Language_modelling_Contextual_retrievability.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "9bb37777",
   "metadata": {},
   "source": [
    "# Language modeling\n",
    "## Focus - Understanding contextual retrievability"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b15e1f3e",
   "metadata": {},
   "source": [
    "### Team\n",
    "1. Ankush Raut\n",
    "2. Tapas Das\n",
    "3. Abhishek Mukundan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab01ae4f",
   "metadata": {},
   "source": [
    "### Problem statement"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b6a814f",
   "metadata": {},
   "source": [
    "Language models have tremendous utility. With the advent of multi-headed attention-based Transformer models, language generation, translation, and many other tasks have now become relatively easy. This has brought about a significant increase in the use of language models across different fields.\n",
    "\n",
    "There is one caveat though. Language model representations of text are not fully interpretable. This makes it harder to understand what exactly are these models inferring from text. This leads to another problem, one of contextual retrievability. How much information are these language models actually able to retrieve from long contexts, or any context for that matter? Does the location of information in a context directs if a language model will be able to infer anything from it? This is what our focus and problem statement is based on. \n",
    "\n",
    "**Determine the ability of a language model to retrieve information from context through a question-answering task. Check if there's a trend in accuracy depending on the location of the answer in the context and the context size.**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93f8278d",
   "metadata": {},
   "source": [
    "### Dataset description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0690960d",
   "metadata": {},
   "source": [
    "We are using the SQuAD-2.0 dataset for this task. It contains multiple questions on a single paragraph, with varying locations of the answers to those questions. It also contains 50k unanswerable questions, i.e. questions that can't be answered from the given context.\n",
    "\n",
    "This dataset was created by the authors of the paper **\"Know What You Don't Know: Unanswerable Questions for SQuAD\"**, and is loaded in this notebook from the datasets API of the Hugging Face library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b288c26f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some libraries necessary for the code to run without errors, this should be installed prior to running the code\n",
    "\n",
    "# !pip install einops\n",
    "# !pip install transformers torch\n",
    "# !pip install datasets evaluate transformers[sentencepiece]\n",
    "# !pip install accelerate\n",
    "# !pip install cloud-tpu-client==0.10 torch==1.9.0 https://storage.googleapis.com/tpu-pytorch/wheels/torch_xla-1.9-cp37-cp37m-linux_x86_64.whl\n",
    "# !apt install git-lfs\n",
    "# !pip install torch\n",
    "# !pip install openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "05747a2e-1d36-4369-8c61-000352cd4d02",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ankushraut/opt/anaconda3/lib/python3.9/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n",
      "  warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n"
     ]
    }
   ],
   "source": [
    "from datasets import load_dataset\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "import transformers\n",
    "import torch\n",
    "from tqdm import tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1e6c59b3-fc5f-4b16-9458-d69d16b36099",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to retrive context length of paragraphs\n",
    "\n",
    "def get_context_length(para):\n",
    "    para['context_length'] = len(para['context'])\n",
    "    return para"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b657bca-12c9-431e-9f93-3c31f978ba87",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load and get context length for paragraphs in the SQUAD V 2.0 dataset\n",
    "\n",
    "dataset = load_dataset(\"squad_v2\")\n",
    "dataset = dataset['train'].map(get_context_length)\n",
    "\n",
    "# convert the dataset into a pandas dataframe\n",
    "df = dataset.to_pandas()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6e6bb55-bfdb-498d-92d4-ae447c1287a7",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "a233e8ba-d330-4567-a3bd-9f53b1846c41",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ankushraut/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
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RERGRyZbJJMwGWeYDPv8X8Cl37x/uQO7+HXdf4+5r6urqJio+ERERkcjkZ/DYe4CFKZ8XAPsGbLMGuMnMAGqB15lZn7vflsG4RERERCKXySRsHXCMmS0F9gKXApenbuDuS5PvzewG4DdKwERERGQ6yFgS5u59ZvYRgqce84Dr3X2zmV0drh+2H5iIiIhILstkTRjuvhZYO2DZoMmXu1+RyVhEREREphKNmC8iIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhFQEiYiIiISASVhIiIiIhHIjzoAEXmxGx/eNeI2l5+5aBIiERGRTFFNmIiIiEgElISJiIiIREBJmIiIiEgElISJiIiIREBJmIiIiEgElISJiIiIREBJmIiIiEgElISJiIiIREBJmIiIiEgElISJiIiIRCCjSZiZXWhmW8xsq5ldM8j6i83sCTPbYGbrzey8TMYjIiIiMlVkbO5IM8sDvgW8GtgDrDOz2939qZTN7gFud3c3s5OAm4FVmYpJREREZKrIZE3YGcBWd9/m7nHgJuDi1A3cvd3dPfxYBjgiIiIi00Amk7D5wO6Uz3vCZUcxs7eY2TPAHcAHBjuQmV0VNleub2hoyEiwIiIiIpMpk0mYDbLsRTVd7v5Ld18FvBm4drADuft33H2Nu6+pq6ub2ChFREREIpDJJGwPsDDl8wJg31Abu/v9wHIzq81gTCIiIiJTQiaTsHXAMWa21MwKgUuB21M3MLMVZmbh+9OAQqApgzGJiIiITAlpPR1pZrcC1wO/dfdEOvu4e5+ZfQS4E8gDrnf3zWZ2dbj+OuBtwHvNrBfoAt6Z0lFfREREJGelO0TFt4H3A183s1uAG9z9mZF2cve1wNoBy65Lef9l4MvphysiIiKSG9JqjnT337v7u4DTgB3A3Wb2FzN7v5kVZDJAERERkVyUdp8wM6sBrgA+BDwOfI0gKbs7I5GJiIiI5LB0+4T9gmAk+x8Db3T3/eGqn5vZ+kwFJyIiIpKr0u0T9r2wf9cRZlbk7j3uviYDcYmIiIjktHSbI78wyLIHJzIQERERkelk2JowM5tDMNVQiZmdyguj4M8ASjMcm4iIiEjOGqk58rUEnfEXAF9NWd4GfDpDMUmO6e1PUJCXyXGBRUREss+wSZi7/xD4oZm9zd1vnaSYJId0xfv59zuf4dXHz+ac5ZqRSkREJGmk5sh3u/tPgCVm9ncD17v7VwfZTeSIbY3t9PQl+OMzB1mzuDrqcERERKaMkdqIysLXcqBikH8iw3q+oYOYQUe8n4e3a1pQERGRpJGaI/8nfP385IQjuWZbQzvL68pJuHP/c410xfspKcyLOiwREZHIpdVb2sz+3cxmmFmBmd1jZo1m9u5MByfZra27l4NtPSyrK+cVq2bT0dPHTx/eGXVYIiIiU0K6j6y9xt1bgTcAe4BjgX/IWFSSE7Y1dgCwvK6MpbVlLKst4zv3b8PdI45MREQkeukmYclJul8H/MzdmzMUj+SQbQ3tFBfEmDezBIDj5s7gYFsPzR3xiCMTERGJXrpJ2K/N7BlgDXCPmdUB3ZkLS3LB8w0dLK0tJ2bBGL/VZYUA7D7UFWVYIiIiU0JaSZi7XwOcDaxx916gA7g4k4FJdjvUGae5I87yurIjy6pKwySsuTOqsERERKaMdCfwBjiOYLyw1H1+NMHxSI7Y1hD0B1tWV35kWVVp0Kq9+5CSMBERkbSSMDP7MbAc2AD0h4sdJWEyhJ1NHZQW5jG7oujIsqKCPKpKC9jdrOZIERGRdGvC1gDHux5rkzQ1tsepqyjCzI5avrC6lD2qCRMREUm7Y/6TwJxMBiK55VBnnOqwD1iqhVWl7FHHfBERkbRrwmqBp8zsEaAnudDd35SRqCSr9fYnaO3qPfI0ZKoF1SXc/VQ9iYQTi9kge4uIiEwP6SZhn8tkEJJbDnf24jBoErawqpR4f4L6tm7mVpZMfnAiIiJTRLpDVNwH7AAKwvfrgMcyGJdksUOdwWCsgyZh1aUA6pwvIiLTXrpzR14J/C/wP+Gi+cBtGYpJslxyRPyqQWvCgtovjRUmIiLTXbod8z8MnAu0Arj7c8CsTAUl2a25I05+zKgoenFrd3IKI40VJiIi0126SViPux+Z8C8csFXDVcigmjviVJcVvmh4CoDigjxmzyjSE5IiIjLtpZuE3WdmnwZKzOzVwC3ArzMXlmSzQ53xQfuDJS2sKlVzpIiITHvpJmHXAA3AJuCvgLXAP2UqKMle7k5zR3zQ/mBJwYCtqgkTEZHpLa0hKtw9YWa3Abe5e0NmQ5Js1hnvp6cvMehArUkLq0r41YYuevsTFOSl+3eAiIhIbhn2N6AFPmdmjcAzwBYzazCzz05OeJJtkk9GDtccuaC6lITDvsOqDRMRkelrpGqIjxM8FXm6u9e4ezVwJnCumf2fTAcn2ad5mDHCkhYcGaZCSZiIiExfIyVh7wUuc/ftyQXuvg14d7hO5CiHkmOEDdscGQ7YqmEqRERkGhspCStw98aBC8N+YQWZCUmyWXNHnIqifArzh7615lYWkxcz9igJExGRaWykJCw+xnUyTY30ZCRAfl6MmrJCGtp6ht1OREQkl430dOTJZtY6yHIDijMQj2S55s44S2rKRtyutryIxnbl8SIiMn0Nm4S5e95kBSLZry+RoKWzl+pFw9eEAdRWFNHYrpowERGZvjRIk0yYls5eHIYdIyyprryIRjVHiojINKYkTCZMcoywkfqEAdRWFNLYHsddU5CKiMj0pCRMJkw6Y4Ql1ZUXEe9P0Nrdl+mwREREpiQlYTJhmjvi5MeMiuKRZ8OqLS8CUL8wERGZtpSEyYRp7ohTVVpIzGzEbZNJmIapEBGR6UpJmEyYQx3xtJoiIegTBqoJExGR6UtJmEwId6cpjYFak440R6omTEREpqmMJmFmdqGZbTGzrWZ2zSDr32VmT4T//mJmJ2cyHsmcrt5+evoSadeEBc2WaMBWERGZtjKWhJlZHvAt4CLgeOAyMzt+wGbbgfPd/STgWuA7mYpHMis5PEU6Y4QB5MWM6jIN2CoiItNXJmvCzgC2uvs2d48DNwEXp27g7n9x90Phx4eABRmMRzLoSBKWZk0YQG15oZIwERGZtjKZhM0Hdqd83hMuG8oHgd9mMB7JoENHBmotSHufuooiGtQcKSIi09TIAzqN3WDjFAw6PLqZvZwgCTtviPVXAVcBLFq0aKLikwnU3BmnrCifovz0pxutLS9iW0NHBqMSERGZujJZE7YHWJjyeQGwb+BGZnYS8D3gYndvGuxA7v4dd1/j7mvq6uoyEqyMT3NHnOrS9GvBIKgJa2zv0dRFIiIyLWUyCVsHHGNmS82sELgUuD11AzNbBPwCeI+7P5vBWCTDmkcxRlhSbXkhPX0J2ns0dZGIiEw/GWuOdPc+M/sIcCeQB1zv7pvN7Opw/XXAZ4Ea4L8tGGW9z93XZComyYze/gQtXb1jSMKSUxfFqSgeXS3aVHTjw7vS2u7yM9WkLiIime0ThruvBdYOWHZdyvsPAR/KZAwyfiMlF80dcRI+uicj4ej5I5fWlo05PhERkWykEfNl3JqPPBk5tiRM80eKiMh0pCRMxu3QKAdqTdL8kSIiMp0pCZNxa+6MkxczZpSMrl9XdWkhZpo/UkREpqeM9gmT6aGpI05VaQExG2xouKHl58WoLi3UgK1DcHce2t7M3kOdHOrspaQgj8vPXDTqchYRkalJSZiMW0Nb95H+XaNVW675I4dS39rDrzfuo7won+KCPLY3dnCgpZt5M0uiDk1ERCaAmiNlXPoSCRraepg9o3hM+9dWaP7IoWza24IBH33FCj543lIAnm9ojzYoERGZMErCZFwa24LhKeaMNQlTTdiQntzXwpLaMiqKC6gsKaCuooitB5WEiYjkCiVhMi4HWrsAmF05jiSsTX3CBqpv7aahrYcT51ceWbZiVjk7mjro7U9EGJmIiEwUJWEyLgdaesgzo26MfcLqKoro6u2nQ1MXHSXZFHnCvBlHlq2oK6e339nV3BldYCIiMmGUhMm41Ld2U1dRRF5sbE/spY6aLy94cm8Li2vKjprOaVltGTFDTZIiIjlCSZiMy4HWbuaMsSkSgkm8QUlYqufq2zjY1sOJ82cctbyoII+FVaVKwkREcoSSMBmzrng/LV29Y34yElKnLlK/sKS1mw4ETZEp/cGSVswqZ9/hLjrjar4VEcl2SsJkzOpbuwGYM2Ns/cEg6BMGqglLdf9zDcyvKmFG8YtnIFgxqxwHnm/omPzARERkQikJkzE7ECZh46kJqw4n/dYk3oH2nj427j7M8rryQdcvqCqlMC/GjiYlYSIi2U5JmIxZfWs3xQUxKkc5Z2SqgrwYVaUFqgkLPbK9ib6ED5mE5cWM2opCmlReIiJZT0mYjNmBlm5mzyjGxjmXoQZsfcEDW5sozI+xuKZ0yG2C8lIfOhGRbKckTMbE3alv6x7zSPmplFS84IGtjaxZXEVB3tDfmjVlRRzqiBPv06CtIiLZTEmYjElLVy/dvYlx9QdLqq1QTRgEDyc8c6CNc1fUDrtdbXkhDhq0VUQkyykJkzHZcyiYrmjeOMYIS6otL6RRHfN5aFsTAOcsrxl2u+SwHtsb1TlfRCSbKQmTMXm2vo3ighjzq4buu5Su2vIiOuL9dMX7JyCy7PXA1iYqivKPmi9yMDXhALc7lISJiGQ1JWEyau7Os/VtrJhVMebpilJprLDAX55v5MxlNeQP0x8MoLQwn9LCPLYpCRMRyWpKwmTUDrR209rdx8rZFRNyvOTk3w3TOAnb1dTJzqZOzl0xfFNkUm15EdsbNX2RiEg2UxImo7blQBsAx84efCyr0Toyifc07hf26yf2AfDq42entX1teSE7GtUxX0QkmykJk1HbUt/GvJnFVAwyrc5Y1FYkJ/GensNUuDu3Pb6X05dUsSDNPnY15UUcaO2mo0dzSIqIZKv8qAOQ7NIV72d3cyfnH1s3YcesKZv6fcJufHhXxo799P42njvYzrVvXp32Psnawx1NHZwwb/iO/CIiMjWpJkxG5bmDbSQcjp2g/mAAhfnB1EfTdf7IX23cS37MeP2Jc9Pep/bIE5JqkhQRyVZKwmRUnq1vo6Qgj4XV4x+aIlVteeGUrgnLlETC+fWGfbzs2Lojk5mnI1l7qM75IiLZS82ROSrd5rPLz1yU9jHjfQme2t/KqjkziI1zvsiBpuv8ket2NLOvpZtPXbRqVPsV5seYM6NYw1SIiGQx1YRJ2jbuPkx3b4IzllRP+LGDqYumX8f8n6/fTUlBHq86Lr2nIlMtqS3VgK0iIllMSZikxd15aHsTc2YUs7hmYpsiIRgrbLoNUfGHZ+r5xWN7efdZiygrGn2l9NLack1dJCKSxZSESVp2NXeyv6Wbs5bVYBPcFAlBn7C2nj66e6fH1EUHW7v5xC1PcNzcGXzitSvHdIxltWUc6uzlcOf0q0EUEckFSsIkLQ9ua6K4IMYpC2dm5PhHBmydBv3CEgnn72/ZSGe8j29cdgpF+XljOk7y4YjdzV0TGZ6IiEwSJWEyorbuXjbvbeW0RVUU5mfmlnlh/sjcrtXp7OnjAz9cx5+ea+SzbziBFbPGPtTHwuoSAHYf0jAVIiLZSE9HyojW7ThEvztnLk1vXsOxmA5TF+091MVPH9lJR08f1158ApedsXBcx0uOrr9HSZiISFZSEibD6utPsG5HMyvqyo/UVmVCbUVuN0c2tvfw/Qe2UZSfxy1XnzMhzbqVJQXMKM5nzyE1R4qIZCM1R8qwfv90PS1dvZy1bOKHpUhVU5acPzL3krDu3n5+/OBOYmZc+dJlE9qvbkFVKbubVRMmIpKNlITJsH704E4qSwpYOWdGRs9TXJBHRXF+zvUJS7hz07pdNHX0cPmZi0Y1Kn46FlaXqCZMRCRLKQmTIW092MZfnm/izKXV5MUmfliKgWZVFFHf2p3x80ymh7c382x9O288eR7Lassn/PgLqkrZc6gLd5/wY4uISGYpCZMh/eShXRTmxViTgRHyBzNvZgn7WnInCevtT3DfloMsqSnLyCwDAAuqSujq7aepI7dqEEVEpgMlYTKozngftz66h9edOIfyMYzmPhZzK4vZdzh3mtYe2d5Ma3cfrzpuVkYGuAVYWJUcK0z9wkREso2SMBnUb57YT1tPH+86a/GknXPezBIa23uI9yUm7ZyZEu9LcN+zDSyrLWNZ3cQ3QyYtCMcKU78wEZHsoyRMBvWzR3axYlY5axZXTdo551WW4E5O9At7eHsT7T19vHIME3OPxgtjhSkJExHJNkrC5EWe3t/K47sOc9kZizLWjDaYuTOLAbK+STLel+D+5xpZUVfO0tqyjJ6rvCifqtICjZovIpKFMpqEmdmFZrbFzLaa2TWDrF9lZg+aWY+ZfSKTsUj6bnpkF4X5Md566vxJPe/cyqBpbV9Ldidhj+5spqOnj5evmjUp51tYXaqaMBGRLJSxJMzM8oBvARcBxwOXmdnxAzZrBv4W+M9MxSGj0xXv5xeP7+V1q+dQNcFjWo1k3pGasOxtjuxLBLVgi2tKM14LlrSgqoQ96pgvIpJ1MlkTdgaw1d23uXscuAm4OHUDdz/o7uuA3gzGIaPw6yf20dbdx2VnLJr0c5cW5jOztID9WVwTtnH3YVq6erng2MmpBYPgCck9h7tIJDRWmIhINslkEjYf2J3yeU+4TKao/oRz3b3Pc9zcGZyxdHLGBhtobmUJ+7O0Jizhzn3PNjC3sphjZ2fuiciBFlSVEO9L0JCDUz6JiOSyTCZhg/XoHtOf6mZ2lZmtN7P1DQ0N4wxLhvLrjfvY1tjBx165YlI75KeaP7OYvVnaMX/j7sM0tsc5/9i6SS2/BdXJJyTVJCkikk0ymYTtARamfF4A7BvLgdz9O+6+xt3X1NXVTUhwcrT+hPP1PzzHqjkVvOb4OZHFMbeyhP1ZOGr+4c44v35iHwuqSlg9v3JSz72wSmOFiYhko0wmYeuAY8xsqZkVApcCt2fwfDIOv3liH9saOvjoK44hNgnzRA5l7sxiWrp66ejpiyyG0Uq4c/P63SQc3rlmIbFJrkVcoFHzRUSyUsbmo3H3PjP7CHAnkAdc7+6bzezqcP11ZjYHWA/MABJm9nHgeHdvzVRc8mKd8T6+fs9zHDu7nItWR1cLBsGArQD7W7pYMasi0ljS4e7c83Q9O5o6ueQlC6gpL5r0GIoL8qgtL2J3s2rCRESySUYnBXT3tcDaAcuuS3l/gKCZUiLS3dvPh364nu2NHVx/xemR1oJBMHURBMNUTNUkrLc/QXt3H883tPPnrY0cbOvhlIUzOWXhzMhiWlxTys7mjsjOLyIiozc5MzPLlNTXn+DqnzzKg9ua+Oo7TuaClZM3rMJQ5lYGY4VNtWEq3J2Ne1q4c/MBWrpeGFFlbmUxbz9tAScvnBnZwwwQJGEPPt8U2flFRGT0lIRNM739CZ450MYz+1vZUt9GZ7yfL731RN5y6tSokJxTWYzZ1BqwdX9LFz98cAfP1rezoKqEM5dWU16UT11FEYuqSyNNvpKW1pTxi8f20hXvp6QwL+pwREQkDUrCpomWrl7+/FwDj+06TFdvP6WFeRw7u4KPv+qYKVEDllSQF2NWRdGUmT+yP+Fc+aP17Gjs5PUnzuXs5TWT3vE+HYvD0fl3Nnewas6MiKMREZF0KAmbBrri/Xz3T9s43Bnn+HmVnL6kiuV15cTMplQCljSVhqn4yUM7eXJvK5eevpCTFsyMOpwhLa0JkrAdjZ1KwkREsoSSsByXcOeWR3dzuDPOlS9dxuKayZnPcDzmzSzmmQNtUYfBwdZu/vPOLbz0mFpOnOSxv0ZrcW0wTMWOJnXOFxHJFpkcJ0ymgHu3NPDMgTZef+LcrEjAIBimYt/hLtyjnQvxi2ufpqcvwb9evHpK9PsazoziAmrKCtmpJExEJGsoCcth+w53cc/T9ZyycCZnLauJOpy0zZ1ZQndvgsOd0c3rvmH3YX61YR9XX7CcpbXZkbwuqS1je6OSMBGRbKEkLIc9sLWRgvwYbzxp3pSvyUk1PxwrbHeEcyFe/+ftVBTlc9XLlkUWw2gtrillZ5NGzRcRyRZKwnJUW3cvT+xt4bRFVVk3ZMExs8sBeLa+PZLz17d2s3bTfi5Zs5DyouzpNrm0poz9Ld10xfujDkVERNKgJCxHPby9mf6Ec87y7GmGTFpSU0ZRfowtB6KZveonD+2k350rzlkSyfnHKnWYChERmfqUhOWg7t5+Ht7ezKo5FdRGMJfheOXFjGNml0fyhGR3bz83PryLV66azaKa0kk//3ikDlMhIiJTn5KwHPTrjfvo6OnjnOW1UYcyZitnz2BLBEnY7Rv30dQR5/3nLpn0c4+XhqkQEckuSsJy0E8e2smsiiKW12XHU32DWTWngoNtPRzqiE/aOd2dHzywg5WzK7KyGVfDVIiIZBclYTlm68F2Nu5pYc3iqqx6InKglXMqACa1SfKR7c08vb+VK85dkrVlt7imVMNUiIhkCSVhOeaXj+8hZnDSwplRhzIuq8IkbDI75//ggR3MLC3gzafMn7RzTrQltWUapkJEJEsoCcshiYRz2+P7OO+YOmYUF0QdzrjUVRQxs7SALfWTUxO2u7mTu546wKWnL8q6IT1SLdEwFSIiWUNJWA5Zt6OZvYe7eOup2VuTk2RmrJxdMWnNkT9+aCdmxnvPXjwp58uUJeEwFeqcLyIy9WXPSJQyol8+vpfSwjxec8Jsbnt8X9ThjNuqORX876N7SCScWGz0fbRufHhXWtu9+dR53PTILi48YQ7zwtH6s1WyGffp/a0cN3dGxNGIiMhwVBOWI7p7+7lj034uPGEOpYW5kVuvnDODjng/ew93ZfQ81/95O63dfXzgvCUZPc9kWF5XTnFBjE17W6IORURERqAkLEfc/VQ9bd19vOW07G+KTJqMJyQPd8b55h+3ctHqObxkcXXGzjNZ8mLG8XNn8KSSMBGRKU9JWI742SO7mD+zhHOzeIDWgVZOwhOSazftB+Azrz8uY+eYbCfOr2TzvlYSCY86FBERGYaSsBywvbGDvzzfxGVnLBxT36mpqrwonwVVJTydoZqwrQfbeXJfKx++YAULqrJriqLhrJ5fSWe8n20aL0xEZEpTEpYDbnpkF3kx4x1rFkYdyoRbs7iKv2xtpK8/MaHHbe3q5ZeP76G6rJArX7ZsQo8dtdXzKwHYvE9NkiIiU5mSsCzX09fPLY/u4VXHzWLWjOKow5lwF66ew6HOXh7Z0Txhx2zr7uX7f95OR7yfd65ZSHFB9o4LNphjZpVTlB9j0x4lYSIiU5mSsCx31+Z6mjviXH5mdo9vNZSXHVtHcUGMO588MCHHa+/p4wcP7OBwV5z3nb2EhdW50wyZlJ8X47i5M3hSNWEiIlOakrAs95OHdrKgqoSXrsidDvmpSgvzOf/YOu7cXD/ujubbGzv45h+eo7G9h/ectYSltdk7wflIVs+fwea96pwvIjKVKQnLYvc928DD25t5/7lLc6pD/kAXrp7DgdZuNu45PKb9E+7cu+Ug3/vTNgryYvz1BctZMat8YoOcYk6cX0lbTx+7mjWPpIjIVJUbo3pOQ/0J59/WPs2i6lLefdaiqMPJqFesnE1+zPjd5gOcuqhqVPu29/Rxy/rdPHewnRPnV/KWU+fnXB+wwZwwL+icv2lvy5GpjEREZGpRTViWuvXRPTxzoI1PXriSovzcTioqSws4e3kNdz55APf0m9d2N3fyzT88x7bGDt508jwuPT33OuEP5djZFRTmxTRoq4jIFKYkLAt1xvv4yt1bOGXhTF5/4tyow5kUF66ew46mTjam+cTf75+q53t/3kZezPjr85dz1rIazHK3yXagwvwYq+ZW8Piuw1GHIiIiQ1Bz5BQz0qTT7s7GPYepb+3hW5efNm0Si9efOJf/d/ezXHPrE9z24XOHrdH62SO7+MwvNzG3soT3nr2YiuKCSYx06jj/2Dq+9cetNLX3UFNeFHU4IiIygJKwLHPvsw3c/VQ9H33FCtYsyf65DgczVCL6+hPn8sMHd/LBG9bx0yvPetH6eF+CL97xFD98cCfnH1vHBSvrcr6pdjgXrZ7LN/6wlbuequeyM3K736CISDZSc2QW2bD7EHc/Vc9bTp3P37362KjDmXQr58zgrGU1PPB8E3/ccvCodTubOrj8uw/xwwd3cuVLl/L9962Z1gkYwHFzK1hcU8pvJ2iMNRERmViqCcsCCXfuD2vAltaW8aW3nThtmiEHumj1HLY1tPP+H6zjpAWVnH9sHY9sb+bh7c2UFOTxjctO5Y0nz4s6zCnBzLho9Vy+96dtHO6MM7O0MOqQREQkhZKwKa4r3s8tj+7mmQNtnLQgGGJhOtfwFOTF+NBLl+Hu3LFpP9/4w1aW1ZbxD69dyVtPm8/cypKoQ5xSLlo9h+vue567n6rnkhycWzRdI/W1BLj8TDXZisjkUhI2Rbk7m/a28Jsn9tMZ7+MNJ83l7Gn2hN9QyovyufzMRfzV+ctp6eplRnG+ymUIJy2oZP7MEn735IFpnYSJiExFSsKmoIa2HtZu2s+W+jbmzSzminOWMG+mangGU1kyPZ98TJeZceHqOfz4wZ20dfdm1ZOi6dRegWqwRCR7KQmbJOn8Qmnp6uXeLQdZt6OZ/LwYF62ewznLa8nL4SmJJPNef9Jcvv/n7fz4oZ38zQUrMn4+Nf2JiKRHSVjEunv72dHYwfqdh3jmQCsApy+p5pXHzaa8SF8eGb/TFlXx2hNm8/V7nuP1J85lcc30msYo3pegtz9BQZ4eBheRqUW/5SdJZ08f9W09NLT1cKgzzuHOOAdauznY2oMDZUX5nLeijtOXVGlgTZlwn3/Tal711fv4p9ue5EcfOGPK96Fzd9q6eznU2Ut7dy/tPf309ieIxYw8M4oLYpQU5FGYH+PRnc24Q2N7D/tbutnd3MXWhnaeP9hOc0ecrt5+AGaWFDBrRhHHzKrgtEVVlBRO3wdcRGRqUBKWIYmEs25HM3c9Vc/dT9Wzq7nzyLqYBX2Z6iqKWD2/kkVVpSytKyM/pr/UJTPmVBbzyQtX8tlfbea2DXt5y6kLMnq+1u5edjV1sru5k4NtPbT39NHe04cZFObFuGndLooL8igpyKO0MHg1MxraezjY2s2eQ1209/Slda7/uX/bUZ+LC2KsmFXOmiVVzJ5RzIzifDbsPkxje5z9LV3csWk/d24+wMkLZvLyVbOoLtPQHSISDSVhE6y3P8GvN+7juvue59n6dgrzYpy7ooYT5s1g9oxi6iqKqCwpIDbFayIk97zrzMX88vG9fOrWTTS1x/nAuUuJhf0Nx9qPq68/QXNHnGfr23nmQCtP7GnhT881cKizF4D8mFFXUcSM4gJmzyjCPfgeqSkrpDPez+HOOPtb+unq7ae/36mtKGJBVQlnLq2mqSNOdVkhFcUFlBflU5BnJBz6E053bz9d8aB27IJVswCoKStkbmUx1WWFL6rpS72+/S1dPLy9mcd3HWLDnsOcvayGC1bWjblcRUTGSknYBOnu7efm9bv5n/u2sfdwFytnV/CVS07mtavnUF6Un/aTXiKZkhczvvfeNVzzi0184Y6nuefpg7zvnCWctmjmkW3cne7eBIe74rR09nK4q5eWrl46evq4/9kGWsLPyX8Da6tmzyhi/swSzl5ey6LqUuZVFpM/SF+sdDrmD/c9k/pU7PnHji6BmltZwptPmc/LV87i90/X88DWRtaHTZrvOXvxsPOSiohMpIwmYWZ2IfA1IA/4nrt/acB6C9e/DugErnD3xzIZ00RrbO/hpw/t4scP7aCxPc5pi2by+TedwCtWzTpSyyASpYHJzAXH1lFRlM8dm/bz4LYmAIryYzhBLVN/wo/aPmZQVphPS2UvlSUFzK0sZtWcCipLC6gsKaC6rJDldeWsnFNBbXlR1vzBUVlSwNtOW8C5y2v53eb9fHHt09zwlx186KVLeftLFmTVcB4ikp0yloSZWR7wLeDVwB5gnZnd7u5PpWx2EXBM+O9M4Nvh65TW3dvP/c828NsnD3DHE/uJ9yd4+co6rj5/OWcsrZ7ynZ5lejMz1iyp5uSFM9nf0s2u5k4OdcSJWVBbVl6UT2VpIZUlBcwsKaC8OJ+YWc4OKzGnspgrzlnKoupSvnr3Fj7/66f4yl3P8saT5/LylbM4d0UtZXpSWUQyIJM/Wc4Atrr7NgAzuwm4GEhNwi4GfuTuDjxkZjPNbK67789gXCPqTzid8T464/20dPVysLWH/S1dbDnQxuZ9rWzYfZiu3n4qivN55+kLueLcJSyvK48yZJFRK8iLsai6lEXVpVGHMiWcd0wt5x1Ty8bdh7nhLzu4fcM+fvbIbvJjxtLaMo6ZXc6i6jJqygqZWVpASWEeBXkxCvKMgrwY+bEYZsFcr+7Ba8KTn51E4oVlHr72u9OfSNDbH9RA9vUn6Es4ff0evoafEy8s7084vf2JI7WWTnBckueCI+dP8he9SWEvvCT/gBzqz8iCvBglhTFKC/MpKcijpDCPssI8SgvzKSvKp7won7KiPMqKgs9lhXnk58XIjxkxM/JiRsyYlD9UPfX6B7vuEaQbov7olvHIZBI2H9id8nkPL67lGmyb+UBkSdiTe1t4wzf+POi6ovwYq+ZUcMmaBbz6+NmctaxGYw+J5IjUZtTTl1Rz6qKZ7Gzq5PmD7dS39fDwtmbufLKe/rH8Rh+nmEHM7MgQHTGDWJjYGMEQN7EYGIaF2xoclU21dYX991JzBk99Cf8f5vL6E068P0Ei7Ds4VnmxFxKy1HMOTBadFydSfuSzD/g85nCmHLMXvkwDE2M7kjSnZM8kk+ij19nRm8gg3nn6Ij77xuMjO795hu5cM7sEeK27fyj8/B7gDHf/aMo2dwD/5u5/Dj/fA3zS3R8dcKyrgKvCjyuBLRkJeni1QGME580lKsPxUfmNj8pvfFR+46cyHJ9sLb/F7j7oE0SZrAnbA6TOGLwA2DeGbXD37wDfmegAR8PM1rv7mihjyHYqw/FR+Y2Pym98VH7jpzIcn1wsv0y2pa0DjjGzpWZWCFwK3D5gm9uB91rgLKAl6v5gIiIiIpMhYzVh7t5nZh8B7iQYouJ6d99sZleH668D1hIMT7GVYIiK92cqHhEREZGpJKPPXbv7WoJEK3XZdSnvHfhwJmOYQJE2h+YIleH4qPzGR+U3Piq/8VMZjk/OlV/GOuaLiIiIyNA0voKIiIhIBJSEpcHMLjSzLWa21cyuiTqebGNmO8xsk5ltMLP1UceTDczsejM7aGZPpiyrNrO7zey58LUqyhinsiHK73Nmtje8DzeY2euijHEqM7OFZvZHM3vazDab2cfC5boH0zBM+ekeTIOZFZvZI2a2MSy/z4fLc+7+U3PkCMLpl54lZfol4LIB0y/JMMxsB7DG3bNxfJdImNnLgHaCGSVWh8v+HWh29y+FfwxUufunooxzqhqi/D4HtLv7f0YZWzYws7nAXHd/zMwqgEeBNwNXoHtwRMOU3zvQPTiicF7pMndvN7MC4M/Ax4C3kmP3n2rCRnZk+iV3jwPJ6ZdEMsbd7weaByy+GPhh+P6HBD/UZRBDlJ+kyd33u/tj4fs24GmC2Ux0D6ZhmPKTNHigPfxYEP5zcvD+UxI2sqGmVpL0OXCXmT0azn4gYzM7OY5e+Dor4niy0UfM7ImwuTLrmzImg5ktAU4FHkb34KgNKD/QPZgWM8szsw3AQeBud8/J+09J2MgGm3ZLbbijc667nwZcBHw4bCoSmWzfBpYDpxDMT/uVSKPJAmZWDtwKfNzdW6OOJ9sMUn66B9Pk7v3ufgrBTDpnmNnqiEPKCCVhI0traiUZmrvvC18PAr8kaOKV0asP+5ok+5wcjDierOLu9eEP9gTwXXQfDivsi3Mr8FN3/0W4WPdgmgYrP92Do+fuh4F7gQvJwftPSdjI0pl+SYZgZmVhx1TMrAx4DfDk8HvJEG4H3he+fx/wqwhjyTrJH96ht6D7cEhhx+jvA0+7+1dTVukeTMNQ5ad7MD1mVmdmM8P3JcCrgGfIwftPT0emIXyM+L94YfqlL0YbUfYws2UEtV8QzNBwo8pvZGb2M+ACoBaoB/4FuA24GVgE7AIucXd1Ph/EEOV3AUEzkAM7gL/SXLWDM7PzgD8Bm4BEuPjTBP2adA+OYJjyuwzdgyMys5MIOt7nEVQW3ezu/2pmNeTY/ackTERERCQCao4UERERiYCSMBEREZEIKAkTERERiYCSMBEREZEIKAkTERERiYCSMBE5ipm5mX0l5fMnwsmvJ+LYN5jZ2yfiWCOc5xIze9rM/pjpcw0470wz+5uUz/PM7H8zcJ6zzey7gyy/18zWTPT5RCQzlISJyEA9wFvNrDbqQFKZWd4oNv8g8Dfu/vJMxTOEmcCRJMzd97l7JpLOC4HfZeC4IjKJlISJyEB9wHeA/zNwxcCaLDNrD18vMLP7zOxmM3vWzL5kZu8ys0fMbJOZLU85zKvM7E/hdm8I988zs/8ws3Xh5MZ/lXLcP5rZjQQDXw6M57Lw+E+a2ZfDZZ8FzgOuM7P/GLC9mdk3zewpM7vDzNYmr8fMdiQTTzNbY2b3hu/LwsmW15nZ42Z2cbj8hPD6NoQxHwN8CVgeLvsPM1tiZk+G2xeb2Q/CeB83s5eHy68ws1+Y2e/M7Dkz+/eUMrkhvLZNZpb69Xgl8HszKzGzm8Lz/xwoGa5swuUXmtljZrbRzO4Z6iYQkczLjzoAEZmSvgU8kUwI0nQycBzQDGwDvufuZ5jZx4CPAh8Pt1sCnE8wkfEfzWwF8F6gxd1PN7Mi4AEzuyvc/gxgtbtvTz2Zmc0Dvgy8BDgE3GVmbw5H1n4F8Al3Xz8gxrcAK4ETgdnAU8D1I1zXZ4A/uPsHwqlUHjGz3wNXA19z959aMKVZHnBNGOspYYxLUo7zYQB3P9HMVoXxHhuuOwU4laAWcouZfQOYBcx399XhsWaGr7VAr7u3mNnfAZ3uflI4yvhjw5UN8ADBnIUvc/ftZlY9wrWLSAapJkxEXsTdW4EfAX87it3Wuft+d+8BngeSSdQmgsQr6WZ3T7j7cwTJ2iqCOUXfa2YbCKbGqQGOCbd/ZGACFjoduNfdG9y9D/gp8LIRYnwZ8LNwEuV9wB/SuK7XANeEsd0LFBNMm/Ig8Gkz+xSw2N27RjjOecCPAdz9GWAnkEzC7nH3FnfvJkgMFxOUzTIz+4aZXQi0psSTLNuXAT8Jj/kE8ES4fKiyOQu4P1me2T7li0i2UxImIkP5L4K+VWUpy/oIf26YmQGFKet6Ut4nUj4nOLrWfeBcaQ4Y8FF3PyX8t9Tdk4lGxxDxWZrXMdBQc7UduTaCRCv1PG9LiW2Ruz/t7jcCbwK6gDvD2rfhDBdvatn1A/nufoigdvFeglq074XrL+Lo/mCDXc9Q57IhtheRCCgJE5FBhbUkNxMkYkk7CJq4AC4GCsZw6EvMLBb2E1sGbAHuBP7azAoAzOxYMysb7iAENWbnm1lt2Gn/MuC+Efa5H7g07G81F0jtuL+DF67tbSnL7wQ+GiadmNmp4esyYJu7fx24HTgJaAMqhjn3u5LXR1CbtmWoQMNmx5i73wr8M3BaGMNJwIZBjrk6XAdDl82D4fKl4T5qjhSJkJIwERnOV4DUpyS/S/BL/BHgTIaupRrOFoKE4LfA1WET3PcImuEeCzuy/w8j9Fl19/3APwJ/BDYCj7n7r0Y49y+B5wiaSL/N0Unb54GvmdmfCGqjkq4lSDafCGO7Nlz+TuDJsJlyFfAjd28i6M/25MCHAoD/BvLMbBPwc+CKsOl2KPOBe8Pj3xBe60uAx909WZv1baDczJ4APgk8MlzZuHsDcBXwCzPbGMYhIhGxF76XRUSmFzO7AfiNu0/4WF6ZYGb/BGx195uijkVExk9PR4qIZAl3/0LUMYjIxFFNmIiIiEgE1CdMREREJAJKwkREREQioCRMREREJAJKwkREREQioCRMREREJAJKwkREREQi8P8BB/LukSqjSLYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the distribution of number of questions per/document for the whole dataset\n",
    "\n",
    "df_by_context = df['context'].value_counts().rename_axis('unique_values').reset_index(name='counts')\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.title('Number of questions per document distribution')\n",
    "sns.distplot(df_by_context['counts'])\n",
    "plt.xlabel('Number of questions/doc') \n",
    "plt.ylabel('Density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "984f91d9-37c9-466a-b16f-9719bc284774",
   "metadata": {},
   "source": [
    "There are mostly **5 or 10 questions** asked per document in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fb7e68d7-0831-425c-b54c-81e87c9b85c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average context length is 754.5662873410631\n"
     ]
    }
   ],
   "source": [
    "avg_context_length = df['context_length'].mean()\n",
    "print(\"Average context length is {}\".format(avg_context_length))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8511e682-020b-4560-801d-df9cb52c19eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ankushraut/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the distribution of context length for the whole dataset\n",
    "\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.title('Context length per document distribution (unbucketed)')\n",
    "ax = sns.distplot(df['context_length'])\n",
    "ax.set(xlabel='Context length', ylabel='Density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e13a8abf-6b8c-47c3-8f1c-e55cdaa6a6a4",
   "metadata": {},
   "source": [
    "We can see a huge spike in the graph above around context length of 700 which is also justified by the \n",
    "the mean context length calculated above for the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f82d4c7f-387b-4b11-bb87-abffff981598",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ankushraut/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the distribution of answer start index for the entire dataset\n",
    "\n",
    "df['answer_text'] = df['answers'].apply(lambda x: x['text'][0] if len(x['text'])>0 else '')\n",
    "df['start_index']= df['answers'].apply(lambda x: x['answer_start'][0] if len(x['answer_start'])>0 else -1)\n",
    "\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.title('Distribution of answer start index (unbucketed)')\n",
    "ax = sns.distplot(df['start_index'])\n",
    "ax.set(xlabel='Answer start index', ylabel='Density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "054ddcc8-18d1-4b06-8eff-cc74236614fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ankushraut/opt/anaconda3/lib/python3.9/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we define a new metric answer_position = start_index/context_length that provides\n",
    "# information about the relative position of answer in the context/paragraph\n",
    "\n",
    "df['answer_pos'] = df['start_index']/df['context_length']\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.title('Distribution of relative answer position in the context')\n",
    "ax = sns.distplot(df['answer_pos'])\n",
    "ax.set(xlabel='Answer position', ylabel='Density')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d27559c4",
   "metadata": {},
   "source": [
    "We can see that the dataset is already stratified on the relative answer position, with a spike at 0 due to unanswerable questions. It's perfect for evaluating the trend of accuracy with the relative position of the answer."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0424f0dd-6ecc-4dfb-a57f-e155ac76d801",
   "metadata": {},
   "source": [
    "## Sampling Strategy"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "05c739dd-f1c0-4c72-a0d9-cc2b1d71f87d",
   "metadata": {},
   "source": [
    "We create 3 samples, all stratified on different characteristics. The reason for that is we have computational limitations preventing us from obtaining predictions on the entire dataset in a small span of time. Here are the criteria for stratifying the sample.\n",
    "\n",
    "1. Equal distribution of answer start index buckets.\n",
    "2. Equal distribution of the buckets of relative answer position.\n",
    "3. Equal distribution of context length buckets.\n",
    "\n",
    "We chose a 1% sample for each of the criteria mentioned above. We'll further our research by predicting on an even larger sample."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "91287497-0099-497d-9784-df302132e32e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [0,600,700,1000,5000]\n",
    "df['context_cat'] = pd.cut(df['context_length'], bins=bins)\n",
    "df_context_cat_valc = df['context_cat'].value_counts().rename_axis('unique_values').reset_index(name='counts')\n",
    "\n",
    "\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.title(\"Distribution of context length across documents (bucketed)\")\n",
    "ax = sns.barplot(x = 'unique_values',\n",
    "            y = 'counts',\n",
    "            data = df_context_cat_valc,\n",
    "            palette = \"Oranges\")\n",
    "\n",
    "ax.set(xlabel='Context length range', ylabel='Count')\n",
    "plt.show()\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "726607f5-1b5c-4c78-a3b4-39091367611f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Barplot showing the number of documents for each of the answer start index range. \n",
    "\n",
    "bins = [-1,0,150,400,5000]\n",
    "df['answer_start_cat']= pd.cut(df['start_index'], bins=bins,include_lowest=True,right=False)\n",
    "df_answer_start_cat = df['answer_start_cat'].value_counts().rename_axis('unique_values').reset_index(name='counts')\n",
    "\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.title('Distribution of answer start index across documents (bucketed)')\n",
    "ax = sns.barplot(x = 'unique_values',\n",
    "            y = 'counts',\n",
    "            data = df_answer_start_cat,\n",
    "            palette = \"Blues\")\n",
    "\n",
    "ax.set(xlabel='Answer start index range', ylabel='Count')\n",
    "plt.show()\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c8996662-0c9e-459f-b35e-f027f5a1450a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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78vem9K7eDJwMfCIzT695X6zz+Qml/R5B+R5AuUvzw5TgfhtKgLG8PkLZpufXdvBTyo+bQYzW/n5MORD+ltKO72PZU3D9lnEZmXkG5WL371B6Uh4P7FWz16Ws68UsvePwCzVvH+D6umxvo9xo0cuqtq3WoPwl1u2UY8ejKJd1PExmXgzcFRHP6sr6PuWGkksoNyscUcc/nRLQXVbzf9g13T6UH9S/plyX974e8zydchw9JSKe2W/ZM/PXlB9519bl25RyV/QplMs47qHsO59Vx7+ScvfxtyltaTGNY2NErEk5BX9kr/Ux0Tp3gUnScomIOZTAafUJ7vmSNAVExIspNxbtMdl1GbaIeDflr27+ZbLrAgZxklaQQZwkTQ5Pp0qSJLWQPXGSJEktZE+cJElSCxnESZIktdD0ya7ARNtoo41yzpw5k10NSZKkUV100UW3Z2bPJ1CsckHcnDlzmD9//mRXQ5IkaVQRccNIeZ5OlSRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqoVXu2anL6+pFl012FbQCnjTrqZNdBUmSxpU9cZIkSS1kECdJktRCBnGSJEktZBAnSZLUQgZxkiRJLWQQJ0mS1EIGcZIkSS1kECdJktRCBnGSJEktZBAnSZLUQgZxkiRJLWQQJ0mS1EIGcZIkSS1kECdJktRCBnGSJEktZBAnSZLUQkMP4iJiWkT8KiJ+WD9vEBGnR8Q19X39xrj7R8SCiPhNROzaSH9mRFxe874SEVHT14iI42v6BRExZ9jLI0mSNBVMRE/ce4GrG58/CpyRmVsBZ9TPRMTWwF7ANsBuwCERMa1O8zVgP2Cr+tqtpu8LLM7MLYEvAZ8b7qJIkiRNDUMN4iJiNvAPwOGN5N2BI+vwkcAejfTjMvP+zLwOWADsEBGbAOtm5nmZmcBRXdN0yjoJ2KXTSydJkrQyG3ZP3JeBfwEeaqRtnJm3ANT3R9X0zYCbGuMtrGmb1eHu9GWmycwlwF3AhuO6BJIkSVPQ0IK4iHgZcFtmXjToJD3Ssk96v2m667JfRMyPiPmLFi0asDqSJElT1zB74p4L/GNEXA8cB+wcEccAt9ZTpNT32+r4C4HNG9PPBm6u6bN7pC8zTURMB2YCd3ZXJDMPy8ztMnO7WbNmjc/SSZIkTaKhBXGZuX9mzs7MOZQbFs7MzNcBpwBz62hzge/X4VOAveodp4+l3MBwYT3lek9E7Fivd3t91zSdsvas83hYT5wkSdLKZvokzPNg4ISI2Be4EXgVQGZeGREnAFcBS4B3ZuaDdZq3A/OAtYDT6gvgCODoiFhA6YHba6IWQpIkaTLFqtZxtd122+X8+fPHPN3Viy4bQm00UZ4066mTXQVJksYsIi7KzO165fnEBkmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFpo+2RWQJEmDefwHd5rsKmg5/e4/zhr3Mu2JkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYaWhAXEWtGxIURcWlEXBkRn6zpG0TE6RFxTX1fvzHN/hGxICJ+ExG7NtKfGRGX17yvRETU9DUi4viafkFEzBnW8kiSJE0lw+yJux/YOTOfBmwL7BYROwIfBc7IzK2AM+pnImJrYC9gG2A34JCImFbL+hqwH7BVfe1W0/cFFmfmlsCXgM8NcXkkSZKmjKEFcVncWz+uXl8J7A4cWdOPBPaow7sDx2Xm/Zl5HbAA2CEiNgHWzczzMjOBo7qm6ZR1ErBLp5dOkiRpZTZ9mIXXnrSLgC2Br2bmBRGxcWbeApCZt0TEo+romwHnNyZfWNMeqMPd6Z1pbqplLYmIu4ANgduHtEjSQOZdfMRkV0HL6Q3P2HeyqyBJAxnqjQ2Z+WBmbgvMpvSqPbnP6L160LJPer9pli04Yr+ImB8R8xctWjRKrSVJkqa+Cbk7NTP/CJxFuZbt1nqKlPp+Wx1tIbB5Y7LZwM01fXaP9GWmiYjpwEzgzh7zPywzt8vM7WbNmjU+CyVJkjSJhnl36qyIWK8OrwW8CPg1cAowt442F/h+HT4F2KvecfpYyg0MF9ZTr/dExI71erfXd03TKWtP4Mx63ZwkSdJKbZjXxG0CHFmvi1sNOCEzfxgR5wEnRMS+wI3AqwAy88qIOAG4ClgCvDMzH6xlvR2YB6wFnFZfAEcAR0fEAkoP3F5DXB5JkqQpY2hBXGZeBjy9R/odwC4jTHMQcFCP9PnAw66ny8z7qEGgJEnSqsQnNkiSJLWQQZwkSVILGcRJkiS1kEGcJElSCxnESZIktZBBnCRJUgsZxEmSJLWQQZwkSVILGcRJkiS1kEGcJElSCxnESZIktZBBnCRJUgsZxEmSJLWQQZwkSVILGcRJkiS1kEGcJElSCxnESZIktZBBnCRJUgsZxEmSJLWQQZwkSVILGcRJkiS1kEGcJElSCxnESZIktZBBnCRJUgsZxEmSJLXQQEFcRDx3kDRJkiRNjEF74v5rwDRJkiRNgOn9MiPi2cBzgFkR8YFG1rrAtGFWTJIkSSPrG8QBjwDWqePNaKTfDew5rEpJkiSpv75BXGaeDZwdEfMy84YJqpMkSZJGMVpPXMcaEXEYMKc5TWbuPIxKSZIkqb9Bg7gTgUOBw4EHh1cdSZIkDWLQIG5JZn5tqDWRJEnSwAb9i5EfRMQ7ImKTiNig8xpqzSRJkjSiQXvi5tb3DzfSEnjc+FZHkiRJgxgoiMvMxw67IpIkSRrcQEFcRLy+V3pmHjW+1ZEkSdIgBj2dun1jeE1gF+BiwCBOkiRpEgx6OvXdzc8RMRM4eig1kiRJ0qgGvTu125+BrcazIpIkSRrcoNfE/YByNyqUB98/CThhWJWSJElSf4NeE/eFxvAS4IbMXDiE+kiSJGkAA51OzcyzgV8DM4D1gb8Os1KSJEnqb6AgLiJeDVwIvAp4NXBBROw5zIpJkiRpZIOeTv0YsH1m3gYQEbOAnwInDatikiRJGtmgd6eu1gngqjvGMK0kSZLG2aA9cT+KiB8Dx9bPrwFOHU6VJEmSNJq+QVxEbAlsnJkfjohXAH8PBHAe8K0JqJ8krdT2PuYdk10FrYBjX3fIZFdBq7DRTol+GbgHIDO/m5kfyMz3U3rhvjzcqkmSJGkkowVxczLzsu7EzJwPzBlKjSRJkjSq0YK4NfvkrTWeFZEkSdLgRgvifhkRb+lOjIh9gYv6TRgRm0fEzyLi6oi4MiLeW9M3iIjTI+Ka+r5+Y5r9I2JBRPwmInZtpD8zIi6veV+JiKjpa0TE8TX9goiYM4ZllyRJaq3Rgrj3AW+MiLMi4j/q62zgzcB7R5l2CfDBzHwSsCPwzojYGvgocEZmbgWcUT9T8/YCtgF2Aw6JiGm1rK8B+wFb1dduNX1fYHFmbgl8CfjcYIstSZLUbn2DuMy8NTOfA3wSuL6+PpmZz87MP4wy7S2ZeXEdvge4GtgM2B04so52JLBHHd4dOC4z78/M64AFwA4RsQmwbmael5kJHNU1Taesk4BdOr10kiRJK7OB/icuM38G/Gx5Z1JPcz4duIDylyW31HJviYhH1dE2A85vTLawpj1Qh7vTO9PcVMtaEhF3ARsCty9vXSVJktpg6E9diIh1gO8A78vMu/uN2iMt+6T3m6a7DvtFxPyImL9o0aLRqixJkjTlDTWIi4jVKQHctzLzuzX51nqKlPreeZzXQmDzxuSzgZtr+uwe6ctMExHTgZnAnd31yMzDMnO7zNxu1qxZ47FokiRJk2poQVy9Nu0I4OrM/GIj6xRgbh2eC3y/kb5XveP0sZQbGC6sp17viYgda5mv75qmU9aewJn1ujlJkqSV2qDPTl0ezwX2AS6PiEtq2v8DDgZOqH9TciPwKoDMvDIiTgCuotzZ+s7MfLBO93ZgHuW/6U6rLyhB4tERsYDSA7fXEJdHkiRpyhhaEJeZv6D3NWsAu4wwzUHAQT3S5wNP7pF+HzUIlCRJWpUM/cYGSZIkjT+DOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYaWhAXEd+IiNsi4opG2gYRcXpEXFPf12/k7R8RCyLiNxGxayP9mRFxec37SkRETV8jIo6v6RdExJxhLYskSdJUM8yeuHnAbl1pHwXOyMytgDPqZyJia2AvYJs6zSERMa1O8zVgP2Cr+uqUuS+wODO3BL4EfG5oSyJJkjTFDC2Iy8xzgDu7kncHjqzDRwJ7NNKPy8z7M/M6YAGwQ0RsAqybmedlZgJHdU3TKeskYJdOL50kSdLKbqKvids4M28BqO+PqumbATc1xltY0zarw93py0yTmUuAu4ANh1ZzSZKkKWSq3NjQqwct+6T3m+bhhUfsFxHzI2L+okWLlrOKkiRJU8dEB3G31lOk1PfbavpCYPPGeLOBm2v67B7py0wTEdOBmTz89C0AmXlYZm6XmdvNmjVrnBZFkiRp8kx0EHcKMLcOzwW+30jfq95x+ljKDQwX1lOu90TEjvV6t9d3TdMpa0/gzHrdnCRJ0kpv+rAKjohjgZ2AjSJiIfAJ4GDghIjYF7gReBVAZl4ZEScAVwFLgHdm5oO1qLdT7nRdCzitvgCOAI6OiAWUHri9hrUskiRJU83QgrjM3HuErF1GGP8g4KAe6fOBJ/dIv48aBEqSJK1qpsqNDZIkSRoDgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqIYM4SZKkFjKIkyRJaiGDOEmSpBYyiJMkSWohgzhJkqQWMoiTJElqodYHcRGxW0T8JiIWRMRHJ7s+kiRJE6HVQVxETAO+CrwE2BrYOyK2ntxaSZIkDV+rgzhgB2BBZl6bmX8FjgN2n+Q6SZIkDV3bg7jNgJsanxfWNEmSpJVaZOZk12G5RcSrgF0z88318z7ADpn57q7x9gP2qx+fCPxmQivaDhsBt092JdQKthWNhe1Fg7Kt9LZFZs7qlTF9omsyzhYCmzc+zwZu7h4pMw8DDpuoSrVRRMzPzO0mux6a+mwrGgvbiwZlWxm7tp9O/SWwVUQ8NiIeAewFnDLJdZIkSRq6VvfEZeaSiHgX8GNgGvCNzLxykqslSZI0dK0O4gAy81Tg1Mmux0rA080alG1FY2F70aBsK2PU6hsbJEmSVlVtvyZOkiRplWQQJ0mS1EIGcVNURMyJiL9ExCUj5EdEfKU+M/ayiHjGCON9qz5b9oqI+EZErF7Td4qIuyLikvr6+AB1emxEXBAR10TE8fWO4F7jza3jXBMRcxvpx0XEVgOtAI1ZrzYzyLOFB21LXdOM2hYiYtuIOC8irqzlvqaRNy8irmu0v21r+ssi4pPLs/wamxVoL6+t2/OyiDg3Ip7WyLs+Ii6v23T+AHUYdD9me5kiVqDdLM8x5121zIyIjfqM92Cj3FMa6Sv/MSczfU3BFzAHuKJP/kuB04AAdgQu6DNe1NexwNtr+k7AD8dYpxOAverwoZ2yusbZALi2vq9fh9eveS8Avj7Z63ZlfXW3Gcod278DHgc8ArgU2Hp529JytIUnAFvV4U2BW4D16ud5wJ49pgngV8Dak70+V/bXCrSX5zS+0y9pthfgemCjMdRh0P2Y7WWKvFag3SzPMefpdX592xVw7wjpK/0xx5649todOCqL84H1ImKT7pEy89Q6TgIXUv4QecwiIoCdgZNq0pHAHj1G3RU4PTPvzMzFwOnAbjXv58CLIqL1d0W3xKDPFh6oLXUM2hYy87eZeU0dvhm4Dej5r+ONaRI4C3hZ/0XTEAzUXjLz3PrdBjif5dynVGNqez3qYnuZfEN7hnlm/iozr1+BIlb6Y45BXHuN6bmx9TTqPsCPGsnPjohLI+K0iNhmlPltCPwxM5eMMr8R65WZDwELgKf1mE7jb9A2MtZnEA/aFv4mInag/Er/XSP5oHoK7UsRsUYjfT7wvH7laSiW51nU+1J60joS+ElEXBTlcYfjOU/by9Q0lm04lmPOWKwZEfMj4vyI2KOTuCoccwzi2it6pPX7v5hDgHMy8+f188WU57E9Dfgv4HvjNL/RxruNcmpNwzde22yFxq89K0cDb6w7VYD9gb8Dtqecev9IYxLbyOQY63Z9ISWIa26752bmMyinWd8ZEc8fp3naXqauQbfhWI85Y/GYLI/r+mfgyxHx+EbeSt0+DOJaIiLe2bhwc1MGfG5snfYTlNNYH+ikZebdmXlvHT4VWL3fhaOUhxKv1+iWHml+o9VrTeAvfeaj8TNoGxm4LVWDtgUiYl3gf4ED6ukyADLzlnoK7X7gm5RTMh22kckxln3KU4HDgd0z845Oej1tTmbeBpzMstt1uedpe5nSBt2GYz3mDKzR7q6lnF5/eiN7pW4fBnEtkZlfzcxt6+tmyjNiX1/v7toRuCszb+meLiLeTLlObe9GLwgR8eh6bVPnVNdqwB318xkRsUx3eL325GfAnjVpLvD9HlX9MfDiiFg/ItYHXlzTOp4A+Gi0iTHos4VHbEsr0hbqPE+mXPN0YlfeJvU9KNfTXdHIfkLXZ02MgdpLRDwG+C6wT2b+tpH+yIiY0RmmfPevqJ/fFeURid0G3Y/ZXqauQdvNmI45g6rHmjXq8EbAc4GrGqOs3Mecyb6zwlfvF6PfnRrAVynXGF0ObNfIOxXYtA4vqeNcUl8fr+nvojTsSykXJz+npq8G3ACs1WOej6PcHLEAOBFYo6ZvBxzeGO9NdZwFlFNonfSNgQsne92urK9ebYZy999vaxv4WCP9bcDb+rWlFW0LwOuABxpt7xJg25p3Zp3XFcAxwDqNsn8IPGWy1+fK/lqB9nI4sLixTec32sSl9XVl1/T/Tfkh2V2HQfdjtpcp8lqBdrM8x5z3UHr6llB69zr7luZ+5jm1bVxa3/dtTL/SH3N87NYUFRFzKLdjP3mC5/tk4E2Z+YFRRx572e8H7s7MI8a7bI1/mxlmW+gzz42Bb2fmLhM1z1XVRO5jIuKHwCuy3L04nuXaXiZYm/Yzq8IxxyBuioqIzYFzgTsyc9tJrs64iIg3Akfn0rsaNY5WhjYTEdsDD2TmJZNdl5Wd7UXLo03tZlU45hjESZIktZA3NkiSJLWQQZwkSVILGcRJmlAR8U9RHmj9d5Ndl2GIiLdFxOvr8Bvq/zp28g6PiK3HYR7/GCM8aHzA6Q+MiA+taD0kTS6viZM0oSLiBGAT4IzMPHAS6zF92Bc8R8RZwIcyc/4w5zNWEXEg5aHhX5jsukhafvbESZowEbEO5c8496X8KWgnfaeIOCsiToqIX0fEtxp/DHpwRFwV5bmZX4iIaRFxbf2D2PUi4qGoj3eKiJ9HxJb1j2e/ERG/jIhfRcTuNf8NEXFiRPwA+ElX3ebUeR9Z53VSRKxd83ap5Vxey12jV91q2oER8aGI2JPyf1bfivKklbXqMm5Xx9u7lndFRHyuUY97I+KgKM+YPL/+jUb3enxDRPx3HZ4XEV+JiHPretmze/w63sci4jcR8VPgiY30bet8LouIk6P8STd1Pf601uPiWPZRRpKmAIM4SRNpD+BHWf7p/86IeEYj7+nA+4CtKX8c+9yI2AD4J2CbzHwq8JnMfJDyx6JbA38PXAQ8rwZWszNzAfAx4MzM3B54IfD5KE8RAHg2MDczd+5RvycCh9V53Q28IyLWBOYBr8nMpwDTgbf3qluzoMw8ifJw9tdmedLK3x79U0+xfg7YGdgW2D6WPrj7kcD5WZ4xeQ7wltFXK5vUdfEy4ODuzIh4JiVofjrwCsozSDuOAj5Sl+Fy4BM1/VvAV2s9ngM87EkKkiaXQZykibQ3cFwdPq5+7rgwMxdmeTzcJZR/hr8buA84PCJeAfy5jvtz4Pn19VlKALM95RFAUB759NGIuITyLMU1gcfUvNMz884R6ndTZv5fHT6mlvtE4Lpc+oipI+t8R6rbILYHzsrMRfWU7rdqmQB/pTyFAEqAOmeA8r6XmQ9l5lWUf6nv9jzg5Mz8c2beTX0sUkTMBNbLzLObyxbl8VmbZebJAJl5X2aOZfkkTQCDOEkTIiI2pPQ8HR4R1wMfBl7TOW0K3N8Y/UGgc83aDsB3qL14Nf/nlMBkB8rjmdYDdqL0XEF5nNMrc+nzhh+TmVfXvD/1qWb3RcJZy3r4iCPXbRA9y6weyKUXKz9I6fkbTXPdjVT2WC6A7lc/SVOEQZykibIncFRmbpGZczJzc+A6Sm9XT/UaupmZeSrlVOu2NesCyim+hzLzPkrP3VspwR3Aj4F3N66re/qAdXxMRDy7Du8N/AL4NTAnIras6fsAZ/epW9M9wIwe6RcAL4iIjSJiWp3X2T3GGy/nAP9Ur8ubAbwcIDPvAhZHxPPqePsAZ9feuoWdU7wRsUbn+kBJU4dBnKSJsjdwclfad4B/7jPNDOCHEXEZJch5P0Bm3g/cRHmQNpTgbQblmi6ATwOrA5dFxBX18yCuBubW+W0AfK0GiW8EToyIy4GHgENHqluXecChnRsbOomZeQuwP/AzyoO7L87M7w9YxzHLzIuB4ynB7ndYGuwCzKVcM3gZJRD9VE3fB3hPTT8XePSw6idp+fgXI5LExD4QXpLGgz1xkiRJLWRPnCRJUgvZEydJktRCBnGSJEktZBAnSZLUQgZxkiRJLWQQJ0mS1EIGcZIkSS30/wFPKsG6BrFGNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [-0.25,0,0.25,0.5,1.5]\n",
    "df['answer_pos_cat']= pd.cut(df['answer_pos'], bins=bins,include_lowest=True,right=False)\n",
    "df_answer_pos_cat = df['answer_pos_cat'].value_counts().rename_axis('unique_values').reset_index(name='counts')\n",
    "\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.title('Distribution of answer relative position in a document across documents (bucketed)')\n",
    "ax = sns.barplot(x = 'unique_values',\n",
    "            y = 'counts',\n",
    "            data = df_answer_pos_cat,\n",
    "            palette = \"Greens\")\n",
    "\n",
    "ax.set(xlabel='Answer position in doc', ylabel='Count')\n",
    "plt.show()\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1bf312be-2c7b-4462-a25a-93b81cff0634",
   "metadata": {},
   "source": [
    "## Stratified Sampling "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a9f12d4-6eac-4f8b-9149-d551ed6d1306",
   "metadata": {},
   "source": [
    "After categorizing the target variables (context length, answer start index and relative answer position in the document) we perform stratified sampling and produce the three samples stratified on different metrics.\n",
    "\n",
    "This has already been done and the stored files are imported below the stratified sampling code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "c3949c84",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.head()\n",
    "\n",
    "# for col in ['context_cat', 'answer_start_cat', 'answer_pos_cat']:\n",
    "#     df[col] = df[col].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b0d62189-5d67-42d7-b7fb-0f5e585a08a4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_context_length, test_context_length = train_test_split(df,\n",
    "#                                                     stratify=df.context_cat, \n",
    "#                                                     test_size=0.01,random_state=42)\n",
    "\n",
    "# test_context_length.to_csv('df_context.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "eacd3295-a018-4570-b484-13a29498f721",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_answer_pos, test_answer_pos = train_test_split(df,\n",
    "#                                                     stratify=df.answer_pos_cat, \n",
    "#                                                     test_size=0.01,random_state=42)\n",
    "\n",
    "# test_answer_pos.to_csv('answer_pos.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e52a62b4-7313-4a4d-b06b-2ac1812f7f3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_answer_start, test_answer_start = train_test_split(df,\n",
    "#                                                     stratify=df.answer_start_cat, \n",
    "#                                                     test_size=0.01,random_state=42)\n",
    "\n",
    "# test_answer_start.to_csv('answer_start.csv', index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6b45b3e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset1 = pd.read_csv('answer_start.csv') \n",
    "dataset2 = pd.read_csv('answer_pos.csv')\n",
    "dataset3 = pd.read_csv('df_context.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09951e6e",
   "metadata": {},
   "source": [
    "# Model pipeline"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b86ea4",
   "metadata": {},
   "source": [
    "This section includes the build of the open-source masked language modeling-based models for question answering. We have used **Longformer (max context length 4096)** and **BERT (max context length 512)**. \n",
    "\n",
    "The reason for this choice of models was to have one large-context and one small-context models and compare their performance on a question answering task. \n",
    "\n",
    "Not only do we require the accuracies but we also want to understand if the contextual retrievability of either of these models is consistent across different regions in the document."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8f0abfd",
   "metadata": {},
   "source": [
    "**Modular code for obtaining predictions** - We build a class that takes the dataset as input, the number of questions that need to be processed, the max context length that needs to be considered, and the model that you are using. Then we initialize this class and run the functions within to obtain predictions.\n",
    "\n",
    "The prediction pipeline is also built using the **Hugging Face** library. It provides an easy way to import language model heads and tokenizers that can be used to build a model for any use case."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2d022fcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "class QAmodel:\n",
    "    def __init__(self, dataset, model, num_questions, max_length):\n",
    "        \n",
    "        # Initialize some variables\n",
    "        \n",
    "        self.dataset = dataset\n",
    "        self.model = model\n",
    "        self.num_questions = num_questions\n",
    "        self.max_length = max_length\n",
    "        self.model_answers_df = None\n",
    "        \n",
    "    \n",
    "    # Function for returning answers to an input set of question and context\n",
    "\n",
    "    def long_context_qa(self, question, context):\n",
    "        \n",
    "        # We need a different Tokenizer and different QuestionAnswering head for the longformer model\n",
    "        # For most other models the AutoTokenizer class and the AutoModel class should work\n",
    "        # If GPU is available, then we load the model to a GPU using cuda\n",
    "        \n",
    "        if self.model == \"allenai/longformer-large-4096-finetuned-triviaqa\":\n",
    "            tokenizer = transformers.LongformerTokenizer.from_pretrained(self.model)\n",
    "            model_call = transformers.LongformerForQuestionAnswering.from_pretrained(self.model)\n",
    "            model_call.to(\"cuda\")\n",
    "        else:\n",
    "            tokenizer = transformers.AutoTokenizer.from_pretrained(self.model)\n",
    "            model_call = transformers.AutoModelForQuestionAnswering.from_pretrained(self.model)\n",
    "            model_call.to('cuda')\n",
    "        \n",
    "        # Here we tokenize the inputs, depending on the model, different tokenizer with different max_context_length will be called\n",
    "        \n",
    "        inputs = tokenizer(question, context, padding=\"max_length\",truncation=True,\n",
    "                       return_tensors=\"pt\",max_length = self.max_length)\n",
    "        \n",
    "        # If GPU is available we load the inputs to GPU using CUDA\n",
    "        \n",
    "        inputs.to(\"cuda\")\n",
    "        \n",
    "        # We'll call the model without calling gradients\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            output = model_call(**inputs)\n",
    "        \n",
    "        # The model gives multiple outputs, it'll give logits corresponding all possible start and end indices\n",
    "        # We'll pick the indices with highest logits \n",
    "        \n",
    "        answer_start = torch.argmax(output['start_logits']).item()\n",
    "        answer_end = torch.argmax(output['end_logits']).item()\n",
    "        \n",
    "        # We'll select the answer from the inputs based on the selected answer start and end logits \n",
    "        \n",
    "        input_ids = inputs[\"input_ids\"][0].cpu().numpy()\n",
    "        answer_tokens = input_ids[answer_start:answer_end + 1]\n",
    "        \n",
    "        # The tokenizer will decode the answer from the selected tokens \n",
    "        answer = tokenizer.decode(answer_tokens)\n",
    "        \n",
    "        return answer\n",
    "\n",
    "    # This function will give you if there's a match\n",
    "    # We're not using exact match, rather we're checking if the actual answer is present in the predicted answer\n",
    "    # This is being done only for answerable questions for now\n",
    "    \n",
    "    @staticmethod\n",
    "    def answer_matcher(actual, predicted):\n",
    "        try:\n",
    "            return 1 if actual in predicted else 0\n",
    "        except:\n",
    "            return np.nan\n",
    "        \n",
    "    \n",
    "    # Here we'll sequentially call the question answering function for all question, context pairs in the dataset\n",
    "    \n",
    "    def process_questions(self):\n",
    "        model_answers = []\n",
    "        \n",
    "        for i in tqdm(range(self.num_questions)):\n",
    "            answer = self.long_context_qa(self.dataset['question'][i], self.dataset['context'][i])\n",
    "            model_answers.append(answer)\n",
    "\n",
    "        self.model_answers_df = pd.DataFrame({\n",
    "            'actual_answer': self.dataset['answer_text'][:self.num_questions],\n",
    "            'predicted_answer': model_answers\n",
    "        })\n",
    "        \n",
    "        # Lowercase actual and predicted answers and call answer matcher to obtain match \n",
    "        \n",
    "        self.model_answers_df.actual_answer = self.model_answers_df.actual_answer.str.strip().str.lower()\n",
    "        self.model_answers_df.predicted_answer = self.model_answers_df.predicted_answer.str.strip().str.lower()\n",
    "        self.model_answers_df['answer_match'] = self.model_answers_df.apply(\n",
    "            lambda x: self.answer_matcher(x['actual_answer'], x['predicted_answer']), axis=1)\n",
    "\n",
    "        return self.model_answers_df\n",
    "    \n",
    "    # This function outputs the accuracy based on the answer_match function\n",
    "    \n",
    "    def get_accuracy(self):\n",
    "        accuracy = sum(self.model_answers_df['answer_match']) * 100 / self.num_questions\n",
    "        return accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6b2e5179",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining a list of models to be tested and their context lengths \n",
    "\n",
    "models = [\"bert-base-uncased\", \"allenai/longformer-large-4096-finetuned-triviaqa\"]\n",
    "context_lengths = [512, 4096]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad7a0e00",
   "metadata": {},
   "source": [
    "It takes quite some time to run the code below, it was run on Colab and the files were saved, this should be run on systems with high RAM and preferably with GPU."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "85f0c451",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Iterate over all datasets and models\n",
    "\n",
    "# i = 0\n",
    "# for dataset in [dataset1, dataset2, dataset3]:\n",
    "#     temp_dataset = dataset.copy()\n",
    "#     for model,context_length in zip(models, context_lengths):\n",
    "        \n",
    "#         # Call the QAmodel class\n",
    "#         d_qamodel = QAmodel(temp_dataset, model, len(temp_dataset), context_length)\n",
    "        \n",
    "#         # Obtain answers using the process_questions function\n",
    "#         answers = d_qamodel.process_questions()\n",
    "        \n",
    "#         # Define a nomenclature for the files to be stored, this can be altered later \n",
    "#         key = model[8:]+'_'+str(i)\n",
    "#         answers.to_csv(key+'.csv')\n",
    "    \n",
    "#     i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b8a4547-d41f-4d77-ab9b-dd76f60feb7d",
   "metadata": {},
   "source": [
    "# Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87404f39",
   "metadata": {},
   "source": [
    "We evaluate the results in this section. We check how the accuracy changes with the answer start index, this trend can be observed for other stratified samples too.\n",
    "\n",
    "The observations below are for one BERT output and 1 Longformer output. We have used the BERT output on the sample stratified on the answer start index, while for Longformer we have used the output on the sample stratified on the context length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "cc09d114-668b-4082-bdbe-af60dd0b959f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# function for postprocessing the predictions and retrieving the results along with original files\n",
    "\n",
    "def prepare_results(df_train, df_predict,by):\n",
    "    \n",
    "    # Convert the answers to lowercase\n",
    "    df_train['answer_text'] = df_train['answer_text'].astype(str).apply(lambda x: x.lower())\n",
    "    \n",
    "    # Join the answers table to the original table\n",
    "    df_merged = pd.concat([df_train, df_predict],axis=1)\n",
    "    \n",
    "    # Replace NaNs with empty strings\n",
    "    df_merged['actual_answer'].fillna('',inplace=True)\n",
    "    df_merged['predicted_answer'].fillna('',inplace=True)\n",
    "    df_merged['answer_match'].fillna(0,inplace=True)\n",
    "    \n",
    "    # To obtain accuracy for unanswerable cases, if the model outputs nothing to unanswerable questions, we consider that an answer match\n",
    "    df_merged['both_empty'] = np.where((df_merged['actual_answer'] == '') & (df_merged['predicted_answer'] == ''),1, 0)\n",
    "    df_merged['answer_match'] = df_merged['answer_match'] + df_merged['both_empty']\n",
    "    df_stats = df_merged[[by,'answer_match']].groupby(by).mean()*100\n",
    "    df_stats = df_stats.reset_index()\n",
    "    return df_stats"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb95c40e-d702-4c0e-9f7c-c4df919afcc1",
   "metadata": {},
   "source": [
    "### BERT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4faf159a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_answer_start_train = pd.read_csv(\"answer_start.csv\")\n",
    "df_bb_astart = pd.read_csv(\"bert-base-uncased_2_1.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e500fb06-c146-4307-8368-02ce6dfdd64e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_x/8vptzk216z9d2gt2lh5spy9m0000gn/T/ipykernel_13164/450668427.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_merged['actual_answer'].fillna('',inplace=True)\n",
      "/var/folders/_x/8vptzk216z9d2gt2lh5spy9m0000gn/T/ipykernel_13164/450668427.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_merged['predicted_answer'].fillna('',inplace=True)\n",
      "/var/folders/_x/8vptzk216z9d2gt2lh5spy9m0000gn/T/ipykernel_13164/450668427.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_merged['answer_match'].fillna(0,inplace=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [-1,0,150,400,700,1000,5000]\n",
    "df_answer_start_train['answer_start_cat']= pd.cut(df_answer_start_train['start_index'], bins=bins,include_lowest=True,right=False)\n",
    "\n",
    "df_stats_bb_astart_df =  prepare_results(df_answer_start_train, df_bb_astart,by='answer_start_cat' )\n",
    "\n",
    "df_stats_bb_astart_df['answer_start_cat'] = df_stats_bb_astart_df['answer_start_cat'].astype('str')\n",
    "\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.title('Trend in accuracy of BERT with answer start index')\n",
    "ax = sns.lineplot(x = 'answer_start_cat',\n",
    "            y = 'answer_match',\n",
    "            data = df_stats_bb_astart_df,\n",
    "            palette = \"Greens\")\n",
    "\n",
    "ax.set(xlabel='Answer Start Index', ylabel='Accuracy')\n",
    "plt.ylim(0, 30)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef20d27b-da50-4a15-a694-2777cdfc92e1",
   "metadata": {},
   "source": [
    "### LongFormer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b77ea6c7-ac40-48c3-913e-c39f9f03cc4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_context_train = pd.read_csv(\"df_context.csv\")\n",
    "df_lf_context = pd.read_csv(\"longformer-large-4096-finetuned-triviaqa_3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b7f5a344-77b0-4246-9d71-a452da6da8a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/_x/8vptzk216z9d2gt2lh5spy9m0000gn/T/ipykernel_13164/450668427.py:12: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_merged['actual_answer'].fillna('',inplace=True)\n",
      "/var/folders/_x/8vptzk216z9d2gt2lh5spy9m0000gn/T/ipykernel_13164/450668427.py:13: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_merged['predicted_answer'].fillna('',inplace=True)\n",
      "/var/folders/_x/8vptzk216z9d2gt2lh5spy9m0000gn/T/ipykernel_13164/450668427.py:14: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_merged['answer_match'].fillna(0,inplace=True)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = [-1,0,150,400,700,1000,5000]\n",
    "df_context_train['answer_start_cat']= pd.cut(df_context_train['start_index'], bins=bins,include_lowest=True,right=False)\n",
    "df_stats_lf_astart_df =  prepare_results(df_context_train, df_lf_context,'answer_start_cat' )\n",
    "df_stats_lf_astart_df['answer_start_cat'] = df_stats_lf_astart_df['answer_start_cat'].astype('str')\n",
    "\n",
    "plt.figure(figsize = (10,5))\n",
    "plt.title('Trend in accuracy of Longformer with answer start index')\n",
    "ax = sns.lineplot(x = 'answer_start_cat',\n",
    "            y = 'answer_match',\n",
    "            data = df_stats_lf_astart_df)\n",
    "\n",
    "ax.set(xlabel='Answer start index', ylabel='Accuracy')\n",
    "plt.ylim(30, 60)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb49edda",
   "metadata": {},
   "source": [
    "## Inference"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "930907b2",
   "metadata": {},
   "source": [
    "It can be inferred that while Longformer in general has a much better accuracy than BERT, both these models perform poorly when the answer is located towards the end of the context."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "151ca194",
   "metadata": {},
   "source": [
    "**Next steps**:\n",
    "\n",
    "1. This is merely an observation on a small sample. To confirm the actual trend, we need to obtain results on the entire dataset without any sampling.\n",
    "\n",
    "2. We also need to vary the language model heads used. How does the accuracy change if we use the CausalLM head (causal language modeling) instead of the question answering head, and pass prompts instead of question-context pairs?\n",
    "\n",
    "3. We need to try bigger datasets and bigger language models.\n",
    "\n",
    "These steps will help arrive at a more reliable observation about the contextual retrievability of large language models. But we do have a certain degree of confidence based on the initial observations that language models lack the ability of inferring from contexts irrespective of the location of the information."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95b7e188",
   "metadata": {},
   "source": [
    "## References"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "57ba24a0",
   "metadata": {},
   "source": [
    "1. Lost in the Middle: How Language Models Use Long Contexts (https://www-cs.stanford.edu/~nfliu/papers/lost-in-the-middle.arxiv2023.pdf)\n",
    "\n",
    "2. Know What You Don't Know: Unanswerable Questions for SQuAD (https://arxiv.org/abs/1806.03822)\n",
    "\n",
    "3. HOTPOTQA: A Dataset for Diverse, Explainable Multi-hop Question Answering (https://nlp.stanford.edu/pubs/yang2018hotpotqa.pdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f68ab71",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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