Copy of Dataset_Creating_For_Fine_Tuning_Llama_2.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":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ulGDcaoPXu_P","outputId":"d57e715b-2428-4513-f710-d52204323964","executionInfo":{"status":"ok","timestamp":1706554134889,"user_tz":-330,"elapsed":18524,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/507.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m30.7/507.1 kB\u001b[0m \u001b[31m1.0 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m112.6/507.1 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.2/507.1 kB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.8/132.8 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.5/85.5 MB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m9.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m8.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m41.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m7.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}],"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","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zdUYSh84xpfy","executionInfo":{"status":"ok","timestamp":1706554157625,"user_tz":-330,"elapsed":22743,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}},"outputId":"c727e0fa-cbf6-4606-d949-98deee181ce6"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive\n"]}]},{"cell_type":"markdown","metadata":{"id":"29CEnxgcZAXg"},"source":["#**03. Set the Hugging Face Token**"]},{"cell_type":"code","execution_count":3,"metadata":{"id":"j-hEiS7CUyXD","executionInfo":{"status":"ok","timestamp":1706554157625,"user_tz":-330,"elapsed":9,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"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":42,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":217,"referenced_widgets":["c17a13c4e894464ab0e1bfc7122b7c38","421bd21c62ae4db28c36adff413cd3c4","bc1c84ad18874a7dbb03509a293a6fd5","88eee0774ef84073846aa84b7cc2ae65","ad651eb72cfc4269acad6523807faf35","fa0244042345451396bc063f70a8c46c","4313862cd2c149e9bb9acecfe5a368aa","73ae728106aa4d209ee081dbcdfe0a55","fd8c3584351e4848a6e48784023e0f06","bb531ad127d949a1bba1186b6c513245","fb035a59de3d4380bdc1fdfff0786e9a","3275c5dc7a8b478f93c204d3a559ba21","5255b246e02e4207acc56b88fde982f2","96d78e28bc96444fb77bda8f15a16a40","d6b284adc7c54bef94fe9cedf30b8124","66402676084845a796ba6702d93ac754","fd207d3f4a424dbab40bdb8b2d0e1bc3","0a1d4a17727045768ce55b9b28238c64","4c803a0e5969452d86f3b6d231c5d7d8","9f4fcc057b77488cbe6e442b8bacb9d3","fb5a0d871f0e4da593b069bbca35cbe9","6701f3088924491c90dc5bdcee926736","140c896a97294ff0b808b07d6ac7944e","a33b45640d89442f96d8daadf049cc20","b7fb174853d640d18954b8a09b0d1af1","bd6fe924b7cb46c1870f1f2e521aa177","5da26788e76b474aa8801faf8fa9df3f","9ad54b15049642a396a463b9e4e73315","f8ae06e1866148fb8a1c4ea30e1d359b","887dfbbf6d99431fa55741c1f38ed535","9521ecd75eab4307a150459174970b9a","9d55b120429f40e6b610d3df0c192368","756b08e6bed249e1b2eb78759473bc51"]},"id":"r8sPoUImZj2W","outputId":"8b5f2c1c-4c16-4608-93dd-d2e867b46b5a","executionInfo":{"status":"ok","timestamp":1706554927106,"user_tz":-330,"elapsed":2391,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/310 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c17a13c4e894464ab0e1bfc7122b7c38"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/1.49M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"3275c5dc7a8b478f93c204d3a559ba21"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/5407 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"140c896a97294ff0b808b07d6ac7944e"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['Input', 'Output'],\n","        num_rows: 5407\n","    })\n","})"]},"metadata":{},"execution_count":42}],"source":["from datasets import load_dataset\n","dataset = load_dataset(\"CatBotHere/med-match-medalpaca-wikidoc-patient-formatted\")\n","dataset"]},{"cell_type":"code","execution_count":23,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"B4VtvKAleA-I","outputId":"baff8543-659f-4413-a438-cab81a362928","executionInfo":{"status":"ok","timestamp":1706554341064,"user_tz":-330,"elapsed":3,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Dataset({\n","    features: ['Input', 'Output'],\n","    num_rows: 5407\n","})"]},"metadata":{},"execution_count":23}],"source":["dataset[\"train\"]"]},{"cell_type":"code","execution_count":24,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"g_6qFaC8ZzTh","outputId":"167e9dfc-c43c-4e38-f311-f9571b9d27e3","executionInfo":{"status":"ok","timestamp":1706554343696,"user_tz":-330,"elapsed":697,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  Input  \\\n","0             Input:  What are the symptoms of Allergy?   \n","1                          Input:  What causes Allergy?   \n","2     Input:  When to seek urgent medical care when ...   \n","3     Input:  What to expect if I have Allergy  (Out...   \n","4         Input:  What are the symptoms of Anaphylaxis?   \n","...                                                 ...   \n","5402   Input:  What are the symptoms of Varicose veins?   \n","5403                Input:  What causes Varicose veins?   \n","5404  Input:  Who is at highest risk for Varicose ve...   \n","5405  Input:  When to seek urgent medical care when ...   \n","5406  Input:  What to expect if I have Varicose vein...   \n","\n","                                                 Output  \n","0     Allergy symptoms vary, but may include:\\nBreat...  \n","1     The immune system normally protects the body a...  \n","2     Call for an appointment with your health care ...  \n","3     Most allergies can be easily treated with medi...  \n","4     Symptoms develop rapidly, often within seconds...  \n","...                                                 ...  \n","5402  Fullness, heaviness, aching, and sometimes pai...  \n","5403  ormal veins, valves in the vein keep blood mov...  \n","5404  Standing for a long time and having increased ...  \n","5405  Call for an appointment with your health care ...  \n","5406  Varicose veins tend to get worse over time. Yo...  \n","\n","[5407 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-285e53dc-3dd1-4904-a81b-a6cbf0305be4\" 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>Input:  What are the symptoms of Allergy?</td>\n","      <td>Allergy symptoms vary, but may include:\\nBreat...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Input:  What causes Allergy?</td>\n","      <td>The immune system normally protects the body a...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Input:  When to seek urgent medical care when ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Input:  What to expect if I have Allergy  (Out...</td>\n","      <td>Most allergies can be easily treated with medi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Input:  What are the symptoms of Anaphylaxis?</td>\n","      <td>Symptoms develop rapidly, often within seconds...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>5402</th>\n","      <td>Input:  What are the symptoms of Varicose veins?</td>\n","      <td>Fullness, heaviness, aching, and sometimes pai...</td>\n","    </tr>\n","    <tr>\n","      <th>5403</th>\n","      <td>Input:  What causes Varicose veins?</td>\n","      <td>ormal veins, valves in the vein keep blood mov...</td>\n","    </tr>\n","    <tr>\n","      <th>5404</th>\n","      <td>Input:  Who is at highest risk for Varicose ve...</td>\n","      <td>Standing for a long time and having increased ...</td>\n","    </tr>\n","    <tr>\n","      <th>5405</th>\n","      <td>Input:  When to seek urgent medical care when ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>5406</th>\n","      <td>Input:  What to expect if I have Varicose vein...</td>\n","      <td>Varicose veins tend to get worse over time. Yo...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5407 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-285e53dc-3dd1-4904-a81b-a6cbf0305be4')\"\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-285e53dc-3dd1-4904-a81b-a6cbf0305be4 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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Analyze the Dataset**"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["12ebf668e2104ef281e0b0722f27f425","9ae9cc907ad846d7853cc0c587be90fb","c2bb718b963b465f810ccf57e0c06670","1ad72e4d11734976b67a751423bdae4e","8069a28a62584e2a920756502bd92872","4cebd06f2fcd4a779392fa0214976c0c","eb6a29c2aaed42fa8015a254a066dd8a","09794204b0064bef8b8b84150cb6ee22","605e67a7134c40928cc3c9f904f34288","8dbcb3ec0d4e4b8a8df40a08cf70a706","9fd086514b9f4486bf0519da495cd62f","dcd8770d4dd44956a58f594ac61ec8dc","a11fdac1c56349e99bf33e3527a8e374","a9e4b43d5f364716900d3b424da40ae2","cb645333ccf543f6ac62ebaeaecc8428","685c1e3b4cfc47debd5f4dfff498c7fa","ba9eda4f4c3c459b90e827fc243b9f75","78eba5f76f8645f3896b728476ad96d7","e722acb9ab5640349932542c50286ea2","68504216633346c99761768b60aee8cf","ec8daf0b9e804b02bbfb167c7c4109ea","4519de322fe846ac961ad0f89caa6c0c","4fbac246d95c4232b8cfb464874c21c7","befd19f1fdbd475184db61790ee9d03f","fb450f9f55f14cf38757b3e20e5a1c9e","5f9db880b07340949bed3241fd8e1d21","19d59619fa8b401ea74ba38b6805f57d","c9f32590569243d69ddb124824e864c4","7e3c4737859e4e9d97b8779bd8fdd076","31da0e67de754266a77a0e0da9f8c5dd","dd6f2c8275d44082aec73d10ceef46cc","77467fcb1f2c4b5ebb6363e0aca4aa4c","1451ed113da94a16b13598c04272a60d","c70f3e5a073e4140b6378e561c875b79","2f145fd3537f4e188f4d559aeb25aa86","10538e39cc8f48b781a6583ec500ba6a","72c7f76932a24f9cb658d9cdde05bf72","490d2d6078da4d088b115521c1540365","a8085917c43a4328b7e6a03892720de2","977e0805ca714a3fbc2c3a345b10497e","54cd2f1cc4fe40f8b76c22482e28eef6","233e45f0eca447078fbcd8b9c7537c95","d85bcf997d3b4a9487a397264aa8faf5","b66fd6c0c0194d4391963be459f799de"]},"id":"Ea3VArojaA1Y","outputId":"f858122c-4d4d-4a40-ca71-b80ea2e26bdb","executionInfo":{"status":"ok","timestamp":1705518546379,"user_tz":-330,"elapsed":7248,"user":{"displayName":"Aishini Bhattacharjee .","userId":"12971578139265296557"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/746 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"12ebf668e2104ef281e0b0722f27f425"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"dcd8770d4dd44956a58f594ac61ec8dc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4fbac246d95c4232b8cfb464874c21c7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/435 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c70f3e5a073e4140b6378e561c875b79"}},"metadata":{}}],"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"wZknnyySb5r1","outputId":"139393ff-b703-4c83-8d10-85e84742bc58","executionInfo":{"status":"ok","timestamp":1705518834438,"user_tz":-330,"elapsed":3971,"user":{"displayName":"Aishini Bhattacharjee .","userId":"12971578139265296557"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Instruction Tokens Count [10, 6, 14, 18, 12, 8, 16, 20, 11, 7, 15, 19, 10, 6, 10, 14, 18, 14, 10, 15, 19, 23, 11, 7, 15, 19, 10, 6, 14, 18, 10, 6, 10, 14, 18, 15, 11, 19, 23, 10, 8, 13, 20, 11, 10, 15, 19, 14, 14, 18, 22, 7, 17, 13, 17, 21, 25, 14, 10, 14, 18, 22, 12, 8, 16, 20, 11, 7, 11, 15, 19, 11, 7, 7, 19, 8, 9, 13, 11, 12, 16, 15, 11, 19, 23, 12, 10, 13, 17, 21, 12, 8, 12, 16, 20, 14, 10, 14, 18, 22, 13, 9, 13, 17, 21, 14, 16, 22, 15, 16, 10, 10, 16, 18, 16, 21, 20, 18, 18, 14, 12, 17, 16, 14, 14, 13, 9, 13, 21, 14, 11, 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7, 11, 15, 19, 8, 6, 14, 11, 18, 10, 6, 10, 14, 18, 13, 9, 13, 17, 21, 17, 13, 21, 25, 13, 9, 13, 17, 21, 13, 9, 13, 17, 21, 15, 11, 13, 17, 21, 17, 16, 17, 21, 25, 10, 6, 10, 14, 18, 25, 13, 9, 13, 17, 21, 14, 10, 14, 18, 14, 10, 14, 18, 22, 11, 7, 11, 15, 19, 15, 11, 15, 19, 23, 14, 10, 18, 13, 9, 13, 17, 21, 11, 7, 11, 15, 19, 11, 7, 11, 15, 19, 12, 8, 14, 18, 22, 9, 10, 14, 6, 10, 14, 15, 11, 19, 23, 11, 7, 15, 19, 9, 5, 13, 17, 10, 6, 14, 12, 16, 6, 10, 14, 18, 11, 7, 15, 19, 11, 7, 15, 19, 11, 11, 15, 19, 14, 10, 17, 21, 15, 15, 19, 23, 9, 9, 5, 13, 17, 13, 9, 19, 23, 11, 7, 12, 11, 12, 16, 20, 11, 10, 15, 20, 16, 20, 24, 28, 12, 8, 16, 20, 7, 9, 10, 11, 9, 7, 9, 8, 9, 13, 17, 12, 8, 16, 11, 7, 15, 11, 7, 11, 15, 19, 12, 8, 16, 20, 12, 8, 20, 14, 10, 14, 22, 13, 9, 13, 17, 21, 14, 10, 7, 22, 9, 5, 13, 17, 7, 11, 7, 9, 8, 7, 17, 14, 18, 22, 8, 9, 10, 9, 10, 9, 16, 10, 12, 9, 14, 10, 9, 8, 20, 14, 16, 24, 15, 14, 13, 17, 10, 6, 10, 14, 18, 12, 8, 16, 20, 16, 12, 16, 20, 24, 13, 12, 13, 19, 22, 13, 15, 19, 22, 10, 12, 16, 19, 15, 23, 14, 18, 26, 13, 9, 13, 17, 21, 13, 17, 21, 15, 11, 17, 21, 25, 14, 10, 16, 20, 10, 6, 14, 18, 7, 15, 19, 7, 16, 20, 11, 7, 11, 15, 19, 10, 6, 10, 14, 18, 13, 9, 17, 21, 8, 12, 16, 20, 10, 6, 14, 18, 10, 9, 9, 9, 17, 14, 10, 16, 20, 24, 14, 10, 14, 18, 22, 11, 7, 11, 15, 19, 14, 22, 6, 13, 9, 13, 17, 21, 10, 6, 14, 18, 12, 8, 10, 14, 18, 12, 11, 11, 11, 8, 13, 10, 6, 10, 14, 18, 10, 6, 9, 13, 17, 10, 6, 14, 18, 14, 10, 18, 22, 11, 7, 15, 19, 14, 10, 18, 22, 14, 10, 14, 18, 22, 13, 9, 18, 22, 26, 15, 11, 15, 19, 23, 11, 7, 16, 20, 13, 9, 13, 17, 21, 16, 12, 16, 20, 24, 12, 8, 12, 16, 20, 12, 8, 12, 16, 20, 13, 9, 13, 17, 21, 14, 10, 14, 18, 22, 11, 7, 11, 15, 19, 10, 6, 14, 18, 13, 9, 13, 17, 21, 14, 10, 13, 17, 21, 10, 6, 13, 17, 10, 6, 11, 15, 19, 16, 12, 16, 20, 24, 10, 6, 10, 14, 18, 9, 10, 9, 14, 10, 14, 18, 22, 11, 7, 11, 15, 19, 13, 9, 12, 16, 20, 11, 7, 11, 15, 19, 12, 8, 12, 16, 20, 7, 11, 15, 19, 11, 7, 11, 15, 19, 8, 11, 15, 19, 10, 6, 14, 18, 8, 16, 17, 13, 17, 21, 25, 14, 10, 14, 18, 22, 12, 8, 14, 18, 22, 11, 7, 15, 19, 10, 13, 13, 14, 10, 15, 19, 10, 6, 14, 18, 11, 7, 15, 19, 14, 10, 18, 22, 11, 7, 15, 19, 12, 8, 16, 20, 16, 12, 16, 20, 24, 18, 14, 18, 22, 26, 12, 8, 16, 5, 12, 8, 16, 20, 13, 9, 17, 12, 8, 16, 20, 7, 10, 6, 14, 18, 9, 12, 8, 12, 16, 20, 9, 5, 13, 17, 12, 8, 12, 16, 13, 17, 21, 9, 5, 13, 17, 6, 14, 12, 8, 16, 20, 11, 7, 15, 19, 10, 6, 15, 19, 19, 9, 5, 13, 16, 10, 6, 10, 14, 18, 9, 10, 9, 9, 5, 7, 6, 16, 12, 20, 24, 10, 6, 14, 18, 12, 8, 12, 16, 20, 11, 7, 15, 19, 13, 9, 17, 21, 12, 8, 12, 16, 20, 10, 6, 10, 14, 18, 11, 9, 14, 21, 14, 10, 18, 22, 15, 12, 16, 20, 24, 11, 10, 8, 16, 12, 20, 24, 12, 8, 16, 20, 13, 9, 17, 21, 11, 7, 17, 20, 16, 12, 16, 20, 11, 10, 11, 15, 19, 16, 9, 7, 12, 19, 14, 10, 14, 18, 22, 12, 8, 20, 16, 12, 8, 12, 16, 20, 7, 15, 12, 8, 12, 16, 20, 15, 11, 17, 21, 25, 16, 12, 16, 20, 24, 10, 10, 14, 18, 17, 13, 23, 27, 31, 14, 11, 14, 18, 13, 22, 13, 13, 17, 21, 14, 10, 14, 18, 22, 13, 15, 19, 23, 16, 12, 20, 24, 16, 16, 20, 24, 17, 14, 22, 16, 26, 10, 6, 10, 14, 18, 7, 6, 10, 9, 7, 7, 8, 7, 12, 11, 7, 7, 9, 9, 20, 11, 10, 9, 14, 13, 7, 7, 8, 6, 11, 18, 9, 8, 13, 12, 7, 7, 10, 11, 11, 11, 12, 12, 8, 16, 20, 12, 8, 12, 16, 20]\n","Length of Input Tokens Count 5942\n"]}],"source":["#Tokenizer Downloaded\n","#Tokenize each row in the Instruction and Output Columns in the  Dataset and Count the Total Number of Tokens\n","input_tokens_count = [len(tokenizer.tokenize(example[\"input\"])) for example in dataset[\"train\"]]\n","print(\"Instruction Tokens Count\", input_tokens_count)\n","print(\"Length of Input Tokens Count\", len(input_tokens_count))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xfgGA5FddfEu","outputId":"c28795db-5fea-4c84-d27d-5d6d16b0262e","executionInfo":{"status":"ok","timestamp":1705519231122,"user_tz":-330,"elapsed":1891,"user":{"displayName":"Aishini Bhattacharjee .","userId":"12971578139265296557"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Output Tokens Count [276, 332, 33, 157, 107, 333, 38, 45, 145, 275, 77, 32, 297, 189, 45, 125, 37, 73, 35, 32, 19, 66, 144, 443, 73, 28, 237, 515, 107, 53, 123, 296, 48, 44, 83, 127, 235, 20, 21, 784, 180, 14, 154, 99, 214, 68, 36, 43, 50, 53, 24, 3714, 134, 43, 56, 32, 47, 219, 209, 163, 42, 94, 107, 79, 44, 32, 147, 155, 33, 67, 53, 100, 239, 28, 13, 209, 621, 128, 209, 621, 128, 40, 178, 14, 18, 151, 183, 64, 75, 35, 217, 205, 173, 40, 92, 161, 226, 42, 58, 51, 248, 368, 166, 71, 217, 396, 323, 131, 104, 361, 309, 44, 145, 634, 199, 21, 160, 458, 266, 439, 155, 18, 176, 457, 249, 102, 149, 88, 42, 117, 434, 15, 50, 13, 347, 83, 63, 23, 22, 132, 110, 246, 76, 20, 61, 222, 162, 70, 28, 125, 122, 170, 241, 92, 14, 156, 311, 47, 156, 71, 224, 104, 290, 53, 161, 128, 271, 29, 84, 181, 345, 75, 50, 64, 104, 168, 82, 17, 37, 164, 378, 99, 76, 16, 42, 171, 490, 75, 46, 58, 13, 31, 65, 93, 46, 22, 196, 220, 173, 95, 159, 40, 222, 162, 70, 28, 125, 76, 236, 44, 60, 47, 94, 51, 21, 64, 64, 314, 23, 102, 66, 65, 155, 44, 53, 175, 357, 66, 55, 33, 360, 227, 83, 84, 99, 113, 67, 58, 20, 91, 564, 276, 240, 18, 119, 179, 163, 35, 130, 148, 113, 43, 38, 68, 69, 28, 68, 94, 56, 54, 30, 86, 327, 73, 16, 11, 238, 361, 74, 102, 472, 63, 98, 35, 20, 40, 55, 228, 17, 68, 149, 55, 46, 79, 13, 121, 65, 93, 46, 22, 204, 272, 181, 1072, 382, 951, 202, 127, 423, 146, 1384, 335, 23, 42, 274, 219, 43, 97, 13, 121, 65, 93, 46, 22, 204, 195, 110, 143, 102, 49, 129, 234, 74, 105, 130, 146, 173, 32, 51, 83, 514, 136, 99, 63, 29, 30, 36, 104, 215, 39, 66, 19, 171, 91, 23, 23, 139, 165, 45, 41, 30, 317, 94, 348, 73, 27, 68, 254, 151, 47, 33, 268, 151, 23, 31, 65, 93, 22, 64, 172, 305, 98, 46, 31, 275, 47, 37, 262, 84, 757, 204, 124, 339, 354, 144, 57, 169, 72, 25, 39, 306, 275, 54, 42, 52, 221, 19, 57, 289, 127, 245, 74, 93, 95, 566, 456, 62, 391, 110, 317, 88, 64, 58, 294, 129, 464, 41, 68, 83, 39, 111, 143, 124, 201, 97, 130, 69, 80, 35, 73, 80, 19, 106, 99, 33, 47, 115, 256, 35, 28, 376, 116, 796, 172, 188, 133, 137, 37, 48, 66, 50, 119, 106, 110, 51, 187, 97, 294, 40, 42, 189, 623, 40, 43, 79, 30, 392, 182, 10, 23, 31, 65, 417, 75, 22, 160, 94, 45, 62, 34, 219, 359, 227, 29, 84, 98, 27, 30, 87, 17, 43, 294, 120, 68, 55, 64, 54, 375, 36, 152, 881, 346, 52, 11, 145, 105, 101, 69, 120, 19, 140, 34, 72, 42, 17, 207, 16, 16, 30, 145, 414, 52, 71, 83, 257, 197, 90, 163, 116, 299, 630, 282, 20, 160, 458, 249, 549, 257, 17, 171, 462, 234, 121, 302, 64, 36, 141, 24, 36, 18, 61, 432, 87, 19, 66, 12, 56, 26, 23, 31, 65, 446, 74, 22, 81, 198, 89, 143, 95, 69, 64, 80, 23, 106, 7, 33, 15, 364, 135, 483, 352, 154, 16, 419, 290, 740, 181, 251, 28, 93, 145, 13, 102, 65, 60, 46, 22, 180, 221, 167, 60, 108, 47, 80, 32, 44, 270, 57, 58, 276, 86, 34, 44, 141, 43, 9, 201, 93, 11, 19, 46, 233, 48, 45, 22, 169, 147, 155, 33, 67, 53, 85, 208, 67, 46, 139, 171, 225, 19, 61, 185, 270, 153, 55, 62, 122, 324, 37, 47, 101, 132, 28, 129, 81, 103, 61, 28, 73, 197, 20, 106, 177, 35, 197, 91, 82, 55, 140, 265, 90, 70, 218, 64, 132, 98, 57, 20, 147, 255, 33, 92, 24, 55, 133, 23, 233, 208, 459, 22, 209, 378, 34, 28, 119, 192, 247, 26, 45, 82, 210, 83, 31, 79, 150, 38, 75, 257, 417, 100, 25, 68, 365, 56, 76, 121, 130, 135, 33, 53, 1730, 73, 553, 37, 26, 36, 64, 469, 175, 105, 26, 45, 82, 177, 29, 49, 103, 26, 26, 45, 281, 441, 27, 43, 123, 22, 279, 57, 132, 55, 86, 174, 69, 47, 26, 53, 153, 107, 96, 289, 18, 69, 42, 72, 13, 32, 121, 227, 58, 210, 78, 91, 254, 86, 239, 26, 69, 56, 67, 106, 127, 25, 71, 78, 198, 122, 68, 62, 99, 262, 55, 11, 92, 332, 38, 107, 140, 541, 186, 5, 282, 198, 100, 237, 11, 194, 92, 84, 38, 50, 170, 325, 67, 274, 152, 53, 764, 179, 84, 499, 530, 49, 200, 141, 215, 2004, 148, 59, 361, 80, 203, 103, 115, 57, 19, 20, 70, 327, 67, 101, 80, 192, 125, 78, 339, 101, 80, 131, 210, 14, 27, 121, 24, 83, 26, 35, 55, 272, 273, 17, 8, 55, 272, 273, 17, 8, 73, 197, 18, 25, 11, 51, 340, 160, 135, 312, 240, 81, 62, 81, 133, 314, 44, 32, 83, 178, 121, 17, 45, 112, 167, 105, 50, 293, 277, 48, 138, 142, 537, 104, 127, 160, 127, 121, 381, 284, 139, 55, 57, 133, 57, 55, 94, 206, 244, 24, 176, 149, 178, 17, 51, 183, 127, 68, 63, 80, 28, 54, 18, 30, 117, 29, 39, 31, 34, 138, 208, 222, 323, 66, 62, 87, 12, 133, 143, 106, 76, 42, 159, 79, 105, 77, 97, 91, 29, 33, 31, 8, 461, 108, 253, 70, 320, 215, 33, 68, 125, 367, 170, 25, 61, 310, 444, 28, 62, 41, 129, 123, 23, 329, 35, 125, 71, 66, 114, 252, 17, 15, 28, 73, 113, 21, 28, 58, 96, 135, 20, 83, 14, 24, 52, 17, 7, 132, 14, 46, 16, 80, 109, 291, 118, 47, 454, 77, 11, 18, 32, 64, 26, 55, 98, 246, 296, 18, 37, 69, 36, 28, 19, 17, 168, 16, 47, 20, 37, 80, 85, 163, 286, 42, 41, 35, 124, 350, 36, 50, 133, 28, 20, 34, 26, 18, 40, 80, 83, 61, 55, 165, 192, 16, 51, 58, 157, 207, 13, 22, 36, 80, 52, 43, 19, 22, 58, 23, 59, 31, 7, 85, 20, 57, 81, 153, 23, 75, 13, 47, 131, 45, 37, 16, 162, 41, 121, 73, 140, 27, 69, 70, 53, 571, 393, 61, 171, 228, 555, 20, 88, 198, 100, 122, 12, 46, 68, 39, 16, 79, 48, 51, 340, 160, 135, 312, 55, 130, 50, 85, 14, 60, 80, 56, 11, 463, 292, 278, 131, 394, 81, 324, 208, 20, 68, 183, 174, 98, 176, 34, 20, 29, 218, 64, 104, 68, 76, 27, 89, 174, 60, 162, 98, 214, 68, 36, 284, 126, 142, 230, 14, 259, 59, 98, 139, 73, 115, 27, 32, 39, 48, 164, 35, 49, 122, 268, 93, 18, 34, 142, 90, 21, 29, 21, 59, 5, 11, 20, 29, 50, 54, 241, 188, 65, 90, 49, 26, 113, 239, 212, 211, 61, 57, 55, 40, 54, 27, 103, 45, 143, 32, 52, 12, 289, 243, 35, 112, 249, 22, 220, 20, 69, 329, 446, 353, 79, 40, 258, 50, 73, 50, 81, 231, 44, 36, 43, 25, 37, 78, 12, 39, 26, 92, 287, 32, 55, 142, 47, 48, 51, 8, 314, 80, 214, 67, 81, 239, 293, 14, 83, 63, 10, 107, 15, 41, 76, 119, 73, 19, 63, 20, 38, 35, 33, 67, 162, 125, 29, 45, 1723, 178, 123, 91, 38, 90, 189, 185, 66, 49, 45, 88, 142, 38, 75, 33, 571, 65, 281, 89, 62, 146, 138, 18, 95, 83, 117, 29, 39, 31, 34, 87, 126, 20, 67, 50, 117, 28, 23, 16, 51, 113, 25, 32, 21, 26, 90, 222, 134, 56, 86, 147, 124, 153, 43, 25, 46, 30, 228, 77, 131, 121, 29, 254, 14, 13, 360, 299, 35, 110, 43, 169, 15, 21, 429, 129, 260, 139, 197, 44, 102, 16, 109, 152, 136, 118, 47, 88, 51, 67, 31, 201, 68, 29, 90, 8, 62, 181, 78, 105, 18, 83, 78, 128, 131, 89, 33, 44, 282, 161, 23, 70, 24, 375, 47, 109, 37, 100, 81, 103, 35, 20, 329, 35, 125, 71, 66, 254, 65, 104, 60, 78, 204, 132, 114, 59, 172, 295, 19, 87, 193, 33, 61, 53, 53, 223, 152, 105, 31, 86, 208, 31, 27, 85, 46, 241, 22, 54, 31, 28, 58, 95, 227, 191, 165, 110, 93, 14, 259, 59, 98, 139, 356, 151, 131, 22, 110, 89, 209, 16, 54, 115, 76, 153, 97, 57, 127, 119, 117, 34, 36, 80, 94, 78, 21, 36, 130, 45, 22, 21, 31, 205, 50, 83, 105, 76, 41, 33, 70, 243, 222, 67, 134, 56, 303, 628, 174, 127, 18, 48, 125, 106, 131, 75, 224, 26, 23, 71, 46, 159, 75, 239, 58, 43, 39, 342, 27, 25, 93, 261, 15, 18, 33, 52, 188, 32, 24, 124, 94, 55, 16, 92, 144, 106, 18, 62, 187, 130, 97, 33, 126, 291, 1340, 20, 49, 49, 174, 29, 54, 138, 162, 33, 96, 271, 292, 43, 12, 85, 191, 315, 21, 28, 70, 81, 69, 128, 34, 127, 155, 249, 38, 83, 16, 409, 1094, 53, 208, 44, 17, 61, 52, 122, 58, 21, 11, 141, 151, 20, 15, 7, 156, 149, 33, 42, 197, 201, 55, 46, 80, 144, 48, 21, 32, 17, 168, 163, 325, 96, 103, 113, 45, 127, 17, 88, 24, 135, 42, 28, 311, 220, 60, 42, 27, 102, 135, 32, 148, 68, 207, 32, 24, 51, 177, 17, 32, 148, 247, 70, 101, 76, 175, 339, 81, 56, 123, 106, 55, 59, 267, 140, 24, 74, 52, 130, 239, 154, 66, 85, 150, 50, 32, 30, 324, 149, 101, 22, 55, 76, 337, 20, 8, 47, 15, 12, 43, 134, 128, 66, 134, 138, 74, 102, 19, 87, 33, 122, 25, 9, 82, 133, 33, 36, 497, 145, 103, 76, 209, 131, 58, 101, 37, 387, 184, 43, 54, 310, 84, 327, 274, 180, 39, 74, 272, 51, 74, 69, 114, 60, 86, 114, 144, 160, 34, 37, 44, 57, 27, 37, 29, 44, 312, 52, 11, 316, 208, 49, 93, 551, 394, 182, 43, 117, 107, 128, 25, 43, 120, 27, 36, 117, 256, 107, 38, 102, 437, 102, 25, 28, 24, 143, 64, 50, 32, 22, 190, 596, 644, 182, 107, 100, 169, 96, 21, 162, 110, 30, 35, 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81, 112, 83, 26, 22, 81, 11, 125, 330, 254, 573, 129, 13, 102, 372, 279, 223, 44, 35, 819, 481, 86, 15, 103, 295, 100, 98, 127, 12, 77, 476, 210, 14, 189, 302, 77, 15, 114, 83, 11, 13, 122, 47, 33, 7, 62, 178, 35, 74, 31]\n","Length of the Output Tokens Count 5942\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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WW0XiMDqevKu","outputId":"bf23fffa-bfe5-4dd3-996d-ed3c796a7f2a","executionInfo":{"status":"ok","timestamp":1705519337810,"user_tz":-330,"elapsed":388,"user":{"displayName":"Aishini Bhattacharjee .","userId":"12971578139265296557"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Combine Tokens Count [286, 338, 47, 175, 119, 341, 54, 65, 156, 282, 92, 51, 307, 195, 55, 139, 55, 87, 45, 47, 38, 89, 155, 450, 88, 47, 247, 521, 121, 71, 133, 302, 58, 58, 101, 142, 246, 39, 44, 794, 188, 27, 174, 110, 224, 83, 55, 57, 64, 71, 46, 3721, 151, 56, 73, 53, 72, 233, 219, 177, 60, 116, 119, 87, 60, 52, 158, 162, 44, 82, 72, 111, 246, 35, 32, 217, 630, 141, 220, 633, 144, 55, 189, 33, 41, 163, 193, 77, 92, 56, 229, 213, 185, 56, 112, 175, 236, 56, 76, 73, 261, 377, 179, 88, 238, 410, 339, 153, 119, 377, 319, 54, 161, 652, 215, 42, 180, 476, 284, 453, 167, 35, 192, 471, 263, 115, 158, 101, 63, 131, 445, 28, 71, 34, 366, 93, 72, 32, 42, 154, 120, 252, 86, 34, 79, 234, 170, 82, 44, 145, 136, 181, 253, 108, 28, 176, 326, 58, 171, 90, 247, 114, 299, 60, 179, 144, 283, 49, 108, 193, 353, 87, 66, 84, 117, 177, 97, 36, 60, 178, 389, 108, 94, 29, 64, 184, 499, 88, 63, 79, 32, 48, 75, 102, 55, 40, 216, 227, 180, 107, 167, 56, 234, 170, 81, 43, 144, 88, 244, 60, 80, 58, 101, 62, 36, 77, 73, 327, 40, 123, 81, 76, 170, 63, 76, 201, 379, 92, 85, 66, 375, 238, 98, 103, 122, 123, 73, 68, 34, 109, 570, 290, 249, 25, 124, 191, 179, 55, 140, 154, 123, 57, 56, 80, 85, 48, 80, 102, 68, 70, 50, 98, 335, 85, 32, 31, 250, 369, 86, 118, 492, 78, 109, 50, 39, 63, 66, 235, 32, 87, 160, 66, 61, 98, 33, 139, 75, 102, 55, 41, 225, 284, 195, 1086, 398, 965, 214, 135, 434, 158, 1391, 343, 33, 51, 283, 224, 50, 103, 31, 137, 75, 102, 55, 39, 223, 208, 119, 156, 119, 63, 139, 248, 92, 127, 142, 154, 185, 48, 71, 95, 522, 148, 119, 79, 41, 50, 60, 120, 227, 59, 90, 38, 189, 110, 46, 50, 153, 175, 61, 61, 54, 324, 114, 367, 92, 50, 82, 264, 167, 71, 43, 279, 158, 45, 51, 75, 102, 43, 87, 183, 315, 109, 61, 50, 290, 60, 47, 280, 100, 782, 213, 138, 355, 360, 158, 73, 181, 88, 45, 63, 317, 282, 65, 57, 71, 232, 27, 63, 299, 132, 254, 87, 110, 104, 571, 465, 75, 408, 120, 324, 98, 78, 76, 302, 135, 474, 52, 75, 98, 58, 118, 151, 142, 211, 105, 137, 76, 88, 46, 80, 95, 38, 116, 105, 47, 65, 126, 263, 50, 47, 386, 126, 802, 182, 202, 151, 152, 48, 63, 85, 73, 136, 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112, 108, 133, 93, 72, 40, 61, 245, 150, 85, 73, 144, 71, 246, 135, 63, 91, 150, 176, 81, 146, 94, 82, 64, 53, 99, 159, 73, 73, 89, 76, 67, 101, 94, 210, 195, 169, 101, 64, 25, 49, 57, 184, 365, 27, 52, 89, 135, 464, 312, 56, 81, 162, 508, 176, 100, 164, 254, 51, 100, 25, 334, 271, 38, 39, 124, 167, 170, 64, 62, 104, 103, 60, 58, 64, 70, 109, 51, 39, 69, 134, 62, 60, 224, 124, 66, 83, 610, 282, 173, 54, 52, 62, 220, 39, 55, 127, 423, 124, 38, 97, 181, 64, 272, 239, 113, 121, 182, 40, 69, 99, 78, 367, 49, 55, 67, 179, 480, 67, 112, 121, 365, 117, 63, 56, 203, 74, 68, 214, 320, 57, 80, 117, 149, 88, 62, 169, 124, 284, 798, 87, 79, 67, 62, 606, 130, 227, 164, 61, 39, 115, 60, 70, 31, 33, 211, 259, 118, 29, 90, 317, 163, 54, 63, 407, 307, 78, 57, 60, 177, 107, 117, 140, 51, 48, 35, 316, 290, 201, 64, 209, 305, 129, 69, 116, 100, 118, 40, 66, 128, 91, 39, 57, 61, 686, 62, 41, 52, 28, 40, 29, 36, 56, 104, 376, 112, 74, 54, 96, 100, 87, 84, 86, 54, 227, 87, 54, 45, 48, 68, 69, 47, 73, 28, 76, 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36, 151, 126, 145, 88, 62, 99, 229, 63, 91, 75, 52, 35, 112, 151, 102, 36, 36, 67, 454, 463, 69, 72, 74, 36, 90, 30, 41, 186, 34, 198, 56, 140, 39, 35, 272, 117, 48, 90, 62, 128, 63, 73, 55, 62, 179, 73, 58, 39, 185, 85, 130, 289, 66, 153, 84, 231, 36, 74, 228, 319, 354, 62, 439, 182, 375, 33, 37, 171, 119, 340, 117, 61, 207, 36, 62, 76, 73, 128, 46, 55, 420, 335, 85, 117, 158, 89, 166, 180, 61, 118, 42, 54, 94, 262, 344, 311, 79, 94, 114, 174, 312, 69, 45, 122, 111, 130, 46, 68, 19, 62, 36, 53, 93, 14, 19, 42, 62, 155, 121, 57, 30, 255, 55, 71, 148, 121, 38, 36, 136, 81, 46, 108, 117, 319, 32, 89, 128, 65, 158, 169, 111, 112, 51, 82, 27, 62, 69, 102, 214, 55, 33, 67, 456, 68, 43, 96, 58, 48, 76, 164, 80, 203, 53, 48, 128, 155, 178, 36, 78, 96, 177, 144, 71, 95, 126, 183, 111, 156, 169, 44, 175, 47, 199, 121, 308, 62, 90, 254, 288, 96, 55, 44, 133, 119, 306, 85, 88, 70, 215, 35, 26, 160, 451, 929, 35, 116, 96, 33, 50, 350, 125, 44, 65, 100, 107, 89, 31, 84, 131, 18, 68, 20, 55, 58, 51, 131, 83, 42, 60, 172, 112, 93, 25, 26, 36, 98, 374, 40, 91, 21, 55, 31, 54, 225, 32, 38, 314, 122, 154, 28, 65, 74, 107, 97, 59, 37, 171, 74, 59, 104, 213, 126, 49, 106, 191, 210, 68, 60, 37, 108, 157, 120, 91, 86, 219, 83, 28, 99, 545, 266, 50, 171, 168, 343, 97, 36, 54, 209, 79, 35, 52, 169, 96, 58, 47, 75, 163, 57, 150, 37, 60, 35, 162, 118, 42, 34, 58, 107, 37, 72, 401, 178, 62, 77, 63, 33, 79, 58, 348, 68, 54, 131, 61, 132, 363, 57, 113, 41, 161, 174, 136, 139, 134, 103, 40, 46, 30, 111, 168, 118, 47, 97, 142, 425, 185, 154, 142, 131, 141, 70, 40, 79, 437, 144, 264, 72, 139, 246, 521, 78, 422, 129, 203, 111, 110, 92, 64, 170, 296, 226, 144, 46, 229, 188, 211, 146, 36, 177, 95, 34, 43, 54, 244, 169, 204, 95, 169, 183, 229, 36, 46, 222, 145, 76, 53, 343, 222, 54, 35, 247, 132, 204, 97, 95, 77, 188, 158, 65, 69, 86, 137, 67, 92, 262, 349, 313, 80, 95, 214, 134, 38, 84, 202, 203, 88, 65, 208, 56, 270, 66, 96, 234, 167, 187, 29, 57, 144, 45, 303, 68, 89, 201, 340, 60, 70, 55, 57, 174, 61, 54, 114, 55, 52, 97, 44, 24, 121, 112, 38, 100, 80, 203, 61, 90, 63, 130, 72, 74, 154, 107, 57, 63, 47, 64, 271, 58, 35, 60, 127, 32, 34, 67, 194, 49, 57, 25, 56, 185, 60, 58, 58, 100, 109, 60, 103, 56, 118, 56, 47, 96, 151, 89, 29, 74, 412, 190, 55, 89, 191, 415, 73, 58, 68, 141, 138, 203, 69, 48, 40, 86, 65, 42, 158, 35, 30, 367, 205, 60, 259, 91, 293, 38, 31, 99, 194, 107, 68, 42, 237, 285, 119, 110, 68, 90, 106, 32, 170, 31, 384, 76, 94, 220, 306, 91, 130, 266, 158, 196, 31, 75, 37, 237, 116, 130, 74, 61, 106, 93, 412, 185, 85, 155, 173, 207, 55, 62, 366, 200, 300, 37, 59, 131, 103, 55, 40, 97, 47, 382, 76, 41, 242, 675, 91, 70, 172, 42, 60, 78, 435, 36, 71, 138, 176, 28, 26, 45, 172, 154, 213, 33, 44, 130, 346, 47, 42, 180, 135, 45, 160, 67, 33, 101, 71, 105, 42, 41, 51, 90, 181, 36, 107, 186, 79, 95, 58, 53, 156, 35, 84, 248, 258, 54, 56, 113, 36, 190, 114, 61, 35, 50, 61, 378, 61, 37, 33, 24, 701, 83, 39, 45, 182, 45, 45, 89, 148, 357, 250, 63, 42, 80, 74, 153, 27, 37, 90, 157, 152, 149, 86, 92, 169, 277, 450, 118, 93, 74, 45, 89, 73, 82, 348, 107, 36, 42, 131, 198, 422, 90, 104, 62, 156, 31, 83, 150, 501, 108, 144, 67, 37, 84, 32, 57, 380, 332, 263, 54, 97, 78, 116, 51, 46, 77, 283, 113, 86, 43, 449, 249, 157, 61, 149, 162, 146, 323, 198, 39, 147, 311, 52, 325, 200, 61, 70, 97, 159, 67, 55, 59, 91, 53, 42, 81, 156, 224, 68, 89, 198, 154, 74, 87, 94, 161, 177, 108, 149, 204, 74, 338, 330, 96, 116, 65, 234, 40, 32, 63, 147, 44, 69, 191, 242, 208, 45, 42, 57, 179, 330, 205, 58, 134, 107, 30, 70, 60, 189, 39, 400, 343, 85, 78, 40, 46, 93, 199, 264, 26, 27, 95, 158, 299, 31, 52, 86, 71, 306, 36, 36, 74, 126, 90, 44, 103, 41, 35, 40, 61, 94, 97, 81, 29, 82, 152, 59, 47, 69, 117, 319, 33, 90, 122, 61, 46, 23, 100, 89, 62, 146, 56, 130, 58, 135, 69, 98, 328, 207, 47, 89, 57, 125, 55, 113, 100, 49, 169, 153, 82, 61, 43, 45, 33, 63, 27, 199, 56, 78, 34, 526, 488, 143, 386, 134, 92, 146, 113, 140, 258, 79, 57, 43, 84, 42, 31, 168, 186, 277, 63, 24, 136, 126, 78, 45, 111, 57, 84, 61, 38, 30, 71, 125, 200, 38, 36, 119, 135, 67, 76, 181, 29, 96, 71, 264, 290, 39, 119, 50, 157, 108, 83, 57, 108, 293, 100, 100, 163, 171, 157, 187, 209, 295, 112, 96, 88, 124, 586, 53, 159, 109, 334, 66, 79, 120, 372, 47, 79, 185, 152, 318, 60, 179, 63, 40, 147, 39, 36, 77, 828, 110, 46, 31, 129, 83, 204, 31, 116, 51, 222, 407, 220, 99, 68, 75, 299, 34, 91, 99, 177, 316, 36, 63, 84, 66, 23, 55, 50, 131, 79, 63, 118, 115, 273, 59, 63, 170, 136, 51, 38, 149, 187, 71, 113, 317, 245, 90, 117, 96, 69, 142, 60, 28, 112, 330, 44, 103, 201, 61, 63, 54, 74, 81, 225, 72, 97, 60, 197, 189, 77, 65, 64, 48, 235, 63, 168, 156, 50, 63, 123, 92, 126, 77, 332, 132, 87, 68, 152, 60, 87, 25, 39, 65, 123, 126, 85, 39, 164, 55, 135, 44, 116, 104, 103, 57, 55, 61, 140, 217, 84, 74, 177, 129, 55, 196, 61, 112, 258, 50, 58, 117, 45, 123, 37, 271, 53, 120, 47, 104, 31, 94, 27, 51, 26, 63, 33, 117, 27, 47, 31, 105, 112, 290, 49, 39, 107, 20, 256, 78, 33, 182, 27, 126, 138, 127, 52, 546, 302, 78, 47, 103, 71, 38, 117, 154, 45, 59, 46, 134, 262, 27, 98, 52, 50, 137, 47, 32, 175, 109, 71, 80, 44, 34, 27, 159, 125, 158, 47, 115, 243, 169, 45, 45, 102, 117, 450, 110, 44, 123, 112, 119, 56, 86, 149, 50, 38, 50, 118, 67, 47, 38, 36, 289, 141, 56, 298, 175, 187, 237, 57, 69, 154, 46, 60, 46, 144, 243, 72, 56, 50, 136, 205, 311, 209, 138, 272, 82, 299, 94, 63, 90, 51, 67, 178, 23, 135, 573, 82, 345, 336, 302, 29, 166, 27, 68, 64, 105, 117, 44, 38, 40, 64, 583, 98, 127, 72, 65, 32, 57, 100, 22, 45, 31, 109, 115, 69, 38, 51, 94, 218, 31, 40, 193, 62, 32, 392, 122, 46, 60, 49, 33, 131, 47, 50, 74, 74, 155, 153, 44, 57, 46, 51, 399, 63, 36, 25, 43, 103, 98, 71, 119, 76, 1808, 100, 1503, 186, 209, 105, 44, 27, 100, 64, 583, 98, 123, 52, 43, 101, 141, 135, 80, 56, 118, 95, 223, 138, 184, 143, 378, 95, 53, 156, 37, 58, 35, 35, 136, 35, 27, 39, 44, 119, 27, 30, 514, 318, 81, 176, 514, 313, 76, 171, 45, 140, 103, 28, 121, 320, 255, 126, 63, 177, 216, 65, 69, 187, 200, 129, 148, 96, 232, 240, 55, 685, 127, 22, 58, 109, 233, 158, 194, 309, 114, 138, 39, 55, 149, 71, 332, 145, 219, 97, 184, 43, 320, 66, 125, 38, 45, 162, 515, 183, 107, 131, 89, 123, 75, 89, 51, 58, 44, 78, 33, 283, 95, 62, 126, 72, 48, 142, 48, 110, 61, 90, 168, 192, 182, 141, 54, 73, 41, 120, 140, 185, 56, 66, 168, 600, 44, 50, 50, 74, 346, 149, 116, 66, 332, 145, 213, 91, 42, 482, 196, 224, 94, 175, 472, 243, 115, 99, 470, 75, 73, 147, 74, 125, 43, 79, 105, 36, 76, 47, 163, 101, 166, 39, 61, 68, 123, 46, 109, 42, 148, 51, 57, 68, 74, 54, 61, 61, 83, 31, 63, 188, 70, 119, 57, 117, 46, 83, 60, 47, 50, 634, 76, 46, 82, 41, 185, 49, 285, 70, 214, 143, 161, 86, 88, 84, 44, 48, 25, 47, 73, 59, 30, 59, 102, 97, 40, 80, 159, 188, 61, 79, 416, 112, 265, 32, 228, 221, 131, 48, 120, 135, 201, 42, 55, 49, 442, 174, 50, 217, 213, 80, 226, 38, 97, 185, 138, 88, 240, 57, 65, 307, 86, 74, 95, 41, 121, 191, 162, 47, 108, 79, 422, 80, 59, 102, 166, 99, 120, 76, 190, 202, 26, 51, 295, 176, 33, 504, 574, 143, 125, 123, 694, 169, 425, 100, 168, 29, 62, 109, 119, 68, 345, 60, 36, 88, 54, 38, 47, 35, 42, 35, 103, 112, 73, 267, 174, 174, 210, 81, 31, 214, 101, 211, 80, 30, 173, 128, 31, 100, 153, 164, 202, 124, 63, 49, 29, 132, 30, 89, 106, 109, 99, 88, 84, 26, 68, 244, 45, 218, 86, 118, 333, 66, 25, 69, 79, 438, 71, 96, 79, 438, 99, 124, 247, 253, 27, 155, 282, 101, 58, 228, 60, 77, 80, 267, 133, 49, 23, 115, 125, 62, 123, 37, 86, 77, 207, 44, 54, 223, 230, 56, 40, 60, 1021, 56, 40, 60, 74, 118, 101, 206, 181, 100, 116, 185, 188, 52, 258, 536, 96, 33, 198, 100, 56, 40, 60, 72, 45, 94, 44, 72, 17, 185, 188, 52, 186, 326, 511, 173, 59, 64, 278, 420, 70, 34, 103, 27, 18, 15, 129, 59, 130, 95, 36, 16, 45, 94, 128, 372, 39, 21, 192, 73, 59, 36, 464, 51, 48, 69, 58, 84, 154, 85, 85, 58, 100, 135, 89, 266, 185, 188, 52, 18, 156, 137, 116, 85, 34, 28, 361, 306, 80, 217, 618, 58, 148, 36, 207, 51, 151, 26, 78, 230, 16, 50, 44, 86, 407, 112, 286, 58, 22, 102, 116, 245, 78, 60, 128, 112, 34, 58, 147, 60, 64, 81, 97, 107, 28, 63, 74, 84, 174, 154, 70, 49, 225, 188, 25, 383, 314, 44, 23, 85, 103, 254, 27, 64, 248, 101, 394, 46, 30, 122, 120, 242, 97, 64, 64, 126, 66, 113, 158, 215, 69, 43, 147, 62, 41, 275, 107, 31, 57, 54, 95, 38, 32, 105, 51, 157, 164, 30, 138, 106, 75, 397, 118, 47, 133, 145, 95, 40, 18, 101, 98, 280, 34, 39, 40, 89, 67, 119, 103, 244, 36, 41, 42, 38, 259, 37, 61, 39, 144, 91, 152, 142, 36, 104, 476, 140, 198, 212, 162, 76, 61, 70, 80, 116, 193, 82, 103, 135, 101, 67, 66, 38, 130, 43, 77, 46, 70, 210, 296, 38, 239, 136, 64, 65, 154, 180, 153, 104, 188, 32, 57, 55, 352, 123, 221, 147, 111, 69, 51, 110, 124, 196, 82, 43, 73, 60, 108, 347, 34, 58, 407, 129, 102, 361, 243, 84, 194, 30, 79, 31, 51, 226, 88, 232, 34, 53, 150, 54, 54, 49, 81, 77, 50, 50, 136, 248, 37, 86, 79, 138, 250, 37, 86, 79, 210, 33, 71, 54, 107, 54, 84, 125, 184, 47, 259, 99, 139, 80, 101, 37, 131, 219, 27, 66, 142, 319, 30, 47, 159, 24, 155, 27, 63, 28, 78, 356, 59, 52, 116, 187, 306, 71, 48, 170, 27, 52, 95, 87, 284, 24, 50, 93, 58, 77, 109, 44, 101, 90, 103, 86, 96, 42, 581, 95, 30, 578, 97, 30, 92, 254, 60, 186, 273, 201, 100, 63, 287, 136, 184, 97, 166, 422, 106, 28, 33, 350, 50, 192, 40, 152, 290, 77, 188, 197, 140, 32, 48, 90, 74, 172, 101, 59, 209, 98, 53, 93, 37, 81, 103, 274, 97, 166, 152, 31, 83, 186, 37, 35, 175, 102, 362, 176, 124, 56, 242, 62, 157, 126, 57, 52, 103, 37, 50, 211, 170, 43, 63, 675, 433, 23, 105, 120, 44, 170, 142, 52, 258, 79, 210, 278, 131, 76, 94, 38, 474, 355, 108, 116, 254, 169, 106, 33, 78, 91, 73, 36, 33, 159, 45, 72, 113, 120, 46, 34, 64, 198, 174, 85, 141, 71, 76, 82, 46, 375, 196, 243, 283, 403, 377, 155, 179, 37, 79, 275, 32, 76, 277, 66, 175, 237, 631, 368, 109, 430, 188, 165, 350, 299, 89, 170, 184, 332, 145, 213, 91, 138, 304, 460, 38, 92, 92, 214, 34, 43, 96, 58, 50, 78, 166, 118, 49, 52, 41, 41, 50, 22, 66, 42, 61, 23, 47, 42, 180, 72, 89, 172, 186, 100, 103, 276, 87, 243, 73, 39, 148, 195, 138, 126, 34, 49, 59, 209, 97, 52, 38, 90, 78, 36, 155, 83, 160, 155, 84, 161, 79, 153, 115, 33, 74, 90, 379, 52, 85, 135, 59, 103, 31, 59, 106, 28, 46, 61, 85, 120, 113, 264, 117, 119, 48, 270, 215, 58, 179, 162, 68, 116, 34, 113, 57, 86, 49, 88, 88, 39, 46, 70, 32, 92, 349, 54, 139, 86, 39, 89, 150, 178, 120, 51, 610, 282, 171, 52, 50, 54, 66, 144, 36, 457, 150, 90, 379, 52, 85, 135, 66, 153, 16, 144, 91, 121, 151, 48, 38, 109, 194, 123, 44, 115, 193, 119, 41, 46, 50, 31, 34, 49, 31, 65, 53, 97, 100, 127, 71, 76, 53, 119, 130, 28, 44, 54, 222, 374, 77, 325, 55, 42, 109, 50, 49, 42, 54, 42, 59, 59, 242, 44, 94, 111, 25, 28, 115, 43, 48, 71, 171, 146, 52, 54, 66, 42, 32, 78, 48, 53, 58, 316, 109, 60, 110, 197, 179, 64, 112, 222, 311, 102, 159, 70, 132, 180, 63, 59, 80, 79, 282, 123, 73, 69, 371, 61, 76, 149, 133, 149, 29, 67, 60, 69, 371, 60, 75, 148, 177, 17, 39, 163, 534, 84, 103, 123, 93, 25, 83, 68, 30, 110, 271, 54, 50, 104, 78, 111, 72, 108, 94, 123, 237, 23, 48, 62, 464, 38, 112, 284, 51, 82, 69, 62, 80, 146, 107, 119, 331, 79, 282, 124, 74, 216, 95, 94, 143, 76, 36, 120, 80, 364, 28, 69, 69, 62, 190, 60, 82, 35, 116, 185, 32, 64, 109, 201, 107, 37, 356, 86, 83, 52, 168, 36, 34, 205, 100, 58, 105, 78, 45, 72, 70, 173, 209, 41, 64, 44, 385, 36, 82, 131, 109, 33, 86, 91, 245, 178, 28, 66, 119, 228, 221, 32, 281, 210, 76, 131, 146, 182, 171, 118, 170, 248, 35, 180, 187, 139, 876, 79, 56, 481, 303, 73, 83, 33, 55, 130, 185, 60, 38, 108, 137, 78, 67, 262, 87, 49, 255, 115, 58, 62, 144, 60, 136, 217, 31, 37, 89, 124, 45, 51, 256, 257, 73, 95, 325, 185, 218, 115, 75, 172, 349, 68, 34, 92, 60, 86, 76, 405, 121, 60, 103, 75, 210, 91, 108, 57, 271, 47, 102, 92, 178, 24, 63, 101, 20, 160, 31, 130, 324, 134, 57, 86, 107, 94, 63, 69, 34, 164, 225, 42, 110, 106, 370, 112, 29, 147, 118, 171, 36, 70, 94, 44, 46, 42, 80, 156, 512, 638, 100, 151, 45, 64, 84, 139, 31, 70, 21, 138, 43, 54, 44, 97, 46, 25, 23, 212, 46, 79, 286, 108, 99, 43, 136, 93, 228, 142, 25, 121, 55, 106, 37, 29, 48, 162, 151, 55, 32, 123, 455, 97, 202, 53, 217, 92, 111, 230, 97, 56, 55, 68, 112, 68, 36, 62, 88, 101, 41, 38, 65, 74, 95, 30, 88, 103, 103, 45, 49, 121, 176, 144, 47, 58, 27, 61, 166, 56, 33, 42, 220, 102, 38, 86, 90, 134, 75, 36, 48, 125, 118, 45, 57, 173, 39, 60, 208, 96, 166, 53, 33, 65, 48, 87, 122, 97, 44, 29, 87, 21, 134, 337, 261, 581, 136, 25, 113, 379, 286, 232, 53, 55, 830, 491, 95, 29, 116, 302, 107, 106, 133, 23, 95, 485, 218, 27, 201, 309, 84, 25, 125, 94, 22, 25, 134, 55, 49, 27, 74, 186, 47, 90, 51]\n","Length of the Combine Tokens Count 5942\n"]}],"source":["#Combine the Instruction Token Count and the Output Token Count\n","combine_tokens_count = [input + output for input, output in zip(input_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":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"KBAcc8Nkfvf5","outputId":"aec111fc-eddf-493e-efd5-933e01eaa382","executionInfo":{"status":"ok","timestamp":1705519396366,"user_tz":-330,"elapsed":4105,"user":{"displayName":"Aishini Bhattacharjee .","userId":"12971578139265296557"}}},"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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4wMrKCgDE/7+JhIQEBAUFQSKRYMiQIWjVqhVSUlKwdetWJCcnY/fu3WjWrFmt15eXlyMkJATx8fGYNGkSFi1aBC0tLQiCgIULFyI+Ph42NjYYOnQoJBIJUlJSsHjxYty8eRPBwcFK6+3duxdJSUlwc3NDz549kZSUhN27d+Px48dYt26dOG/fvn1YtmwZzMzMMGjQIJiYmKCgoAAXLlzATz/9BA8Pjzd+bYiIiIio/rCRTkRERETvNEtLS0ycOBERERGIiorCxIkT6+VxMjIyEBsbi3bt2gEApk6dikGDBuGrr75Cq1atEBcXB1NTUwB/flimr68vwsPDa2ykZ2Vl4dChQ2jdujUAIDAwEFOmTEFiYiISExPFpuuNGzewcuVKSKVS7Ny5Ey1bthTX+O6777Bu3Trs2bMHU6ZMUVg/JSUFq1atwqhRo9R6blVVVVi0aBFKSkoQFhaGvn37imP/+Mc/EB4ejrVr12LVqlUwNjbGnDlzsHnzZrGR3qNHjzq8krWTy+X4/PPPoaOjg/3798POzg4AsGDBAgQFBeHo0aMICwvDrFmzary+qKgIc+fORXJyMoKCghAQECCOHThwAPHx8Rg5ciRCQ0PFN1zKysowd+5cREREwMvLCw4ODgprpqam4uDBg+IHqQYGBsLb2xtHjx7FZ599BgsLCwBAdHQ09PT0cOjQIbRq1UphjRfv6CciIiKixolHuxARERHRO2/69OkwNjbGt99+i6Kionp5DD8/P7GJDgBt2rSBi4sLCgsLMWPGDLGJDgDOzs5o164drl69WuNaMplMbKIDgEQiwfz58wEAsbGxYnz//v2oqKjA559/rtBEBwB/f3+YmpoiPj5eaf1OnTqp3UQHgLNnz+LWrVvo16+fQhMdAGbNmgUTExPEx8ejrKxM7TVfx/Hjx1FYWIhRo0aJTXQA0NbWxqeffgpdXV2F1+dFjx49wqRJk3D69GmsWrVKoYkOAHv27IGBgQGWLl2q8FsLEokEgYGBAIAjR44orSuTycQmOgDo6+tj6NChqKqqwm+//aYwV09PD7q6yvcyvfy9IyIiIqLGh3ekExEREdE777333sO0adOwbt06RERE1MuHV778YZkAYGZmBgAKTd8Xx86fP1/jWl27dlWKde7cGbq6urh06ZIYy8rKAgAkJSXh9OnTStfo6uoiJydHKf7yXdWvUv2Y3bt3Vxpr0aIFHBwckJycjJycHLXPW38dly9frnUflpaWaNu2LXJzcyGXy2FoaCiOPXjwAOPGjUN+fj62bNmidAZ+SUkJrl27BnNzc+zYsUNp7YqKCgBAdna20linTp2UYtVvgjx79kyM/fWvf8XXX3+NoUOHYujQoejZsydcXFwU9klEREREjRcb6URERETUJMhkMuzduxcREREYP378W1+/poZo9d3HtY1VN2hf9vLRHwCgo6MDExMTFBYWirGnT58CALZt21anvb7//vt1mi+Xy1VeV/2GQfW8+vKqfZibmyM3NxdFRUUKr3lBQQHkcjmsra3h7OysdN2zZ88gCALu3buHLVu21Pr4xcXFSrGavrc6OjoA/jwSp9rUqVNhYmKCf/3rX/j+++8REREBXV1d9O/fH4sWLVL4bQYiIiIianzYSCciIiKiJkFfXx9z5szB4sWLsWXLFnh7e9c4T0tLC+Xl5TWOvdjErk8PHz5UOC4EACorK/HkyROFJnt1EzcjI6NOdzZraWnVaT/Vaz948KDG8YKCAoV59UXdfbRo0UIhbm9vjxEjRmDJkiWQyWTYtWuXQjO+en6nTp0QExNTH1uHlpYWRo8ejdGjR+Px48fIyMhAfHw8EhIS8Pvvv+Pw4cNiA56IiIiIGh+ekU5ERERETYaPjw8++ugjHDhwAL///nuNc9577z08evRI6W7x4uLiWq95286cOaMUy8zMREVFBTp27CjGnJycAPzviJf6Uv2Y6enpSmPFxcW4ePEi9PX18cEHH9TrPqqPz6lpH3fv3sUff/yBdu3a1djQHzVqFFavXo3s7GzIZDKFZryhoSE6dOiA7OxsheNY6kvLli0xcOBAbNy4ET179sSNGzcaLLeIiIiI6PWwkU5ERERETYaOjg4CAwNRXl5e6xEeDg4OKC8vR1xcnBgTBAHr16+v8WiP+hAZGYn8/Hzx67KyMmzcuBHAn28GVBs/fjx0dXWxfPly3LlzR2mdZ8+eKZyp/rq6dOmC9u3b45dffkFqaqrC2D//+U88efIEXl5ekEgkb/xYqgwcOBBGRkaIiYnB9evXxbggCFi7di0qKioUXp+XjRgxAqtXr0ZOTg78/PzEO9iBPz8stqSkBEuWLKnx+/zHH38gLy/vtfeelpYGQRAUYuXl5eLxPM2aNXvttYmIiIio/vFoFyIiIiJqUtzd3eHi4oKMjIwaxydOnIiYmBgsWbIEKSkpMDU1xZkzZ1BYWAg7OztcuXKl3vfo7OwMb29vDBkyBM2bN8fJkyeRk5ODwYMHw8PDQ5xna2uLpUuX4ssvv4Snpyf69++Pdu3aoaioCHl5eUhPT4ePjw9CQ0PfaD/a2tpYvXo1/P39ERAQAA8PD1hZWSEzMxPp6elo3749Fi5c+KZP+5UMDQ2xfPlyBAUFwdfXF0OGDIGpqSlSU1Px22+/wcnJCf7+/irXGDFiBLS1tRESEgI/Pz9ERkbC3NwcY8eORVZWFmJjY3H27Fn06tUL5ubmePjwIbKzs5GVlYV169ahbdu2r7X3WbNmwdDQEM7OzrC0tERFRQVSU1Nx48YN8fUkIiIiosaLjXQiIiIianIWLlyIcePG1Thma2uLsLAwrF+/HomJiTAwMED//v0RHByM+fPnN8j+Fi9ejISEBERHR+POnTswNzfHnDlzEBAQoDTX19cXdnZ22LlzJ3799VecPHkShoaGsLS0xOTJkzFixIi3sqeuXbsiKioKW7duRUpKCuRyOczNzSGTyTBjxgyYmpq+lcd5lSFDhsDMzAzbt2/HTz/9hJKSElhZWWHmzJmYNm2aWnd2Dx8+HNra2vjss8/EM9MtLCywZs0a9OvXDwcOHMCpU6dQXFwMU1NTWFtbIzg4GK6urq+97wULFiApKQkXLlzAyZMn0bx5c7Rv3x5ffvklRo8e/drrEhEREVHD0BJe/v1CIiIiIiIiIiIiIiIS8Yx0IiIiIiIiIiIiIiIV2EgnIiIiIiIiIiIiIlKBjXQiIiIiIiIiIiIiIhXYSCciIiIiIiIiIiIiUoGNdCIiIiIiIiIiIiIiFdhIJyIiIiIiIiIiIiJSgY10IiIiIiIiIiIiIiIV2EgnIiIiIiIiIiIiIlKBjXQiIiIiIiIiIiIiIhXYSCciIiIiIiIiIiIiUoGNdCIiIiIiIiIiIiIiFdhIJyIiIiIiIiIiIiJSgY10IiIiIiIiIiIiIiIV/g/EUiDe1oQslAAAAABJRU5ErkJggg==\n"},"metadata":{}}],"source":["# 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(input_tokens_count, \"Distribution of the tokens count for Input 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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":611},"id":"f2rjgLRoiG-_","outputId":"16d1ab07-f051-4173-cebf-ccc8a2bc711f","executionInfo":{"status":"ok","timestamp":1705519511446,"user_tz":-330,"elapsed":2951,"user":{"displayName":"Aishini Bhattacharjee .","userId":"12971578139265296557"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of Valid Rows: 5942\n","Removing: 0 rows....\n"]},{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x600 with 1 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\n"},"metadata":{}}],"source":["#We 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":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Wt-7JggVyfOJ","outputId":"a4dd4bff-9699-41fa-90aa-af84b6166155","executionInfo":{"status":"ok","timestamp":1706554166008,"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'],\n","        num_rows: 5407\n","    })\n","})"]},"metadata":{},"execution_count":7}],"source":["dataset"]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"FcqDkXvGyhgo","outputId":"eb8a973d-098d-46f8-df90-4b0f2f0d6e34","executionInfo":{"status":"ok","timestamp":1706554166008,"user_tz":-330,"elapsed":10,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  Input  \\\n","0             Input:  What are the symptoms of Allergy?   \n","1                          Input:  What causes Allergy?   \n","2     Input:  When to seek urgent medical care when ...   \n","3     Input:  What to expect if I have Allergy  (Out...   \n","4         Input:  What are the symptoms of Anaphylaxis?   \n","...                                                 ...   \n","5402   Input:  What are the symptoms of Varicose veins?   \n","5403                Input:  What causes Varicose veins?   \n","5404  Input:  Who is at highest risk for Varicose ve...   \n","5405  Input:  When to seek urgent medical care when ...   \n","5406  Input:  What to expect if I have Varicose vein...   \n","\n","                                                 Output  \n","0     Allergy symptoms vary, but may include:\\nBreat...  \n","1     The immune system normally protects the body a...  \n","2     Call for an appointment with your health care ...  \n","3     Most allergies can be easily treated with medi...  \n","4     Symptoms develop rapidly, often within seconds...  \n","...                                                 ...  \n","5402  Fullness, heaviness, aching, and sometimes pai...  \n","5403  ormal veins, valves in the vein keep blood mov...  \n","5404  Standing for a long time and having increased ...  \n","5405  Call for an appointment with your health care ...  \n","5406  Varicose veins tend to get worse over time. Yo...  \n","\n","[5407 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-e581c892-b703-479b-8ffe-2c5e743816c7\" 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>Input:  What are the symptoms of Allergy?</td>\n","      <td>Allergy symptoms vary, but may include:\\nBreat...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Input:  What causes Allergy?</td>\n","      <td>The immune system normally protects the body a...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Input:  When to seek urgent medical care when ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Input:  What to expect if I have Allergy  (Out...</td>\n","      <td>Most allergies can be easily treated with medi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Input:  What are the symptoms of Anaphylaxis?</td>\n","      <td>Symptoms develop rapidly, often within seconds...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>5402</th>\n","      <td>Input:  What are the symptoms of Varicose veins?</td>\n","      <td>Fullness, heaviness, aching, and sometimes pai...</td>\n","    </tr>\n","    <tr>\n","      <th>5403</th>\n","      <td>Input:  What causes Varicose veins?</td>\n","      <td>ormal veins, valves in the vein keep blood mov...</td>\n","    </tr>\n","    <tr>\n","      <th>5404</th>\n","      <td>Input:  Who is at highest risk for Varicose ve...</td>\n","      <td>Standing for a long time and having increased ...</td>\n","    </tr>\n","    <tr>\n","      <th>5405</th>\n","      <td>Input:  When to seek urgent medical care when ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>5406</th>\n","      <td>Input:  What to expect if I have Varicose vein...</td>\n","      <td>Varicose veins tend to get worse over time. Yo...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5407 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-e581c892-b703-479b-8ffe-2c5e743816c7')\"\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-e581c892-b703-479b-8ffe-2c5e743816c7 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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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 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care ...   \n","3     Most allergies can be easily treated with medi...   \n","4     Symptoms develop rapidly, often within seconds...   \n","...                                                 ...   \n","5402  Fullness, heaviness, aching, and sometimes pai...   \n","5403  In normal veins, valves in the vein keep blood...   \n","5404  Standing for a long time and having increased ...   \n","5405  Call for an appointment with your health care ...   \n","5406  Varicose veins tend to get worse over time. Yo...   \n","\n","                                                  input  \\\n","0                     What are the symptoms of Allergy?   \n","1                                  What causes Allergy?   \n","2     When to seek urgent medical care when I have A...   \n","3     What to expect if I have Allergy  (Outlook/Pro...   \n","4                 What are the symptoms of Anaphylaxis?   \n","...                                                 ...   \n","5402           What are the symptoms of Varicose veins?   \n","5403                        What causes Varicose veins?   \n","5404        Who is at highest risk for Varicose veins ?   \n","5405  When to seek urgent medical care when I have V...   \n","5406  What to expect if I have Varicose veins  (Outl...   \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","5402  Answer this question truthfully  \n","5403  Answer this question truthfully  \n","5404  Answer this question truthfully  \n","5405  Answer this question truthfully  \n","5406  Answer this question truthfully  \n","\n","[5407 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":9,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":177},"id":"lsTjo77f_fy3","outputId":"2177e28f-d69c-4e17-9dbf-73b8d45b2749","executionInfo":{"status":"error","timestamp":1706554166008,"user_tz":-330,"elapsed":9,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'deduped_dataset' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-9-89746e06c7ce>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdeduped_dataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'deduped_dataset' is not defined"]}],"source":["deduped_dataset"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"i7LLGx7BCY5A","outputId":"34ef966e-acd0-459e-ed09-fcc3df6d13da","executionInfo":{"status":"ok","timestamp":1705519758488,"user_tz":-330,"elapsed":377,"user":{"displayName":"Aishini Bhattacharjee .","userId":"12971578139265296557"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                 output  \\\n","0     Allergy symptoms vary, but may include:\\nBreat...   \n","1     The immune system normally protects the body a...   \n","2     Call for an appointment with your health care ...   \n","3     Most allergies can be easily treated with medi...   \n","4     Symptoms develop rapidly, often within seconds...   \n","...                                                 ...   \n","5402  Fullness, heaviness, aching, and sometimes pai...   \n","5403  In normal veins, valves in the vein keep blood...   \n","5404  Standing for a long time and having increased ...   \n","5405  Call for an appointment with your health care ...   \n","5406  Varicose veins tend to get worse over time. Yo...   \n","\n","                                                  input  \\\n","0                     What are the symptoms of Allergy?   \n","1                                  What causes Allergy?   \n","2     When to seek urgent medical care when I have A...   \n","3     What to expect if I have Allergy  (Outlook/Pro...   \n","4                 What are the symptoms of Anaphylaxis?   \n","...                                                 ...   \n","5402           What are the symptoms of Varicose veins?   \n","5403                        What causes Varicose veins?   \n","5404        Who is at highest risk for Varicose veins ?   \n","5405  When to seek urgent medical care when I have V...   \n","5406  What to expect if I have Varicose veins  (Outl...   \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","5402  Answer this question truthfully  \n","5403  Answer this question truthfully  \n","5404  Answer this question truthfully  \n","5405  Answer this question truthfully  \n","5406  Answer this question truthfully  \n","\n","[5407 rows x 3 columns]"],"text/html":["\n","  <div id=\"df-5a05f446-728d-4e3a-b44f-b5f68f50b4b1\" 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>output</th>\n","      <th>input</th>\n","      <th>instruction</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Allergy symptoms vary, but may include:\\nBreat...</td>\n","      <td>What are the symptoms of Allergy?</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>The immune system normally protects the body a...</td>\n","      <td>What causes Allergy?</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Call for an appointment with your health care ...</td>\n","      <td>When to seek urgent medical care when I have A...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Most allergies can be easily treated with medi...</td>\n","      <td>What to expect if I have Allergy  (Outlook/Pro...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Symptoms develop rapidly, often within seconds...</td>\n","      <td>What are the symptoms of Anaphylaxis?</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>5402</th>\n","      <td>Fullness, heaviness, aching, and sometimes pai...</td>\n","      <td>What are the symptoms of Varicose veins?</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>5403</th>\n","      <td>In normal veins, valves in the vein keep blood...</td>\n","      <td>What causes Varicose veins?</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>5404</th>\n","      <td>Standing for a long time and having increased ...</td>\n","      <td>Who is at highest risk for Varicose veins ?</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>5405</th>\n","      <td>Call for an appointment with your health care ...</td>\n","      <td>When to seek urgent medical care when I have V...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","    <tr>\n","      <th>5406</th>\n","      <td>Varicose veins tend to get worse over time. Yo...</td>\n","      <td>What to expect if I have Varicose veins  (Outl...</td>\n","      <td>Answer this question truthfully</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5407 rows × 3 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-5a05f446-728d-4e3a-b44f-b5f68f50b4b1')\"\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-5a05f446-728d-4e3a-b44f-b5f68f50b4b1 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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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-e99778c6-3bcb-4c31-b460-87b13f4e7644\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e99778c6-3bcb-4c31-b460-87b13f4e7644')\"\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-e99778c6-3bcb-4c31-b460-87b13f4e7644 button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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\u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Number of rows in the Original Dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\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[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Number of rows in the deduped dataset\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdeduped_dataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\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      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"Number of rows removed: {len(dataset['train']) - len(deduped_dataset['train'])}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mNameError\u001b[0m: name 'deduped_dataset' is not defined"]}],"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":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WIws4AQe-1yv","outputId":"5a7ead32-d260-4c67-d8fd-87d63e364ce9","executionInfo":{"status":"ok","timestamp":1705519882910,"user_tz":-330,"elapsed":5132,"user":{"displayName":"Aishini Bhattacharjee .","userId":"12971578139265296557"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Input Tokens Count [10, 6, 14, 18, 12, 8, 16, 20, 11, 7, 15, 19, 10, 6, 10, 14, 18, 14, 10, 15, 19, 23, 11, 7, 15, 19, 10, 6, 14, 18, 10, 6, 10, 14, 18, 15, 11, 19, 23, 10, 8, 13, 20, 11, 10, 15, 19, 14, 14, 18, 22, 7, 17, 13, 17, 21, 25, 14, 10, 14, 18, 22, 12, 8, 16, 20, 11, 7, 11, 15, 19, 11, 7, 7, 19, 8, 9, 13, 11, 12, 16, 15, 11, 19, 23, 12, 10, 13, 17, 21, 12, 8, 12, 16, 20, 14, 10, 14, 18, 22, 13, 9, 13, 17, 21, 14, 16, 22, 15, 16, 10, 10, 16, 18, 16, 21, 20, 18, 18, 14, 12, 17, 16, 14, 14, 13, 9, 13, 21, 14, 11, 13, 21, 21, 19, 10, 9, 9, 20, 22, 10, 6, 10, 14, 18, 12, 8, 12, 16, 20, 14, 11, 12, 16, 14, 20, 15, 11, 15, 19, 23, 10, 9, 7, 18, 16, 12, 20, 24, 12, 8, 12, 16, 20, 13, 9, 15, 19, 23, 14, 11, 9, 18, 13, 22, 13, 9, 13, 17, 21, 19, 17, 10, 9, 9, 18, 20, 7, 7, 12, 8, 16, 12, 8, 11, 15, 19, 12, 8, 16, 20, 11, 7, 11, 15, 13, 9, 13, 17, 21, 15, 11, 15, 19, 23, 26, 22, 26, 30, 33, 15, 11, 15, 19, 23, 10, 6, 10, 14, 18, 6, 14, 9, 7, 5, 12, 16, 20, 10, 6, 10, 14, 18, 12, 16, 20, 12, 8, 12, 16, 20, 12, 8, 12, 16, 20, 12, 8, 12, 16, 20, 15, 11, 15, 19, 23, 11, 7, 15, 19, 11, 11, 15, 19, 20, 18, 10, 9, 9, 19, 21, 12, 14, 14, 16, 14, 12, 8, 11, 12, 7, 8, 10, 9, 9, 5, 7, 6, 18, 16, 10, 9, 9, 17, 19, 13, 9, 13, 17, 14, 10, 14, 18, 22, 12, 8, 12, 16, 20, 12, 8, 12, 20, 16, 12, 20, 24, 16, 12, 20, 24, 19, 18, 19, 23, 27, 14, 10, 16, 20, 24, 7, 20, 19, 19, 23, 14, 10, 16, 24, 10, 11, 7, 22, 20, 10, 9, 21, 23, 11, 10, 11, 15, 19, 15, 13, 10, 18, 16, 25, 9, 14, 16, 6, 14, 16, 12, 16, 20, 24, 11, 7, 11, 15, 19, 11, 8, 6, 10, 5, 9, 13, 17, 9, 5, 9, 13, 17, 10, 7, 10, 14, 18, 8, 6, 10, 11, 7, 15, 19, 7, 8, 18, 10, 8, 7, 7, 8, 11, 7, 15, 19, 10, 6, 14, 18, 11, 7, 15, 19, 10, 10, 6, 10, 14, 18, 15, 11, 15, 19, 23, 17, 13, 17, 21, 25, 11, 7, 15, 19, 15, 11, 15, 19, 23, 10, 6, 14, 18, 24, 22, 10, 8, 9, 23, 25, 13, 13, 17, 21, 17, 15, 11, 15, 19, 23, 13, 9, 13, 17, 21, 12, 8, 12, 16, 20, 12, 8, 17, 21, 13, 10, 14, 12, 18, 13, 9, 17, 21, 11, 7, 10, 15, 19, 12, 8, 12, 16, 20, 12, 8, 12, 16, 20, 12, 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9, 9, 5, 7, 6, 16, 12, 20, 24, 10, 6, 14, 18, 12, 8, 12, 16, 20, 11, 7, 15, 19, 13, 9, 17, 21, 12, 8, 12, 16, 20, 10, 6, 10, 14, 18, 11, 9, 14, 21, 14, 10, 18, 22, 15, 12, 16, 20, 24, 11, 10, 8, 16, 12, 20, 24, 12, 8, 16, 20, 13, 9, 17, 21, 11, 7, 17, 20, 16, 12, 16, 20, 11, 10, 11, 15, 19, 16, 9, 7, 12, 19, 14, 10, 14, 18, 22, 12, 8, 20, 16, 12, 8, 12, 16, 20, 7, 15, 12, 8, 12, 16, 20, 15, 11, 17, 21, 25, 16, 12, 16, 20, 24, 10, 10, 14, 18, 17, 13, 23, 27, 31, 14, 11, 14, 18, 13, 22, 13, 13, 17, 21, 14, 10, 14, 18, 22, 13, 15, 19, 23, 16, 12, 20, 24, 16, 16, 20, 24, 17, 14, 22, 16, 26, 10, 6, 10, 14, 18, 7, 6, 10, 9, 7, 7, 8, 7, 12, 11, 7, 7, 9, 9, 20, 11, 10, 9, 14, 13, 7, 7, 8, 6, 11, 18, 9, 8, 13, 12, 7, 7, 10, 11, 11, 11, 12, 12, 8, 16, 20, 12, 8, 12, 16, 20]\n","Length of Input Tokens Count 5942\n","Output Tokens Count [276, 332, 33, 157, 107, 333, 38, 45, 145, 275, 77, 32, 297, 189, 45, 125, 37, 73, 35, 32, 19, 66, 144, 443, 73, 28, 237, 515, 107, 53, 123, 296, 48, 83, 127, 235, 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23, 67, 48, 334, 55, 40, 113, 39, 120, 355, 93, 29, 150, 162, 120, 119, 124, 94, 30, 32, 12, 92, 153, 24, 70, 119, 133, 58, 24, 59, 428, 139, 255, 59, 122, 231, 510, 63, 403, 106, 194, 100, 103, 81, 49, 151, 282, 216, 126, 24, 217, 180, 199, 130, 16, 163, 85, 20, 32, 234, 163, 194, 81, 151, 170, 220, 19, 25, 204, 131, 54, 27, 330, 213, 37, 14, 232, 121, 197, 82, 76, 60, 175, 44, 44, 71, 126, 48, 69, 202, 126, 23, 65, 185, 190, 67, 40, 198, 50, 260, 52, 78, 224, 161, 177, 39, 113, 31, 289, 46, 63, 185, 328, 44, 50, 31, 43, 161, 48, 41, 96, 33, 41, 90, 29, 5, 107, 98, 20, 78, 71, 198, 48, 73, 51, 110, 59, 61, 137, 92, 42, 54, 265, 48, 21, 42, 115, 20, 18, 47, 185, 44, 48, 12, 39, 174, 53, 45, 41, 79, 90, 47, 105, 39, 26, 84, 143, 73, 9, 62, 404, 178, 39, 69, 57, 131, 127, 184, 54, 37, 25, 67, 42, 28, 148, 17, 8, 356, 198, 240, 80, 286, 23, 12, 89, 188, 97, 54, 24, 227, 278, 108, 95, 49, 78, 94, 16, 154, 16, 369, 56, 70, 209, 299, 80, 115, 145, 187, 18, 58, 16, 223, 106, 112, 52, 47, 96, 71, 400, 177, 69, 135, 157, 195, 35, 38, 346, 190, 23, 41, 118, 94, 38, 19, 82, 32, 371, 57, 18, 232, 669, 77, 59, 165, 27, 41, 64, 425, 18, 49, 128, 170, 18, 12, 27, 159, 145, 200, 16, 23, 114, 334, 31, 22, 156, 117, 144, 77, 59, 97, 30, 25, 31, 76, 171, 12, 179, 68, 80, 39, 40, 147, 18, 63, 235, 249, 41, 39, 92, 22, 180, 100, 43, 17, 28, 51, 372, 51, 23, 15, 13, 694, 68, 20, 30, 171, 26, 22, 77, 140, 345, 242, 51, 26, 60, 62, 145, 15, 21, 70, 134, 67, 69, 157, 266, 439, 102, 73, 64, 39, 79, 59, 64, 337, 100, 21, 23, 118, 186, 410, 50, 148, 15, 63, 138, 493, 92, 124, 56, 30, 73, 17, 38, 367, 323, 250, 37, 76, 61, 103, 34, 25, 52, 269, 103, 72, 21, 238, 142, 42, 138, 151, 130, 311, 180, 17, 121, 305, 38, 315, 194, 47, 52, 79, 145, 49, 33, 33, 75, 37, 22, 57, 143, 215, 55, 72, 177, 147, 63, 75, 82, 154, 161, 93, 133, 191, 53, 328, 324, 82, 98, 54, 227, 25, 13, 50, 138, 31, 52, 170, 231, 201, 32, 25, 36, 168, 323, 39, 124, 101, 20, 56, 42, 179, 30, 390, 325, 73, 70, 28, 30, 73, 189, 258, 16, 13, 77, 147, 292, 20, 37, 67, 57, 296, 14, 60, 116, 72, 22, 91, 33, 22, 23, 40, 139, 50, 34, 52, 50, 39, 12, 85, 70, 139, 41, 46, 127, 53, 78, 315, 198, 30, 68, 44, 113, 42, 101, 88, 29, 156, 140, 65, 40, 33, 39, 23, 49, 9, 186, 47, 61, 13, 514, 480, 131, 366, 118, 80, 128, 87, 132, 246, 67, 41, 23, 74, 36, 20, 156, 177, 266, 48, 117, 112, 68, 31, 93, 35, 70, 51, 24, 12, 49, 115, 194, 28, 18, 100, 120, 40, 64, 173, 17, 80, 51, 254, 284, 25, 101, 35, 146, 93, 64, 34, 96, 285, 88, 84, 143, 159, 145, 167, 197, 287, 100, 80, 68, 113, 575, 38, 96, 325, 49, 58, 110, 366, 37, 65, 167, 141, 307, 45, 168, 44, 30, 141, 25, 18, 66, 821, 99, 31, 12, 111, 70, 18, 206, 395, 204, 79, 44, 63, 288, 18, 71, 93, 40, 74, 57, 14, 42, 39, 124, 68, 48, 99, 104, 262, 44, 122, 35, 18, 131, 173, 49, 87, 305, 237, 79, 102, 77, 56, 130, 43, 102, 324, 192, 56, 54, 41, 57, 70, 218, 61, 82, 38, 229, 109, 78, 108, 55, 319, 123, 74, 51, 131, 49, 80, 14, 24, 46, 110, 117, 72, 22, 143, 46, 130, 35, 103, 125, 206, 69, 55, 154, 114, 40, 177, 38, 101, 247, 35, 39, 105, 37, 111, 21, 251, 41, 112, 28, 81, 20, 86, 12, 40, 20, 107, 12, 16, 82, 103, 285, 40, 26, 90, 8, 248, 62, 21, 171, 115, 131, 108, 41, 539, 63, 28, 87, 51, 18, 93, 143, 34, 44, 27, 122, 254, 15, 82, 32, 38, 129, 31, 163, 97, 51, 71, 35, 21, 10, 140, 110, 139, 24, 88, 229, 159, 31, 80, 105, 442, 98, 28, 103, 98, 109, 38, 64, 135, 40, 20, 28, 105, 54, 30, 17, 24, 281, 129, 40, 278, 163, 179, 225, 49, 131, 234, 59, 39, 29, 126, 199, 288, 182, 107, 267, 68, 281, 77, 46, 69, 26, 55, 170, 11, 124, 566, 67, 334, 329, 287, 21, 154, 16, 52, 44, 107, 30, 20, 18, 53, 573, 88, 116, 61, 50, 13, 45, 92, 10, 29, 29, 84, 212, 24, 29, 186, 50, 16, 382, 104, 33, 51, 36, 16, 117, 37, 36, 56, 52, 142, 144, 31, 40, 25, 39, 390, 52, 21, 24, 87, 83, 56, 100, 66, 1799, 91, 1490, 172, 195, 94, 31, 14, 79, 108, 38, 29, 83, 130, 69, 41, 99, 83, 215, 122, 164, 131, 370, 88, 39, 146, 23, 40, 13, 21, 125, 14, 18, 31, 107, 16, 18, 502, 301, 60, 151, 35, 130, 89, 10, 101, 301, 236, 105, 38, 163, 206, 51, 170, 187, 112, 127, 71, 221, 230, 45, 671, 116, 11, 43, 90, 228, 149, 181, 292, 103, 131, 24, 36, 143, 57, 317, 131, 200, 74, 173, 27, 306, 56, 111, 20, 23, 102, 76, 110, 57, 67, 36, 30, 60, 15, 261, 69, 51, 114, 56, 28, 115, 36, 98, 44, 43, 58, 22, 97, 129, 175, 46, 52, 154, 587, 30, 32, 28, 60, 336, 135, 98, 44, 35, 474, 187, 207, 80, 165, 459, 225, 93, 87, 462, 63, 57, 127, 61, 113, 31, 67, 94, 29, 65, 144, 85, 154, 23, 41, 44, 107, 34, 93, 22, 124, 36, 46, 53, 55, 31, 50, 54, 72, 44, 175, 61, 104, 38, 94, 34, 75, 48, 31, 30, 618, 31, 68, 28, 169, 36, 276, 53, 202, 135, 149, 70, 68, 71, 32, 36, 37, 67, 49, 16, 41, 89, 88, 25, 61, 146, 175, 44, 58, 401, 101, 18, 216, 213, 119, 32, 100, 125, 195, 32, 41, 31, 432, 164, 36, 204, 201, 67, 214, 31, 84, 176, 123, 65, 236, 45, 57, 291, 68, 60, 77, 19, 95, 178, 153, 30, 87, 65, 412, 62, 45, 80, 156, 89, 106, 58, 176, 192, 22, 44, 281, 163, 565, 128, 106, 100, 676, 156, 413, 90, 162, 19, 48, 91, 113, 54, 46, 66, 43, 31, 36, 20, 23, 21, 93, 92, 49, 255, 164, 162, 202, 63, 16, 192, 90, 204, 69, 15, 161, 118, 16, 78, 144, 147, 191, 115, 52, 41, 15, 121, 19, 74, 87, 96, 88, 76, 68, 14, 48, 231, 36, 205, 69, 97, 324, 57, 14, 48, 69, 429, 54, 75, 237, 245, 14, 135, 275, 86, 48, 222, 50, 63, 72, 261, 125, 41, 12, 97, 113, 54, 111, 21, 66, 64, 198, 27, 33, 215, 214, 41, 27, 48, 994, 63, 105, 87, 196, 170, 87, 108, 177, 163, 38, 243, 525, 77, 10, 187, 61, 38, 82, 28, 52, 11, 173, 317, 157, 53, 56, 269, 411, 60, 17, 93, 16, 11, 8, 119, 48, 118, 78, 27, 5, 26, 80, 117, 368, 23, 4, 173, 60, 49, 23, 456, 39, 40, 57, 42, 65, 142, 77, 73, 80, 124, 246, 2, 149, 127, 104, 75, 26, 18, 353, 295, 75, 211, 610, 40, 138, 18, 198, 46, 138, 13, 61, 223, 7, 42, 36, 81, 400, 105, 234, 63, 41, 115, 103, 17, 37, 61, 97, 18, 49, 56, 71, 165, 141, 53, 28, 216, 181, 13, 364, 38, 12, 67, 89, 244, 46, 226, 90, 387, 31, 11, 109, 111, 229, 52, 118, 54, 93, 146, 207, 57, 27, 127, 51, 34, 266, 98, 18, 40, 42, 87, 29, 27, 92, 34, 145, 156, 18, 122, 86, 63, 389, 106, 31, 113, 134, 88, 25, 6, 93, 85, 271, 21, 22, 19, 78, 60, 104, 24, 249, 23, 43, 17, 136, 78, 143, 129, 19, 83, 465, 133, 179, 200, 154, 45, 62, 102, 175, 60, 77, 119, 89, 47, 42, 23, 119, 28, 58, 23, 58, 202, 280, 18, 228, 129, 57, 46, 145, 175, 140, 90, 178, 16, 37, 31, 347, 110, 212, 136, 96, 54, 87, 109, 185, 67, 20, 54, 49, 101, 336, 19, 39, 399, 123, 88, 350, 225, 74, 188, 20, 65, 13, 38, 217, 75, 215, 13, 36, 137, 33, 29, 64, 123, 239, 24, 69, 58, 193, 54, 33, 82, 44, 78, 115, 170, 234, 86, 130, 67, 16, 117, 209, 13, 48, 128, 309, 16, 29, 137, 13, 148, 16, 48, 9, 63, 345, 44, 33, 93, 173, 296, 53, 35, 161, 14, 35, 74, 76, 277, 47, 70, 98, 29, 82, 78, 95, 72, 78, 20, 85, 77, 243, 41, 163, 262, 194, 85, 44, 278, 131, 171, 80, 92, 16, 17, 344, 40, 178, 22, 141, 283, 62, 169, 186, 133, 17, 29, 79, 63, 157, 82, 45, 199, 81, 32, 78, 22, 62, 80, 265, 88, 161, 139, 14, 70, 177, 18, 12, 164, 95, 350, 165, 112, 40, 222, 51, 147, 111, 37, 36, 83, 13, 22, 199, 162, 27, 43, 668, 424, 13, 94, 111, 37, 161, 134, 43, 245, 62, 198, 270, 115, 65, 87, 23, 463, 348, 97, 101, 235, 157, 98, 17, 58, 79, 65, 16, 19, 149, 31, 50, 100, 111, 33, 17, 43, 184, 164, 78, 119, 62, 71, 69, 29, 368, 185, 236, 274, 395, 370, 267, 23, 66, 268, 56, 166, 221, 100, 416, 178, 156, 342, 279, 80, 206, 18, 23, 105, 37, 39, 22, 19, 37, 7, 47, 20, 51, 11, 31, 165, 49, 154, 87, 94, 70, 222, 60, 22, 127, 109, 24, 28, 84, 64, 18, 148, 68, 141, 68, 146, 104, 18, 55, 80, 373, 42, 71, 117, 46, 94, 14, 38, 98, 16, 30, 41, 75, 114, 99, 246, 107, 110, 39, 261, 198, 44, 169, 146, 92, 20, 103, 43, 68, 27, 77, 81, 28, 31, 51, 18, 70, 343, 41, 130, 73, 68, 140, 172, 106, 33, 42, 55, 133, 25, 449, 137, 56, 147, 7, 131, 74, 111, 145, 34, 20, 95, 184, 105, 22, 32, 40, 13, 12, 35, 21, 51, 35, 75, 87, 118, 53, 54, 27, 104, 119, 13, 31, 211, 367, 61, 305, 42, 33, 28, 26, 42, 26, 39, 35, 230, 36, 82, 95, 16, 107, 31, 32, 51, 158, 137, 39, 37, 45, 28, 22, 64, 30, 47, 309, 98, 45, 91, 187, 173, 50, 94, 209, 302, 49, 118, 170, 50, 42, 59, 69, 276, 110, 56, 59, 365, 50, 61, 130, 117, 137, 13, 47, 36, 168, 7, 30, 149, 524, 70, 85, 101, 82, 18, 72, 53, 11, 97, 262, 42, 34, 84, 67, 104, 61, 93, 75, 111, 229, 11, 32, 42, 457, 27, 97, 265, 58, 138, 96, 104, 312, 110, 208, 79, 77, 130, 59, 95, 66, 354, 14, 51, 47, 50, 182, 46, 64, 13, 105, 178, 17, 45, 99, 188, 94, 23, 346, 71, 64, 42, 162, 22, 16, 194, 93, 43, 86, 64, 35, 54, 48, 162, 202, 26, 45, 32, 377, 20, 62, 115, 97, 17, 66, 67, 227, 164, 10, 44, 93, 216, 213, 16, 276, 68, 115, 126, 169, 162, 101, 158, 240, 19, 160, 180, 129, 870, 65, 38, 472, 291, 65, 71, 17, 35, 121, 180, 47, 21, 96, 129, 66, 51, 249, 70, 28, 246, 110, 45, 45, 138, 46, 124, 209, 15, 17, 78, 117, 30, 32, 246, 251, 58, 76, 306, 59, 162, 343, 58, 20, 74, 51, 76, 67, 396, 116, 53, 59, 198, 71, 84, 47, 265, 33, 84, 80, 170, 12, 47, 81, 9, 153, 16, 111, 311, 65, 95, 86, 51, 53, 14, 219, 359, 103, 15, 126, 104, 161, 18, 48, 79, 32, 30, 22, 56, 145, 502, 630, 84, 139, 25, 40, 8, 129, 7, 200, 30, 59, 275, 98, 88, 28, 117, 77, 219, 135, 13, 102, 41, 96, 23, 11, 26, 143, 35, 111, 447, 85, 33, 210, 77, 222, 35, 53, 101, 51, 15, 37, 72, 89, 25, 41, 64, 85, 16, 70, 86, 90, 22, 90, 162, 133, 33, 40, 14, 39, 153, 43, 16, 21, 206, 92, 24, 68, 68, 60, 17, 25, 109, 106, 25, 33, 157, 23, 40, 184, 79, 152, 31, 17, 39, 38, 81, 112, 83, 26, 22, 81, 11, 125, 330, 254, 573, 129, 13, 102, 372, 279, 223, 44, 35, 819, 481, 86, 15, 103, 295, 100, 98, 127, 12, 77, 476, 210, 14, 189, 302, 77, 15, 114, 83, 11, 13, 122, 47, 33, 7, 62, 178, 35, 74, 31]\n","Length of the Output Tokens Count 5407\n","Combine Tokens Count [286, 338, 47, 175, 119, 341, 54, 65, 156, 282, 92, 51, 307, 195, 55, 139, 55, 87, 45, 47, 38, 89, 155, 450, 88, 47, 247, 521, 121, 71, 133, 302, 58, 101, 142, 246, 39, 44, 794, 188, 27, 174, 110, 224, 83, 55, 57, 64, 71, 46, 3721, 151, 56, 73, 53, 72, 233, 219, 177, 60, 116, 119, 87, 60, 52, 158, 162, 44, 82, 72, 111, 246, 35, 32, 217, 630, 141, 55, 189, 33, 41, 163, 193, 77, 92, 56, 185, 175, 236, 56, 76, 73, 261, 377, 179, 88, 238, 410, 339, 153, 119, 377, 319, 54, 161, 652, 215, 42, 180, 476, 284, 453, 192, 471, 263, 115, 158, 101, 63, 131, 445, 28, 71, 34, 366, 93, 72, 32, 42, 154, 86, 34, 79, 234, 170, 82, 44, 145, 136, 181, 253, 108, 28, 176, 326, 58, 171, 90, 247, 114, 299, 60, 179, 144, 283, 49, 108, 193, 353, 87, 66, 84, 117, 177, 97, 36, 60, 178, 389, 108, 94, 29, 64, 184, 499, 88, 63, 79, 48, 75, 102, 55, 40, 216, 227, 180, 107, 167, 56, 88, 244, 60, 80, 58, 101, 62, 36, 77, 73, 327, 123, 81, 76, 170, 63, 76, 201, 379, 92, 85, 66, 375, 238, 98, 103, 122, 123, 73, 68, 34, 109, 570, 290, 249, 25, 124, 191, 179, 55, 140, 154, 123, 57, 56, 80, 85, 48, 80, 102, 68, 70, 50, 98, 335, 85, 32, 31, 250, 369, 86, 118, 492, 78, 109, 50, 39, 63, 66, 235, 32, 87, 160, 66, 61, 98, 139, 225, 284, 195, 1086, 398, 965, 214, 135, 434, 158, 1391, 343, 33, 51, 283, 224, 50, 103, 208, 119, 156, 119, 63, 139, 248, 92, 127, 142, 154, 185, 48, 71, 95, 522, 148, 119, 79, 41, 50, 60, 120, 227, 59, 90, 38, 189, 110, 46, 50, 153, 175, 61, 61, 54, 324, 114, 367, 92, 50, 82, 264, 167, 71, 43, 279, 158, 45, 87, 183, 315, 109, 61, 50, 290, 60, 47, 280, 100, 782, 138, 355, 360, 158, 73, 181, 88, 45, 63, 317, 282, 65, 57, 71, 232, 27, 63, 299, 132, 254, 87, 110, 104, 571, 465, 75, 408, 120, 324, 98, 78, 76, 302, 135, 474, 52, 75, 98, 58, 118, 151, 142, 211, 105, 137, 76, 88, 46, 80, 95, 38, 116, 105, 47, 65, 126, 263, 50, 47, 386, 126, 802, 182, 202, 151, 152, 48, 63, 85, 73, 136, 119, 127, 72, 212, 108, 301, 55, 61, 204, 634, 55, 62, 102, 40, 398, 196, 28, 425, 84, 185, 107, 58, 79, 55, 236, 44, 40, 39, 100, 34, 64, 306, 128, 80, 71, 84, 66, 53, 173, 894, 356, 66, 23, 163, 118, 110, 86, 141, 30, 147, 44, 87, 61, 29, 215, 28, 32, 50, 157, 422, 64, 87, 103, 269, 205, 106, 183, 129, 308, 647, 297, 40, 268, 132, 309, 79, 55, 153, 35, 48, 34, 81, 438, 93, 29, 80, 30, 66, 40, 100, 208, 95, 157, 111, 85, 84, 104, 36, 115, 20, 50, 36, 371, 150, 493, 363, 160, 31, 438, 302, 752, 192, 258, 39, 108, 164, 121, 69, 202, 236, 178, 75, 127, 70, 94, 47, 63, 283, 80, 73, 287, 105, 57, 55, 148, 58, 28, 220, 111, 30, 42, 73, 245, 56, 58, 39, 190, 98, 217, 80, 63, 160, 183, 233, 35, 81, 198, 280, 166, 72, 83, 135, 333, 68, 116, 143, 43, 104, 116, 71, 45, 86, 206, 33, 127, 187, 41, 210, 104, 99, 76, 153, 274, 106, 75, 139, 109, 72, 39, 161, 265, 47, 110, 46, 63, 137, 36, 243, 215, 470, 41, 217, 383, 43, 41, 136, 207, 258, 45, 68, 99, 223, 104, 56, 88, 155, 51, 92, 271, 430, 114, 38, 82, 70, 94, 143, 144, 37, 60, 1741, 48, 41, 55, 75, 476, 193, 118, 47, 70, 96, 187, 43, 71, 119, 42, 46, 69, 294, 450, 40, 60, 144, 35, 288, 70, 149, 76, 99, 183, 82, 47, 65, 161, 119, 109, 298, 35, 90, 55, 81, 26, 49, 146, 246, 77, 233, 105, 104, 263, 97, 246, 37, 84, 75, 81, 116, 141, 43, 93, 207, 135, 83, 114, 273, 34, 108, 344, 58, 131, 149, 556, 195, 17, 294, 209, 211, 105, 101, 59, 75, 178, 333, 77, 282, 160, 779, 186, 93, 512, 539, 58, 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140, 32, 48, 90, 74, 172, 101, 59, 209, 98, 53, 93, 37, 81, 103, 274, 97, 166, 152, 31, 83, 186, 37, 35, 175, 102, 362, 176, 124, 56, 242, 62, 157, 126, 57, 52, 103, 37, 50, 211, 170, 43, 63, 675, 433, 23, 105, 120, 44, 170, 142, 52, 258, 79, 210, 278, 131, 76, 94, 38, 474, 355, 108, 116, 254, 169, 106, 33, 78, 91, 73, 36, 33, 159, 45, 72, 113, 120, 46, 34, 64, 198, 174, 85, 141, 71, 76, 82, 46, 375, 196, 243, 283, 403, 377, 275, 32, 76, 277, 66, 175, 237, 109, 430, 188, 165, 350, 299, 92, 214, 34, 43, 118, 49, 52, 41, 41, 50, 22, 66, 42, 61, 23, 47, 180, 72, 172, 100, 103, 87, 243, 73, 39, 148, 126, 49, 38, 90, 78, 36, 155, 83, 160, 79, 153, 115, 33, 74, 90, 379, 52, 85, 135, 59, 103, 31, 59, 106, 28, 46, 61, 85, 120, 113, 264, 117, 119, 48, 270, 215, 58, 179, 162, 116, 34, 113, 57, 86, 49, 88, 88, 39, 46, 70, 32, 92, 349, 54, 139, 86, 89, 150, 178, 120, 51, 54, 66, 144, 36, 457, 150, 66, 153, 16, 144, 91, 121, 151, 48, 38, 109, 194, 123, 44, 46, 50, 31, 34, 49, 31, 65, 53, 97, 100, 127, 71, 76, 53, 119, 130, 28, 54, 222, 374, 77, 325, 55, 42, 49, 42, 54, 42, 59, 59, 242, 44, 94, 111, 28, 115, 43, 48, 71, 171, 146, 52, 54, 66, 42, 32, 78, 48, 58, 316, 109, 60, 110, 197, 179, 64, 112, 222, 311, 70, 132, 180, 63, 59, 80, 79, 282, 123, 73, 69, 371, 61, 76, 149, 133, 149, 29, 67, 60, 177, 17, 39, 163, 534, 84, 103, 123, 93, 25, 83, 68, 30, 110, 271, 54, 50, 104, 78, 111, 72, 108, 94, 123, 237, 23, 48, 62, 464, 38, 112, 284, 69, 146, 107, 119, 331, 124, 216, 95, 94, 143, 76, 120, 80, 364, 28, 69, 69, 62, 190, 60, 82, 35, 116, 185, 32, 64, 109, 201, 107, 37, 356, 86, 83, 52, 168, 36, 34, 205, 100, 58, 105, 78, 45, 72, 70, 173, 209, 41, 64, 44, 385, 36, 82, 131, 109, 33, 86, 91, 245, 178, 28, 66, 119, 228, 221, 32, 281, 76, 131, 146, 182, 171, 118, 170, 248, 35, 180, 187, 139, 876, 79, 56, 481, 303, 73, 83, 33, 55, 130, 185, 60, 38, 108, 137, 78, 67, 262, 87, 49, 255, 115, 58, 62, 144, 60, 136, 217, 31, 37, 89, 124, 45, 51, 256, 257, 73, 95, 325, 75, 172, 349, 68, 34, 92, 60, 86, 76, 405, 121, 60, 75, 210, 91, 108, 57, 271, 47, 102, 92, 178, 24, 63, 101, 20, 160, 31, 130, 324, 86, 107, 94, 63, 69, 34, 225, 370, 112, 29, 147, 118, 171, 36, 70, 94, 44, 46, 42, 80, 156, 512, 638, 100, 151, 45, 64, 21, 138, 23, 212, 46, 79, 286, 108, 99, 43, 136, 93, 228, 142, 25, 121, 55, 106, 37, 29, 48, 151, 55, 123, 455, 97, 53, 217, 92, 230, 55, 68, 112, 68, 36, 62, 88, 101, 41, 65, 74, 95, 30, 88, 103, 103, 45, 121, 176, 144, 47, 58, 27, 61, 166, 56, 33, 42, 220, 102, 38, 86, 90, 75, 36, 48, 125, 118, 45, 57, 173, 39, 60, 208, 96, 166, 53, 33, 65, 48, 87, 122, 97, 44, 29, 87, 21, 134, 337, 261, 581, 136, 25, 113, 379, 286, 232, 53, 55, 830, 491, 95, 29, 116, 302, 107, 106, 133, 23, 95, 485, 218, 27, 201, 309, 84, 25, 125, 94, 22, 25, 134, 55, 49, 27, 74, 186, 47, 90, 51]\n","Length of the Combine Tokens Count 5407\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(\"Input Tokens Count\", input_tokens_count)\n","print(\"Length of Input Tokens Count\", len(input_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":"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":{"colab":{"base_uri":"https://localhost:8080/"},"id":"a4imRjhF7KkC","outputId":"2ab37a17-da68-4110-d229-80c1d87add86"},"outputs":[{"name":"stdout","output_type":"stream","text":["Top K Indices [17950, 833, 17908, 1508, 3194, 9174, 15131, 5188, 16808, 16929, 16656, 16051, 17353, 16896, 16824, 15310, 15575, 16752, 3384, 16559, 10433, 15036, 17078, 802, 15115, 1609, 16977, 6090, 15432, 16100, 5121, 15195, 16041, 16838, 7586, 17087, 6656, 15420, 17265, 3019, 16923, 4727, 15322, 17045, 210, 7803, 14921, 17345, 16138, 18137, 9170, 15643, 13489, 15613, 15659, 15006, 17726, 16310, 15364, 16134, 16153, 16570, 16091, 16837, 4703, 17990, 16768, 16594, 17597, 15660, 3469, 16693, 7693, 17029, 16018, 16640, 17680, 15858, 16811, 17211, 16534, 5508, 17234, 15328, 17093, 15449, 16709, 16892, 16424, 15109, 15763, 16607, 16508, 7238, 15384, 17594, 605, 15900, 16698, 16198, 16296, 4770, 15333, 16972, 15359, 16924, 16434, 15100, 17024, 3312, 7657, 15217, 3074, 10345, 3208, 15741, 16593, 532, 17376, 10101, 17571, 5165, 10110, 15481, 15140, 16889, 1832, 18004, 5378, 15153, 15183, 1619, 14309, 10203, 15711, 17149, 15015, 17052, 15467, 10569, 17006, 15941, 18010, 17116, 16995, 15888, 14090, 16116, 15903, 15972, 4878, 16622, 15542, 16155, 3204, 4955, 478, 16442, 3357, 1847, 17155, 15417, 15783, 13896, 16411, 16655, 15120, 15464, 15334, 17958, 15150, 3457, 16935, 17651, 16405, 3285, 17129, 15896, 15241, 16783, 17001, 17120, 15077, 15247, 16213, 17293, 15598, 17044, 13830, 17281, 15401, 16490, 16699, 17156, 15729, 16264, 3080, 10414, 16158, 15820, 15690, 16429, 16749, 17018, 17788, 16873, 15724, 16238, 15409, 15596, 17639, 3174, 744, 16090, 16763, 3810, 15447, 5420, 8973, 11652, 16565, 16677, 16390, 16339, 14154, 16864, 15996, 4853, 16255, 3294, 14885, 16964, 10371, 13834, 15549, 16139, 17128, 7742, 15872, 17641, 4913, 17565, 15856, 16314, 15721, 16003, 16237, 15938, 15133, 15156, 17094, 16117, 4865, 6825, 2844, 2913, 15969, 15645, 16516, 17187, 11074, 13836, 16465, 16681, 4907, 15251, 5288, 17214, 17459, 16072, 16341, 17289, 15098, 15964, 16179, 17929, 15604, 919, 16989, 1735, 15505, 9173, 16849, 15276, 15376, 16979, 7707, 15124, 17135, 489, 15799, 15294, 16043, 15565, 16870, 17163, 17551, 16441, 4906, 747, 13974, 15658, 8260, 11611, 13873, 16032, 16019, 3246, 4747, 18136, 13832, 15104, 8884, 18134, 7972, 16374, 2880, 16951, 13886, 15869, 16192, 16898, 16143, 15656, 5814, 15341, 15200, 11605, 15404, 16474, 13607, 15117, 798, 3107, 15414, 15891, 13280, 15286, 16532, 16891, 18068, 13624, 17340, 14335, 14935, 15053, 16449, 11591, 13710, 15705, 16285, 16775, 3079, 14143, 4860, 16207, 16868, 16829, 16497, 14526, 17013, 1469, 16067, 17196, 17676, 17084, 16552, 16181, 16176, 16782, 17953, 15148, 16934, 16629, 16949, 14965, 13863, 13798, 8904, 17752, 14315, 15134, 17259, 16365, 16624, 17181, 17192, 13652, 17273, 15709, 16706, 16770, 5171, 9086, 13926, 15810, 17043, 15037, 15663, 3754, 13944, 16579, 17266, 14452, 16409, 17145, 16024, 855, 4679, 2922, 14599, 15235, 5186, 15175, 17297, 10301, 15981, 16130, 16740, 17739, 1415, 1836, 15710, 3207, 17035, 14506, 15677, 13898, 3795, 15436, 17932, 14786, 485, 2943, 3149, 11246, 16437, 17074, 3454, 7990, 16626, 15870, 17174, 1008, 7923, 16332, 18039, 2925, 13787, 15188, 15958, 16731, 16647, 16059, 15582, 3100, 16053, 17792, 3385, 3393, 15179, 15181, 5803, 15277, 15416, 14999, 16764, 17106, 13709, 16224, 3089, 4719, 14199, 9049, 9186, 230, 10049, 16270, 17249, 5275, 16107, 16925, 14704, 754, 9285, 16568, 16881, 17215, 10045, 16159, 14277, 16661, 11578, 16983, 15300, 16625, 17204, 7685, 13833, 16599, 17177, 15608, 16697, 16900, 15428, 17167, 17591, 5131, 14071, 16423, 7689, 16334, 5048, 13122, 15016, 16040, 13853, 3353, 14793, 7724, 13917, 15935, 16767, 9033, 15180, 15991, 17069, 2895, 6348, 16961, 16499, 17390, 17892, 3507, 3050, 16932, 17339, 16156, 475, 1407, 4760, 16160, 3183, 8370, 13909, 15665, 15622, 5396, 16252, 16464, 17467, 15340, 16659, 17369, 8309, 10111, 14044, 16956, 15784, 15289, 16950, 1949, 2979, 8292, 15118, 3243, 15528, 15541, 17397, 8098, 9232, 17261, 16511, 16732, 8241, 3088, 14522, 18042, 17099, 10370, 16200, 16496, 866, 4855, 14920, 15863, 15845, 16211, 16212, 9152, 16986, 15097, 15574, 16673, 15444, 4217, 5691, 15475, 16833, 2888, 10072, 11472, 1244, 3344, 17191, 14795, 3552, 7752, 15144, 15448, 15987, 1490, 14852, 15570, 15943, 6317, 13742, 15509, 17625, 14915, 15738, 5406, 6764, 15538, 17745, 16557, 10070, 17327, 14702, 16571, 5013, 15079, 15908, 6153, 10985, 13693, 10400, 15885, 16148, 16592, 16976, 17922, 15060, 15250, 7981, 3077, 15632, 15988, 15942, 15521, 13942, 15034, 15815, 15853, 16394, 17012, 15182, 17137, 2602, 13788, 15424, 15532, 16440, 89, 3229, 9211, 15478, 13899, 16756, 286, 8020, 14643, 17589, 964, 1777, 4942, 4968, 11551, 16786, 16933, 17011, 7639, 15067, 17158, 14633, 15089, 14359, 16494, 17150, 157, 2950, 10214, 15239, 15386, 16027, 7969, 16648, 10291, 13689, 15577, 15733, 15311, 16930, 1072, 1931, 3471, 9740, 18156, 4775, 14561, 16258, 16735, 17847, 4877, 10067, 15336, 16701, 15, 16680, 16311, 16445, 1559, 1629, 1652, 5235, 7686, 13945, 17595, 1897, 13953, 15697, 17278, 17755, 940, 14060, 14807, 15476, 17869, 13125, 15152, 15646, 15775, 732, 5326, 16330, 15671, 17530, 14543, 14700, 15187, 15805, 7601, 14137, 17168, 14236, 14711, 15675, 11604, 3400, 16352, 16867, 3328, 15331, 17317, 9488, 8254, 8120, 16118, 16191, 16307, 16981, 17059, 17505, 15095, 15882, 7773, 10413, 14113, 14712, 16157, 16260, 767, 5269, 13782, 16827, 9251, 13966, 17126, 17533, 2978, 16005, 17712, 3381, 14243, 1703, 16595, 1622, 9397, 15267, 15624, 16189, 16712, 17212, 5128, 13946, 8105, 15125, 15798, 16621, 1533, 10176, 9067, 15623, 17947, 1542, 14185, 1928, 4856, 16690, 17563, 13673, 15847, 16306, 9868, 17561, 1669, 4871, 9245, 13856, 14602, 3029, 10235, 10453, 11552, 13697, 14070, 16188, 17382, 15995, 3056, 14437, 15309, 17048, 7642, 14142, 15584, 15873, 3051, 15258, 1394, 8281, 14300, 16054, 18044, 18150, 4785, 10126, 1948, 2878, 5046, 8917, 13773, 17207, 3365, 8050, 14764, 17336, 17936, 1584, 16637, 17022, 17331, 723, 16936, 1099, 2494, 13694, 14877, 16244, 17332, 9000, 16184, 17109, 18077, 3363, 4764, 9227, 17308, 1765, 14040, 14358, 16643, 16759, 16805, 766, 13629, 14847, 15691, 1403, 8252, 15967, 16554, 16990, 1655, 14439, 6440, 6919, 9003, 15045, 4418, 4845, 10227, 14732, 14785, 16065, 17147, 17267, 17500, 14158, 15022, 16746, 17073, 1546, 15128, 17741, 11549, 14030, 16174, 17441, 14135, 16104, 3111, 8178, 10204, 15279, 13792, 16724, 16093, 16395, 17987, 8954, 15453, 14962, 15460, 16975, 8318, 9209, 16056, 16916, 17324, 11538, 16219, 10234, 15132, 15522, 16602, 16850, 17637, 4762, 15635, 17924, 16284, 17773, 4777, 16079, 16488, 17113, 17957, 15707, 15993, 3411, 13700, 15026, 15666, 16747, 17845, 13613, 14410, 14780, 14815, 15320, 4671, 5335, 15606, 16288, 17925, 14428, 15978, 16030, 17153, 3429, 14688, 14922, 15860, 17363, 18152, 2622, 5628, 17054, 17183]\n","Length of Top K Indices 1000\n"]}],"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":{"colab":{"base_uri":"https://localhost:8080/"},"id":"vr26xT9iCGIj","outputId":"297a2d2e-df06-4e75-ed86-144ee30254cb"},"outputs":[{"data":{"text/plain":["Dataset({\n","    features: ['instruction', 'output'],\n","    num_rows: 1000\n","})"]},"execution_count":20,"metadata":{},"output_type":"execute_result"}],"source":["top_k_dataset"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"0CkmEMv9CQmV","colab":{"base_uri":"https://localhost:8080/","height":177},"executionInfo":{"status":"error","timestamp":1706554191988,"user_tz":-330,"elapsed":441,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}},"outputId":"c12588e7-4114-4ee4-82f7-5330ad61bfa8"},"outputs":[{"output_type":"error","ename":"NameError","evalue":"name 'DatasetDict' is not defined","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-11-3b679af33d32>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdataset\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDatasetDict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0;34m\"train\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtop_k_dataset\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[0;31mNameError\u001b[0m: name 'DatasetDict' is not defined"]}],"source":["dataset = DatasetDict({\"train\": top_k_dataset})"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bvty8s1oCw7x","outputId":"77394ed6-7e6e-47a8-f42d-b36ef5ae3bc9","executionInfo":{"status":"ok","timestamp":1706554196756,"user_tz":-330,"elapsed":454,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['Input', 'Output'],\n","        num_rows: 5407\n","    })\n","})"]},"metadata":{},"execution_count":12}],"source":["dataset"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"jRB04juICyI_","outputId":"a1bb7b84-7576-4e50-e24d-54769f122f66","executionInfo":{"status":"ok","timestamp":1706554199689,"user_tz":-330,"elapsed":5,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  Input  \\\n","0             Input:  What are the symptoms of Allergy?   \n","1                          Input:  What causes Allergy?   \n","2     Input:  When to seek urgent medical care when ...   \n","3     Input:  What to expect if I have Allergy  (Out...   \n","4         Input:  What are the symptoms of Anaphylaxis?   \n","...                                                 ...   \n","5402   Input:  What are the symptoms of Varicose veins?   \n","5403                Input:  What causes Varicose veins?   \n","5404  Input:  Who is at highest risk for Varicose ve...   \n","5405  Input:  When to seek urgent medical care when ...   \n","5406  Input:  What to expect if I have Varicose vein...   \n","\n","                                                 Output  \n","0     Allergy symptoms vary, but may include:\\nBreat...  \n","1     The immune system normally protects the body a...  \n","2     Call for an appointment with your health care ...  \n","3     Most allergies can be easily treated with medi...  \n","4     Symptoms develop rapidly, often within seconds...  \n","...                                                 ...  \n","5402  Fullness, heaviness, aching, and sometimes pai...  \n","5403  ormal veins, valves in the vein keep blood mov...  \n","5404  Standing for a long time and having increased ...  \n","5405  Call for an appointment with your health care ...  \n","5406  Varicose veins tend to get worse over time. Yo...  \n","\n","[5407 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-baa35a76-2e9d-4453-94ea-a8ee06223ed1\" 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>Input:  What are the symptoms of Allergy?</td>\n","      <td>Allergy symptoms vary, but may include:\\nBreat...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Input:  What causes Allergy?</td>\n","      <td>The immune system normally protects the body a...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Input:  When to seek urgent medical care when ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Input:  What to expect if I have Allergy  (Out...</td>\n","      <td>Most allergies can be easily treated with medi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Input:  What are the symptoms of Anaphylaxis?</td>\n","      <td>Symptoms develop rapidly, often within seconds...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>5402</th>\n","      <td>Input:  What are the symptoms of Varicose veins?</td>\n","      <td>Fullness, heaviness, aching, and sometimes pai...</td>\n","    </tr>\n","    <tr>\n","      <th>5403</th>\n","      <td>Input:  What causes Varicose veins?</td>\n","      <td>ormal veins, valves in the vein keep blood mov...</td>\n","    </tr>\n","    <tr>\n","      <th>5404</th>\n","      <td>Input:  Who is at highest risk for Varicose ve...</td>\n","      <td>Standing for a long time and having increased ...</td>\n","    </tr>\n","    <tr>\n","      <th>5405</th>\n","      <td>Input:  When to seek urgent medical care when ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>5406</th>\n","      <td>Input:  What to expect if I have Varicose vein...</td>\n","      <td>Varicose veins tend to get worse over time. Yo...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5407 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-baa35a76-2e9d-4453-94ea-a8ee06223ed1')\"\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-baa35a76-2e9d-4453-94ea-a8ee06223ed1 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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1162, 1071, 195, 1152, 977, 1083, 372, 18, 170, 1141, 257, 86, 215, 329, 332, 401, 312, 196, 479, 246, 125, 1154, 371, 1100, 1052, 140, 1136, 1081, 75, 75, 1121, 345, 721, 1131, 1158, 1132, 363, 920, 217, 1112, 490, 307, 1142, 446, 1103, 82, 140, 829, 298, 322, 1128, 817, 1046, 1110, 1084, 1086, 127, 164, 1150, 1122, 669, 150, 1122, 1095, 252, 305, 1035, 700, 1040, 1034, 30, 1084, 1099, 1128, 946, 963, 177, 109, 1120, 189, 1088, 1114, 1089, 1113, 185, 1086, 184, 376, 1143, 949, 1094, 274, 240, 102, 1066, 875, 219, 1142, 321, 872, 164, 97, 83, 1067, 322, 1047, 1049, 209, 322, 1061, 998, 1060, 1109, 278, 1069, 578, 1067, 1084, 975, 1092, 733, 1050, 1029, 1013, 984, 219, 34, 1076, 1107, 287, 154, 113, 260, 1084, 1081, 238, 426, 128, 1098, 307, 176, 546, 234, 112, 1093, 1015, 421, 300, 83, 1047, 695, 1059, 1094, 151, 146, 402, 595, 235, 242, 1057, 122, 751, 1080, 1079, 1050, 701, 1049, 375, 1074, 1070, 615, 1019, 976, 1086, 1060, 217, 1070, 963, 923, 473, 1064, 1052, 104, 805, 870, 1012, 84, 552, 1066, 1043, 1053, 202, 357, 42, 1037, 487, 689, 168, 244, 155, 982, 1034, 996, 1044, 130, 219, 81, 77, 1035, 858, 999, 557, 1038, 1056, 1050, 978, 1061, 1028, 516, 231, 346, 221, 322, 94, 960, 1044, 576, 1044, 980, 97, 216, 158, 1033, 651, 1028, 332, 1062, 958, 1053, 1047, 165, 1049, 967, 993, 696, 309, 317, 688, 1051, 480, 351, 49, 1066, 991, 218, 43, 339, 23, 1045, 194, 1029, 291, 802, 617, 1049, 336, 1058, 240, 101, 153, 82, 1006, 175, 1031, 184, 451, 986, 1048, 996, 113, 954, 982, 1026, 195, 668, 980, 1009, 201, 1016, 133, 174, 341, 1032, 310, 991, 1028, 759, 225, 166, 1015, 1024, 945, 854, 686, 1007, 990, 474, 1049, 920, 990, 125, 1047, 1015, 114, 955, 127, 1021, 181, 215, 941, 194, 210, 345, 841, 368, 996, 977, 260, 171, 945, 997, 952, 972, 1010, 901, 254, 947, 640, 1020, 1042, 466, 917, 210, 151, 1009, 942, 903, 979, 478, 374, 972, 800, 966, 66, 427, 292, 986, 137, 998, 982, 167, 218, 911, 877, 172, 319, 991, 980, 92, 1007, 937, 66, 954, 908, 983, 696, 968, 669, 1012, 883, 949, 726, 367, 994, 916, 105, 952, 922, 950, 936, 93, 894, 979, 174, 937, 987, 429, 305, 279, 1007, 745, 1005, 1002, 986, 916, 868, 955, 237, 354, 242, 225, 971, 981, 130, 47, 261, 119, 126, 952, 953, 907, 169, 976, 47, 196, 173, 160, 982, 971, 699, 873, 733, 189, 130, 988, 976, 174, 1006, 102, 990, 264, 954, 930, 141, 987, 157, 325, 341, 987, 161, 961, 914, 1004, 378, 254, 450, 949, 212, 911, 964, 905, 273, 84, 115, 184, 829, 934, 308, 98, 913, 931, 963, 709, 190, 986, 277, 216, 955, 892, 949, 982, 957, 993, 861, 938, 193, 908, 871, 928, 331, 958, 358, 957, 886, 311, 941, 556, 153, 98, 321, 352, 538, 363, 928, 13, 896, 255, 172, 77, 962, 874, 206, 256, 946, 386, 726, 755, 909, 259, 188]\n","Length of the Output Tokens Count 1000\n","Combine Tokens Count [2043, 2038, 2035, 2029, 1995, 1984, 1979, 1977, 1955, 1955, 1952, 1951, 1951, 1947, 1935, 1934, 1934, 1933, 1925, 1920, 1917, 1917, 1914, 1910, 1908, 1907, 1901, 1899, 1885, 1881, 1874, 1874, 1871, 1863, 1862, 1862, 1854, 1851, 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1456, 1455, 1454, 1454, 1451, 1451, 1450, 1448, 1446, 1446, 1446, 1444, 1443, 1441, 1440, 1440, 1439, 1438, 1434, 1433, 1431, 1430, 1428, 1427, 1427, 1426, 1424, 1420, 1419, 1418, 1415, 1415, 1413, 1412, 1412, 1410, 1408, 1408, 1406, 1406, 1404, 1404, 1404, 1400, 1399, 1399, 1396, 1395, 1393, 1392, 1391, 1391, 1388, 1387, 1387, 1386, 1384, 1384, 1382, 1381, 1380, 1380, 1379, 1379, 1378, 1377, 1376, 1376, 1374, 1374, 1374, 1374, 1373, 1372, 1370, 1369, 1369, 1366, 1366, 1364, 1363, 1363, 1360, 1359, 1359, 1358, 1358, 1357, 1356, 1353, 1353, 1353, 1353, 1352, 1351, 1350, 1350, 1350, 1349, 1349, 1349, 1349, 1347, 1344, 1344, 1344, 1343, 1343, 1342, 1341, 1340, 1339, 1337, 1334, 1333, 1333, 1333, 1333, 1330, 1329, 1327, 1327, 1326, 1324, 1324, 1323, 1322, 1322, 1319, 1319, 1319, 1314, 1313, 1313, 1313, 1313, 1313, 1311, 1311, 1310, 1310, 1310, 1309, 1308, 1307, 1307, 1307, 1307, 1305, 1304, 1303, 1302, 1301, 1300, 1298, 1295, 1295, 1292, 1291, 1289, 1289, 1288, 1287, 1285, 1284, 1283, 1283, 1281, 1281, 1279, 1279, 1278, 1277, 1276, 1274, 1273, 1272, 1270, 1268, 1267, 1267, 1266, 1266, 1264, 1264, 1263, 1262, 1262, 1261, 1260, 1260, 1257, 1257, 1256, 1256, 1255, 1254, 1254, 1254, 1253, 1252, 1252, 1251, 1250, 1250, 1249, 1249, 1247, 1246, 1246, 1245, 1244, 1244, 1244, 1242, 1241, 1240, 1240, 1240, 1239, 1239, 1237, 1237, 1236, 1235, 1234, 1234, 1233, 1232, 1229, 1229, 1229, 1229, 1229, 1228, 1228, 1228, 1227, 1227, 1226, 1226, 1226, 1226, 1225, 1225, 1225, 1225, 1225, 1224, 1223, 1221, 1220, 1220, 1219, 1218, 1218, 1218, 1218, 1217, 1217, 1217, 1216, 1216, 1214, 1213, 1213, 1212, 1212, 1212, 1211, 1211, 1210, 1210, 1210, 1210, 1209, 1207, 1207, 1206, 1205, 1205, 1205, 1205, 1205, 1204, 1203, 1202, 1201, 1200, 1200, 1199, 1199, 1199, 1198, 1198, 1198, 1197, 1195, 1195, 1195, 1194, 1194, 1194, 1193, 1193, 1193, 1192, 1192, 1191, 1191, 1191, 1191, 1189, 1188, 1188, 1187, 1187, 1187, 1187, 1185, 1184, 1184, 1184, 1183, 1183, 1183, 1182, 1181, 1181, 1180, 1179, 1179, 1179, 1178, 1176, 1175, 1174, 1174, 1173, 1173, 1173, 1173, 1172, 1171, 1170, 1170, 1169, 1168, 1168, 1168, 1167, 1167, 1167, 1167, 1166, 1164, 1164, 1163, 1163, 1163, 1163, 1162, 1162, 1162, 1162, 1161, 1161, 1161, 1160, 1159, 1157, 1155, 1154, 1153, 1152, 1151, 1151, 1150, 1149, 1149, 1149, 1149, 1148, 1147, 1147, 1146, 1146, 1145, 1145, 1144, 1143, 1142, 1142, 1142, 1142, 1141, 1141, 1141, 1140, 1140, 1140, 1139, 1138, 1138, 1136, 1135, 1135, 1133, 1133, 1133, 1133, 1132, 1132, 1132, 1132, 1131, 1130, 1128, 1128, 1128, 1128, 1127, 1126, 1126, 1125, 1125, 1124, 1124, 1124, 1123, 1123, 1123, 1122, 1122, 1122, 1122, 1122, 1122, 1121, 1121, 1120, 1119, 1119, 1119, 1118, 1116, 1114, 1113, 1113, 1113, 1113, 1113, 1112, 1112, 1111, 1111, 1111, 1111, 1111, 1110, 1110, 1110, 1108, 1107, 1107, 1106, 1106, 1106, 1106, 1104, 1104, 1104, 1104, 1104, 1103, 1103, 1102, 1101, 1101, 1101, 1100, 1100, 1098, 1098, 1098, 1097, 1097, 1097, 1097, 1097, 1097, 1096, 1096, 1095, 1095, 1095, 1095, 1094, 1094, 1093, 1093, 1093, 1093, 1093, 1092, 1092, 1092, 1092, 1092, 1091, 1091, 1091, 1091, 1090, 1090, 1089, 1089, 1088, 1087, 1087, 1087, 1087, 1087, 1087, 1086, 1086, 1086, 1086, 1086, 1085, 1085, 1085, 1085, 1085, 1084, 1084, 1084, 1084, 1083, 1083, 1083, 1082, 1082, 1081, 1081, 1081, 1081, 1080, 1080, 1080, 1079, 1079, 1079, 1078, 1077, 1077, 1077, 1076, 1076, 1075, 1074, 1073, 1072, 1072, 1072, 1072, 1072, 1072, 1072, 1071, 1071, 1070, 1070, 1070, 1070, 1070, 1070, 1068, 1068, 1068, 1068, 1067, 1067, 1067, 1067, 1066, 1066, 1066, 1065, 1065, 1064, 1064, 1063, 1063, 1063, 1063, 1063, 1062, 1062, 1061, 1061, 1060, 1060, 1060, 1060, 1059, 1059, 1058, 1058, 1058, 1057, 1057, 1056, 1056, 1056, 1056, 1055, 1055, 1055, 1054, 1054, 1053, 1053, 1053, 1052, 1052, 1051, 1051, 1051, 1051, 1051, 1051, 1051, 1051, 1050, 1048, 1048, 1048, 1048, 1047, 1047, 1047, 1047, 1046, 1046, 1045, 1045, 1045, 1045, 1045, 1045, 1044, 1044, 1043, 1043, 1043, 1043, 1043, 1043, 1042, 1042, 1042, 1042, 1042, 1041, 1041, 1041, 1040, 1039, 1038, 1037, 1037, 1037, 1037, 1036, 1036, 1035, 1035, 1035, 1035, 1034, 1034, 1034, 1034, 1033, 1033, 1033, 1033, 1033, 1033, 1032, 1032, 1031, 1031, 1030, 1030, 1030, 1030, 1030, 1029, 1029, 1028, 1028, 1028, 1028, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1026, 1025, 1025, 1025, 1025, 1024, 1024, 1024, 1023, 1023, 1023, 1023, 1022, 1022, 1021, 1021, 1021, 1021, 1019, 1019, 1018, 1018, 1018, 1017, 1016, 1015, 1015, 1015, 1014, 1014, 1014, 1014, 1014, 1013, 1013, 1012, 1012, 1012, 1012, 1012, 1012, 1011, 1011, 1011, 1010, 1010, 1009, 1009, 1009, 1008, 1008, 1007, 1007, 1006, 1006, 1006, 1006, 1006, 1006, 1005, 1005, 1005, 1005, 1005, 1004, 1004, 1004, 1004, 1004, 1003, 1003, 1003, 1003, 1002, 1002, 1002, 1002, 1002, 1002, 1001, 1001, 1001, 1001]\n","Length of the Combine Tokens Count 1000\n"]}],"source":["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":{"colab":{"base_uri":"https://localhost:8080/","height":458},"id":"FLS7FyuuDMx2","outputId":"945cbfd5-4561-463c-9fb9-3e6dfc3fb522"},"outputs":[{"data":{"image/png":"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\n","text/plain":["<Figure size 1500x600 with 1 Axes>"]},"metadata":{},"output_type":"display_data"}],"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":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"uKLI8hJVDVIe","outputId":"235d83fa-b1e9-45ce-a0f1-a6e7a7992eba","executionInfo":{"status":"ok","timestamp":1706554320307,"user_tz":-330,"elapsed":460,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  Input  \\\n","0             Input:  What are the symptoms of Allergy?   \n","1                          Input:  What causes Allergy?   \n","2     Input:  When to seek urgent medical care when ...   \n","3     Input:  What to expect if I have Allergy  (Out...   \n","4         Input:  What are the symptoms of Anaphylaxis?   \n","...                                                 ...   \n","5402   Input:  What are the symptoms of Varicose veins?   \n","5403                Input:  What causes Varicose veins?   \n","5404  Input:  Who is at highest risk for Varicose ve...   \n","5405  Input:  When to seek urgent medical care when ...   \n","5406  Input:  What to expect if I have Varicose vein...   \n","\n","                                                 Output  \n","0     Allergy symptoms vary, but may include:\\nBreat...  \n","1     The immune system normally protects the body a...  \n","2     Call for an appointment with your health care ...  \n","3     Most allergies can be easily treated with medi...  \n","4     Symptoms develop rapidly, often within seconds...  \n","...                                                 ...  \n","5402  Fullness, heaviness, aching, and sometimes pai...  \n","5403  ormal veins, valves in the vein keep blood mov...  \n","5404  Standing for a long time and having increased ...  \n","5405  Call for an appointment with your health care ...  \n","5406  Varicose veins tend to get worse over time. Yo...  \n","\n","[5407 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-13972f87-5ae0-4357-be46-18dfd4434219\" 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>Input:  What are the symptoms of Allergy?</td>\n","      <td>Allergy symptoms vary, but may include:\\nBreat...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Input:  What causes Allergy?</td>\n","      <td>The immune system normally protects the body a...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Input:  When to seek urgent medical care when ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Input:  What to expect if I have Allergy  (Out...</td>\n","      <td>Most allergies can be easily treated with medi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Input:  What are the symptoms of Anaphylaxis?</td>\n","      <td>Symptoms develop rapidly, often within seconds...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>5402</th>\n","      <td>Input:  What are the symptoms of Varicose veins?</td>\n","      <td>Fullness, heaviness, aching, and sometimes pai...</td>\n","    </tr>\n","    <tr>\n","      <th>5403</th>\n","      <td>Input:  What causes Varicose veins?</td>\n","      <td>ormal veins, valves in the vein keep blood mov...</td>\n","    </tr>\n","    <tr>\n","      <th>5404</th>\n","      <td>Input:  Who is at highest risk for Varicose ve...</td>\n","      <td>Standing for a long time and having increased ...</td>\n","    </tr>\n","    <tr>\n","      <th>5405</th>\n","      <td>Input:  When to seek urgent medical care when ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>5406</th>\n","      <td>Input:  What to expect if I have Varicose vein...</td>\n","      <td>Varicose veins tend to get worse over time. Yo...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5407 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-13972f87-5ae0-4357-be46-18dfd4434219')\"\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-13972f87-5ae0-4357-be46-18dfd4434219 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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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-69c2e7e5-5a54-4352-b9fb-608d02f8e544\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-69c2e7e5-5a54-4352-b9fb-608d02f8e544')\"\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-69c2e7e5-5a54-4352-b9fb-608d02f8e544 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":21}],"source":["# Read as pandas DataFrame\n","dataset['train'].to_pandas()"]},{"cell_type":"markdown","source":["##**Removal of instructions column**"],"metadata":{"id":"d_sluWqT913A"}},{"cell_type":"code","source":["df = dataset[\"train\"].to_pandas()"],"metadata":{"id":"3AZJcOqt-WAj","executionInfo":{"status":"ok","timestamp":1706554307674,"user_tz":-330,"elapsed":460,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"3WfcuTH6kDlR","executionInfo":{"status":"ok","timestamp":1706554313845,"user_tz":-330,"elapsed":5,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}},"outputId":"2880aeca-d3b6-4dc3-d8b4-c9a308c836fb"},"execution_count":20,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  Input  \\\n","0             Input:  What are the symptoms of Allergy?   \n","1                          Input:  What causes Allergy?   \n","2     Input:  When to seek urgent medical care when ...   \n","3     Input:  What to expect if I have Allergy  (Out...   \n","4         Input:  What are the symptoms of Anaphylaxis?   \n","...                                                 ...   \n","5402   Input:  What are the symptoms of Varicose veins?   \n","5403                Input:  What causes Varicose veins?   \n","5404  Input:  Who is at highest risk for Varicose ve...   \n","5405  Input:  When to seek urgent medical care when ...   \n","5406  Input:  What to expect if I have Varicose vein...   \n","\n","                                                 Output  \n","0     Allergy symptoms vary, but may include:\\nBreat...  \n","1     The immune system normally protects the body a...  \n","2     Call for an appointment with your health care ...  \n","3     Most allergies can be easily treated with medi...  \n","4     Symptoms develop rapidly, often within seconds...  \n","...                                                 ...  \n","5402  Fullness, heaviness, aching, and sometimes pai...  \n","5403  ormal veins, valves in the vein keep blood mov...  \n","5404  Standing for a long time and having increased ...  \n","5405  Call for an appointment with your health care ...  \n","5406  Varicose veins tend to get worse over time. Yo...  \n","\n","[5407 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-97e196c2-bdd7-43b2-a223-2f518b48e19d\" 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>Input:  What are the symptoms of Allergy?</td>\n","      <td>Allergy symptoms vary, but may include:\\nBreat...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Input:  What causes Allergy?</td>\n","      <td>The immune system normally protects the body a...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Input:  When to seek urgent medical care when ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Input:  What to expect if I have Allergy  (Out...</td>\n","      <td>Most allergies can be easily treated with medi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Input:  What are the symptoms of Anaphylaxis?</td>\n","      <td>Symptoms develop rapidly, often within seconds...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>5402</th>\n","      <td>Input:  What are the symptoms of Varicose veins?</td>\n","      <td>Fullness, heaviness, aching, and sometimes pai...</td>\n","    </tr>\n","    <tr>\n","      <th>5403</th>\n","      <td>Input:  What causes Varicose veins?</td>\n","      <td>ormal veins, valves in the vein keep blood mov...</td>\n","    </tr>\n","    <tr>\n","      <th>5404</th>\n","      <td>Input:  Who is at highest risk for Varicose ve...</td>\n","      <td>Standing for a long time and having increased ...</td>\n","    </tr>\n","    <tr>\n","      <th>5405</th>\n","      <td>Input:  When to seek urgent medical care when ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>5406</th>\n","      <td>Input:  What to expect if I have Varicose vein...</td>\n","      <td>Varicose veins tend to get worse over time. Yo...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5407 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-97e196c2-bdd7-43b2-a223-2f518b48e19d')\"\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-97e196c2-bdd7-43b2-a223-2f518b48e19d button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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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-940cbe2f-8bf0-4509-8d26-3062435eb3fb\">\n","  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-940cbe2f-8bf0-4509-8d26-3062435eb3fb')\"\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-940cbe2f-8bf0-4509-8d26-3062435eb3fb button');\n","      quickchartButtonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Assuming dataset[\"train\"] is your DataFrame\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"instruction\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\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/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\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    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\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/pandas/core/frame.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   5397\u001b[0m                 \u001b[0mweight\u001b[0m  \u001b[0;36m1.0\u001b[0m     \u001b[0;36m0.8\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5398\u001b[0m         \"\"\"\n\u001b[0;32m-> 5399\u001b[0;31m         return super().drop(\n\u001b[0m\u001b[1;32m   5400\u001b[0m             \u001b[0mlabels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5401\u001b[0m             \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\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/pandas/util/_decorators.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    329\u001b[0m                     \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfind_stack_level\u001b[0m\u001b[0;34m(\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    330\u001b[0m                 )\n\u001b[0;32m--> 331\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    332\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    333\u001b[0m         \u001b[0;31m# error: \"Callable[[VarArg(Any), KwArg(Any)], Any]\" has no\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/pandas/core/generic.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, axis, index, columns, level, inplace, errors)\u001b[0m\n\u001b[1;32m   4503\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32min\u001b[0m \u001b[0maxes\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\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   4504\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mlabels\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\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[0;32m-> 4505\u001b[0;31m                 \u001b[0mobj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_drop_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4506\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4507\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0minplace\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/pandas/core/generic.py\u001b[0m in \u001b[0;36m_drop_axis\u001b[0;34m(self, labels, axis, level, errors, only_slice)\u001b[0m\n\u001b[1;32m   4544\u001b[0m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4545\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 4546\u001b[0;31m                 \u001b[0mnew_axis\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\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   4547\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_axis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4548\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mdrop\u001b[0;34m(self, labels, errors)\u001b[0m\n\u001b[1;32m   6932\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0many\u001b[0m\u001b[0;34m(\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   6933\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0merrors\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"ignore\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6934\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{list(labels[mask])} not found in axis\"\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   6935\u001b[0m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m~\u001b[0m\u001b[0mmask\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6936\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdelete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: \"['instruction'] not found in axis\""]}]},{"cell_type":"code","source":["new_column_names = ['Input', 'Output']\n","df.columns = new_column_names\n","chars_to_remove = '\\n\\n### Response: '\n","df['Output'] = df['Output'].map(lambda x: x.rstrip('\\n\\n### Response:'))\n","df['Input'] = df['Input'].map(lambda x: x.lstrip('Input: '))\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"V1Ck4h49AMyL","executionInfo":{"status":"ok","timestamp":1706554948540,"user_tz":-330,"elapsed":463,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}},"outputId":"7f174b41-751f-4e9e-f6c5-9a8d0bafe86e"},"execution_count":43,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  Input  \\\n","0                     What are the symptoms of Allergy?   \n","1                                  What causes Allergy?   \n","2     When to seek urgent medical care when I have A...   \n","3     What to expect if I have Allergy  (Outlook/Pro...   \n","4                 What are the symptoms of Anaphylaxis?   \n","...                                                 ...   \n","5402           What are the symptoms of Varicose veins?   \n","5403                        What causes Varicose veins?   \n","5404        Who is at highest risk for Varicose veins ?   \n","5405  When to seek urgent medical care when I have V...   \n","5406  What to expect if I have Varicose veins  (Outl...   \n","\n","                                                 Output  \n","0     Allergy symptoms vary, but may include:\\nBreat...  \n","1     The immune system normally protects the body a...  \n","2     Call for an appointment with your health care ...  \n","3     Most allergies can be easily treated with medi...  \n","4     Symptoms develop rapidly, often within seconds...  \n","...                                                 ...  \n","5402  Fullness, heaviness, aching, and sometimes pai...  \n","5403  ormal veins, valves in the vein keep blood mov...  \n","5404  Standing for a long time and having increased ...  \n","5405  Call for an appointment with your health care ...  \n","5406  Varicose veins tend to get worse over time. Yo...  \n","\n","[5407 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-7fa50498-e9d7-4f79-9de7-52be4bc0abc0\" 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>What are the symptoms of Allergy?</td>\n","      <td>Allergy symptoms vary, but may include:\\nBreat...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>What causes Allergy?</td>\n","      <td>The immune system normally protects the body a...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>When to seek urgent medical care when I have A...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>What to expect if I have Allergy  (Outlook/Pro...</td>\n","      <td>Most allergies can be easily treated with medi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>What are the symptoms of Anaphylaxis?</td>\n","      <td>Symptoms develop rapidly, often within seconds...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>5402</th>\n","      <td>What are the symptoms of Varicose veins?</td>\n","      <td>Fullness, heaviness, aching, and sometimes pai...</td>\n","    </tr>\n","    <tr>\n","      <th>5403</th>\n","      <td>What causes Varicose veins?</td>\n","      <td>ormal veins, valves in the vein keep blood mov...</td>\n","    </tr>\n","    <tr>\n","      <th>5404</th>\n","      <td>Who is at highest risk for Varicose veins ?</td>\n","      <td>Standing for a long time and having increased ...</td>\n","    </tr>\n","    <tr>\n","      <th>5405</th>\n","      <td>When to seek urgent medical care when I have V...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>5406</th>\n","      <td>What to expect if I have Varicose veins  (Outl...</td>\n","      <td>Varicose veins tend to get worse over time. Yo...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>5407 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-7fa50498-e9d7-4f79-9de7-52be4bc0abc0')\"\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-7fa50498-e9d7-4f79-9de7-52be4bc0abc0 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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'block' : 'none';\n","    })();\n","  </script>\n","</div>\n","    </div>\n","  </div>\n"]},"metadata":{},"execution_count":43}]},{"cell_type":"code","source":["from datasets import Dataset, DatasetDict"],"metadata":{"id":"Nk1nLm-UTF4Z","executionInfo":{"status":"ok","timestamp":1706554954756,"user_tz":-330,"elapsed":706,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"execution_count":44,"outputs":[]},{"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","source":["dataset[\"train\"] = Dataset.from_pandas(df)\n","dataset"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"celMnPn7DQ_m","executionInfo":{"status":"ok","timestamp":1706554958537,"user_tz":-330,"elapsed":426,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}},"outputId":"ce21f6f3-dd7a-4617-e930-ba0ec74c07fa"},"execution_count":45,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['Input', 'Output'],\n","        num_rows: 5407\n","    })\n","})"]},"metadata":{},"execution_count":45}]},{"cell_type":"code","execution_count":46,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["935b955bb4a24e05ae0c0d2aea0c07ed","260d2070ac8d4fc383d5792af874c293","1dce936690c34a12aaedb41aaa4a6fad","8152c497577a4007b01b94a331c2b98d","a8019adcf5a44d30b2d6122505116ba5","89b2de8cffa44435ad0694e65b3d4859","04a369a2ab4c42ff91e5b125ec036c2c","b583c3c68def4e78831c15df4150fcf4","b767749deb264ed6899ebf4053dbe14b","a19adff04e404fdcabaa0e9e1344a585","6b514e163e2a4fc5b47e4dfcaa2c86e5"]},"id":"PF39ozdtD6mu","outputId":"1eef4ca1-4bef-4465-de8f-412b5ca1ab89","executionInfo":{"status":"ok","timestamp":1706554961237,"user_tz":-330,"elapsed":437,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/5407 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"935b955bb4a24e05ae0c0d2aea0c07ed"}},"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":47,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"c1lvbGrgEflj","outputId":"eb7fa285-bed8-44d4-c06a-5157ce8f91ce","executionInfo":{"status":"ok","timestamp":1706554964067,"user_tz":-330,"elapsed":425,"user":{"displayName":"Deepak C Nayak .","userId":"06766587012951415164"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                                  Input  \\\n","0     ### Instruction:\\nWhat are the symptoms of All...   \n","1     ### Instruction:\\nWhat causes Allergy?\\n\\n### ...   \n","2     ### Instruction:\\nWhen to seek urgent medical ...   \n","3     ### Instruction:\\nWhat to expect if I have All...   \n","4     ### Instruction:\\nWhat are the symptoms of Ana...   \n","...                                                 ...   \n","5402  ### Instruction:\\nWhat are the symptoms of Var...   \n","5403  ### Instruction:\\nWhat causes Varicose veins?\\...   \n","5404  ### Instruction:\\nWho is at highest risk for V...   \n","5405  ### Instruction:\\nWhen to seek urgent medical ...   \n","5406  ### Instruction:\\nWhat to expect if I have Var...   \n","\n","                                                 Output  \n","0     Allergy symptoms vary, but may include:\\nBreat...  \n","1     The immune system normally protects the body a...  \n","2     Call for an appointment with your health care ...  \n","3     Most allergies can be easily treated with medi...  \n","4     Symptoms develop rapidly, often within seconds...  \n","...                                                 ...  \n","5402  Fullness, heaviness, aching, and sometimes pai...  \n","5403  ormal veins, valves in the vein keep blood mov...  \n","5404  Standing for a long time and having increased ...  \n","5405  Call for an appointment with your health care ...  \n","5406  Varicose veins tend to get worse over time. Yo...  \n","\n","[5407 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-58fac8f4-ca1f-4ee8-ab5d-e2df6eddf157\" 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:\\nWhat are the symptoms of All...</td>\n","      <td>Allergy symptoms vary, but may include:\\nBreat...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>### Instruction:\\nWhat causes Allergy?\\n\\n### ...</td>\n","      <td>The immune system normally protects the body a...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>### Instruction:\\nWhen to seek urgent medical ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>### Instruction:\\nWhat to expect if I have All...</td>\n","      <td>Most allergies can be easily treated with medi...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>### Instruction:\\nWhat are the symptoms of Ana...</td>\n","      <td>Symptoms develop rapidly, often within seconds...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>5402</th>\n","      <td>### Instruction:\\nWhat are the symptoms of Var...</td>\n","      <td>Fullness, heaviness, aching, and sometimes pai...</td>\n","    </tr>\n","    <tr>\n","      <th>5403</th>\n","      <td>### Instruction:\\nWhat causes Varicose veins?\\...</td>\n","      <td>ormal veins, valves in the vein keep blood mov...</td>\n","    </tr>\n","    <tr>\n","      <th>5404</th>\n","      <td>### Instruction:\\nWho is at highest risk for V...</td>\n","      <td>Standing for a long time and having increased ...</td>\n","    </tr>\n","    <tr>\n","      <th>5405</th>\n","      <td>### Instruction:\\nWhen to seek urgent medical ...</td>\n","      <td>Call for an appointment with your health care ...</td>\n","    </tr>\n","    <tr>\n","      <th>5406</th>\n","      <td>### Instruction:\\nWhat to expect if I have Var...</td>\n","      <td>Varicose veins tend to get worse over time. 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