HF-QuestionAnswering.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"machine_shape":"hm","authorship_tag":"ABX9TyNPIg5wM6Ruvhj8bmEq/Nim"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"TPU"},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qDDYeK7Bn_eL","executionInfo":{"status":"ok","timestamp":1693787581411,"user_tz":420,"elapsed":15755,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"7d0bf59a-8a6e-4b17-ecf9-9b14c423718f"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (1.3.0)\n","Requirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.23.5)\n","Requirement already satisfied: scipy>=1.5.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.10.1)\n","Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (1.3.2)\n","Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn) (3.2.0)\n"]}],"source":["!pip install datasets transformers sentencepiece accelerate -q\n","!pip install scikit-learn --upgrade"]},{"cell_type":"code","source":["from datasets import get_dataset_config_names\n","\n","domains=get_dataset_config_names(\"subjqa\")\n","domains"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qIx6jhvQot-j","executionInfo":{"status":"ok","timestamp":1693787583490,"user_tz":420,"elapsed":2083,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"5ed917f7-7bd8-47e0-dfc8-92a9e0fe0ad9"},"execution_count":2,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['books', 'electronics', 'grocery', 'movies', 'restaurants', 'tripadvisor']"]},"metadata":{},"execution_count":2}]},{"cell_type":"code","source":["from datasets import load_dataset\n","subjqa=load_dataset(\"subjqa\",name='electronics')"],"metadata":{"id":"Vcaiu5shoiS3","executionInfo":{"status":"ok","timestamp":1693787584893,"user_tz":420,"elapsed":1406,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":3,"outputs":[]},{"cell_type":"code","source":["print(subjqa['train']['answers'][1])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2aQWCzkhpEhT","executionInfo":{"status":"ok","timestamp":1693787584893,"user_tz":420,"elapsed":4,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"0d4b24d0-bd95-4dd0-b4a1-8cb187f4aa4a"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["{'text': ['Bass is weak as expected', 'Bass is weak as expected, even with EQ adjusted up'], 'answer_start': [1302, 1302], 'answer_subj_level': [1, 1], 'ans_subj_score': [0.5083333253860474, 0.5083333253860474], 'is_ans_subjective': [True, True]}\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","\n","dfs={split: dset.to_pandas() for split,dset in subjqa.flatten().items()}\n","\n","for split,df in dfs.items():\n","    print(f\"Number of questions in {split}: {df['id'].nunique()}\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rVEUkXsnpwO9","executionInfo":{"status":"ok","timestamp":1693787585103,"user_tz":420,"elapsed":212,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"9b416085-ec41-41e6-8e48-4f76336d30f8"},"execution_count":5,"outputs":[{"output_type":"stream","name":"stdout","text":["Number of questions in train: 1295\n","Number of questions in test: 358\n","Number of questions in validation: 255\n"]}]},{"cell_type":"code","source":["qa_cols=['title','question','answers.text','answers.answer_start','context']\n","sample_df=dfs['train'][qa_cols].sample(2,random_state=7)\n","sample_df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":112},"id":"XnEIoqkxsOqL","executionInfo":{"status":"ok","timestamp":1693787585104,"user_tz":420,"elapsed":8,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"b9c6c225-b539-4d8b-ebc3-f96a36007013"},"execution_count":6,"outputs":[{"output_type":"execute_result","data":{"text/plain":["           title                        question                answers.text  \\\n","791   B005DKZTMG  Does the keyboard lightweight?  [this keyboard is compact]   \n","1159  B00AAIPT76             How is the battery?                          []   \n","\n","     answers.answer_start                                            context  \n","791                 [215]  I really like this keyboard.  I give it 4 star...  \n","1159                   []  I bought this after the first spare gopro batt...  "],"text/html":["\n","  <div id=\"df-64eb71ed-7f57-4ea8-9349-5778da409837\" 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>title</th>\n","      <th>question</th>\n","      <th>answers.text</th>\n","      <th>answers.answer_start</th>\n","      <th>context</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>791</th>\n","      <td>B005DKZTMG</td>\n","      <td>Does the keyboard lightweight?</td>\n","      <td>[this keyboard is compact]</td>\n","      <td>[215]</td>\n","      <td>I really like this keyboard.  I give it 4 star...</td>\n","    </tr>\n","    <tr>\n","      <th>1159</th>\n","      <td>B00AAIPT76</td>\n","      <td>How is the battery?</td>\n","      <td>[]</td>\n","      <td>[]</td>\n","      <td>I bought this after the first spare gopro batt...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64eb71ed-7f57-4ea8-9349-5778da409837')\"\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-64eb71ed-7f57-4ea8-9349-5778da409837 button.colab-df-convert');\n","      buttonEl.style.display =\n","        google.colab.kernel.accessAllowed ? 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plt\n","\n","counts={}\n","question_types=['What','How','Is','Does','Do','Was','Where','Why']\n","\n","for q in question_types:\n","    counts[q]=dfs['train']['question'].str.startswith(q).value_counts()[True]\n","\n","pd.Series(counts).sort_values().plot.barh()\n","plt.title(\"Frequency of Question Types\")\n","plt.show()\n","\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":452},"id":"H3wBkt2xvwEF","executionInfo":{"status":"ok","timestamp":1693787585463,"user_tz":420,"elapsed":365,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"5d305a07-6b2a-49d2-abd1-2eae6445ad96"},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAjwAAAGzCAYAAAArAc0KAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAA8AElEQVR4nO3de1wWZf7/8fcNwi3IyQMKKCiKZ8VUzMz0BrU85WonyyjFDt8t9bvalpW2pdQaltuu5e6m1aa1aa520GpXzcONqRlqSmqamWmyZZ7loIgC1+8Pv8yvOzxgCTcMr+fjMY+H9zXXzHyuGeR+M/fM3A5jjBEAAICN+Xi7AAAAgPJG4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AFQae3evVs33HCDQkND5XA4tGjRIm+XVG727dsnh8OhOXPmeLsUwJYIPICkOXPmyOFwnHd6/PHHvV1etTVixAht27ZNU6ZM0T//+U8lJCRctP/Ro0c1fvx4tWzZUjVr1lSdOnXUt29f/fvf/66gii9t3rx5mj59urfLkCSlp6df8Of+5xNQ1dXwdgFAZfL0008rNjbWo61du3ZeqqZ6y8/P1/r16/XEE09ozJgxl+y/a9cu9e7dW4cPH9bIkSOVkJCgEydOaO7cubrxxhv12GOPaerUqRVQ+cXNmzdP27dv17hx4zzaGzdurPz8fPn5+VVYLa1bt9Y///lPj7YJEyYoKChITzzxRIXVAVQEAg/wE/3797/kWYQSp0+flr+/v3x8OFFaHg4fPixJCgsLu2Tfs2fP6tZbb9Xx48f1ySefqGvXrta8hx56SMnJyXruuefUuXNn3XbbbeVV8q/icDhUs2bNCt1mgwYNdNddd3m0TZ06VfXq1SvVDlR1/KYGyqDk1P/8+fP1hz/8QQ0bNlRgYKBycnIkSRkZGerXr59CQ0MVGBgol8uldevWlVrP2rVr1aVLF9WsWVPNmjXTrFmzNHnyZI+PDC52LYfD4dDkyZM92r7//nvdc889atCggZxOp9q2bavXX3/9vPUvWLBAU6ZMUaNGjVSzZk317t1b33zzTantZGRkaMCAAapdu7Zq1aql+Ph4vfjii5Kk2bNny+FwaMuWLaWWe/bZZ+Xr66vvv//+ovtzy5Yt6t+/v0JCQhQUFKTevXvrs88+s+ZPnjxZjRs3liSNHz9eDodDTZo0ueD63n33XW3fvl2PP/64R9iRJF9fX82aNUthYWGaNGmS1V7yMea+ffvOu6/S09NL7ZNLHePc3FyNGzdOTZo0kdPpVP369XX99ddr8+bNkqTExET9+9//1nfffWd9VFQyrgsd91WrVqlHjx6qVauWwsLCNHjwYO3cudOjT8nP0DfffKOUlBSFhYUpNDRUI0eO1KlTpy643y7FGKMmTZpo8ODBpeadPn1aoaGh+u1vf+ux3/71r39p4sSJioiIUK1atfSb3/xGWVlZpZa/EvsTuByc4QF+Ijs7W0eOHPFoq1evnvXvZ555Rv7+/nrkkUdUUFAgf39/rVq1Sv3791fnzp01adIk+fj4aPbs2erVq5fWrFmjq6++WpK0bds23XDDDQoPD9fkyZNVWFioSZMmqUGDBr+43oMHD+qaa66Rw+HQmDFjFB4eriVLlujee+9VTk5OqY9Npk6dKh8fHz3yyCPKzs7W888/r+TkZGVkZFh9li9frhtvvFGRkZEaO3asIiIitHPnTn300UcaO3asbr31Vo0ePVpz585Vx44dPdY/d+5cJSYmqmHDhhes+csvv1SPHj0UEhKiRx99VH5+fpo1a5YSExO1evVqde3aVTfffLPCwsL00EMPadiwYRowYICCgoIuuM4PP/xQkjR8+PDzzg8NDdXgwYP1xhtvaM+ePWrWrNmldq2Hsh7jBx54QO+8847GjBmjNm3a6OjRo1q7dq127typTp066YknnlB2drb++9//6i9/+YskXXRcK1asUP/+/dW0aVNNnjxZ+fn5mjFjhrp3767NmzeXCoFDhw5VbGys0tLStHnzZr322muqX7++nnvuucsabwmHw6G77rpLzz//vI4dO6Y6depY8z788EPl5OSUOhM0ZcoUORwOPfbYYzp06JCmT5+uPn36KDMzUwEBAVd0fwKXxQAws2fPNpLOOxljjNvtNpJM06ZNzalTp6zliouLTfPmzU3fvn1NcXGx1X7q1CkTGxtrrr/+eqttyJAhpmbNmua7776z2nbs2GF8fX3NT/8r7t2710gys2fPLlWnJDNp0iTr9b333msiIyPNkSNHPPrdcccdJjQ01Kq1pP7WrVubgoICq9+LL75oJJlt27YZY4wpLCw0sbGxpnHjxub48eMe6/zp+IYNG2aioqJMUVGR1bZ58+YL1v1TQ4YMMf7+/mbPnj1W2w8//GCCg4NNz549S+2HadOmXXR9xhhz1VVXmdDQ0Iv2+fOf/2wkmQ8++MAY8/+P+d69ez36lewrt9ttjLm8YxwaGmpGjx590ToGDhxoGjduXKr9fMf9qquuMvXr1zdHjx612r744gvj4+Njhg8fbrVNmjTJSDL33HOPxzpvuukmU7du3YvW83Nt27Y1LpfLer1r1y4jybz88sse/X7zm9+YJk2aWPukZL81bNjQ5OTkWP0WLFhgJJkXX3zRGHPl9ydQVnykBfzE3/72Ny1fvtxj+qkRI0ZYf6VKUmZmpnbv3q0777xTR48e1ZEjR3TkyBGdPHlSvXv31ieffKLi4mIVFRVp2bJlGjJkiGJiYqzlW7durb59+/6iWo0xevfddzVo0CAZY6xtHzlyRH379lV2dnapU/8jR46Uv7+/9bpHjx6SpG+//VbSuY+a9u7dq3HjxpW6duanH7sNHz5cP/zwg9xut9U2d+5cBQQE6JZbbrlgzUVFRfr44481ZMgQNW3a1GqPjIzUnXfeqbVr11ofE16O3NxcBQcHX7RPyfzc3NzLWndZj7F07nqjjIwM/fDDD5c9hp87cOCAMjMzlZKS4nFmJT4+Xtdff73+85//lFrmgQce8Hjdo0cPHT169Bft0xItWrRQ165dNXfuXKvt2LFjWrJkiZKTk0vdwTV8+HCPY3HrrbcqMjLSqtdb+xPgIy3gJ66++uqLXrT88zu4du/eLelcELqQ7OxsFRQUKD8/X82bNy81v2XLlud987qUw4cP68SJE3rllVf0yiuvnLfPoUOHPF7/NGxJUu3atSVJx48flyTt2bNH0qXvTLv++usVGRmpuXPnqnfv3iouLtbbb7+twYMHXzR4HD58WKdOnVLLli1LzWvdurWKi4uVlZWltm3bXnT7PxccHFzqo8ifKwk69evXv6x1l/UY165dW88//7xGjBih6Ohode7cWQMGDNDw4cM9wl1Zfffdd5J0wX21bNkynTx5UrVq1bLaL3Z8Q0JCLruGEsOHD9eYMWP03XffqXHjxlq4cKHOnj2ru+++u1Tfn/+MOxwOxcXFWddKeWt/AgQe4DL89OyOJOsv0WnTpumqq6467zJBQUEqKCgo8zYu9MyToqKi8277rrvuuuCbR3x8vMdrX1/f8/YzxpS5vpL13HnnnXr11Vf197//XevWrdMPP/zgtTt72rRpo8zMTO3fv7/Um36JrVu3SpL1Znm5+/lSx1g6dw1Njx499P777+vjjz/WtGnT9Nxzz+m9995T//79L3tcl+tKHd+fu+OOO/TQQw9p7ty5mjhxot566y0lJCScN4xdSlXan7AXAg/wK5Rc/BoSEqI+ffpcsF94eLgCAgKsv25/ateuXR6vS/4qP3HihEd7yV/8P11ncHCwioqKLrrty1Eynu3bt19yncOHD9cLL7ygDz/8UEuWLFF4ePglP54LDw9XYGBgqTFL0ldffSUfHx9FR0dfdt2DBg3SvHnz9Oabb+oPf/hDqfk5OTlavHixOnXqZAWesu7nsh7jEpGRkRo1apRGjRqlQ4cOqVOnTpoyZYr1Bl3Wh/iV3KV2oX1Vr149j7M75alOnToaOHCg5s6dq+TkZK1bt+6CD0/8+c+4MUbffPONFb6v9P4EyopreIBfoXPnzmrWrJn+9Kc/KS8vr9T8kmfJ+Pr6qm/fvlq0aJH2799vzd+5c6eWLVvmsUxISIjq1aunTz75xKP973//u8drX19f3XLLLdYt2Rfa9uXo1KmTYmNjNX369FJB4OdnCeLj4xUfH6/XXntN7777ru644w7VqHHxv6F8fX11ww03aPHixR63gx88eFDz5s3Tdddd94s+ernlllvUtm1bTZ06VZs2bfKYV1xcrAcffFDHjx/3eJheyRvvT/dzUVFRqY8Hy3qMi4qKlJ2d7TGvfv36ioqK8jjDV6tWrVL9zicyMlJXXXWV3njjDY9jsX37dn388ccaMGDAJddxJd19993asWOHxo8fL19fX91xxx3n7ffmm296XCf1zjvv6MCBA1ZAudL7EygrzvAAv4KPj49ee+019e/fX23bttXIkSPVsGFDff/993K73QoJCbFumU5NTdXSpUvVo0cPjRo1SoWFhZoxY4batm1rfdxS4r777tPUqVN13333KSEhQZ988om+/vrrUtufOnWq3G63unbtqvvvv19t2rTRsWPHtHnzZq1YsULHjh277PG8/PLLGjRokK666iqNHDlSkZGR+uqrr/Tll1+WCmfDhw/XI488Ikll/jjrj3/8o5YvX67rrrtOo0aNUo0aNTRr1iwVFBTo+eefv6x6S/j5+endd99Vr169dN1113k8aXnevHnavHmzJk6cqJtvvtlapm3btrrmmms0YcIE65br+fPnq7CwsNQ+Kcsxzs3NVaNGjXTrrbeqQ4cOCgoK0ooVK7Rx40a98MIL1vo6d+6sf/3rX/r973+vLl26KCgoSIMGDTrvuKZNm6b+/furW7duuvfee63b0kNDQ0s9j6m8DRw4UHXr1tXChQvVv3//C14LVadOHesYHDx4UNOnT1dcXJzuv/9+SVd+fwJl5s1bxIDKouQW5Y0bN553fskttwsXLjzv/C1btpibb77Z1K1b1zidTtO4cWMzdOhQs3LlSo9+q1evNp07dzb+/v6madOmZubMmdYtxT916tQpc++995rQ0FATHBxshg4dag4dOlTqtnRjjDl48KAZPXq0iY6ONn5+fiYiIsL07t3bvPLKK5es/0K3wK9du9Zcf/31Jjg42NSqVcvEx8ebGTNmlBr3gQMHjK+vr2nRosV598uFbN682fTt29cEBQWZwMBAk5SUZD799NPz1laW29JLHD582Dz88MMmLi7O+Pv7W48W+Mc//nHe/nv27DF9+vQxTqfTNGjQwEycONEsX77c47b0Epc6xgUFBWb8+PGmQ4cO1n7r0KGD+fvf/+6xnry8PHPnnXeasLAwI8m6Rf1Cx2LFihWme/fuJiAgwISEhJhBgwaZHTt2ePQp+Rk6fPiwR/uFbr2/mJ/flv5To0aNMpLMvHnzSs0r+Rl7++23zYQJE0z9+vVNQECAGThwoMejGEpcqf0JlJXDmF95NRuAX2Xy5MlKTU391ReWesORI0cUGRmpp556Sk8++aS3yyll27Zt6tGjh6Kjo7V27VqFhoZ6u6Qq7aGHHtI//vEP/fjjjwoMDPSYl56erqSkJC1cuFC33nqrlyoELoxreAD8YnPmzFFRUdF5b0+uDNq3b6/Fixdr9+7dGjJkiM6cOePtkqqs06dP66233tItt9xSKuwAVQHX8AC4bKtWrdKOHTs0ZcoUDRky5KLfc+VtLpdLp0+f9nYZVdahQ4e0YsUKvfPOOzp69KjGjh3r7ZKAX4TAA+CyPf300/r000/VvXt3zZgxw9vloBzt2LFDycnJql+/vl566aULPjsHqOy4hgcAANge1/AAAADbI/AAAADbq/bX8BQXF+uHH35QcHBwmR/5DgAAvMsYo9zcXEVFRcnH59Lnb6p94Pnhhx9+0Xf3AAAA78vKylKjRo0u2a/aB57g4GBJ53bYL/kOHwAAUPFycnIUHR1tvY9fSrUPPCUfY4WEhBB4AACoYsp6OQoXLQMAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANur9g8eLNFu0jL5OAO9XQYAALaxb+pAb5dg4QwPAACwPQIPAACwPQIPAACwPQIPAACwPQIPAACwPQIPAACwPQIPAACwvQoLPCkpKRoyZEip9vT0dDkcDp04caKiSgEAANUMZ3gAAIDtVbrA8+6776pt27ZyOp1q0qSJXnjhBWveX//6V7Vr1856vWjRIjkcDs2cOdNq69Onj/7whz9UaM0AAKByq1SB5/PPP9fQoUN1xx13aNu2bZo8ebKefPJJzZkzR5Lkcrm0Y8cOHT58WJK0evVq1atXT+np6ZKks2fPav369UpMTLzgNgoKCpSTk+MxAQAAe6vQwPPRRx8pKCjIY+rfv781/89//rN69+6tJ598Ui1atFBKSorGjBmjadOmSZLatWunOnXqaPXq1ZLOXf/z8MMPW683bNigs2fP6tprr71gDWlpaQoNDbWm6OjochwxAACoDCo08CQlJSkzM9Njeu2116z5O3fuVPfu3T2W6d69u3bv3q2ioiI5HA717NlT6enpOnHihHbs2KFRo0apoKBAX331lVavXq0uXbooMPDCXwI6YcIEZWdnW1NWVla5jRcAAFQOFfpt6bVq1VJcXJxH23//+9/LWkdiYqJeeeUVrVmzRh07dlRISIgVglavXi2Xy3XR5Z1Op5xO52XXDgAAqq5KdQ1P69attW7dOo+2devWqUWLFvL19ZX0/6/jWbhwoXWtTmJiolasWKF169Zd9PodAABQPVWqwPPwww9r5cqVeuaZZ/T111/rjTfe0F//+lc98sgjVp/4+HjVrl1b8+bN8wg8ixYtUkFBQamPxAAAACpV4OnUqZMWLFig+fPnq127dnrqqaf09NNPKyUlxerjcDjUo0cPORwOXXfddZLOhaCQkBAlJCSoVq1aXqoeAABUVg5jjPF2Ed6Uk5Nz7m6tcQvk47zwxc4AAODy7Js6sNzWXfL+nZ2drZCQkEv2r1RneAAAAMoDgQcAANgegQcAANgegQcAANhehT54sDLbntq3TBc9AQCAqoczPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPZqeLuAyqLdpGXycQZ6uwxUMfumDvR2CQCAMuAMDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsD0CDwAAsL1KEXgcDocWLVrk7TIAAIBNXdHAM3PmTAUHB6uwsNBqy8vLk5+fnxITEz36pqeny+FwaM+ePVdk2ykpKRoyZMgVWRcAALCXKxp4kpKSlJeXp02bNllta9asUUREhDIyMnT69Gmr3e12KyYmRs2aNbuSJQAAAJRyRQNPy5YtFRkZqfT0dKstPT1dgwcPVmxsrD777DOP9qSkJOv1kSNHdNNNNykwMFDNmzfXBx98YM0rKirSvffeq9jYWAUEBKhly5Z68cUXrfmTJ0/WG2+8ocWLF8vhcMjhcHjUAAAAqrcrfg1PUlKS3G639drtdisxMVEul8tqz8/PV0ZGhkfgSU1N1dChQ7V161YNGDBAycnJOnbsmCSpuLhYjRo10sKFC7Vjxw499dRTmjhxohYsWCBJeuSRRzR06FD169dPBw4c0IEDB3Tttdeet76CggLl5OR4TAAAwN7KJfCsW7dOhYWFys3N1ZYtW+RyudSzZ0/rrMv69etVUFDgEXhSUlI0bNgwxcXF6dlnn1VeXp42bNggSfLz81NqaqoSEhIUGxur5ORkjRw50go8QUFBCggIkNPpVEREhCIiIuTv73/e+tLS0hQaGmpN0dHRV3oXAACASuaKB57ExESdPHlSGzdu1Jo1a9SiRQuFh4fL5XJZ1/Gkp6eradOmiomJsZaLj4+3/l2rVi2FhITo0KFDVtvf/vY3de7cWeHh4QoKCtIrr7yi/fv3X3Z9EyZMUHZ2tjVlZWX9ugEDAIBK74p/W3pcXJwaNWokt9ut48ePy+VySZKioqIUHR2tTz/9VG63W7169fJYzs/Pz+O1w+FQcXGxJGn+/Pl65JFH9MILL6hbt24KDg7WtGnTlJGRcdn1OZ1OOZ3OXzg6AABQFV3xwCOd+1grPT1dx48f1/jx4632nj17asmSJdqwYYMefPDBMq9v3bp1uvbaazVq1Cir7ee3s/v7+6uoqOjXFw8AAGynXB48mJSUpLVr1yozM9M6wyNJLpdLs2bN0pkzZzyu37mU5s2ba9OmTVq2bJm+/vprPfnkk9q4caNHnyZNmmjr1q3atWuXjhw5orNnz16x8QAAgKqt3AJPfn6+4uLi1KBBA6vd5XIpNzfXun29rH7729/q5ptv1u23366uXbvq6NGjHmd7JOn+++9Xy5YtlZCQoPDwcK1bt+6KjQcAAFRtDmOM8XYR3pSTk3Pubq1xC+TjDPR2Oahi9k0d6O0SAKBaKnn/zs7OVkhIyCX7V4rv0gIAAChPBB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB75fLgwapoe2rfMl3lDQAAqh7O8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANur4e0CKot2k5bJxxno7TIqxL6pA71dAgAAFYozPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPYIPAAAwPaqZOBJSUnRkCFDvF0GAACoIqpk4AEAALgcVT7wvPPOO2rfvr0CAgJUt25d9enTRydPnvR2WQAAoBKp0k9aPnDggIYNG6bnn39eN910k3Jzc7VmzRoZYy64TEFBgQoKCqzXOTk5FVEqAADwoiofeAoLC3XzzTercePGkqT27dtfdJm0tDSlpqZWRHkAAKCSqNIfaXXo0EG9e/dW+/btddttt+nVV1/V8ePHL7rMhAkTlJ2dbU1ZWVkVVC0AAPCWKh14fH19tXz5ci1ZskRt2rTRjBkz1LJlS+3du/eCyzidToWEhHhMAADA3qp04JEkh8Oh7t27KzU1VVu2bJG/v7/ef/99b5cFAAAqkSp9DU9GRoZWrlypG264QfXr11dGRoYOHz6s1q1be7s0AABQiVTpwBMSEqJPPvlE06dPV05Ojho3bqwXXnhB/fv393ZpAACgEqmSgWfOnDnWv5cuXeq9QgAAQJVQ5a/hAQAAuBQCDwAAsD0CDwAAsD0CDwAAsD0CDwAAsL0qeZdWedie2penLgMAYFOc4QEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZXw9sFVBbtJi2TjzPQ22X8KvumDvR2CQAAVEqc4QEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZH4AEAALZXKQNPSkqKHA6HHA6H/Pz81KBBA11//fV6/fXXVVxc7O3yAABAFVMpA48k9evXTwcOHNC+ffu0ZMkSJSUlaezYsbrxxhtVWFjo7fIAAEAVUmkDj9PpVEREhBo2bKhOnTpp4sSJWrx4sZYsWaI5c+ZIkvbv36/BgwcrKChIISEhGjp0qA4ePHjR9RYUFCgnJ8djAgAA9lZpA8/59OrVSx06dNB7772n4uJiDR48WMeOHdPq1au1fPlyffvtt7r99tsvuo60tDSFhoZaU3R0dAVVDwAAvKXKfZdWq1attHXrVq1cuVLbtm3T3r17rdDy5ptvqm3bttq4caO6dOly3uUnTJig3//+99brnJwcQg8AADZXpc7wSJIxRg6HQzt37lR0dLRHWGnTpo3CwsK0c+fOCy7vdDoVEhLiMQEAAHurcoFn586dio2N9XYZAACgCqlSgWfVqlXatm2bbrnlFrVu3VpZWVnKysqy5u/YsUMnTpxQmzZtvFglAACobCrtNTwFBQX68ccfVVRUpIMHD2rp0qVKS0vTjTfeqOHDh8vHx0ft27dXcnKypk+frsLCQo0aNUoul0sJCQneLh8AAFQilTbwLF26VJGRkapRo4Zq166tDh066KWXXtKIESPk43PuxNTixYv1v//7v+rZs6d8fHzUr18/zZgxw8uVAwCAysZhjDHeLsKbcnJyzt2ePm6BfJyB3i7nV9k3daC3SwAAoEKUvH9nZ2eX6QakKnUNDwAAwC9B4AEAALZH4AEAALZH4AEAALZXae/SqmjbU/vy1GUAAGyKMzwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2CDwAAMD2ani7gMqi3aRl8nEGlvt29k0dWO7bAAAAnjjDAwAAbI/AAwAAbI/AAwAAbI/AAwAAbI/AAwAAbI/AAwAAbI/AAwAAbK/cAk9KSoocDoccDof8/PzUoEEDXX/99Xr99ddVXFxcXpsFAAAopVzP8PTr108HDhzQvn37tGTJEiUlJWns2LG68cYbVVhYWJ6bBgAAsJRr4HE6nYqIiFDDhg3VqVMnTZw4UYsXL9aSJUs0Z84cSdL+/fs1ePBgBQUFKSQkREOHDtXBgwc91rN48WJ16tRJNWvWVNOmTZWammoFJmOMJk+erJiYGDmdTkVFRel3v/tdeQ4LAABUMRV+DU+vXr3UoUMHvffeeyouLtbgwYN17NgxrV69WsuXL9e3336r22+/3eq/Zs0aDR8+XGPHjtWOHTs0a9YszZkzR1OmTJEkvfvuu/rLX/6iWbNmaffu3Vq0aJHat29/we0XFBQoJyfHYwIAAPbmle/SatWqlbZu3aqVK1dq27Zt2rt3r6KjoyVJb775ptq2bauNGzeqS5cuSk1N1eOPP64RI0ZIkpo2bapnnnlGjz76qCZNmqT9+/crIiJCffr0kZ+fn2JiYnT11VdfcNtpaWlKTU2tkHECAIDKwSt3aRlj5HA4tHPnTkVHR1thR5LatGmjsLAw7dy5U5L0xRdf6Omnn1ZQUJA13X///Tpw4IBOnTql2267Tfn5+WratKnuv/9+vf/++xe9PmjChAnKzs62pqysrHIfLwAA8C6vnOHZuXOnYmNjy9Q3Ly9Pqampuvnmm0vNq1mzpqKjo7Vr1y6tWLFCy5cv16hRozRt2jStXr1afn5+pZZxOp1yOp2/egwAAKDqqPDAs2rVKm3btk0PPfSQGjVqpKysLGVlZVlneXbs2KETJ06oTZs2kqROnTpp165diouLu+A6AwICNGjQIA0aNEijR49Wq1attG3bNnXq1KlCxgQAACq3cg08BQUF+vHHH1VUVKSDBw9q6dKlSktL04033qjhw4fLx8dH7du3V3JysqZPn67CwkKNGjVKLpdLCQkJkqSnnnpKN954o2JiYnTrrbfKx8dHX3zxhbZv364//vGPmjNnjoqKitS1a1cFBgbqrbfeUkBAgBo3blyeQwMAAFVIuV7Ds3TpUkVGRqpJkybq16+f3G63XnrpJS1evFi+vr5yOBxavHixateurZ49e6pPnz5q2rSp/vWvf1nr6Nu3rz766CN9/PHH6tKli6655hr95S9/sQJNWFiYXn31VXXv3l3x8fFasWKFPvzwQ9WtW7c8hwYAAKoQhzHGeLsIb8rJyVFoaKiixy2QjzOw3Le3b+rAct8GAAB2V/L+nZ2drZCQkEv257u0AACA7RF4AACA7RF4AACA7RF4AACA7XnlwYOV0fbUvmW66AkAAFQ9nOEBAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2V8PbBVQW7SYtk48z8Bcvv2/qwCtYDQAAuJI4wwMAAGyPwAMAAGyPwAMAAGyPwAMAAGyPwAMAAGyPwAMAAGyv3AOPw+HQokWLynszAAAAF1TmwDNz5kwFBwersLDQasvLy5Ofn58SExM9+qanp8vhcGjPnj1XrFAAAIBfqsyBJykpSXl5edq0aZPVtmbNGkVERCgjI0OnT5+22t1ut2JiYtSsWbMrW+3/OXPmTLmsFwAA2FOZA0/Lli0VGRmp9PR0qy09PV2DBw9WbGysPvvsM4/2pKQk6/WRI0d00003KTAwUM2bN9cHH3zgse7t27erf//+CgoKUoMGDXT33XfryJEj1vzExESNGTNG48aNU7169dS3b98yLQcAACBd5jU8SUlJcrvd1mu3263ExES5XC6rPT8/XxkZGR6BJzU1VUOHDtXWrVs1YMAAJScn69ixY5KkEydOqFevXurYsaM2bdqkpUuX6uDBgxo6dKjHtt944w35+/tr3bp1mjlzZpmX+7mCggLl5OR4TAAAwN4u67u0kpKSNG7cOBUWFio/P19btmyRy+XS2bNnNXPmTEnS+vXrVVBQ4BF4UlJSNGzYMEnSs88+q5deekkbNmxQv3799Ne//lUdO3bUs88+a/V//fXXFR0dra+//lotWrSQJDVv3lzPP/+81eePf/xjmZb7ubS0NKWmpl7OsAEAQBV3WWd4EhMTdfLkSW3cuFFr1qxRixYtFB4eLpfLZV3Hk56erqZNmyomJsZaLj4+3vp3rVq1FBISokOHDkmSvvjiC7ndbgUFBVlTq1atJMnjoufOnTt71FLW5X5uwoQJys7OtqasrKzL2QUAAKAKuqwzPHFxcWrUqJHcbreOHz8ul8slSYqKilJ0dLQ+/fRTud1u9erVy2M5Pz8/j9cOh0PFxcWSzt3pNWjQID333HOlthcZGWn9u1atWh7zyrrczzmdTjmdzkuMFAAA2MllBR7p3Mda6enpOn78uMaPH2+19+zZU0uWLNGGDRv04IMPlnl9nTp10rvvvqsmTZqoRo2yl/NLlwMAANXPZT94MCkpSWvXrlVmZqZ1hkeSXC6XZs2apTNnznhcv3Mpo0eP1rFjxzRs2DBt3LhRe/bs0bJlyzRy5EgVFRVd8eUAAED184sCT35+vuLi4tSgQQOr3eVyKTc317p9vayioqK0bt06FRUV6YYbblD79u01btw4hYWFycfnwuX90uUAAED14zDGGG8X4U05OTkKDQ1V9LgF8nEG/uL17Js68ApWBQAALqbk/Ts7O1shISGX7M+pEAAAYHsEHgAAYHsEHgAAYHsEHgAAYHs8wOb/bE/tW6aLngAAQNXDGR4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7BB4AAGB7NbxdQGXRbtIy+TgDy9R339SB5VwNAAC4kjjDAwAAbI/AAwAAbI/AAwAAbI/AAwAAbI/AAwAAbI/AAwAAbK9SBx6Hw6FFixZ5uwwAAFDFVUjgmTlzpoKDg1VYWGi15eXlyc/PT4mJiR5909PT5XA4tGfPnoooDQAAVAMVEniSkpKUl5enTZs2WW1r1qxRRESEMjIydPr0aavd7XYrJiZGzZo1q4jSAABANVAhgadly5aKjIxUenq61Zaenq7BgwcrNjZWn332mUd7UlKS9frIkSO66aabFBgYqObNm+uDDz6QJBljFBcXpz/96U8e28rMzJTD4dA333xTvoMCAABVRoVdw5OUlCS32229drvdSkxMlMvlstrz8/OVkZHhEXhSU1M1dOhQbd26VQMGDFBycrKOHTsmh8Ohe+65R7Nnz/bYzuzZs9WzZ0/FxcWdt46CggLl5OR4TAAAwN4qNPCsW7dOhYWFys3N1ZYtW+RyudSzZ0/rzM/69etVUFDgEXhSUlI0bNgwxcXF6dlnn1VeXp42bNhgzdu1a5f1+uzZs5o3b57uueeeC9aRlpam0NBQa4qOji6/QQMAgEqhwgJPYmKiTp48qY0bN2rNmjVq0aKFwsPD5XK5rOt40tPT1bRpU8XExFjLxcfHW/+uVauWQkJCdOjQIUlSVFSUBg4cqNdff12S9OGHH6qgoEC33XbbBeuYMGGCsrOzrSkrK6ucRgwAACqLCgs8cXFxatSokdxut9xut1wul6RzoSU6Olqffvqp3G63evXq5bGcn5+fx2uHw6Hi4mLr9X333af58+crPz9fs2fP1u23367AwAt/67nT6VRISIjHBAAA7K1Cn8OTlJSk9PR0paene9yO3rNnTy1ZskQbNmzw+DirLAYMGKBatWrp5Zdf1tKlSy/6cRYAAKieKjzwrF27VpmZmdYZHklyuVyaNWuWzpw5c9mBx9fXVykpKZowYYKaN2+ubt26XemyAQBAFVfhgSc/P19xcXFq0KCB1e5yuZSbm2vdvn657r33Xp05c0YjR468kuUCAACbqFGRG2vSpImMMaXaGzdufN7287WdOHGiVNv3338vPz8/DR8+/IrUCQAA7KVCA8+VVlBQoMOHD2vy5Mm67bbbPM4aAQAAlKjUXx56KW+//bYaN26sEydO6Pnnn/d2OQAAoJKq0oEnJSVFRUVF+vzzz9WwYUNvlwMAACqpKh14AAAAyoLAAwAAbK9KX7R8JW1P7ctTlwEAsCnO8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANsj8AAAANur4e0CKot2k5bJxxlYqn3f1IFeqAYAAFxJnOEBAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2R+ABAAC2V6GBZ+bMmQoODlZhYaHVlpeXJz8/PyUmJnr0TU9Pl8Ph0J49eyqyRAAAYEMVGniSkpKUl5enTZs2WW1r1qxRRESEMjIydPr0aavd7XYrJiZGzZo1q8gSAQCADVVo4GnZsqUiIyOVnp5utaWnp2vw4MGKjY3VZ5995tGelJSkf/7zn0pISFBwcLAiIiJ055136tChQ1a/48ePKzk5WeHh4QoICFDz5s01e/bsihwWAACo5Cr8Gp6kpCS53W7rtdvtVmJiolwul9Wen5+vjIwMJSUl6ezZs3rmmWf0xRdfaNGiRdq3b59SUlKs5Z988knt2LFDS5Ys0c6dO/Xyyy+rXr16F9x+QUGBcnJyPCYAAGBvFf7VEklJSRo3bpwKCwuVn5+vLVu2yOVy6ezZs5o5c6Ykaf369SooKFBSUpJiYmKsZZs2baqXXnpJXbp0UV5enoKCgrR//3517NhRCQkJkqQmTZpcdPtpaWlKTU0tt/EBAIDKp8LP8CQmJurkyZPauHGj1qxZoxYtWig8PFwul8u6jic9PV1NmzZVTEyMPv/8cw0aNEgxMTEKDg6Wy+WSJO3fv1+S9OCDD2r+/Pm66qqr9Oijj+rTTz+96PYnTJig7Oxsa8rKyir3MQMAAO+q8MATFxenRo0aye12y+12WwEmKipK0dHR+vTTT+V2u9WrVy+dPHlSffv2VUhIiObOnauNGzfq/ffflySdOXNGktS/f3999913euihh/TDDz+od+/eeuSRRy64fafTqZCQEI8JAADYm1eew5OUlKT09HSlp6d73I7es2dPLVmyRBs2bFBSUpK++uorHT16VFOnTlWPHj3UqlUrjwuWS4SHh2vEiBF66623NH36dL3yyisVOBoAAFDZVfg1PNK5wDN69GidPXvWOsMjSS6XS2PGjNGZM2eUlJSkGjVqyN/fXzNmzNADDzyg7du365lnnvFY11NPPaXOnTurbdu2Kigo0EcffaTWrVtX9JAAAEAl5rUzPPn5+YqLi1ODBg2sdpfLpdzcXOv29fDwcM2ZM0cLFy5UmzZtNHXqVP3pT3/yWJe/v78mTJig+Ph49ezZU76+vpo/f35FDwkAAFRiDmOM8XYR3pSTk6PQ0FBFj1sgH2dgqfn7pg70QlUAAOBiSt6/s7Ozy3Q9Lt+lBQAAbI/AAwAAbI/AAwAAbI/AAwAAbI/AAwAAbM8rz+GpjLan9uWpywAA2BRneAAAgO0ReAAAgO0ReAAAgO0ReAAAgO0ReAAAgO0ReAAAgO0ReAAAgO0ReAAAgO0ReAAAgO1V+yctG2MkSTk5OV6uBAAAlFXJ+3bJ+/ilVPvAc/ToUUlSdHS0lysBAACXKzc3V6GhoZfsV+0DT506dSRJ+/fvL9MOq+pycnIUHR2trKysavPdYdVtzNVtvBJjrg5jrm7jlarfmC93vMYY5ebmKioqqkzrr/aBx8fn3GVMoaGh1eIHqkRISEi1Gq9U/cZc3cYrMebqoLqNV6p+Y76c8V7OiQouWgYAALZH4AEAALZX7QOP0+nUpEmT5HQ6vV1Khahu45Wq35ir23glxlwdVLfxStVvzOU9Xocp6/1cAAAAVVS1P8MDAADsj8ADAABsj8ADAABsj8ADAABsj8ADAABsr1oHnr/97W9q0qSJatasqa5du2rDhg3eLukX++STTzRo0CBFRUXJ4XBo0aJFHvONMXrqqacUGRmpgIAA9enTR7t37/boc+zYMSUnJyskJERhYWG69957lZeXV4GjKLu0tDR16dJFwcHBql+/voYMGaJdu3Z59Dl9+rRGjx6tunXrKigoSLfccosOHjzo0Wf//v0aOHCgAgMDVb9+fY0fP16FhYUVOZQyefnllxUfH289gbRbt25asmSJNd9OY72QqVOnyuFwaNy4cVabncY9efJkORwOj6lVq1bWfDuN9ae+//573XXXXapbt64CAgLUvn17bdq0yZpvt99dTZo0KXWcHQ6HRo8eLcl+x7moqEhPPvmkYmNjFRAQoGbNmumZZ57x+MLPCjvGppqaP3++8ff3N6+//rr58ssvzf3332/CwsLMwYMHvV3aL/Kf//zHPPHEE+a9994zksz777/vMX/q1KkmNDTULFq0yHzxxRfmN7/5jYmNjTX5+flWn379+pkOHTqYzz77zKxZs8bExcWZYcOGVfBIyqZv375m9uzZZvv27SYzM9MMGDDAxMTEmLy8PKvPAw88YKKjo83KlSvNpk2bzDXXXGOuvfZaa35hYaFp166d6dOnj9myZYv5z3/+Y+rVq2cmTJjgjSFd1AcffGD+/e9/m6+//trs2rXLTJw40fj5+Znt27cbY+w11vPZsGGDadKkiYmPjzdjx4612u007kmTJpm2bduaAwcOWNPhw4et+XYaa4ljx46Zxo0bm5SUFJORkWG+/fZbs2zZMvPNN99Yfez2u+vQoUMex3j58uVGknG73cYY+x3nKVOmmLp165qPPvrI7N271yxcuNAEBQWZF1980epTUce42gaeq6++2owePdp6XVRUZKKiokxaWpoXq7oyfh54iouLTUREhJk2bZrVduLECeN0Os3bb79tjDFmx44dRpLZuHGj1WfJkiXG4XCY77//vsJq/6UOHTpkJJnVq1cbY86Nz8/PzyxcuNDqs3PnTiPJrF+/3hhzLiT6+PiYH3/80erz8ssvm5CQEFNQUFCxA/gFateubV577TXbjzU3N9c0b97cLF++3LhcLivw2G3ckyZNMh06dDjvPLuNtcRjjz1mrrvuugvOrw6/u8aOHWuaNWtmiouLbXmcBw4caO655x6PtptvvtkkJycbYyr2GFfLj7TOnDmjzz//XH369LHafHx81KdPH61fv96LlZWPvXv36scff/QYb2hoqLp27WqNd/369QoLC1NCQoLVp0+fPvLx8VFGRkaF13y5srOzJUl16tSRJH3++ec6e/asx5hbtWqlmJgYjzG3b99eDRo0sPr07dtXOTk5+vLLLyuw+stTVFSk+fPn6+TJk+rWrZutxypJo0eP1sCBAz3GJ9nzGO/evVtRUVFq2rSpkpOTtX//fkn2HKskffDBB0pISNBtt92m+vXrq2PHjnr11Vet+Xb/3XXmzBm99dZbuueee+RwOGx5nK+99lqtXLlSX3/9tSTpiy++0Nq1a9W/f39JFXuMq+W3pR85ckRFRUUePzCS1KBBA3311Vdeqqr8/Pjjj5J03vGWzPvxxx9Vv359j/k1atRQnTp1rD6VVXFxscaNG6fu3burXbt2ks6Nx9/fX2FhYR59fz7m8+2TknmVzbZt29StWzedPn1aQUFBev/999WmTRtlZmbabqwl5s+fr82bN2vjxo2l5tntGHft2lVz5sxRy5YtdeDAAaWmpqpHjx7avn277cZa4ttvv9XLL7+s3//+95o4caI2btyo3/3ud/L399eIESNs/7tr0aJFOnHihFJSUiTZ72dakh5//HHl5OSoVatW8vX1VVFRkaZMmaLk5GRJFfv+VC0DD+xl9OjR2r59u9auXevtUspVy5YtlZmZqezsbL3zzjsaMWKEVq9e7e2yyk1WVpbGjh2r5cuXq2bNmt4up9yV/MUrSfHx8eratasaN26sBQsWKCAgwIuVlZ/i4mIlJCTo2WeflSR17NhR27dv18yZMzVixAgvV1f+/vGPf6h///6KiorydinlZsGCBZo7d67mzZuntm3bKjMzU+PGjVNUVFSFH+Nq+ZFWvXr15OvrW+rK94MHDyoiIsJLVZWfkjFdbLwRERE6dOiQx/zCwkIdO3asUu+TMWPG6KOPPpLb7VajRo2s9oiICJ05c0YnTpzw6P/zMZ9vn5TMq2z8/f0VFxenzp07Ky0tTR06dNCLL75oy7FK5z7GOXTokDp16qQaNWqoRo0aWr16tV566SXVqFFDDRo0sOW4S4SFhalFixb65ptvbHuMIyMj1aZNG4+21q1bWx/l2fl313fffacVK1bovvvus9rseJzHjx+vxx9/XHfccYfat2+vu+++Ww899JDS0tIkVewxrpaBx9/fX507d9bKlSuttuLiYq1cuVLdunXzYmXlIzY2VhERER7jzcnJUUZGhjXebt266cSJE/r888+tPqtWrVJxcbG6du1a4TVfijFGY8aM0fvvv69Vq1YpNjbWY37nzp3l5+fnMeZdu3Zp//79HmPetm2bx3+k5cuXKyQkpNQv4cqouLhYBQUFth1r7969tW3bNmVmZlpTQkKCkpOTrX/bcdwl8vLytGfPHkVGRtr2GHfv3r3U4yS+/vprNW7cWJI9f3eVmD17turXr6+BAwdabXY8zqdOnZKPj2fU8PX1VXFxsaQKPsa/4uLrKm3+/PnG6XSaOXPmmB07dpj/+Z//MWFhYR5Xvlclubm5ZsuWLWbLli1Gkvnzn/9stmzZYr777jtjzLnb/sLCwszixYvN1q1bzeDBg89721/Hjh1NRkaGWbt2rWnevHmlvbXzwQcfNKGhoSY9Pd3jFs9Tp05ZfR544AETExNjVq1aZTZt2mS6detmunXrZs0vub3zhhtuMJmZmWbp0qUmPDy8Ut7e+fjjj5vVq1ebvXv3mq1bt5rHH3/cOBwO8/HHHxtj7DXWi/npXVrG2GvcDz/8sElPTzd79+4169atM3369DH16tUzhw4dMsbYa6wlNmzYYGrUqGGmTJlidu/ebebOnWsCAwPNW2+9ZfWx2+8uY87dFRwTE2Mee+yxUvPsdpxHjBhhGjZsaN2W/t5775l69eqZRx991OpTUce42gYeY4yZMWOGiYmJMf7+/ubqq682n332mbdL+sXcbreRVGoaMWKEMebcrX9PPvmkadCggXE6naZ3795m165dHus4evSoGTZsmAkKCjIhISFm5MiRJjc31wujubTzjVWSmT17ttUnPz/fjBo1ytSuXdsEBgaam266yRw4cMBjPfv27TP9+/c3AQEBpl69eubhhx82Z8+ereDRXNo999xjGjdubPz9/U14eLjp3bu3FXaMsddYL+bngcdO47799ttNZGSk8ff3Nw0bNjS33367x/No7DTWn/rwww9Nu3btjNPpNK1atTKvvPKKx3y7/e4yxphly5YZSaXGYYz9jnNOTo4ZO3asiYmJMTVr1jRNmzY1TzzxhMct9BV1jB3G/ORxhwAAADZULa/hAQAA1QuBBwAA2B6BBwAA2B6BBwAA2B6BBwAA2B6BBwAA2B6BBwAA2B6BBwAA2B6BBwAA2B6BBwAA2B6BBwAA2N7/A2v6WbNWcbqGAAAAAElFTkSuQmCC\n"},"metadata":{}}]},{"cell_type":"code","source":["for question_type in ['How','What','Is']:\n","    for question in (\n","        dfs['train'][dfs['train'].question.str.startswith(question_type)]\n","        .sample(n=3,random_state=42)['question']):\n","        print(question)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"TqINpuMgwRou","executionInfo":{"status":"ok","timestamp":1693787585464,"user_tz":420,"elapsed":6,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"326719c7-0678-4f12-bd07-469df9610566"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["How is the camera?\n","How do you like the control?\n","How fast is the charger?\n","What is direction?\n","What is the quality of the construction of the bag?\n","What is your impression of the product?\n","Is this how zoom works?\n","Is sound clear?\n","Is it a wireless keyboard?\n"]}]},{"cell_type":"markdown","source":["#Extracting Answers from Text\n","\n","- Frame the supervised learning problem\n","- Tokenize and encode text for QA tasks\n","- Deal with long passages that exceed a model's maximum context size"],"metadata":{"id":"7lr6YuO3yTM7"}},{"cell_type":"markdown","source":["### Span Classification\n","\n","- Most common way to extract answers from text is by framing the porblem as a span classification task, where the start and end tokens of an answer span act as the labels that a model needs to predict\n","\n","\n","\n","![image.png](data:image/png;base64,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)"],"metadata":{"id":"4aDfiVGIyiuN"}},{"cell_type":"code","source":["from transformers import  AutoTokenizer\n","\n","model_ckpt=\"deepset/minilm-uncased-squad2\"\n","tokenizer=AutoTokenizer.from_pretrained(model_ckpt)\n"],"metadata":{"id":"U6l70pr1w_su","executionInfo":{"status":"ok","timestamp":1693787586460,"user_tz":420,"elapsed":999,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["question=\"How much music can this hold?\"\n","context=\"\"\"\n","An MP# is about 1MB/minute, so about 6000 hours depending on file size.\n","\"\"\"\n","\n","inputs=tokenizer(question,context,return_tensors='pt')"],"metadata":{"id":"GWf_Iw7xz-cW","executionInfo":{"status":"ok","timestamp":1693787588616,"user_tz":420,"elapsed":2159,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":11,"outputs":[]},{"cell_type":"code","source":["print(tokenizer.decode(inputs['input_ids'][0]))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"T_DEYyLc0Qwq","executionInfo":{"status":"ok","timestamp":1693787592028,"user_tz":420,"elapsed":3416,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"c7f77cf3-c69d-4513-e7f7-7b858124a38d"},"execution_count":12,"outputs":[{"output_type":"stream","name":"stdout","text":["[CLS] how much music can this hold? [SEP] an mp # is about 1mb / minute, so about 6000 hours depending on file size. [SEP]\n"]}]},{"cell_type":"code","source":["import torch\n","from transformers import AutoModelForQuestionAnswering\n","\n","model=AutoModelForQuestionAnswering.from_pretrained(model_ckpt)\n","\n","with torch.no_grad():\n","    outputs=model(**inputs)\n","print(outputs)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9W9OhyD90bVL","executionInfo":{"status":"ok","timestamp":1693787593408,"user_tz":420,"elapsed":1383,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"d98572b8-0654-4bbc-9ba0-8339308eb15a"},"execution_count":13,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at deepset/minilm-uncased-squad2 were not used when initializing BertForQuestionAnswering: ['bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n","- This IS expected if you are initializing BertForQuestionAnswering from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForQuestionAnswering from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"]},{"output_type":"stream","name":"stdout","text":["QuestionAnsweringModelOutput(loss=None, start_logits=tensor([[ 1.7627, -5.2762, -5.7905, -5.3906, -5.5060, -5.8739, -5.2696, -6.5243,\n","          1.7627, -0.4839,  0.2375, -1.1819, -2.1969,  3.1938,  3.5504, -3.9401,\n","         -4.7159, -2.6179, -5.1319, -1.4217,  3.7794,  4.5681, -0.5047, -3.6367,\n","         -5.4666, -2.3854, -3.6333, -4.3608,  1.7627]]), end_logits=tensor([[ 1.9853, -5.8393, -5.3206, -5.3707, -5.7119, -5.9227, -5.4298, -4.9016,\n","          1.9852, -4.4776, -1.6492,  0.0102, -5.1159, -3.5019, -2.6105, -1.0012,\n","         -4.1588,  3.6553,  0.2192, -3.9306, -3.4069,  1.2676,  5.1932, -0.6512,\n","         -5.5073, -3.4533,  0.9701,  0.8371,  1.9852]]), hidden_states=None, attentions=None)\n"]}]},{"cell_type":"code","source":["start_logits=outputs.start_logits\n","end_logits=outputs.end_logits"],"metadata":{"id":"Uvy2s0AR05U0","executionInfo":{"status":"ok","timestamp":1693787593408,"user_tz":420,"elapsed":5,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["print(f\"Input IDs shape: {inputs.input_ids.size()}\")\n","print(f\"Start logits shape: {start_logits.size()}\")\n","print(f\"End logits shape: {end_logits.size()}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Qp0vTBWb1RHO","executionInfo":{"status":"ok","timestamp":1693787593408,"user_tz":420,"elapsed":5,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"196b8ed1-4a7c-4ebd-af16-dc27ebf71f16"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["Input IDs shape: torch.Size([1, 29])\n","Start logits shape: torch.Size([1, 29])\n","End logits shape: torch.Size([1, 29])\n"]}]},{"cell_type":"code","source":["import torch\n","\n","start_idx=torch.argmax(start_logits)\n","end_idx=torch.argmax(end_logits)+1\n","answer_span=inputs[\"input_ids\"][0][start_idx:end_idx]\n","answer=tokenizer.decode(answer_span)\n","print(f\"Question:{question}\")\n","print(f\"Answwr:{answer}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MDedB55T1jJr","executionInfo":{"status":"ok","timestamp":1693787593408,"user_tz":420,"elapsed":3,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"45cd4d97-8569-4f71-8c96-2dd0f0d09341"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["Question:How much music can this hold?\n","Answwr:6000 hours\n"]}]},{"cell_type":"code","source":["from transformers import pipeline\n","\n","pipe=pipeline(\"question-answering\",model=model,tokenizer=tokenizer)\n","pipe(question=question,context=context,topk=3)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9c4JDWNr20Og","executionInfo":{"status":"ok","timestamp":1693787594883,"user_tz":420,"elapsed":1477,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"949f37c2-d94c-434e-ad30-e99f70b8df0e"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/transformers/pipelines/question_answering.py:326: UserWarning: topk parameter is deprecated, use top_k instead\n","  warnings.warn(\"topk parameter is deprecated, use top_k instead\", UserWarning)\n"]},{"output_type":"execute_result","data":{"text/plain":["[{'score': 0.34934380650520325,\n","  'start': 38,\n","  'end': 48,\n","  'answer': '6000 hours'},\n"," {'score': 0.15876197814941406,\n","  'start': 32,\n","  'end': 48,\n","  'answer': 'about 6000 hours'},\n"," {'score': 0.12626370787620544,\n","  'start': 17,\n","  'end': 48,\n","  'answer': '1MB/minute, so about 6000 hours'}]"]},"metadata":{},"execution_count":17}]},{"cell_type":"code","source":["pipe(question=\"Qhy is there no data?\",context=context,handle_impossible_answer=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rKPElqis3gWl","executionInfo":{"status":"ok","timestamp":1693787595242,"user_tz":420,"elapsed":360,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"439ca83b-b33e-4eae-d2c3-b9de30b72e4e"},"execution_count":18,"outputs":[{"output_type":"execute_result","data":{"text/plain":["{'score': 0.987058699131012, 'start': 0, 'end': 0, 'answer': ''}"]},"metadata":{},"execution_count":18}]},{"cell_type":"markdown","source":["- For other tasks like text classification we can truncate long texts under the assumption that enough information was contained in the embedding of the [CLS] token to generate accurate predictions. For QA, however, this strategy is problematic because the answer to a question could lie near the end of the context and thus would be removed by truncation.\n","\n","- Standard way to deal with long text size is to apply a **sliding window **across the inputs, where each window contains a passage of tokens in the models context:\n","\n","\n","\n","\n","![image.png](data:image/png;base64,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)\n","\n","### *How the sliding window creates multiple question-context pairs for long documents- the first bar corresponds to the question, while the second bar is the context captured in each window*"],"metadata":{"id":"jOnvk9C84Es6"}},{"cell_type":"code","source":["example=dfs['train'].iloc[0][['question','context']]\n","tokenized_example=tokenizer(example['question'],example['context'],return_overflowing_tokens=True,max_length=100,stride=25)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dx7MVh223sAC","executionInfo":{"status":"ok","timestamp":1693787595243,"user_tz":420,"elapsed":6,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"6eb546d4-e82c-4e97-a64c-3f92648a79e7"},"execution_count":19,"outputs":[{"output_type":"stream","name":"stderr","text":["Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n"]}]},{"cell_type":"code","source":["for idx, window in enumerate(tokenized_example['input_ids']):\n","    print(f\"Window #{idx} has {len(window)} tokens\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"V8G2YsSf5ylU","executionInfo":{"status":"ok","timestamp":1693787595243,"user_tz":420,"elapsed":4,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"71c3ad35-6375-4bf6-b123-c2c57487c0d1"},"execution_count":20,"outputs":[{"output_type":"stream","name":"stdout","text":["Window #0 has 100 tokens\n","Window #1 has 88 tokens\n"]}]},{"cell_type":"code","source":["for window in tokenized_example['input_ids']:\n","    print(f\"{tokenizer.decode(window)}. \\n\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"fVC9jPB-5_r2","executionInfo":{"status":"ok","timestamp":1693787595243,"user_tz":420,"elapsed":3,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"8f756e75-c53c-40fd-9eb0-9bec13b3ea01"},"execution_count":21,"outputs":[{"output_type":"stream","name":"stdout","text":["[CLS] how is the bass? [SEP] i have had koss headphones in the past, pro 4aa and qz - 99. the koss portapro is portable and has great bass response. the work great with my android phone and can be \" rolled up \" to be carried in my motorcycle jacket or computer bag without getting crunched. they are very light and do not feel heavy or bear down on your ears even after listening to music with them on all day. the sound is [SEP]. \n","\n","[CLS] how is the bass? [SEP] and do not feel heavy or bear down on your ears even after listening to music with them on all day. the sound is night and day better than any ear - bud could be and are almost as good as the pro 4aa. they are \" open air \" headphones so you cannot match the bass to the sealed types, but it comes close. for $ 32, you cannot go wrong. [SEP]. \n","\n"]}]},{"cell_type":"markdown","source":["## Using Haystack to Build a QA Pipeline\n","\n","- In our simple answer extraction example, we provided both the question and the context to the model. However, in reality our system's users will only provide a question about a product, so we need some way of selecting relevant passages from among all the reviews in our corpus.\n","\n","- One way to do this would be to concatenate all the reviews of a given product together and feed them to the model as a single, long context.\n","\n","    - Although simple, the drawback of this approach is that the context can become extremely long and thereby introduce an unacceptable latency for out users queries.\n","\n","    - To handle this, modern QA systems are typically based on the retriever-reader architecture which has two main components:\n","\n","    1. **Retriever**\n","        Responsible for retrieving relevant documents for a given query.Retriever are usually categorized as sparse or dense. Sparse retrievers use word frequencies to represent each document is then determined by computing an inner product of the vectors.\n","\n","        On the other hand, dense retrivers use encoders liketransformers to represent the query and document as contextualized embeddings (which are dense vectors)\n","\n","        These embeddings encode semantice meaning and allow dense retrievers to imporve search accuracy by understanding the content of the query\n","\n","    2. **Reader**\n","        Responsible for extracting an answer from the documents provided by the retriever.The reader is usually a reading comprehension model althoguh at the end o the chapter we'll see examples of mdoels that can genrate free-form answer"],"metadata":{"id":"y6pQJwD_77Rr"}},{"cell_type":"markdown","source":["- In addition to retriever and reader there are two more components involved when building a QA pipeline with Haystack:\n","\n","    Document store\n","        - A document oriented databae that sotres documents and metadata which are provided to the retriever at query time\n","\n","    Pipeline\n","        - Combines all. the components of a QA system to enable custom query flows, margin documents from multiple retrievers,and more"],"metadata":{"id":"kBiL0owO-qVd"}},{"cell_type":"code","source":["url = \"\"\"https://artifacts.elastic.co/downloads/elasticsearch/\\\n","elasticsearch-7.9.2-linux-x86_64.tar.gz\"\"\"\n","!wget -nc -q {url}\n","!tar -xzf elasticsearch-7.9.2-linux-x86_64.tar.gz"],"metadata":{"id":"6dOl8xuZ671g","executionInfo":{"status":"ok","timestamp":1693787600676,"user_tz":420,"elapsed":5435,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["import os\n","from subprocess import Popen, PIPE, STDOUT\n","\n","# Run Elasticsearch as a background process\n","!chown -R daemon:daemon elasticsearch-7.9.2\n","es_server = Popen(args=['elasticsearch-7.9.2/bin/elasticsearch'],\n","                  stdout=PIPE, stderr=STDOUT, preexec_fn=lambda: os.setuid(1))\n","# Wait until Elasticsearch has started\n","!sleep 30"],"metadata":{"id":"QGReK-yW_xnY","executionInfo":{"status":"ok","timestamp":1693787631572,"user_tz":420,"elapsed":30898,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":23,"outputs":[]},{"cell_type":"code","source":["!curl -X GET \"localhost:9200/?pretty\"\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"E8wTx1YoAJ3E","executionInfo":{"status":"ok","timestamp":1693787631572,"user_tz":420,"elapsed":6,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"0682c2ad-9b4d-4cfd-b62f-7c8a91f66082"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["curl: (7) Failed to connect to localhost port 9200 after 0 ms: Connection refused\n"]}]},{"cell_type":"markdown","source":["## Haystack\n","\n","- FARMReader\n","    - Based on deepset's FARM framework for finetuning and deployign transfromers. Compatible with model trained using hf.\n","\n","\n","- TransformerReader\n","    - Based on the QA Pipeline from HF Transformers. Suitable for running inference only\n","\n","\n","Although both readers handle amodels weights in the same ,way there are some differences in thw way the predictions are converted. to produce answers:\n","\n","- In Transformers, the QA pipileine normalizes the start and end logits with a softmax in each passage.\n","    - This means that it is only meaningful to compare answer scores between answers extracgted from the same passage, where the probabilities sum to 1\n","\n","    - For example an answer score of 0.9 from one passage is nnot necessarily better than a score of 0.8 in another.\n","\n","\n","    - In Farm, the logits are not normalized, so inter-passage answers can be compared more easily\n","\n","- The TransformerReaer sometiemz predicts the same answer twice, but with different scores. This can happen in long contexts if the answer lies across two overlapping windows. In FARM, these duplicates are removed"],"metadata":{"id":"Ot2VFf1JiFIm"}},{"cell_type":"code","source":["from haystack.document_stores import ElasticsearchDocumentStore\n","\n","# Return the document embedding for later use with dense retriever\n","document_store = ElasticsearchDocumentStore(return_embedding=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"jXNTIcmcAP2v","executionInfo":{"status":"error","timestamp":1693787633488,"user_tz":420,"elapsed":1918,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}},"outputId":"5ef4cb2b-1855-4bb8-9016-366ff30c8f93"},"execution_count":25,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:elastic_transport.node_pool:Node <Urllib3HttpNode(http://localhost:9200)> has failed for 1 times in a row, putting on 1 second timeout\n","WARNING:elastic_transport.transport:Retrying request after failure (attempt 0 of 3)\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/elastic_transport/_transport.py\", line 329, in perform_request\n","    meta, raw_data = node.perform_request(\n","  File \"/usr/local/lib/python3.10/dist-packages/elastic_transport/_node/_http_urllib3.py\", line 199, in perform_request\n","    raise err from None\n","elastic_transport.ConnectionError: Connection error caused by: NewConnectionError(<urllib3.connection.HTTPConnection object at 0x7c33ea68ad70>: Failed to establish a new connection: [Errno 111] Connection refused)\n","WARNING:elastic_transport.node_pool:Node <Urllib3HttpNode(http://localhost:9200)> has failed for 2 times in a row, putting on 2 second timeout\n","WARNING:elastic_transport.transport:Retrying request after failure (attempt 1 of 3)\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/elastic_transport/_transport.py\", line 329, in perform_request\n","    meta, raw_data = node.perform_request(\n","  File \"/usr/local/lib/python3.10/dist-packages/elastic_transport/_node/_http_urllib3.py\", line 199, in perform_request\n","    raise err from None\n","elastic_transport.ConnectionError: Connection error caused by: NewConnectionError(<urllib3.connection.HTTPConnection object at 0x7c33ea68b310>: Failed to establish a new connection: [Errno 111] Connection refused)\n","WARNING:elastic_transport.node_pool:Node <Urllib3HttpNode(http://localhost:9200)> has failed for 3 times in a row, putting on 4 second timeout\n","WARNING:elastic_transport.transport:Retrying request after failure (attempt 2 of 3)\n","Traceback (most recent call last):\n","  File \"/usr/local/lib/python3.10/dist-packages/elastic_transport/_transport.py\", line 329, in perform_request\n","    meta, raw_data = node.perform_request(\n","  File \"/usr/local/lib/python3.10/dist-packages/elastic_transport/_node/_http_urllib3.py\", line 199, in perform_request\n","    raise err from None\n","elastic_transport.ConnectionError: Connection error caused by: NewConnectionError(<urllib3.connection.HTTPConnection object at 0x7c33ea68b430>: Failed to establish a new connection: [Errno 111] Connection refused)\n","WARNING:elastic_transport.node_pool:Node <Urllib3HttpNode(http://localhost:9200)> has failed for 4 times in a row, putting on 8 second timeout\n"]},{"output_type":"error","ename":"ConnectionError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mConnectionError\u001b[0m                           Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/haystack/document_stores/elasticsearch/es8.py\u001b[0m in \u001b[0;36m_init_elastic_client\u001b[0;34m(host, port, username, password, api_key_id, api_key, scheme, ca_certs, verify_certs, timeout, use_system_proxy, aws4auth)\u001b[0m\n\u001b[1;32m    287\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mstatus\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 288\u001b[0;31m                     raise ConnectionError(\n\u001b[0m\u001b[1;32m    289\u001b[0m                         \u001b[0;34mf\"Initial connection to Elasticsearch failed. Make sure you run an Elasticsearch instance \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mConnectionError\u001b[0m: Initial connection to Elasticsearch failed. Make sure you run an Elasticsearch instance at `[{'host': 'localhost', 'port': 9200, 'scheme': 'http'}]` and that it has finished the initial ramp up (can take > 30s). Also, make sure you are using the correct credentials if you are using a secured Elasticsearch instance.","\nDuring handling of the above exception, another exception occurred:\n","\u001b[0;31mConnectionError\u001b[0m                           Traceback (most recent call last)","\u001b[0;32m<ipython-input-25-f09a929b7a83>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Return the document embedding for later use with dense retriever\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mdocument_store\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mElasticsearchDocumentStore\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreturn_embedding\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\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/haystack/nodes/base.py\u001b[0m in \u001b[0;36mwrapper_exportable_to_yaml\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m     44\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     45\u001b[0m         \u001b[0;31m# Call the actuall __init__ function with all the arguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m         \u001b[0minit_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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     47\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     48\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mwrapper_exportable_to_yaml\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/haystack/document_stores/elasticsearch/es8.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, host, port, username, password, api_key_id, api_key, aws4auth, index, label_index, search_fields, content_field, name_field, embedding_field, embedding_dim, custom_mapping, excluded_meta_data, analyzer, scheme, ca_certs, verify_certs, recreate_index, create_index, refresh_type, similarity, timeout, return_embedding, duplicate_documents, scroll, skip_missing_embeddings, synonyms, synonym_type, use_system_proxy, batch_size)\u001b[0m\n\u001b[1;32m    148\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_RequestError\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRequestError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m         \u001b[0;31m# Initiate the Elasticsearch client for version 8.x\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m         client = ElasticsearchDocumentStore._init_elastic_client(\n\u001b[0m\u001b[1;32m    151\u001b[0m             \u001b[0mhost\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mhost\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m             \u001b[0mport\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mport\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/haystack/document_stores/elasticsearch/es8.py\u001b[0m in \u001b[0;36m_init_elastic_client\u001b[0;34m(host, port, username, password, api_key_id, api_key, scheme, ca_certs, verify_certs, timeout, use_system_proxy, aws4auth)\u001b[0m\n\u001b[1;32m    292\u001b[0m                     )\n\u001b[1;32m    293\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 294\u001b[0;31m             raise ConnectionError(\n\u001b[0m\u001b[1;32m    295\u001b[0m                 \u001b[0;34mf\"Initial connection to Elasticsearch failed. Make sure you run an Elasticsearch instance at `{hosts}` \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    296\u001b[0m                 \u001b[0;34mf\"and that it has finished the initial ramp up (can take > 30s). Also, make sure you are using the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mConnectionError\u001b[0m: Initial connection to Elasticsearch failed. Make sure you run an Elasticsearch instance at `[{'host': 'localhost', 'port': 9200, 'scheme': 'http'}]` and that it has finished the initial ramp up (can take > 30s). Also, make sure you are using the correct credentials if you are using a secured Elasticsearch instance."]}]},{"cell_type":"markdown","source":["## Dense Passage Retrieval\n","\n","- We've seen that we get almost perfect recall when our sparse retriever returns k=10 documents, but can we do better at smaller values of k?\n","- The advantage of doing so is that we can pass fewer documents to the reader and thereby reduce the overall latency of our QA pipeline\n","\n","\n","- A well known limitation of sparse retrivevers like BM25 is that they can fail to capture the relevant documents if the user query contains terms that dont match exctly those of the review.\n","\n","- One promising alternative is to use dense embeddings to represent the question and document, and the current state of the art in an architecture known as Dense. Passage Retrieveal (DPR)\n","\n","    - The main idea behind is to use two BERT models as encoders for the qeustion and the passage.\n","\n","![image.png](data:image/png;base64,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)\n","\n","#### **DPR's bi-encoder architecture for computing the relevance of a document and query**\n","\n","Where distance Metric could be:\n","- Cosine Similarity, Dot Product Similarity, Euclidean etc.\n"],"metadata":{"id":"3wD2iDRfm1ZD"}},{"cell_type":"code","source":[],"metadata":{"id":"EsQnc9pXmz5_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["if len(document_store.get_all_documents()) or len(document_store.get_all_labels()) > 0:\n","    document_store.delete_documents(\"document\")\n","    document_store.delete_documents(\"label\")"],"metadata":{"id":"V_xZ5nKyAYGM","executionInfo":{"status":"aborted","timestamp":1693787633489,"user_tz":420,"elapsed":4,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from haystack.retriever.sparse import ElasticsearchRetriever\n","\n","es_retriever=ElasticsearchRetriever(document_store=document_store)"],"metadata":{"id":"0no15Tk9epIb","executionInfo":{"status":"aborted","timestamp":1693787633489,"user_tz":420,"elapsed":4,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","for split, df in dfs.items():\n","    # Exclude duplicate reviews\n","    docs = [{\"text\": row[\"context\"],\n","             \"meta\":{\"item_id\": row[\"title\"], \"question_id\": row[\"id\"],\n","                     \"split\": split}}\n","        for _,row in df.drop_duplicates(subset=\"context\").iterrows()]\n","    document_store.write_documents(docs, index=\"document\")\n","\n","print(f\"Loaded {document_store.get_document_count()} documents\")"],"metadata":{"id":"zuu8CHUlAy0t","executionInfo":{"status":"aborted","timestamp":1693787633489,"user_tz":420,"elapsed":4,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","from haystack.retriever.sparse import ElasticsearchRetriever\n","\n","es_retriever = ElasticsearchRetriever(document_store=document_store)\n","\n","\n","item_id = \"B0074BW614\"\n","query = \"Is it good for reading?\"\n","retrieved_docs = es_retriever.retrieve(\n","    query=query, top_k=3, filters={\"item_id\":[item_id], \"split\":[\"train\"]})"],"metadata":{"id":"MnQfUszmBE7N","executionInfo":{"status":"aborted","timestamp":1693787633489,"user_tz":420,"elapsed":4,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from haystack.reader.farm import FARMReader\n","\n","model_ckpt = \"deepset/minilm-uncased-squad2\"\n","max_seq_length, doc_stride = 384, 128\n","reader = FARMReader(model_name_or_path=model_ckpt, progress_bar=False,\n","                    max_seq_len=max_seq_length, doc_stride=doc_stride,\n","                    return_no_answer=True)"],"metadata":{"id":"1d7-izzeC6Gr","executionInfo":{"status":"aborted","timestamp":1693787633489,"user_tz":420,"elapsed":4,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(reader.predict_on_texts(question=question, texts=[context], top_k=1))\n"],"metadata":{"id":"1lwzYgH8EfrU","executionInfo":{"status":"aborted","timestamp":1693787633490,"user_tz":420,"elapsed":4,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["from haystack.pipeline import ExtractiveQAPipeline\n","\n","pipe = ExtractiveQAPipeline(reader, es_retriever)"],"metadata":{"id":"83AB2UGuEp7q","executionInfo":{"status":"aborted","timestamp":1693787633490,"user_tz":420,"elapsed":4,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["n_answers = 3\n","preds = pipe.run(query=query, top_k_retriever=3, top_k_reader=n_answers,\n","                 filters={\"item_id\": [item_id], \"split\":[\"train\"]})\n","\n","print(f\"Question: {preds['query']} \\n\")\n","for idx in range(n_answers):\n","    print(f\"Answer {idx+1}: {preds['answers'][idx]['answer']}\")\n","    print(f\"Review snippet: ...{preds['answers'][idx]['context']}...\")\n","    print(\"\\n\\n\")\n",""],"metadata":{"id":"8SXnquZ2ErUa","executionInfo":{"status":"aborted","timestamp":1693787633490,"user_tz":420,"elapsed":4,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","from haystack.pipeline import Pipeline\n","from haystack.eval import EvalDocuments\n","\n","class EvalRetrieverPipeline:\n","    def __init__(self, retriever):\n","        self.retriever = retriever\n","        self.eval_retriever = EvalDocuments()\n","        pipe = Pipeline()\n","        pipe.add_node(component=self.retriever, name=\"ESRetriever\",\n","                      inputs=[\"Query\"])\n","        pipe.add_node(component=self.eval_retriever, name=\"EvalRetriever\",\n","                      inputs=[\"ESRetriever\"])\n","        self.pipeline = pipe\n","\n","\n","pipe = EvalRetrieverPipeline(es_retriever)"],"metadata":{"id":"B19A2SgDEs6j","executionInfo":{"status":"aborted","timestamp":1693787633491,"user_tz":420,"elapsed":5,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","from haystack import Label\n","\n","labels = []\n","for i, row in dfs[\"test\"].iterrows():\n","    # Metadata used for filtering in the Retriever\n","    meta = {\"item_id\": row[\"title\"], \"question_id\": row[\"id\"]}\n","    # Populate labels for questions with answers\n","    if len(row[\"answers.text\"]):\n","        for answer in row[\"answers.text\"]:\n","            label = Label(\n","                question=row[\"question\"], answer=answer, id=i, origin=row[\"id\"],\n","                meta=meta, is_correct_answer=True, is_correct_document=True,\n","                no_answer=False)\n","            labels.append(label)\n","    # Populate labels for questions without answers\n","    else:\n","        label = Label(\n","            question=row[\"question\"], answer=\"\", id=i, origin=row[\"id\"],\n","            meta=meta, is_correct_answer=True, is_correct_document=True,\n","            no_answer=True)\n","        labels.append(label)\n",""],"metadata":{"id":"qcSm6T19FBCT","executionInfo":{"status":"aborted","timestamp":1693787633491,"user_tz":420,"elapsed":5,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(labels[0])\n"],"metadata":{"id":"NsBE0J9vFHRI","executionInfo":{"status":"aborted","timestamp":1693787633491,"user_tz":420,"elapsed":5,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["document_store.write_labels(labels, index=\"label\")\n","print(f\"\"\"Loaded {document_store.get_label_count(index=\"label\")} \\\n","question-answer pairs\"\"\")\n",""],"metadata":{"id":"r2rgRi2IFI3n","executionInfo":{"status":"aborted","timestamp":1693787633491,"user_tz":420,"elapsed":5,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","labels_agg = document_store.get_all_labels_aggregated(\n","    index=\"label\",\n","    open_domain=True,\n","    aggregate_by_meta=[\"item_id\"]\n",")\n","print(len(labels_agg))"],"metadata":{"id":"Tnx_2aPXFfL2","executionInfo":{"status":"aborted","timestamp":1693787633491,"user_tz":420,"elapsed":5,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["print(labels_agg[109])\n"],"metadata":{"id":"AvkVG7AtFg71","executionInfo":{"status":"aborted","timestamp":1693787633491,"user_tz":420,"elapsed":5,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","def run_pipeline(pipeline, top_k_retriever=10, top_k_reader=4):\n","    for l in labels_agg:\n","        _ = pipeline.pipeline.run(\n","            query=l.question,\n","            top_k_retriever=top_k_retriever,\n","            top_k_reader=top_k_reader,\n","            top_k_eval_documents=top_k_retriever,\n","            labels=l,\n","            filters={\"item_id\": [l.meta[\"item_id\"]], \"split\": [\"test\"]})"],"metadata":{"id":"SnNqPUGNFoGt","executionInfo":{"status":"aborted","timestamp":1693787633491,"user_tz":420,"elapsed":5,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","run_pipeline(pipe, top_k_retriever=3)\n","print(f\"Recall@3: {pipe.eval_retriever.recall:.2f}\")"],"metadata":{"id":"7kcLs5_FFpzg","executionInfo":{"status":"aborted","timestamp":1693787633491,"user_tz":420,"elapsed":5,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","#hide_output\n","def evaluate_retriever(retriever, topk_values = [1,3,5,10,20]):\n","    topk_results = {}\n","\n","    for topk in topk_values:\n","        # Create Pipeline\n","        p = EvalRetrieverPipeline(retriever)\n","        # Loop over each question-answers pair in test set\n","        run_pipeline(p, top_k_retriever=topk)\n","        # Get metrics\n","        topk_results[topk] = {\"recall\": p.eval_retriever.recall}\n","\n","    return pd.DataFrame.from_dict(topk_results, orient=\"index\")\n","\n","\n","es_topk_df = evaluate_retriever(es_retriever)"],"metadata":{"id":"an56zHDUFrKs","executionInfo":{"status":"aborted","timestamp":1693787633492,"user_tz":420,"elapsed":68216,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","def plot_retriever_eval(dfs, retriever_names):\n","    fig, ax = plt.subplots()\n","    for df, retriever_name in zip(dfs, retriever_names):\n","        df.plot(y=\"recall\", ax=ax, label=retriever_name)\n","    plt.xticks(df.index)\n","    plt.ylabel(\"Top-k Recall\")\n","    plt.xlabel(\"k\")\n","    plt.show()\n","\n","plot_retriever_eval([es_topk_df], [\"BM25\"])"],"metadata":{"id":"kviucjg3Fsp9","executionInfo":{"status":"aborted","timestamp":1693787633492,"user_tz":420,"elapsed":68215,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["![image.png](data:image/png;base64,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)"],"metadata":{"id":"TGiexGOXGKs0"}},{"cell_type":"code","source":["\n","#hide_output\n","from haystack.retriever.dense import DensePassageRetriever\n","\n","dpr_retriever = DensePassageRetriever(document_store=document_store,\n","    query_embedding_model=\"facebook/dpr-question_encoder-single-nq-base\",\n","    passage_embedding_model=\"facebook/dpr-ctx_encoder-single-nq-base\",\n","    embed_title=False)"],"metadata":{"id":"geGFNvsMFugv","executionInfo":{"status":"aborted","timestamp":1693787633492,"user_tz":420,"elapsed":68213,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["document_store.update_embeddings(retriever=dpr_retriever)\n"],"metadata":{"id":"Bis8_8XPGMwr","executionInfo":{"status":"aborted","timestamp":1693787633492,"user_tz":420,"elapsed":68212,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","dpr_topk_df = evaluate_retriever(dpr_retriever)\n","plot_retriever_eval([es_topk_df, dpr_topk_df], [\"BM25\", \"DPR\"])"],"metadata":{"id":"59bhc55pGWNS","executionInfo":{"status":"aborted","timestamp":1693787633492,"user_tz":420,"elapsed":68210,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","from farm.evaluation.squad_evaluation import compute_f1, compute_exact\n","\n","pred = \"about 6000 hours\"\n","label = \"6000 hours\"\n","print(f\"EM: {compute_exact(label, pred)}\")\n","print(f\"F1: {compute_f1(label, pred)}\")"],"metadata":{"id":"LmFu4Gk5GYEE","executionInfo":{"status":"aborted","timestamp":1693787633492,"user_tz":420,"elapsed":68209,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","pred = \"about 6000 dollars\"\n","print(f\"EM: {compute_exact(label, pred)}\")\n","print(f\"F1: {compute_f1(label, pred)}\")"],"metadata":{"id":"Sp1FRgrtGaPw","executionInfo":{"status":"aborted","timestamp":1693787633492,"user_tz":420,"elapsed":68208,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["\n","#hide_output\n","from haystack.eval import EvalAnswers\n","\n","def evaluate_reader(reader):\n","    score_keys = ['top_1_em', 'top_1_f1']\n","    eval_reader = EvalAnswers(skip_incorrect_retrieval=False)\n","    pipe = Pipeline()\n","    pipe.add_node(component=reader, name=\"QAReader\", inputs=[\"Query\"])\n","    pipe.add_node(component=eval_reader, name=\"EvalReader\", inputs=[\"QAReader\"])\n","\n","    for l in labels_agg:\n","        doc = document_store.query(l.question,\n","                                   filters={\"question_id\":[l.origin]})\n","        _ = pipe.run(query=l.question, documents=doc, labels=l)\n","\n","    return {k:v for k,v in eval_reader.__dict__.items() if k in score_keys}\n","\n","reader_eval = {}\n","reader_eval[\"Fine-tune on SQuAD\"] = evaluate_reader(reader)"],"metadata":{"id":"X-4f7VtrGbgC","executionInfo":{"status":"aborted","timestamp":1693787633492,"user_tz":420,"elapsed":68206,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":[],"metadata":{"id":"IyQyZuFhGwV_","executionInfo":{"status":"aborted","timestamp":1693787633492,"user_tz":420,"elapsed":68205,"user":{"displayName":"Canberk Kandemir","userId":"18419208864442465365"}}},"execution_count":null,"outputs":[]}]}