130 m. How high is the balloon above the field (that is, the length of $OH$)? [asy]\\nsize(200);\\npair A, B, C, D, O, H, W, X, Y, Z;\\nO=(0,0);\\nA=(1,1);\\nD=(1.5,-.3);\\nB=(-1.5,.3);\\nC=(-1,-1);\\nH=(0,2.5);\\nW=(5/3)*(A+D);\\nX=(5/3)*(A+B);\\nY=(-1)*(W);\\nZ=(-1)*(X);\\ndraw(W--X--Y--Z--W);\\ndraw(A--C);\\ndraw(B--D);\\ndraw(O--H, linewidth(1));\\ndraw(A--H, dashed);\\ndraw(B--H, dashed);\\ndraw(C--H, dashed);\\ndraw(D--H, dashed);\\ndot(A);\\ndot(B);\\ndot(C);\\ndot(D);\\ndot(O);\\ndot(H);\\nlabel(\\\"A\\\", A, NE);\\nlabel(\\\"B\\\", B, SW);\\nlabel(\\\"C\\\", C, SE);\\nlabel(\\\"D\\\", D, NE);\\nlabel(\\\"O\\\", O, SE);\\nlabel(\\\"H\\\", H, NW);\\n[/asy]\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"data_source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"airoboros\",\n          \"ARB\",\n          \"reclor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "dataset[\"train\"].to_pandas()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**For near-deduplication using embeddings, consider utilizing models available on the MTEB (Model Text Embedding Benchmarks) leaderboard on Hugging Face. These models offer advanced capabilities for embedding text data, which can be leveraged to identify and remove near-duplicate instances within a dataset.**"
      ],
      "metadata": {
        "id": "9ET9-8aKeq5u"
      }
    },
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          "text": [
            "Convert the text to embeddings....\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Batches:   0%|          | 0/778 [00:00<?, ?it/s]"
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        {
          "output_type": "stream",
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          "text": [
            "Dimensions of the embedding (24895, 1024)\n",
            "Filtering out near duplicates....\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Filtering:   0%|          | 0/24895 [00:00<?, ?it/s]"
            ],
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              "version_minor": 0,
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          "text": [
            "      input                                             output  \\\n",
            "0            To find the probability of the spinner landing...   \n",
            "1            I need to choose 6 people out of 14, and the o...   \n",
            "2            First we count the number of all 4-letter word...   \n",
            "3            She can do this if and only if at least one of...   \n",
            "4            Think of the problem as a sequence of H's and ...   \n",
            "...     ...                                                ...   \n",
            "18183        There are several formulas that generate all p...   \n",
            "18184        Sure, here are some of the best universities:\\...   \n",
            "18185        [Enter stage left]\\n\\nFirst Citizen: Good morr...   \n",
            "18186        That's a very thoughtful idea! Here are some s...   \n",
            "18187        Certainly! Here's an implementation of the Tim...   \n",
            "\n",
            "                                             instruction    data_source  \n",
            "0      A board game spinner is divided into three par...  MATH/PRM-800K  \n",
            "1      My school's math club has 6 boys and 8 girls. ...  MATH/PRM-800K  \n",
            "2      How many 4-letter words with at least one cons...  MATH/PRM-800K  \n",
            "3      Melinda will roll two standard six-sided dice ...  MATH/PRM-800K  \n",
            "4      Let $p$ be the probability that, in the proces...  MATH/PRM-800K  \n",
            "...                                                  ...            ...  \n",
            "18183  Can we find a formula to generate all prime nu...        guanaco  \n",
            "18184  What are some of the best university's for stu...        guanaco  \n",
            "18185  Write me a SPL (Shakespeare Programming Langua...        guanaco  \n",
            "18186  Hi. I want to make an embossed picture for my ...        guanaco  \n",
            "18187  Please implement the Timsort algorithm on Lean...        guanaco  \n",
            "\n",
            "[18188 rows x 4 columns]\n"
          ]
        }
      ],
      "source": [
        "# Import necessary libraries and modules\n",
        "from sentence_transformers import SentenceTransformer\n",
        "import faiss\n",
        "from datasets import Dataset, DatasetDict\n",
        "from tqdm.autonotebook import tqdm\n",
        "import numpy as np\n",
        "\n",
        "# Define a function to deduplicate a dataset based on embeddings similarity\n",
        "def deduplicate_dataset(dataset: Dataset, model: str, threshold: float):\n",
        "    # Initialize the SentenceTransformer model with the specified name\n",
        "    sentence_model = SentenceTransformer(model)\n",
        "\n",
        "    # Extract the output column from the dataset\n",
        "    outputs = [example[\"output\"] for example in dataset[\"train\"]]\n",
        "\n",
        "    # Encode the text into embeddings using the SentenceTransformer model\n",
        "    print(\"Convert the text to embeddings....\")\n",
        "    embeddings = sentence_model.encode(outputs, show_progress_bar=True)\n",
        "    dimensions = embeddings.shape[1]  # Get the dimensions of the embeddings\n",
        "    print(\"Dimensions of the embedding\", embeddings.shape)\n",
        "\n",
        "    # Create an index using Faiss as the Vector Database\n",
        "    index = faiss.IndexFlatIP(dimensions)\n",
        "\n",
        "    # Normalize the embeddings\n",
        "    normalized_embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)\n",
        "\n",
        "    # Add the normalized embeddings to the index\n",
        "    index.add(normalized_embeddings)\n",
        "    print(\"Filtering out near duplicates....\")\n",
        "\n",
        "    # Search for nearest neighbors for each embedding\n",
        "    D, I = index.search(normalized_embeddings, k=2)\n",
        "\n",
        "    # Initialize a list to store indices of samples to keep\n",
        "    to_keep = []\n",
        "\n",
        "    # Iterate over each embedding\n",
        "    for i in tqdm(range(len(embeddings)), desc=\"Filtering\"):\n",
        "        # If the similarity with the second closest vector is above the threshold\n",
        "        if D[i, 1] >= threshold:\n",
        "            # Check if the current item or its nearest neighbor is already in the to_keep list\n",
        "            nearest_neighbor = I[i, 1]\n",
        "            if i not in to_keep and nearest_neighbor not in to_keep:\n",
        "                # If not, add the current item to the list\n",
        "                to_keep.append(i)\n",
        "        else:\n",
        "            # If the similarity is below the threshold, always keep the current item\n",
        "            to_keep.append(i)\n",
        "\n",
        "    # Filter the dataset based on the indices to keep\n",
        "    dataset = dataset[\"train\"].select(to_keep)\n",
        "\n",
        "    # Print the filtered dataset\n",
        "    print(dataset.to_pandas())\n",
        "\n",
        "    # Return the deduplicated dataset wrapped in a DatasetDict\n",
        "    return DatasetDict({\"train\": dataset})\n",
        "\n",
        "# Call the deduplicate_dataset function to deduplicate the dataset using the specified model and threshold\n",
        "deduped_dataset = deduplicate_dataset(dataset, \"thenlper/gte-large\", 0.95)\n"
      ]
    },
    {
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      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "qQGcbdwUbIOv",
        "outputId": "7ac3aeb2-d85b-4b85-eaf4-f1d7efa6b135"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['input', 'output', 'instruction', 'data_source'],\n",
              "        num_rows: 18188\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "deduped_dataset"
      ]
    },
    {
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      "metadata": {
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        "id": "4nEfgTDlbPls",
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              "      input                                             output  \\\n",
              "0            To find the probability of the spinner landing...   \n",
              "1            I need to choose 6 people out of 14, and the o...   \n",
              "2            First we count the number of all 4-letter word...   \n",
              "3            She can do this if and only if at least one of...   \n",
              "4            Think of the problem as a sequence of H's and ...   \n",
              "...     ...                                                ...   \n",
              "18183        There are several formulas that generate all p...   \n",
              "18184        Sure, here are some of the best universities:\\...   \n",
              "18185        [Enter stage left]\\n\\nFirst Citizen: Good morr...   \n",
              "18186        That's a very thoughtful idea! Here are some s...   \n",
              "18187        Certainly! Here's an implementation of the Tim...   \n",
              "\n",
              "                                             instruction    data_source  \n",
              "0      A board game spinner is divided into three par...  MATH/PRM-800K  \n",
              "1      My school's math club has 6 boys and 8 girls. ...  MATH/PRM-800K  \n",
              "2      How many 4-letter words with at least one cons...  MATH/PRM-800K  \n",
              "3      Melinda will roll two standard six-sided dice ...  MATH/PRM-800K  \n",
              "4      Let $p$ be the probability that, in the proces...  MATH/PRM-800K  \n",
              "...                                                  ...            ...  \n",
              "18183  Can we find a formula to generate all prime nu...        guanaco  \n",
              "18184  What are some of the best university's for stu...        guanaco  \n",
              "18185  Write me a SPL (Shakespeare Programming Langua...        guanaco  \n",
              "18186  Hi. I want to make an embossed picture for my ...        guanaco  \n",
              "18187  Please implement the Timsort algorithm on Lean...        guanaco  \n",
              "\n",
              "[18188 rows x 4 columns]"
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              "      <td>She can do this if and only if at least one of...</td>\n",
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              "      <td>Please implement the Timsort algorithm on Lean...</td>\n",
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              "    .colab-df-convert:hover {\n",
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              "      background-color: #3B4455;\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        document.querySelector('#df-3fd1f888-f3aa-445d-90f7-1f483fdaae68 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-3fd1f888-f3aa-445d-90f7-1f483fdaae68');\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",
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              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "      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-2493bb0b-147a-4922-b878-865fd52ae718 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"deduped_dataset[\\\"train\\\"]\",\n  \"rows\": 18188,\n  \"fields\": [\n    {\n      \"column\": \"input\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"\",\n          \"Choose A, B, C or D as your solution.\",\n          \"When you review a fellow student's writing, try to make your feedback clear and specific. You can use questions such as the following to guide your feedback:\\nIdeas and development: Does the writer express a clear main idea and develop it with evidence, examples, and analysis?\\nOrganization: Does the writer order ideas in a clear, logical way so that the ideas build on one another and are easy to follow?\\nSentence fluency: Do the writer's sentences vary in structure and length, or does the writing sound choppy, rambling, or repetitive?\\nWord choice: Does the writer use words accurately and precisely, avoiding excessive repetition or inappropriate language to create clear, effective, and engaging writing?\\nGrammar and mechanics: Does the writer use accurate spelling, punctuation, and grammar to create writing that is correct and easy to read?\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18155,\n        \"samples\": [\n          \"\\ndef k_distant_indices(nums, key, k):\\n    result = []\\n    for i in range(len(nums)):\\n        for j in range(max(0, i - k), min(i + k + 1, len(nums))):\\n            if nums[j] == key:\\n                result.append(i)\\n                break\\n    return result\\n\",\n          \"Let $z = x + yi,$ where $x$ and $y$ are real numbers.\\n\\nFrom the equation $\\\\left| \\\\frac{z - 4}{z - 8} \\\\right| = 1,$ $|z - 4| = |z - 8|.$  Then\\n\\\\[|x + yi - 4| = |x + yi - 8|,\\\\]so $(x - 4)^2 + y^2 = (x - 8)^2 + y^2.$  This simplifies to $x = 6.$\\n\\nFrom the equation $\\\\left| \\\\frac{z - 12}{z - 8i} \\\\right| = \\\\frac{5}{3},$ $3|z - 12| = 5|z - 8i|.$  Then\\n\\\\[3|6 + yi - 12| = 5|6 + yi - 8i|,\\\\]so $9(36 + y^2) = 25(36 + (y - 8)^2).$  This simplifies to $16y^2 - 400y + 2176 = 0,$ which factors as $16(y - 8)(y - 17) = 0.$  Hence, $y = 8$ or $y = 17.$\\n\\nThus, the solutions in $z$ are $6 + 8i, 6 + 17i.$\",\n          \"To find the number of positive divisors of a number, I need to factor it into prime factors and use a formula based on the exponents of those factors. 6! means 6 times 5 times 4 times 3 times 2 times 1, so I can factor it as 2^4 times 3^2 times 5^1. The formula for the number of divisors is to add one to each exponent and multiply them together. So in this case, the number of divisors is (4 + 1) times (2 + 1) times (1 + 1), which is 5 times 3 times 2, which is 30. Therefore, 6! has 30 positive divisors. \"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"instruction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 18188,\n        \"samples\": [\n          \"The expression $x^2 + 18x - 63$ can be written in the form $(x - a)(x + b)$, where $a$ and $b$ are both nonnegative real numbers. What is the value of $b$?\",\n          \"Implement a C++ function that checks if a given number is prime or not.\\nPLAINFORMAT\",\n          \"Select the animal.\\nA: Basil has green leaves.\\nB: Grizzly bears walk and run.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"data_source\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"airoboros\",\n          \"ARB\",\n          \"tigerbot-kaggle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "deduped_dataset[\"train\"].to_pandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_vsUAKlbR3A",
        "outputId": "76aa3bbc-a793-400d-cc9e-1bad066ab19a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of rows in the Original Dataset 24895\n",
            "Number of rows in the deduped dataset 18188\n",
            "Number of rows removed: 6707\n"
          ]
        }
      ],
      "source": [
        "# Print the number of rows in the original dataset\n",
        "# `dataset[\"train\"]` refers to the training split of the dataset\n",
        "print(\"Number of rows in the Original Dataset\", len(dataset[\"train\"]))\n",
        "\n",
        "# Print the number of rows in the deduplicated dataset\n",
        "# `deduped_dataset[\"train\"]` refers to the training split of the dataset after duplicates have been removed\n",
        "print(\"Number of rows in the deduped dataset\", len(deduped_dataset[\"train\"]))\n",
        "\n",
        "# Calculate and print the number of rows removed during the deduplication process\n",
        "# This is computed by subtracting the number of rows in the deduplicated dataset from the original dataset\n",
        "print(f\"Number of rows removed: {len(dataset['train']) - len(deduped_dataset['train'])}\")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2swLwl1ObUV_",
        "outputId": "a6b2afa6-a691-4848-d886-9c724082ecf7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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36, 127, 50, 77, 8, 17, 14, 48, 951, 32, 315, 14, 49, 732, 151, 19, 11, 137, 708, 48, 16, 12, 113, 12, 28, 9, 35, 22, 21, 32, 31, 14, 28, 15, 52, 17, 24, 279, 46, 46, 16, 223, 61, 23, 39, 48, 24, 54, 54, 79, 646, 54, 21, 24, 16, 12, 18, 20, 19, 44, 15, 43, 23, 16, 72, 21, 8, 87, 21, 24, 1298, 26, 31, 28, 9, 54, 12, 139, 39, 19, 23, 8, 26, 7, 69, 37, 59, 40, 63, 29, 30, 23, 28, 64, 17, 25, 24, 31, 13, 39, 15, 16, 11, 85, 137, 17, 30, 17, 15, 14, 16, 25, 17, 18, 41, 11, 7, 39, 37, 29, 111, 39, 14, 16, 31, 72, 15, 9, 14, 246, 5, 15, 18, 16, 23, 7, 63, 49, 16, 48, 16, 26, 59, 38, 25, 104, 18, 37, 49, 51, 15, 64, 17, 12, 24, 31, 29, 278, 14, 47, 105, 23, 267, 12, 18, 18, 343, 16, 16, 20, 255, 22, 10, 56, 87, 52, 39, 7, 12, 59, 12, 18, 14, 50, 24, 62, 13, 14, 69, 19, 44, 11, 72, 105, 84, 9, 573, 16, 30, 8, 103, 20, 145, 11, 86, 44, 425, 87, 38, 39, 62, 14, 276, 17, 9, 63, 49, 19, 21, 14, 31, 69, 15]\n",
            "Length of Instruction Tokens Count 18188\n",
            "Output Tokens Count [223, 105, 193, 96, 440, 217, 324, 80, 267, 267, 135, 194, 251, 194, 130, 958, 158, 76, 137, 201, 256, 206, 384, 59, 306, 558, 634, 98, 122, 423, 122, 142, 178, 210, 215, 306, 124, 306, 209, 216, 40, 513, 239, 278, 130, 505, 228, 315, 112, 169, 625, 326, 544, 186, 375, 125, 421, 374, 87, 96, 141, 149, 104, 32, 227, 195, 133, 181, 432, 78, 553, 609, 437, 428, 167, 355, 413, 143, 322, 165, 317, 108, 414, 197, 20, 101, 483, 426, 356, 805, 277, 293, 312, 355, 189, 74, 58, 76, 124, 245, 301, 183, 195, 236, 222, 170, 143, 339, 278, 246, 190, 151, 150, 81, 321, 78, 244, 212, 426, 273, 338, 212, 113, 258, 180, 206, 117, 153, 108, 241, 194, 161, 307, 188, 244, 214, 220, 230, 515, 202, 246, 123, 226, 53, 202, 104, 272, 243, 103, 66, 213, 74, 110, 199, 347, 112, 226, 858, 275, 102, 318, 99, 277, 263, 229, 569, 78, 187, 175, 63, 200, 372, 88, 330, 105, 226, 166, 151, 259, 627, 200, 432, 349, 121, 285, 327, 67, 569, 152, 364, 53, 158, 294, 212, 108, 285, 185, 167, 138, 216, 101, 85, 103, 428, 101, 280, 56, 281, 64, 38, 1801, 102, 63, 495, 82, 120, 165, 889, 152, 313, 235, 204, 410, 135, 254, 552, 131, 93, 167, 523, 1026, 213, 235, 131, 546, 70, 187, 176, 220, 60, 209, 139, 433, 145, 123, 232, 240, 440, 213, 83, 65, 124, 211, 107, 348, 306, 345, 214, 286, 102, 399, 518, 77, 494, 229, 263, 87, 220, 165, 162, 245, 233, 82, 167, 105, 73, 68, 256, 93, 58, 316, 274, 315, 80, 321, 358, 1043, 144, 553, 231, 697, 145, 270, 144, 138, 161, 49, 253, 446, 193, 332, 184, 258, 174, 121, 100, 229, 573, 281, 260, 88, 56, 249, 167, 647, 123, 317, 535, 143, 235, 222, 319, 686, 194, 159, 78, 66, 235, 646, 72, 358, 141, 232, 67, 269, 659, 124, 133, 131, 363, 747, 279, 123, 166, 38, 109, 94, 397, 113, 213, 61, 182, 251, 86, 159, 229, 131, 497, 138, 181, 370, 233, 191, 104, 263, 266, 579, 240, 466, 73, 365, 59, 160, 310, 76, 111, 209, 285, 210, 465, 43, 484, 146, 392, 601, 180, 241, 163, 159, 216, 113, 165, 551, 75, 140, 159, 281, 752, 377, 71, 687, 155, 81, 73, 132, 146, 308, 80, 63, 256, 446, 222, 200, 47, 192, 258, 272, 324, 109, 192, 194, 472, 76, 215, 154, 189, 481, 80, 141, 223, 164, 233, 96, 131, 201, 176, 173, 140, 93, 110, 332, 180, 363, 32, 150, 152, 421, 111, 209, 121, 233, 141, 159, 189, 143, 389, 196, 45, 72, 437, 254, 163, 169, 163, 167, 246, 237, 889, 153, 376, 85, 146, 234, 393, 146, 1150, 205, 211, 1409, 169, 262, 99, 589, 348, 256, 254, 161, 60, 306, 1209, 145, 73, 139, 408, 309, 234, 157, 234, 36, 164, 328, 158, 290, 111, 315, 471, 369, 164, 175, 375, 205, 461, 362, 493, 164, 97, 277, 167, 144, 169, 87, 72, 327, 163, 195, 406, 240, 688, 353, 235, 283, 313, 1011, 419, 120, 117, 266, 271, 246, 856, 99, 187, 447, 295, 254, 262, 43, 385, 244, 279, 314, 361, 219, 299, 175, 84, 328, 116, 437, 272, 295, 197, 190, 177, 213, 304, 157, 253, 352, 109, 203, 109, 201, 282, 268, 153, 214, 96, 164, 381, 161, 274, 242, 509, 213, 356, 716, 95, 439, 176, 210, 585, 94, 142, 182, 107, 148, 249, 676, 275, 441, 218, 246, 340, 81, 1494, 462, 321, 231, 213, 327, 134, 246, 333, 188, 64, 207, 254, 175, 296, 160, 179, 125, 160, 93, 294, 287, 342, 177, 302, 160, 293, 361, 157, 166, 112, 218, 49, 247, 454, 201, 78, 387, 398, 69, 195, 187, 138, 127, 80, 192, 714, 335, 429, 156, 278, 48, 273, 28, 376, 162, 50, 33, 299, 110, 123, 105, 143, 175, 376, 97, 133, 227, 653, 177, 129, 182, 162, 167, 276, 188, 277, 232, 128, 366, 262, 348, 102, 278, 290, 279, 139, 296, 94, 189, 163, 52, 110, 140, 158, 476, 101, 200, 175, 126, 303, 137, 268, 325, 104, 264, 594, 836, 539, 120, 211, 424, 234, 77, 137, 188, 273, 704, 979, 155, 226, 276, 367, 585, 264, 157, 608, 802, 672, 109, 577, 136, 221, 890, 899, 427, 555, 480, 682, 1336, 330, 941, 1117, 64, 102, 407, 233, 298, 308, 1152, 873, 513, 346, 275, 695, 122, 551, 101, 116, 260, 223, 47, 945, 741, 52, 181, 513, 250, 533, 184, 296, 260, 242, 268, 159, 275, 287, 593, 498, 409, 587, 481, 284, 111, 121, 222, 345, 292, 778, 553, 654, 473, 101, 1266, 160, 618, 229, 1831, 437, 803, 289, 641, 320, 188, 170, 223, 388, 565, 56, 175, 889, 305, 126, 590, 174, 184, 202, 427, 449, 301, 343, 231, 398, 324, 308, 726, 305, 302, 1837, 330, 132, 321, 786, 739, 320, 316, 812, 429, 299, 312, 64, 656, 174, 261, 551, 472, 749, 152, 114, 391, 1174, 307, 788, 178, 353, 715, 548, 347, 520, 134, 117, 83, 748, 300, 229, 636, 400, 252, 195, 702, 204, 347, 230, 734, 469, 525, 84, 108, 493, 372, 303, 236, 667, 907, 889, 254, 271, 508, 357, 256, 383, 58, 402, 417, 183, 439, 191, 79, 83, 148, 265, 395, 398, 228, 101, 164, 434, 223, 311, 781, 157, 134, 210, 240, 1265, 215, 538, 364, 291, 553, 433, 437, 74, 614, 511, 221, 232, 378, 425, 937, 373, 255, 869, 279, 141, 991, 666, 271, 264, 96, 382, 748, 179, 741, 540, 289, 560, 455, 276, 89, 305, 452, 172, 340, 185, 273, 199, 690, 221, 42, 93, 273, 285, 179, 767, 560, 624, 316, 360, 493, 224, 821, 319, 573, 489, 515, 386, 178, 231, 278, 220, 391, 189, 138, 384, 124, 223, 562, 553, 432, 78, 285, 164, 562, 103, 226, 557, 159, 217, 202, 74, 150, 139, 1103, 281, 345, 166, 316, 313, 134, 284, 114, 98, 277, 208, 130, 382, 517, 209, 377, 99, 542, 430, 63, 249, 131, 289, 170, 332, 689, 220, 307, 395, 325, 314, 145, 614, 216, 122, 537, 321, 120, 544, 359, 425, 584, 425, 428, 339, 479, 300, 555, 409, 647, 216, 167, 244, 567, 277, 248, 314, 96, 727, 148, 167, 566, 106, 94, 460, 624, 232, 371, 678, 929, 165, 51, 294, 473, 216, 170, 373, 316, 461, 289, 180, 695, 239, 130, 271, 90, 466, 344, 674, 343, 937, 265, 490, 103, 148, 159, 310, 245, 449, 389, 254, 330, 88, 244, 52, 259, 325, 118, 180, 96, 605, 354, 245, 443, 359, 663, 261, 648, 431, 864, 799, 149, 499, 724, 298, 235, 250, 565, 804, 310, 188, 150, 449, 235, 398, 367, 353, 167, 656, 195, 386, 181, 134, 520, 458, 145, 303, 131, 284, 275, 859, 105, 183, 183, 338, 136, 143, 529, 306, 560, 208, 538, 542, 413, 300, 392, 299, 131, 183, 592, 485, 661, 251, 611, 416, 792, 378, 296, 280, 160, 245, 280, 776, 112, 147, 148, 80, 49, 304, 135, 202, 333, 439, 140, 75, 188, 459, 87, 210, 321, 83, 162, 244, 609, 331, 501, 278, 304, 159, 208, 632, 413, 472, 302, 513, 188, 205, 856, 476, 159, 296, 269, 121, 183, 229, 590, 123, 268, 714, 122, 448, 152, 317, 264, 73, 1029, 372, 349, 225, 240, 366, 354, 301, 157, 365, 612, 697, 650, 293, 392, 58, 328, 776, 237, 232, 297, 182, 258, 107, 494, 184, 756, 705, 447, 310, 332, 803, 180, 287, 347, 195, 213, 182, 244, 275, 281, 491, 274, 69, 192, 422, 131, 403, 734, 472, 311, 562, 380, 343, 522, 248, 249, 333, 530, 599, 206, 262, 194, 109, 441, 311, 521, 389, 368, 203, 462, 204, 446, 356, 407, 164, 501, 500, 170, 552, 195, 728, 407, 455, 240, 374, 223, 525, 440, 537, 657, 98, 393, 660, 433, 247, 457, 516, 701, 500, 306, 515, 726, 176, 304, 245, 272, 637, 468, 78, 100, 193, 230, 220, 777, 781, 83, 262, 630, 209, 801, 204, 497, 73, 477, 302, 927, 449, 452, 639, 229, 212, 857, 500, 255, 133, 214, 346, 487, 344, 357, 383, 149, 272, 722, 220, 186, 221, 253, 164, 1007, 643, 609, 546, 287, 255, 454, 392, 487, 952, 518, 251, 343, 1122, 180, 446, 833, 250, 545, 806, 227, 1186, 180, 439, 376, 466, 385, 261, 133, 132, 346, 24, 322, 177, 447, 837, 263, 416, 489, 542, 271, 407, 456, 47, 537, 65, 219, 255, 380, 251, 307, 662, 315, 490, 186, 89, 234, 327, 139, 393, 661, 523, 130, 155, 266, 512, 467, 336, 265, 257, 204, 526, 770, 360, 259, 1194, 543, 532, 251, 372, 157, 192, 120, 480, 276, 186, 481, 684, 442, 61, 357, 200, 466, 112, 260, 132, 113, 499, 228, 228, 561, 249, 171, 65, 136, 351, 501, 378, 201, 204, 373, 368, 177, 734, 1931, 623, 532, 189, 288, 339, 159, 561, 424, 414, 249, 324, 550, 234, 397, 188, 66, 258, 272, 264, 154, 242, 198, 190, 433, 945, 223, 107, 173, 351, 107, 180, 213, 199, 901, 278, 220, 252, 930, 241, 250, 398, 405, 115, 169, 248, 802, 454, 323, 312, 93, 1049, 294, 278, 380, 162, 843, 426, 114, 346, 203, 256, 544, 382, 357, 538, 116, 221, 125, 287, 235, 693, 460, 210, 642, 107, 950, 103, 493, 808, 309, 307, 166, 168, 372, 195, 264, 529, 617, 293, 421, 222, 243, 113, 160, 225, 315, 155, 444, 530, 87, 1857, 434, 153, 287, 755, 96, 423, 459, 246, 673, 1417, 267, 313, 1021, 413, 208, 273, 617, 306, 294, 967, 259, 426, 54, 230, 884, 379, 295, 207, 504, 104, 80, 75, 563, 346, 208, 158, 339, 418, 509, 433, 768, 71, 993, 360, 481, 47, 521, 465, 251, 357, 304, 237, 561, 718, 231, 865, 642, 579, 283, 942, 514, 288, 418, 532, 384, 148, 366, 296, 145, 224, 667, 505, 465, 128, 396, 417, 146, 117, 208, 417, 293, 264, 89, 221, 235, 132, 205, 344, 734, 364, 472, 260, 432, 955, 269, 484, 587, 351, 477, 410, 176, 205, 367, 234, 312, 373, 75, 283, 179, 201, 747, 210, 450, 270, 282, 223, 251, 697, 444, 867, 348, 273, 121, 280, 762, 1316, 358, 194, 297, 233, 618, 117, 196, 303, 196, 371, 630, 228, 369, 422, 111, 441, 207, 269, 119, 158, 561, 411, 337, 234, 454, 311, 122, 242, 158, 354, 201, 466, 269, 254, 347, 338, 430, 452, 822, 102, 318, 1037, 521, 406, 374, 516, 43, 283, 217, 127, 368, 542, 788, 609, 725, 123, 234, 343, 226, 178, 381, 182, 468, 272, 151, 281, 113, 279, 462, 282, 522, 164, 252, 510, 150, 359, 248, 268, 540, 806, 185, 101, 223, 104, 124, 533, 612, 275, 525, 270, 186, 135, 232, 488, 338, 372, 1457, 145, 480, 382, 1176, 214, 433, 447, 605, 241, 824, 335, 357, 350, 170, 1392, 448, 197, 114, 173, 668, 248, 140, 394, 494, 233, 223, 352, 376, 471, 333, 171, 232, 71, 352, 502, 543, 201, 533, 273, 189, 304, 188, 578, 584, 153, 298, 58, 187, 374, 42, 192, 400, 225, 507, 852, 153, 462, 324, 751, 112, 295, 222, 136, 294, 1051, 355, 356, 283, 660, 220, 206, 260, 733, 470, 238, 220, 418, 449, 236, 315, 304, 801, 527, 498, 107, 480, 395, 745, 183, 327, 71, 221, 408, 575, 433, 947, 677, 272, 960, 309, 156, 396, 460, 635, 399, 255, 231, 549, 208, 122, 468, 269, 335, 280, 565, 669, 1114, 211, 274, 817, 145, 102, 216, 318, 215, 169, 323, 862, 236, 139, 303, 607, 167, 586, 255, 140, 380, 243, 592, 200, 562, 624, 205, 475, 468, 119, 340, 135, 86, 192, 472, 192, 287, 175, 516, 240, 168, 322, 125, 282, 145, 153, 274, 53, 221, 369, 380, 359, 249, 113, 466, 355, 524, 285, 278, 70, 117, 334, 273, 83, 159, 165, 672, 186, 346, 376, 392, 191, 195, 310, 164, 146, 62, 356, 420, 295, 185, 374, 193, 267, 330, 105, 102, 200, 453, 344, 335, 50, 261, 290, 228, 59, 96, 191, 293, 772, 330, 150, 270, 248, 372, 249, 249, 609, 354, 257, 343, 182, 307, 152, 541, 311, 103, 457, 743, 288, 336, 412, 129, 231, 311, 23, 39, 361, 190, 108, 180, 152, 204, 318, 182, 396, 142, 106, 502, 564, 112, 79, 168, 536, 462, 275, 288, 152, 479, 315, 243, 202, 482, 125, 665, 178, 288, 197, 334, 74, 253, 62, 71, 83, 238, 109, 611, 144, 217, 648, 352, 408, 605, 190, 310, 289, 140, 344, 338, 146, 279, 257, 486, 332, 228, 152, 258, 556, 455, 222, 194, 121, 369, 144, 519, 216, 531, 202, 145, 260, 107, 385, 214, 230, 168, 548, 467, 117, 373, 768, 137, 333, 136, 212, 247, 283, 123, 55, 971, 302, 332, 245, 284, 317, 135, 402, 131, 298, 389, 202, 196, 284, 255, 125, 210, 800, 44, 406, 86, 166, 158, 215, 129, 282, 200, 315, 151, 306, 96, 110, 180, 518, 252, 107, 60, 85, 388, 424, 423, 369, 312, 74, 295, 300, 160, 137, 199, 209, 234, 436, 116, 162, 258, 94, 309, 437, 537, 462, 160, 341, 288, 241, 74, 166, 149, 173, 342, 238, 232, 126, 158, 153, 110, 164, 342, 378, 158, 208, 257, 285, 394, 132, 205, 366, 385, 484, 365, 211, 281, 697, 422, 638, 317, 65, 278, 228, 162, 260, 191, 220, 161, 182, 298, 365, 377, 447, 204, 72, 162, 227, 154, 190, 195, 143, 158, 75, 311, 148, 321, 151, 122, 110, 219, 221, 197, 257, 354, 562, 462, 228, 142, 328, 486, 642, 172, 302, 38, 315, 139, 921, 199, 70, 301, 353, 339, 461, 208, 188, 424, 129, 698, 222, 277, 157, 228, 174, 189, 74, 490, 100, 173, 174, 637, 581, 256, 346, 238, 336, 715, 89, 80, 110, 262, 222, 294, 186, 223, 174, 327, 499, 117, 133, 169, 618, 492, 329, 182, 216, 238, 160, 296, 114, 212, 343, 236, 493, 163, 168, 280, 334, 246, 506, 252, 481, 582, 233, 136, 351, 104, 461, 595, 293, 387, 365, 129, 711, 254, 186, 253, 294, 190, 141, 463, 53, 256, 503, 105, 209, 65, 169, 158, 492, 404, 380, 213, 319, 332, 92, 460, 283, 184, 237, 328, 276, 183, 382, 304, 706, 312, 234, 50, 343, 182, 330, 182, 384, 408, 118, 614, 165, 316, 235, 487, 374, 139, 283, 141, 639, 168, 246, 272, 172, 369, 86, 216, 870, 392, 257, 102, 557, 250, 747, 108, 230, 296, 247, 358, 79, 104, 378, 111, 432, 264, 604, 248, 126, 235, 501, 264, 62, 338, 151, 221, 749, 176, 216, 387, 108, 99, 307, 305, 524, 154, 368, 95, 539, 222, 238, 268, 308, 113, 212, 380, 300, 240, 987, 120, 159, 163, 199, 192, 236, 187, 66, 362, 274, 218, 209, 184, 98, 741, 505, 309, 153, 148, 150, 219, 367, 96, 399, 501, 711, 73, 401, 181, 111, 709, 172, 182, 606, 427, 453, 265, 82, 131, 418, 367, 186, 236, 270, 173, 239, 285, 141, 228, 163, 497, 256, 629, 102, 483, 178, 221, 97, 264, 123, 132, 249, 310, 246, 327, 66, 182, 302, 545, 653, 297, 158, 153, 156, 142, 573, 268, 77, 239, 158, 302, 262, 201, 229, 231, 409, 112, 196, 97, 227, 681, 152, 498, 380, 524, 168, 736, 236, 223, 167, 120, 321, 150, 328, 395, 86, 113, 923, 257, 69, 155, 444, 361, 205, 235, 332, 317, 199, 397, 595, 630, 472, 310, 125, 269, 601, 439, 755, 239, 247, 155, 150, 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794, 670, 1015, 460, 1855, 303, 233, 470, 89, 868, 1376, 348, 609, 1083, 594, 1699, 1047, 869, 142, 152, 855, 491, 406, 110, 1203, 194, 583, 282, 5, 563, 371, 837, 19, 369, 140, 450, 309, 1057, 466, 276, 127, 55, 525, 4, 246, 611, 391, 277, 364, 529, 552, 558, 131, 361, 794, 617, 249, 137, 200, 145, 175, 474, 325, 152, 499, 624, 171, 313, 350, 1215, 401, 113, 460, 342, 296, 132, 401, 39, 5, 180, 690, 800, 936, 757, 592, 1676, 415, 4, 971, 322, 444, 295, 1079, 1061, 543, 1, 626, 210, 857, 734, 313, 81, 6, 281, 1099, 158, 86, 3, 3, 645, 967, 272, 157, 474, 506, 496, 880, 377, 278, 204, 179, 79, 143, 282, 415, 1053, 224, 143, 265, 816, 44, 266, 154, 639, 258, 1020, 566, 38, 114, 93, 367, 412, 371, 227, 205, 809, 1062, 380, 228, 643, 17, 192, 310, 715, 1609, 267, 932, 210, 314, 311, 494, 322, 645, 286, 254, 958, 254, 801, 646, 949, 782, 366, 112, 830, 938, 468, 211, 102, 268, 464, 158, 367, 423, 392, 349, 1207, 903, 212, 239, 60, 222, 264, 311, 35, 320, 804, 240, 438, 335, 216, 682, 1066, 180, 852, 981, 3, 149, 684, 146, 132, 198, 815, 1142, 540, 430, 211, 262, 740, 685, 208, 1197, 779, 267, 206, 376, 140, 675, 1153, 285, 238, 172, 244, 772, 198, 237, 830, 157, 231, 209, 45, 4, 369, 584, 44, 622, 316, 126, 726, 243, 675, 130, 147, 711, 487, 498, 246, 1677, 415, 331, 764, 273, 540, 148, 4, 972, 556, 480, 2, 908, 1893, 344, 178, 1007, 272, 187, 217, 692, 82, 975, 847, 897, 715, 81, 386, 170, 181, 79, 681, 662, 812, 754, 793, 578, 613, 1329, 216, 69, 568, 94, 537, 27, 558, 344, 202, 620, 390, 803, 219, 266, 287, 541, 303, 954, 1264, 174, 164, 113, 116, 55, 20, 758, 6, 695, 120, 90, 178, 372, 519, 4, 145, 166, 176, 612, 680, 1521, 245, 324, 429, 12, 324, 41, 261, 4, 1181, 409, 1100, 208, 806, 203, 206, 643, 215, 56, 817, 78, 890, 761, 7, 101, 863, 182, 98, 154, 284, 689, 144, 720, 574, 333, 409, 1071, 689, 39, 802, 325, 322, 736, 1086, 155, 1253, 1387, 174, 281, 66, 84, 36, 166, 342, 224, 3, 285, 294, 183, 49, 252, 1088, 379, 716, 299, 94, 48, 109, 302, 405, 3, 658, 322, 809, 896, 139, 626, 207, 627, 276, 24, 421, 532, 482, 366, 497, 184, 122, 513, 617, 301, 176, 174, 3, 329, 21, 889, 998, 774, 180, 266, 976, 278, 7, 94, 452, 473, 51, 298, 481, 797, 125, 463, 630, 424, 9, 74, 225, 292, 286, 2, 172, 982, 217, 83, 694, 509, 443, 127, 284, 850, 1, 594, 93, 3, 404, 860, 721, 44, 72, 179, 174, 930, 123, 69, 186, 316, 182, 848, 116, 371, 77, 333, 14, 1195, 181, 509, 100, 273, 911, 531, 605, 219, 325, 662, 259, 786, 159, 407, 199, 341, 633, 77, 686, 53, 586, 220, 233, 122, 214, 140, 300, 246, 7, 954, 69, 317, 724, 388, 146, 839, 145, 774, 641, 334, 1166, 5, 397, 489, 239, 159, 167, 634, 432, 463, 470, 51, 235, 450, 105, 321, 518, 789, 177, 323, 179, 187, 242, 850, 88, 1002, 790, 169, 289, 908, 588, 502, 215, 171, 59, 411, 360, 592, 172, 739, 231, 145, 1049, 678, 223, 313, 630, 141, 114, 347, 592, 1237, 182, 963, 753, 169, 707, 536, 184, 642, 139, 1155, 70, 976, 301, 1486, 1035, 86, 344, 962, 30, 1353, 109, 382, 1044, 429, 559, 10, 3, 22, 27, 433, 262, 140, 1031, 202, 43, 250, 522, 8, 225, 392, 332, 125, 5, 269, 2, 108, 40, 188, 693, 530, 520, 355, 12, 171, 312, 984, 314, 675, 52, 850, 96, 14, 864, 254, 354, 134, 427, 86, 312, 671, 7, 367, 729, 401, 534, 199, 345, 605, 164, 18, 5, 275, 429, 254, 255, 217, 375, 750, 312, 130, 738, 546, 56, 245, 73, 393, 307, 103, 132, 644, 393, 86, 27, 240, 17, 460, 230, 512, 625, 201, 52, 918, 813, 334, 93, 639, 723, 550, 34, 882, 133, 880, 483, 77, 227, 274, 244, 204, 246, 283, 409, 174, 239, 810, 468, 5, 870, 281, 45, 269, 264, 260, 49, 200, 45, 383, 530, 472, 144, 39, 777, 348, 552, 142, 246, 284, 382, 1440, 845, 152, 302, 1220, 211, 824, 627, 531, 558, 185, 113, 51, 597, 51, 237, 5, 255, 102, 114, 614, 246, 826, 695, 195, 411, 774, 780, 860, 521, 7, 913, 236, 99, 300, 866, 676, 185, 894, 1007, 241, 288, 871, 952, 128, 417, 127, 90, 223, 832, 567, 72, 185, 323, 236, 97, 627, 2, 161, 968, 101, 351, 716, 176, 199, 24, 346, 64, 276, 506, 386, 74, 615, 782, 256, 573, 1128, 257, 4, 371, 615, 321, 398, 381, 60, 298, 311, 197, 96, 137, 718, 37, 747, 316, 434, 147, 777, 817, 346, 237, 313, 738, 320, 463, 1143, 245, 491, 305, 794, 436, 295, 110, 549, 368, 357, 641, 216, 369, 420, 458, 406, 421, 212, 351, 720, 138, 321, 211, 749, 324, 21, 282, 327, 65, 277, 659, 373, 329, 394, 127, 431, 153, 107, 277, 964, 466, 87, 251, 987, 392, 305, 204, 134, 478, 358, 503, 461, 140, 410, 246, 105, 126, 623, 525, 69, 380, 460, 79, 113, 518, 474, 70, 64, 367, 30, 246, 490, 285, 480, 714, 593, 267, 22, 315, 437, 251, 50, 437, 335, 404, 290, 289, 236, 208, 355, 556, 447, 438, 187, 522, 380, 358, 258, 335, 395, 702, 492, 1006, 647, 389, 96, 492, 1032, 89, 181, 228, 526, 455, 463, 451, 654, 514, 688, 585, 57, 233, 572, 836, 440, 400, 447, 377, 53, 39, 444, 490, 258, 1058, 691, 695, 920, 555, 872, 842, 545, 68, 93, 471, 385, 482, 426, 513, 779, 412, 129, 525, 495, 546, 1027, 379, 986, 338, 265, 336, 130, 308, 433, 825, 1009, 222, 1042, 471, 1390, 115, 512, 178, 743, 655, 1562, 88, 755, 269, 697, 780, 930, 526, 465, 351, 349, 554, 324, 656, 525, 765, 371, 219, 357, 262, 1136, 803, 726, 1189, 688, 703, 1624, 376, 395, 104, 289, 340, 403, 400, 126, 273, 377, 502, 393, 133, 450, 217, 229, 800, 425, 448, 371, 350, 153, 668, 147, 352, 290, 405, 1098, 54, 636, 533, 294, 272, 741, 723, 61, 247, 181, 448, 955, 144, 1346, 920, 1401, 523, 651, 165, 425, 153, 105, 271, 187, 106, 1452, 625, 147, 329, 164, 295, 27, 338, 234, 522, 232, 365, 420, 347, 54, 672, 107, 316, 880, 501, 491, 798, 542, 130, 308, 1263, 653, 208, 837, 374, 404, 958, 261, 651, 448, 366, 443, 95, 158, 363, 134, 678, 151, 482, 393, 363, 153, 352, 561, 283, 483, 561, 186, 361, 717, 46, 155, 556, 292, 412, 537, 1047, 140, 510, 322, 384, 278, 266, 425, 464, 680, 513, 454, 885, 549, 1079, 235, 875, 492, 875, 351, 562, 347, 298, 207, 318, 307, 293, 1224, 591, 987, 89, 540, 290, 1093, 585, 439, 690, 126, 125, 506, 775, 830, 193, 1066, 664, 393, 662, 454, 436, 387, 462, 142, 781, 39, 272, 652, 480, 413, 233, 311, 611, 993, 890, 567, 636, 252, 621, 807, 594, 145, 121, 463, 163, 656, 275, 386, 1422, 262, 280, 334, 1200, 159, 63, 232, 361, 228, 382, 362, 231, 236, 501, 422, 118, 301, 444, 336, 151, 338, 708, 216, 79, 285, 249, 256, 179, 141, 217, 43, 212, 325, 120, 608, 210, 585, 30, 435, 172, 451, 805, 552, 767, 457, 338, 109, 583, 154, 508, 318, 557, 342, 117, 554, 63, 941, 270, 1033, 268, 288, 660, 564, 434, 457, 530, 136, 504, 321, 300, 560, 458, 491, 40, 249, 719, 712, 554, 437, 126, 23, 803, 365, 778, 184, 292, 197, 405, 466, 192, 383, 783, 349, 392, 254, 541, 718, 799, 926, 170, 709, 96, 617, 1046, 607, 274, 578, 538, 394, 511, 747, 901, 524, 239, 986, 756, 514, 147, 95, 1482, 688, 207, 65, 262, 435, 677, 87, 235, 780, 264, 24, 314, 25, 751, 772, 982, 896, 419, 442, 383, 1314, 252, 389, 1073, 836, 773, 57, 922, 520, 710, 794, 587, 399, 538, 593, 657, 548, 210, 1010, 11, 814, 1728, 306, 388, 551, 43, 605, 61, 871, 807, 600, 196, 630, 553, 199, 460, 77, 164, 514, 358, 245, 325, 446, 351, 225, 274, 209, 169, 337, 62, 66, 81, 253, 271, 136, 509, 466, 203, 964, 566, 570, 1110, 691, 280, 219, 618, 183, 768, 366, 347, 405, 352, 434, 137, 254, 1509, 283, 991, 645, 122, 276, 259, 178, 284, 878, 209, 220, 113, 278, 99, 80, 89, 175, 700, 220, 386, 379, 183, 192, 357, 153, 355, 229, 498, 930, 579, 378, 270, 341, 556, 1187, 222, 143, 1142, 273, 908, 702, 756, 302, 205, 461, 368, 653, 145, 340, 435, 164, 366, 440, 417, 420, 395, 927, 134, 162, 380, 528, 414, 582, 1299, 623, 217, 629, 474, 102, 135, 223, 30, 986, 162, 91, 248, 593, 690, 701, 707, 387, 143, 411, 354, 111, 320, 136, 352, 300, 582, 355, 678, 579, 881, 129, 444, 174, 264, 205, 297, 323, 155, 387, 810, 441, 235, 122, 321, 253, 334, 298, 537, 621, 183, 290, 611, 75, 456, 267, 557, 87, 754, 296, 148, 774, 537, 615, 386, 161, 1257, 95, 771, 1792, 183, 507, 182, 490, 458, 712, 97, 75, 392, 31, 295, 376, 983, 479, 726, 431, 342, 360, 1044, 233, 142, 519, 213, 628, 796]\n",
            "Length of the Output Tokens Count 18188\n",
            "Combine Tokens Count [308, 158, 268, 182, 522, 247, 370, 116, 368, 308, 154, 296, 285, 214, 184, 1090, 262, 113, 308, 263, 279, 258, 427, 208, 490, 682, 681, 210, 182, 478, 217, 256, 237, 224, 267, 357, 277, 329, 235, 260, 61, 596, 258, 340, 185, 516, 293, 352, 212, 213, 686, 480, 632, 198, 412, 157, 612, 522, 164, 126, 166, 218, 131, 54, 342, 248, 164, 216, 518, 150, 672, 626, 567, 453, 177, 380, 474, 176, 348, 202, 410, 138, 607, 218, 31, 189, 544, 448, 375, 1110, 358, 380, 385, 406, 300, 138, 124, 101, 153, 594, 427, 243, 312, 257, 262, 224, 206, 369, 352, 314, 228, 175, 215, 156, 388, 157, 354, 286, 597, 357, 710, 291, 131, 289, 202, 233, 130, 221, 228, 284, 235, 209, 972, 220, 279, 261, 317, 340, 572, 253, 263, 165, 251, 300, 247, 160, 372, 253, 113, 401, 285, 116, 137, 212, 405, 285, 236, 1097, 330, 156, 440, 124, 349, 319, 239, 583, 113, 515, 216, 117, 264, 474, 145, 558, 337, 266, 223, 184, 289, 682, 238, 568, 457, 174, 351, 416, 90, 697, 275, 441, 84, 172, 401, 249, 259, 329, 329, 213, 175, 259, 296, 168, 185, 451, 423, 450, 71, 327, 142, 82, 1825, 136, 266, 581, 234, 160, 214, 942, 175, 367, 268, 246, 471, 250, 309, 659, 396, 151, 185, 798, 1210, 249, 301, 190, 628, 101, 275, 249, 260, 108, 246, 212, 494, 165, 231, 258, 263, 520, 266, 136, 104, 258, 248, 120, 453, 346, 371, 253, 385, 137, 571, 556, 131, 557, 246, 366, 141, 313, 230, 183, 272, 248, 115, 221, 137, 89, 99, 303, 120, 78, 353, 318, 335, 140, 347, 382, 1106, 426, 612, 278, 721, 188, 325, 177, 165, 201, 90, 283, 495, 251, 540, 199, 299, 204, 155, 190, 329, 784, 326, 326, 172, 104, 339, 203, 741, 138, 346, 710, 181, 307, 289, 329, 724, 342, 230, 256, 97, 262, 923, 175, 491, 239, 290, 94, 335, 715, 143, 226, 230, 395, 796, 332, 141, 245, 90, 136, 125, 508, 169, 265, 99, 229, 267, 101, 208, 267, 210, 555, 234, 210, 392, 295, 337, 159, 315, 345, 694, 309, 684, 216, 407, 91, 220, 368, 200, 238, 248, 307, 229, 489, 73, 880, 185, 414, 729, 212, 295, 181, 215, 506, 173, 246, 746, 129, 186, 208, 346, 972, 413, 116, 793, 211, 696, 113, 230, 188, 380, 126, 75, 278, 612, 301, 221, 83, 305, 284, 299, 513, 146, 228, 308, 697, 124, 242, 199, 266, 568, 180, 210, 276, 199, 266, 160, 434, 239, 239, 240, 171, 206, 206, 598, 402, 512, 59, 203, 328, 511, 243, 230, 178, 317, 230, 212, 678, 214, 487, 218, 101, 322, 467, 273, 363, 566, 211, 255, 273, 428, 971, 211, 640, 137, 168, 277, 445, 199, 1176, 283, 251, 1533, 285, 326, 268, 712, 421, 329, 1232, 199, 94, 358, 1358, 210, 93, 208, 464, 394, 256, 177, 463, 86, 218, 394, 168, 352, 166, 326, 492, 407, 354, 219, 417, 229, 554, 566, 595, 262, 116, 405, 235, 201, 224, 164, 108, 451, 225, 337, 430, 541, 760, 368, 273, 304, 367, 1596, 488, 426, 163, 305, 323, 315, 973, 164, 264, 553, 392, 344, 312, 96, 427, 294, 303, 338, 435, 245, 365, 246, 186, 387, 151, 586, 314, 355, 293, 240, 195, 246, 357, 171, 292, 405, 141, 362, 155, 408, 390, 328, 234, 343, 149, 210, 416, 191, 391, 292, 595, 236, 831, 820, 135, 476, 214, 224, 714, 154, 189, 327, 136, 162, 300, 849, 307, 511, 272, 356, 425, 130, 1652, 481, 356, 257, 252, 689, 203, 370, 441, 378, 212, 238, 300, 202, 322, 313, 317, 358, 187, 118, 368, 435, 394, 201, 441, 257, 347, 492, 184, 268, 151, 229, 72, 269, 544, 229, 450, 533, 469, 358, 222, 225, 157, 207, 194, 250, 767, 374, 459, 221, 377, 120, 355, 46, 407, 237, 69, 84, 329, 181, 229, 172, 267, 285, 484, 160, 198, 267, 719, 218, 158, 219, 255, 219, 380, 389, 335, 247, 172, 430, 346, 417, 137, 347, 308, 405, 296, 340, 141, 232, 344, 63, 186, 207, 208, 557, 120, 240, 286, 187, 383, 223, 280, 380, 166, 470, 721, 869, 628, 178, 340, 469, 307, 113, 180, 271, 309, 825, 1039, 211, 309, 316, 455, 624, 297, 195, 667, 1083, 750, 163, 763, 183, 254, 979, 953, 479, 595, 537, 914, 1446, 376, 999, 1350, 106, 136, 480, 272, 397, 366, 1205, 927, 564, 421, 331, 983, 167, 585, 124, 135, 307, 301, 1032, 1068, 982, 91, 209, 600, 270, 549, 226, 372, 403, 284, 299, 192, 337, 381, 679, 569, 464, 641, 508, 335, 137, 186, 278, 376, 339, 842, 664, 674, 550, 135, 1319, 204, 707, 330, 1910, 498, 929, 331, 860, 400, 222, 238, 296, 417, 639, 90, 270, 947, 351, 217, 698, 211, 240, 290, 517, 495, 397, 454, 291, 456, 382, 347, 816, 363, 340, 2038, 376, 188, 352, 842, 786, 372, 371, 898, 608, 359, 367, 108, 724, 244, 316, 627, 511, 770, 207, 138, 512, 1250, 356, 848, 211, 388, 776, 655, 383, 612, 163, 162, 1149, 795, 345, 342, 676, 470, 289, 241, 802, 256, 402, 264, 778, 564, 569, 133, 155, 537, 473, 378, 272, 753, 969, 956, 335, 326, 596, 394, 321, 415, 81, 500, 561, 269, 565, 242, 132, 114, 203, 381, 441, 454, 252, 135, 218, 457, 284, 343, 832, 203, 201, 254, 341, 1372, 283, 592, 423, 395, 628, 480, 502, 154, 695, 608, 249, 280, 400, 662, 994, 439, 362, 901, 331, 181, 1085, 758, 323, 586, 154, 432, 791, 242, 813, 572, 331, 617, 541, 401, 155, 367, 555, 209, 387, 235, 345, 285, 735, 291, 1104, 139, 311, 338, 255, 843, 616, 691, 338, 420, 572, 282, 982, 432, 619, 592, 576, 424, 212, 255, 379, 261, 428, 232, 165, 424, 159, 270, 626, 657, 466, 151, 363, 226, 754, 123, 414, 627, 208, 282, 283, 93, 214, 172, 1226, 348, 396, 212, 377, 397, 172, 357, 142, 169, 363, 258, 171, 493, 576, 235, 438, 128, 641, 464, 273, 283, 157, 374, 273, 406, 744, 276, 362, 423, 369, 411, 188, 708, 285, 187, 585, 417, 145, 647, 381, 602, 629, 555, 482, 378, 589, 479, 593, 461, 729, 250, 204, 277, 613, 356, 300, 427, 157, 812, 345, 207, 615, 131, 1093, 513, 736, 278, 417, 733, 995, 216, 82, 362, 570, 275, 338, 422, 638, 516, 325, 292, 746, 268, 156, 341, 107, 551, 391, 704, 440, 1037, 318, 597, 128, 183, 185, 555, 288, 488, 461, 291, 413, 124, 288, 144, 339, 594, 195, 240, 144, 699, 505, 337, 499, 457, 696, 317, 736, 465, 945, 828, 183, 531, 820, 401, 259, 316, 607, 853, 396, 214, 214, 513, 275, 674, 457, 417, 213, 757, 285, 473, 287, 172, 836, 528, 262, 355, 176, 392, 360, 972, 143, 220, 276, 379, 169, 211, 580, 366, 585, 249, 601, 637, 596, 355, 446, 344, 166, 231, 660, 545, 730, 285, 659, 470, 837, 449, 377, 361, 210, 301, 316, 877, 137, 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            "Length of the Combine Tokens Count 18188\n"
          ]
        }
      ],
      "source": [
        "# Tokenize each row in the 'instruction' column of the deduplicated dataset and count the tokens\n",
        "# The tokenizer.tokenize method splits the text into tokens (words/subwords)\n",
        "instruction_tokens_count = [len(tokenizer.tokenize(example[\"instruction\"])) for example in deduped_dataset[\"train\"]]\n",
        "print(\"Instruction Tokens Count\", instruction_tokens_count)\n",
        "# Print the total number of entries processed for instruction token counts\n",
        "print(\"Length of Instruction Tokens Count\", len(instruction_tokens_count))\n",
        "\n",
        "# Tokenize each row in the 'output' column of the deduplicated dataset and count the tokens\n",
        "output_tokens_count = [len(tokenizer.tokenize(example[\"output\"])) for example in deduped_dataset[\"train\"]]\n",
        "print(\"Output Tokens Count\", output_tokens_count)\n",
        "# Print the total number of entries processed for output token counts\n",
        "print(\"Length of the Output Tokens Count\", len(output_tokens_count))\n",
        "\n",
        "# Combine the token counts for instruction and output by adding corresponding counts together\n",
        "# This shows the total number of tokens per row across both columns\n",
        "combine_tokens_count = [instruction + output for instruction, output in zip(instruction_tokens_count, output_tokens_count)]\n",
        "print(\"Combine Tokens Count\", combine_tokens_count)\n",
        "# Print the total number of entries for which combined token counts have been calculated\n",
        "print(\"Length of the Combine Tokens Count\", len(combine_tokens_count))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdMYZwiNbWXT",
        "outputId": "9625e1e7-3f36-4978-d208-d2e3e3616281"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top K Indices [17975, 833, 17933, 1508, 3194, 9174, 15156, 5188, 16833, 16954, 16681, 16077, 17378, 16921, 16849, 15335, 15600, 16777, 3384, 16584, 10433, 15061, 17103, 802, 15140, 1609, 17002, 6090, 15457, 16126, 5121, 15220, 16067, 16863, 7586, 17112, 6656, 15445, 17290, 3019, 16948, 4727, 15347, 17070, 210, 7803, 14947, 17370, 16163, 18162, 9170, 15668, 13504, 15638, 15684, 15031, 17751, 16335, 15389, 16160, 16178, 16595, 16117, 16862, 4703, 18015, 16793, 16619, 17622, 15685, 3469, 16718, 7693, 17054, 16044, 16665, 17705, 15883, 16836, 17236, 16559, 5508, 17259, 15353, 17118, 15474, 16734, 16917, 16449, 15134, 15788, 16632, 16533, 7238, 15409, 17619, 605, 15925, 16723, 16223, 16321, 4770, 15358, 16997, 15384, 16949, 16459, 15125, 17049, 3312, 7657, 15242, 3074, 10345, 3208, 15766, 16618, 532, 17401, 10101, 17596, 5165, 10110, 15506, 15165, 16914, 1832, 18029, 5378, 15178, 15208, 1619, 14335, 10203, 15736, 17174, 15040, 17077, 15492, 10569, 17031, 15966, 18035, 17141, 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            "Length of Top K Indices 1000\n"
          ]
        }
      ],
      "source": [
        "# Define a function to retrieve the top k rows with the highest number of tokens\n",
        "def get_top_k_rows(dataset, tokens_count, k):\n",
        "    # Sort indices of the rows based on the token counts in descending order\n",
        "    sorted_indices = sorted(range(len(tokens_count)), key=lambda i: tokens_count[i], reverse=True)\n",
        "    # Select the top k indices from the sorted list\n",
        "    top_k_indices = sorted_indices[:k]\n",
        "    print(\"Top K Indices\", top_k_indices)\n",
        "    # Print the number of top k indices to confirm the size\n",
        "    print(\"Length of Top K Indices\", len(top_k_indices))\n",
        "\n",
        "    # Extract the Top K rows based on the indices and store them in a dictionary\n",
        "    top_k_data = {\n",
        "        \"instruction\": [dataset[\"train\"][i][\"instruction\"] for i in top_k_indices],\n",
        "        \"output\": [dataset[\"train\"][i][\"output\"] for i in top_k_indices]\n",
        "    }\n",
        "    # Return the top k rows as a new Dataset object\n",
        "    return Dataset.from_dict(top_k_data)\n",
        "\n",
        "# Number of rows to retrieve\n",
        "k = 1000\n",
        "# Get the top 1000 rows with the most number of tokens\n",
        "top_k_dataset = get_top_k_rows(deduped_dataset, combine_tokens_count, k)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oAv-KO4Wbdes",
        "outputId": "3c502d58-fb4f-4da4-f355-6f2965aa801e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Dataset({\n",
              "    features: ['instruction', 'output'],\n",
              "    num_rows: 1000\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ],
      "source": [
        "top_k_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ayr-sieZbgXt"
      },
      "outputs": [],
      "source": [
        "\n",
        "dataset = DatasetDict({\"train\": top_k_dataset})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dPJU8vwXbjg6",
        "outputId": "9431e96d-5b9e-4b24-abbb-5c5fdcdf2b4b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['instruction', 'output'],\n",
              "        num_rows: 1000\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MqztEBGBble-",
        "outputId": "06de285d-8364-49d3-b761-35eddaa91c15"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           instruction  \\\n",
              "0    Let's come up with a rich and complex alien wo...   \n",
              "1    Let\\n$$p(x,y) = a_0 + a_1x + a_2y + a_3x^2 + a...   \n",
              "2    Given the code below, refactor it, add comment...   \n",
              "3    Find the area of the region described by $x \\g...   \n",
              "4    Let $P$ be the plane passing through the origi...   \n",
              "..                                                 ...   \n",
              "995  Hello. My name is Mike. I have a message left ...   \n",
              "996  Given a prime $p$ and an integer $a$, we say t...   \n",
              "997  Let $f$ be defined by  \\[f(x) = \\left\\{\\n\\begi...   \n",
              "998  BEGININPUT\\nBEGINCONTEXT\\ndate: August 5, 2021...   \n",
              "999  BEGININPUT\\nBEGINCONTEXT\\ndate: August 14, 201...   \n",
              "\n",
              "                                                output  \n",
              "0    Planet Name: Xylothar\\n\\nXylothar is a diverse...  \n",
              "1    Observe that  \\begin{align*}\\np(0,0) &= a_0 = ...  \n",
              "2    Here is the refactored and commented version:\\...  \n",
              "3    Let $n = \\lfloor x \\rfloor,$ and let $\\{x\\} = ...  \n",
              "4    Let $\\mathbf{v} = \\begin{pmatrix} x \\\\ y \\\\ z ...  \n",
              "..                                                 ...  \n",
              "995  Hello Mike, it's nice to meet you. As an AI la...  \n",
              "996  To find the primitive roots $\\pmod 7$, I need ...  \n",
              "997  The number $f^{-1}(-3)$ is the value of $x$ su...  \n",
              "998  Dr. Eleanor Thompson's study found that partic...  \n",
              "999  Charles Leroux won the Golden Bread Award in 1...  \n",
              "\n",
              "[1000 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9d39687d-cbb3-4c48-ba43-dc52840b664e\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>instruction</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Let's come up with a rich and complex alien wo...</td>\n",
              "      <td>Planet Name: Xylothar\\n\\nXylothar is a diverse...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Let\\n$$p(x,y) = a_0 + a_1x + a_2y + a_3x^2 + a...</td>\n",
              "      <td>Observe that  \\begin{align*}\\np(0,0) &amp;= a_0 = ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Given the code below, refactor it, add comment...</td>\n",
              "      <td>Here is the refactored and commented version:\\...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Find the area of the region described by $x \\g...</td>\n",
              "      <td>Let $n = \\lfloor x \\rfloor,$ and let $\\{x\\} = ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Let $P$ be the plane passing through the origi...</td>\n",
              "      <td>Let $\\mathbf{v} = \\begin{pmatrix} x \\\\ y \\\\ z ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>Hello. My name is Mike. I have a message left ...</td>\n",
              "      <td>Hello Mike, it's nice to meet you. As an AI la...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>Given a prime $p$ and an integer $a$, we say t...</td>\n",
              "      <td>To find the primitive roots $\\pmod 7$, I need ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>Let $f$ be defined by  \\[f(x) = \\left\\{\\n\\begi...</td>\n",
              "      <td>The number $f^{-1}(-3)$ is the value of $x$ su...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>BEGININPUT\\nBEGINCONTEXT\\ndate: August 5, 2021...</td>\n",
              "      <td>Dr. Eleanor Thompson's study found that partic...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>BEGININPUT\\nBEGINCONTEXT\\ndate: August 14, 201...</td>\n",
              "      <td>Charles Leroux won the Golden Bread Award in 1...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 2 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-9d39687d-cbb3-4c48-ba43-dc52840b664e')\"\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-9d39687d-cbb3-4c48-ba43-dc52840b664e 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-9d39687d-cbb3-4c48-ba43-dc52840b664e');\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-7be7b0d7-026e-44a6-aec0-48fd760b4fa5\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7be7b0d7-026e-44a6-aec0-48fd760b4fa5')\"\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-7be7b0d7-026e-44a6-aec0-48fd760b4fa5 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\": \"dataset[\\\"train\\\"]\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"instruction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"BEGININPUT\\nBEGINCONTEXT\\ndate: September 15, 2021\\nurl: www.telecomhistory.com/evolution-video-transcript\\nauthor: Patricia Williams\\ntitle: The Evolution of Telecommunication: A Video Transcript\\nENDCONTEXT\\nIn this video transcript, we will explore the fascinating journey of telecommunication from its humble beginnings to the advanced technologies we use today. Our story begins in the early 19th century with Samuel Morse's invention of the electric telegraph.\\n\\nThe electric telegraph was invented by Samuel Morse in 1837 and became operational in 1844. This revolutionary device allowed people to send messages over long distances using a series of electrical signals. Messages were sent in Morse code, a system of dots and dashes that represented letters and numbers. The first message sent via telegraph was \\\"What hath God wrought?\\\" on May 24, 1844, between Washington D.C. and Baltimore.\\n\\nAlexander Graham Bell is credited with inventing the telephone in 1876. His invention revolutionized communication by allowing people to speak directly to each other over long distances. On March 10, 1876, Bell made the first successful telephone call to his assistant, Thomas Watson, saying, \\\"Mr. Watson, come here; I want you.\\\" By the end of the 19th century, telephone lines had been installed across the United States and Europe.\\n\\nWireless communication took a significant leap forward in 1895 when Guglielmo Marconi developed the wireless telegraph, also known as the radio. Marconi's invention used electromagnetic waves to transmit messages without wires. In 1901, he successfully sent a wireless signal across the Atlantic Ocean, proving that radio could be used for long-distance communication.\\n\\nTelevision emerged as a new form of telecommunication in the 1920s. Scottish inventor John Logie Baird demonstrated the first working television system on January 26, 1926. Early television sets were large and expensive, but by the 1950s, they had become more affordable and widespread.\\n\\nThe development of satellites in the 1960s allowed for global communication on an unprecedented scale. On July 10, 1962, Telstar 1, the first active communications satellite, was launched into orbit. This satellite enabled live transatlantic television broadcasts and telephone calls between the United States and Europe.\\n\\nIn the 1980s, mobile phones began to gain popularity. The first commercially available cell phone, the Motorola DynaTAC 8000X, was released in 1983. It weighed nearly two pounds and cost $3,995. Over time, mobile phones became smaller, lighter, and more affordable, leading to their widespread adoption around the world.\\n\\nThe invention of the internet in the late 20th century revolutionized telecommunication once again. Developed initially as a military project in the 1960s, the internet became publicly accessible in the 1990s. Today, it serves as a platform for countless applications, including email, social media, video conferencing, and online gaming.\\n\\nAs we look to the future, advancements in telecommunication technology continue to evolve at a rapid pace. From 5G networks to virtual reality, the possibilities are endless. One thing is certain: the history of telecommunication is a testament to human ingenuity and our desire to connect with one another.\\nENDINPUT\\n\\nBEGININSTRUCTION\\n- Summarize the key milestones in the evolution of telecommunication from the electric telegraph to the internet.\\nENDINSTRUCTION\",\n          \"For each following Query, return a series of clear, factual and concise Summary Bullet Points of the Query using only information available in the Query.\\n###\\nQuery:\\nArtificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of \\\"intelligent agents\\\": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.3 Colloquially, the term \\\"artificial intelligence\\\" is often used to describe machines (or computers) that mimic \\\"cognitive\\\" functions that humans associate with the human mind, such as \\\"learning\\\" and \\\"problem solving\\\".[4]\\n\\nAs machines become increasingly capable, tasks considered to require \\\"intelligence\\\" are often removed from the definition of AI, a phenomenon known as the AI effect.[5] A quip in Tesler's Theorem says \\\"AI is whatever hasn't been done yet.\\\"[6] For instance, optical character recognition is frequently excluded from things considered to be AI,[7] having become a routine technology.[8] Modern machine capabilities generally classified as AI include successfully understanding human speech,[9] competing at the highest level in strategic game systems (such as chess and Go),[10] autonomously operating cars, intelligent routing in content delivery networks, and military simulations.[11]\\n\\nSummary:\\n- The text discusses artificial intelligence (AI) and its various applications. \\n- It defines AI as intelligence demonstrated by machines\\n- AI is often distinguished from natural intelligence by its lack of consciousness and emotionality. \\n- Discusses the AI effect, which is the tendency for tasks to be removed from the definition of AI as machines become increasingly capable.\\n###\\nQuery:\\nFishing is the activity of trying to catch fish. Fish are often caught as wildlife from the natural environments, but may also be caught from stocked bodies of water such as ponds, canals, park wetlands and reservoirs. Fishing techniques include hand-gathering, spearing, netting, angling, shooting and trapping, as well as more destructive and often illegal techniques such as electrocution, blasting and poisoning.\\n\\nThe term fishing broadly includes catching aquatic animals other than fish, such as crustaceans (shrimp/lobsters/crabs), shellfish, cephalopods (octopus/squid) and echinoderms (starfish/sea urchins). The term is not normally applied to harvesting fish raised in controlled cultivations (fish farming). Nor is it normally applied to hunting aquatic mammals, where terms like whaling and sealing are used instead.\\n\\nFishing has been an important part of human culture since hunter-gatherer times, and is one of the few food production activities that have persisted from prehistory into modern age, surviving both the Neolithic Revolution and successive Industrial Revolutions. In addition to being caught to be eaten for food, fish are caught as recreational pastimes. Fishing tournaments are held, and caught fish are sometimes kept long-term as preserved or living trophies. When bioblitzes occur, fish are typically caught, identified, and then released.\\n\\nAccording to the United Nations FAO statistics, the total number of commercial fishers and fish farmers is estimated to be 38 million. Fishing industries and aquaculture provide direct and indirect employment to over 500 million people in developing countries.[1] In 2005, the worldwide per capita consumption of fish captured from wild fisheries was 14.4 kilograms (32 lb), with an additional 7.4 kilograms (16 lb) harvested from fish farms.[2]\\n\\nSummary:\\n- Fishing is the activity of trying to catch fish.\\n- Fishing techniques include hand-gathering, spearing, netting, angling, shooting and trapping.\\n- The term \\\"fishing\\\" broadly includes catching aquatic animals other than fish, such as crustaceans, shellfish, cephalopods and echinoderms.\\n- The term fishing is not normally applied to hunting aquatic mammals.\\n- Fishing has been an important part of human culture since hunter-gatherer times\\n###\\nQuery: {}\\n\\nSummary:\",\n          \"Develop a C++ program that implements a simple ray-tracing algorithm to render 3D scenes with basic shapes (spheres, planes) and lighting. The program should output the rendered image as a PNG file.\\nPLAINFORMAT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"The key milestones in the evolution of telecommunication include:\\n\\n1. Invention of the electric telegraph by Samuel Morse in 1837, which became operational in 1844 and allowed long-distance communication using Morse code.\\n2. Alexander Graham Bell's invention of the telephone in 1876, enabling direct speech over long distances.\\n3. Guglielmo Marconi's development of the wireless telegraph (radio) in 1895, using electromagnetic waves for wire-free messaging.\\n4. The emergence of television as a new form of telecommunication in the 1920s, with John Logie Baird demonstrating the first working system in 1926.\\n5. Launch of Telstar 1, the first active communications satellite, in 1962, allowing global communication through live transatlantic broadcasts and phone calls.\\n6. The rise of mobile phones in the 1980s, starting with the release of the Motorola DynaTAC 8000X in 1983.\\n7. The invention of the internet in the late 20th century, initially developed as a military project and becoming publicly accessible in the 1990s, leading to various applications like email, social media, and video conferencing.\",\n          \"Thank you for the examples, I'm ready to summarize now. \\n\\nWhat is your query?\",\n          \"```cpp\\n#include <iostream>\\n#include <vector>\\n#include <limits>\\n#include <memory>\\n#include <Eigen/Dense>\\n#include \\\"stb_image_write.h\\\"\\n\\nusing namespace std;\\nusing namespace Eigen;\\n\\nclass Vector3 {\\npublic:\\n    Vector3(float x = 0, float y = 0, float z = 0) : v(x, y, z) {}\\n\\n    float x() const { return v.x(); }\\n    float y() const { return v.y(); }\\n    float z() const { return v.z(); }\\n\\n    Vector3 operator+(const Vector3 &other) const {\\n        return Vector3(v + other.v);\\n    }\\n\\nprivate:\\n    Vector3(const Eigen::Vector3f &v) : v(v) {}\\n\\n    Eigen::Vector3f v;\\n};\\n\\nclass Ray {\\npublic:\\n    Ray(const Vector3 &origin, const Vector3 &direction)\\n        : origin(origin), direction(direction.normalized()) {}\\n\\n    Vector3 origin;\\n    Vector3 direction;\\n};\\n\\nclass Shape {\\npublic:\\n    virtual bool intersect(const Ray &ray, float &t) const = 0;\\n};\\n\\nclass Sphere : public Shape {\\npublic:\\n    Sphere(const Vector3 &center, float radius) : center(center), radius(radius) {}\\n\\n    bool intersect(const Ray &ray, float &t) const override {\\n        // ... (intersection algorithm)\\n    }\\n\\n    Vector3 center;\\n    float radius;\\n};\\n\\nclass Plane : public Shape {\\npublic:\\n    Plane(const Vector3 &point, const Vector3 &normal)\\n        : point(point), normal(normal.normalized()) {}\\n\\n    bool intersect(const Ray &ray, float &t) const override {\\n        // ... (intersection algorithm)\\n    }\\n\\n    Vector3 point;\\n    Vector3 normal;\\n};\\n\\nclass Light {\\npublic:\\n    Light(const Vector3 &position, float intensity)\\n        : position(position), intensity(intensity) {}\\n\\n    Vector3 position;\\n    float intensity;\\n};\\n\\nclass Scene {\\npublic:\\n    void addShape(unique_ptr<Shape> shape) {\\n        shapes.push_back(move(shape));\\n    }\\n\\n    void addLight(const Light &light) {\\n        lights.push_back(light);\\n    }\\n\\n    const Shape *intersect(const Ray &ray, float &t) const {\\n        // ... (find the closest intersection)\\n    }\\n\\n    vector<unique_ptr<Shape>> shapes;\\n    vector<Light> lights;\\n};\\n\\nVector3 shade(const Ray &ray, const Scene &scene) {\\n    float t;\\n    const Shape *shape = scene.intersect(ray, t);\\n\\n    if (!shape) {\\n        return Vector3(0);  // background color\\n    }\\n\\n    // Compute the shading based on the intersection and lighting\\n    // ...\\n\\n    return color;\\n}\\n\\nvoid render(const Scene &scene, const string &outputFile) {\\n    int width = 800;\\n    int height = 600;\\n\\n    vector<uint8_t> pixels(width * height * 3);\\n\\n    for (int y = 0; y < height; ++y) {\\n        for (int x = 0; x < width; ++x) {\\n            // Cast a ray through the pixel\\n            Ray ray = ...;\\n\\n            Vector3 color = shade(ray, scene);\\n\\n            int index = (y * width + x) * 3;\\n            pixels[index] = color.x() * 255;\\n            pixels[index + 1] = color.y() * 255;\\n            pixels[index + 2] = color.z() * 255;\\n        }\\n    }\\n\\n    stbi_write_png(outputFile.c_str(), width, height, 3, pixels.data(), width * 3);\\n}\\n\\nint main() {\\n    // Create your 3D scene with shapes and lights\\n    // ...\\n\\n    // Render the scene and save it as a PNG file\\n    render(scene, \\\"output.png\\\");\\n\\n    return 0;\\n}\\n```\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "\n",
        "dataset[\"train\"].to_pandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LFcru3J9bnCz",
        "outputId": "5b3eb86f-52d4-400f-d878-a12dc696ff67"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Instruction Tokens Count [315, 201, 553, 98, 77, 125, 47, 1200, 1468, 1630, 1871, 1512, 1850, 1686, 42, 1673, 1616, 1495, 94, 65, 416, 62, 1768, 79, 1473, 50, 1388, 877, 71, 1831, 290, 1259, 1512, 1477, 93, 1373, 897, 1694, 1559, 95, 1504, 114, 1506, 1317, 24, 40, 1735, 1630, 47, 16, 204, 1597, 1759, 55, 50, 1642, 708, 1465, 37, 73, 51, 61, 31, 1676, 531, 646, 1215, 1476, 128, 56, 721, 1620, 105, 1551, 1202, 45, 1345, 1403, 39, 1515, 1398, 531, 1572, 39, 1238, 1548, 84, 1260, 1295, 110, 140, 1549, 1339, 1442, 51, 470, 158, 30, 1424, 1314, 48, 1352, 1138, 1154, 1447, 1223, 1423, 1201, 1218, 849, 228, 48, 105, 144, 84, 1398, 1131, 585, 1214, 477, 27, 142, 428, 33, 60, 65, 124, 72, 568, 1284, 132, 153, 1273, 234, 130, 81, 1326, 1239, 44, 46, 1335, 1170, 1298, 1341, 1079, 1365, 1246, 47, 1232, 133, 447, 1228, 1221, 1431, 66, 379, 124, 1403, 169, 137, 175, 1176, 1405, 1056, 1024, 1212, 50, 45, 1309, 708, 52, 493, 124, 59, 1420, 655, 1188, 1297, 54, 1213, 1034, 1136, 41, 71, 33, 52, 37, 1308, 1013, 1102, 50, 1403, 1268, 1362, 1145, 1309, 90, 495, 1071, 41, 89, 50, 1139, 1172, 34, 1400, 1234, 37, 725, 1089, 104, 22, 110, 1112, 1300, 487, 1177, 123, 335, 245, 63, 1295, 82, 69, 1202, 1143, 1221, 172, 44, 50, 1260, 1281, 569, 804, 1317, 69, 1190, 1064, 104, 9, 104, 18, 1166, 50, 1236, 1122, 119, 727, 81, 1073, 926, 1124, 153, 23, 99, 51, 1261, 1033, 57, 1031, 961, 752, 1155, 1157, 258, 73, 179, 1215, 918, 1234, 994, 1234, 72, 128, 926, 60, 1253, 107, 1104, 53, 57, 82, 37, 1221, 1249, 1062, 423, 871, 122, 149, 54, 1125, 963, 43, 1189, 1331, 326, 1193, 190, 233, 847, 1125, 107, 75, 819, 56, 44, 76, 398, 573, 663, 1205, 29, 84, 296, 970, 123, 955, 813, 52, 73, 152, 1231, 73, 84, 1028, 951, 169, 57, 965, 958, 1059, 53, 434, 50, 71, 193, 45, 45, 989, 14, 765, 1221, 1232, 1151, 54, 104, 253, 1095, 1065, 1230, 110, 76, 1132, 264, 1096, 37, 1113, 105, 1150, 1212, 98, 156, 1193, 26, 122, 123, 1083, 51, 130, 732, 981, 28, 983, 1027, 1115, 860, 680, 107, 498, 949, 74, 1191, 69, 52, 1158, 952, 696, 983, 114, 1070, 1051, 406, 61, 728, 1124, 62, 62, 860, 1054, 757, 51, 845, 1152, 1028, 97, 68, 76, 260, 308, 893, 67, 213, 858, 25, 483, 56, 943, 35, 17, 54, 64, 70, 500, 923, 915, 115, 775, 454, 838, 161, 1052, 978, 119, 274, 510, 1172, 1160, 43, 58, 1115, 920, 1002, 123, 55, 52, 39, 144, 776, 48, 61, 1014, 145, 61, 117, 413, 69, 19, 293, 85, 876, 57, 505, 70, 862, 59, 1084, 972, 820, 44, 352, 1000, 1063, 380, 43, 184, 487, 1041, 876, 213, 45, 136, 1011, 53, 228, 122, 833, 1187, 1034, 62, 945, 1115, 985, 871, 867, 798, 887, 1002, 719, 952, 1072, 41, 824, 95, 142, 1054, 58, 112, 1118, 1118, 71, 847, 470, 60, 33, 59, 826, 268, 971, 75, 697, 880, 45, 739, 81, 1102, 1044, 354, 885, 861, 54, 364, 135, 70, 95, 93, 1052, 1014, 26, 53, 505, 1024, 51, 78, 921, 868, 137, 471, 130, 136, 1139, 84, 69, 40, 221, 204, 990, 1058, 46, 975, 76, 49, 74, 50, 978, 76, 978, 786, 19, 212, 67, 887, 920, 1057, 91, 280, 935, 11, 831, 279, 987, 1053, 1066, 82, 827, 102, 99, 938, 825, 85, 148, 85, 36, 866, 74, 564, 75, 58, 167, 49, 408, 91, 111, 127, 156, 920, 1104, 62, 29, 848, 981, 1020, 873, 49, 52, 894, 706, 1004, 34, 824, 954, 582, 894, 1016, 35, 112, 705, 826, 1042, 78, 429, 65, 30, 972, 977, 721, 527, 887, 880, 65, 1000, 371, 41, 42, 70, 418, 70, 744, 44, 46, 499, 94, 137, 27, 53, 896, 42, 149, 188, 638, 47, 59, 1007, 305, 240, 98, 1024, 555, 41, 63, 53, 904, 749, 1062, 67, 617, 415, 936, 859, 948, 120, 67, 105, 57, 970, 881, 1017, 1021, 63, 239, 98, 540, 59, 41, 47, 118, 35, 67, 579, 864, 749, 873, 772, 999, 133, 49, 517, 49, 112, 995, 876, 934, 59, 440, 63, 759, 29, 132, 37, 42, 924, 39, 120, 94, 391, 778, 770, 399, 35, 606, 735, 1037, 20, 94, 867, 1042, 746, 1062, 39, 890, 55, 793, 281, 466, 34, 746, 24, 841, 980, 928, 999, 74, 905, 49, 895, 628, 93, 30, 81, 964, 123, 94, 50, 880, 406, 93, 63, 871, 56, 939, 898, 731, 40, 761, 80, 42, 311, 845, 904, 55, 46, 123, 214, 382, 61, 77, 593, 18, 147, 76, 941, 19, 50, 951, 109, 937, 42, 882, 848, 122, 869, 852, 717, 220, 693, 64, 83, 800, 889, 114, 62, 106, 86, 48, 156, 803, 109, 416, 36, 14, 589, 138, 845, 903, 45, 111, 150, 74, 574, 678, 79, 251, 85, 985, 624, 759, 65, 914, 52, 66, 881, 830, 137, 170, 875, 728, 56, 66, 954, 38, 108, 979, 91, 137, 62, 348, 76, 374, 31, 160, 94, 317, 676, 48, 126, 937, 90, 120, 91, 105, 948, 146, 60, 864, 100, 50, 608, 732, 757, 29, 290, 30, 33, 49, 118, 166, 79, 797, 679, 791, 808, 62, 52, 903, 985, 771, 912, 905, 78, 77, 123, 861, 54, 982, 833, 855, 868, 46, 57, 327, 153, 293, 837, 896, 38, 50, 852, 20, 923, 35, 761, 71, 94, 883, 37, 866, 698, 682, 36, 861, 61, 107, 17, 643, 767, 569, 70, 806, 107, 54, 112, 743, 931, 900, 831, 185, 80, 706, 916, 101, 82, 50, 303, 822, 26, 735, 796, 57, 119, 62, 29, 53, 17, 148, 71, 816, 100, 137, 79, 676, 48, 648, 49, 120, 695, 65, 449, 852, 907, 684, 653, 466, 641, 76, 991, 108, 748, 831, 926, 41, 128, 796, 746, 56, 616, 276, 246, 92, 742, 813]\n",
            "Length of Instruction Tokens Count 1000\n",
            "Output Tokens Count [1728, 1837, 1482, 1931, 1918, 1859, 1932, 777, 487, 325, 81, 439, 101, 261, 1893, 261, 318, 438, 1831, 1855, 1501, 1855, 146, 1831, 435, 1857, 513, 1022, 1814, 50, 1584, 615, 359, 386, 1769, 489, 957, 157, 283, 1743, 333, 1718, 323, 509, 1801, 1779, 81, 185, 1766, 1792, 1602, 208, 39, 1743, 1745, 152, 1079, 317, 1742, 1706, 1715, 1699, 1728, 81, 1225, 1110, 540, 276, 1624, 1686, 1016, 114, 1626, 179, 519, 1676, 374, 313, 1677, 199, 315, 1176, 132, 1664, 463, 145, 1609, 429, 390, 1573, 1543, 131, 337, 228, 1608, 1189, 1494, 1622, 227, 336, 1596, 286, 500, 482, 185, 409, 207, 428, 404, 772, 1387, 1562, 1503, 1461, 1516, 201, 466, 1011, 381, 1114, 1562, 1446, 1160, 1555, 1526, 1521, 1457, 1509, 1010, 294, 1439, 1417, 297, 1334, 1437, 1486, 238, 325, 1518, 1515, 225, 387, 259, 215, 473, 186, 302, 1500, 314, 1413, 1094, 313, 319, 107, 1471, 1155, 1409, 128, 1361, 1392, 1353, 350, 121, 469, 501, 313, 1472, 1476, 207, 807, 1461, 1019, 1387, 1452, 87, 850, 313, 202, 1444, 285, 463, 360, 1453, 1422, 1459, 1440, 1453, 181, 475, 383, 1428, 75, 205, 109, 325, 160, 1378, 973, 397, 1426, 1372, 1410, 320, 284, 1422, 55, 220, 1417, 726, 362, 1346, 1426, 1336, 334, 146, 957, 266, 1318, 1105, 1195, 1376, 143, 1352, 1364, 229, 287, 207, 1255, 1383, 1376, 164, 139, 850, 614, 98, 1346, 223, 348, 1308, 1401, 1304, 1390, 240, 1356, 168, 282, 1285, 673, 1318, 326, 470, 271, 1240, 1369, 1292, 1340, 127, 354, 1330, 355, 423, 632, 227, 224, 1122, 1307, 1200, 164, 460, 143, 382, 142, 1302, 1246, 448, 1314, 120, 1265, 266, 1316, 1312, 1284, 1329, 143, 114, 301, 937, 488, 1237, 1209, 1304, 232, 393, 1310, 164, 22, 1027, 159, 1161, 1117, 503, 225, 1242, 1274, 530, 1293, 1303, 1268, 946, 771, 680, 138, 1313, 1257, 1044, 369, 1214, 379, 520, 1281, 1260, 1181, 99, 1256, 1243, 299, 375, 1155, 1267, 358, 364, 263, 1266, 885, 1269, 1243, 1120, 1268, 1268, 324, 1299, 546, 90, 78, 159, 1256, 1205, 1055, 212, 242, 77, 1197, 1229, 172, 1039, 206, 1264, 187, 1193, 145, 83, 1194, 1135, 96, 1263, 1166, 1164, 202, 1233, 1153, 551, 300, 1253, 296, 252, 163, 417, 596, 1167, 775, 323, 1196, 77, 1198, 1215, 108, 314, 568, 281, 1149, 192, 211, 855, 1199, 532, 133, 1195, 1194, 396, 201, 497, 1203, 409, 101, 224, 1155, 1183, 1174, 990, 941, 356, 1180, 1033, 388, 1220, 761, 1188, 301, 1207, 1224, 1186, 1176, 1170, 739, 316, 322, 1122, 461, 781, 396, 1073, 181, 254, 1110, 955, 719, 57, 69, 1185, 1170, 113, 307, 225, 1103, 1171, 1174, 1187, 1081, 449, 1177, 1164, 211, 1079, 1162, 1104, 807, 1151, 1200, 925, 1133, 342, 1161, 712, 1147, 355, 1157, 132, 242, 393, 1169, 860, 212, 149, 831, 1168, 1026, 723, 169, 334, 996, 1162, 1071, 195, 1152, 977, 1083, 372, 18, 170, 1141, 257, 86, 215, 329, 332, 401, 312, 196, 479, 246, 125, 1154, 371, 1100, 1052, 140, 1136, 1081, 75, 75, 1121, 345, 721, 1131, 1158, 1132, 363, 920, 217, 1112, 490, 307, 1142, 446, 1103, 82, 140, 829, 298, 322, 1128, 817, 1046, 1110, 1084, 1086, 127, 164, 1150, 1122, 669, 150, 1122, 1095, 252, 305, 1035, 700, 1040, 1034, 30, 1084, 1099, 1128, 946, 963, 177, 109, 1120, 189, 1088, 1114, 1089, 1113, 185, 1086, 184, 376, 1143, 949, 1094, 274, 240, 102, 1066, 875, 219, 1142, 321, 872, 164, 97, 83, 1067, 322, 1047, 1049, 209, 322, 1061, 998, 1060, 1109, 278, 1069, 578, 1067, 1084, 975, 1092, 733, 1050, 1029, 1013, 984, 219, 34, 1076, 1107, 287, 154, 113, 260, 1084, 1081, 238, 426, 128, 1098, 307, 176, 546, 234, 112, 1093, 1015, 421, 300, 83, 1047, 695, 1059, 1094, 151, 146, 402, 595, 235, 242, 1057, 122, 751, 1080, 1079, 1050, 701, 1049, 375, 1074, 1070, 615, 1019, 976, 1086, 1060, 217, 1070, 963, 923, 473, 1064, 1052, 104, 805, 870, 1012, 84, 552, 1066, 1043, 1053, 202, 357, 42, 1037, 487, 689, 168, 244, 155, 982, 1034, 996, 1044, 130, 219, 81, 77, 1035, 858, 999, 557, 1038, 1056, 1050, 978, 1061, 1028, 516, 231, 346, 221, 322, 94, 960, 1044, 576, 1044, 980, 97, 216, 158, 1033, 651, 1028, 332, 1062, 958, 1053, 1047, 165, 1049, 967, 993, 696, 309, 317, 688, 1051, 480, 351, 49, 1066, 991, 218, 43, 339, 23, 1045, 194, 1029, 291, 802, 617, 1049, 336, 1058, 240, 101, 153, 82, 1006, 175, 1031, 184, 451, 986, 1048, 996, 113, 954, 982, 1026, 195, 668, 980, 1009, 201, 1016, 133, 174, 341, 1032, 310, 991, 1028, 759, 225, 166, 1015, 1024, 945, 854, 686, 1007, 990, 474, 1049, 920, 990, 125, 1047, 1015, 114, 955, 127, 1021, 181, 215, 941, 194, 210, 345, 841, 368, 996, 977, 260, 171, 945, 997, 952, 972, 1010, 901, 254, 947, 640, 1020, 1042, 466, 917, 210, 151, 1009, 942, 903, 979, 478, 374, 972, 800, 966, 66, 427, 292, 986, 137, 998, 982, 167, 218, 911, 877, 172, 319, 991, 980, 92, 1007, 937, 66, 954, 908, 983, 696, 968, 669, 1012, 883, 949, 726, 367, 994, 916, 105, 952, 922, 950, 936, 93, 894, 979, 174, 937, 987, 429, 305, 279, 1007, 745, 1005, 1002, 986, 916, 868, 955, 237, 354, 242, 225, 971, 981, 130, 47, 261, 119, 126, 952, 953, 907, 169, 976, 47, 196, 173, 160, 982, 971, 699, 873, 733, 189, 130, 988, 976, 174, 1006, 102, 990, 264, 954, 930, 141, 987, 157, 325, 341, 987, 161, 961, 914, 1004, 378, 254, 450, 949, 212, 911, 964, 905, 273, 84, 115, 184, 829, 934, 308, 98, 913, 931, 963, 709, 190, 986, 277, 216, 955, 892, 949, 982, 957, 993, 861, 938, 193, 908, 871, 928, 331, 958, 358, 957, 886, 311, 941, 556, 153, 98, 321, 352, 538, 363, 928, 13, 896, 255, 172, 77, 962, 874, 206, 256, 946, 386, 726, 755, 909, 259, 188]\n",
            "Length of the Output Tokens Count 1000\n",
            "Combine Tokens Count [2043, 2038, 2035, 2029, 1995, 1984, 1979, 1977, 1955, 1955, 1952, 1951, 1951, 1947, 1935, 1934, 1934, 1933, 1925, 1920, 1917, 1917, 1914, 1910, 1908, 1907, 1901, 1899, 1885, 1881, 1874, 1874, 1871, 1863, 1862, 1862, 1854, 1851, 1842, 1838, 1837, 1832, 1829, 1826, 1825, 1819, 1816, 1815, 1813, 1808, 1806, 1805, 1798, 1798, 1795, 1794, 1787, 1782, 1779, 1779, 1766, 1760, 1759, 1757, 1756, 1756, 1755, 1752, 1752, 1742, 1737, 1734, 1731, 1730, 1721, 1721, 1719, 1716, 1716, 1714, 1713, 1707, 1704, 1703, 1701, 1693, 1693, 1689, 1685, 1683, 1683, 1680, 1676, 1670, 1659, 1659, 1652, 1652, 1651, 1650, 1644, 1638, 1638, 1636, 1632, 1632, 1630, 1629, 1622, 1621, 1615, 1610, 1608, 1605, 1600, 1599, 1597, 1596, 1595, 1591, 1589, 1588, 1588, 1588, 1586, 1586, 1581, 1581, 1578, 1578, 1571, 1570, 1570, 1568, 1567, 1567, 1564, 1564, 1562, 1561, 1560, 1557, 1557, 1556, 1552, 1551, 1548, 1547, 1546, 1546, 1541, 1541, 1540, 1538, 1537, 1534, 1533, 1531, 1530, 1529, 1528, 1526, 1526, 1525, 1525, 1525, 1522, 1521, 1516, 1515, 1513, 1512, 1511, 1511, 1507, 1505, 1501, 1499, 1498, 1498, 1497, 1496, 1494, 1493, 1492, 1492, 1490, 1489, 1488, 1485, 1478, 1478, 1473, 1471, 1470, 1469, 1468, 1468, 1468, 1467, 1461, 1460, 1459, 1456, 1456, 1455, 1454, 1454, 1451, 1451, 1450, 1448, 1446, 1446, 1446, 1444, 1443, 1441, 1440, 1440, 1439, 1438, 1434, 1433, 1431, 1430, 1428, 1427, 1427, 1426, 1424, 1420, 1419, 1418, 1415, 1415, 1413, 1412, 1412, 1410, 1408, 1408, 1406, 1406, 1404, 1404, 1404, 1400, 1399, 1399, 1396, 1395, 1393, 1392, 1391, 1391, 1388, 1387, 1387, 1386, 1384, 1384, 1382, 1381, 1380, 1380, 1379, 1379, 1378, 1377, 1376, 1376, 1374, 1374, 1374, 1374, 1373, 1372, 1370, 1369, 1369, 1366, 1366, 1364, 1363, 1363, 1360, 1359, 1359, 1358, 1358, 1357, 1356, 1353, 1353, 1353, 1353, 1352, 1351, 1350, 1350, 1350, 1349, 1349, 1349, 1349, 1347, 1344, 1344, 1344, 1343, 1343, 1342, 1341, 1340, 1339, 1337, 1334, 1333, 1333, 1333, 1333, 1330, 1329, 1327, 1327, 1326, 1324, 1324, 1323, 1322, 1322, 1319, 1319, 1319, 1314, 1313, 1313, 1313, 1313, 1313, 1311, 1311, 1310, 1310, 1310, 1309, 1308, 1307, 1307, 1307, 1307, 1305, 1304, 1303, 1302, 1301, 1300, 1298, 1295, 1295, 1292, 1291, 1289, 1289, 1288, 1287, 1285, 1284, 1283, 1283, 1281, 1281, 1279, 1279, 1278, 1277, 1276, 1274, 1273, 1272, 1270, 1268, 1267, 1267, 1266, 1266, 1264, 1264, 1263, 1262, 1262, 1261, 1260, 1260, 1257, 1257, 1256, 1256, 1255, 1254, 1254, 1254, 1253, 1252, 1252, 1251, 1250, 1250, 1249, 1249, 1247, 1246, 1246, 1245, 1244, 1244, 1244, 1242, 1241, 1240, 1240, 1240, 1239, 1239, 1237, 1237, 1236, 1235, 1234, 1234, 1233, 1232, 1229, 1229, 1229, 1229, 1229, 1228, 1228, 1228, 1227, 1227, 1226, 1226, 1226, 1226, 1225, 1225, 1225, 1225, 1225, 1224, 1223, 1221, 1220, 1220, 1219, 1218, 1218, 1218, 1218, 1217, 1217, 1217, 1216, 1216, 1214, 1213, 1213, 1212, 1212, 1212, 1211, 1211, 1210, 1210, 1210, 1210, 1209, 1207, 1207, 1206, 1205, 1205, 1205, 1205, 1205, 1204, 1203, 1202, 1201, 1200, 1200, 1199, 1199, 1199, 1198, 1198, 1198, 1197, 1195, 1195, 1195, 1194, 1194, 1194, 1193, 1193, 1193, 1192, 1192, 1191, 1191, 1191, 1191, 1189, 1188, 1188, 1187, 1187, 1187, 1187, 1185, 1184, 1184, 1184, 1183, 1183, 1183, 1182, 1181, 1181, 1180, 1179, 1179, 1179, 1178, 1176, 1175, 1174, 1174, 1173, 1173, 1173, 1173, 1172, 1171, 1170, 1170, 1169, 1168, 1168, 1168, 1167, 1167, 1167, 1167, 1166, 1164, 1164, 1163, 1163, 1163, 1163, 1162, 1162, 1162, 1162, 1161, 1161, 1161, 1160, 1159, 1157, 1155, 1154, 1153, 1152, 1151, 1151, 1150, 1149, 1149, 1149, 1149, 1148, 1147, 1147, 1146, 1146, 1145, 1145, 1144, 1143, 1142, 1142, 1142, 1142, 1141, 1141, 1141, 1140, 1140, 1140, 1139, 1138, 1138, 1136, 1135, 1135, 1133, 1133, 1133, 1133, 1132, 1132, 1132, 1132, 1131, 1130, 1128, 1128, 1128, 1128, 1127, 1126, 1126, 1125, 1125, 1124, 1124, 1124, 1123, 1123, 1123, 1122, 1122, 1122, 1122, 1122, 1122, 1121, 1121, 1120, 1119, 1119, 1119, 1118, 1116, 1114, 1113, 1113, 1113, 1113, 1113, 1112, 1112, 1111, 1111, 1111, 1111, 1111, 1110, 1110, 1110, 1108, 1107, 1107, 1106, 1106, 1106, 1106, 1104, 1104, 1104, 1104, 1104, 1103, 1103, 1102, 1101, 1101, 1101, 1100, 1100, 1098, 1098, 1098, 1097, 1097, 1097, 1097, 1097, 1097, 1096, 1096, 1095, 1095, 1095, 1095, 1094, 1094, 1093, 1093, 1093, 1093, 1093, 1092, 1092, 1092, 1092, 1092, 1091, 1091, 1091, 1091, 1090, 1090, 1089, 1089, 1088, 1087, 1087, 1087, 1087, 1087, 1087, 1086, 1086, 1086, 1086, 1086, 1085, 1085, 1085, 1085, 1085, 1084, 1084, 1084, 1084, 1083, 1083, 1083, 1082, 1082, 1081, 1081, 1081, 1081, 1080, 1080, 1080, 1079, 1079, 1079, 1078, 1077, 1077, 1077, 1076, 1076, 1075, 1074, 1073, 1072, 1072, 1072, 1072, 1072, 1072, 1072, 1071, 1071, 1070, 1070, 1070, 1070, 1070, 1070, 1068, 1068, 1068, 1068, 1067, 1067, 1067, 1067, 1066, 1066, 1066, 1065, 1065, 1064, 1064, 1063, 1063, 1063, 1063, 1063, 1062, 1062, 1061, 1061, 1060, 1060, 1060, 1060, 1059, 1059, 1058, 1058, 1058, 1057, 1057, 1056, 1056, 1056, 1056, 1055, 1055, 1055, 1054, 1054, 1053, 1053, 1053, 1052, 1052, 1051, 1051, 1051, 1051, 1051, 1051, 1051, 1051, 1050, 1048, 1048, 1048, 1048, 1047, 1047, 1047, 1047, 1046, 1046, 1045, 1045, 1045, 1045, 1045, 1045, 1044, 1044, 1043, 1043, 1043, 1043, 1043, 1043, 1042, 1042, 1042, 1042, 1042, 1041, 1041, 1041, 1040, 1039, 1038, 1037, 1037, 1037, 1037, 1036, 1036, 1035, 1035, 1035, 1035, 1034, 1034, 1034, 1034, 1033, 1033, 1033, 1033, 1033, 1033, 1032, 1032, 1031, 1031, 1030, 1030, 1030, 1030, 1030, 1029, 1029, 1028, 1028, 1028, 1028, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1025, 1025, 1025, 1025, 1024, 1024, 1024, 1023, 1023, 1023, 1023, 1022, 1022, 1021, 1021, 1021, 1021, 1019, 1019, 1018, 1018, 1018, 1017, 1016, 1015, 1015, 1015, 1014, 1014, 1014, 1014, 1014, 1013, 1013, 1012, 1012, 1012, 1012, 1012, 1012, 1011, 1011, 1011, 1010, 1010, 1009, 1009, 1009, 1008, 1008, 1007, 1007, 1006, 1006, 1006, 1006, 1006, 1006, 1005, 1005, 1005, 1005, 1005, 1004, 1004, 1004, 1004, 1004, 1003, 1003, 1003, 1003, 1002, 1002, 1002, 1002, 1002, 1002, 1001, 1001, 1001, 1001]\n",
            "Length of the Combine Tokens Count 1000\n"
          ]
        }
      ],
      "source": [
        "# Tokenize each row in the 'instruction' column of the dataset and count the number of tokens\n",
        "# Using a list comprehension to apply tokenizer.tokenize() to each instruction\n",
        "instruction_tokens_count = [len(tokenizer.tokenize(example[\"instruction\"])) for example in dataset[\"train\"]]\n",
        "# Print the total number of tokens for each instruction in the dataset\n",
        "print(\"Instruction Tokens Count\", instruction_tokens_count)\n",
        "# Print the number of entries that were processed to get token counts for instructions\n",
        "print(\"Length of Instruction Tokens Count\", len(instruction_tokens_count))\n",
        "\n",
        "# Tokenize each row in the 'output' column of the dataset and count the number of tokens\n",
        "# Using a list comprehension to apply tokenizer.tokenize() to each output\n",
        "output_tokens_count = [len(tokenizer.tokenize(example[\"output\"])) for example in dataset[\"train\"]]\n",
        "# Print the total number of tokens for each output in the dataset\n",
        "print(\"Output Tokens Count\", output_tokens_count)\n",
        "# Print the number of entries that were processed to get token counts for outputs\n",
        "print(\"Length of the Output Tokens Count\", len(output_tokens_count))\n",
        "\n",
        "# Combine the token counts from the instruction and output by adding them together for each row\n",
        "# Using zip to iterate over paired instruction and output token counts\n",
        "combine_tokens_count = [instruction + output for instruction, output in zip(instruction_tokens_count, output_tokens_count)]\n",
        "# Print the combined total number of tokens for each row across both instruction and output columns\n",
        "print(\"Combine Tokens Count\", combine_tokens_count)\n",
        "# Print the total number of entries for which combined token counts have been calculated\n",
        "print(\"Length of the Combine Tokens Count\", len(combine_tokens_count))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "F3Tgs35-bpOk",
        "outputId": "bcfae2d2-9223-4586-bee8-a5c819848122"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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ds2aNkpKSFBERoWuuuUYZGRnasmWLXnrpJR0+fFhPPPFEuewfAFw0KwDAK3Tu3NlqNBqtCxYsKHHZoUOHWo1Go3X8+PGFpicmJlqNRqN1/fr19mkbN260Go1Ga9++fa2nT58usq0dO3ZYjx8/XmwtBw4cKLb99evXW41Go9VoNFpvuukm69GjRy+4T+dvx1an0Wi0vvPOO4XmbdiwwdqiRQur0Wi0rl69usT9O9drr71mNRqN1tdee63Q9AULFliNRqP1scceK3Y9q9Vqfeyxx4o9/p999pnVaDRa27VrZ921a5d9ekFBgb29hIQE67Fjx4rd95iYGOuKFSuKrSc+Pt5qsVgc1nS+V155xWo0Gq3dunUrdExzcnKsjz/+uNVoNFq7dOliPXv2bKn2rTTKci307du30LWQnZ1tHT58uNVoNFr/97//FVrPYrFYu3XrZjUajdZXX321UM1ZWVnWcePGWY1Go3XChAmlrtVWS2JiosNlnD2Gtn20SU9Pt9588832GnNycgotb6v/vvvus5rN5kLzkpKSrEaj0dq9e3drXl5ekfrLeiwHDx5sNRqN1sWLFxfZ31OnTlm3bdvm8HicLzk52RoTE2ONioqyfvzxx9b8/PxC8//++29rcnKy/d8ZGRnW+Ph4q9FotL711lvWgoIC+7wdO3ZY27RpYzUajdZ58+YV2k5J16SjPmubbjQarcOHDy90Tztx4oT9nJx/Xzlw4IDVaDRaO3fuXOpjcb7zr4FzXex9omnTptYff/yxzDX9+OOPVqPRaG3evLn122+/LTL/jz/+sO7Zs8f+75SUFGuzZs2sUVFR1kWLFhVaduXKldaYmBir0Wi0rlmzptA82/kq7jx//PHHVqPRaO3YsaO1TZs2Rbb7wgsvWI1Go3Xo0KFF6rNts0WLFta1a9cWmvf+++/b75Pn9yFHnwXn1jl9+vRC/Wv37t3WVq1aWY1Go3Xr1q2F1lu9erU1KirK2q5dO+vGjRsLzUtJSbF27NjRajQarRs2bCg0r3///laj0Wh96KGHrNnZ2fbpBw8etN/fLvSZVRzb9XL+fcx2Dduu4z///NM+Ly8vzzpx4kR7vzjXhAkT7OftfBaLpcj+luY+eu5xnjp1apH7xLn7cf7ncUntHDp0yH5PmTp1apF7sdlstm7atKnQtJI+pxz1/7Nnz9rXnTRpUqH7eFpamn3e9OnTC63n7H3IWbbr3dH+lVZOTo41OjraajQai/RTR2zXz5133llo+oXuh1ZryX30Qj+POHtfcGdNjsyYMcNqNBqtXbt2LfO6VqvVumrVKvs9/Pz77vz58+0/R6amppaqZld8tt53333WrKws+7ydO3damzVrZo2Ojrb27t3bOmnSJGtubq59/g8//GA1Go3W1q1bF1rPai388/7nn39eaN7atWutUVFR1qZNm1oPHz5cwpECAO/AcC4AUAHZnggrzVPfZrNZ0r9PY9qekj1X8+bNi33CrLQmTZqksLAwp9Zt1qyZ7rvvvkLT2rZtq0GDBkn69+lMT5s9e7akf7+me+5TSQaDQaNHj1ZUVJROnTql+fPnF7t+YmKiOnfuXGjaLbfcoiuvvFKnT58uMiSPI2fPntWnn34q6d+nEM99ErlKlSp68sknFRoaqr///lvff/99mfbRFQwGg6ZMmVLoWqhWrZoefPBBSdLatWsLLb9o0SKlpaWpc+fOGjNmTKGn/KpXr65nn31WderU0ZIlS3Ty5EmX1OiqY5iSkqLbbrtNJpNJDz74oKZMmVJo3P+9e/fq22+/Vd26dTV16lTVqVOn0PpDhw5Vp06dtH//fq1evbrI9st6LI8dOyZJ6tSpU5Ft1ahRQ61atXK4L+d7++23lZubq8TERA0ePFh+foV/VGzYsGGhb7/Mnz9fp0+fVkxMjO6//34ZDAb7vObNm2vkyJGSpFmzZpW6htIICgrSlClTCt3TLrnkEvtTcOcfI3e72PtE3759ix1HtyS24QbGjh2r3r17F5kfGRmpxo0b2//98ccfKy8vT9dff7369u1baNlOnTpp4MCBkhyfrxYtWmjkyJGFzvMdd9yhWrVqKT09Xe3bty+y3fvvv1+StGnTJuXm5ha73YEDB6p9+/aFpt19992KjY3V6dOn9cUXXxS7niMxMTEaM2ZMoaf5jUaj+vTpI6no9fH666/LarXqmWeeUZs2bQrNO3fog08++cQ+fcuWLUpOTlZQUJCeeuqpQk/1NmjQQI899liZai6LJ598Uo0aNbL/29/f3/7S6I0bNxY6zhe6PwQGBhbZ37K44oorNGbMmCL3iYuRlJSk06dPq3Pnznr44YeLPAFep04dh99oKKtly5bp4MGDqlu3rp544olC9/HLL7/cfg7nzJmjs2fPFlnf2+5DJTl58qR9SI7zP5ccsT3Nf/z4cbfV5Yir7wvuYDsupT2e57N9dgwaNEjXXHNNoXm33nqrOnfurNzcXPuQMe4WFBSkF154odA4+TExMerYsaMKCgqUlZWlxx9/XAEB/z+gQbdu3WQ0GpWZmenwZ9ru3bvr9ttvLzStffv26tChg/Lz87V+/Xr37BAAuBghOgBUQLZfgs4NMhyJiYmRv7+/FixYoE8//VRHjx51WR0X+8uso6++2kKYLVu2FDseaXlJT09XWlqapMJDONgYDAb7MAmOvrp/foBuYwu2SjtuanJysrKyslSrVq1ix3WtXr26PURz5zACjjRo0EDR0dFFpjvaT9sYw7169Sp2e8HBwYqNjVVeXl6ZxwR1xBXH8JdfftGgQYN07Ngxvfzyyxo1alSRZVatWiWr1aqOHTsW+4crSfavxRc3hnFZj2WLFi0kSePHj9fmzZuVl5dXbJslyc/Pt4c+t912W6nWsY2TW1z/kGT/Cvr+/ftdOkZwbGys6tatW2S6bZii8hyP2BX3ibIOMSNJGRkZMplM8vPzK/VX/Ut7vjZv3lzsvbdjx45FPncCAgLUsGFDScUHtZdeeqlq1aql3Nxch3/4dVSP7bPg/PGYS9K5c+diPx+L60PHjx/Xjh07FBgY6PB+3a5dO0myj8V9bk3XXnttsX+I7tq1q1uG8ggICCg0zIpNWFiYLrnkEuXk5BQ6zrb7w9NPP61ffvml2DDYWd26dSv1sEOl9csvv0iS/Q867mQ7hzfccEOxw7V0795dl1xyic6cOVNsOOjq+9B7772nCRMmFPnvzz//lCS9/PLLxc53Z8Bt9eB41a6+L3ibvLw8+z2lpHtyef1cFxsbW+wfBGwv1G7Xrl2xwwDZ5jv6HaOkn4Vd+bsJALgTY6IDQAX0zz//SPr3iaeSREREaOLEiXr55Zf17LPP6tlnn1XDhg3VqlUrXXfdderZs2exvzyWhi04cdb5Y2SfPz07O1snTpxw+gmfi2X7BbhWrVoOw1DbmMiOfllu0KBBsdNt2yttoGH7BeNCx7ykWtwpPDy82Om2/Tz/ZX4HDhyQJD366KN69NFHL7htVwUErjiGI0eOVF5enl555RX7U63ns+3bl19+qS+//PKCNRW3b2U9luPGjdPu3bu1evVqrV69WoGBgWrWrJn95ZLnPol8ISdOnFBWVpYkFXrK9UJsx8lRX65Zs6b9HQ5HjhxRvXr1SrXdkpT1GLmTK+4Tjo7fhdheThsWFlbqsLak82V7Kd7Zs2eLvfc6Ou62cbAvNP/EiRMO73clfRakp6cXO9+Rslwff//9t6xWq7Kzs9W8efMLbtf22XtuTY7uJwaDQQ0bNlRKSkqZai9JWFhYoSemzxUSEqKTJ08WOs4jRozQli1btHbtWt19992qUqWKoqKi1KZNG/Xu3dsesjvjYn8GKM6hQ4ckle6dJRerpP5gMBh02WWX6eTJk8X2XVffh3755ZcLBsOOviE1evRo1a5du8TtX3LJJfLz81NBQYH9GwolsS1Xmu27mqvvC+5gOy6lPZ7nOveeWNI9ubx+rnN0TdveNVDSZ4Cje3xJfcWVf9wDAHciRAeACsZqtcpkMklSoZf7XcjgwYPVq1cvrVixQlu2bNGWLVv07bff6ttvv9Xrr7+uTz/9tNinqUoSGBhY5nXKqixPQdme0Pcmpfm2gC8o69f5befq2muvLfHlb47+EOEJffv21ZdffqmZM2cqLi7O/gvuuWz71rRp02KfKD/XuS8AtCnrsQwLC9OCBQu0ceNGrV27Vlu3btWOHTu0detWvfvuuxo3bpzDl315q5L6siuHj/AGJb3gz1uUdNzddV7K+jRsWeqwbduZl856QlmPcfXq1ZWUlKQdO3bol19+0bZt27Rt2zbt3LlTSUlJGjRokJ566imnarmYnwG88fO6rFx9vc+ZM6fY6YMHD9bGjRv1008/OfUHN5sqVarIaDQqJSVFv/32W5Ghl4pj+yZYaV62fa7yOL9lvS+4oybbcfn777/1zz//XNTwiOXhYj9bnb3mfe0zG0DlRYgOABXMqlWr7GNEd+jQodTrhYaG6rbbbrMP07B371498cQT2rZtm6ZNm6aXXnrJLfVeyN9//13s9IMHD0r6N1iqVauWfbrt6bszZ84Uu57tCTZXsT01e+LECWVmZhb7lKntqWNXPWHriO2PHLZjU5zyqsUVwsPD9eeff2rAgAHq2bNnubTpimP4/PPPKygoSB9//LHuvPNOJSUlFXnS2/bEVevWrTVp0iRXlF4ig8Ggdu3a2YedOHv2rBYuXKhnn31Wr776qnr27Gl/GtqRWrVqqXr16rJYLNq3b1+p/khXr149/fnnn/bjdr7Tp0/bh5Y495iWd192J0/dJ2zXWUZGhk6fPl2qp9Hr1auntLQ0HThwoNjza7snV6tWrVTfdHKVv//+u9BY8ja2vlq/fn23tW3btsFg0OTJk0sd9tjO5YXuJ950Hbdo0cL+1HleXp5+/PFHPfbYY/rss8/Uo0cPXXXVVS5tz9k+Hh4ern379unPP//Uf/7zH5fWdD7bOXR0/5L+v09UhM/V0ujatatSUlL03XffacK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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#Combine Tokens Count\n",
        "plot_distribution(combine_tokens_count, \"Distribution of the tokens count for combined Instruction + Output Column\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "sYzE1V-mbsBT",
        "outputId": "8263a4ca-9fe5-43ee-acb8-61f5c5efaed4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                           instruction  \\\n",
              "0    Let's come up with a rich and complex alien wo...   \n",
              "1    Let\\n$$p(x,y) = a_0 + a_1x + a_2y + a_3x^2 + a...   \n",
              "2    Given the code below, refactor it, add comment...   \n",
              "3    Find the area of the region described by $x \\g...   \n",
              "4    Let $P$ be the plane passing through the origi...   \n",
              "..                                                 ...   \n",
              "995  Hello. My name is Mike. I have a message left ...   \n",
              "996  Given a prime $p$ and an integer $a$, we say t...   \n",
              "997  Let $f$ be defined by  \\[f(x) = \\left\\{\\n\\begi...   \n",
              "998  BEGININPUT\\nBEGINCONTEXT\\ndate: August 5, 2021...   \n",
              "999  BEGININPUT\\nBEGINCONTEXT\\ndate: August 14, 201...   \n",
              "\n",
              "                                                output  \n",
              "0    Planet Name: Xylothar\\n\\nXylothar is a diverse...  \n",
              "1    Observe that  \\begin{align*}\\np(0,0) &= a_0 = ...  \n",
              "2    Here is the refactored and commented version:\\...  \n",
              "3    Let $n = \\lfloor x \\rfloor,$ and let $\\{x\\} = ...  \n",
              "4    Let $\\mathbf{v} = \\begin{pmatrix} x \\\\ y \\\\ z ...  \n",
              "..                                                 ...  \n",
              "995  Hello Mike, it's nice to meet you. As an AI la...  \n",
              "996  To find the primitive roots $\\pmod 7$, I need ...  \n",
              "997  The number $f^{-1}(-3)$ is the value of $x$ su...  \n",
              "998  Dr. Eleanor Thompson's study found that partic...  \n",
              "999  Charles Leroux won the Golden Bread Award in 1...  \n",
              "\n",
              "[1000 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-87dfbe95-5e48-47e9-9bf9-f0e53f49e1bd\" 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>instruction</th>\n",
              "      <th>output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Let's come up with a rich and complex alien wo...</td>\n",
              "      <td>Planet Name: Xylothar\\n\\nXylothar is a diverse...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Let\\n$$p(x,y) = a_0 + a_1x + a_2y + a_3x^2 + a...</td>\n",
              "      <td>Observe that  \\begin{align*}\\np(0,0) &amp;= a_0 = ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Given the code below, refactor it, add comment...</td>\n",
              "      <td>Here is the refactored and commented version:\\...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Find the area of the region described by $x \\g...</td>\n",
              "      <td>Let $n = \\lfloor x \\rfloor,$ and let $\\{x\\} = ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Let $P$ be the plane passing through the origi...</td>\n",
              "      <td>Let $\\mathbf{v} = \\begin{pmatrix} x \\\\ y \\\\ z ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>995</th>\n",
              "      <td>Hello. My name is Mike. I have a message left ...</td>\n",
              "      <td>Hello Mike, it's nice to meet you. As an AI la...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>996</th>\n",
              "      <td>Given a prime $p$ and an integer $a$, we say t...</td>\n",
              "      <td>To find the primitive roots $\\pmod 7$, I need ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>997</th>\n",
              "      <td>Let $f$ be defined by  \\[f(x) = \\left\\{\\n\\begi...</td>\n",
              "      <td>The number $f^{-1}(-3)$ is the value of $x$ su...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>998</th>\n",
              "      <td>BEGININPUT\\nBEGINCONTEXT\\ndate: August 5, 2021...</td>\n",
              "      <td>Dr. Eleanor Thompson's study found that partic...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>999</th>\n",
              "      <td>BEGININPUT\\nBEGINCONTEXT\\ndate: August 14, 201...</td>\n",
              "      <td>Charles Leroux won the Golden Bread Award in 1...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1000 rows × 2 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-87dfbe95-5e48-47e9-9bf9-f0e53f49e1bd')\"\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-87dfbe95-5e48-47e9-9bf9-f0e53f49e1bd 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-87dfbe95-5e48-47e9-9bf9-f0e53f49e1bd');\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-a9b8931c-9371-45c4-8bfc-15844854378e\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a9b8931c-9371-45c4-8bfc-15844854378e')\"\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-a9b8931c-9371-45c4-8bfc-15844854378e 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\": \"dataset['train']\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"instruction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"BEGININPUT\\nBEGINCONTEXT\\ndate: September 15, 2021\\nurl: www.telecomhistory.com/evolution-video-transcript\\nauthor: Patricia Williams\\ntitle: The Evolution of Telecommunication: A Video Transcript\\nENDCONTEXT\\nIn this video transcript, we will explore the fascinating journey of telecommunication from its humble beginnings to the advanced technologies we use today. Our story begins in the early 19th century with Samuel Morse's invention of the electric telegraph.\\n\\nThe electric telegraph was invented by Samuel Morse in 1837 and became operational in 1844. This revolutionary device allowed people to send messages over long distances using a series of electrical signals. Messages were sent in Morse code, a system of dots and dashes that represented letters and numbers. The first message sent via telegraph was \\\"What hath God wrought?\\\" on May 24, 1844, between Washington D.C. and Baltimore.\\n\\nAlexander Graham Bell is credited with inventing the telephone in 1876. His invention revolutionized communication by allowing people to speak directly to each other over long distances. On March 10, 1876, Bell made the first successful telephone call to his assistant, Thomas Watson, saying, \\\"Mr. Watson, come here; I want you.\\\" By the end of the 19th century, telephone lines had been installed across the United States and Europe.\\n\\nWireless communication took a significant leap forward in 1895 when Guglielmo Marconi developed the wireless telegraph, also known as the radio. Marconi's invention used electromagnetic waves to transmit messages without wires. In 1901, he successfully sent a wireless signal across the Atlantic Ocean, proving that radio could be used for long-distance communication.\\n\\nTelevision emerged as a new form of telecommunication in the 1920s. Scottish inventor John Logie Baird demonstrated the first working television system on January 26, 1926. Early television sets were large and expensive, but by the 1950s, they had become more affordable and widespread.\\n\\nThe development of satellites in the 1960s allowed for global communication on an unprecedented scale. On July 10, 1962, Telstar 1, the first active communications satellite, was launched into orbit. This satellite enabled live transatlantic television broadcasts and telephone calls between the United States and Europe.\\n\\nIn the 1980s, mobile phones began to gain popularity. The first commercially available cell phone, the Motorola DynaTAC 8000X, was released in 1983. It weighed nearly two pounds and cost $3,995. Over time, mobile phones became smaller, lighter, and more affordable, leading to their widespread adoption around the world.\\n\\nThe invention of the internet in the late 20th century revolutionized telecommunication once again. Developed initially as a military project in the 1960s, the internet became publicly accessible in the 1990s. Today, it serves as a platform for countless applications, including email, social media, video conferencing, and online gaming.\\n\\nAs we look to the future, advancements in telecommunication technology continue to evolve at a rapid pace. From 5G networks to virtual reality, the possibilities are endless. One thing is certain: the history of telecommunication is a testament to human ingenuity and our desire to connect with one another.\\nENDINPUT\\n\\nBEGININSTRUCTION\\n- Summarize the key milestones in the evolution of telecommunication from the electric telegraph to the internet.\\nENDINSTRUCTION\",\n          \"For each following Query, return a series of clear, factual and concise Summary Bullet Points of the Query using only information available in the Query.\\n###\\nQuery:\\nArtificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of \\\"intelligent agents\\\": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.3 Colloquially, the term \\\"artificial intelligence\\\" is often used to describe machines (or computers) that mimic \\\"cognitive\\\" functions that humans associate with the human mind, such as \\\"learning\\\" and \\\"problem solving\\\".[4]\\n\\nAs machines become increasingly capable, tasks considered to require \\\"intelligence\\\" are often removed from the definition of AI, a phenomenon known as the AI effect.[5] A quip in Tesler's Theorem says \\\"AI is whatever hasn't been done yet.\\\"[6] For instance, optical character recognition is frequently excluded from things considered to be AI,[7] having become a routine technology.[8] Modern machine capabilities generally classified as AI include successfully understanding human speech,[9] competing at the highest level in strategic game systems (such as chess and Go),[10] autonomously operating cars, intelligent routing in content delivery networks, and military simulations.[11]\\n\\nSummary:\\n- The text discusses artificial intelligence (AI) and its various applications. \\n- It defines AI as intelligence demonstrated by machines\\n- AI is often distinguished from natural intelligence by its lack of consciousness and emotionality. \\n- Discusses the AI effect, which is the tendency for tasks to be removed from the definition of AI as machines become increasingly capable.\\n###\\nQuery:\\nFishing is the activity of trying to catch fish. Fish are often caught as wildlife from the natural environments, but may also be caught from stocked bodies of water such as ponds, canals, park wetlands and reservoirs. Fishing techniques include hand-gathering, spearing, netting, angling, shooting and trapping, as well as more destructive and often illegal techniques such as electrocution, blasting and poisoning.\\n\\nThe term fishing broadly includes catching aquatic animals other than fish, such as crustaceans (shrimp/lobsters/crabs), shellfish, cephalopods (octopus/squid) and echinoderms (starfish/sea urchins). The term is not normally applied to harvesting fish raised in controlled cultivations (fish farming). Nor is it normally applied to hunting aquatic mammals, where terms like whaling and sealing are used instead.\\n\\nFishing has been an important part of human culture since hunter-gatherer times, and is one of the few food production activities that have persisted from prehistory into modern age, surviving both the Neolithic Revolution and successive Industrial Revolutions. In addition to being caught to be eaten for food, fish are caught as recreational pastimes. Fishing tournaments are held, and caught fish are sometimes kept long-term as preserved or living trophies. When bioblitzes occur, fish are typically caught, identified, and then released.\\n\\nAccording to the United Nations FAO statistics, the total number of commercial fishers and fish farmers is estimated to be 38 million. Fishing industries and aquaculture provide direct and indirect employment to over 500 million people in developing countries.[1] In 2005, the worldwide per capita consumption of fish captured from wild fisheries was 14.4 kilograms (32 lb), with an additional 7.4 kilograms (16 lb) harvested from fish farms.[2]\\n\\nSummary:\\n- Fishing is the activity of trying to catch fish.\\n- Fishing techniques include hand-gathering, spearing, netting, angling, shooting and trapping.\\n- The term \\\"fishing\\\" broadly includes catching aquatic animals other than fish, such as crustaceans, shellfish, cephalopods and echinoderms.\\n- The term fishing is not normally applied to hunting aquatic mammals.\\n- Fishing has been an important part of human culture since hunter-gatherer times\\n###\\nQuery: {}\\n\\nSummary:\",\n          \"Develop a C++ program that implements a simple ray-tracing algorithm to render 3D scenes with basic shapes (spheres, planes) and lighting. The program should output the rendered image as a PNG file.\\nPLAINFORMAT\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"The key milestones in the evolution of telecommunication include:\\n\\n1. Invention of the electric telegraph by Samuel Morse in 1837, which became operational in 1844 and allowed long-distance communication using Morse code.\\n2. Alexander Graham Bell's invention of the telephone in 1876, enabling direct speech over long distances.\\n3. Guglielmo Marconi's development of the wireless telegraph (radio) in 1895, using electromagnetic waves for wire-free messaging.\\n4. The emergence of television as a new form of telecommunication in the 1920s, with John Logie Baird demonstrating the first working system in 1926.\\n5. Launch of Telstar 1, the first active communications satellite, in 1962, allowing global communication through live transatlantic broadcasts and phone calls.\\n6. The rise of mobile phones in the 1980s, starting with the release of the Motorola DynaTAC 8000X in 1983.\\n7. The invention of the internet in the late 20th century, initially developed as a military project and becoming publicly accessible in the 1990s, leading to various applications like email, social media, and video conferencing.\",\n          \"Thank you for the examples, I'm ready to summarize now. \\n\\nWhat is your query?\",\n          \"```cpp\\n#include <iostream>\\n#include <vector>\\n#include <limits>\\n#include <memory>\\n#include <Eigen/Dense>\\n#include \\\"stb_image_write.h\\\"\\n\\nusing namespace std;\\nusing namespace Eigen;\\n\\nclass Vector3 {\\npublic:\\n    Vector3(float x = 0, float y = 0, float z = 0) : v(x, y, z) {}\\n\\n    float x() const { return v.x(); }\\n    float y() const { return v.y(); }\\n    float z() const { return v.z(); }\\n\\n    Vector3 operator+(const Vector3 &other) const {\\n        return Vector3(v + other.v);\\n    }\\n\\nprivate:\\n    Vector3(const Eigen::Vector3f &v) : v(v) {}\\n\\n    Eigen::Vector3f v;\\n};\\n\\nclass Ray {\\npublic:\\n    Ray(const Vector3 &origin, const Vector3 &direction)\\n        : origin(origin), direction(direction.normalized()) {}\\n\\n    Vector3 origin;\\n    Vector3 direction;\\n};\\n\\nclass Shape {\\npublic:\\n    virtual bool intersect(const Ray &ray, float &t) const = 0;\\n};\\n\\nclass Sphere : public Shape {\\npublic:\\n    Sphere(const Vector3 &center, float radius) : center(center), radius(radius) {}\\n\\n    bool intersect(const Ray &ray, float &t) const override {\\n        // ... (intersection algorithm)\\n    }\\n\\n    Vector3 center;\\n    float radius;\\n};\\n\\nclass Plane : public Shape {\\npublic:\\n    Plane(const Vector3 &point, const Vector3 &normal)\\n        : point(point), normal(normal.normalized()) {}\\n\\n    bool intersect(const Ray &ray, float &t) const override {\\n        // ... (intersection algorithm)\\n    }\\n\\n    Vector3 point;\\n    Vector3 normal;\\n};\\n\\nclass Light {\\npublic:\\n    Light(const Vector3 &position, float intensity)\\n        : position(position), intensity(intensity) {}\\n\\n    Vector3 position;\\n    float intensity;\\n};\\n\\nclass Scene {\\npublic:\\n    void addShape(unique_ptr<Shape> shape) {\\n        shapes.push_back(move(shape));\\n    }\\n\\n    void addLight(const Light &light) {\\n        lights.push_back(light);\\n    }\\n\\n    const Shape *intersect(const Ray &ray, float &t) const {\\n        // ... (find the closest intersection)\\n    }\\n\\n    vector<unique_ptr<Shape>> shapes;\\n    vector<Light> lights;\\n};\\n\\nVector3 shade(const Ray &ray, const Scene &scene) {\\n    float t;\\n    const Shape *shape = scene.intersect(ray, t);\\n\\n    if (!shape) {\\n        return Vector3(0);  // background color\\n    }\\n\\n    // Compute the shading based on the intersection and lighting\\n    // ...\\n\\n    return color;\\n}\\n\\nvoid render(const Scene &scene, const string &outputFile) {\\n    int width = 800;\\n    int height = 600;\\n\\n    vector<uint8_t> pixels(width * height * 3);\\n\\n    for (int y = 0; y < height; ++y) {\\n        for (int x = 0; x < width; ++x) {\\n            // Cast a ray through the pixel\\n            Ray ray = ...;\\n\\n            Vector3 color = shade(ray, scene);\\n\\n            int index = (y * width + x) * 3;\\n            pixels[index] = color.x() * 255;\\n            pixels[index + 1] = color.y() * 255;\\n            pixels[index + 2] = color.z() * 255;\\n        }\\n    }\\n\\n    stbi_write_png(outputFile.c_str(), width, height, 3, pixels.data(), width * 3);\\n}\\n\\nint main() {\\n    // Create your 3D scene with shapes and lights\\n    // ...\\n\\n    // Render the scene and save it as a PNG file\\n    render(scene, \\\"output.png\\\");\\n\\n    return 0;\\n}\\n```\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "\n",
        "# Read as pandas DataFrame\n",
        "dataset['train'].to_pandas()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Chat template**"
      ],
      "metadata": {
        "id": "1k1EkmzEe3hV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "b16f12ccbbcc46d29327c134d84652de",
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            "e1d1aa12229c4e51aa773cdc8ad0e814",
            "7f85fb39d1c24f808a4763de18cd712a",
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            "a087c38e5b0e4920bb9ea09210ee1896",
            "5289fda84aa648bfad773e6f54e48d0c",
            "bba89b70ea6541e8bddc689710676b6a",
            "2c1cd8a9de7d44208ac7e61bf292dc73"
          ]
        },
        "id": "1CLdxLwcbt-W",
        "outputId": "10c24308-7be6-4f63-b514-c3beb6cc53c2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b16f12ccbbcc46d29327c134d84652de"
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          "metadata": {}
        }
      ],
      "source": [
        "# Define a function to format each example in the dataset with a template for chat interaction\n",
        "def chat_template(example):\n",
        "    # Append a formatted template to the 'instruction' field of each example\n",
        "    # This format separates the instruction from the expected response, clearly defining the structure\n",
        "    example[\"instruction\"] = f\"### Instruction:\\n{example['instruction']}\\n\\n### Response:\\n\"\n",
        "    return example\n",
        "\n",
        "# Apply the chat_template function to each example in the dataset\n",
        "# This uses the .map method of the dataset, which applies a specified function to each element\n",
        "dataset = dataset.map(chat_template)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "buGTLbYtbxlO",
        "outputId": "70d928cf-6f6d-43f6-9942-631847fc1913"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "                                           instruction  \\\n",
              "0    ### Instruction:\\nLet's come up with a rich an...   \n",
              "1    ### Instruction:\\nLet\\n$$p(x,y) = a_0 + a_1x +...   \n",
              "2    ### Instruction:\\nGiven the code below, refact...   \n",
              "3    ### Instruction:\\nFind the area of the region ...   \n",
              "4    ### Instruction:\\nLet $P$ be the plane passing...   \n",
              "..                                                 ...   \n",
              "995  ### Instruction:\\nHello. My name is Mike. I ha...   \n",
              "996  ### Instruction:\\nGiven a prime $p$ and an int...   \n",
              "997  ### Instruction:\\nLet $f$ be defined by  \\[f(x...   \n",
              "998  ### Instruction:\\nBEGININPUT\\nBEGINCONTEXT\\nda...   \n",
              "999  ### Instruction:\\nBEGININPUT\\nBEGINCONTEXT\\nda...   \n",
              "\n",
              "                                                output  \n",
              "0    Planet Name: Xylothar\\n\\nXylothar is a diverse...  \n",
              "1    Observe that  \\begin{align*}\\np(0,0) &= a_0 = ...  \n",
              "2    Here is the refactored and commented version:\\...  \n",
              "3    Let $n = \\lfloor x \\rfloor,$ and let $\\{x\\} = ...  \n",
              "4    Let $\\mathbf{v} = \\begin{pmatrix} x \\\\ y \\\\ z ...  \n",
              "..                                                 ...  \n",
              "995  Hello Mike, it's nice to meet you. As an AI la...  \n",
              "996  To find the primitive roots $\\pmod 7$, I need ...  \n",
              "997  The number $f^{-1}(-3)$ is the value of $x$ su...  \n",
              "998  Dr. Eleanor Thompson's study found that partic...  \n",
              "999  Charles Leroux won the Golden Bread Award in 1...  \n",
              "\n",
              "[1000 rows x 2 columns]"
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              "      <td>### Instruction:\\nLet\\n$$p(x,y) = a_0 + a_1x +...</td>\n",
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              "      <td>### Instruction:\\nFind the area of the region ...</td>\n",
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              "      <th>4</th>\n",
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              "      <td>### Instruction:\\nLet $f$ be defined by  \\[f(x...</td>\n",
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              "    <tr>\n",
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              "      <td>### Instruction:\\nBEGININPUT\\nBEGINCONTEXT\\nda...</td>\n",
              "      <td>Dr. Eleanor Thompson's study found that partic...</td>\n",
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              "      <td>### Instruction:\\nBEGININPUT\\nBEGINCONTEXT\\nda...</td>\n",
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              "        const element = document.querySelector('#df-afd8c399-88b5-4eaf-9d92-cf672e180a15');\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",
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              "summary": "{\n  \"name\": \"dataset[\\\"train\\\"]\",\n  \"rows\": 1000,\n  \"fields\": [\n    {\n      \"column\": \"instruction\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"### Instruction:\\nBEGININPUT\\nBEGINCONTEXT\\ndate: September 15, 2021\\nurl: www.telecomhistory.com/evolution-video-transcript\\nauthor: Patricia Williams\\ntitle: The Evolution of Telecommunication: A Video Transcript\\nENDCONTEXT\\nIn this video transcript, we will explore the fascinating journey of telecommunication from its humble beginnings to the advanced technologies we use today. Our story begins in the early 19th century with Samuel Morse's invention of the electric telegraph.\\n\\nThe electric telegraph was invented by Samuel Morse in 1837 and became operational in 1844. This revolutionary device allowed people to send messages over long distances using a series of electrical signals. Messages were sent in Morse code, a system of dots and dashes that represented letters and numbers. The first message sent via telegraph was \\\"What hath God wrought?\\\" on May 24, 1844, between Washington D.C. and Baltimore.\\n\\nAlexander Graham Bell is credited with inventing the telephone in 1876. His invention revolutionized communication by allowing people to speak directly to each other over long distances. On March 10, 1876, Bell made the first successful telephone call to his assistant, Thomas Watson, saying, \\\"Mr. Watson, come here; I want you.\\\" By the end of the 19th century, telephone lines had been installed across the United States and Europe.\\n\\nWireless communication took a significant leap forward in 1895 when Guglielmo Marconi developed the wireless telegraph, also known as the radio. Marconi's invention used electromagnetic waves to transmit messages without wires. In 1901, he successfully sent a wireless signal across the Atlantic Ocean, proving that radio could be used for long-distance communication.\\n\\nTelevision emerged as a new form of telecommunication in the 1920s. Scottish inventor John Logie Baird demonstrated the first working television system on January 26, 1926. Early television sets were large and expensive, but by the 1950s, they had become more affordable and widespread.\\n\\nThe development of satellites in the 1960s allowed for global communication on an unprecedented scale. On July 10, 1962, Telstar 1, the first active communications satellite, was launched into orbit. This satellite enabled live transatlantic television broadcasts and telephone calls between the United States and Europe.\\n\\nIn the 1980s, mobile phones began to gain popularity. The first commercially available cell phone, the Motorola DynaTAC 8000X, was released in 1983. It weighed nearly two pounds and cost $3,995. Over time, mobile phones became smaller, lighter, and more affordable, leading to their widespread adoption around the world.\\n\\nThe invention of the internet in the late 20th century revolutionized telecommunication once again. Developed initially as a military project in the 1960s, the internet became publicly accessible in the 1990s. Today, it serves as a platform for countless applications, including email, social media, video conferencing, and online gaming.\\n\\nAs we look to the future, advancements in telecommunication technology continue to evolve at a rapid pace. From 5G networks to virtual reality, the possibilities are endless. One thing is certain: the history of telecommunication is a testament to human ingenuity and our desire to connect with one another.\\nENDINPUT\\n\\nBEGININSTRUCTION\\n- Summarize the key milestones in the evolution of telecommunication from the electric telegraph to the internet.\\nENDINSTRUCTION\\n\\n### Response:\\n\",\n          \"### Instruction:\\nFor each following Query, return a series of clear, factual and concise Summary Bullet Points of the Query using only information available in the Query.\\n###\\nQuery:\\nArtificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of \\\"intelligent agents\\\": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.3 Colloquially, the term \\\"artificial intelligence\\\" is often used to describe machines (or computers) that mimic \\\"cognitive\\\" functions that humans associate with the human mind, such as \\\"learning\\\" and \\\"problem solving\\\".[4]\\n\\nAs machines become increasingly capable, tasks considered to require \\\"intelligence\\\" are often removed from the definition of AI, a phenomenon known as the AI effect.[5] A quip in Tesler's Theorem says \\\"AI is whatever hasn't been done yet.\\\"[6] For instance, optical character recognition is frequently excluded from things considered to be AI,[7] having become a routine technology.[8] Modern machine capabilities generally classified as AI include successfully understanding human speech,[9] competing at the highest level in strategic game systems (such as chess and Go),[10] autonomously operating cars, intelligent routing in content delivery networks, and military simulations.[11]\\n\\nSummary:\\n- The text discusses artificial intelligence (AI) and its various applications. \\n- It defines AI as intelligence demonstrated by machines\\n- AI is often distinguished from natural intelligence by its lack of consciousness and emotionality. \\n- Discusses the AI effect, which is the tendency for tasks to be removed from the definition of AI as machines become increasingly capable.\\n###\\nQuery:\\nFishing is the activity of trying to catch fish. Fish are often caught as wildlife from the natural environments, but may also be caught from stocked bodies of water such as ponds, canals, park wetlands and reservoirs. Fishing techniques include hand-gathering, spearing, netting, angling, shooting and trapping, as well as more destructive and often illegal techniques such as electrocution, blasting and poisoning.\\n\\nThe term fishing broadly includes catching aquatic animals other than fish, such as crustaceans (shrimp/lobsters/crabs), shellfish, cephalopods (octopus/squid) and echinoderms (starfish/sea urchins). The term is not normally applied to harvesting fish raised in controlled cultivations (fish farming). Nor is it normally applied to hunting aquatic mammals, where terms like whaling and sealing are used instead.\\n\\nFishing has been an important part of human culture since hunter-gatherer times, and is one of the few food production activities that have persisted from prehistory into modern age, surviving both the Neolithic Revolution and successive Industrial Revolutions. In addition to being caught to be eaten for food, fish are caught as recreational pastimes. Fishing tournaments are held, and caught fish are sometimes kept long-term as preserved or living trophies. When bioblitzes occur, fish are typically caught, identified, and then released.\\n\\nAccording to the United Nations FAO statistics, the total number of commercial fishers and fish farmers is estimated to be 38 million. Fishing industries and aquaculture provide direct and indirect employment to over 500 million people in developing countries.[1] In 2005, the worldwide per capita consumption of fish captured from wild fisheries was 14.4 kilograms (32 lb), with an additional 7.4 kilograms (16 lb) harvested from fish farms.[2]\\n\\nSummary:\\n- Fishing is the activity of trying to catch fish.\\n- Fishing techniques include hand-gathering, spearing, netting, angling, shooting and trapping.\\n- The term \\\"fishing\\\" broadly includes catching aquatic animals other than fish, such as crustaceans, shellfish, cephalopods and echinoderms.\\n- The term fishing is not normally applied to hunting aquatic mammals.\\n- Fishing has been an important part of human culture since hunter-gatherer times\\n###\\nQuery: {}\\n\\nSummary:\\n\\n### Response:\\n\",\n          \"### Instruction:\\nDevelop a C++ program that implements a simple ray-tracing algorithm to render 3D scenes with basic shapes (spheres, planes) and lighting. The program should output the rendered image as a PNG file.\\nPLAINFORMAT\\n\\n### Response:\\n\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"output\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1000,\n        \"samples\": [\n          \"The key milestones in the evolution of telecommunication include:\\n\\n1. Invention of the electric telegraph by Samuel Morse in 1837, which became operational in 1844 and allowed long-distance communication using Morse code.\\n2. Alexander Graham Bell's invention of the telephone in 1876, enabling direct speech over long distances.\\n3. Guglielmo Marconi's development of the wireless telegraph (radio) in 1895, using electromagnetic waves for wire-free messaging.\\n4. The emergence of television as a new form of telecommunication in the 1920s, with John Logie Baird demonstrating the first working system in 1926.\\n5. Launch of Telstar 1, the first active communications satellite, in 1962, allowing global communication through live transatlantic broadcasts and phone calls.\\n6. The rise of mobile phones in the 1980s, starting with the release of the Motorola DynaTAC 8000X in 1983.\\n7. The invention of the internet in the late 20th century, initially developed as a military project and becoming publicly accessible in the 1990s, leading to various applications like email, social media, and video conferencing.\",\n          \"Thank you for the examples, I'm ready to summarize now. \\n\\nWhat is your query?\",\n          \"```cpp\\n#include <iostream>\\n#include <vector>\\n#include <limits>\\n#include <memory>\\n#include <Eigen/Dense>\\n#include \\\"stb_image_write.h\\\"\\n\\nusing namespace std;\\nusing namespace Eigen;\\n\\nclass Vector3 {\\npublic:\\n    Vector3(float x = 0, float y = 0, float z = 0) : v(x, y, z) {}\\n\\n    float x() const { return v.x(); }\\n    float y() const { return v.y(); }\\n    float z() const { return v.z(); }\\n\\n    Vector3 operator+(const Vector3 &other) const {\\n        return Vector3(v + other.v);\\n    }\\n\\nprivate:\\n    Vector3(const Eigen::Vector3f &v) : v(v) {}\\n\\n    Eigen::Vector3f v;\\n};\\n\\nclass Ray {\\npublic:\\n    Ray(const Vector3 &origin, const Vector3 &direction)\\n        : origin(origin), direction(direction.normalized()) {}\\n\\n    Vector3 origin;\\n    Vector3 direction;\\n};\\n\\nclass Shape {\\npublic:\\n    virtual bool intersect(const Ray &ray, float &t) const = 0;\\n};\\n\\nclass Sphere : public Shape {\\npublic:\\n    Sphere(const Vector3 &center, float radius) : center(center), radius(radius) {}\\n\\n    bool intersect(const Ray &ray, float &t) const override {\\n        // ... (intersection algorithm)\\n    }\\n\\n    Vector3 center;\\n    float radius;\\n};\\n\\nclass Plane : public Shape {\\npublic:\\n    Plane(const Vector3 &point, const Vector3 &normal)\\n        : point(point), normal(normal.normalized()) {}\\n\\n    bool intersect(const Ray &ray, float &t) const override {\\n        // ... (intersection algorithm)\\n    }\\n\\n    Vector3 point;\\n    Vector3 normal;\\n};\\n\\nclass Light {\\npublic:\\n    Light(const Vector3 &position, float intensity)\\n        : position(position), intensity(intensity) {}\\n\\n    Vector3 position;\\n    float intensity;\\n};\\n\\nclass Scene {\\npublic:\\n    void addShape(unique_ptr<Shape> shape) {\\n        shapes.push_back(move(shape));\\n    }\\n\\n    void addLight(const Light &light) {\\n        lights.push_back(light);\\n    }\\n\\n    const Shape *intersect(const Ray &ray, float &t) const {\\n        // ... (find the closest intersection)\\n    }\\n\\n    vector<unique_ptr<Shape>> shapes;\\n    vector<Light> lights;\\n};\\n\\nVector3 shade(const Ray &ray, const Scene &scene) {\\n    float t;\\n    const Shape *shape = scene.intersect(ray, t);\\n\\n    if (!shape) {\\n        return Vector3(0);  // background color\\n    }\\n\\n    // Compute the shading based on the intersection and lighting\\n    // ...\\n\\n    return color;\\n}\\n\\nvoid render(const Scene &scene, const string &outputFile) {\\n    int width = 800;\\n    int height = 600;\\n\\n    vector<uint8_t> pixels(width * height * 3);\\n\\n    for (int y = 0; y < height; ++y) {\\n        for (int x = 0; x < width; ++x) {\\n            // Cast a ray through the pixel\\n            Ray ray = ...;\\n\\n            Vector3 color = shade(ray, scene);\\n\\n            int index = (y * width + x) * 3;\\n            pixels[index] = color.x() * 255;\\n            pixels[index + 1] = color.y() * 255;\\n            pixels[index + 2] = color.z() * 255;\\n        }\\n    }\\n\\n    stbi_write_png(outputFile.c_str(), width, height, 3, pixels.data(), width * 3);\\n}\\n\\nint main() {\\n    // Create your 3D scene with shapes and lights\\n    // ...\\n\\n    // Render the scene and save it as a PNG file\\n    render(scene, \\\"output.png\\\");\\n\\n    return 0;\\n}\\n```\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "dataset[\"train\"].to_pandas()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2PBjwceVbzXU"
      },
      "outputs": [],
      "source": [
        "! pip install huggingface_hub --q"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wpakk8XZXGq4",
        "outputId": "6ecf5d9c-953b-4ef0-c7c9-667c667b9109"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n",
            "    Setting a new token will erase the existing one.\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) Y\n",
            "Token is valid (permission: write).\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.push_to_hub(\"malikashish997/create_data\")"
      ],
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        {
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            "text/plain": [
              "Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"
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