Dataset_Creating_For_Fine_Tuning_Llama_2(1).ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"ivUAZC1AUs1P"},"source":["#**Dataset Creation**\n","\n","There are different types of datasets which we can use to fine-tune Large Language Models.\n","\n","1. Instruction Datasets\n","\n","2. Raw Completion\n","\n","3. Preference Datasets\n","\n","Usually instruction dataset is used to fine-tune the Llama 2 Model. And as we will be fine-tuning the Llama 2 Model using Supervised Fine Tuning, so we can only used the instruction dataset for this.\n","\n","So, we have two options\n","\n","1. Create our own Instruction Dataset\n","\n","2. Modify an existing instruction dataset i.e. filter it, modify it and enrich it\n"]},{"cell_type":"markdown","metadata":{"id":"QXAinDUcVWxM"},"source":["**So we will go it with the option 02, we will modify an existing instruction daraset**"]},{"cell_type":"markdown","metadata":{"id":"8Q2UipukXhkh"},"source":["#**01. Install All the Required Libraries**"]},{"cell_type":"code","execution_count":29,"metadata":{"executionInfo":{"elapsed":5685,"status":"ok","timestamp":1705521307655,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"ulGDcaoPXu_P"},"outputs":[],"source":["#Install Datasets library to load the dataset from hugging face into the Google Colab Notebook.\n","#Install Transformers library to import the Autotokenizer this will convert the raw text into tokens\n","#Install Sentence Transformers Library to download the Embedding Model\n","!pip install -q datasets transformers sentence_transformers faiss-gpu"]},{"cell_type":"code","execution_count":30,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":340},"executionInfo":{"elapsed":4572,"status":"error","timestamp":1705521312217,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"zdUYSh84xpfy","outputId":"b19d78dd-7f5b-40d6-e2d6-257a5452d81e"},"outputs":[{"output_type":"error","ename":"MessageError","evalue":"Error: credential propagation was unsuccessful","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mMessageError\u001b[0m                              Traceback (most recent call last)","\u001b[0;32m<ipython-input-30-d5df0069828e>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mgoogle\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolab\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdrive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdrive\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'/content/drive'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36mmount\u001b[0;34m(mountpoint, force_remount, timeout_ms, readonly)\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mmount\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_ms\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m120000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreadonly\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     99\u001b[0m   \u001b[0;34m\"\"\"Mount your Google Drive at the specified mountpoint path.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 100\u001b[0;31m   return _mount(\n\u001b[0m\u001b[1;32m    101\u001b[0m       \u001b[0mmountpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    102\u001b[0m       \u001b[0mforce_remount\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mforce_remount\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/drive.py\u001b[0m in \u001b[0;36m_mount\u001b[0;34m(mountpoint, force_remount, timeout_ms, ephemeral, readonly)\u001b[0m\n\u001b[1;32m    127\u001b[0m   )\n\u001b[1;32m    128\u001b[0m   \u001b[0;32mif\u001b[0m \u001b[0mephemeral\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 129\u001b[0;31m     _message.blocking_request(\n\u001b[0m\u001b[1;32m    130\u001b[0m         \u001b[0;34m'request_auth'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m'authType'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'dfs_ephemeral'\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    131\u001b[0m     )\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mblocking_request\u001b[0;34m(request_type, request, timeout_sec, parent)\u001b[0m\n\u001b[1;32m    174\u001b[0m       \u001b[0mrequest_type\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrequest\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparent\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpect_reply\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    175\u001b[0m   )\n\u001b[0;32m--> 176\u001b[0;31m   \u001b[0;32mreturn\u001b[0m \u001b[0mread_reply_from_input\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrequest_id\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout_sec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/google/colab/_message.py\u001b[0m in \u001b[0;36mread_reply_from_input\u001b[0;34m(message_id, timeout_sec)\u001b[0m\n\u001b[1;32m    101\u001b[0m     ):\n\u001b[1;32m    102\u001b[0m       \u001b[0;32mif\u001b[0m \u001b[0;34m'error'\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 103\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mMessageError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreply\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'error'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    104\u001b[0m       \u001b[0;32mreturn\u001b[0m \u001b[0mreply\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mMessageError\u001b[0m: Error: credential propagation was unsuccessful"]}],"source":["from google.colab import drive\n","drive.mount('/content/drive')"]},{"cell_type":"markdown","metadata":{"id":"29CEnxgcZAXg"},"source":["#**03. Set the Hugging Face Token**"]},{"cell_type":"code","execution_count":31,"metadata":{"executionInfo":{"elapsed":598,"status":"ok","timestamp":1705521316874,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"j-hEiS7CUyXD"},"outputs":[],"source":["import os\n","os.environ[\"HF_TOKEN\"] = \"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\""]},{"cell_type":"markdown","metadata":{"id":"3E_G5bbIZnQt"},"source":["#**04. Load the Dataset**"]},{"cell_type":"code","execution_count":32,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1888,"status":"ok","timestamp":1705521320801,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"r8sPoUImZj2W","outputId":"818cdaa2-3716-4335-9eb2-07d432310ca3"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input', 'output', 'instruction'],\n","        num_rows: 10000\n","    })\n","})"]},"metadata":{},"execution_count":32}],"source":["from datasets import load_dataset\n","dataset = load_dataset(\"medalpaca/medical_meadow_wikidoc\")\n","dataset"]},{"cell_type":"code","execution_count":33,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":381,"status":"ok","timestamp":1705521325689,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"B4VtvKAleA-I","outputId":"7ed29f53-335f-4f81-c7d7-0dc3ac23e057"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['input', 'output', 'instruction'],\n","    num_rows: 10000\n","})"]},"metadata":{},"execution_count":33}],"source":["dataset[\"train\"]"]},{"cell_type":"code","execution_count":34,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"elapsed":1146,"status":"ok","timestamp":1705521330405,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"g_6qFaC8ZzTh","outputId":"3ae249be-430f-4c09-a9f0-2a525b201cb8"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  input  \\\n","0     Can you provide an overview of the lung's squa...   \n","1                         What does \"Clear: cell\" mean?   \n","2     Can you provide me with information regarding ...   \n","3     What are the historical background and symptom...   \n","4     What does the \"Hypotension: Resident Survival ...   \n","...                                                 ...   \n","9995  What are some biochemistry topics that can be ...   \n","9996  What information is available on esophageal st...   \n","9997  What is the main way to prevent basal cell car...   \n","9998  Could you provide me with the text you want me...   \n","9999  What is the medical treatment for De Quervain'...   \n","\n","                                                 output  \\\n","0     Squamous cell carcinoma of the lung may be cla...   \n","1     Clear cell tumors are part of the surface epit...   \n","2     Two Japanese scientists commenced research int...   \n","3     Symptoms of vulvovaginitis caused by Candida s...   \n","4     Hypotension is the term for low blood pressure...   \n","...                                                 ...   \n","9995                                            Water -   \n","9996  Esophageal stricture is a narrowing of the eso...   \n","9997  The development of basal cell carcinoma can be...   \n","9998  No medical treatment is currently known or pra...   \n","9999  The mainstay of therapy for de Quervain's thyr...   \n","\n","                          instruction  \n","0     Answer this question truthfully  \n","1     Answer this question truthfully  \n","2     Answer this question truthfully  \n","3     Answer this question truthfully  \n","4     Answer this question truthfully  \n","...                               ...  \n","9995  Answer this question truthfully  \n","9996  Answer this question truthfully  \n","9997  Answer this question truthfully  \n","9998  Answer this question truthfully  \n","9999  Answer this question truthfully  \n","\n","[10000 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-22c49458-ef59-49a0-afe8-3ef096de3431\" class=\"colab-df-container\">\n","    <div>\n","<style 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<th>2</th>\n","      <td>Can you provide me with information regarding ...</td>\n","      <td>Two Japanese scientists commenced research int...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>What are the historical background and symptom...</td>\n","      <td>Symptoms of vulvovaginitis caused by Candida s...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>What does the \"Hypotension: Resident Survival ...</td>\n","      <td>Hypotension is the term for low blood pressure...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>What are some biochemistry topics that can be ...</td>\n","      <td>Water -</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>9996</th>\n","      <td>What information is available on esophageal st...</td>\n","      <td>Esophageal stricture is a narrowing of the eso...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>9997</th>\n","      <td>What is the main way to prevent basal cell car...</td>\n","      <td>The development of basal cell carcinoma can be...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>9998</th>\n","      <td>Could you provide me with the text you want me...</td>\n","      <td>No medical treatment is currently known or pra...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>9999</th>\n","      <td>What is the medical treatment for De Quervain'...</td>\n","      <td>The mainstay of therapy for de Quervain's thyr...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","  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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-5872a755-f794-4db9-8573-5b1fefc6d1ba\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5872a755-f794-4db9-8573-5b1fefc6d1ba')\"\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-5872a755-f794-4db9-8573-5b1fefc6d1ba button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":34}],"source":["# Read the Dataset as the Pandas Dataset\n","dataset[\"train\"].to_pandas()"]},{"cell_type":"markdown","metadata":{"id":"XvTvVY2AaWnv"},"source":["#**05. Analyze the Dataset**"]},{"cell_type":"code","execution_count":35,"metadata":{"executionInfo":{"elapsed":5,"status":"ok","timestamp":1705521339547,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"Ea3VArojaA1Y"},"outputs":[],"source":["#Use Transformers library to import the Autotokenizer this will convert the raw text into tokens\n","from transformers import AutoTokenizer\n","# Import Matplotlib and Seaborn Library to plot and visualize the data\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","#Next I will load the Tokenizer from Llama 2, I will use the Nous Research Version of Llama 2 and not the official one from meta,\n","# to use the official version of Llama 2 from meta you need to have an Hugging Face pro account\n","tokenizer = AutoTokenizer.from_pretrained(\"NousResearch/Llama-2-7b-hf\")"]},{"cell_type":"code","execution_count":36,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":1573,"status":"ok","timestamp":1705521344798,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"wZknnyySb5r1","outputId":"31ef8bdc-2869-432b-9cf9-9921111a91e3"},"outputs":[{"output_type":"stream","name":"stdout","text":["Instruction Tokens Count [19, 9, 10, 21, 19, 11, 16, 15, 13, 13, 21, 14, 22, 19, 21, 24, 24, 13, 25, 11, 15, 10, 14, 16, 25, 23, 10, 11, 14, 15, 18, 19, 19, 12, 15, 17, 26, 16, 10, 13, 9, 11, 18, 9, 15, 14, 12, 0, 18, 23, 10, 16, 13, 21, 16, 10, 17, 17, 21, 17, 29, 22, 15, 16, 16, 15, 14, 9, 15, 17, 11, 16, 13, 14, 8, 18, 30, 17, 13, 24, 25, 11, 14, 11, 16, 11, 25, 17, 9, 12, 16, 10, 15, 9, 18, 18, 12, 13, 17, 14, 14, 17, 14, 18, 17, 11, 14, 12, 15, 14, 22, 23, 21, 9, 20, 12, 10, 11, 22, 26, 12, 20, 7, 18, 17, 23, 25, 26, 17, 9, 17, 16, 16, 12, 13, 13, 13, 20, 13, 33, 15, 14, 16, 26, 20, 26, 16, 18, 22, 15, 10, 14, 16, 22, 20, 20, 19, 17, 15, 13, 20, 17, 14, 23, 19, 15, 12, 20, 16, 13, 14, 11, 16, 16, 14, 7, 16, 12, 20, 9, 18, 14, 19, 12, 16, 11, 9, 18, 21, 10, 14, 16, 16, 18, 10, 14, 22, 15, 17, 11, 14, 10, 15, 13, 14, 9, 8, 25, 10, 18, 14, 17, 15, 20, 14, 17, 12, 15, 9, 17, 18, 12, 20, 21, 18, 15, 12, 12, 14, 16, 13, 14, 11, 9, 10, 19, 16, 15, 18, 16, 17, 20, 17, 9, 10, 23, 11, 61, 12, 12, 23, 15, 10, 18, 12, 13, 17, 16, 14, 14, 17, 14, 9, 11, 29, 17, 21, 14, 16, 15, 14, 15, 25, 29, 13, 15, 18, 21, 16, 11, 14, 16, 15, 22, 16, 14, 9, 10, 8, 18, 18, 25, 17, 17, 17, 15, 13, 21, 17, 18, 15, 20, 9, 10, 11, 16, 20, 14, 21, 13, 17, 16, 14, 11, 15, 24, 19, 14, 12, 12, 9, 19, 18, 28, 20, 17, 11, 10, 9, 12, 19, 16, 12, 23, 17, 12, 8, 15, 12, 15, 15, 17, 20, 14, 17, 16, 12, 12, 19, 22, 13, 24, 15, 10, 16, 10, 21, 22, 13, 16, 18, 17, 20, 15, 19, 30, 13, 15, 11, 23, 15, 8, 15, 17, 20, 19, 11, 18, 16, 20, 14, 16, 17, 19, 20, 15, 10, 15, 17, 14, 13, 13, 15, 14, 12, 19, 16, 35, 23, 15, 15, 12, 14, 14, 13, 20, 18, 14, 16, 26, 12, 17, 21, 14, 12, 19, 18, 15, 20, 19, 10, 12, 13, 17, 14, 19, 11, 14, 19, 19, 10, 14, 15, 11, 16, 12, 18, 24, 8, 18, 20, 14, 11, 15, 10, 13, 17, 14, 10, 8, 10, 17, 16, 17, 13, 12, 14, 19, 13, 15, 9, 25, 21, 11, 15, 15, 13, 15, 30, 17, 28, 17, 16, 10, 25, 10, 11, 27, 15, 18, 16, 12, 19, 14, 18, 14, 17, 14, 16, 27, 17, 11, 15, 11, 9, 13, 18, 14, 26, 13, 11, 9, 10, 17, 16, 16, 18, 14, 24, 14, 19, 19, 16, 13, 42, 14, 13, 12, 9, 13, 23, 17, 17, 16, 21, 16, 11, 14, 20, 22, 16, 29, 18, 14, 11, 18, 10, 11, 24, 13, 15, 26, 22, 14, 19, 11, 12, 11, 14, 31, 17, 16, 17, 18, 13, 17, 11, 18, 25, 16, 13, 10, 22, 15, 19, 17, 17, 10, 12, 12, 24, 15, 19, 12, 16, 17, 12, 19, 18, 8, 18, 11, 9, 14, 10, 13, 15, 17, 22, 15, 24, 10, 14, 16, 16, 28, 11, 16, 12, 10, 14, 18, 16, 17, 12, 13, 15, 10, 12, 13, 16, 14, 17, 13, 10, 10, 14, 12, 23, 16, 18, 21, 20, 15, 15, 10, 9, 21, 15, 15, 15, 9, 19, 14, 14, 12, 13, 20, 13, 18, 13, 8, 15, 13, 18, 13, 14, 20, 15, 12, 12, 14, 13, 20, 17, 11, 17, 11, 21, 14, 15, 11, 14, 14, 11, 20, 11, 20, 13, 15, 11, 18, 14, 17, 22, 10, 16, 23, 18, 13, 14, 13, 11, 28, 17, 14, 9, 16, 13, 13, 19, 12, 10, 21, 11, 16, 13, 13, 17, 18, 13, 13, 18, 16, 21, 14, 16, 13, 18, 15, 16, 11, 14, 17, 36, 14, 14, 25, 14, 18, 15, 14, 19, 14, 14, 18, 11, 12, 25, 13, 24, 15, 23, 16, 16, 10, 9, 17, 12, 9, 13, 12, 13, 27, 12, 18, 21, 14, 12, 26, 20, 22, 20, 40, 13, 15, 16, 21, 17, 18, 22, 11, 14, 21, 17, 22, 21, 19, 17, 17, 19, 11, 14, 17, 17, 9, 10, 37, 14, 11, 16, 22, 15, 13, 17, 15, 34, 17, 11, 12, 17, 13, 16, 14, 9, 13, 16, 17, 15, 21, 10, 16, 14, 9, 13, 17, 11, 20, 12, 14, 11, 23, 8, 17, 11, 16, 13, 15, 23, 11, 22, 15, 16, 17, 20, 9, 19, 8, 13, 18, 17, 15, 16, 13, 13, 14, 14, 17, 17, 11, 12, 27, 27, 20, 19, 12, 14, 15, 27, 21, 14, 17, 22, 25, 15, 18, 13, 14, 20, 14, 12, 16, 16, 16, 15, 22, 18, 15, 15, 22, 19, 12, 15, 32, 19, 14, 15, 11, 41, 14, 11, 25, 23, 13, 18, 13, 15, 13, 14, 31, 13, 13, 12, 23, 18, 20, 17, 35, 14, 21, 14, 20, 16, 16, 14, 24, 16, 13, 14, 31, 17, 27, 13, 26, 22, 33, 12, 19, 16, 15, 14, 19, 14, 19, 17, 13, 15, 18, 21, 6, 16, 12, 14, 21, 19, 13, 18, 14, 26, 26, 20, 10, 12, 13, 20, 11, 15, 15, 20, 23, 12, 9, 12, 22, 22, 19, 12, 18, 12, 16, 12, 14, 7, 14, 15, 12, 14, 18, 16, 14, 25, 12, 15, 10, 15, 13, 19, 10, 13, 13, 19, 22, 21, 17, 9, 18, 19, 23, 13, 11, 12, 16, 33, 25, 18, 20, 12, 13, 24, 20, 14, 20, 27, 21, 11, 12, 9, 15, 13, 20, 16, 22, 10, 8, 16, 14, 21, 14, 16, 13, 9, 28, 12, 11, 12, 19, 17, 14, 25, 16, 18, 19, 12, 10, 15, 9, 12, 14, 19, 12, 15, 13, 17, 11, 15, 16, 20, 18, 15, 15, 12, 17, 18, 12, 9, 17, 14, 16, 12, 9, 20, 18, 18, 10, 12, 16, 36, 15, 13, 13, 15, 22, 13, 12, 8, 17, 17, 13, 18, 17, 10, 19, 27, 13, 21, 22, 11, 11, 20, 13, 19, 19, 17, 10, 21, 14, 20, 13, 22, 21, 9, 14, 13, 18, 13, 18, 24, 13, 13, 20, 21, 17, 19, 14, 15, 18, 16, 18, 24, 16, 21, 16, 14, 15, 12, 9, 20, 15, 12, 16, 19, 14, 11, 15, 10, 19, 12, 19, 19, 13, 9, 16, 21, 28, 13, 25, 23, 11, 10, 15, 15, 17, 14, 11, 16, 21, 8, 13, 11, 16, 12, 16, 12, 12, 20, 20, 15, 15, 10, 18, 15, 19, 18, 21, 15, 17, 15, 13, 16, 12, 13, 23, 16, 16, 11, 19, 16, 11, 14, 17, 17, 22, 13, 11, 12, 17, 15, 15, 10, 11, 18, 11, 11, 16, 21, 12, 8, 8, 18, 13, 11, 9, 21, 13, 18, 19, 17, 16, 26, 16, 9, 15, 16, 13, 16, 29, 13, 11, 16, 13, 22, 15, 24, 16, 13, 19, 20, 7, 14, 15, 13, 10, 12, 16, 15, 15, 16, 14, 12, 13, 13, 12, 11, 18, 8, 17, 16, 15, 11, 18, 24, 14, 10, 16, 15, 20, 10, 17, 14, 9, 10, 10, 10, 15, 22, 17, 17, 10, 42, 18, 18, 11, 12, 18, 14, 13, 18, 16, 16, 10, 13, 13, 14, 13, 14, 21, 18, 19, 14, 14, 12, 25, 15, 12, 12, 21, 14, 8, 26, 21, 15, 14, 14, 13, 17, 12, 10, 19, 10, 22, 14, 13, 11, 20, 11, 12, 17, 10, 9, 15, 7, 15, 24, 15, 15, 14, 22, 16, 8, 16, 18, 28, 22, 16, 15, 14, 22, 11, 16, 15, 10, 35, 23, 12, 19, 15, 14, 18, 20, 23, 12, 13, 14, 14, 11, 16, 15, 12, 12, 12, 27, 17, 19, 16, 21, 15, 12, 13, 13, 20, 18, 13, 17, 22, 17, 27, 14, 18, 26, 12, 15, 38, 16, 18, 13, 22, 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Tokens Count 10000\n"]}],"source":["#Tokenizer Downloaded\n","#Tokenize each row in the Input and Output Columns in the  Dataset and Count the Total Number of Tokens\n","instruction_tokens_count = [len(tokenizer.tokenize(example[\"input\"])) for example in dataset[\"train\"]]\n","print(\"Instruction Tokens Count\", instruction_tokens_count)\n","print(\"Length of Instruction Tokens Count\", len(instruction_tokens_count))"]},{"cell_type":"code","execution_count":37,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8012,"status":"ok","timestamp":1705521356629,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"xfgGA5FddfEu","outputId":"89768814-2cf6-4a9e-d351-68929d206511"},"outputs":[{"output_type":"stream","name":"stdout","text":["Output Tokens Count [45, 174, 170, 120, 268, 145, 162, 146, 66, 133, 168, 166, 12, 83, 99, 86, 94, 131, 124, 134, 69, 730, 220, 13, 396, 62, 61, 302, 42, 1650, 62, 445, 84, 120, 30, 199, 53, 190, 373, 1311, 72, 134, 1009, 34, 228, 35, 47, 95, 207, 52, 153, 169, 151, 82, 290, 102, 127, 66, 16, 136, 30, 47, 116, 1105, 53, 115, 631, 400, 86, 58, 131, 100, 140, 12, 63, 45, 100, 75, 137, 350, 172, 82, 85, 730, 25, 17, 37, 1079, 44, 45, 277, 641, 1428, 336, 59, 427, 77, 90, 27, 273, 12, 156, 189, 47, 195, 1404, 48, 277, 76, 106, 31, 673, 47, 118, 26, 228, 255, 60, 22, 1028, 79, 19, 823, 38, 318, 319, 112, 62, 16, 107, 684, 99, 24, 43, 196, 94, 104, 117, 148, 121, 216, 94, 80, 11, 138, 264, 64, 140, 536, 157, 34, 97, 71, 322, 166, 234, 238, 16, 1955, 43, 21, 162, 22, 215, 22, 106, 136, 308, 267, 202, 233, 124, 622, 479, 184, 65, 80, 29, 145, 214, 324, 115, 33, 374, 154, 304, 196, 118, 19, 34, 274, 87, 33, 69, 117, 30, 64, 123, 287, 60, 195, 40, 75, 47, 148, 197, 224, 99, 83, 157, 146, 354, 131, 133, 93, 52, 440, 175, 11, 121, 36, 266, 129, 1702, 158, 33, 51, 66, 315, 265, 4, 15, 89, 91, 44, 426, 159, 121, 144, 173, 88, 71, 430, 17, 23, 140, 493, 101, 80, 98, 1614, 139, 4, 32, 666, 74, 38, 211, 15, 312, 18, 87, 462, 613, 108, 182, 470, 381, 61, 36, 22, 583, 15, 450, 32, 158, 88, 149, 254, 883, 114, 68, 34, 159, 28, 27, 123, 114, 41, 30, 180, 18, 50, 109, 18, 178, 41, 37, 18, 45, 64, 172, 68, 427, 159, 973, 227, 124, 290, 140, 88, 206, 57, 69, 36, 176, 602, 34, 89, 79, 40, 77, 72, 30, 42, 541, 64, 19, 133, 98, 106, 81, 57, 630, 224, 206, 130, 107, 68, 21, 1013, 21, 349, 182, 89, 21, 24, 32, 126, 178, 226, 131, 509, 377, 118, 85, 53, 45, 54, 46, 69, 70, 1764, 121, 536, 26, 27, 459, 18, 32, 106, 66, 33, 30, 62, 15, 234, 206, 114, 522, 52, 216, 42, 307, 253, 78, 12, 225, 50, 325, 341, 287, 1172, 258, 58, 306, 86, 158, 100, 68, 67, 226, 19, 152, 8, 37, 387, 91, 559, 14, 89, 340, 330, 124, 241, 58, 172, 175, 13, 731, 71, 7, 34, 341, 219, 48, 23, 96, 1286, 107, 209, 282, 606, 133, 169, 110, 266, 28, 142, 85, 51, 601, 423, 18, 609, 49, 745, 31, 200, 56, 63, 24, 180, 15, 137, 171, 47, 550, 60, 13, 28, 747, 163, 366, 101, 383, 69, 147, 230, 42, 65, 103, 141, 228, 212, 60, 11, 1387, 65, 261, 154, 76, 108, 106, 71, 89, 40, 105, 1752, 50, 98, 247, 163, 113, 34, 41, 1116, 94, 144, 11, 128, 244, 415, 91, 127, 91, 337, 52, 1344, 98, 877, 297, 32, 33, 634, 250, 72, 52, 124, 31, 37, 187, 136, 83, 1622, 43, 2762, 22, 528, 76, 15, 187, 128, 81, 148, 343, 14, 15, 91, 134, 20, 1255, 20, 973, 208, 140, 114, 34, 2102, 204, 323, 609, 324, 141, 1028, 53, 45, 88, 57, 125, 54, 207, 105, 57, 210, 86, 20, 25, 46, 258, 157, 578, 75, 44, 21, 201, 24, 60, 91, 272, 47, 80, 6049, 303, 18, 100, 142, 1366, 121, 369, 126, 162, 213, 534, 136, 78, 844, 57, 174, 187, 652, 870, 143, 24, 60, 239, 127, 107, 64, 28, 124, 1767, 71, 70, 188, 609, 109, 3, 97, 245, 27, 179, 122, 133, 13, 918, 234, 313, 152, 223, 4713, 408, 48, 179, 34, 78, 135, 9, 79, 49, 50, 52, 257, 102, 532, 699, 48, 39, 77, 190, 224, 5, 813, 41, 108, 30, 428, 148, 20, 91, 98, 80, 55, 576, 201, 110, 62, 46, 142, 17, 36, 48, 357, 83, 101, 109, 362, 248, 767, 1124, 766, 73, 63, 116, 362, 34, 303, 682, 116, 194, 307, 44, 81, 21, 250, 32, 4928, 52, 34, 39, 345, 180, 267, 143, 140, 195, 152, 839, 104, 1310, 18, 109, 50, 120, 269, 151, 247, 125, 1675, 150, 33, 601, 16, 18, 952, 891, 259, 121, 89, 93, 288, 30, 19, 171, 93, 145, 325, 15, 543, 366, 16, 1594, 19, 20, 479, 77, 418, 129, 91, 236, 521, 56, 57, 90, 84, 11, 1973, 18, 121, 59, 76, 102, 27, 83, 18, 73, 51, 60, 253, 791, 240, 126, 209, 359, 34, 147, 100, 459, 223, 161, 59, 75, 61, 188, 198, 183, 1768, 70, 200, 222, 93, 128, 657, 202, 1725, 62, 53, 29, 53, 54, 182, 115, 477, 167, 115, 108, 29, 139, 278, 268, 189, 254, 35, 65, 145, 16, 40, 27, 17, 78, 1650, 40, 124, 64, 46, 1515, 298, 17, 116, 9, 37, 445, 105, 52, 1384, 212, 172, 211, 252, 15, 299, 15, 23, 42, 33, 270, 27, 76, 641, 169, 491, 88, 232, 304, 76, 208, 17, 48, 87, 129, 2277, 86, 20, 110, 176, 187, 60, 475, 87, 116, 179, 51, 45, 1482, 56, 398, 150, 1710, 122, 230, 254, 36, 59, 163, 561, 1412, 73, 201, 35, 99, 58, 574, 35, 165, 24, 101, 64, 75, 92, 177, 130, 55, 201, 45, 145, 70, 91, 304, 61, 88, 29, 92, 137, 52, 367, 19, 251, 316, 69, 90, 56, 16, 1432, 392, 867, 423, 240, 130, 90, 72, 40, 24, 249, 64, 50, 262, 227, 35, 187, 92, 174, 81, 25, 104, 66, 485, 40, 146, 30, 212, 152, 102, 182, 79, 300, 433, 641, 185, 71, 51, 110, 340, 64, 551, 147, 90, 818, 424, 883, 551, 31, 66, 32, 47, 63, 34, 385, 562, 94, 244, 118, 68, 3311, 26, 1476, 158, 158, 34, 111, 15, 3888, 245, 183, 111, 496, 62, 54, 158, 130, 9189, 15, 48, 17, 153, 838, 327, 68, 105, 97, 410, 134, 96, 515, 93, 186, 14, 420, 278, 189, 147, 86, 24, 55, 1628, 43, 39, 59, 87, 113, 100, 391, 338, 40, 21, 13, 65, 173, 414, 26, 55, 35, 1459, 206, 55, 2079, 159, 688, 31, 433, 145, 325, 56, 94, 25, 680, 81, 645, 189, 102, 563, 20, 136, 396, 62, 13, 868, 293, 635, 851, 194, 111, 245, 44, 31, 89, 125, 291, 27, 357, 996, 48, 31, 2169, 143, 580, 62, 19, 213, 789, 868, 45, 220, 842, 135, 53, 89, 243, 204, 62, 82, 156, 29, 46, 21, 184, 132, 901, 16, 108, 70, 103, 18, 140, 46, 210, 472, 662, 20, 24, 182, 1606, 122, 45, 385, 99, 109, 196, 177, 17, 73, 367, 45, 268, 229, 140, 578, 260, 165, 106, 28, 553, 481, 116, 142, 118, 80, 112, 134, 181, 363, 278, 539, 345, 54, 216, 948, 260, 837, 69, 50, 334, 104, 52, 7, 261, 24, 69, 39, 46, 51, 105, 45, 57, 52, 179, 751, 63, 19, 183, 29, 54, 74, 116, 21, 1282, 3759, 47, 523, 407, 73, 18, 119, 218, 738, 398, 107, 62, 95, 912, 243, 221, 446, 57, 73, 307, 203, 119, 181, 17, 56, 35, 112, 652, 16, 62, 409, 46, 1120, 80, 43, 281, 728, 92, 241, 39, 294, 133, 1981, 374, 39, 215, 197, 561, 97, 134, 63, 76, 428, 252, 200, 173, 91, 407, 185, 10, 67, 33, 28, 126, 162, 55, 39, 123, 65, 66, 28, 159, 320, 177, 64, 145, 127, 194, 918, 49, 86, 39, 987, 17, 178, 219, 242, 246, 57, 21, 267, 121, 34, 733, 395, 66, 257, 343, 160, 65, 134, 46, 84, 297, 99, 179, 307, 167, 185, 173, 66, 30, 63, 67, 163, 56, 145, 371, 647, 19, 345, 206, 160, 156, 19, 156, 1088, 14, 29, 155, 64, 84, 13, 208, 62, 264, 68, 46, 77, 458, 60, 27, 768, 199, 34, 476, 1085, 136, 347, 179, 34, 283, 87, 9, 18, 124, 41, 27, 478, 166, 580, 769, 188, 138, 70, 67, 80, 58, 117, 140, 255, 280, 235, 22, 10, 290, 62, 346, 91, 93, 14, 9, 373, 42, 36, 979, 46, 187, 192, 34, 3459, 116, 60, 73, 111, 1085, 31, 197, 105, 79, 695, 1129, 61, 86, 135, 51, 1547, 247, 19, 15, 350, 64, 161, 138, 60, 107, 534, 48, 209, 149, 42, 111, 518, 197, 302, 459, 29, 34, 316, 34, 274, 416, 368, 98, 20, 85, 45, 853, 182, 78, 126, 153, 150, 156, 69, 44, 69, 49, 145, 101, 273, 480, 79, 37, 82, 124, 173, 2084, 36, 74, 145, 86, 209, 174, 42, 45, 62, 43, 258, 123, 94, 103, 40, 72, 32, 66, 66, 50, 32, 168, 20, 65, 158, 11009, 115, 78, 617, 54, 34, 35, 68, 160, 146, 62, 50, 62, 66, 808, 333, 78, 451, 42, 73, 356, 134, 54, 14392, 24, 42, 12, 116, 28, 33, 13, 655, 36, 72, 18, 51, 146, 98, 111, 16, 87, 267, 133, 42, 107, 200, 672, 379, 17, 50, 48, 25, 167, 507, 31, 135, 69, 151, 88, 92, 190, 249, 205, 112, 139, 566, 84, 213, 33, 369, 68, 94, 791, 87, 573, 51, 92, 597, 71, 84, 372, 23, 23, 31, 514, 362, 347, 54, 42, 17, 26, 57, 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21, 313, 473, 352, 573, 177, 42, 2445, 914, 76, 219, 23, 286, 188, 1902, 117, 221, 241, 164, 29, 1071, 37, 15, 357, 157, 39, 152, 165, 197, 18, 31, 1650, 409, 90, 19, 522, 149, 38, 121, 90, 66, 34, 2198, 523, 156, 68, 118, 850, 2027, 110, 79, 81, 72, 179, 162, 39, 358, 68, 19, 374, 97, 100, 858, 73, 903, 164, 64, 191, 20, 200, 225, 51, 35, 67, 321, 44, 1113, 216, 126, 348, 7, 506, 2673, 117, 186, 164, 124, 135, 1227, 20, 340, 374, 57, 55, 460, 362, 144, 223, 44, 84, 98, 54, 178, 364, 41, 174, 103, 7853, 37, 561, 90, 276, 139, 160, 25, 118, 134, 34, 58, 200, 18, 407, 58, 28, 161, 29, 116, 152, 78, 16, 51, 161, 186, 88, 15, 344, 60, 449, 39, 26, 1660, 216, 26, 138, 52, 144, 38, 40, 172, 48, 69, 96, 192, 573, 773, 43, 180, 99, 29, 50, 190, 77, 74, 99, 248, 80, 105, 159, 126, 135, 272, 86, 281, 118, 75, 175, 44, 72, 78, 285, 62, 174, 109, 56, 69, 63, 229, 126, 16, 40, 812, 95, 113, 397, 599, 53, 107, 149, 2266, 1016, 377, 21, 76, 147, 50, 215, 118, 167, 78, 315, 605, 141, 79, 42, 58, 302, 410, 111, 1307, 11, 56, 54, 96, 780, 39, 386, 26, 13, 72, 144, 28, 40, 333, 83, 196, 144, 90, 17, 229, 58, 26, 13, 1923, 110, 58, 1052, 183, 119, 123, 250, 97, 556, 326, 53, 732, 339, 446, 231, 42, 45, 358, 389, 116, 378, 64, 171, 431, 41, 30, 19, 123, 819, 36, 197, 77, 152, 84, 109, 698, 15, 171, 307, 183, 40, 347, 15, 179, 123, 70, 526, 26, 50, 57, 33, 214, 436, 52, 48, 70, 112, 280, 403, 164, 160, 85, 66, 264, 174, 34, 65, 809, 146, 8, 67, 838, 94, 61, 209, 17, 127, 92, 22, 1846, 62, 83, 105, 11, 203, 12, 253, 57, 121, 574, 455, 53, 41, 11, 308, 331, 65, 87, 115, 46, 54, 85, 105, 328, 113, 45, 45, 2739, 1118, 102, 106, 187, 110, 41, 73, 48, 38, 80, 11, 43, 4, 570, 11, 17, 42, 20, 283, 111, 253, 341, 5, 115, 417, 264, 46, 221, 197, 118, 47, 429, 902, 43, 265, 9, 67, 115, 179, 82, 53, 84, 1547, 69, 262, 88, 25, 246, 2044, 510, 2131, 72, 46, 860, 96, 131, 87, 72, 19, 163, 324, 39, 39, 394, 28, 56, 458, 194, 86, 65, 133, 148, 45, 85, 27, 191, 21, 98, 209, 611, 18, 188, 58, 69, 87, 674, 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244, 70, 557, 1721, 46, 575, 121, 229, 92, 2231, 214, 124, 143, 419, 379, 27, 168, 47, 338, 37, 270, 18, 364, 17, 9, 585, 108, 91, 20, 86, 239, 33, 173, 61, 165, 296, 61, 13, 52, 180, 358, 85, 304, 43, 264, 968, 99, 50, 336, 242, 169, 207, 144, 52, 865, 36, 434, 32, 169, 169, 273, 505, 330, 1814, 32, 75, 42, 79, 31, 109, 69, 51, 100, 359, 47, 583, 50, 70, 1157, 159, 347, 44, 202, 36, 40, 143, 15, 202, 635, 321, 105, 233, 15, 20, 18, 511, 6, 97, 307, 41, 37, 790, 84, 16, 816, 446, 60, 72, 67, 15, 88, 383, 95, 262, 172, 109, 170, 40, 156, 839, 133, 438, 25, 35, 197, 100, 14, 12, 341, 73, 98, 40, 71, 30, 35, 499, 188, 92, 408, 226, 486, 353, 175, 50, 5, 451, 28, 89, 62, 2, 45, 92, 234, 102]\n","Length of the Output Tokens Count 10000\n"]}],"source":["output_tokens_count = [len(tokenizer.tokenize(example[\"output\"])) for example in dataset[\"train\"]]\n","print(\"Output Tokens Count\", output_tokens_count)\n","print(\"Length of the Output Tokens Count\", len(output_tokens_count))"]},{"cell_type":"code","execution_count":38,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":7,"status":"ok","timestamp":1705521356629,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"WW0XiMDqevKu","outputId":"3f15a31a-f20d-4bcb-9cbb-13d2eda3c178"},"outputs":[{"output_type":"stream","name":"stdout","text":["Combine Tokens Count [64, 183, 180, 141, 287, 156, 178, 161, 79, 146, 189, 180, 34, 102, 120, 110, 118, 144, 149, 145, 84, 740, 234, 29, 421, 85, 71, 313, 56, 1665, 80, 464, 103, 132, 45, 216, 79, 206, 383, 1324, 81, 145, 1027, 43, 243, 49, 59, 95, 225, 75, 163, 185, 164, 103, 306, 112, 144, 83, 37, 153, 59, 69, 131, 1121, 69, 130, 645, 409, 101, 75, 142, 116, 153, 26, 71, 63, 130, 92, 150, 374, 197, 93, 99, 741, 41, 28, 62, 1096, 53, 57, 293, 651, 1443, 345, 77, 445, 89, 103, 44, 287, 26, 173, 203, 65, 212, 1415, 62, 289, 91, 120, 53, 696, 68, 127, 46, 240, 265, 71, 44, 1054, 91, 39, 830, 56, 335, 342, 137, 88, 33, 116, 701, 115, 40, 55, 209, 107, 117, 137, 161, 154, 231, 108, 96, 37, 158, 290, 80, 158, 558, 172, 44, 111, 87, 344, 186, 254, 257, 33, 1970, 56, 41, 179, 36, 238, 41, 121, 148, 328, 283, 215, 247, 135, 638, 495, 198, 72, 96, 41, 165, 223, 342, 129, 52, 386, 170, 315, 205, 136, 40, 44, 288, 103, 49, 87, 127, 44, 86, 138, 304, 71, 209, 50, 90, 60, 162, 206, 232, 124, 93, 175, 160, 371, 146, 153, 107, 69, 452, 190, 20, 138, 54, 278, 149, 1723, 176, 48, 63, 78, 329, 281, 17, 29, 100, 100, 54, 445, 175, 136, 162, 189, 105, 91, 447, 26, 33, 163, 504, 162, 92, 110, 1637, 154, 14, 50, 678, 87, 55, 227, 29, 326, 35, 101, 471, 624, 137, 199, 491, 395, 77, 51, 36, 598, 40, 479, 45, 173, 106, 170, 270, 894, 128, 84, 49, 181, 44, 41, 132, 124, 49, 48, 198, 43, 67, 126, 35, 193, 54, 58, 35, 63, 79, 192, 77, 437, 170, 989, 247, 138, 311, 153, 105, 222, 71, 80, 51, 200, 621, 48, 101, 91, 49, 96, 90, 58, 62, 558, 75, 29, 142, 110, 125, 97, 69, 653, 241, 218, 138, 122, 80, 36, 1028, 38, 369, 196, 106, 37, 36, 44, 145, 200, 239, 155, 524, 387, 134, 95, 74, 67, 67, 62, 87, 87, 1784, 136, 555, 56, 40, 474, 29, 55, 121, 74, 48, 47, 82, 34, 245, 224, 130, 542, 66, 232, 59, 326, 273, 93, 22, 240, 67, 339, 354, 300, 1187, 272, 70, 325, 102, 193, 123, 83, 82, 238, 33, 166, 21, 57, 405, 105, 575, 40, 101, 357, 351, 138, 253, 77, 190, 190, 33, 750, 81, 19, 47, 358, 233, 67, 34, 110, 1305, 126, 219, 296, 621, 144, 185, 122, 284, 52, 150, 103, 71, 615, 434, 33, 619, 62, 762, 45, 210, 64, 73, 41, 196, 32, 150, 183, 61, 569, 73, 28, 37, 772, 184, 377, 116, 398, 82, 162, 260, 59, 93, 120, 157, 238, 237, 70, 22, 1414, 80, 279, 170, 88, 127, 120, 89, 103, 57, 119, 1768, 77, 115, 258, 178, 124, 43, 54, 1134, 108, 170, 24, 139, 253, 425, 108, 143, 107, 355, 66, 1368, 112, 896, 316, 48, 46, 676, 264, 85, 64, 133, 44, 60, 204, 153, 99, 1643, 59, 2773, 36, 548, 98, 31, 216, 146, 95, 159, 361, 24, 26, 115, 147, 35, 1281, 42, 987, 227, 151, 126, 45, 2116, 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91, 17, 57, 106, 259, 118]\n","Length of the Combine Tokens Count 10000\n"]}],"source":["#Combine the Instruction Token Count and the Output Token Count\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(\"Length of the Combine Tokens Count\", len(combine_tokens_count))"]},{"cell_type":"code","execution_count":39,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"elapsed":1798,"status":"ok","timestamp":1705521359284,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"KBAcc8Nkfvf5","outputId":"7cc2129a-a4ec-4495-e242-615e200d37ea"},"outputs":[{"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 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Create a Histogram using the Matplotlib Library to see the distribution of our token counts\n","def plot_distribution(tokens_count, title):\n","  sns.set_style(\"whitegrid\")\n","  plt.figure(figsize=(15, 6))\n","  plt.hist(tokens_count, bins=50, color='#3498db', edgecolor='black')\n","  plt.title(title, fontsize=16)\n","  plt.xlabel(\"Number of Tokens\", fontsize=14)\n","  plt.ylabel(\"Number of Examples\", fontsize=14)\n","  plt.xticks(fontsize=12)\n","  plt.yticks(fontsize=12)\n","  plt.tight_layout()\n","  plt.show()\n","\n","#Insruction Tokens Count\n","plot_distribution(instruction_tokens_count, \"Distribution of the tokens count for Instruction Column\")\n","#Output Tokens Count\n","plot_distribution(output_tokens_count, \"Distribution of the tokens count for Output Column\")\n","#Combine Tokens Count\n","plot_distribution(combine_tokens_count, \"Distribution of the tokens count for combined Instruction + Output Column\")\n","#The mean is around 500 tokens but there is a long tail distribution which goes up to 5000 tokens"]},{"cell_type":"markdown","metadata":{"id":"mKxDv_yBjvC-"},"source":["**Now we have find out the Number of Tokens in the Instruction Column and in the Output Column and Combine Instruction and Output Column**\n","But the Question remains is why we need to know the number of tokens because the Llama 2 and other LLMs, have a certain context window (input tokens limit)(Maximum Context Size of Llama 2 by default is 4096)  and if the tokens goes beyond the this context window then it is not going to be helpful. So, its important to know the number of tokens in our dataset"]},{"cell_type":"markdown","metadata":{"id":"8vxUEv8wseGV"},"source":["#**06. Filter out rows with more than 2048 tokens in the Combine Token Count (Instruction Column + Output Column)**"]},{"cell_type":"markdown","metadata":{"id":"XEtJiIxztCsB"},"source":["**Maximum Context Size of Llama 2 by default = 4096 tokens**"]},{"cell_type":"code","execution_count":40,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":642},"executionInfo":{"elapsed":1756,"status":"ok","timestamp":1705521366453,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"f2rjgLRoiG-_","outputId":"1736887f-72b5-4c15-f302-343a56bd0b32"},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of Valid Rows: 9976\n","Removing: 24 rows....\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x600 with 1 Axes>"],"image/png":"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will remove samples with more than 2048 tokens in the Combine Token Count\n","#First, filter  rows with less or equal to 2048 tokens\n","valid_indices = [i for i, count in enumerate(combine_tokens_count) if count <= 4096]\n","print(f\"Number of Valid Rows: {len(valid_indices)}\")\n","#Number of Rows with more than 2048 tokens\n","print(f\"Removing: {len(dataset['train']) - len(valid_indices)} rows....\")\n","#Second, 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_distribution(token_counts, \"New distribution of Combine Total Count (Instruction + Output Columns)\")"]},{"cell_type":"code","execution_count":41,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":3,"status":"ok","timestamp":1705521370943,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"Wt-7JggVyfOJ","outputId":"2baece0c-3849-4e51-c7ba-a0e6d784fb45"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input', 'output', 'instruction'],\n","        num_rows: 9976\n","    })\n","})"]},"metadata":{},"execution_count":41}],"source":["dataset"]},{"cell_type":"code","execution_count":42,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"elapsed":382,"status":"ok","timestamp":1705521378303,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"},"user_tz":-330},"id":"FcqDkXvGyhgo","outputId":"9047bfa3-11e7-47b8-c6fc-5f491770122c"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  input  \\\n","0     Can you provide an overview of the lung's squa...   \n","1                         What does \"Clear: cell\" mean?   \n","2     Can you provide me with information regarding ...   \n","3     What are the historical background and symptom...   \n","4     What does the \"Hypotension: Resident Survival ...   \n","...                                                 ...   \n","9971  What are some biochemistry topics that can be ...   \n","9972  What information is available on esophageal st...   \n","9973  What is the main way to prevent basal cell car...   \n","9974  Could you provide me with the text you want me...   \n","9975  What is the medical treatment for De Quervain'...   \n","\n","                                                 output  \\\n","0     Squamous cell carcinoma of the lung may be cla...   \n","1     Clear cell tumors are part of the surface epit...   \n","2     Two Japanese scientists commenced research int...   \n","3     Symptoms of vulvovaginitis caused by Candida s...   \n","4     Hypotension is the term for low blood pressure...   \n","...                                                 ...   \n","9971                                            Water -   \n","9972  Esophageal stricture is a narrowing of the eso...   \n","9973  The development of basal cell carcinoma can be...   \n","9974  No medical treatment is currently known or pra...   \n","9975  The mainstay of therapy for de Quervain's thyr...   \n","\n","                          instruction  \n","0     Answer this question truthfully  \n","1     Answer this question truthfully  \n","2     Answer this question truthfully  \n","3     Answer this question truthfully  \n","4     Answer this question truthfully  \n","...                               ...  \n","9971  Answer this question truthfully  \n","9972  Answer this question truthfully  \n","9973  Answer this question truthfully  \n","9974  Answer this question truthfully  \n","9975  Answer this question truthfully  \n","\n","[9976 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-f793997f-93ae-4c98-a2ac-3ae66970e3af\" class=\"colab-df-container\">\n","    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Near-deduplication using Embeddings**"]},{"cell_type":"markdown","metadata":{"id":"KWdt3t9ExXDh"},"source":["You can check the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) on Hugging face to choose the embedding model"]},{"cell_type":"code","execution_count":43,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":865,"referenced_widgets":["c8ead1419e564c4bb9816cc573e9af8a","16c9caf076574d95833f2e052d6cbfe5","f560256bdd1f443584ede9abe4c16232","85b7284c8aa94b579e3f4c95ad199099","8098cafcd8194f7fb9f44ae8f398164d","0b83e79d8e77425dbeb023799322bc54","0276ce55b2ac48bfbad957153315adb2","af0aaeea33f0433f86fa486a279c8141","def8332bfde04f06977e78b195ec3222","96c959d5b9e343d4aa3cecd47ba8cff3","8e42b7fd03084c9a8fa97c0b913853c9","7091655610ca49d798995e555076cc56","572b7e6afe5b481585f4b88152eb077b","982405e6ee1946e4b7219d5d57b1c7de","f507068e3add41e3bc12f71af57debd5","3ab83ab8e05242a6a7d2d2705abf2926","32216ebe88a94add817664983d3078ff","987e5affff0148dd9cf4c439d99f08f5","c0b8334ced3d4493870e7507e59a9feb","f950e511cb5a4f74af73e0d179d91e96","1db45e9719a546258ceedbecfe281955","ebfdf44b20aa47d6a8d1302a54aa4b4c"]},"id":"tzB6zQ-_vP83","executionInfo":{"status":"ok","timestamp":1705521764906,"user_tz":-330,"elapsed":377477,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}},"outputId":"3d00d0db-3922-41a9-8956-6f4340cf360f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Convert the text to embeddings....\n"]},{"output_type":"display_data","data":{"text/plain":["Batches:   0%|          | 0/312 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c8ead1419e564c4bb9816cc573e9af8a"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Dimensions of the embedding (9976, 1024)\n","Filtering out near duplicates....\n"]},{"output_type":"display_data","data":{"text/plain":["Filtering:   0%|          | 0/9976 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7091655610ca49d798995e555076cc56"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["List [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 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9962, 9963, 9964, 9965, 9966, 9967, 9968, 9969, 9970, 9971, 9972, 9973, 9974]\n","                                                  input  \\\n","0     Can you provide an overview of the lung's squa...   \n","1                         What does \"Clear: cell\" mean?   \n","2     Can you provide me with information regarding ...   \n","3     What are the historical background and symptom...   \n","4     What does the \"Hypotension: Resident Survival ...   \n","...                                                 ...   \n","9510  What are the potential treatments or therapies...   \n","9511  What are some biochemistry topics that can be ...   \n","9512  What information is available on esophageal st...   \n","9513  What is the main way to prevent basal cell car...   \n","9514  Could you provide me with the text you want me...   \n","\n","                                                 output  \\\n","0     Squamous cell carcinoma of the lung may be cla...   \n","1     Clear cell tumors are part of the surface epit...   \n","2     Two Japanese scientists commenced research int...   \n","3     Symptoms of vulvovaginitis caused by Candida s...   \n","4     Hypotension is the term for low blood pressure...   \n","...                                                 ...   \n","9510  Future or investigational therapies of multipl...   \n","9511                                            Water -   \n","9512  Esophageal stricture is a narrowing of the eso...   \n","9513  The development of basal cell carcinoma can be...   \n","9514  No medical treatment is currently known or pra...   \n","\n","                          instruction  \n","0     Answer this question truthfully  \n","1     Answer this question truthfully  \n","2     Answer this question truthfully  \n","3     Answer this question truthfully  \n","4     Answer this question truthfully  \n","...                               ...  \n","9510  Answer this question truthfully  \n","9511  Answer this question truthfully  \n","9512  Answer this question truthfully  \n","9513  Answer this question truthfully  \n","9514  Answer this question truthfully  \n","\n","[9515 rows x 3 columns]\n"]}],"source":["# In this notebook i am using the gte-large embedding model its not the best embedding model but it is fast\n","#I will use the sentence transformers library to download the embedding model\n","from sentence_transformers import SentenceTransformer\n","#Faiss will be used as our vector database which is not very fast but very simple to use\n","import faiss\n","from datasets import Dataset, DatasetDict\n","#tqdm creates a nice loading bar\n","from tqdm.autonotebook import tqdm\n","import numpy as np\n","\n","def deduplicate_dataset(dataset: Dataset, model: str, threshold: float):\n","  #Here i will pass the name of the embedding model\n","  sentence_model = SentenceTransformer(model)\n","  #Embed every sample every row in  dataset output column\n","  outputs = [example[\"output\"] for example in dataset[\"train\"]]\n","  # Using the Embedding Model we will convert the text into embeddings\n","  print(\"Convert the text to embeddings....\")\n","  embeddings = sentence_model.encode(outputs, show_progress_bar=True)\n","  dimensions = embeddings.shape[1]\n","  print(\"Dimensions of the embedding\", embeddings.shape)\n","  #Create an index using the Faiss as our Vector Database\n","  index = faiss.IndexFlatIP(dimensions)\n","  #Normalize the Embeddings\n","  normalized_embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)\n","  index.add(normalized_embeddings)\n","  print(\"Filtering out near duplicates....\")\n","  #k=2, means atmost two vectors\n","  D, I = index.search(normalized_embeddings, k=2)\n","  #In the below list, we will add the list of samples we want to keep\n","  to_keep=[]\n","  #We will define the threshold below, if the embedding is 95% similar to other embedding then we will remove that embedding\n","  for i in tqdm(range(len(embeddings)), desc=\"Filtering\"):\n","    #If the second closest vector (D[i,1]) has cosine similarity 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","  print(\"List\", to_keep)\n","  dataset = dataset[\"train\"].select(to_keep)\n","  print(dataset.to_pandas())\n","  return DatasetDict({\"train\": dataset})\n","\n","deduped_dataset = deduplicate_dataset(dataset, \"thenlper/gte-large\", 0.95)"]},{"cell_type":"code","execution_count":44,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lsTjo77f_fy3","outputId":"e0ec5ec7-7499-4241-fcb9-e51f9399a72a","executionInfo":{"status":"ok","timestamp":1705521764907,"user_tz":-330,"elapsed":12,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input', 'output', 'instruction'],\n","        num_rows: 9515\n","    })\n","})"]},"metadata":{},"execution_count":44}],"source":["deduped_dataset"]},{"cell_type":"code","execution_count":45,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"i7LLGx7BCY5A","outputId":"217ca7bb-7cf0-4d5b-90d0-c937af384145","executionInfo":{"status":"ok","timestamp":1705521778163,"user_tz":-330,"elapsed":395,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  input  \\\n","0     Can you provide an overview of the lung's squa...   \n","1                         What does \"Clear: cell\" mean?   \n","2     Can you provide me with information regarding ...   \n","3     What are the historical background and symptom...   \n","4     What does the \"Hypotension: Resident Survival ...   \n","...                                                 ...   \n","9510  What are the potential treatments or therapies...   \n","9511  What are some biochemistry topics that can be ...   \n","9512  What information is available on esophageal st...   \n","9513  What is the main way to prevent basal cell car...   \n","9514  Could you provide me with the text you want me...   \n","\n","                                                 output  \\\n","0     Squamous cell carcinoma of the lung may be cla...   \n","1     Clear cell tumors are part of the surface epit...   \n","2     Two Japanese scientists commenced research int...   \n","3     Symptoms of vulvovaginitis caused by Candida s...   \n","4     Hypotension is the term for low blood pressure...   \n","...                                                 ...   \n","9510  Future or investigational therapies of multipl...   \n","9511                                            Water -   \n","9512  Esophageal stricture is a narrowing of the eso...   \n","9513  The development of basal cell carcinoma can be...   \n","9514  No medical treatment is currently known or pra...   \n","\n","                          instruction  \n","0     Answer this question truthfully  \n","1     Answer this question truthfully  \n","2     Answer this question truthfully  \n","3     Answer this question truthfully  \n","4     Answer this question truthfully  \n","...                               ...  \n","9510  Answer this question truthfully  \n","9511  Answer this question truthfully  \n","9512  Answer this question truthfully  \n","9513  Answer this question truthfully  \n","9514  Answer this question truthfully  \n","\n","[9515 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-14c0c067-1c43-4b2f-9a82-8f4bc2eb89d6\" 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>input</th>\n","      <th>output</th>\n","      <th>instruction</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Can you provide an overview of the lung's squa...</td>\n","      <td>Squamous cell carcinoma of the lung may be 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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-9cdd4b75-636f-468a-9e53-869af38c1bdb\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9cdd4b75-636f-468a-9e53-869af38c1bdb')\"\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-9cdd4b75-636f-468a-9e53-869af38c1bdb button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":45}],"source":["deduped_dataset[\"train\"].to_pandas()"]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RUxyIS194Pdl","outputId":"df81e1d9-567e-412b-f55e-ee3a9fb77661","executionInfo":{"status":"ok","timestamp":1705521781265,"user_tz":-330,"elapsed":404,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of rows in the Original Dataset 9976\n","Number of rows in the deduped dataset 9515\n","Number of rows removed: 461\n"]}],"source":["print(\"Number of rows in the Original Dataset\", (len(dataset[\"train\"])))\n","print(\"Number of rows in the deduped dataset\", (len(deduped_dataset[\"train\"])))\n","print(f\"Number of rows removed: {len(dataset['train']) - len(deduped_dataset['train'])}\")"]},{"cell_type":"code","execution_count":47,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WIws4AQe-1yv","outputId":"1b6d4c76-cb98-417d-b6c0-8c5f0f25c6b4","executionInfo":{"status":"ok","timestamp":1705521802336,"user_tz":-330,"elapsed":11110,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Instruction Tokens Count [19, 9, 10, 21, 19, 11, 16, 15, 13, 13, 21, 14, 22, 19, 21, 24, 24, 13, 25, 11, 15, 10, 14, 16, 25, 23, 10, 11, 14, 15, 18, 19, 19, 12, 15, 17, 26, 16, 10, 13, 9, 11, 18, 9, 15, 14, 12, 0, 18, 23, 10, 16, 13, 21, 16, 10, 17, 17, 21, 17, 29, 22, 15, 16, 16, 15, 14, 9, 15, 17, 11, 16, 13, 14, 8, 18, 30, 17, 13, 24, 25, 11, 14, 11, 16, 11, 25, 17, 9, 12, 16, 10, 15, 9, 18, 18, 12, 13, 17, 14, 14, 17, 14, 18, 17, 11, 14, 12, 15, 14, 22, 23, 21, 9, 20, 12, 10, 11, 22, 26, 12, 20, 7, 18, 17, 23, 25, 26, 17, 9, 17, 16, 16, 12, 13, 13, 13, 20, 13, 33, 15, 14, 16, 26, 20, 26, 16, 18, 22, 15, 10, 14, 16, 22, 20, 20, 19, 17, 15, 13, 20, 17, 14, 23, 19, 15, 12, 20, 16, 13, 14, 11, 16, 16, 14, 7, 16, 12, 20, 9, 18, 14, 19, 12, 16, 11, 9, 18, 21, 10, 14, 16, 16, 18, 10, 14, 22, 15, 17, 11, 14, 10, 15, 13, 14, 9, 8, 25, 10, 18, 14, 17, 15, 20, 14, 17, 12, 15, 9, 17, 18, 12, 20, 21, 18, 15, 12, 12, 14, 16, 13, 14, 11, 9, 10, 19, 16, 15, 18, 16, 17, 20, 17, 9, 10, 23, 11, 61, 12, 12, 23, 15, 10, 18, 12, 13, 17, 16, 14, 14, 17, 14, 9, 11, 29, 17, 21, 14, 16, 15, 14, 15, 25, 29, 13, 15, 18, 21, 16, 11, 14, 16, 15, 22, 16, 14, 9, 10, 8, 18, 18, 25, 17, 17, 17, 15, 13, 21, 17, 18, 15, 20, 9, 10, 11, 16, 20, 14, 21, 13, 17, 16, 14, 11, 15, 24, 19, 14, 12, 12, 9, 19, 18, 28, 20, 17, 11, 10, 9, 12, 19, 16, 12, 23, 17, 12, 8, 15, 12, 15, 15, 17, 20, 14, 17, 16, 12, 12, 22, 13, 24, 15, 10, 16, 10, 21, 22, 13, 16, 18, 17, 20, 15, 19, 30, 13, 15, 11, 23, 15, 8, 15, 17, 20, 19, 11, 18, 16, 20, 14, 16, 17, 19, 20, 15, 10, 15, 17, 14, 13, 13, 15, 14, 12, 19, 16, 35, 23, 15, 15, 12, 14, 14, 13, 20, 18, 14, 16, 26, 12, 17, 21, 14, 12, 19, 18, 15, 20, 19, 10, 12, 13, 17, 14, 19, 11, 14, 19, 19, 10, 14, 15, 11, 16, 12, 18, 24, 8, 18, 20, 14, 11, 15, 10, 13, 17, 14, 10, 8, 10, 17, 16, 17, 13, 12, 14, 19, 13, 15, 9, 25, 21, 11, 15, 15, 13, 15, 30, 17, 28, 17, 16, 10, 25, 10, 11, 27, 15, 18, 16, 12, 19, 14, 18, 14, 17, 14, 16, 27, 17, 11, 15, 11, 9, 13, 18, 14, 26, 13, 11, 9, 10, 17, 16, 16, 18, 14, 24, 14, 19, 19, 16, 13, 42, 14, 13, 12, 9, 13, 23, 17, 17, 16, 21, 16, 11, 14, 20, 22, 16, 29, 18, 14, 11, 18, 10, 11, 24, 13, 15, 26, 14, 19, 11, 12, 11, 14, 31, 17, 16, 17, 18, 13, 17, 11, 18, 25, 16, 13, 10, 22, 15, 19, 17, 17, 10, 12, 12, 24, 15, 19, 12, 16, 17, 12, 19, 18, 8, 18, 11, 14, 10, 13, 15, 17, 22, 15, 24, 10, 14, 16, 16, 28, 11, 16, 12, 10, 14, 18, 16, 17, 12, 13, 15, 10, 12, 13, 16, 14, 17, 13, 10, 10, 14, 12, 23, 16, 18, 21, 20, 15, 15, 10, 9, 21, 15, 15, 9, 19, 14, 14, 12, 13, 20, 13, 18, 13, 8, 15, 13, 18, 13, 14, 20, 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14, 13, 15, 13, 19, 14, 12, 9, 15, 22, 10, 9, 12, 18, 20, 29, 18, 18, 20, 7, 12, 14, 13, 10, 19, 14, 18, 20, 19, 18, 18, 15, 21, 16, 15, 16, 13, 12, 14, 12, 15, 15, 19, 19, 18, 19, 21, 15, 18, 14, 12, 22, 18, 20, 18, 27, 16, 15, 12, 29, 16, 18, 15, 14, 14, 18, 10, 14, 14, 13, 22, 12, 16, 28, 16, 13, 15, 12, 23, 12, 12, 17, 17, 16, 15, 15, 10, 12, 14, 15, 15, 22, 18, 15, 18, 10, 16, 19, 21, 23, 26, 22, 13, 18, 28, 12, 15, 17, 14, 31, 13, 11, 14, 18, 16, 14, 22, 23, 50, 13, 19, 14, 20, 16, 18, 13, 14, 11, 20, 18, 17, 12, 16, 13, 17, 15, 22, 21, 12, 11, 14, 13, 18, 26, 13, 25, 15, 14, 14, 13, 17, 26, 20, 26, 16, 12, 18, 14, 12, 15, 15, 17, 18, 18, 14, 16, 11, 16, 14, 16, 14, 12, 8, 24, 38, 12, 10, 19, 11, 12, 16, 11, 17, 18, 15, 18, 9, 11, 19, 16, 26, 15, 23, 16, 14, 13, 16, 21, 10, 14, 5, 24, 15, 23, 16, 14, 29, 14, 29, 13, 21, 10, 18, 17, 17, 16, 17, 16, 21, 9, 14, 11, 10, 10, 14, 16, 28, 25, 16, 20, 19, 26, 17, 15, 12, 11, 17, 25, 17, 19, 17, 18, 18, 18, 18, 11, 14, 18, 11, 14, 17, 11, 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10, 16, 11, 17, 10, 7, 15, 8, 17, 18, 11, 13, 14, 6, 16, 15, 19, 12, 20, 13, 16, 12, 14, 14, 13, 9, 15, 16, 14, 15, 15, 14, 15, 18, 13, 13, 15, 20, 20, 19, 12, 12, 9, 22, 11, 13, 20, 28, 22, 14, 12, 10, 16, 11, 10, 26, 17, 29, 12, 11, 20, 11, 27, 20, 11, 15, 17, 16, 18, 15, 17, 29, 17, 12, 12, 20, 14, 12, 11, 21, 14, 26, 14, 19, 17, 13, 12, 20, 23, 17, 17, 12, 15, 15, 13, 15, 28, 9, 16, 18, 16, 18, 14, 17, 10, 10, 15, 14, 17, 12, 25, 14, 10, 11, 9, 13, 14, 13, 14, 21, 12, 12, 14, 10, 15, 25, 21, 10, 11, 13, 23, 14, 28, 16, 14, 11, 15, 22, 14, 24, 22, 17, 11, 26, 20, 11, 18, 11, 26, 19, 13, 16, 15, 20, 20, 9, 21, 10, 22, 9, 18, 14, 20, 17, 14, 10, 17, 13, 15, 14, 12, 14, 13, 13, 20, 12, 9, 17, 13, 18, 20, 13, 15, 17, 53, 19, 10, 9, 23, 18, 16, 20, 23, 23, 30, 18, 10, 19, 20, 14, 26, 9, 15, 23, 20, 16, 16, 15, 16, 16, 17, 10, 19, 15, 17, 13, 21, 20, 14, 10, 20, 28, 15, 15, 31, 15, 16, 12, 17, 16, 25, 14, 12, 19, 14, 9, 12, 15, 14, 10, 15, 32, 23, 25, 17, 9, 21, 23, 14, 15, 17, 29, 15, 12, 14, 25]\n","Length of Instruction Tokens Count 9515\n","Output Tokens Count [45, 174, 170, 120, 268, 145, 162, 146, 66, 133, 168, 166, 12, 83, 99, 86, 94, 131, 124, 134, 69, 730, 220, 13, 396, 62, 61, 302, 42, 1650, 62, 445, 84, 120, 30, 199, 53, 190, 373, 1311, 72, 134, 1009, 34, 228, 35, 47, 95, 207, 52, 153, 169, 151, 82, 290, 102, 127, 66, 16, 136, 30, 47, 116, 1105, 53, 115, 631, 400, 86, 58, 131, 100, 140, 12, 63, 45, 100, 75, 137, 350, 172, 82, 85, 730, 25, 17, 37, 1079, 44, 45, 277, 641, 1428, 336, 59, 427, 77, 90, 27, 273, 12, 156, 189, 47, 195, 1404, 48, 277, 76, 106, 31, 673, 47, 118, 26, 228, 255, 60, 22, 1028, 79, 19, 823, 38, 318, 319, 112, 62, 16, 107, 684, 99, 24, 43, 196, 94, 104, 117, 148, 121, 216, 94, 80, 11, 138, 264, 64, 140, 536, 157, 34, 97, 71, 322, 166, 234, 238, 16, 1955, 43, 21, 162, 22, 215, 22, 106, 136, 308, 267, 202, 233, 124, 622, 479, 184, 65, 80, 29, 145, 214, 324, 115, 33, 374, 154, 304, 196, 118, 19, 34, 274, 87, 33, 69, 117, 30, 64, 123, 287, 60, 195, 40, 75, 47, 148, 197, 224, 99, 83, 157, 146, 354, 131, 133, 93, 52, 440, 175, 11, 121, 36, 266, 129, 1702, 158, 33, 51, 66, 315, 265, 4, 15, 89, 91, 44, 426, 159, 121, 144, 173, 88, 71, 430, 17, 23, 140, 493, 101, 80, 98, 1614, 139, 4, 32, 666, 74, 38, 211, 15, 312, 18, 87, 462, 613, 108, 182, 470, 381, 61, 36, 22, 583, 15, 450, 32, 158, 88, 149, 254, 883, 114, 68, 34, 159, 28, 27, 123, 114, 41, 30, 180, 18, 50, 109, 18, 178, 41, 37, 18, 45, 64, 172, 68, 427, 159, 973, 227, 124, 290, 140, 88, 206, 57, 69, 36, 176, 602, 34, 89, 79, 40, 77, 72, 30, 42, 541, 64, 19, 133, 98, 106, 81, 57, 630, 224, 206, 130, 107, 68, 21, 1013, 21, 349, 182, 89, 21, 24, 32, 178, 226, 131, 509, 377, 118, 85, 53, 45, 54, 46, 69, 70, 1764, 121, 536, 26, 27, 459, 18, 32, 106, 66, 33, 30, 62, 15, 234, 206, 114, 522, 52, 216, 42, 307, 253, 78, 12, 225, 50, 325, 341, 287, 1172, 258, 58, 306, 86, 158, 100, 68, 67, 226, 19, 152, 8, 37, 387, 91, 559, 14, 89, 340, 330, 124, 241, 58, 172, 175, 13, 731, 71, 7, 34, 341, 219, 48, 23, 96, 1286, 107, 209, 282, 606, 133, 169, 110, 266, 28, 142, 85, 51, 601, 423, 18, 609, 49, 745, 31, 200, 56, 63, 24, 180, 15, 137, 171, 47, 550, 60, 13, 28, 747, 163, 366, 101, 383, 69, 147, 230, 42, 65, 103, 141, 228, 212, 60, 11, 1387, 65, 261, 154, 76, 108, 106, 71, 89, 40, 105, 1752, 50, 98, 247, 163, 113, 34, 41, 1116, 94, 144, 11, 128, 244, 415, 91, 127, 91, 337, 52, 1344, 98, 877, 297, 32, 33, 634, 250, 72, 52, 124, 31, 37, 187, 136, 83, 1622, 43, 2762, 22, 528, 76, 15, 187, 128, 81, 148, 343, 14, 15, 91, 134, 20, 1255, 973, 208, 140, 114, 34, 2102, 204, 323, 609, 324, 141, 1028, 53, 45, 88, 57, 125, 54, 207, 105, 57, 210, 86, 20, 25, 46, 258, 157, 578, 75, 44, 21, 201, 24, 60, 91, 272, 47, 80, 303, 18, 100, 142, 1366, 121, 369, 126, 162, 213, 534, 136, 78, 844, 57, 174, 187, 652, 870, 143, 24, 60, 239, 127, 107, 64, 28, 124, 1767, 71, 70, 188, 609, 109, 3, 97, 245, 27, 179, 122, 133, 13, 918, 234, 313, 152, 223, 408, 48, 179, 34, 78, 135, 9, 79, 49, 50, 52, 257, 102, 532, 699, 48, 39, 77, 190, 224, 5, 813, 41, 108, 30, 428, 148, 20, 91, 98, 80, 55, 201, 110, 62, 46, 142, 17, 36, 48, 357, 83, 101, 109, 362, 248, 767, 1124, 766, 73, 63, 116, 362, 34, 303, 682, 116, 194, 307, 44, 81, 21, 250, 32, 52, 34, 39, 345, 180, 267, 143, 140, 195, 152, 839, 104, 1310, 18, 109, 50, 120, 269, 151, 247, 125, 1675, 150, 33, 601, 16, 18, 952, 891, 259, 121, 89, 93, 288, 30, 19, 171, 93, 145, 325, 15, 543, 366, 16, 1594, 19, 20, 479, 77, 418, 129, 91, 236, 521, 56, 57, 90, 84, 11, 1973, 18, 121, 59, 76, 102, 27, 83, 18, 73, 51, 60, 253, 791, 240, 126, 209, 359, 34, 147, 100, 459, 223, 161, 59, 75, 61, 188, 198, 183, 1768, 70, 200, 222, 93, 128, 657, 202, 1725, 62, 53, 29, 53, 54, 182, 115, 167, 115, 108, 29, 139, 278, 268, 189, 254, 35, 65, 145, 16, 40, 27, 17, 78, 40, 124, 64, 46, 1515, 298, 17, 116, 9, 37, 445, 105, 52, 1384, 212, 172, 211, 252, 15, 299, 15, 23, 42, 33, 270, 27, 76, 641, 169, 491, 88, 232, 304, 76, 208, 17, 48, 87, 129, 2277, 86, 20, 110, 176, 187, 60, 475, 87, 116, 179, 51, 45, 1482, 56, 398, 150, 1710, 122, 230, 254, 36, 59, 163, 561, 1412, 73, 201, 35, 99, 58, 574, 35, 165, 24, 101, 64, 75, 92, 177, 130, 55, 201, 45, 145, 70, 91, 304, 61, 88, 29, 92, 137, 52, 367, 19, 251, 316, 69, 90, 56, 16, 1432, 392, 867, 423, 240, 130, 90, 72, 40, 24, 249, 64, 50, 262, 227, 35, 187, 92, 174, 81, 25, 104, 66, 485, 40, 146, 30, 212, 152, 102, 182, 79, 300, 433, 641, 185, 71, 51, 110, 340, 64, 551, 147, 90, 818, 424, 883, 551, 31, 66, 32, 47, 63, 34, 385, 562, 94, 244, 118, 68, 3311, 26, 1476, 158, 158, 111, 15, 3888, 245, 183, 111, 496, 62, 54, 158, 130, 15, 48, 17, 153, 838, 327, 68, 105, 97, 410, 134, 96, 515, 93, 186, 14, 420, 278, 189, 147, 86, 24, 55, 1628, 43, 39, 59, 87, 113, 100, 391, 338, 40, 21, 13, 65, 173, 414, 26, 55, 35, 1459, 206, 55, 2079, 159, 688, 31, 433, 145, 325, 56, 94, 25, 680, 81, 645, 189, 102, 563, 20, 136, 396, 62, 13, 868, 293, 635, 851, 194, 111, 245, 44, 31, 89, 125, 291, 27, 357, 996, 48, 31, 2169, 143, 580, 62, 19, 213, 789, 868, 45, 220, 842, 135, 53, 89, 243, 204, 62, 82, 156, 29, 46, 21, 184, 132, 901, 16, 108, 70, 103, 18, 140, 46, 210, 472, 662, 20, 24, 182, 1606, 122, 45, 385, 99, 109, 196, 177, 17, 73, 367, 45, 268, 229, 140, 578, 260, 165, 106, 28, 553, 481, 116, 142, 118, 80, 112, 134, 181, 363, 278, 539, 345, 54, 216, 948, 260, 837, 69, 50, 334, 104, 52, 7, 261, 24, 69, 39, 46, 51, 105, 45, 57, 52, 179, 751, 63, 19, 183, 29, 54, 74, 116, 21, 1282, 3759, 47, 523, 407, 73, 18, 119, 218, 738, 398, 107, 62, 95, 912, 243, 221, 446, 57, 73, 307, 203, 119, 181, 17, 56, 35, 112, 652, 16, 62, 409, 46, 1120, 80, 43, 281, 728, 92, 241, 39, 294, 133, 1981, 374, 39, 215, 197, 561, 97, 134, 63, 76, 428, 252, 200, 173, 91, 407, 185, 10, 67, 33, 28, 126, 162, 55, 39, 123, 65, 66, 28, 159, 320, 177, 64, 145, 127, 194, 918, 49, 86, 987, 17, 178, 219, 242, 246, 57, 21, 267, 121, 34, 733, 395, 66, 257, 343, 160, 65, 134, 46, 84, 297, 99, 179, 307, 167, 185, 173, 30, 63, 67, 163, 56, 145, 371, 647, 19, 345, 206, 160, 156, 19, 156, 1088, 14, 29, 155, 64, 84, 13, 208, 62, 264, 68, 46, 77, 458, 60, 27, 768, 199, 34, 476, 1085, 136, 347, 179, 34, 283, 87, 9, 18, 124, 41, 27, 478, 166, 580, 769, 188, 138, 70, 67, 80, 58, 117, 140, 255, 280, 235, 22, 10, 290, 62, 346, 91, 93, 14, 9, 373, 42, 36, 979, 46, 187, 192, 34, 3459, 116, 60, 73, 111, 1085, 31, 197, 105, 79, 695, 1129, 61, 86, 135, 51, 1547, 247, 19, 15, 350, 64, 161, 138, 60, 107, 534, 48, 209, 149, 42, 111, 518, 197, 302, 459, 29, 34, 316, 34, 274, 416, 368, 98, 20, 85, 45, 853, 182, 78, 126, 153, 150, 156, 69, 44, 69, 49, 145, 101, 273, 480, 79, 37, 82, 124, 173, 2084, 36, 74, 145, 86, 209, 174, 42, 45, 62, 43, 258, 123, 94, 103, 40, 72, 32, 66, 66, 50, 32, 168, 20, 65, 158, 115, 78, 617, 54, 34, 35, 68, 160, 146, 62, 50, 62, 66, 808, 333, 78, 451, 42, 73, 356, 134, 54, 24, 42, 12, 116, 28, 33, 13, 655, 36, 72, 18, 51, 146, 98, 111, 16, 87, 267, 133, 42, 107, 200, 672, 379, 17, 50, 48, 25, 167, 507, 31, 135, 69, 151, 88, 92, 190, 249, 205, 112, 139, 566, 84, 213, 33, 369, 68, 94, 791, 87, 573, 51, 92, 597, 71, 84, 372, 23, 23, 31, 514, 362, 347, 54, 42, 17, 26, 57, 102, 305, 142, 266, 270, 489, 149, 59, 238, 149, 234, 141, 37, 103, 98, 72, 442, 80, 204, 35, 166, 215, 196, 309, 94, 126, 106, 417, 152, 31, 350, 322, 85, 62, 146, 22, 143, 398, 65, 567, 93, 65, 25, 799, 12, 189, 18, 123, 78, 128, 112, 134, 143, 610, 219, 33, 158, 14, 45, 88, 136, 529, 44, 704, 103, 129, 87, 62, 64, 104, 78, 84, 302, 18, 90, 323, 89, 118, 168, 15, 46, 33, 56, 53, 156, 25, 18, 1172, 294, 101, 34, 361, 168, 3442, 97, 993, 528, 324, 330, 156, 41, 52, 275, 54, 98, 148, 174, 18, 96, 211, 756, 242, 514, 104, 51, 79, 63, 65, 127, 772, 19, 263, 100, 50, 32, 304, 621, 1177, 119, 155, 12, 276, 251, 227, 190, 109, 57, 161, 208, 31, 28, 43, 686, 461, 196, 318, 171, 171, 44, 972, 89, 742, 77, 35, 74, 63, 800, 131, 249, 247, 1124, 726, 265, 124, 179, 48, 68, 14, 137, 90, 49, 397, 488, 313, 69, 17, 43, 80, 35, 28, 25, 265, 26, 203, 94, 121, 576, 51, 17, 79, 106, 819, 52, 66, 204, 122, 65, 172, 106, 334, 38, 99, 864, 17, 375, 201, 165, 213, 68, 198, 271, 38, 38, 21, 313, 473, 352, 573, 177, 42, 2445, 914, 76, 219, 23, 286, 188, 1902, 117, 221, 241, 164, 1071, 37, 15, 357, 157, 39, 152, 165, 197, 18, 31, 1650, 90, 19, 522, 149, 38, 121, 90, 66, 34, 2198, 523, 156, 68, 118, 850, 2027, 110, 79, 81, 72, 179, 162, 39, 358, 68, 19, 374, 97, 100, 858, 73, 903, 164, 64, 191, 20, 200, 225, 51, 35, 67, 321, 44, 1113, 216, 126, 348, 7, 506, 2673, 117, 186, 164, 124, 135, 1227, 20, 340, 374, 57, 55, 460, 362, 144, 223, 44, 84, 98, 54, 178, 364, 41, 174, 103, 37, 561, 90, 276, 139, 160, 25, 118, 134, 34, 58, 200, 18, 407, 58, 28, 161, 29, 116, 152, 78, 16, 51, 161, 186, 88, 15, 344, 60, 449, 39, 26, 1660, 216, 26, 138, 52, 144, 38, 40, 172, 48, 69, 96, 192, 573, 773, 43, 180, 99, 29, 50, 190, 77, 74, 99, 248, 80, 105, 159, 126, 135, 272, 86, 281, 118, 75, 175, 44, 72, 78, 285, 62, 174, 109, 56, 69, 63, 229, 126, 16, 40, 812, 95, 113, 397, 599, 53, 107, 149, 2266, 1016, 377, 21, 76, 147, 50, 215, 118, 167, 315, 605, 141, 79, 42, 58, 302, 410, 111, 1307, 11, 56, 54, 96, 780, 39, 386, 26, 13, 72, 144, 28, 40, 333, 83, 196, 144, 90, 17, 229, 58, 26, 13, 1923, 110, 58, 1052, 183, 119, 123, 250, 97, 556, 326, 53, 732, 339, 446, 231, 42, 45, 358, 389, 116, 378, 64, 171, 431, 41, 30, 19, 123, 819, 36, 197, 77, 152, 84, 109, 698, 15, 171, 307, 183, 40, 347, 15, 179, 123, 70, 526, 26, 50, 57, 33, 214, 436, 52, 48, 70, 112, 280, 403, 164, 160, 85, 66, 264, 174, 34, 65, 809, 146, 8, 67, 838, 94, 61, 209, 17, 127, 92, 22, 1846, 62, 83, 105, 11, 203, 12, 57, 121, 574, 455, 53, 41, 11, 308, 331, 65, 87, 115, 46, 54, 85, 105, 328, 113, 45, 45, 2739, 1118, 102, 106, 187, 110, 41, 73, 48, 38, 80, 11, 43, 4, 570, 11, 17, 42, 20, 283, 111, 253, 5, 115, 417, 264, 46, 221, 197, 118, 47, 429, 902, 43, 265, 9, 67, 115, 179, 82, 53, 84, 1547, 69, 262, 88, 25, 246, 2044, 510, 2131, 72, 46, 860, 96, 131, 72, 19, 163, 324, 39, 39, 394, 28, 56, 458, 194, 86, 133, 148, 45, 85, 27, 191, 21, 98, 209, 611, 18, 188, 58, 69, 87, 674, 143, 200, 30, 221, 165, 4, 149, 156, 19, 1755, 61, 256, 74, 17, 66, 63, 46, 350, 722, 389, 222, 89, 50, 31, 55, 536, 71, 201, 66, 449, 92, 195, 53, 82, 187, 435, 85, 90, 11, 638, 189, 45, 64, 209, 120, 99, 72, 209, 162, 133, 17, 31, 84, 288, 383, 97, 91, 573, 72, 142, 32, 34, 95, 38, 410, 135, 183, 17, 70, 283, 36, 326, 30, 16, 639, 16, 1643, 127, 3607, 105, 370, 1037, 67, 2674, 75, 7, 148, 237, 70, 93, 133, 21, 20, 378, 242, 617, 336, 272, 238, 145, 48, 268, 140, 1100, 254, 313, 22, 518, 1380, 150, 251, 53, 37, 30, 34, 57, 435, 173, 103, 118, 77, 208, 156, 116, 123, 114, 1036, 1565, 371, 64, 15, 83, 26, 493, 41, 260, 152, 160, 143, 563, 193, 120, 73, 129, 487, 50, 203, 15, 325, 70, 62, 360, 342, 19, 75, 81, 498, 60, 403, 19, 1013, 81, 38, 286, 144, 112, 61, 158, 37, 285, 48, 166, 34, 31, 95, 493, 113, 489, 36, 83, 109, 129, 179, 74, 16, 42, 324, 273, 23, 127, 118, 240, 124, 73, 32, 951, 45, 19, 140, 75, 72, 386, 45, 32, 238, 151, 34, 20, 720, 152, 72, 725, 118, 120, 13, 158, 30, 895, 111, 37, 341, 192, 187, 68, 28, 395, 165, 192, 96, 35, 32, 77, 263, 100, 526, 2039, 241, 75, 104, 548, 34, 34, 140, 65, 116, 131, 27, 318, 130, 81, 71, 288, 509, 1487, 126, 10, 104, 601, 101, 49, 225, 74, 71, 97, 243, 55, 414, 56, 179, 438, 75, 394, 551, 38, 237, 49, 44, 35, 172, 215, 255, 304, 1179, 56, 60, 69, 80, 35, 243, 408, 63, 319, 57, 135, 561, 171, 625, 121, 1017, 49, 180, 132, 17, 587, 29, 64, 71, 130, 567, 266, 123, 19, 35, 47, 90, 61, 101, 74, 23, 200, 573, 235, 157, 440, 927, 338, 453, 966, 399, 204, 87, 286, 15, 142, 31, 67, 14, 331, 52, 30, 530, 69, 191, 137, 20, 1358, 243, 37, 117, 162, 51, 113, 429, 256, 72, 47, 190, 1482, 362, 688, 161, 700, 301, 109, 218, 223, 1332, 171, 100, 33, 170, 42, 44, 19, 253, 113, 31, 70, 67, 115, 186, 8, 189, 85, 100, 762, 749, 212, 265, 237, 27, 739, 73, 792, 97, 931, 61, 39, 215, 226, 281, 75, 96, 339, 66, 217, 108, 28, 87, 79, 67, 40, 648, 966, 146, 54, 754, 97, 597, 724, 344, 517, 195, 157, 42, 211, 855, 654, 217, 76, 54, 1345, 113, 79, 65, 117, 81, 41, 26, 120, 168, 24, 42, 137, 60, 139, 82, 62, 129, 91, 13, 123, 16, 166, 54, 65, 53, 2594, 634, 81, 297, 48, 257, 125, 144, 182, 44, 168, 16, 45, 54, 73, 76, 212, 30, 33, 220, 135, 47, 150, 211, 25, 93, 21, 31, 96, 140, 27, 127, 16, 877, 1211, 53, 38, 679, 97, 108, 34, 201, 19, 13, 127, 630, 69, 20, 57, 50, 193, 25, 53, 508, 459, 57, 148, 265, 467, 73, 27, 468, 90, 66, 60, 52, 60, 143, 678, 133, 352, 95, 264, 329, 111, 1941, 1718, 315, 76, 740, 315, 25, 96, 41, 108, 309, 420, 97, 76, 84, 122, 46, 308, 102, 775, 111, 106, 127, 38, 461, 42, 37, 80, 88, 279, 40, 307, 144, 185, 201, 119, 385, 255, 134, 135, 670, 17, 103, 44, 184, 262, 1465, 90, 319, 612, 26, 84, 60, 121, 146, 107, 2259, 27, 30, 77, 24, 55, 83, 70, 58, 42, 23, 80, 50, 541, 100, 35, 145, 31, 111, 313, 14, 142, 884, 331, 95, 267, 29, 48, 94, 273, 75, 262, 47, 12, 139, 79, 463, 6, 97, 54, 218, 129, 59, 430, 51, 85, 48, 46, 69, 115, 107, 60, 935, 125, 33, 317, 148, 78, 64, 65, 508, 24, 365, 171, 19, 229, 537, 176, 150, 215, 226, 45, 369, 767, 287, 20, 62, 39, 300, 620, 951, 422, 597, 764, 70, 4, 751, 444, 140, 143, 84, 238, 347, 193, 115, 272, 217, 146, 48, 96, 76, 170, 79, 373, 33, 59, 125, 102, 122, 24, 186, 89, 212, 152, 104, 579, 51, 38, 155, 936, 128, 193, 395, 480, 67, 18, 16, 215, 47, 26, 1394, 820, 239, 74, 61, 115, 145, 1690, 113, 57, 52, 140, 124, 105, 530, 48, 140, 113, 10, 154, 279, 84, 208, 2482, 409, 114, 792, 138, 12, 154, 230, 11, 25, 20, 1247, 20, 111, 718, 456, 68, 104, 104, 52, 85, 49, 130, 200, 333, 97, 548, 22, 44, 239, 28, 1161, 210, 23, 157, 121, 75, 258, 981, 295, 34, 30, 79, 312, 15, 18, 347, 468, 53, 230, 92, 184, 100, 213, 51, 115, 82, 204, 34, 352, 81, 44, 60, 53, 81, 139, 168, 45, 77, 283, 265, 37, 215, 192, 33, 312, 61, 55, 62, 40, 479, 210, 20, 151, 127, 88, 94, 75, 431, 31, 36, 152, 204, 15, 109, 291, 32, 521, 251, 32, 648, 157, 523, 941, 45, 95, 37, 626, 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130, 54, 36, 22, 1083, 452, 47, 17, 115, 199, 169, 1488, 843, 14, 266, 1016, 36, 31, 2101, 378, 68, 11, 78, 69, 117, 64, 3112, 63, 1204, 38, 33, 96, 18, 125, 440, 181, 118, 109, 73, 101, 60, 340, 80, 13, 889, 2199, 347, 25, 202, 54, 197, 139, 32, 59, 7, 806, 225, 15, 41, 52, 536, 103, 571, 244, 427, 401, 77, 52, 83, 44, 93, 312, 96, 108, 53, 884, 282, 208, 44, 465, 9, 16, 1284, 156, 179, 129, 17, 16, 318, 37, 129, 427, 530, 52, 68, 133, 87, 671, 78, 26, 62, 87, 44, 86, 73, 285, 42, 87, 596, 69, 21, 140, 153, 100, 300, 27, 891, 349, 104, 140, 155, 30, 12, 1279, 203, 74, 553, 154, 72, 654, 555, 140, 132, 45, 182, 920, 120, 123, 13, 97, 37, 3782, 38, 85, 38, 233, 36, 276, 135, 89, 123, 620, 112, 27, 8, 244, 127, 177, 141, 119, 174, 3358, 86, 1300, 19, 50, 497, 20, 562, 994, 492, 93, 470, 82, 246, 222, 164, 79, 8, 403, 213, 197, 53, 158, 87, 333, 25, 105, 155, 552, 140, 128, 426, 65, 103, 24, 393, 82, 100, 456, 234, 1394, 470, 68, 3895, 57, 745, 299, 873, 269, 45, 185, 24, 22, 41, 18, 36, 488, 99, 181, 82, 257, 61, 23, 260, 87, 83, 182, 423, 619, 65, 1063, 33, 93, 858, 18, 113, 613, 78, 191, 259, 1928, 1762, 64, 15, 58, 26, 77, 332, 175, 710, 79, 114, 351, 99, 3396, 569, 374, 480, 54, 130, 71, 73, 104, 70, 2074, 668, 63, 66, 39, 46, 249, 230, 14, 706, 161, 438, 182, 36, 129, 41, 324, 504, 31, 61, 48, 263, 72, 185, 71, 214, 18, 406, 289, 23, 89, 15, 27, 399, 186, 248, 52, 22, 21, 243, 106, 42, 83, 89, 40, 147, 25, 37, 63, 287, 72, 8, 105, 44, 110, 34, 29, 1921, 177, 139, 280, 92, 35, 139, 486, 484, 96, 73, 506, 290, 179, 45, 218, 454, 321, 961, 142, 77, 246, 14, 60, 18, 682, 65, 110, 192, 138, 256, 52, 860, 49, 15, 167, 278, 21, 19, 461, 102, 77, 80, 21, 429, 145, 72, 162, 218, 800, 115, 344, 114, 34, 376, 111, 399, 115, 51, 304, 500, 73, 159, 114, 54, 363, 49, 133, 570, 348, 227, 26, 617, 1124, 25, 232, 2561, 59, 695, 34, 42, 348, 375, 303, 94, 43, 16, 100, 138, 32, 370, 138, 473, 15, 1001, 14, 309, 78, 44, 68, 113, 88, 1311, 75, 483, 119, 18, 444, 96, 213, 48, 41, 65, 29, 212, 41, 197, 73, 421, 25, 992, 77, 27, 28, 28, 25, 220, 173, 198, 100, 149, 15, 86, 134, 351, 127, 149, 240, 69, 343, 844, 44, 26, 978, 769, 35, 133, 106, 61, 94, 51, 34, 748, 43, 82, 91, 231, 61, 83, 106, 171, 82, 23, 183, 103, 71, 58, 194, 36, 51, 18, 87, 389, 307, 11, 98, 25, 30, 24, 66, 299, 104, 38, 274, 32, 122, 348, 29, 13, 44, 279, 1576, 105, 35, 57, 25, 555, 785, 91, 111, 456, 1804, 17, 240, 177, 224, 102, 798, 67, 64, 217, 86, 109, 51, 592, 150, 94, 100, 76, 133, 550, 106, 62, 83, 122, 13, 286, 534, 96, 176, 121, 223, 234, 184, 270, 26, 28, 52, 42, 851, 170, 35, 548, 74, 221, 13, 342, 252, 208, 534, 2097, 65, 74, 133, 62, 175, 56, 734, 16, 16, 14, 361, 41, 62, 38, 162, 133, 151, 122, 65, 216, 243, 52, 288, 75, 73, 338, 564, 137, 49, 149, 91, 31, 120, 296, 49, 117, 81, 119, 14, 93, 377, 74, 390, 2797, 227, 101, 19, 314, 63, 213, 39, 96, 57, 225, 41, 37, 280, 309, 492, 109, 611, 40, 181, 56, 57, 755, 15, 166, 349, 84, 100, 594, 72, 51, 31, 168, 67, 11, 68, 31, 98, 275, 192, 1937, 297, 84, 267, 154, 29, 56, 83, 42, 90, 47, 803, 244, 216, 253, 213, 80, 154, 44, 177, 2584, 16, 64, 58, 372, 124, 55, 35, 12, 17, 190, 59, 204, 200, 96, 15, 93, 287, 51, 17, 19, 96, 139, 39, 49, 723, 45, 36, 47, 67, 59, 663, 125, 89, 27, 67, 42, 236, 1312, 192, 181, 27, 1317, 35, 118, 316, 70, 323, 1466, 318, 161, 162, 21, 39, 125, 66, 962, 1096, 16, 290, 320, 147, 91, 91, 12, 691, 67, 23, 61, 111, 307, 283, 123, 115, 32, 24, 47, 56, 615, 12, 34, 79, 105, 20, 144, 118, 96, 85, 154, 41, 21, 32, 238, 195, 2093, 295, 681, 92, 67, 56, 78, 458, 343, 118, 173, 56, 802, 68, 364, 270, 406, 292, 17, 109, 168, 36, 67, 404, 83, 388, 110, 33, 329, 152, 620, 253, 285, 49, 362, 60, 31, 328, 87, 172, 194, 370, 148, 124, 58, 103, 16, 123, 349, 96, 593, 226, 150, 114, 1952, 13, 121, 147, 68, 18, 16, 106, 300, 426, 25, 265, 300, 131, 70, 32, 11, 17, 42, 72, 37, 71, 546, 64, 26, 22, 64, 212, 65, 1464, 145, 124, 16, 335, 501, 204, 37, 60, 582, 128, 48, 197, 84, 48, 38, 432, 274, 567, 60, 38, 310, 69, 10, 475, 78, 16, 66, 9, 77, 90, 11, 307, 103, 1010, 249, 51, 101, 198, 161, 320, 27, 53, 310, 249, 127, 148, 110, 162, 73, 578, 36, 34, 721, 1785, 248, 30, 541, 232, 157, 492, 34, 40, 23, 88, 735, 8, 373, 22, 35, 308, 35, 27, 201, 341, 73, 36, 647, 37, 46, 89, 332, 87, 51, 122, 479, 145, 37, 796, 32, 131, 174, 23, 402, 349, 64, 1389, 71, 93, 211, 49, 29, 299, 93, 31, 41, 81, 131, 78, 272, 64, 346, 358, 123, 316, 67, 12, 315, 255, 89, 209, 46, 190, 24, 141, 365, 179, 143, 64, 22, 66, 34, 16, 18, 94, 188, 1748, 90, 46, 116, 45, 76, 204, 821, 64, 137, 382, 187, 80, 307, 779, 45, 125, 528, 309, 571, 15, 103, 33, 149, 40, 134, 144, 207, 89, 499, 50, 16, 326, 75, 97, 218, 179, 228, 19, 86, 63, 87, 13, 74, 549, 62, 47, 143, 295, 145, 21, 84, 35, 74, 185, 564, 158, 46, 197, 158, 293, 59, 239, 115, 205, 239, 632, 584, 712, 40, 24, 159, 165, 40, 121, 93, 72, 218, 29, 46, 12, 1321, 162, 127, 786, 78, 62, 282, 17, 142, 1208, 683, 193, 79, 42, 799, 35, 228, 3369, 168, 20, 104, 14, 185, 351, 2022, 46, 27, 624, 605, 80, 174, 392, 200, 67, 352, 640, 735, 45, 87, 1128, 21, 22, 123, 128, 82, 46, 46, 58, 293, 275, 347, 68, 112, 286, 556, 38, 113, 632, 3, 157, 100, 98, 33, 25, 39, 29, 18, 570, 53, 12, 82, 23, 150, 130, 82, 36, 1208, 1371, 36, 59, 60, 18, 337, 42, 224, 38, 153, 88, 27, 995, 1112, 91, 47, 507, 172, 24, 153, 226, 107, 91, 43, 242, 48, 189, 17, 35, 1432, 664, 179, 1999, 289, 56, 32, 77, 171, 132, 514, 196, 103, 53, 52, 194, 1319, 80, 430, 150, 395, 53, 301, 4, 101, 163, 377, 243, 34, 44, 514, 450, 614, 169, 1580, 57, 37, 90, 118, 316, 42, 143, 284, 337, 49, 210, 86, 513, 297, 240, 615, 196, 117, 1344, 483, 594, 240, 47, 46, 213, 287, 65, 87, 4, 298, 17, 100, 252, 568, 93, 29, 98, 662, 223, 50, 63, 397, 296, 30, 20, 290, 110, 51, 68, 18, 222, 328, 312, 77, 459, 164, 1917, 240, 27, 54, 99, 52, 103, 192, 197, 1354, 278, 33, 191, 146, 118, 307, 19, 139, 390, 279, 114, 68, 3878, 88, 179, 991, 63, 63, 37, 135, 257, 440, 389, 269, 631, 30, 17, 51, 91, 74, 25, 50, 1280, 16, 59, 23, 151, 129, 55, 217, 246, 1116, 229, 72, 195, 157, 2172, 42, 442, 378, 84, 54, 208, 55, 269, 18, 59, 217, 203, 386, 883, 653, 38, 80, 41, 772, 184, 34, 134, 137, 808, 669, 280, 258, 22, 64, 613, 123, 390, 26, 239, 17, 212, 1262, 128, 46, 49, 30, 19, 416, 2690, 301, 78, 126, 45, 53, 131, 1142, 166, 823, 109, 321, 97, 197, 174, 150, 114, 45, 127, 130, 116, 231, 87, 17, 157, 211, 205, 28, 99, 120, 80, 144, 268, 33, 19, 106, 72, 19, 601, 67, 78, 15, 284, 390, 18, 109, 384, 509, 57, 60, 56, 106, 281, 966, 235, 128, 42, 495, 175, 21, 1866, 39, 375, 913, 68, 95, 277, 214, 82, 867, 142, 58, 13, 136, 369, 738, 301, 118, 62, 258, 50, 3043, 136, 74, 1125, 91, 18, 96, 357, 50, 129, 248, 63, 426, 79, 15, 548, 17, 195, 61, 98, 49, 56, 748, 318, 86, 285, 86, 160, 132, 98, 16, 129, 38, 68, 294, 27, 465, 95, 46, 69, 2040, 21, 56, 277, 445, 137, 184, 53, 187, 86, 28, 34, 1187, 17, 561, 158, 132, 176, 35, 340, 133, 127, 1305, 108, 172, 346, 22, 61, 70, 166, 27, 85, 100, 386, 53, 126, 1381, 106, 423, 58, 512, 52, 1050, 130, 111, 1664, 394, 71, 18, 206, 439, 540, 106, 389, 141, 5, 135, 466, 192, 164, 180, 141, 43, 49, 123, 133, 354, 33, 94, 106, 38, 413, 31, 20, 9, 56, 75, 23, 112, 1199, 514, 464, 137, 235, 528, 64, 60, 296, 122, 108, 22, 31, 240, 47, 545, 106, 134, 169, 23, 17, 97, 248, 26, 491, 28, 525, 221, 47, 33, 113, 11, 396, 208, 862, 155, 184, 40, 22, 246, 37, 36, 84, 60, 3522, 224, 50, 222, 268, 139, 61, 72, 314, 18, 130, 20, 174, 223, 34, 99, 722, 88, 131, 32, 151, 121, 88, 147, 190, 221, 29, 46, 14, 238, 236, 39, 22, 130, 103, 65, 1660, 1025, 1208, 52, 233, 28, 891, 128, 50, 136, 92, 545, 232, 47, 242, 102, 102, 96, 754, 60, 655, 109, 184, 38, 154, 602, 336, 24, 227, 39, 600, 159, 225, 74, 1134, 185, 328, 265, 2664, 139, 121, 168, 343, 69, 570, 81, 62, 980, 285, 259, 125, 70, 14, 90, 119, 610, 172, 244, 335, 134, 62, 18, 52, 29, 26, 535, 288, 140, 179, 47, 423, 412, 44, 152, 9, 30, 5, 924, 157, 30, 39, 113, 45, 157, 75, 74, 58, 236, 53, 47, 145, 118, 137, 686, 11, 200, 206, 104, 63, 48, 370, 29, 27, 68, 88, 205, 169, 104, 127, 59, 33, 129, 456, 23, 114, 128, 845, 144, 96, 121, 23, 144, 23, 64, 15, 211, 402, 4, 165, 577, 75, 53, 27, 96, 184, 141, 536, 261, 72, 154, 87, 209, 404, 557, 26, 805, 166, 52, 325, 44, 172, 407, 115, 16, 16, 72, 106, 43, 30, 47, 189, 440, 118, 1870, 348, 88, 93, 113, 29, 74, 111, 1142, 605, 70, 39, 95, 297, 65, 38, 583, 121, 62, 130, 222, 69, 169, 220, 127, 1040, 59, 242, 21, 370, 193, 111, 302, 102, 22, 64, 71, 89, 167, 129, 221, 120, 1022, 46, 125, 1084, 96, 15, 421, 81, 501, 33, 312, 157, 609, 2050, 104, 101, 229, 90, 19, 79, 187, 136, 30, 96, 84, 17, 70, 137, 56, 354, 67, 447, 147, 60, 137, 101, 386, 26, 91, 28, 124, 290, 153, 64, 733, 4, 91, 36, 522, 307, 567, 52, 284, 87, 270, 123, 165, 307, 404, 102, 93, 66, 133, 28, 252, 85, 76, 174, 196, 111, 30, 78, 146, 16, 152, 91, 32, 22, 124, 378, 134, 94, 335, 16, 134, 136, 22, 76, 18, 136, 88, 199, 549, 529, 19, 330, 14, 19, 328, 865, 57, 185, 85, 164, 115, 622, 110, 121, 103, 171, 446, 119, 469, 114, 105, 242, 795, 110, 37, 45, 215, 171, 15, 171, 192, 768, 220, 260, 87, 417, 235, 136, 18, 298, 121, 17, 61, 214, 53, 1230, 26, 22, 257, 38, 97, 351, 98, 64, 151, 125, 38, 221, 65, 182, 228, 83, 1768, 43, 222, 534, 580, 3298, 85, 28, 125, 121, 14, 46, 1173, 99, 108, 60, 89, 28, 14, 797, 76, 487, 146, 69, 115, 49, 292, 169, 104, 42, 187, 670, 133, 74, 260, 9, 631, 78, 275, 16, 41, 43, 163, 50, 56, 140, 108, 83, 198, 58, 171, 202, 91, 41, 583, 41, 94, 46, 121, 950, 208, 78, 394, 150, 123, 100, 8, 1227, 63, 96, 33, 131, 153, 257, 405, 94, 566, 93, 435, 12, 122, 246, 70, 36, 248, 1787, 113, 27, 39, 105, 220, 21, 83, 269, 282, 1026, 75, 309, 117, 72, 20, 478, 33, 349, 101, 226, 92, 145, 343, 111, 120, 893, 102, 576, 196, 547, 218, 26, 169, 122, 112, 53, 96, 385, 51, 40, 93, 54, 134, 419, 471, 314, 25, 224, 111, 625, 97, 104, 598, 70, 86, 141, 5, 126, 96, 34, 56, 49, 73, 352, 89, 96, 81, 83, 33, 72, 73, 36, 40, 57, 80, 50, 118, 118, 126, 939, 59, 72, 252, 171, 1009, 100, 246, 130, 67, 32, 75, 259, 92, 45, 145, 557, 13, 604, 61, 121, 81, 60, 382, 11, 154, 58, 422, 125, 79, 164, 244, 152, 206, 54, 237, 165, 158, 181, 103, 305, 16, 90, 102, 60, 34, 29, 366, 185, 296, 37, 50, 154, 86, 116, 51, 198, 5, 208, 110, 30, 231, 21, 778, 95, 32, 87, 170, 56, 139, 99, 79, 18, 67, 44, 196, 734, 38, 13, 43, 925, 31, 210, 29, 161, 15, 194, 58, 39, 618, 316, 50, 464, 176, 631, 90, 38, 1056, 449, 245, 110, 101, 76, 93, 71, 1534, 402, 21, 495, 747, 118, 20, 72, 173, 161, 79, 265, 53, 201, 53, 24, 146, 47, 50, 300, 874, 212, 21, 173, 20, 44, 280, 86, 134, 81, 436, 165, 96, 253, 346, 85, 266, 28, 266, 823, 40, 456, 125, 71, 53, 184, 86, 37, 424, 153, 88, 186, 48, 85, 1155, 98, 152, 110, 82, 253, 218, 69, 238, 27, 291, 98, 75, 18, 1606, 199, 40, 37, 53, 1571, 824, 25, 141, 149, 17, 244, 2336, 546, 225, 109, 257, 3, 70, 81, 988, 108, 496, 99, 415, 18, 41, 341, 111, 81, 64, 43, 553, 161, 605, 51, 176, 39, 196, 19, 96, 404, 270, 82, 572, 343, 292, 31, 101, 212, 50, 877, 165, 1049, 149, 124, 70, 144, 299, 244, 70, 557, 1721, 46, 575, 121, 229, 92, 214, 124, 143, 419, 379, 27, 168, 338, 37, 270, 18, 364, 17, 9, 585, 108, 20, 86, 33, 173, 61, 296, 61, 13, 52, 180, 358, 85, 304, 43, 264, 968, 99, 50, 242, 169, 207, 52, 865, 36, 434, 32, 169, 169, 273, 505, 330, 32, 75, 42, 31, 69, 51, 100, 359, 47, 583, 50, 70, 1157, 159, 347, 44, 202, 36, 40, 143, 15, 202, 635, 321, 105, 233, 15, 20, 511, 6, 97, 307, 41, 37, 790, 84, 16, 446, 60, 72, 67, 15, 88, 383, 95, 262, 172, 109, 170, 40, 156, 839, 133, 438, 25, 35, 197, 100, 14, 12, 341, 73, 98, 40, 71, 30, 35, 499, 188, 92, 408, 226, 486, 353, 175, 50, 5, 451, 28, 89, 62, 2, 45, 92, 234]\n","Length of the Output Tokens Count 9515\n","Combine Tokens Count [64, 183, 180, 141, 287, 156, 178, 161, 79, 146, 189, 180, 34, 102, 120, 110, 118, 144, 149, 145, 84, 740, 234, 29, 421, 85, 71, 313, 56, 1665, 80, 464, 103, 132, 45, 216, 79, 206, 383, 1324, 81, 145, 1027, 43, 243, 49, 59, 95, 225, 75, 163, 185, 164, 103, 306, 112, 144, 83, 37, 153, 59, 69, 131, 1121, 69, 130, 645, 409, 101, 75, 142, 116, 153, 26, 71, 63, 130, 92, 150, 374, 197, 93, 99, 741, 41, 28, 62, 1096, 53, 57, 293, 651, 1443, 345, 77, 445, 89, 103, 44, 287, 26, 173, 203, 65, 212, 1415, 62, 289, 91, 120, 53, 696, 68, 127, 46, 240, 265, 71, 44, 1054, 91, 39, 830, 56, 335, 342, 137, 88, 33, 116, 701, 115, 40, 55, 209, 107, 117, 137, 161, 154, 231, 108, 96, 37, 158, 290, 80, 158, 558, 172, 44, 111, 87, 344, 186, 254, 257, 33, 1970, 56, 41, 179, 36, 238, 41, 121, 148, 328, 283, 215, 247, 135, 638, 495, 198, 72, 96, 41, 165, 223, 342, 129, 52, 386, 170, 315, 205, 136, 40, 44, 288, 103, 49, 87, 127, 44, 86, 138, 304, 71, 209, 50, 90, 60, 162, 206, 232, 124, 93, 175, 160, 371, 146, 153, 107, 69, 452, 190, 20, 138, 54, 278, 149, 1723, 176, 48, 63, 78, 329, 281, 17, 29, 100, 100, 54, 445, 175, 136, 162, 189, 105, 91, 447, 26, 33, 163, 504, 162, 92, 110, 1637, 154, 14, 50, 678, 87, 55, 227, 29, 326, 35, 101, 471, 624, 137, 199, 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63, 126, 101, 133, 24, 111, 385, 86, 401, 2823, 245, 113, 34, 325, 80, 231, 56, 117, 65, 238, 63, 51, 296, 320, 505, 123, 630, 54, 195, 73, 67, 763, 27, 171, 361, 136, 114, 606, 90, 73, 47, 181, 87, 31, 87, 47, 131, 289, 204, 1960, 316, 102, 286, 175, 49, 71, 95, 55, 103, 76, 816, 259, 237, 271, 219, 98, 179, 53, 194, 2593, 34, 79, 73, 382, 134, 70, 48, 23, 29, 203, 80, 229, 206, 119, 29, 104, 301, 60, 32, 35, 105, 150, 51, 62, 738, 57, 48, 56, 76, 99, 669, 136, 109, 54, 84, 55, 258, 1325, 214, 202, 41, 1331, 44, 130, 333, 85, 336, 1477, 329, 169, 177, 41, 51, 135, 79, 979, 1120, 24, 311, 335, 163, 124, 108, 24, 707, 87, 35, 75, 126, 333, 295, 157, 129, 46, 44, 54, 71, 626, 25, 49, 100, 120, 35, 163, 128, 111, 111, 164, 50, 37, 44, 254, 210, 2103, 311, 695, 109, 85, 70, 94, 477, 362, 133, 187, 85, 814, 87, 380, 286, 417, 301, 33, 133, 187, 48, 80, 419, 96, 403, 159, 52, 342, 163, 638, 279, 298, 61, 373, 73, 42, 340, 100, 184, 207, 377, 163, 143, 74, 120, 34, 145, 363, 113, 608, 237, 167, 125, 1963, 26, 130, 160, 77, 34, 38, 124, 309, 442, 40, 290, 310, 144, 79, 43, 24, 31, 61, 92, 57, 85, 556, 72, 36, 47, 82, 223, 82, 1476, 159, 141, 29, 344, 510, 226, 46, 72, 610, 156, 64, 213, 99, 74, 57, 444, 285, 582, 73, 48, 320, 80, 25, 488, 95, 25, 82, 28, 93, 106, 24, 321, 119, 1023, 262, 64, 112, 220, 178, 334, 43, 68, 326, 267, 151, 160, 135, 187, 101, 590, 55, 45, 735, 1804, 264, 41, 561, 245, 172, 509, 55, 53, 42, 106, 747, 19, 392, 36, 47, 327, 64, 48, 222, 365, 88, 55, 662, 50, 61, 105, 345, 99, 68, 140, 495, 152, 55, 807, 48, 156, 185, 38, 423, 366, 78, 1410, 82, 102, 224, 67, 43, 315, 110, 46, 55, 100, 142, 99, 287, 77, 361, 369, 131, 336, 82, 22, 325, 271, 108, 220, 61, 208, 32, 155, 384, 193, 160, 79, 34, 80, 47, 26, 42, 106, 205, 1759, 102, 59, 126, 62, 87, 212, 837, 83, 157, 402, 204, 94, 329, 795, 59, 142, 541, 317, 583, 26, 115, 52, 177, 54, 150, 162, 220, 107, 511, 60, 28, 341, 92, 113, 232, 190, 245, 32, 100, 74, 104, 25, 91, 564, 84, 59, 156, 308, 166, 38, 98, 51, 86, 209, 577, 169, 69, 216, 170, 309, 85, 249, 124, 215, 253, 647, 604, 733, 57, 34, 172, 174, 58, 136, 105, 86, 238, 48, 67, 24, 1335, 177, 153, 805, 95, 75, 298, 32, 158, 1225, 700, 203, 97, 61, 811, 54, 243, 3381, 184, 35, 121, 25, 200, 360, 2032, 57, 41, 644, 629, 94, 188, 411, 217, 83, 363, 662, 754, 62, 106, 1152, 36, 36, 137, 153, 97, 55, 61, 74, 302, 288, 357, 90, 135, 298, 571, 57, 133, 644, 25, 167, 116, 110, 49, 37, 53, 55, 29, 583, 70, 31, 92, 35, 179, 140, 100, 51, 1227, 1397, 47, 71, 80, 36, 356, 55, 237, 55, 167, 102, 34, 1015, 1128, 101, 55, 519, 178, 37, 172, 245, 122, 111, 67, 257, 68, 200, 30, 49, 1448, 677, 197, 2011, 302, 69, 62, 94, 181, 152, 527, 205, 115, 64, 71, 207, 1336, 94, 446, 167, 411, 64, 318, 16, 114, 180, 386, 257, 46, 56, 527, 469, 642, 183, 1595, 82, 51, 104, 133, 337, 64, 165, 296, 352, 58, 222, 110, 528, 312, 256, 636, 216, 132, 1358, 495, 609, 251, 57, 58, 229, 296, 80, 104, 21, 312, 30, 116, 266, 582, 144, 40, 111, 675, 241, 63, 80, 413, 311, 38, 33, 303, 125, 61, 88, 37, 234, 342, 323, 94, 472, 177, 1927, 258, 40, 65, 114, 66, 119, 204, 206, 1363, 299, 49, 208, 166, 126, 332, 42, 150, 399, 290, 128, 90, 3888, 102, 192, 1006, 79, 85, 53, 155, 275, 449, 397, 280, 641, 39, 33, 70, 116, 105, 34, 71, 1292, 32, 74, 34, 164, 141, 65, 229, 258, 1134, 242, 84, 208, 174, 2199, 61, 463, 389, 98, 71, 223, 67, 287, 35, 71, 227, 212, 399, 898, 674, 53, 94, 59, 789, 199, 54, 147, 153, 819, 689, 299, 274, 35, 78, 639, 134, 420, 34, 252, 28, 226, 1275, 141, 55, 65, 54, 33, 433, 2700, 330, 93, 141, 58, 71, 141, 1159, 182, 839, 120, 334, 122, 220, 188, 163, 126, 60, 141, 145, 130, 244, 115, 34, 167, 231, 220, 43, 127, 130, 97, 154, 284, 48, 32, 130, 88, 33, 614, 80, 92, 28, 302, 408, 34, 123, 395, 530, 71, 79, 73, 124, 296, 979, 255, 140, 54, 509, 190, 38, 1881, 53, 388, 936, 81, 115, 296, 229, 97, 881, 161, 71, 22, 151, 380, 750, 312, 130, 74, 278, 65, 3054, 150, 85, 1142, 105, 30, 112, 380, 74, 140, 260, 71, 438, 96, 21, 564, 32, 208, 78, 111, 63, 68, 761, 334, 106, 295, 105, 173, 152, 113, 29, 144, 65, 100, 306, 37, 474, 113, 64, 77, 2063, 36, 68, 293, 461, 148, 199, 62, 201, 103, 36, 46, 1200, 41, 581, 172, 146, 189, 49, 360, 147, 139, 1312, 126, 190, 360, 39, 71, 96, 176, 40, 108, 115, 400, 70, 153, 1396, 118, 444, 69, 527, 68, 1061, 141, 125, 1685, 410, 84, 40, 218, 457, 559, 114, 401, 156, 17, 153, 475, 208, 177, 191, 158, 56, 63, 139, 150, 373, 53, 115, 121, 57, 425, 51, 32, 26, 74, 88, 37, 138, 1210, 532, 477, 159, 251, 545, 81, 80, 307, 136, 129, 36, 54, 247, 57, 559, 143, 150, 182, 46, 33, 111, 261, 38, 505, 41, 538, 228, 61, 50, 134, 20, 410, 223, 875, 170, 197, 58, 37, 264, 56, 50, 96, 70, 3536, 238, 62, 243, 290, 152, 79, 86, 335, 35, 136, 34, 187, 235, 44, 118, 734, 101, 142, 50, 173, 144, 101, 169, 211, 237, 39, 59, 30, 267, 255, 54, 46, 141, 126, 82, 1668, 1037, 1220, 63, 248, 41, 899, 164, 66, 145, 103, 573, 244, 57, 256, 113, 127, 112, 767, 72, 668, 123, 204, 57, 170, 617, 354, 45, 247, 62, 611, 174, 241, 86, 1150, 199, 347, 292, 2674, 150, 146, 179, 350, 84, 582, 94, 74, 997, 314, 276, 134, 85, 30, 108, 133, 623, 183, 256, 344, 151, 76, 38, 67, 44, 39, 545, 305, 156, 207, 89, 436, 423, 54, 161, 19, 40, 20, 941, 175, 49, 60, 128, 71, 177, 93, 89, 75, 259, 65, 65, 159, 134, 145, 707, 27, 220, 218, 117, 81, 64, 398, 50, 44, 87, 99, 224, 185, 119, 146, 70, 45, 146, 472, 35, 130, 141, 861, 158, 116, 143, 39, 160, 42, 77, 31, 233, 425, 15, 178, 588, 91, 71, 44, 110, 196, 159, 558, 269, 88, 171, 97, 222, 421, 572, 45, 817, 173, 65, 344, 58, 189, 427, 128, 32, 29, 101, 123, 59, 44, 67, 201, 457, 134, 1887, 361, 103, 110, 130, 43, 90, 124, 1158, 619, 86, 63, 109, 305, 80, 65, 603, 138, 81, 149, 237, 79, 179, 238, 140, 1056, 69, 255, 32, 384, 210, 118, 318, 115, 43, 78, 88, 107, 186, 151, 242, 137, 1039, 62, 145, 1092, 113, 33, 434, 95, 513, 50, 338, 170, 618, 2069, 116, 119, 245, 101, 35, 102, 200, 154, 40, 128, 100, 33, 80, 155, 81, 375, 89, 460, 165, 83, 157, 113, 397, 42, 103, 43, 142, 299, 166, 77, 746, 13, 118, 59, 529, 325, 585, 69, 297, 101, 283, 145, 180, 322, 428, 112, 110, 91, 146, 40, 269, 99, 90, 192, 211, 126, 42, 99, 162, 35, 168, 103, 40, 45, 140, 392, 150, 110, 354, 38, 149, 151, 30, 91, 36, 158, 101, 212, 566, 543, 37, 342, 26, 29, 340, 878, 71, 206, 97, 178, 127, 635, 131, 136, 117, 180, 454, 141, 479, 131, 119, 257, 802, 126, 54, 59, 244, 181, 36, 179, 209, 784, 233, 277, 106, 427, 251, 157, 28, 324, 132, 27, 70, 232, 67, 1246, 40, 29, 271, 48, 110, 366, 138, 78, 168, 142, 60, 232, 76, 198, 244, 93, 1782, 61, 233, 546, 590, 3309, 94, 46, 139, 134, 26, 59, 1186, 114, 121, 72, 103, 44, 25, 819, 99, 503, 161, 91, 128, 65, 320, 180, 119, 57, 202, 678, 143, 88, 274, 27, 643, 89, 289, 31, 57, 52, 175, 63, 69, 155, 120, 96, 211, 72, 189, 215, 102, 52, 593, 74, 108, 65, 138, 961, 220, 90, 405, 162, 135, 111, 17, 1252, 77, 115, 46, 149, 168, 273, 413, 110, 578, 117, 469, 27, 140, 254, 89, 53, 258, 1793, 126, 40, 57, 121, 234, 37, 108, 284, 297, 1039, 92, 321, 131, 92, 38, 495, 53, 365, 112, 238, 111, 155, 356, 125, 134, 915, 116, 598, 215, 563, 236, 43, 185, 144, 127, 71, 111, 410, 60, 55, 113, 65, 149, 430, 482, 330, 39, 237, 128, 643, 110, 117, 618, 87, 107, 151, 15, 144, 109, 47, 69, 66, 84, 360, 103, 115, 95, 95, 45, 88, 89, 48, 51, 72, 92, 69, 127, 137, 138, 950, 73, 92, 264, 188, 1020, 111, 257, 151, 79, 46, 83, 270, 103, 56, 157, 570, 24, 614, 72, 131, 94, 71, 395, 27, 163, 71, 432, 135, 96, 182, 270, 164, 226, 70, 247, 180, 175, 198, 118, 319, 30, 106, 123, 78, 54, 44, 385, 198, 309, 51, 67, 176, 99, 134, 64, 228, 19, 215, 146, 40, 244, 35, 800, 114, 55, 102, 182, 76, 154, 112, 98, 32, 80, 61, 220, 744, 54, 22, 56, 939, 51, 223, 42, 171, 31, 208, 78, 55, 626, 337, 61, 475, 191, 647, 104, 52, 1065, 467, 259, 124, 112, 94, 116, 84, 1546, 419, 32, 505, 768, 147, 28, 86, 190, 183, 94, 277, 63, 217, 64, 41, 156, 54, 65, 308, 891, 230, 32, 186, 34, 50, 296, 101, 153, 93, 456, 178, 112, 265, 360, 99, 279, 37, 281, 839, 54, 471, 140, 85, 68, 202, 99, 50, 439, 173, 108, 205, 60, 97, 1164, 120, 163, 123, 102, 281, 240, 83, 250, 37, 307, 109, 85, 44, 1623, 228, 52, 48, 73, 1582, 851, 45, 152, 164, 34, 260, 2354, 561, 242, 138, 274, 15, 82, 101, 1002, 120, 507, 120, 429, 44, 55, 360, 128, 94, 76, 63, 576, 178, 622, 63, 191, 54, 209, 34, 124, 413, 286, 100, 588, 361, 306, 48, 111, 222, 65, 891, 182, 1061, 174, 138, 80, 155, 308, 257, 84, 570, 1735, 67, 587, 133, 243, 102, 229, 149, 164, 429, 390, 40, 191, 352, 65, 286, 32, 375, 32, 31, 599, 132, 42, 103, 44, 199, 81, 307, 79, 24, 78, 199, 371, 101, 319, 63, 284, 977, 120, 60, 264, 178, 225, 66, 885, 53, 448, 42, 186, 182, 288, 519, 342, 46, 88, 55, 51, 81, 60, 117, 372, 65, 603, 63, 85, 1174, 212, 366, 54, 211, 59, 58, 159, 35, 225, 658, 351, 123, 243, 34, 40, 525, 32, 106, 322, 64, 57, 806, 100, 31, 462, 76, 89, 77, 34, 103, 400, 108, 283, 192, 123, 180, 60, 184, 854, 148, 469, 40, 51, 209, 117, 30, 37, 355, 85, 117, 54, 80, 42, 50, 513, 198, 107, 440, 249, 511, 370, 184, 71, 28, 465, 43, 106, 91, 17, 57, 106, 259]\n","Length of the Combine Tokens Count 9515\n"]}],"source":["#Tokenize each row in the Instruction and Output Columns in the  Dataset and Count the Total Number of Tokens\n","instruction_tokens_count = [len(tokenizer.tokenize(example[\"input\"])) for example in deduped_dataset[\"train\"]]\n","print(\"Instruction Tokens Count\", instruction_tokens_count)\n","print(\"Length of Instruction Tokens Count\", len(instruction_tokens_count))\n","\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(\"Length of the Output Tokens Count\", len(output_tokens_count))\n","\n","#Combine the Instruction Token Count and the Output Token Count\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(\"Length of the Combine Tokens Count\", len(combine_tokens_count))"]},{"cell_type":"code","source":["dataset_p = deduped_dataset[\"train\"].to_pandas()\n","dataset_p = dataset_p.drop([\"instruction\"], axis=1)\n","dataset[\"train\"] = Dataset.from_pandas(dataset_p)\n","dataset\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"SjZojvzT5rXo","executionInfo":{"status":"ok","timestamp":1705521805953,"user_tz":-330,"elapsed":380,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}},"outputId":"4cf30d8c-31ec-48fa-a6ec-4d09470df1b6"},"execution_count":48,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input', 'output'],\n","        num_rows: 9515\n","    })\n","})"]},"metadata":{},"execution_count":48}]},{"cell_type":"markdown","metadata":{"id":"5IW-e8Mf72Aa"},"source":["#**08. Top K-Sampling**\n","\n","In Top K-Sampling i will separate the Top 1000 rows from my dataset based on the number of tokens.\n","So as my daraset has 18,168 rows So, i will take those 1000 rows which have the most number of tokens"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"a4imRjhF7KkC","executionInfo":{"status":"aborted","timestamp":1705521312223,"user_tz":-330,"elapsed":13,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[],"source":["#Get the Top 1000 rows with most number of tokens\n","def get_top_k_rows(dataset, tokens_count, k):\n","  #Sort by descending token count and get the top 1000 rows\n","  sorted_indices = sorted(range(len(tokens_count)), key=lambda i: tokens_count[i], reverse=True)\n","  top_k_indices = sorted_indices[:k]\n","  print(\"Top K Indices\", top_k_indices)\n","  print(\"Length of Top K Indices\", len(top_k_indices))\n","\n","  #Extract the Top K rows\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 Dataset.from_dict(top_k_data)\n","\n","k=1000\n","top_k_dataset = get_top_k_rows(deduped_dataset, combine_tokens_count, k)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vr26xT9iCGIj","executionInfo":{"status":"aborted","timestamp":1705521312223,"user_tz":-330,"elapsed":13,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[],"source":["top_k_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0CkmEMv9CQmV","executionInfo":{"status":"aborted","timestamp":1705521312223,"user_tz":-330,"elapsed":13,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[],"source":["dataset = DatasetDict({\"train\": top_k_dataset})"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bvty8s1oCw7x","executionInfo":{"status":"aborted","timestamp":1705521312223,"user_tz":-330,"elapsed":13,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[],"source":["dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jRB04juICyI_","executionInfo":{"status":"aborted","timestamp":1705521312223,"user_tz":-330,"elapsed":13,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[],"source":["dataset[\"train\"].to_pandas()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qV-WxYD8C2fe","executionInfo":{"status":"aborted","timestamp":1705521312223,"user_tz":-330,"elapsed":12,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[],"source":["instruction_tokens_count = [len(tokenizer.tokenize(example[\"instruction\"])) for example in dataset[\"train\"]]\n","print(\"Instruction Tokens Count\", instruction_tokens_count)\n","print(\"Length of Instruction Tokens Count\", len(instruction_tokens_count))\n","\n","\n","output_tokens_count = [len(tokenizer.tokenize(example[\"output\"])) for example in dataset[\"train\"]]\n","print(\"Output Tokens Count\", output_tokens_count)\n","print(\"Length of the Output Tokens Count\", len(output_tokens_count))\n","\n","\n","#Combine the Instruction Token Count and the Output Token Count\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(\"Length of the Combine Tokens Count\", len(combine_tokens_count))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FLS7FyuuDMx2","executionInfo":{"status":"aborted","timestamp":1705521312223,"user_tz":-330,"elapsed":12,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[],"source":["#Combine Tokens Count\n","plot_distribution(combine_tokens_count, \"Distribution of the tokens count for combined Instruction + Output Column\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uKLI8hJVDVIe","executionInfo":{"status":"aborted","timestamp":1705521312223,"user_tz":-330,"elapsed":12,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[],"source":["# Read as pandas DataFrame\n","dataset['train'].to_pandas()"]},{"cell_type":"markdown","metadata":{"id":"U1wJ9pZqEPCq"},"source":["#**09. Chat templates**"]},{"cell_type":"markdown","metadata":{"id":"bO397K3BEDRx"},"source":["In the case of Llama 2, the authors used the following template for the **chat models**:\n","\n","```\n","<s>[INST] <<SYS>>\n","System prompt\n","<</SYS>>\n","\n","User prompt [/INST] Model answer </s>\n","```"]},{"cell_type":"code","execution_count":49,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["daf603de114d42e8be381662dad33324","179c8d2f69e941e98a4396817b286fb7","4161f8f96c984f59a0ce5b8518492691","e9c7dee291b84d9693e77711b2de507f","88dd126427db4a7da71f77b31159b14f","7546e9f1f0334e52acf09ed3016d4679","96c59dc257224caab73ab837e4eb6db9","e65593197b194ec197d818ebede3ceef","d3bce44df11342a293eff023e498e44f","74095547dcd443ee88f8707ed61fd1b1","eee1a00bcf56429ebd0682e42bb87919"]},"id":"PF39ozdtD6mu","outputId":"b722ccfd-a877-468d-c292-17169adbb711","executionInfo":{"status":"ok","timestamp":1705521814677,"user_tz":-330,"elapsed":904,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/9515 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"daf603de114d42e8be381662dad33324"}},"metadata":{}}],"source":["def chat_template(example):\n","    example[\"input\"] = f\"### Instruction:\\n{example['input']}\\n\\n### Response:\\n\"\n","    return example\n","\n","dataset = dataset.map(chat_template)"]},{"cell_type":"code","execution_count":51,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"c1lvbGrgEflj","outputId":"0f202b8c-281d-4a4c-9674-81a4d6f2f9d4","executionInfo":{"status":"ok","timestamp":1705521919624,"user_tz":-330,"elapsed":6,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  input  \\\n","0     ### Instruction:\\nCan you provide an overview ...   \n","1     ### Instruction:\\nWhat does \"Clear: cell\" mean...   \n","2     ### Instruction:\\nCan you provide me with info...   \n","3     ### Instruction:\\nWhat are the historical back...   \n","4     ### Instruction:\\nWhat does the \"Hypotension: ...   \n","...                                                 ...   \n","9510  ### Instruction:\\nWhat are the potential treat...   \n","9511  ### Instruction:\\nWhat are some biochemistry t...   \n","9512  ### Instruction:\\nWhat information is availabl...   \n","9513  ### Instruction:\\nWhat is the main way to prev...   \n","9514  ### Instruction:\\nCould you provide me with th...   \n","\n","                                                 output  \n","0     Squamous cell carcinoma of the lung may be cla...  \n","1     Clear cell tumors are part of the surface epit...  \n","2     Two Japanese scientists commenced research int...  \n","3     Symptoms of vulvovaginitis caused by Candida s...  \n","4     Hypotension is the term for low blood pressure...  \n","...                                                 ...  \n","9510  Future or investigational therapies of multipl...  \n","9511                                            Water -  \n","9512  Esophageal stricture is a narrowing of the eso...  \n","9513  The development of basal cell carcinoma can be...  \n","9514  No medical treatment is currently known or pra...  \n","\n","[9515 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-a76441b3-8dcf-4592-9c30-9cf84611a966\" 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>input</th>\n","      <th>output</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>### Instruction:\\nCan you provide an overview ...</td>\n","      <td>Squamous cell carcinoma of the lung may be cla...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>### Instruction:\\nWhat does \"Clear: cell\" mean...</td>\n","      <td>Clear cell tumors are part of the surface epit...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>### Instruction:\\nCan you provide me with info...</td>\n","      <td>Two Japanese scientists commenced research int...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>### Instruction:\\nWhat are the historical back...</td>\n","      <td>Symptoms of vulvovaginitis caused by Candida s...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>### Instruction:\\nWhat does the \"Hypotension: ...</td>\n","      <td>Hypotension is the term for low blood pressure...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>9510</th>\n","      <td>### Instruction:\\nWhat are the potential treat...</td>\n","      <td>Future or investigational therapies of multipl...</td>\n","    </tr>\n","    <tr>\n","      <th>9511</th>\n","      <td>### Instruction:\\nWhat are some biochemistry t...</td>\n","      <td>Water -</td>\n","    </tr>\n","    <tr>\n","      <th>9512</th>\n","      <td>### Instruction:\\nWhat information is availabl...</td>\n","      <td>Esophageal stricture is a narrowing of the eso...</td>\n","    </tr>\n","    <tr>\n","      <th>9513</th>\n","      <td>### Instruction:\\nWhat is the main way to prev...</td>\n","      <td>The development of basal cell carcinoma can be...</td>\n","    </tr>\n","    <tr>\n","      <th>9514</th>\n","      <td>### Instruction:\\nCould you provide me with th...</td>\n","      <td>No medical treatment is currently known or pra...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>9515 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-a76441b3-8dcf-4592-9c30-9cf84611a966')\"\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-a76441b3-8dcf-4592-9c30-9cf84611a966 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Push the Dataset to Hugging Face Hub**"]},{"cell_type":"code","execution_count":52,"metadata":{"id":"ytid7UuZFH9v","executionInfo":{"status":"ok","timestamp":1705521927592,"user_tz":-330,"elapsed":608,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[],"source":["import os\n","os.environ[\"HF_TOKEN\"] = \"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\""]},{"cell_type":"code","execution_count":53,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":116,"referenced_widgets":["1aec262131ea4ddabe0bb4dbf0ff6ab1","3901a638c43f452db8bdcea22e59e472","816ebb9e8f21463faeeff68393df1b98","ef9cde5891574d4da02258f963d54d02","4ee8b18102904479abc8fccec7bf3255","a024e6f95eb9414cbbe42968d69e7d56","cae1772f9ef947b5b5abcbd0d90c945c","ed1c851db12d4ad6b728142ce889f27a","c4d719b2990a4ef7b36bdf1d13a7a1db","e2c5d7f142954048a20b6281389d1304","10592c1d3e6a4a419ae56e9c1caf8561","a3b4ff4f73f442fa86d599b1ec33ff6c","41241ccb288c4cf3b6786fb6cbc3b8de","f4854bb4051e49678ccdd029122dbf18","4e4e032aed6d4efb8395c08e03b476bb","6f22a4eab0624426a758300976abade9","014af3fefae84defa36141195b5ab354","6e1ea10fd42e4809837aa4fce1c3e3a7","72928cea4d244107abe33aaa2493f9a3","5c91f3418f49490e954c0d1475846b1a","9b7d004964e745488c6eb06937cf5050","bd723ad9ff0c4202ba80567c8a01e80e"]},"id":"8DJTVOL6Eito","outputId":"0c199949-393e-4dad-adbf-91cfc95f0a94","executionInfo":{"status":"ok","timestamp":1705521931528,"user_tz":-330,"elapsed":2002,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Uploading the dataset shards:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"1aec262131ea4ddabe0bb4dbf0ff6ab1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Creating parquet from Arrow format:   0%|          | 0/10 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a3b4ff4f73f442fa86d599b1ec33ff6c"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["CommitInfo(commit_url='https://huggingface.co/datasets/deepakachu/med-match-medalpaca-wikidoc-formatted/commit/0566c75cfb674f2b5988e450832925b3c5bb7c17', commit_message='Upload dataset', commit_description='', oid='0566c75cfb674f2b5988e450832925b3c5bb7c17', pr_url=None, pr_revision=None, 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