{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c8ebdd6d",
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:13.588603Z",
     "iopub.status.busy": "2026-07-24T12:15:13.587967Z",
     "iopub.status.idle": "2026-07-24T12:15:15.372828Z",
     "shell.execute_reply": "2026-07-24T12:15:15.372222Z"
    },
    "papermill": {
     "duration": 1.797184,
     "end_time": "2026-07-24T12:15:15.374546+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:13.577362+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/competitions/smart-mcq-solver-challenge/sample_submission.csv\n",
      "/kaggle/input/competitions/smart-mcq-solver-challenge/train.csv\n",
      "/kaggle/input/competitions/smart-mcq-solver-challenge/test.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session\n",
    "\n",
    "# Use the kagglehub client library to attach Kaggle resources like competitions, datasets, and models to your session\n",
    "# Learn more about kagglehub: https://github.com/Kaggle/kagglehub/blob/main/README.md\n",
    "\n",
    "import kagglehub\n",
    "# kagglehub.dataset_download('<owner>/<dataset-slug>')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec1432a",
   "metadata": {
    "papermill": {
     "duration": 0.00823,
     "end_time": "2026-07-24T12:15:15.391547+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:15.383317+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7ab763c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:15.409561Z",
     "iopub.status.busy": "2026-07-24T12:15:15.408753Z",
     "iopub.status.idle": "2026-07-24T12:15:21.049491Z",
     "shell.execute_reply": "2026-07-24T12:15:21.048757Z"
    },
    "papermill": {
     "duration": 5.65207,
     "end_time": "2026-07-24T12:15:21.051424+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:15.399354+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.2/12.2 MB\u001b[0m \u001b[31m93.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "dask-cuda 26.2.0 requires cuda-core==0.3.*, but you have cuda-core 1.0.1 which is incompatible.\r\n",
      "dask-cuda 26.2.0 requires numba-cuda<0.23.0,>=0.22.1, but you have numba-cuda 0.30.2 which is incompatible.\r\n",
      "distributed-ucxx-cu12 0.48.0 requires numba-cuda[cu12]<0.23.0,>=0.22.1, but you have numba-cuda 0.30.2 which is incompatible.\r\n",
      "cuml-cu12 26.2.0 requires numba<0.62.0,>=0.60.0, but you have numba 0.65.1 which is incompatible.\r\n",
      "cuml-cu12 26.2.0 requires numba-cuda[cu12]<0.23.0,>=0.22.1, but you have numba-cuda 0.30.2 which is incompatible.\r\n",
      "ucxx-cu12 0.48.0 requires numba-cuda[cu12]<0.23.0,>=0.22.1, but you have numba-cuda 0.30.2 which is incompatible.\r\n",
      "cudf-cu12 26.2.1 requires numba<0.62.0,>=0.60.0, but you have numba 0.65.1 which is incompatible.\r\n",
      "cudf-cu12 26.2.1 requires numba-cuda[cu12]<0.23.0,>=0.22.2, but you have numba-cuda 0.30.2 which is incompatible.\u001b[0m\u001b[31m\r\n",
      "\u001b[0mDone!\n"
     ]
    }
   ],
   "source": [
    "!pip install sentence-transformers -q\n",
    "print('Done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2773597f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:21.069328Z",
     "iopub.status.busy": "2026-07-24T12:15:21.068914Z",
     "iopub.status.idle": "2026-07-24T12:15:51.957690Z",
     "shell.execute_reply": "2026-07-24T12:15:51.956802Z"
    },
    "papermill": {
     "duration": 30.908735,
     "end_time": "2026-07-24T12:15:51.968450+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:21.059715+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sentence transformer loaded\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "\n",
    "print(\"Sentence transformer loaded\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebcdb1d3",
   "metadata": {
    "papermill": {
     "duration": 0.012144,
     "end_time": "2026-07-24T12:15:51.991050+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:51.978906+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e7b5ff69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:52.011978Z",
     "iopub.status.busy": "2026-07-24T12:15:52.011243Z",
     "iopub.status.idle": "2026-07-24T12:15:52.501250Z",
     "shell.execute_reply": "2026-07-24T12:15:52.500433Z"
    },
    "papermill": {
     "duration": 0.502307,
     "end_time": "2026-07-24T12:15:52.502873+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:52.000566+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Imports Done\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import re\n",
    "import random\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\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",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.metrics import accuracy_score\n",
    "import wandb\n",
    "seed = 42\n",
    "random.seed(seed)\n",
    "np.random.seed(seed)\n",
    "sns.set_style('whitegrid')\n",
    "print('Imports Done')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c10820f1",
   "metadata": {
    "papermill": {
     "duration": 0.008261,
     "end_time": "2026-07-24T12:15:52.519429+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:52.511168+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Load sentence - BERT Encoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4611d864",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:52.537636Z",
     "iopub.status.busy": "2026-07-24T12:15:52.536799Z",
     "iopub.status.idle": "2026-07-24T12:15:57.168420Z",
     "shell.execute_reply": "2026-07-24T12:15:57.167526Z"
    },
    "papermill": {
     "duration": 4.64266,
     "end_time": "2026-07-24T12:15:57.170107+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:52.527447+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d97c3d06e72d47ddb129d6f65015691f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c5191dc2f1784d41be5834c40c1a4bf1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "121a403994ee4d83b23974788343b1fd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8ea8d698781c4cfeae82ade6a9f3480a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b49c3f55d8c149968d2c36f1e8a17c59",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4097cb6dea1046ac9b9144f3cb2e9370",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "53db24e86619428fbde222b1f0719816",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading weights:   0%|          | 0/103 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "BertModel LOAD REPORT from: sentence-transformers/all-MiniLM-L6-v2\n",
      "Key                     | Status     |  | \n",
      "------------------------+------------+--+-\n",
      "embeddings.position_ids | UNEXPECTED |  | \n",
      "\n",
      "Notes:\n",
      "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2364ee1aa8314d3e8979de059107ce69",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "69075447c1084f33bd2f79316754da49",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "vocab.txt: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d990535a0d2b458e9f684dcd3947d6e8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json: 0.00B [00:00, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a599e911f29f4b1ea31b77fad5f5d06c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "afe3086af9e84270a7ab14ff8c2201f0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "encoder = SentenceTransformer('all-MiniLM-L6-v2')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6732a7f9",
   "metadata": {
    "papermill": {
     "duration": 0.0095,
     "end_time": "2026-07-24T12:15:57.189479+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:57.179979+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Experiment Tracking - WandB Login \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8d253a6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:57.209638Z",
     "iopub.status.busy": "2026-07-24T12:15:57.208916Z",
     "iopub.status.idle": "2026-07-24T12:15:57.400261Z",
     "shell.execute_reply": "2026-07-24T12:15:57.399347Z"
    },
    "papermill": {
     "duration": 0.20294,
     "end_time": "2026-07-24T12:15:57.401893+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:57.198953+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: [wandb.login()] Using explicit session credentials for https://api.wandb.ai.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: No netrc file found, creating one.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33m23f3000653\u001b[0m (\u001b[33m23f3000653-indian-institute-of-technology-madras\u001b[0m) to \u001b[32mhttps://api.wandb.ai\u001b[0m. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ W&B login successful!\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import wandb\n",
    "from kaggle_secrets import UserSecretsClient\n",
    "\n",
    "# Kaggle Secret se key lo — automatically milegi\n",
    "secrets = UserSecretsClient()\n",
    "os.environ[\"WANDB_API_KEY\"] = secrets.get_secret(\"WANDB_API_KEY\")\n",
    "wandb.login(key=os.environ[\"WANDB_API_KEY\"], relogin=True)\n",
    "print(\"✅ W&B login successful!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68ec145f",
   "metadata": {
    "papermill": {
     "duration": 0.010231,
     "end_time": "2026-07-24T12:15:57.422315+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:57.412084+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a7798ac8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:57.444522Z",
     "iopub.status.busy": "2026-07-24T12:15:57.443775Z",
     "iopub.status.idle": "2026-07-24T12:15:57.517449Z",
     "shell.execute_reply": "2026-07-24T12:15:57.516539Z"
    },
    "papermill": {
     "duration": 0.08636,
     "end_time": "2026-07-24T12:15:57.519090+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:57.432730+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv(\"/kaggle/input/competitions/smart-mcq-solver-challenge/train.csv\")\n",
    "test_dt = pd.read_csv(\"/kaggle/input/competitions/smart-mcq-solver-challenge/test.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc61307e",
   "metadata": {
    "papermill": {
     "duration": 0.009187,
     "end_time": "2026-07-24T12:15:57.537593+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:57.528406+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0cbed895",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:57.557674Z",
     "iopub.status.busy": "2026-07-24T12:15:57.557342Z",
     "iopub.status.idle": "2026-07-24T12:15:57.561203Z",
     "shell.execute_reply": "2026-07-24T12:15:57.560548Z"
    },
    "papermill": {
     "duration": 0.015586,
     "end_time": "2026-07-24T12:15:57.562825+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:57.547239+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2000, 8)\n",
      "(500, 7)\n"
     ]
    }
   ],
   "source": [
    "print(train_df.shape)\n",
    "print(test_dt.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7f707318",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:57.582763Z",
     "iopub.status.busy": "2026-07-24T12:15:57.582079Z",
     "iopub.status.idle": "2026-07-24T12:15:57.608877Z",
     "shell.execute_reply": "2026-07-24T12:15:57.608042Z"
    },
    "papermill": {
     "duration": 0.038378,
     "end_time": "2026-07-24T12:15:57.610231+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:57.571853+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>id</th>\n",
       "      <th>prompt</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>answer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Pick the best possible answer: What is Martin ...</td>\n",
       "      <td>Martin Heidegger believes that humans exist wi...</td>\n",
       "      <td>Martin Heidegger believes that humans do not e...</td>\n",
       "      <td>Martin Heidegger does not believe in the exist...</td>\n",
       "      <td>Martin Heidegger believes that the relationshi...</td>\n",
       "      <td>Martin Heidegger believes that time is an illu...</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>What is accelerator-based light-ion fusion?</td>\n",
       "      <td>Accelerator-based light-ion fusion is a techni...</td>\n",
       "      <td>Accelerator-based light-ion fusion is a techni...</td>\n",
       "      <td>Accelerator-based light-ion fusion is a techni...</td>\n",
       "      <td>Accelerator-based light-ion fusion is a techni...</td>\n",
       "      <td>Accelerator-based light-ion fusion is a techni...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Determine the correct option: What is the term...</td>\n",
       "      <td>Blueshifting</td>\n",
       "      <td>Redshifting</td>\n",
       "      <td>Reddening</td>\n",
       "      <td>Whitening</td>\n",
       "      <td>Yellowing</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Select the most accurate option: What is Marti...</td>\n",
       "      <td>Martin Heidegger believes that humans exist wi...</td>\n",
       "      <td>Martin Heidegger believes that humans do not e...</td>\n",
       "      <td>Martin Heidegger does not believe in the exist...</td>\n",
       "      <td>Martin Heidegger believes that the relationshi...</td>\n",
       "      <td>Martin Heidegger believes that time is an illu...</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Identify the correct statement: What is the co...</td>\n",
       "      <td>Simultaneity is relative, meaning that two eve...</td>\n",
       "      <td>Simultaneity is relative, meaning that two eve...</td>\n",
       "      <td>Simultaneity is absolute, meaning that two eve...</td>\n",
       "      <td>Simultaneity is a concept that applies only to...</td>\n",
       "      <td>Simultaneity is a concept that applies only to...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                             prompt  \\\n",
       "0   1  Pick the best possible answer: What is Martin ...   \n",
       "1   2        What is accelerator-based light-ion fusion?   \n",
       "2   3  Determine the correct option: What is the term...   \n",
       "3   4  Select the most accurate option: What is Marti...   \n",
       "4   5  Identify the correct statement: What is the co...   \n",
       "\n",
       "                                                   A  \\\n",
       "0  Martin Heidegger believes that humans exist wi...   \n",
       "1  Accelerator-based light-ion fusion is a techni...   \n",
       "2                                       Blueshifting   \n",
       "3  Martin Heidegger believes that humans exist wi...   \n",
       "4  Simultaneity is relative, meaning that two eve...   \n",
       "\n",
       "                                                   B  \\\n",
       "0  Martin Heidegger believes that humans do not e...   \n",
       "1  Accelerator-based light-ion fusion is a techni...   \n",
       "2                                        Redshifting   \n",
       "3  Martin Heidegger believes that humans do not e...   \n",
       "4  Simultaneity is relative, meaning that two eve...   \n",
       "\n",
       "                                                   C  \\\n",
       "0  Martin Heidegger does not believe in the exist...   \n",
       "1  Accelerator-based light-ion fusion is a techni...   \n",
       "2                                          Reddening   \n",
       "3  Martin Heidegger does not believe in the exist...   \n",
       "4  Simultaneity is absolute, meaning that two eve...   \n",
       "\n",
       "                                                   D  \\\n",
       "0  Martin Heidegger believes that the relationshi...   \n",
       "1  Accelerator-based light-ion fusion is a techni...   \n",
       "2                                          Whitening   \n",
       "3  Martin Heidegger believes that the relationshi...   \n",
       "4  Simultaneity is a concept that applies only to...   \n",
       "\n",
       "                                                   E answer  \n",
       "0  Martin Heidegger believes that time is an illu...      B  \n",
       "1  Accelerator-based light-ion fusion is a techni...      A  \n",
       "2                                          Yellowing      C  \n",
       "3  Martin Heidegger believes that time is an illu...      B  \n",
       "4  Simultaneity is a concept that applies only to...      A  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "efcd3b1c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:57.630644Z",
     "iopub.status.busy": "2026-07-24T12:15:57.630442Z",
     "iopub.status.idle": "2026-07-24T12:15:57.653232Z",
     "shell.execute_reply": "2026-07-24T12:15:57.652246Z"
    },
    "papermill": {
     "duration": 0.034646,
     "end_time": "2026-07-24T12:15:57.654599+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:57.619953+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2000 entries, 0 to 1999\n",
      "Data columns (total 8 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   id      2000 non-null   int64 \n",
      " 1   prompt  2000 non-null   object\n",
      " 2   A       2000 non-null   object\n",
      " 3   B       2000 non-null   object\n",
      " 4   C       2000 non-null   object\n",
      " 5   D       2000 non-null   object\n",
      " 6   E       2000 non-null   object\n",
      " 7   answer  2000 non-null   object\n",
      "dtypes: int64(1), object(7)\n",
      "memory usage: 125.1+ KB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3509e9bf",
   "metadata": {
    "papermill": {
     "duration": 0.009231,
     "end_time": "2026-07-24T12:15:57.673144+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:57.663913+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Sentence - BERT Embedding Baseline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "530dc2ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:15:57.693104Z",
     "iopub.status.busy": "2026-07-24T12:15:57.692543Z",
     "iopub.status.idle": "2026-07-24T12:16:03.140657Z",
     "shell.execute_reply": "2026-07-24T12:16:03.139814Z"
    },
    "papermill": {
     "duration": 5.459612,
     "end_time": "2026-07-24T12:16:03.142061+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:15:57.682449+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0fc32833df24c758193e99ddaea51f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c82b30b7d9204fbab6eb883dc5fa895d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c043991fafd14841b8c0275108ab2287",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e4aa5634d0dc4496a8ee70b195a9431a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d03dfa73eaf4ccd8a29423580f2eb64",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "81d0c47939544f239001d6021b37b865",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Batches:   0%|          | 0/32 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Embeddings created\n"
     ]
    }
   ],
   "source": [
    "train_questions = train_df['prompt'].astype(str).tolist()\n",
    "\n",
    "\n",
    "question_embeddings = encoder.encode(\n",
    "    train_questions,\n",
    "    batch_size=64,\n",
    "    show_progress_bar=True\n",
    ")\n",
    "\n",
    "\n",
    "option_embeddings = {}\n",
    "\n",
    "\n",
    "for col in ['A','B','C','D','E']:\n",
    "\n",
    "    option_embeddings[col] = encoder.encode(\n",
    "        train_df[col].astype(str).tolist(),\n",
    "        batch_size=64,\n",
    "        show_progress_bar=True\n",
    "    )\n",
    "\n",
    "\n",
    "print(\"Embeddings created\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69f021fc",
   "metadata": {
    "papermill": {
     "duration": 0.010027,
     "end_time": "2026-07-24T12:16:03.162539+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:03.152512+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Null Values Check"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ab3261a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:03.183821Z",
     "iopub.status.busy": "2026-07-24T12:16:03.183269Z",
     "iopub.status.idle": "2026-07-24T12:16:03.190044Z",
     "shell.execute_reply": "2026-07-24T12:16:03.189255Z"
    },
    "papermill": {
     "duration": 0.019001,
     "end_time": "2026-07-24T12:16:03.191606+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:03.172605+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id        0\n",
      "prompt    0\n",
      "A         0\n",
      "B         0\n",
      "C         0\n",
      "D         0\n",
      "E         0\n",
      "answer    0\n",
      "dtype: int64\n",
      "answer\n",
      "B    490\n",
      "C    459\n",
      "A    369\n",
      "D    358\n",
      "E    324\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(train_df.isnull().sum())\n",
    "print(train_df['answer'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aebf35f8",
   "metadata": {
    "papermill": {
     "duration": 0.009788,
     "end_time": "2026-07-24T12:16:03.211408+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:03.201620+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Answer Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dad512eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:03.232799Z",
     "iopub.status.busy": "2026-07-24T12:16:03.232491Z",
     "iopub.status.idle": "2026-07-24T12:16:03.437070Z",
     "shell.execute_reply": "2026-07-24T12:16:03.436108Z"
    },
    "papermill": {
     "duration": 0.217195,
     "end_time": "2026-07-24T12:16:03.438672+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:03.221477+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Answer Distribution')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.countplot(x=train_df['answer'])\n",
    "plt.title('Answer Distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e4fce56",
   "metadata": {
    "papermill": {
     "duration": 0.010459,
     "end_time": "2026-07-24T12:16:03.459794+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:03.449335+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Text Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9ac8e8d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:03.482530Z",
     "iopub.status.busy": "2026-07-24T12:16:03.481865Z",
     "iopub.status.idle": "2026-07-24T12:16:03.672504Z",
     "shell.execute_reply": "2026-07-24T12:16:03.671713Z"
    },
    "papermill": {
     "duration": 0.203485,
     "end_time": "2026-07-24T12:16:03.673931+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:03.470446+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BEFORE: Pick the best possible answer: What is Martin Heidegger's view on the relationsh\n",
      "AFTER:  pick the best possible answer what is martin heideggers view on the relationship\n"
     ]
    }
   ],
   "source": [
    "OPTION_COLS = ['A', 'B', 'C', 'D', 'E']   # The 5 answer choices\n",
    "\n",
    "def clean_text(text):\n",
    "    \"\"\"Clean a piece of text: lowercase → remove special chars → trim spaces.\"\"\"\n",
    "    text = str(text).lower()                    # 'Hello' → 'hello'\n",
    "    text = re.sub(r'[^a-z0-9\\s]', '', text)    # Remove !, ?, commas etc.\n",
    "    text = re.sub(r'\\s+', ' ', text).strip()    # Remove double spaces\n",
    "    return text\n",
    "\n",
    "# Apply cleaning to prompt and all 5 option columns\n",
    "train_df['prompt_clean'] = train_df['prompt'].apply(clean_text)\n",
    "test_dt['prompt_clean']  = test_dt['prompt'].apply(clean_text)\n",
    "\n",
    "for col in OPTION_COLS:\n",
    "    train_df[f'{col}_clean'] = train_df[col].apply(clean_text)\n",
    "    test_dt[f'{col}_clean']  = test_dt[col].apply(clean_text)\n",
    "\n",
    "# Show before/after example\n",
    "print('BEFORE:', train_df['prompt'].iloc[0][:80])\n",
    "print('AFTER: ', train_df['prompt_clean'].iloc[0][:80])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2613711",
   "metadata": {
    "papermill": {
     "duration": 0.010332,
     "end_time": "2026-07-24T12:16:03.695061+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:03.684729+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Baseline Models\n",
    "\n",
    "# #TF-IDF Retrieval Baseline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "850cc4c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:03.717874Z",
     "iopub.status.busy": "2026-07-24T12:16:03.717091Z",
     "iopub.status.idle": "2026-07-24T12:16:07.280121Z",
     "shell.execute_reply": "2026-07-24T12:16:07.279078Z"
    },
    "papermill": {
     "duration": 3.576329,
     "end_time": "2026-07-24T12:16:07.282269+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:03.705940+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>id</th>\n",
       "      <th>prompt</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>prompt_clean</th>\n",
       "      <th>A_clean</th>\n",
       "      <th>B_clean</th>\n",
       "      <th>C_clean</th>\n",
       "      <th>D_clean</th>\n",
       "      <th>E_clean</th>\n",
       "      <th>prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Pick the best possible answer: What is the rel...</td>\n",
       "      <td>For every eigenstate of one Hamiltonian, its p...</td>\n",
       "      <td>For every eigenstate of one Hamiltonian, its p...</td>\n",
       "      <td>For every eigenstate of one Hamiltonian, its p...</td>\n",
       "      <td>For every eigenstate of one Hamiltonian, its p...</td>\n",
       "      <td>For every eigenstate of one Hamiltonian, its p...</td>\n",
       "      <td>pick the best possible answer what is the rela...</td>\n",
       "      <td>for every eigenstate of one hamiltonian its pa...</td>\n",
       "      <td>for every eigenstate of one hamiltonian its pa...</td>\n",
       "      <td>for every eigenstate of one hamiltonian its pa...</td>\n",
       "      <td>for every eigenstate of one hamiltonian its pa...</td>\n",
       "      <td>for every eigenstate of one hamiltonian its pa...</td>\n",
       "      <td>[B, A, C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>What is the estimated redshift of CEERS-93316,...</td>\n",
       "      <td>Approximately z = 6.0, corresponding to 1 bill...</td>\n",
       "      <td>Approximately z = 16.7, corresponding to 235.8...</td>\n",
       "      <td>Approximately z = 3.0, corresponding to 5 bill...</td>\n",
       "      <td>Approximately z = 10.0, corresponding to 13 bi...</td>\n",
       "      <td>Approximately z = 13.0, corresponding to 30 bi...</td>\n",
       "      <td>what is the estimated redshift of ceers93316 a...</td>\n",
       "      <td>approximately z 60 corresponding to 1 billion ...</td>\n",
       "      <td>approximately z 167 corresponding to 2358 mill...</td>\n",
       "      <td>approximately z 30 corresponding to 5 billion ...</td>\n",
       "      <td>approximately z 100 corresponding to 13 billio...</td>\n",
       "      <td>approximately z 130 corresponding to 30 billio...</td>\n",
       "      <td>[C, D, E]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Pick the best possible answer: What is the rea...</td>\n",
       "      <td>The sun appears yellowish due to a reflection ...</td>\n",
       "      <td>The longer wavelengths of light, such as red a...</td>\n",
       "      <td>The sun appears yellowish due to the scatterin...</td>\n",
       "      <td>The sun emits a yellow light due to its own sp...</td>\n",
       "      <td>The atmosphere absorbs the shorter wavelengths...</td>\n",
       "      <td>pick the best possible answer what is the reas...</td>\n",
       "      <td>the sun appears yellowish due to a reflection ...</td>\n",
       "      <td>the longer wavelengths of light such as red an...</td>\n",
       "      <td>the sun appears yellowish due to the scatterin...</td>\n",
       "      <td>the sun emits a yellow light due to its own sp...</td>\n",
       "      <td>the atmosphere absorbs the shorter wavelengths...</td>\n",
       "      <td>[D, A, C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>What is the significance of the redshift-dista...</td>\n",
       "      <td>Observations of the redshift-distance relation...</td>\n",
       "      <td>Observations of the redshift-distance relation...</td>\n",
       "      <td>Observations of the redshift-distance relation...</td>\n",
       "      <td>Observations of the redshift-distance relation...</td>\n",
       "      <td>Observations of the redshift-distance relation...</td>\n",
       "      <td>what is the significance of the redshiftdistan...</td>\n",
       "      <td>observations of the redshiftdistance relations...</td>\n",
       "      <td>observations of the redshiftdistance relations...</td>\n",
       "      <td>observations of the redshiftdistance relations...</td>\n",
       "      <td>observations of the redshiftdistance relations...</td>\n",
       "      <td>observations of the redshiftdistance relations...</td>\n",
       "      <td>[A, E, C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>What is the Landau-Lifshitz-Gilbert equation u...</td>\n",
       "      <td>The Landau-Lifshitz-Gilbert equation is a diff...</td>\n",
       "      <td>The Landau-Lifshitz-Gilbert equation is a diff...</td>\n",
       "      <td>The Landau-Lifshitz-Gilbert equation is a diff...</td>\n",
       "      <td>The Landau-Lifshitz-Gilbert equation is a diff...</td>\n",
       "      <td>The Landau-Lifshitz-Gilbert equation is a diff...</td>\n",
       "      <td>what is the landaulifshitzgilbert equation use...</td>\n",
       "      <td>the landaulifshitzgilbert equation is a differ...</td>\n",
       "      <td>the landaulifshitzgilbert equation is a differ...</td>\n",
       "      <td>the landaulifshitzgilbert equation is a differ...</td>\n",
       "      <td>the landaulifshitzgilbert equation is a differ...</td>\n",
       "      <td>the landaulifshitzgilbert equation is a differ...</td>\n",
       "      <td>[C, A, D]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                             prompt  \\\n",
       "0   1  Pick the best possible answer: What is the rel...   \n",
       "1   2  What is the estimated redshift of CEERS-93316,...   \n",
       "2   3  Pick the best possible answer: What is the rea...   \n",
       "3   4  What is the significance of the redshift-dista...   \n",
       "4   5  What is the Landau-Lifshitz-Gilbert equation u...   \n",
       "\n",
       "                                                   A  \\\n",
       "0  For every eigenstate of one Hamiltonian, its p...   \n",
       "1  Approximately z = 6.0, corresponding to 1 bill...   \n",
       "2  The sun appears yellowish due to a reflection ...   \n",
       "3  Observations of the redshift-distance relation...   \n",
       "4  The Landau-Lifshitz-Gilbert equation is a diff...   \n",
       "\n",
       "                                                   B  \\\n",
       "0  For every eigenstate of one Hamiltonian, its p...   \n",
       "1  Approximately z = 16.7, corresponding to 235.8...   \n",
       "2  The longer wavelengths of light, such as red a...   \n",
       "3  Observations of the redshift-distance relation...   \n",
       "4  The Landau-Lifshitz-Gilbert equation is a diff...   \n",
       "\n",
       "                                                   C  \\\n",
       "0  For every eigenstate of one Hamiltonian, its p...   \n",
       "1  Approximately z = 3.0, corresponding to 5 bill...   \n",
       "2  The sun appears yellowish due to the scatterin...   \n",
       "3  Observations of the redshift-distance relation...   \n",
       "4  The Landau-Lifshitz-Gilbert equation is a diff...   \n",
       "\n",
       "                                                   D  \\\n",
       "0  For every eigenstate of one Hamiltonian, its p...   \n",
       "1  Approximately z = 10.0, corresponding to 13 bi...   \n",
       "2  The sun emits a yellow light due to its own sp...   \n",
       "3  Observations of the redshift-distance relation...   \n",
       "4  The Landau-Lifshitz-Gilbert equation is a diff...   \n",
       "\n",
       "                                                   E  \\\n",
       "0  For every eigenstate of one Hamiltonian, its p...   \n",
       "1  Approximately z = 13.0, corresponding to 30 bi...   \n",
       "2  The atmosphere absorbs the shorter wavelengths...   \n",
       "3  Observations of the redshift-distance relation...   \n",
       "4  The Landau-Lifshitz-Gilbert equation is a diff...   \n",
       "\n",
       "                                        prompt_clean  \\\n",
       "0  pick the best possible answer what is the rela...   \n",
       "1  what is the estimated redshift of ceers93316 a...   \n",
       "2  pick the best possible answer what is the reas...   \n",
       "3  what is the significance of the redshiftdistan...   \n",
       "4  what is the landaulifshitzgilbert equation use...   \n",
       "\n",
       "                                             A_clean  \\\n",
       "0  for every eigenstate of one hamiltonian its pa...   \n",
       "1  approximately z 60 corresponding to 1 billion ...   \n",
       "2  the sun appears yellowish due to a reflection ...   \n",
       "3  observations of the redshiftdistance relations...   \n",
       "4  the landaulifshitzgilbert equation is a differ...   \n",
       "\n",
       "                                             B_clean  \\\n",
       "0  for every eigenstate of one hamiltonian its pa...   \n",
       "1  approximately z 167 corresponding to 2358 mill...   \n",
       "2  the longer wavelengths of light such as red an...   \n",
       "3  observations of the redshiftdistance relations...   \n",
       "4  the landaulifshitzgilbert equation is a differ...   \n",
       "\n",
       "                                             C_clean  \\\n",
       "0  for every eigenstate of one hamiltonian its pa...   \n",
       "1  approximately z 30 corresponding to 5 billion ...   \n",
       "2  the sun appears yellowish due to the scatterin...   \n",
       "3  observations of the redshiftdistance relations...   \n",
       "4  the landaulifshitzgilbert equation is a differ...   \n",
       "\n",
       "                                             D_clean  \\\n",
       "0  for every eigenstate of one hamiltonian its pa...   \n",
       "1  approximately z 100 corresponding to 13 billio...   \n",
       "2  the sun emits a yellow light due to its own sp...   \n",
       "3  observations of the redshiftdistance relations...   \n",
       "4  the landaulifshitzgilbert equation is a differ...   \n",
       "\n",
       "                                             E_clean prediction  \n",
       "0  for every eigenstate of one hamiltonian its pa...  [B, A, C]  \n",
       "1  approximately z 130 corresponding to 30 billio...  [C, D, E]  \n",
       "2  the atmosphere absorbs the shorter wavelengths...  [D, A, C]  \n",
       "3  observations of the redshiftdistance relations...  [A, E, C]  \n",
       "4  the landaulifshitzgilbert equation is a differ...  [C, A, D]  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "options = [\"A\",\"B\",\"C\",\"D\",\"E\"]\n",
    "\n",
    "\n",
    "# TF-IDF corpus (saare options)\n",
    "corpus = []\n",
    "\n",
    "for col in options:\n",
    "    corpus.extend(train_df[col].astype(str).tolist())\n",
    "\n",
    "\n",
    "vectorizer = TfidfVectorizer(\n",
    "    lowercase=True,\n",
    "    stop_words=\"english\",\n",
    "    ngram_range=(1,2)\n",
    ")\n",
    "\n",
    "\n",
    "tfidf_options = vectorizer.fit_transform(corpus)\n",
    "\n",
    "\n",
    "# option vectors\n",
    "option_vectors = {}\n",
    "\n",
    "for col in options:\n",
    "    option_vectors[col] = vectorizer.transform(\n",
    "        train_df[col].astype(str)\n",
    "    )\n",
    "\n",
    "\n",
    "\n",
    "def retrieve_top3(question):\n",
    "\n",
    "    q_vec = vectorizer.transform([question])\n",
    "\n",
    "    scores = []\n",
    "\n",
    "    for col in options:\n",
    "\n",
    "        sims = cosine_similarity(\n",
    "            q_vec,\n",
    "            option_vectors[col]\n",
    "        )[0]\n",
    "\n",
    "        scores.append(sims.max())\n",
    "\n",
    "\n",
    "    top3 = np.argsort(scores)[::-1][:3]\n",
    "\n",
    "    return [\n",
    "        options[i] for i in top3\n",
    "    ]\n",
    "\n",
    "\n",
    "\n",
    "# test prediction\n",
    "\n",
    "test_dt[\"prediction\"] = test_dt[\"prompt\"].apply(\n",
    "    retrieve_top3\n",
    ")\n",
    "\n",
    "\n",
    "test_dt.head()\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b5cbd72e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:07.311894Z",
     "iopub.status.busy": "2026-07-24T12:16:07.310899Z",
     "iopub.status.idle": "2026-07-24T12:16:07.317265Z",
     "shell.execute_reply": "2026-07-24T12:16:07.316616Z"
    },
    "papermill": {
     "duration": 0.022437,
     "end_time": "2026-07-24T12:16:07.318956+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:07.296519+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def sbert_predict(question):\n",
    "\n",
    "    q_embedding = encoder.encode([question])\n",
    "\n",
    "\n",
    "    scores=[]\n",
    "\n",
    "\n",
    "    for col in ['A','B','C','D','E']:\n",
    "\n",
    "\n",
    "        sim = cosine_similarity(\n",
    "            q_embedding,\n",
    "            option_embeddings[col]\n",
    "        )\n",
    "\n",
    "\n",
    "        scores.append(sim.max())\n",
    "\n",
    "\n",
    "    top3 = np.argsort(scores)[::-1][:3]\n",
    "\n",
    "\n",
    "    return [\n",
    "        ['A','B','C','D','E'][i]\n",
    "        for i in top3\n",
    "    ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "feabed34",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:07.346788Z",
     "iopub.status.busy": "2026-07-24T12:16:07.346225Z",
     "iopub.status.idle": "2026-07-24T12:16:17.914270Z",
     "shell.execute_reply": "2026-07-24T12:16:17.913631Z"
    },
    "papermill": {
     "duration": 10.583918,
     "end_time": "2026-07-24T12:16:17.916794+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:07.332876+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>id</th>\n",
       "      <th>prompt</th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "      <th>prompt_clean</th>\n",
       "      <th>A_clean</th>\n",
       "      <th>B_clean</th>\n",
       "      <th>C_clean</th>\n",
       "      <th>D_clean</th>\n",
       "      <th>E_clean</th>\n",
       "      <th>prediction</th>\n",
       "      <th>sbert_prediction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Pick the best possible answer: What is the rel...</td>\n",
       "      <td>For every eigenstate of one Hamiltonian, its p...</td>\n",
       "      <td>For every eigenstate of one Hamiltonian, its p...</td>\n",
       "      <td>For every eigenstate of one Hamiltonian, its p...</td>\n",
       "      <td>For every eigenstate of one Hamiltonian, its p...</td>\n",
       "      <td>For every eigenstate of one Hamiltonian, its p...</td>\n",
       "      <td>pick the best possible answer what is the rela...</td>\n",
       "      <td>for every eigenstate of one hamiltonian its pa...</td>\n",
       "      <td>for every eigenstate of one hamiltonian its pa...</td>\n",
       "      <td>for every eigenstate of one hamiltonian its pa...</td>\n",
       "      <td>for every eigenstate of one hamiltonian its pa...</td>\n",
       "      <td>for every eigenstate of one hamiltonian its pa...</td>\n",
       "      <td>[B, A, C]</td>\n",
       "      <td>[B, E, A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>What is the estimated redshift of CEERS-93316,...</td>\n",
       "      <td>Approximately z = 6.0, corresponding to 1 bill...</td>\n",
       "      <td>Approximately z = 16.7, corresponding to 235.8...</td>\n",
       "      <td>Approximately z = 3.0, corresponding to 5 bill...</td>\n",
       "      <td>Approximately z = 10.0, corresponding to 13 bi...</td>\n",
       "      <td>Approximately z = 13.0, corresponding to 30 bi...</td>\n",
       "      <td>what is the estimated redshift of ceers93316 a...</td>\n",
       "      <td>approximately z 60 corresponding to 1 billion ...</td>\n",
       "      <td>approximately z 167 corresponding to 2358 mill...</td>\n",
       "      <td>approximately z 30 corresponding to 5 billion ...</td>\n",
       "      <td>approximately z 100 corresponding to 13 billio...</td>\n",
       "      <td>approximately z 130 corresponding to 30 billio...</td>\n",
       "      <td>[C, D, E]</td>\n",
       "      <td>[B, C, D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Pick the best possible answer: What is the rea...</td>\n",
       "      <td>The sun appears yellowish due to a reflection ...</td>\n",
       "      <td>The longer wavelengths of light, such as red a...</td>\n",
       "      <td>The sun appears yellowish due to the scatterin...</td>\n",
       "      <td>The sun emits a yellow light due to its own sp...</td>\n",
       "      <td>The atmosphere absorbs the shorter wavelengths...</td>\n",
       "      <td>pick the best possible answer what is the reas...</td>\n",
       "      <td>the sun appears yellowish due to a reflection ...</td>\n",
       "      <td>the longer wavelengths of light such as red an...</td>\n",
       "      <td>the sun appears yellowish due to the scatterin...</td>\n",
       "      <td>the sun emits a yellow light due to its own sp...</td>\n",
       "      <td>the atmosphere absorbs the shorter wavelengths...</td>\n",
       "      <td>[D, A, C]</td>\n",
       "      <td>[A, C, D]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>What is the significance of the redshift-dista...</td>\n",
       "      <td>Observations of the redshift-distance relation...</td>\n",
       "      <td>Observations of the redshift-distance relation...</td>\n",
       "      <td>Observations of the redshift-distance relation...</td>\n",
       "      <td>Observations of the redshift-distance relation...</td>\n",
       "      <td>Observations of the redshift-distance relation...</td>\n",
       "      <td>what is the significance of the redshiftdistan...</td>\n",
       "      <td>observations of the redshiftdistance relations...</td>\n",
       "      <td>observations of the redshiftdistance relations...</td>\n",
       "      <td>observations of the redshiftdistance relations...</td>\n",
       "      <td>observations of the redshiftdistance relations...</td>\n",
       "      <td>observations of the redshiftdistance relations...</td>\n",
       "      <td>[A, E, C]</td>\n",
       "      <td>[A, E, C]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>What is the Landau-Lifshitz-Gilbert equation u...</td>\n",
       "      <td>The Landau-Lifshitz-Gilbert equation is a diff...</td>\n",
       "      <td>The Landau-Lifshitz-Gilbert equation is a diff...</td>\n",
       "      <td>The Landau-Lifshitz-Gilbert equation is a diff...</td>\n",
       "      <td>The Landau-Lifshitz-Gilbert equation is a diff...</td>\n",
       "      <td>The Landau-Lifshitz-Gilbert equation is a diff...</td>\n",
       "      <td>what is the landaulifshitzgilbert equation use...</td>\n",
       "      <td>the landaulifshitzgilbert equation is a differ...</td>\n",
       "      <td>the landaulifshitzgilbert equation is a differ...</td>\n",
       "      <td>the landaulifshitzgilbert equation is a differ...</td>\n",
       "      <td>the landaulifshitzgilbert equation is a differ...</td>\n",
       "      <td>the landaulifshitzgilbert equation is a differ...</td>\n",
       "      <td>[C, A, D]</td>\n",
       "      <td>[B, C, D]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id                                             prompt  \\\n",
       "0   1  Pick the best possible answer: What is the rel...   \n",
       "1   2  What is the estimated redshift of CEERS-93316,...   \n",
       "2   3  Pick the best possible answer: What is the rea...   \n",
       "3   4  What is the significance of the redshift-dista...   \n",
       "4   5  What is the Landau-Lifshitz-Gilbert equation u...   \n",
       "\n",
       "                                                   A  \\\n",
       "0  For every eigenstate of one Hamiltonian, its p...   \n",
       "1  Approximately z = 6.0, corresponding to 1 bill...   \n",
       "2  The sun appears yellowish due to a reflection ...   \n",
       "3  Observations of the redshift-distance relation...   \n",
       "4  The Landau-Lifshitz-Gilbert equation is a diff...   \n",
       "\n",
       "                                                   B  \\\n",
       "0  For every eigenstate of one Hamiltonian, its p...   \n",
       "1  Approximately z = 16.7, corresponding to 235.8...   \n",
       "2  The longer wavelengths of light, such as red a...   \n",
       "3  Observations of the redshift-distance relation...   \n",
       "4  The Landau-Lifshitz-Gilbert equation is a diff...   \n",
       "\n",
       "                                                   C  \\\n",
       "0  For every eigenstate of one Hamiltonian, its p...   \n",
       "1  Approximately z = 3.0, corresponding to 5 bill...   \n",
       "2  The sun appears yellowish due to the scatterin...   \n",
       "3  Observations of the redshift-distance relation...   \n",
       "4  The Landau-Lifshitz-Gilbert equation is a diff...   \n",
       "\n",
       "                                                   D  \\\n",
       "0  For every eigenstate of one Hamiltonian, its p...   \n",
       "1  Approximately z = 10.0, corresponding to 13 bi...   \n",
       "2  The sun emits a yellow light due to its own sp...   \n",
       "3  Observations of the redshift-distance relation...   \n",
       "4  The Landau-Lifshitz-Gilbert equation is a diff...   \n",
       "\n",
       "                                                   E  \\\n",
       "0  For every eigenstate of one Hamiltonian, its p...   \n",
       "1  Approximately z = 13.0, corresponding to 30 bi...   \n",
       "2  The atmosphere absorbs the shorter wavelengths...   \n",
       "3  Observations of the redshift-distance relation...   \n",
       "4  The Landau-Lifshitz-Gilbert equation is a diff...   \n",
       "\n",
       "                                        prompt_clean  \\\n",
       "0  pick the best possible answer what is the rela...   \n",
       "1  what is the estimated redshift of ceers93316 a...   \n",
       "2  pick the best possible answer what is the reas...   \n",
       "3  what is the significance of the redshiftdistan...   \n",
       "4  what is the landaulifshitzgilbert equation use...   \n",
       "\n",
       "                                             A_clean  \\\n",
       "0  for every eigenstate of one hamiltonian its pa...   \n",
       "1  approximately z 60 corresponding to 1 billion ...   \n",
       "2  the sun appears yellowish due to a reflection ...   \n",
       "3  observations of the redshiftdistance relations...   \n",
       "4  the landaulifshitzgilbert equation is a differ...   \n",
       "\n",
       "                                             B_clean  \\\n",
       "0  for every eigenstate of one hamiltonian its pa...   \n",
       "1  approximately z 167 corresponding to 2358 mill...   \n",
       "2  the longer wavelengths of light such as red an...   \n",
       "3  observations of the redshiftdistance relations...   \n",
       "4  the landaulifshitzgilbert equation is a differ...   \n",
       "\n",
       "                                             C_clean  \\\n",
       "0  for every eigenstate of one hamiltonian its pa...   \n",
       "1  approximately z 30 corresponding to 5 billion ...   \n",
       "2  the sun appears yellowish due to the scatterin...   \n",
       "3  observations of the redshiftdistance relations...   \n",
       "4  the landaulifshitzgilbert equation is a differ...   \n",
       "\n",
       "                                             D_clean  \\\n",
       "0  for every eigenstate of one hamiltonian its pa...   \n",
       "1  approximately z 100 corresponding to 13 billio...   \n",
       "2  the sun emits a yellow light due to its own sp...   \n",
       "3  observations of the redshiftdistance relations...   \n",
       "4  the landaulifshitzgilbert equation is a differ...   \n",
       "\n",
       "                                             E_clean prediction  \\\n",
       "0  for every eigenstate of one hamiltonian its pa...  [B, A, C]   \n",
       "1  approximately z 130 corresponding to 30 billio...  [C, D, E]   \n",
       "2  the atmosphere absorbs the shorter wavelengths...  [D, A, C]   \n",
       "3  observations of the redshiftdistance relations...  [A, E, C]   \n",
       "4  the landaulifshitzgilbert equation is a differ...  [C, A, D]   \n",
       "\n",
       "  sbert_prediction  \n",
       "0        [B, E, A]  \n",
       "1        [B, C, D]  \n",
       "2        [A, C, D]  \n",
       "3        [A, E, C]  \n",
       "4        [B, C, D]  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_dt['sbert_prediction'] = test_dt['prompt'].apply(\n",
    "    sbert_predict\n",
    ")\n",
    "\n",
    "\n",
    "test_dt.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b312b94c",
   "metadata": {
    "papermill": {
     "duration": 0.018891,
     "end_time": "2026-07-24T12:16:17.955671+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:17.936780+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Train/Validation Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "058157c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:17.997175Z",
     "iopub.status.busy": "2026-07-24T12:16:17.996888Z",
     "iopub.status.idle": "2026-07-24T12:16:18.008316Z",
     "shell.execute_reply": "2026-07-24T12:16:18.007638Z"
    },
    "papermill": {
     "duration": 0.034949,
     "end_time": "2026-07-24T12:16:18.010088+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:17.975139+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train: (1600, 14)\n",
      "Validation: (400, 14)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# tumhare existing train_df ke according validation split\n",
    "dl_train_df, dl_val_df = train_test_split(\n",
    "    train_df,\n",
    "    test_size=0.2,\n",
    "    random_state=42\n",
    ")\n",
    "\n",
    "print(\"Train:\", dl_train_df.shape)\n",
    "print(\"Validation:\", dl_val_df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fad2d2e7",
   "metadata": {
    "papermill": {
     "duration": 0.012405,
     "end_time": "2026-07-24T12:16:18.041641+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:18.029236+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8322b67d",
   "metadata": {
    "papermill": {
     "duration": 0.010986,
     "end_time": "2026-07-24T12:16:18.063578+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:18.052592+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# # MAP@3 Score  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "07fb4ce7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:18.088462Z",
     "iopub.status.busy": "2026-07-24T12:16:18.087955Z",
     "iopub.status.idle": "2026-07-24T12:16:18.092905Z",
     "shell.execute_reply": "2026-07-24T12:16:18.092172Z"
    },
    "papermill": {
     "duration": 0.017762,
     "end_time": "2026-07-24T12:16:18.094133+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:18.076371+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "def map_at_3(actual,predicted):\n",
    "    scores = []\n",
    "    for true_ans , pred_list in zip(actual,predicted):\n",
    "        score = 0\n",
    "        for rank,pred in enumerate(pred_list[:3],start=1):\n",
    "            if pred ==true_ans:\n",
    "                score = 1/rank\n",
    "                break\n",
    "        scores.append(score)\n",
    "    return np.mean(scores)\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9da191cb",
   "metadata": {
    "papermill": {
     "duration": 0.010612,
     "end_time": "2026-07-24T12:16:18.115426+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:18.104814+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# SBERT Baseline Validation Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7ef96f4b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:18.138846Z",
     "iopub.status.busy": "2026-07-24T12:16:18.138622Z",
     "iopub.status.idle": "2026-07-24T12:16:26.543995Z",
     "shell.execute_reply": "2026-07-24T12:16:26.542699Z"
    },
    "papermill": {
     "duration": 8.42055,
     "end_time": "2026-07-24T12:16:26.547042+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:18.126492+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBERT MAP@3 = 0.3804166666666666\n"
     ]
    }
   ],
   "source": [
    "val_pred=[]\n",
    "\n",
    "for q in dl_val_df['prompt']:\n",
    "    \n",
    "    val_pred.append(\n",
    "        sbert_predict(q)\n",
    "    )\n",
    "\n",
    "\n",
    "score = map_at_3(\n",
    "    dl_val_df['answer'].tolist(),\n",
    "    val_pred\n",
    ")\n",
    "\n",
    "\n",
    "print(\"SBERT MAP@3 =\", score)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9dbfc9a",
   "metadata": {
    "papermill": {
     "duration": 0.019547,
     "end_time": "2026-07-24T12:16:26.590970+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:26.571423+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# SBERT Baseline - Accuracy & F1 Score (Top-1 Prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5da4aedd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:26.632726Z",
     "iopub.status.busy": "2026-07-24T12:16:26.632349Z",
     "iopub.status.idle": "2026-07-24T12:16:26.871560Z",
     "shell.execute_reply": "2026-07-24T12:16:26.870766Z"
    },
    "papermill": {
     "duration": 0.262685,
     "end_time": "2026-07-24T12:16:26.873180+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:26.610495+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SBERT Baseline -> Top-1 Accuracy   : 0.2225\n",
      "SBERT Baseline -> F1 Score (macro) : 0.2211\n",
      "SBERT Baseline -> F1 Score (weighted): 0.2226\n",
      "\n",
      "Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.22      0.29      0.25        68\n",
      "           B       0.32      0.22      0.26        97\n",
      "           C       0.25      0.22      0.24        98\n",
      "           D       0.16      0.13      0.14        83\n",
      "           E       0.17      0.28      0.21        54\n",
      "\n",
      "    accuracy                           0.22       400\n",
      "   macro avg       0.23      0.23      0.22       400\n",
      "weighted avg       0.23      0.22      0.22       400\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import f1_score, classification_report, confusion_matrix\n",
    "\n",
    "# sbert_predict() returns top-3 letters ranked by similarity; top-1 = first element\n",
    "sbert_top1_preds = [p[0] for p in val_pred]\n",
    "sbert_actuals = dl_val_df['answer'].tolist()\n",
    "\n",
    "sbert_accuracy = accuracy_score(sbert_actuals, sbert_top1_preds)\n",
    "sbert_f1_macro = f1_score(sbert_actuals, sbert_top1_preds, average='macro')\n",
    "sbert_f1_weighted = f1_score(sbert_actuals, sbert_top1_preds, average='weighted')\n",
    "\n",
    "print(f\"SBERT Baseline -> Top-1 Accuracy   : {sbert_accuracy:.4f}\")\n",
    "print(f\"SBERT Baseline -> F1 Score (macro) : {sbert_f1_macro:.4f}\")\n",
    "print(f\"SBERT Baseline -> F1 Score (weighted): {sbert_f1_weighted:.4f}\")\n",
    "print(\"\\nClassification Report:\\n\")\n",
    "print(classification_report(sbert_actuals, sbert_top1_preds, labels=['A','B','C','D','E']))\n",
    "\n",
    "sbert_cm = confusion_matrix(sbert_actuals, sbert_top1_preds, labels=['A','B','C','D','E'])\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "sns.heatmap(sbert_cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['A','B','C','D','E'], yticklabels=['A','B','C','D','E'])\n",
    "plt.title('SBERT Baseline - Confusion Matrix (Validation, Top-1)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9340abd",
   "metadata": {
    "papermill": {
     "duration": 0.012216,
     "end_time": "2026-07-24T12:16:26.898561+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:26.886345+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Deep Learning Models\n",
    "\n",
    "# Pytorch Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ca515a8b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:26.924478Z",
     "iopub.status.busy": "2026-07-24T12:16:26.924144Z",
     "iopub.status.idle": "2026-07-24T12:16:26.928387Z",
     "shell.execute_reply": "2026-07-24T12:16:26.927734Z"
    },
    "papermill": {
     "duration": 0.018818,
     "end_time": "2026-07-24T12:16:26.929745+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:26.910927+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from collections import Counter\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import copy\n",
    "from torch.optim.lr_scheduler import StepLR\n",
    "from sklearn.model_selection import train_test_split\n",
    "import wandb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44107c66",
   "metadata": {
    "papermill": {
     "duration": 0.011183,
     "end_time": "2026-07-24T12:16:26.952014+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:26.940831+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Device Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2227dcba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:26.975833Z",
     "iopub.status.busy": "2026-07-24T12:16:26.975591Z",
     "iopub.status.idle": "2026-07-24T12:16:26.979783Z",
     "shell.execute_reply": "2026-07-24T12:16:26.979010Z"
    },
    "papermill": {
     "duration": 0.017872,
     "end_time": "2026-07-24T12:16:26.981243+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:26.963371+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "print(f'Using device: {device}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cac73483",
   "metadata": {
    "papermill": {
     "duration": 0.012236,
     "end_time": "2026-07-24T12:16:27.005298+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:26.993062+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "ffd6848d",
   "metadata": {
    "papermill": {
     "duration": 0.012021,
     "end_time": "2026-07-24T12:16:27.029502+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:27.017481+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Vocabulary Building"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4aad75ed",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:27.054751Z",
     "iopub.status.busy": "2026-07-24T12:16:27.054524Z",
     "iopub.status.idle": "2026-07-24T12:16:27.122563Z",
     "shell.execute_reply": "2026-07-24T12:16:27.121690Z"
    },
    "papermill": {
     "duration": 0.082449,
     "end_time": "2026-07-24T12:16:27.124015+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:27.041566+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vocabulary size built successfully: 3957\n"
     ]
    }
   ],
   "source": [
    "class MCQVocabulary:\n",
    "    def __init__(self, max_size=25000):\n",
    "        self.max_size = max_size\n",
    "        self.word2idx = {\"<PAD>\": 0, \"<UNK>\": 1}\n",
    "        self.idx2word = {0: \"<PAD>\", 1: \"<UNK>\"}\n",
    "        \n",
    "    def build_vocab(self, text_list):\n",
    "        word_counts = Counter()\n",
    "        for text in text_list:\n",
    "            word_counts.update(str(text).split())\n",
    "            \n",
    "        most_common = word_counts.most_common(self.max_size)\n",
    "        for word, _ in most_common:\n",
    "            if word not in self.word2idx:\n",
    "                idx = len(self.word2idx)\n",
    "                self.word2idx[word] = idx\n",
    "                self.idx2word[idx] = word\n",
    "                \n",
    "    def numericalize(self, text):\n",
    "        return [self.word2idx.get(word, self.word2idx[\"<UNK>\"]) for word in str(text).split()]\n",
    "\n",
    "# Pura raw text combine karke vocabulary build kar rahe hain\n",
    "raw_texts = train_df['prompt'].astype(str).tolist()\n",
    "for col in ['A', 'B', 'C', 'D', 'E']:\n",
    "    raw_texts += train_df[col].astype(str).tolist()\n",
    "\n",
    "vocab = MCQVocabulary()\n",
    "vocab.build_vocab(raw_texts)\n",
    "print(f\"Vocabulary size built successfully: {len(vocab.word2idx)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d575ea70",
   "metadata": {
    "papermill": {
     "duration": 0.012073,
     "end_time": "2026-07-24T12:16:27.148656+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:27.136583+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Dataset Class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0dd352d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:27.174954Z",
     "iopub.status.busy": "2026-07-24T12:16:27.174557Z",
     "iopub.status.idle": "2026-07-24T12:16:27.182684Z",
     "shell.execute_reply": "2026-07-24T12:16:27.181936Z"
    },
    "papermill": {
     "duration": 0.022828,
     "end_time": "2026-07-24T12:16:27.184284+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:27.161456+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done\n"
     ]
    }
   ],
   "source": [
    "class DeepMCQDataset(Dataset):\n",
    "    def __init__(self, df, vocab, max_len=128, is_test=False):\n",
    "        self.df = df.reset_index(drop=True)\n",
    "        self.vocab = vocab\n",
    "        self.max_len = max_len\n",
    "        self.is_test = is_test\n",
    "        self.label_map = {'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4}\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "        \n",
    "    def pad_and_truncate(self, tokens):\n",
    "        if len(tokens) < self.max_len:\n",
    "            return tokens + [self.vocab.word2idx[\"<PAD>\"]] * (self.max_len - len(tokens))\n",
    "        return tokens[:self.max_len]\n",
    "        \n",
    "    def __getitem__(self, idx):\n",
    "        row = self.df.iloc[idx]\n",
    "        prompt_tokens = self.vocab.numericalize(row['prompt'])\n",
    "        \n",
    "        input_sequences = []\n",
    "        for option in ['A', 'B', 'C', 'D', 'E']:\n",
    "            option_tokens = self.vocab.numericalize(row[option])\n",
    "            combined_tokens = prompt_tokens + option_tokens\n",
    "            padded_tokens = self.pad_and_truncate(combined_tokens)\n",
    "            input_sequences.append(padded_tokens)\n",
    "            \n",
    "        X = torch.tensor(input_sequences, dtype=torch.long)\n",
    "        \n",
    "        if self.is_test:\n",
    "            return X, torch.tensor(row['id'], dtype=torch.int)\n",
    "            \n",
    "        y = self.label_map[row['answer']]\n",
    "        return X, torch.tensor(y, dtype=torch.long)\n",
    "print('Done')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f47e6e97",
   "metadata": {
    "papermill": {
     "duration": 0.012037,
     "end_time": "2026-07-24T12:16:27.208688+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:27.196651+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Architecture - BIGRU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "da6c5a84",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:27.232779Z",
     "iopub.status.busy": "2026-07-24T12:16:27.232348Z",
     "iopub.status.idle": "2026-07-24T12:16:27.238409Z",
     "shell.execute_reply": "2026-07-24T12:16:27.237710Z"
    },
    "papermill": {
     "duration": 0.019784,
     "end_time": "2026-07-24T12:16:27.239785+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:27.220001+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "class\n"
     ]
    }
   ],
   "source": [
    "class BiGRUMCQModel(nn.Module):\n",
    "    def __init__(self, vocab_size, embed_dim=128, hidden_dim=256):\n",
    "        super(BiGRUMCQModel, self).__init__()\n",
    "        self.embedding = nn.Embedding(vocab_size, embed_dim, padding_idx=0)\n",
    "        self.gru = nn.GRU(embed_dim, hidden_dim, batch_first=True, bidirectional=True, num_layers=2)\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Linear(hidden_dim * 2, 64),\n",
    "            nn.ReLU(),\n",
    "            nn.Dropout(0.5),\n",
    "            nn.Linear(64, 1)\n",
    "        )\n",
    "        \n",
    "    def forward(self, x):\n",
    "        batch_size, num_options, max_len = x.shape\n",
    "        x = x.view(-1, max_len)\n",
    "        \n",
    "        embedded = self.embedding(x)\n",
    "        gru_out, _ = self.gru(embedded)\n",
    "        \n",
    "        pooled = torch.mean(gru_out, dim=1)\n",
    "        scores = self.classifier(pooled)\n",
    "        return scores.view(batch_size, num_options)\n",
    "print(\"class\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c209d513",
   "metadata": {
    "papermill": {
     "duration": 0.011989,
     "end_time": "2026-07-24T12:16:27.263151+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:27.251162+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# DataLoaders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "faf492eb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:27.287550Z",
     "iopub.status.busy": "2026-07-24T12:16:27.286975Z",
     "iopub.status.idle": "2026-07-24T12:16:27.296681Z",
     "shell.execute_reply": "2026-07-24T12:16:27.295978Z"
    },
    "papermill": {
     "duration": 0.023464,
     "end_time": "2026-07-24T12:16:27.298097+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:27.274633+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "done\n"
     ]
    }
   ],
   "source": [
    "dl_train_df, dl_val_df = train_test_split(train_df, test_size=0.2, random_state=seed)\n",
    "\n",
    "train_dataset = DeepMCQDataset(dl_train_df, vocab, max_len=128)\n",
    "val_dataset = DeepMCQDataset(dl_val_df, vocab, max_len=128)\n",
    "test_dataset = DeepMCQDataset(test_dt, vocab, max_len=128, is_test=True)\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)\n",
    "val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)\n",
    "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
    "print(\"done\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19b74ff9",
   "metadata": {
    "papermill": {
     "duration": 0.011235,
     "end_time": "2026-07-24T12:16:27.320868+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:27.309633+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# W&B Experiment Initialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cfe069c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:27.344659Z",
     "iopub.status.busy": "2026-07-24T12:16:27.344444Z",
     "iopub.status.idle": "2026-07-24T12:16:29.323652Z",
     "shell.execute_reply": "2026-07-24T12:16:29.322992Z"
    },
    "papermill": {
     "duration": 1.992755,
     "end_time": "2026-07-24T12:16:29.325135+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:27.332380+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.25.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/kaggle/working/wandb/run-20260724_121627-mxsey7x8\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mrnn-bigru-regularized-0.77\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/23f3000653-indian-institute-of-technology-madras/DL-23f3000653-notebook-t22026\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/23f3000653-indian-institute-of-technology-madras/DL-23f3000653-notebook-t22026/runs/mxsey7x8\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/23f3000653-indian-institute-of-technology-madras/DL-23f3000653-notebook-t22026/runs/mxsey7x8?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x79cb600128a0>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import wandb\n",
    "\n",
    "wandb.init(\n",
    "    mode=\"disabled\"\n",
    ")\n",
    "wandb.init(\n",
    "    project=\"DL-23f3000653-notebook-t22026\",\n",
    "    name=\"rnn-bigru-regularized-0.77\",\n",
    "    config={\n",
    "        \"architecture\": \"BiGRU\",\n",
    "        \"embedding_dim\": 128,\n",
    "        \"hidden_dim\": 256,\n",
    "        \"epochs\": 10,\n",
    "        \"batch_size\": 32,\n",
    "        \"learning_rate\": 5e-4,\n",
    "        \"dropout\": 0.5\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a65205eb",
   "metadata": {
    "papermill": {
     "duration": 0.012064,
     "end_time": "2026-07-24T12:16:29.349908+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:29.337844+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8d911553",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:16:29.375306Z",
     "iopub.status.busy": "2026-07-24T12:16:29.374977Z",
     "iopub.status.idle": "2026-07-24T12:17:50.783789Z",
     "shell.execute_reply": "2026-07-24T12:17:50.782842Z"
    },
    "papermill": {
     "duration": 81.43667,
     "end_time": "2026-07-24T12:17:50.798603+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:16:29.361933+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting deep learning sequence training...\n",
      "Epoch 1/10 -> Train Loss: 1.6050 | Val Loss: 1.3988 | Val MAP@3: 0.6233\n",
      "Epoch 2/10 -> Train Loss: 1.2315 | Val Loss: 0.8725 | Val MAP@3: 0.7833\n",
      "Epoch 3/10 -> Train Loss: 0.7543 | Val Loss: 0.5868 | Val MAP@3: 0.8579\n",
      "Epoch 4/10 -> Train Loss: 0.4840 | Val Loss: 0.3536 | Val MAP@3: 0.9287\n",
      "Epoch 5/10 -> Train Loss: 0.3212 | Val Loss: 0.2845 | Val MAP@3: 0.9608\n",
      "Epoch 6/10 -> Train Loss: 0.1977 | Val Loss: 0.1705 | Val MAP@3: 0.9804\n",
      "Epoch 7/10 -> Train Loss: 0.1820 | Val Loss: 0.1420 | Val MAP@3: 0.9817\n",
      "Epoch 8/10 -> Train Loss: 0.1274 | Val Loss: 0.1003 | Val MAP@3: 0.9879\n",
      "Epoch 9/10 -> Train Loss: 0.1031 | Val Loss: 0.0889 | Val MAP@3: 0.9888\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: uploading summary; updating run metadata\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 10/10 -> Train Loss: 0.0854 | Val Loss: 0.0785 | Val MAP@3: 0.9900\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: uploading summary\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: uploading history steps 9-9, summary, console lines 10-10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run history:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:      epoch ▁▂▃▃▄▅▆▆▇█\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:         lr █▄▄▃▃▂▂▁▁▁\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: train_loss █▆▄▃▂▂▁▁▁▁\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   val_loss █▅▄▂▂▁▁▁▁▁\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   val_map3 ▁▄▅▇▇█████\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run summary:\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:      epoch 10\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:         lr 2e-05\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: train_loss 0.08545\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   val_loss 0.07852\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m:   val_map3 0.99\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run \u001b[33mrnn-bigru-regularized-0.77\u001b[0m at: \u001b[34m\u001b[4mhttps://wandb.ai/23f3000653-indian-institute-of-technology-madras/DL-23f3000653-notebook-t22026/runs/mxsey7x8\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at: \u001b[34m\u001b[4mhttps://wandb.ai/23f3000653-indian-institute-of-technology-madras/DL-23f3000653-notebook-t22026\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Synced 5 W&B file(s), 0 media file(s), 0 artifact file(s) and 0 other file(s)\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Find logs at: \u001b[35m\u001b[1m./wandb/run-20260724_121627-mxsey7x8/logs\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "model = BiGRUMCQModel(len(vocab.word2idx)).to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-3)\n",
    "scheduler = StepLR(optimizer, step_size=2, gamma=0.5)\n",
    "idx_to_letter = {0: 'A', 1: 'B', 2: 'C', 3: 'D', 4: 'E'}\n",
    "epochs = 10\n",
    "best_map3 = 0.0\n",
    "best_model_wts = copy.deepcopy(model.state_dict())\n",
    "\n",
    "print(\"Starting deep learning sequence training...\")\n",
    "for epoch in range(epochs):\n",
    "    model.train()\n",
    "    train_loss = 0.0\n",
    "    for X_batch, y_batch in train_loader:\n",
    "        X_batch, y_batch = X_batch.to(device), y_batch.to(device)\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(X_batch)\n",
    "        loss = criterion(outputs, y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        \n",
    "        train_loss += loss.item() * X_batch.size(0)\n",
    "        \n",
    "    scheduler.step() # Apply learning rate decay\n",
    "    epoch_train_loss = train_loss / len(train_loader.dataset)\n",
    "    \n",
    "    # Validation\n",
    "    model.eval()\n",
    "    val_loss = 0.0\n",
    "    actuals_list = []\n",
    "    predictions_list = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for X_val_batch, y_val_batch in val_loader:\n",
    "            X_val_batch, y_val_batch = X_val_batch.to(device), y_val_batch.to(device)\n",
    "            val_outputs = model(X_val_batch)\n",
    "            \n",
    "            loss = criterion(val_outputs, y_val_batch)\n",
    "            val_loss += loss.item() * X_val_batch.size(0)\n",
    "            \n",
    "            sorted_indices = torch.argsort(val_outputs, dim=1, descending=True).cpu().numpy()\n",
    "            for idxs, true_lbl in zip(sorted_indices, y_val_batch.cpu().numpy()):\n",
    "                predictions_list.append([idx_to_letter[i] for i in idxs[:3]])\n",
    "                actuals_list.append(idx_to_letter[true_lbl])\n",
    "                \n",
    "    epoch_val_loss = val_loss / len(val_loader.dataset)\n",
    "    epoch_map3 = map_at_3(actuals_list, predictions_list)\n",
    "    \n",
    "    # Save the absolute best model checkpoint based on validation tracking\n",
    "    if epoch_map3 >= best_map3:\n",
    "        best_map3 = epoch_map3\n",
    "        best_model_wts = copy.deepcopy(model.state_dict())\n",
    "        \n",
    "    wandb.log({\n",
    "        \"epoch\": epoch + 1,\n",
    "        \"train_loss\": epoch_train_loss,\n",
    "        \"val_loss\": epoch_val_loss,\n",
    "        \"val_map3\": epoch_map3,\n",
    "        \"lr\": optimizer.param_groups[0]['lr']\n",
    "    })\n",
    "    \n",
    "    print(f\"Epoch {epoch+1}/{epochs} -> Train Loss: {epoch_train_loss:.4f} | Val Loss: {epoch_val_loss:.4f} | Val MAP@3: {epoch_map3:.4f}\")\n",
    "\n",
    "# Load back the best weights before test evaluation\n",
    "model.load_state_dict(best_model_wts)\n",
    "wandb.finish()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31984810",
   "metadata": {
    "papermill": {
     "duration": 0.013846,
     "end_time": "2026-07-24T12:17:50.825207+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:50.811361+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# BiGRU - Final Validation Metrics (Accuracy, F1, Confusion Matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b2a1349b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:17:50.851720Z",
     "iopub.status.busy": "2026-07-24T12:17:50.851397Z",
     "iopub.status.idle": "2026-07-24T12:17:51.503384Z",
     "shell.execute_reply": "2026-07-24T12:17:51.502508Z"
    },
    "papermill": {
     "duration": 0.667236,
     "end_time": "2026-07-24T12:17:51.504856+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:50.837620+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "BiGRU (best checkpoint) -> Top-1 Accuracy   : 0.9800\n",
      "BiGRU (best checkpoint) -> F1 Score (macro) : 0.9804\n",
      "BiGRU (best checkpoint) -> F1 Score (weighted): 0.9801\n",
      "\n",
      "Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.92      1.00      0.96        68\n",
      "           B       1.00      1.00      1.00        97\n",
      "           C       0.98      0.96      0.97        98\n",
      "           D       1.00      0.95      0.98        83\n",
      "           E       1.00      1.00      1.00        54\n",
      "\n",
      "    accuracy                           0.98       400\n",
      "   macro avg       0.98      0.98      0.98       400\n",
      "weighted avg       0.98      0.98      0.98       400\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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j6dOn+PTTT7Xrq1SporNfzod0YabH1q9fHzY2NggPD4eFhQWsra3RvHnzfPcPCwvD2rVrce/ePZ2k7N++8b/L0NAQlSpVKvD+w4cPx/79+3H58mVMnDgRderUKfBj8xv74OHhgSNHjuDYsWPw8PDAX3/9hcePH+tUQN68eYOgoCDs2rULz5490zlWXmNT3ufx48ewtrbOlZDn9e/94sULBAYGIiIiAgkJCTrbCnveHE+ePMmzOznndfPkyROdSooQ11ZB4wLybgcbGxtcuHBBZ52BgUGugbI5j815rScmJiIrK0u73dTUtMBfhP5Jo9GUiNlZ9PGYuBQDAwMDNGvWDBs2bMCDBw90PrBymJubw9raGnfu3Mm1LedN6n0DCRs3bqwtrbZt2xYeHh6YNGkSdu3apVO9UKlUePPmTZ7HyFn/vhky0dHRuUrix44dy/fDzsbGBjdu3IBarRZ8dsTHTD/O7w3s3TfJnP2WLVuGixcv4sSJEzh9+jSmTZuGdevWYdu2bR/8JvpPec0UA/L/sM5Pt27dsGXLFpQuXRqdO3fO97h79uyBn58f3N3dtWNUlEolgoKCCvVNXKVS5XuOvDx69Eg7MPT27dsFeoylpSUUCkW+H7Rt27aFubk5wsPD4eHhgfDwcCiVSu39XoC3U5p37dqFIUOGwMnJCebm5lAoFJgwYUKRTnX+5ptvEB0djeHDh6NevXowNTVFdnY2RowYUeRTrHMIdW2JoXfv3jpfWHx8fODr6/tBx0pOTkbZsmWFCo1ExMSlmOR8IOZVUcnh5uaG7du3f/QU5tKlS8PHxwdTp07FgQMHdN7Aq1Spgnv37uX5uJz1//yG9i47OzusW7dOZ521tXW++7dt2xbR0dE4fPhwnrNb3pVz3piYGJ1vYmq1GrGxsdqZAUJ491vnu10QOd8a/8nJyQlOTk6YMGEC9u3bh0mTJiEiIiLPO3KWK1cOJiYmebZzTEwMDAwMCjQz7EN4eHhg2bJliIuLw6JFi/Ld79ChQ6hevToCAwN1krh/DowU8htqdnY2/Pz8YGZmhiFDhmDVqlXo2LFjvgN6cxgaGqJGjRr5Ju4qlQodO3bEnj17EB8fj4MHD6J58+Y612XOOJZ3Z+ekp6d/UNWjatWqOHfuHF6/fq2TuP7z3/vly5eIjIyEr68vfHx8tOvv37+f65iFaef8XsMxMTHa7WLIOe+9e/d07veSs+6fcWVnZ+PRo0c6FZqc55Vzn6pFixYhPT1du/1jpjLHxsb+6yBwkgaOcSkGGRkZOHv2LIyMjPKcEpxjxIgRMDExwbRp0xAfH59re2G+IXl4eKBSpUo604OBt/f7uHTpEq5evaqzPjk5Gfv27UO9evXem4hYWlrC1dVVZ3lfhaZ///6wtrbG/Pnz83yzTUhIwMqVKwG8nbJoZGSEkJAQnee6Y8cOvHr1Cm3atCnQcy+InGm1UVFR2nWpqanYvXu3zn454yveVa9ePQBvE6q8KJVKtGjRAseOHdP5sI2Pj0d4eDgaNWoEMzMzIZ5GLjVq1MC0adPwn//8573Jb0616t3ndunSpVzTtXPGSAnRrbBu3TpER0dj9uzZ+Prrr+Hs7IxZs2YVaNq3k5NTrmv2XR4eHsjIyMCMGTOQmJiYa2xPXtW5kJCQXBW2gmjdujUyMzN1bu6YlZWVa8B1fhXB9evX51qX084FSaTatGmDy5cvIzo6WrsuNTUVoaGhqFq1aqG634Rkb28PKysrbN26Vee1cerUKdy9ezfP2W3vjtfLuW2BkZGRNvFp1KiRznvNhyYur169wsOHD//1fi8kDay4FIHffvtN++0nMTER+/btw/379+Ht7f3eD6xatWph8eLF+M9//oNOnTpp75yr0WgQGxuL8PBwGBgYFGhMgZGREby8vLBw4UL89ttvaN26NQDA29sbBw8exJdffol+/frBxsYGz58/R1hYGJ4/f4558+YJ0wj/n6WlJVasWAFvb2/06NFD5865169fR3h4uPbNpFy5chg1ahQCAwMxYsQItGvXDvfu3cPmzZvRsGHDf51OXhgtWrRAlSpV8N133yEmJgZKpRI7d+5E2bJldaouYWFh2LJlC9zd3VGjRg28fv0aoaGhMDMz07ZpXr755hv8/vvvGDhwIAYOHAilUolt27ZBrVbnO05JKEOGDPnXfdzc3HD48GGMGzcObm5uiI2NxdatW1GnTh2dqqCxsTHq1KmDAwcOoFatWihTpgw+/fTTQs9GuXv3rva+MTn3RJk/fz569OgBf39/LF269L2Pb9++Pfbs2YN79+7lOYaiadOmqFSpEo4dOwZjY2OdKds5z3fPnj0wMzNDnTp1cPHiRfz+++8oU6ZMoZ4H8Pa+Kp999hl+/PFHPH78GHXq1MHhw4dzJR1mZmZo0qQJVq9ejYyMDFSsWBFnz57Ns3KU85r473//iy5dusDIyAht27bNc7C+t7e39maDgwcPhqWlJXbv3o3Y2FgsX768UF13Oc6fPw8vL6+P6ooxMjLCpEmTMHXqVHz55Zfo2rUrEhISsGHDBlStWjXXbMtSpUrh9OnTmDJlChwcHHD69GmcPHkSo0ePLtCMouPHj2vv/5ORkYFbt25pvwS1a9dOp7ry+++/Q6PR8M65MsHEpQi8W24vVaoUbGxsMGvWLPTv3/9fH+vu7o59+/Zh7dq1OHv2LHbu3AmFQoEqVaqgTZs2GDBgQIHLnf369cPPP/+M4OBg7Yds+fLlsX37dixfvhwHDhxAQkICzMzM4OzsjP/+97+C3EPmnxwdHbFv3z6sWbMGJ0+exJ49e2BgYAAbGxt4e3vrzMbw9fVFuXLlsHHjRgQEBMDS0hJ9+/bFxIkTBb2DqZGREQIDA7UfmtbW1hgyZAgsLCx0bi7XtGlTXLlyBREREYiPj4e5uTkcHBywePHi9377+/TTT7Fp0yb8+OOPCAoKgkajgYODAxYtWlQkbVxYnp6eiI+Px7Zt23DmzBnUqVMHixYtwsGDB/HHH3/o7Dtnzhz88MMPCAgIQEZGBnx8fAqVuGRlZWHKlCkoW7Yspk2bpl1fq1YtTJw4EXPnzkVERAS6dOmS7zHatm2LsmXL4sCBA7luWAi8HcfRtWtXrFmzBm3bts31BeG7776DgYEB9u3bh/T0dHz22WdYt26ddjpzYRgYGODnn3/GvHnzsHfvXigUCu1N4v45rfrHH3/EDz/8gM2bN0Oj0aBFixYIDg7O9XtZDg4O+Prrr7F161acPn0a2dnZOHbsWJ6JS/ny5bF161bttPr09HTY2tpi1apVeVY1CiInWX1ftbUgPD09YWxsjODgYCxevBimpqZwd3fH5MmTc80KUyqVWL16NWbNmoVFixZpu7jHjRtXoHMdPnwYYWFh2r+vX7+unSFWqVIlnffJgwcPolGjRsX2cwRUtBQaKYzQIiK9t2LFCuzatQuHDx8W9Xeh5GjhwoXYv38/jhw5Uiw/MeDn54dDhw7pdHcVlbi4OLRv3x5LlizJ8z5ZJD0c40JEkvDVV18hNTUV+/fvFzsU2Tl//jzGjh1bYn4XSUjr169H3bp1mbTICCsuRERUrIqz4kLyw4oLERERSQYrLkRERCQZrLgQERGRZDBxISIiIslg4kJERESSIcsb0DnOPCZ2CLJz/nvecZKI6EMZF9OnrYmzz7/vVAhp0YGCHk8IrLgQERGRZMiy4kJERKSXFPKvRzBxISIikguFQuwIipz8UzMiIiKSDVZciIiI5EIPuork/wyJiIhINlhxISIikgs9GOPCxIWIiEgu2FVEREREVHKw4kJERCQX7CoiIiIiyWBXEREREVHJwYoLERGRXOhBVxErLkRERCQZrLgQERHJhR6McWHiQkREJBfsKiIiIiIqOVhxISIikgt2FREREZFksKuIiIiIqORgxYWIiEgu2FVEREREkqEHiYv8nyERERHJBisuREREcmHAwblEREREJQYrLkRERHKhB2NcmLgQERHJBe/jQkRERFRysOJCREQkF+wqIiIiIslgVxERERFRycGKCxERkVzoQVeR/J8hERERyQYrLkRERHLBMS4khArmpTDPsz5OTWmN89PdsGNsM9SvYq7dbqJSYmqXujg8sQXOT3fDrnHN0adxVREjlp6tmzehc4d2aOLcEIP698GVy5fFDknS2J7CY5sKi+2ZD4WBsEsJVDKjkhFzY0P8OrwRMrM1GLfxIjwDz+HHQ3eQnJap3WdSx0/hWscK03ZdQ8/Ac9h07iH8utRFG9vyIkYuHQcPRGDxwgCMGjsOW7eHwdbWDmNGDUdCQoLYoUkS21N4bFNhsT31W4lOXG7fvi12CB9tWMuaeJacjhm7b+Dq42Q8fvEGkXcTEZuUpt3Hqbol9l16ij/vv8CTF2+w88IT3H6WAvuqFiJGLh0h69fBs3df9OjZC7Xr1MH0mf4wNjbG7l07xQ5NktiewmObCovt+R4KhbBLCVTiEpeUlBRs27YNvXv3xhdffCF2OB+tja01rj1JxqK+9jgxuRW2jW4Kz0ZVdPa5+Ogl2thao4J5KQBAk1plUdPKFJF3E8UIWVIy1GrcuH4NzV1ctesMDAzQvLkrLl+KFjEyaWJ7Co9tKiy257/Qg66iEjM4NyoqCjt27MDhw4dRoUIFdOjQATNmzBA7rI9Wrawx+jauipDIR1jz2300qGqBKZ3rIiMzG/su/Q0AmB9xCzO618ORSS2RkZUNjQbw33sDfz14IW7wEpD0IglZWVmwsrLSWW9lZYV792JEikq62J7CY5sKi+1JoiYucXFxCAsLw44dO5CSkoLOnTtDrVZjxYoVqFOnjpihCcZAocC1J8lYfuwuAODm3ymoU8EMfZpU0yYuA5pVh0M1C4zfdAlPXr5Bo5plMK2rLeJepeN8TJKY4RMRkZSU0O4dIYmWuIwePRpRUVFwc3PDtGnT0KpVKyiVSmzdulWskIpEXEo6YuJe66yLiX8N9/rWAIBShgYY3742Jmy9jNN33g4su/MsBbaVzDDEtSYTl39RtkxZKJXKXIPyEhISUL48BzcXFttTeGxTYbE9/0UJ7d4RkmjP8LfffkPv3r3h6+sLNzc3KJVKsUIpUhcfvkSt8qV11tW0MsWTF28AAIZKBYwMDZCt0X1ctgYwkH/i/NGMVCrUq98A589FatdlZ2fj/PlIODg6ixiZNLE9hcc2FRbbk0RLXDZv3ozXr1/D09MTffr0wcaNG5GYKL/BqBsjH6JhNQsMb1UT1cuZoHPDiujdqCq2/RELAHidnoWoe0mY+HkdNK5VBlXLGKO7U2V0c6yEYzfjRI5eGgYPGYpdO0Kxd3cYYu7exZzZs5CWloYePT3FDk2S2J7CY5sKi+35HhycW3ScnJzg5OSEadOmISIiAjt37sT8+fORnZ2Ns2fPolKlSjAzMxMrPMFce/IKE7dexnj3OhjV5hM8fvEGCw/eRsSVZ9p9puy4iq/dayOgVwNYmBjh6Ys3CDx2F9ujHosYuXR06twFSYmJWBm4DPHxcbC1q4eVQathxbLxB2F7Co9tKiy2p35TaDQazb/vVjxiYmKwY8cO7N27F8nJyXB1dcWqVasKfRzHmceKIDr9dv779mKHQEQkWcbFVCYw6f6zoMdL2ztG0OMJoUTVgWxsbPDtt9/i1KlTWLJkidjhEBERSQu7isShVCrh7u4Od3d3sUMhIiKiEqREJi5ERET0AXgfFyIiIpKMEtq9IyT5P0MiIiKSDVZciIiI5IJdRURERCQVCj1IXNhVRERERJLBigsREZFMsOJCREREVIKw4kJERCQX8i+4MHEhIiKSC3YVEREREZUgrLgQERHJhD5UXJi4EBERyYQ+JC7sKiIiIiLJYMWFiIhIJlhxISIiIipBWHEhIiKSC/kXXJi4EBERyQW7ioiIiIhKECYuREREMqFQKARdCiMrKws//fQT2rVrBwcHB7i7u2PFihXQaDTafTQaDZYuXYqWLVvCwcEBX331Fe7fv1+o8zBxISIikgkxE5fg4GBs2bIFM2bMQEREBCZNmoTVq1cjJCREZ5+QkBDMmjULoaGhMDExwfDhw5Genl7g8zBxISIioo8WHR2N9u3bw83NDdWqVUOnTp3QsmVLXL58GcDbasuGDRswZswYuLu7w87ODgsXLsTz589x9OjRAp+HiQsREZFMCF1xUavVSElJ0VnUanWe53Z2dsa5c+dw7949AMDNmzdx4cIFtG7dGgAQGxuLuLg4uLq6ah9jbm4OR0dHREdHF/g5clYRERGRXAg8qSgoKAiBgYE663x8fODr65trX29vb6SkpKBz585QKpXIysrChAkT0L17dwBAXFwcAMDKykrncVZWVoiPjy9wTExciIiIKE+jRo3C0KFDddapVKo89z1w4AD27duHH3/8EXXq1MGNGzcQEBCAChUqoGfPnoLFxMSFiIhIJoS+j4tKpco3UfmnhQsXwtvbG127dgUA2Nra4smTJwgKCkLPnj1hbW0NAEhISECFChW0j0tISICdnV2BY+IYFyIiIvpob968yZU4KZVK7XToatWqwdraGpGRkdrtKSkpuHTpEpydnQt8HlZciIiIZELMO+e2bdsWq1atQpUqVbRdRevWrUOvXr20sXl5eeHnn39GzZo1Ua1aNSxduhQVKlSAu7t7gc/DxIWIiEgmxExcpk+fjqVLl8Lf31/bHdSvXz+MGzdOu8/IkSORlpaGGTNmIDk5GY0aNcLq1atRqlSpAp9HoXn3lnYy4TjzmNghyM7579uLHQIRkWQZF1OZoMKwUEGP93xtX0GPJwRWXIiIiORC/r+xyMSFiIhILvjr0EREREQliCwrLhyPIbyyTXzEDkFWkqIC/30nIqJC0oeKiywTFyIiIn2kD4kLu4qIiIhIMlhxISIikglWXIiIiIhKEFZciIiI5EL+BRcmLkRERHLBriIiIiKiEoQVFyIiIpnQh4oLExciIiKZ0IfEhV1FREREJBmsuBAREcmF/AsurLgQERGRdLDiQkREJBP6MMaFiQsREZFM6EPiwq4iIiIikgxWXIiIiGRCHyouTFyIiIhkQh8SF3YVERERkWSw4kJERCQX8i+4MHEhIiKSC3YVEREREZUgrLgQERHJBCsuRERERCUIKy5EREQyoQcFFyYuREREcsGuIiIiIqIShBUXIiIimdCDggsTFyIiIrlgVxERERFRCcKKCxERkUzoQcGFiQsREZFcGBjIP3NhVxERERFJBisuREREMqEPXUWsuBAREZFkMHERydbNm9C5Qzs0cW6IQf374Mrly2KHJBlmpqWwaFIv3IqYjcTIJTjx60Q0ql9Duz0tOjDPZYJXexGjlhZen8JjmwqL7Zk3hUIh6FISMXERwcEDEVi8MACjxo7D1u1hsLW1w5hRw5GQkCB2aJLw84yBaNfcDsOmr0fjvvNwNPIm9q/yRRVrSwBALfepOov3zI3Izs5G2LGL4gYuEbw+hcc2FRbbM38KhbBLScTERQQh69fBs3df9OjZC7Xr1MH0mf4wNjbG7l07xQ6txDMuZYQe7Z3w3U+7cfavu4h5FI+5QRG4+ygOI/u0AgA8S3ils3i4NcSpqDu4/5hvagXB61N4bFNhsT31W4lIXJKSkrT///TpUyxduhQLFizAn3/+KWJURSNDrcaN69fQ3MVVu87AwADNm7vi8qVoESOTBkOlAQwNlXijztBZ/yY9A67OtXPtX6GcOTq1tMf63ZHFFaKk8foUHttUWGzP92NXURG7desW2rVrB1dXV3Tq1Ak3btxA79698euvvyI0NBReXl44evSomCEKLulFErKysmBlZaWz3srKCvHx8SJFJR0pqek4dykGU0d2RmVrSxgYKNC/SxM0c/gElcpb5Nr/S49meJX6BruPXyz+YCWI16fw2KbCYnu+HxOXIrZo0SLUrVsXGzduRNOmTTFq1Ci0adMGFy5cQFRUFPr164dffvlFzBCpBBo2fQMUCiDm8Fy8PP8Txg1og9CDfyI7W5NrX68vmmPbgT+Rrs4UIVIiIhKaqPdxuXLlCtavXw87OzvY2dkhNDQUAwcOhIHB23zqyy+/RL9+/cQMUXBly5SFUqnMNYgsISEB5cuXFykqabkXG4/PRyyFqbEKFmbG+Ds+GSHzh+LeY91vWy2ca8P2k0oY7LdOpEilh9en8NimwmJ7vl8JLZIIStSKy8uXL2FtbQ0AKF26NExMTGBpaandbmlpidevX4sVXpEwUqlQr34DnD/3f2MusrOzcf58JBwcnUWMTHpS36jxd3wyypibwN21HsJPXtHZPqSHCy5cf4grtx+LFKH08PoUHttUWGxPEv3OuSW1D60oDR4yFN9Pm4IGDexh39ABG0PWIy0tDT16eoodmiS4u9SDQgHcvv8ctatbY96EHrh97xk27P2/NzLz0sbw7OAMvyVhIkYqTbw+hcc2FRbbM3/68JkqeuLi5+cHlUoFAFCr1Zg1axZMTEy0f8tRp85dkJSYiJWByxAfHwdbu3pYGbQaVixzFoilmTFm+3ZH1YplkPgyFXuOXcTMFfuQmZmt3adPx0ZQQIHQg/KbmVbUeH0Kj20qLLZn/vQgb4FCo9HkHtFYTKZOnVqg/QICAgp13Dcchym4sk18xA5BVpKiAsUOgYiKkXExlQk+m31c0OP9NaOdoMcTgqgVl8ImJERERJQ/dhURERGRZOhB3lIy7pxLREREVBCsuBAREckEu4qIiIhIMvQgb2FXEREREUkHKy5EREQyoQ9dRay4EBERkWSw4kJERCQTelBwYeJCREQkF+wqIiIiIipBWHEhIiKSCT0ouDBxISIikgt2FRERERGVIKy4EBERyYQeFFxYcSEiIiLpYMWFiIhIJvRhjAsTFyIiIpnQh8SFXUVEREQkGay4EBERyYQeFFyYuBAREckFu4qIiIiIShBWXIiIiGRCDwourLgQERHJhUKhEHQprGfPnmHSpElo1qwZHBwc4OHhgStXrmi3azQaLF26FC1btoSDgwO++uor3L9/v1DnYOJCREREH+3ly5cYMGAAjIyMEBwcjP3792PKlCmwtLTU7hMcHIyQkBDMmjULoaGhMDExwfDhw5Genl7g87CriIiISCbE7CoKDg5GpUqVEBAQoF1XvXp17f9rNBps2LABY8aMgbu7OwBg4cKFcHV1xdGjR9G1a9cCnYcVFyIiIvpox48fh729PcaPHw8XFxf06NEDoaGh2u2xsbGIi4uDq6urdp25uTkcHR0RHR1d4POw4kJERCQTBgKXXNRqNdRqtc46lUoFlUqVa99Hjx5hy5YtGDp0KEaPHo0rV65gzpw5MDIyQs+ePREXFwcAsLKy0nmclZUV4uPjCxwTExciIiKZELqrKCgoCIGBgTrrfHx84Ovrm2tfjUYDe3t7TJw4EQBQv3593LlzB1u3bkXPnj0Fi4mJCxEREeVp1KhRGDp0qM66vKotAGBtbY3atWvrrLOxscGhQ4e02wEgISEBFSpU0O6TkJAAOzu7AsfEMS5EREQyIfR0aJVKBTMzM50lv8Tls88+w71793TW3b9/H1WrVgUAVKtWDdbW1oiMjNRuT0lJwaVLl+Ds7Fzg58jEhYiISCYMFMIuhTFkyBBcunQJq1atwoMHD7Bv3z6EhoZi4MCBAN4mVV5eXvj5559x7Ngx3Lp1C99++y0qVKignWVUEOwqIiIioo/m4OCAwMBALFmyBCtWrEC1atUwbdo0dO/eXbvPyJEjkZaWhhkzZiA5ORmNGjXC6tWrUapUqQKfR6HRaDRF8QTE9CZT7Ajkp2wTH7FDkJWkqMB/34mIZMO4mMoEXVb9IejxIkY3FfR4QmDFhYiISCb04beKZJm4vMnIEjsE2WGFQFhlW04ROwRZSTqzQOwQiKiYyDJxISIi0kcKyL/kwllFREREJBmsuBAREclEYacwSxETFyIiIplQ6MHoXHYVERERkWSw4kJERCQTelBwYeJCREQkFwZ6kLmwq4iIiIgkgxUXIiIimdCDggsrLkRERCQdrLgQERHJhD5Mh2biQkREJBN6kLewq4iIiIikgxUXIiIimdCH6dBMXIiIiGRC/mkLu4qIiIhIQlhxISIikgnOKiIiIiLJMJB/3sKuIiIiIpIOVlyIiIhkgl1F/9+xY8cKfMD27dt/cDBERERE71OgxGXcuHEFOphCocCNGzc+KiAiIiL6MHpQcClY4nLz5s2ijoOIiIg+kj50FXFwLhEREUnGBw3OTU1NRVRUFJ48eYKMjAydbV5eXoIERkRERIWjD9OhC524XL9+Hd7e3khLS0NaWhosLS2RlJQEExMTlCtXjokLERGRSNhVlIeAgAC0bdsWUVFRKFWqFEJDQ3HixAk0aNAAU6ZMKYoYiYiIiAB8QOJy48YNDB06FAYGBlAqlVCr1ahcuTImT56MJUuWFEWMREREVAAKgZeSqNCJi6GhIQwM3j7MysoKT548AQCYmZnh77//FjY6IiIiKjADhULQpSQq9BiX+vXr48qVK6hVqxaaNGmCZcuWISkpCXv27MGnn35aFDESERERAfiAisuECRNgbW2t/X8LCwvMmjULSUlJ+OGHHwQPkIiIiApGoRB2KYkKXXFp2LCh9v+trKywZs0aQQMiIiIiyg9/ZJGIiEgm9GE6dKETl3bt2r23YQrzg4xEREQkHD3IWwo/xmXIkCHw8vLSLgMHDoSTkxNSUlLQr1+/oohR1tavDUYzp/pYsjBA7FAkbevmTejcoR2aODfEoP59cOXyZbFDkgwzUxUWfeOBW2F+SDw5Byd+GYtG9arlue+yb3si7dwC+PRrWcxRSh+vUWGxPfVXoSsuQ4YMyXP9pk2bcPXq1Y8OSJ9cv3oFYTtCUaeurdihSNrBAxFYvDAA02f6o2FDR2wKWY8xo4ZjT/hBWFlZiR1eiffztN6ob1MJw/y34Wl8MgZ0csb+5SPx2YAf8SQuWbtf9zYN0NS+Bp48fylitNLEa1RYbM/8ldQpzEIS7EcWW7dujUOHDhV4/8jISHTp0gUpKSm5tr169Qpdu3bFn3/+KVR4JU5q6mvMmPYtps3wh4W5hdjhSFrI+nXw7N0XPXr2Qu06dTB9pj+MjY2xe9dOsUMr8YxLGaKHmz2+C4zA2Yv3EBObgLmrj+JubDxGejbX7lfF2gJL/vMFhs7cioysLBEjliZeo8Jie+ZPH2YVCZa4HDx4EGXKlCnw/uvXr0ffvn1hZmaWa5u5uTn69euHdevWCRVeibNo3hy0aNUGTZu7ih2KpGWo1bhx/Rqau/xfOxoYGKB5c1dcvhQtYmTSYKg0gKGhEm/Uuj+W+iY9A66OtQC8Hey3ZmY//HfjKdy490yEKKWN16iw2J5U6K6iHj166AzO1Wg0iI+PR2JiImbOnFng49y6dQuTJ0/Od3uLFi2wdu3awoYnCYcPRuDWzetYtylU7FAkL+lFErKysnKVh62srHDvXoxIUUlHSqoa5y4/wNRh7XHr/nM8S0xB38+d0My+Ju7GJgAA/jO4DTKzsrEi9KzI0UoTr1FhsT3fj7OK8tC+fXudhlEoFChXrhyaNm2K2rVrF/g48fHxMDTM//SGhoZITEwsbHgl3rO/n2LJwgAsX7UapUqVEjscIgzz34qg7/ogJnw6MjOzcPHWE4QeuQhnu2pwtq2Kcf1awnXIUrHDJCIC8AGJi6+vryAnrlixIu7cuYOaNWvmuf3WrVvaO/TKyc3r15CUmIAhA3pr12VlZSH6rz+xY9tmnP7jIpRKpYgRSkvZMmWhVCqRkJCgsz4hIQHly5cXKSppufc4EZ+PDYKpsREsShvj74RXCJkzEPceJ6CF0yeoULY0bu+eqt3f0FCJ+eO7wqd/C9j1XCBi5NLAa1RYbM/3E2z8RwlW6OdYr169XBcMACQlJaFevXoFPk6bNm2wdOlSpKen59r25s0bLF++HG3bti1seCVe42Yu2LxjD0K27dIu9erbo2OXbgjZtotJSyEZqVSoV78Bzp+L1K7Lzs7G+fORcHB0FjEy6Ul9k4G/E16hjLkJ3JvVRfhv17H5wF9o8uVPaOa1VLs8ef4S/910Ch5f867ZBcFrVFhsz/dTKBSCLiVRoSsuGo0mz/VqtRpGRkYFPs6YMWNw+PBhdOzYEYMGDcInn3wCAIiJicHmzZuRlZWF0aNHFza8Eq906dKoXUf3xyhNTExgaVkm13oqmMFDhuL7aVPQoIE97Bs6YGPIeqSlpaFHT0+xQ5ME92Z1oVAAtx/EoXb18pjn0wW3H8RhQ/ifyMzKRmJyqs7+GVlZeJaQgjsP40WKWHp4jQqL7anfCpy4bNiwAcDbbG779u0wNTXVbsvOzkZUVBRsbGwKfOLy5ctj69atmDVrFpYsWaJNiBQKBVq2bIkZM2aw7EcF0qlzFyQlJmJl4DLEx8fB1q4eVgathhWvnwKxNDPG7DGdULWCJRKTU7HnxFXMXHUImVnZYocmG7xGhcX2zJ9BySySCEqhya+E8g/t2rUDADx58gSVKlWCgcH/9TIZGRmhWrVqGD9+PBwdHQsdxMuXL/HgwQMAQM2aNWFpaVnoY7zrRRrvMyE0YyN2YQmpbMspYocgK0lnONaGSjbjYvplwIl7bwp6vCXd7QQ9nhAK3JTHjx8HAAwePBiBgYEfnVy8y9LSEg4ODoIdj4iIiOSp0DlgSEhIUcRBREREH6mkDqgVUqFnFfn6+uKXX37JtT44OBjjx48XJCgiIiIqPAOFsEtJVOjEJSoqCm3atMm1vnXr1rL+bSEiIiISX6G7ilJTU/Oc9mxoaJjnDyYSERFR8dCDnqLCV1zq1q2LiIiIXOsjIiJQp04dQYIiIiIiykuhKy5jx46Fr68vHj16hObN3/7sfWRkJMLDw7Fs2TLBAyQiIqKCMdCDkkuhE5d27dphxYoVWLVqFQ4dOoRSpUrBzs4O69evF3SKNBERERWOPvxW0QfdEsfNzQ1ubm4AgJSUFISHh2PBggW4du0abty4IWR8RERERFoffC+/qKgo7NixA4cPH0aFChXQoUMHzJgxQ8jYiIiIqBD0oKeocIlLXFwcwsLCsGPHDqSkpKBz585Qq9VYsWIFB+YSERGJjGNc3jF69GhERUXBzc0N06ZNQ6tWraBUKrF169aijI+IiIhIq8CJy2+//YbBgwdjwIABqFWrVhGGRERERB9CDwouBR+AvHnzZrx+/Rqenp7o06cPNm7ciMTExKKMjYiIiAqBt/x/h5OTE+bMmYMzZ86gX79+2L9/P1q3bo3s7GycPXuWd80lIiKiIlfoKd+mpqbo3bs3tmzZgr1792Lo0KEIDg6Gq6srRo8eXRQxEhERUQEYKBSCLiXRR92rxsbGBt9++y1OnTqFJUuWCBUTERERUZ4++D4u71IqlXB3d4e7u7sQhyMiIqIPUEKLJIISJHEhIiIi8ZXUAbVC0oefNSAiIiKZYMWFiIhIJhSQf8mFiQsREZFMsKuIiIiIqARhxYWIiEgmWHEhIiIiKkGYuBAREcmEQqEQdPkYv/zyC2xtbTF37lztuvT0dPj7+6NZs2ZwdnaGr68v4uPjC3VcJi5EREQyUVJ+ZPHy5cvYunUrbG1tddbPmzcPJ06cwE8//YSQkBA8f/4cPj4+hXuOHx4WERERka7Xr19j8uTJmDNnDiwtLbXrX716hZ07d8LPzw8uLi6wt7fHvHnzEB0djYsXLxb4+ExciIiIZEKhEHb5ELNnz0abNm3g6uqqs/7q1avIyMjQWV+7dm1UqVKlUIkLZxURERHJhNC/6KxWq6FWq3XWqVQqqFSqPPffv38/rl+/jh07duTaFh8fDyMjI1hYWOist7KyQlxcXIFjYuJCREREeQoKCkJgYKDOOh8fH/j6+uba9+nTp5g7dy7Wrl2LUqVKFVlMTFyIiIhkQuj7uIwaNQpDhw7VWZdfteXatWtISEiAp6endl1WVhaioqKwadMmrFmzBhkZGUhOTtapuiQkJMDa2rrAMTFxISIikgmBe4re2y30T82bN8e+fft01k2dOhU2NjYYOXIkKleuDCMjI0RGRqJjx44AgJiYGDx58gROTk4FjomJCxEREX00MzMz1K1bV2edqakpypQpo13fq1cvzJ8/H5aWljAzM8OcOXPg7OzMxIWIiEgfGZTwX4eeNm0aDAwMMH78eKjVarRs2RIzZ84s1DEUGo1GU0TxiSYlXXZPSXSGypL9YpCazCxeo0L6ZHSo2CHIyqPgfmKHIDvGxVQmWHH2vqDHG9eilqDHEwIrLkRERDIh9BiXkoiJCxERkUzw16GJiIiIShBWXIiIiGRC6DvnlkRMXIiIiGRCD/IWdhURERGRdLDiQkREJBPsKiIiIiLJ0IO8hV1FREREJB2suBAREcmEPlQj9OE5EhERkUyw4kJERCQTCj0Y5MLEhYiISCbkn7awq4iIiIgkhBUXIiIimeB9XIiIiEgy5J+2sKuIiIiIJIQVFyIiIpnQg54iVlyIiIhIOlhxISIikgnex4WIiIgkQx+6UfThORIREZFMsOJCREQkE+wqIiIiIsmQf9rCriIiIiKSEFZciIiIZIJdRURERCQZ+tCNog/PkYiIiGSCFRciIiKZ0IeuIlZciIiISDJYcSEiIpIJ+ddbmLgQERHJhh70FLGriIiIiKSDFZditnZ1EE4cO4L792JQqpQxHJycMf6b/6DWJzZihyZpWzdvwvp1axAfH4e6tnbwm/Y9Gjo4iB2WJPEa/TgXFnVDjfKlc61fe+wOpmz8C7WsS2NWPyc0q1sepQyVOH7lKaZu+gtxyekiRCtdfM3nzUAPOotYcSlmf/0ZhT79B+LXjduw8pe1yMzMxLjRI5CWmip2aJJ18EAEFi8MwKix47B1exhsbe0wZtRwJCQkiB2aJPEa/Tifzz6CBl/v0S69Fp0EAOyJegRTlRKhk9ygAeC58CS6zjsGI0MDbPy6lV6U+IXC13z+FAphl5JIodFoNGIHIbSUdOk8paTERLi7uSJ4bQg+a9xE7HDyZagsoVcwgEH9+6CBfUNMmz4DAJCdnY3P27fBgIGDMXykt8jR5S0zi9eokD4ZHSp2CPmaM8AZnztWRlO/CLg1qIitE1ujzrgwpLzJBACYmxjhf4E90efHU/jt+jORo33rUXA/sUN4Lym+5o2LqX8j/Kqw11A3+4qCHk8IrLiILCXlFQDAwtJS5EikKUOtxo3r19DcxVW7zsDAAM2bu+LypWgRI5MPXqMfzkhpgN4uNbH59D0AgMpQCY0GUGdma/dJz8hCtkaDZp+WFytMSeFr/v0UAv9XEok+xiU7Oxu7du3CkSNH8PjxYygUClStWhWdOnXCF198Ieub6WRnZ2PxwnlwdP4MdT6tK3Y4kpT0IglZWVmwsrLSWW9lZYV792JEiko+eI1+nC6fVYWlqRG2nH2buFyISUBqeiZm9HHE3J2XoQDwfR9HGCoNULGMibjBSgRf8yRq4qLRaDBmzBicOnUKdnZ2qFu3LjQaDe7evQs/Pz8cPnwYK1euFDPEIjV/7mzc/d8drPl1s9ihEOWJ1+jHGdT6Exy78hTPXrwBACS8Ssfwlb9joVdjjHT/FNkaDXadf4hL9xORnS2d7kMquWT8XV9L1MRl165diIqKwq+//ormzZvrbIuMjMS4ceOwe/du9OjRQ5wAi9CCebNx5reTCF63ERUrVRI7HMkqW6YslEplrkF5CQkJKF+epfePwWv041SzMkXr+hXxVeBZnfUnrz1D0yn7Uc5MhcwsDZLTMnDtp+54EJciUqTSwtf8+3FWURHbv38/Ro8enStpAQAXFxd4e3tj3759IkRWdDQaDRbMm40Tx49i1epfUbVaNbFDkjQjlQr16jfA+XOR2nXZ2dk4fz4SDo7OIkYmXbxGhTGg5SeIT07HkUtP89yemKJGcloGWtargPLmxjh48UkxRyhNfM2TqBWXW7duYfLkyflub926NUJCQooxoqI3f+5sHDwQjiVLV8C0dGnEx8cBAMzMzGFsbCxydNI0eMhQfD9tCho0sId9QwdsDFmPtLQ09OjpKXZoksRr9OMpFG8Tl21n7yPrH11AA1p+gttPkpHw6g0a1ymPuQOdserwbdz9+5VI0UoPX/P5Y1dREXv58mWuAVbvsrKywsuXL4sxoqK3I3QLAMB7mJfO+pk/zEP3L/ii+xCdOndBUmIiVgYuQ3x8HGzt6mFl0GpYsWz8QXiNfrw29SuievnS2HQ692DROpXMMb13Q5QprcKj+FT8d991rDp8W4QopYuv+fzpQ+Ii6n1c6tWrh7Nnz6JcuXJ5bo+Pj0erVq1w48aNQh1XSvdxkYqSfB8XKZLSfVykoCTfx0WKSvp9XKSouO7jcvhGnKDH+7yetaDHE4Los4r8/PygUqny3K5Wq4s5IiIiIukqqfdeEZKoiUvPnj3/dR85zigiIiIqCgbyz1vETVwCAgLEPD0RERFJjOh3ziUiIiJh6ENXEX+riIiIiCSDFRciIiKZ0Ifp0ExciIiIZIJdRUREREQlCCsuREREMsHp0ERERCQZ7CoiIiIiKkFYcSEiIpIJzioiIiIiydCDvIVdRURERCQdrLgQERHJhIEe9BWx4kJERESSwYoLERGRTMi/3sLEhYiISD70IHNhVxERERFJBisuREREMqEPd85l4kJERCQTejCpiF1FREREJB2suBAREcmEHhRcWHEhIiIi6WDFhYiISC70oOTCxIWIiEgm9GFWEbuKiIiISDJYcSEiIpIJfZgOzcSFiIhIJvQgb2FXEREREX28oKAg9OrVC87OznBxccHYsWMRExOjs096ejr8/f3RrFkzODs7w9fXF/Hx8YU6DxMXIiIiuVAIvBTCH3/8gUGDBiE0NBTr1q1DZmYmhg8fjtTUVO0+8+bNw4kTJ/DTTz8hJCQEz58/h4+PT6HOw64iIiIimRBzVtGaNWt0/p4/fz5cXFxw7do1NGnSBK9evcLOnTuxePFiuLi4AHibyHTp0gUXL16Ek5NTgc7DigsRERHlSa1WIyUlRWdRq9UFeuyrV68AAJaWlgCAq1evIiMjA66urtp9ateujSpVquDixYsFjomJCxERkUwoFMIuQUFBaNSokc4SFBT0r3FkZ2dj3rx5+Oyzz1C3bl0AQHx8PIyMjGBhYaGzr5WVFeLi4gr8HNlVRERERHkaNWoUhg4dqrNOpVL96+P8/f1x584dbN68WfCYmLgQERHJhNAjXFQqVYESlXfNnj0bJ0+exMaNG1GpUiXt+vLlyyMjIwPJyck6VZeEhARYW1sX+PiyTFwMlfowk52kjNeosB4F9xM7BFkZuvmi2CHIzhYvp+I5kYhvLRqNBj/88AOOHDmCkJAQVK9eXWe7vb09jIyMEBkZiY4dOwIAYmJi8OTJkwIPzAVkmrgQERFR8fL390d4eDhWrlyJ0qVLa8etmJubw9jYGObm5ujVqxfmz58PS0tLmJmZYc6cOXB2dmbiQkREpI/EnA69ZcsWAMDgwYN11gcEBMDT0xMAMG3aNBgYGGD8+PFQq9Vo2bIlZs6cWajzKDQajUaYkEuON5liR0BEJF3sKhJecXUVXYlNEfR4DauZCXo8IXA6NBEREUkGu4qIiIhkQh+G/TNxISIikgs9yFzYVURERESSwYoLERGRTIg5q6i4sOJCREREksGKCxERkUwo5F9wYeJCREQkF3qQt7CriIiIiKSDFRciIiK50IOSCxMXIiIimeCsIiIiIqIShBUXIiIimdCHWUWsuBAREZFksOJCREQkE3pQcGHiQkREJBt6kLmwq4iIiIgkgxUXIiIimdCH6dBMXIiIiGSCs4qIiIiIShBWXIiIiGRCDwouTFyIiIhkQw8yF3YVERERkWSw4kJERCQT+jCriBUXIiIikgxWXIiIiGRCH6ZDM3EhIiKSCT3IW9hVRERERNLBigsREZFc6EHJhYkLERGRTHBWERWZrZs3oXOHdmji3BCD+vfBlcuXxQ5J0tiewmJ7Co9t+mF6OVbCFi8nnWXxF3Z57julvQ22eDmhcXXLYo6SihMTFxEcPBCBxQsDMGrsOGzdHgZbWzuMGTUcCQkJYocmSWxPYbE9hcc2/TiPktIwOvSqdvE/eCfXPp3rWUOjESG4EkahEHYpiZi4iCBk/Tp49u6LHj17oXadOpg+0x/GxsbYvWun2KFJEttTWGxP4bFNP06WBnj5JlO7vErP0tles6wJuta3RtDvD0WKsORQCLyURExcilmGWo0b16+huYurdp2BgQGaN3fF5UvRIkYmTWxPYbE9hcc2/XiVzFVY2bsBfupZD+Na1oBVaSPtNpVSAZ9WNbHuj1i8fJMpYpRUXJi4FLOkF0nIysqClZWVznorKyvEx8eLFJV0sT2FxfYUHtv04/wv7jVW/f4Q84/exdrzsahgVgozO34KY8O3H1+Dm1TF7bjXuPAoWeRISwZ2FRWxkSNH4tWrV9q/f/nlFyQn/9/Fl5SUhC5duogRGhERlQCXnrzC+Qcv8fDFG1x+8goLjsWgtEqJ5rXKoFE1CzSoZI4NUY/FDpOKkajToc+cOQO1Wq39e9WqVejcuTMsLCwAAFlZWbh3755Y4RWJsmXKQqlU5hqUl5CQgPLly4sUlXSxPYXF9hQe21RYqRlZeJqcjkrmpVCjjAkqmquwpn9DnX0mtKmFm89f44fD/xMpSjGV0DKJgEStuGj+MQT8n3/LkZFKhXr1G+D8uUjtuuzsbJw/HwkHR2cRI5Mmtqew2J7CY5sKq5ShASqaq5CUloE9V59hyr5b8Av/vwUANvz5GKv0dKCuPnQV8QZ0Ihg8ZCi+nzYFDRrYw76hAzaGrEdaWhp69PQUOzRJYnsKi+0pPLbphxvUqAr+in2JuJQMlDU1RB/HysjWAL/fS8Kr9Kw8B+QmvM5AXIo6j6ORHIiauCgUCihKakpXhDp17oKkxESsDFyG+Pg42NrVw8qg1bBi2fiDsD2FxfYUHtv0w5UzNYJvq1owK6VE8ptM3Hr+Gt9H3M41JZre0odPVIVGxP4ZOzs7tG7dGiqVCgBw4sQJNG/eHCYmJgAAtVqN06dP48aNG4U6LmfEERF9uKGbL4odguxs8XIqlvM8fSlspamypUrQ4wlB1IpLz549df7u3r17rn169OhRTNEQERFRSSdq4hIQECDm6YmIiGSFP7JIREREVIJwVhEREZFcyL/gwsSFiIhILvQgb2FXEREREUkHKy5EREQyoQ+3RmPiQkREJBOcVURERERUgrDiQkREJBfyL7gwcSEiIpILPchb2FVERERE0sGKCxERkUzow6wiVlyIiIhIMlhxISIikgl9mA7NxIWIiEgm2FVEREREVIIwcSEiIiLJYFcRERGRTLCriIiIiKgEYcWFiIhIJvRhVhErLkRERCQZrLgQERHJhD6McWHiQkREJBN6kLewq4iIiIikgxUXIiIiudCDkgsTFyIiIpngrCIiIiKiEoQVFyIiIpngrCIiIiKSDD3IW9hVRERERNLBxIWIiEguFAIvH2DTpk1o164dGjZsiD59+uDy5csf8YRyY+JCREREgoiIiEBAQADGjRuHsLAw2NnZYfjw4UhISBDsHExciIiIZEIh8H+FtW7dOvTt2xe9evVCnTp14O/vD2NjY+zcuVOw58jEhYiISCYUCmGXwlCr1bh27RpcXV216wwMDODq6oro6GjBniNnFREREVGe1Go11Gq1zjqVSgWVSpVr36SkJGRlZcHKykpnvZWVFWJiYgSLSZaJi7EsnxURUfHY4uUkdgj0gYT+/Fu+PAiBgYE663x8fODr6yvsiQqBH/FERESUp1GjRmHo0KE66/KqtgBA2bJloVQqcw3ETUhIQPny5QWLiWNciIiIKE8qlQpmZmY6S36Ji0qlQoMGDRAZGaldl52djcjISDg7OwsWEysuREREJIihQ4diypQpsLe3h4ODA9avX4+0tDR4enoKdg4mLkRERCSILl26IDExEcuWLUNcXBzq1auH1atXC9pVpNBoNBrBjkZERERUhDjGhYiIiCSDiQsRERFJBhMXIiIikgwmLkRERCQZnFUkkujoaAwcOBCtWrXCL7/8InY4kuXn54ewsDDt32XKlIG9vT0mT54MOzs7ESOTtri4OKxatQonT57Es2fPYGVlhXr16mHIkCFwcXEROzzJePf6NDQ0hKWlJWxtbdG1a1d4enrCwIDfHT/EP1/3OVq2bIk1a9aIEBEVJyYuItmxYwe+/PJL7NixA8+ePUPFihXFDkmyWrVqhYCAAABAfHw8fvrpJ4wePRonT54UNzCJio2NxYABA2BhYYFvv/0WdevWRWZmJs6cOQN/f38cPHhQ7BAlJef6zM7ORnx8PE6fPo25c+fi0KFD+Pnnn2FoyLfhD/Hu6z5HfjdGI3nhK0YEr1+/RkREBHbu3In4+HiEhYVh9OjRYoclWSqVCtbW1gAAa2trjBw5EoMGDUJiYiLKlSsncnTS4+/vD4VCge3bt8PU1FS7/tNPP0WvXr1EjEya3r0+K1asiAYNGsDR0RFfffUVwsLC0KdPH5EjlKZ325X0C+uUIjhw4ABsbGxgY2OD7t27Y+fOneDtdITx+vVr7N27FzVr1kSZMmXEDkdyXrx4gdOnT2PQoEE6SUsOCwsLEaKSHxcXF9jZ2eHw4cNih0IkOay4iGDHjh3o3r07gLflzlevXuGPP/5As2bNRI5Mmk6ePKn9HYzU1FRYW1sjKCiI4wc+wMOHD6HRaGBjYyN2KLJnY2ODW7duiR2GZL37us8xatQoVq/1ABOXYhYTE4MrV65gxYoVAN4O2OvSpQt27NjBxOUDNWvWDLNmzQIAvHz5Elu2bMHIkSOxfft2VK1aVdzgJIaVv+Kj0WigUCjEDkOy3n3d57C0tBQnGCpWTFyK2Y4dO5CZmYlWrVpp12k0GqhUKsyYMQPm5uYiRidNJiYmqFmzpvbvBg0aoHHjxggNDcWECRNEjEx6atasCYVCgZiYGLFDkb27d++iWrVqYochWf983ZP+YC29GGVmZmLPnj3w8/PD7t27tcuePXtQoUIFhIeHix2iLCgUCigUCqSnp4sdiuSUKVMGLVu2xKZNm5Camppre3JysghRyU9kZCRu376Nzz//XOxQiCSHFZdidPLkSbx8+RK9e/fOVVn5/PPPsWPHDgwYMECk6KRLrVYjLi4OwNsP1o0bNyI1NRVt27YVOTJpmjlzJgYMGIA+ffpg/PjxsLW1RVZWFs6ePYstW7bgwIEDYocoKTnX57vToYOCgtC2bVv06NFD7PAk693XfQ6lUsmZhHqAiUsx2rFjB1xdXfPsDurYsSNWr16Nmzdv8sZphXT69Gm0bNkSAFC6dGnY2Nhg6dKlHDP0gapXr45du3Zh1apVWLBgAZ4/f45y5cqhQYMGucYU0L/LuT4NDQ1hYWEBOzs7TJ8+HT179uQA8o/w7us+xyeffML7DOkBhYaj8YiIiEgimO4TERGRZDBxISIiIslg4kJERESSwcSFiIiIJIOJCxEREUkGExciIiKSDCYuREREJBlMXIiIiEgymLgQEQDAz88PY8eO1f49ePBgzJ07t9jjOH/+PGxtbfm7SESUJ97yn6iE8/PzQ1hYGADAyMgIlStXxhdffIHRo0fD0LDoXsLLly8v8PHPnz8PLy8vREVFwcLCoshiIiJi4kIkAa1atUJAQADUajVOnTqF2bNnw8jICKNGjdLZT61WQ6VSCXLOMmXKCHIcIiIhMXEhkgCVSgVra2sAwMCBA3H06FEcP34c9+7dQ3JyMho2bIhNmzZBpVLh+PHjePr0KebPn4+zZ8/CwMAAjRo1wnfffYdq1aoBALKysrBw4ULs3LkTSqUSvXr1wj9/tmzw4MGws7PDd999B+BtUrR06VKEh4cjISEBlStXhre3N1xcXODl5QUAaNKkCQCgZ8+emD9/PrKzsxEcHIxt27YhPj4etWrVwtixY9GpUyfteU6dOoV58+bh6dOncHR0RM+ePYu8PYlIupi4EElQqVKl8OLFCwBAZGQkzMzMsG7dOgBARkYGhg8fDicnJ2zatAmGhoZYuXIlRowYgb1790KlUmHt2rUICwvDvHnzULt2baxduxZHjhxB8+bN8z3nt99+i4sXL2L69Omws7NDbGwskpKSULlyZSxfvhy+vr44ePAgzMzMYGxsDAAICgrC3r174e/vj1q1aiEqKgqTJ09GuXLl0LRpUzx9+hQ+Pj4YNGgQ+vbti6tXr2LBggVF3n5EJF1MXIgkRKPRIDIyEmfOnMGXX36JpKQkmJqaYs6cOdouoj179iA7Oxtz586FQqEAAAQEBKBJkyb4448/0LJlS6xfvx7e3t74/PPPAQD+/v44c+ZMvue9d+8eDhw4gHXr1sHV1RUAUL16de12S0tLAICVlZV2jItarUZQUBDWrVsHZ2dn7WMuXLiAbdu2oWnTptiyZQtq1KgBPz8/AICNjQ1u376N4OBgIZuNiGSEiQuRBJw8eRLOzs7IyMiARqNBt27d4Ovri9mzZ6Nu3bo641pu3ryJhw8f4rPPPtM5Rnp6Oh4+fIhXr14hLi4Ojo6O2m2Ghoawt7fP1V2U48aNG1AqldquoIJ48OAB0tLSMGzYMJ31GRkZqFevHgDg7t27cHBw0Nnu5ORU4HMQkf5h4kIkAc2aNcOsWbNgZGSEChUq6Mz2MTEx0dk3NTUVDRo0wOLFi3Mdp1y5ch90/pyun8JITU0F8La7qGLFijrbhBpATET6h4kLkQSYmJigZs2aBdq3QYMGOHDgAKysrGBmZpbnPtbW1rh06ZK2gpKZmYlr166hfv36ee5ft25dZGdnIyoqSttV9C4jIyMAbwf95qhduzZUKhWePHmCpk2b5nnc2rVr4/jx4zrrLl269O9Pkoj0Fm9ARyQzHh4eKFu2LMaMGYM///wTjx49wvnz5zFnzhz8/fffAAAvLy8EBwfj6NGjuHv3Lvz9/d97w7dq1aqhZ8+emDZtGo4ePao9ZkREBACgatWqUCgUOHnyJBITE/H69WuYmZlh2LBhCAgIQFhYGB4+fIhr164hJCREe1+a/v374/79+1iwYAFiYmKwb98+7TYiorwwcSGSGRMTE2zcuBFVqlSBj48PunTpgu+++w7p6enaCsywYcPQvXt3TJkyBf3790fp0qXRoUOH9x531qxZ6NixI2bNmoXOnTvj+++/R1paGgCgYsWK8PX1xY8//ghXV1f88MMPAIBvvvkGY8eORVBQELp06YIRI0bg5MmT2mnZVapUwfLly3Hs2DF88cUX2Lp1KyZMmFCErUNEUqfQ5Dcaj4iIiKiEYcWFiIiIJIOJCxEREUkGExciIiKSDCYuREREJBlMXIiIiEgymLgQERGRZDBxISIiIslg4kJERESSwcSFiIiIJIOJCxEREUkGExciIiKSDCYuREREJBn/D+hkUZMCnnXFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.eval()\n",
    "bigru_val_actuals = []\n",
    "bigru_val_top1_preds = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for X_val_batch, y_val_batch in val_loader:\n",
    "        X_val_batch = X_val_batch.to(device)\n",
    "        val_outputs = model(X_val_batch)\n",
    "        top1_idx = torch.argmax(val_outputs, dim=1).cpu().numpy()\n",
    "        for pred_i, true_i in zip(top1_idx, y_val_batch.numpy()):\n",
    "            bigru_val_top1_preds.append(idx_to_letter[pred_i])\n",
    "            bigru_val_actuals.append(idx_to_letter[true_i])\n",
    "\n",
    "bigru_accuracy = accuracy_score(bigru_val_actuals, bigru_val_top1_preds)\n",
    "bigru_f1_macro = f1_score(bigru_val_actuals, bigru_val_top1_preds, average='macro')\n",
    "bigru_f1_weighted = f1_score(bigru_val_actuals, bigru_val_top1_preds, average='weighted')\n",
    "\n",
    "print(f\"BiGRU (best checkpoint) -> Top-1 Accuracy   : {bigru_accuracy:.4f}\")\n",
    "print(f\"BiGRU (best checkpoint) -> F1 Score (macro) : {bigru_f1_macro:.4f}\")\n",
    "print(f\"BiGRU (best checkpoint) -> F1 Score (weighted): {bigru_f1_weighted:.4f}\")\n",
    "print(\"\\nClassification Report:\\n\")\n",
    "print(classification_report(bigru_val_actuals, bigru_val_top1_preds, labels=['A','B','C','D','E']))\n",
    "\n",
    "bigru_cm = confusion_matrix(bigru_val_actuals, bigru_val_top1_preds, labels=['A','B','C','D','E'])\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "sns.heatmap(bigru_cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['A','B','C','D','E'], yticklabels=['A','B','C','D','E'])\n",
    "plt.title('BiGRU - Confusion Matrix (Validation, Top-1)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "db218bbe",
   "metadata": {
    "papermill": {
     "duration": 0.013598,
     "end_time": "2026-07-24T12:17:51.532274+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:51.518676+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Install and Imports - DEBERTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "838f774c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:17:51.560758Z",
     "iopub.status.busy": "2026-07-24T12:17:51.560503Z",
     "iopub.status.idle": "2026-07-24T12:17:54.884326Z",
     "shell.execute_reply": "2026-07-24T12:17:54.883281Z"
    },
    "papermill": {
     "duration": 3.340347,
     "end_time": "2026-07-24T12:17:54.886019+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:51.545672+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Transformers library loaded\n"
     ]
    }
   ],
   "source": [
    "!pip install -q transformers\n",
    "from transformers import AutoTokenizer, AutoModel\n",
    "print('Transformers library loaded')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45389a26",
   "metadata": {
    "papermill": {
     "duration": 0.014772,
     "end_time": "2026-07-24T12:17:54.915127+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:54.900355+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Tokenizer Setup - DEBERTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bb5a60a1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:17:54.944050Z",
     "iopub.status.busy": "2026-07-24T12:17:54.943682Z",
     "iopub.status.idle": "2026-07-24T12:17:56.727610Z",
     "shell.execute_reply": "2026-07-24T12:17:56.726730Z"
    },
    "papermill": {
     "duration": 1.800249,
     "end_time": "2026-07-24T12:17:56.729103+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:54.928854+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ea23d3c12e3412fa331caf5a17d6ac3",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "config.json:   0%|          | 0.00/578 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f739f636c79d491c8e22f86256ce7360",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/52.0 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1614726ef80e492db102cb315119725e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "spm.model:   0%|          | 0.00/2.46M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DeBERTa tokenizer loaded: microsoft/deberta-v3-small\n"
     ]
    }
   ],
   "source": [
    "MODEL_NAME = \"microsoft/deberta-v3-small\"\n",
    "tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)\n",
    "print(\"DeBERTa tokenizer loaded:\", MODEL_NAME)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c912d204",
   "metadata": {
    "papermill": {
     "duration": 0.015568,
     "end_time": "2026-07-24T12:17:56.759820+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:56.744252+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Dataset Class For DEBERTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "a591081d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:17:56.788448Z",
     "iopub.status.busy": "2026-07-24T12:17:56.788167Z",
     "iopub.status.idle": "2026-07-24T12:17:56.796085Z",
     "shell.execute_reply": "2026-07-24T12:17:56.795204Z"
    },
    "papermill": {
     "duration": 0.024209,
     "end_time": "2026-07-24T12:17:56.797822+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:56.773613+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DebertaMCQDataset class ready\n"
     ]
    }
   ],
   "source": [
    "class DebertaMCQDataset(Dataset):\n",
    "    def __init__(self, df, tokenizer, max_len=64, is_test=False):\n",
    "        self.df = df.reset_index(drop=True)\n",
    "        self.tokenizer = tokenizer\n",
    "        self.max_len = max_len\n",
    "        self.is_test = is_test\n",
    "        self.label_map = {'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4}\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.df)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        row = self.df.iloc[idx]\n",
    "        prompt = str(row['prompt'])\n",
    "\n",
    "        input_ids_list = []\n",
    "        attn_mask_list = []\n",
    "\n",
    "        for option in ['A', 'B', 'C', 'D', 'E']:\n",
    "            option_text = str(row[option])\n",
    "            encoded = self.tokenizer(\n",
    "                prompt,\n",
    "                option_text,\n",
    "                truncation=True,\n",
    "                max_length=self.max_len,\n",
    "                padding='max_length',\n",
    "                return_tensors='pt'\n",
    "            )\n",
    "            input_ids_list.append(encoded['input_ids'].squeeze(0))\n",
    "            attn_mask_list.append(encoded['attention_mask'].squeeze(0))\n",
    "\n",
    "        input_ids = torch.stack(input_ids_list)          # (5, max_len)\n",
    "        attention_mask = torch.stack(attn_mask_list)      # (5, max_len)\n",
    "\n",
    "        if self.is_test:\n",
    "            return input_ids, attention_mask, torch.tensor(row['id'], dtype=torch.int)\n",
    "\n",
    "        y = self.label_map[row['answer']]\n",
    "        return input_ids, attention_mask, torch.tensor(y, dtype=torch.long)\n",
    "print('DebertaMCQDataset class ready')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6f5e20b",
   "metadata": {
    "papermill": {
     "duration": 0.014459,
     "end_time": "2026-07-24T12:17:56.826104+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:56.811645+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Model Architecture - DEBERTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "17bb1f86",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:17:56.855559Z",
     "iopub.status.busy": "2026-07-24T12:17:56.854947Z",
     "iopub.status.idle": "2026-07-24T12:17:56.861420Z",
     "shell.execute_reply": "2026-07-24T12:17:56.860557Z"
    },
    "papermill": {
     "duration": 0.023196,
     "end_time": "2026-07-24T12:17:56.863317+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:56.840121+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DeBERTaMCQModel class ready\n"
     ]
    }
   ],
   "source": [
    "class DeBERTaMCQModel(nn.Module):\n",
    "    def __init__(self, model_name=MODEL_NAME, hidden_dropout=0.3):\n",
    "        super(DeBERTaMCQModel, self).__init__()\n",
    "        # torch_dtype=torch.float32 explicitly set — kuch checkpoints (deberta-v3-small)\n",
    "        # naye transformers versions mein by default fp16 (Half) mein load ho jaate hain,\n",
    "        # jisse classifier Linear (fp32) ke saath matmul dtype mismatch error aata hai.\n",
    "        self.deberta = AutoModel.from_pretrained(model_name, torch_dtype=torch.float32)\n",
    "        hidden_size = self.deberta.config.hidden_size\n",
    "        self.classifier = nn.Sequential(\n",
    "            nn.Dropout(hidden_dropout),\n",
    "            nn.Linear(hidden_size, 1)\n",
    "        )\n",
    "\n",
    "    def forward(self, input_ids, attention_mask):\n",
    "        batch_size, num_options, seq_len = input_ids.shape\n",
    "        input_ids = input_ids.view(-1, seq_len)\n",
    "        attention_mask = attention_mask.view(-1, seq_len)\n",
    "\n",
    "        outputs = self.deberta(input_ids=input_ids, attention_mask=attention_mask)\n",
    "        # .float() safety cast — agar kabhi bhi hidden_state fp16 mein aaye to bhi\n",
    "        # classifier ke saath dtype match rahega\n",
    "        cls_output = outputs.last_hidden_state[:, 0, :].float()   # CLS token representation\n",
    "        scores = self.classifier(cls_output)\n",
    "        return scores.view(batch_size, num_options)\n",
    "print('DeBERTaMCQModel class ready')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dc87ff7",
   "metadata": {
    "papermill": {
     "duration": 0.013927,
     "end_time": "2026-07-24T12:17:56.892173+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:56.878246+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# DataLoaders - DEBERTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "1bab15be",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:17:56.921728Z",
     "iopub.status.busy": "2026-07-24T12:17:56.921425Z",
     "iopub.status.idle": "2026-07-24T12:17:56.932317Z",
     "shell.execute_reply": "2026-07-24T12:17:56.931654Z"
    },
    "papermill": {
     "duration": 0.02788,
     "end_time": "2026-07-24T12:17:56.933838+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:56.905958+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DeBERTa dataloaders ready\n"
     ]
    }
   ],
   "source": [
    "deberta_train_dataset = DebertaMCQDataset(dl_train_df, tokenizer, max_len=64)\n",
    "deberta_val_dataset   = DebertaMCQDataset(dl_val_df, tokenizer, max_len=64)\n",
    "deberta_test_dataset  = DebertaMCQDataset(test_dt, tokenizer, max_len=64, is_test=True)\n",
    "\n",
    "deberta_train_loader = DataLoader(deberta_train_dataset, batch_size=8, shuffle=True)\n",
    "deberta_val_loader   = DataLoader(deberta_val_dataset, batch_size=8, shuffle=False)\n",
    "deberta_test_loader  = DataLoader(deberta_test_dataset, batch_size=8, shuffle=False)\n",
    "print('DeBERTa dataloaders ready')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bb890ac6",
   "metadata": {
    "papermill": {
     "duration": 0.013669,
     "end_time": "2026-07-24T12:17:56.961604+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:56.947935+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# W&B Experiment Initialization - DEBERTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d19268b7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:17:56.990174Z",
     "iopub.status.busy": "2026-07-24T12:17:56.989904Z",
     "iopub.status.idle": "2026-07-24T12:17:58.805870Z",
     "shell.execute_reply": "2026-07-24T12:17:58.805028Z"
    },
    "papermill": {
     "duration": 1.832179,
     "end_time": "2026-07-24T12:17:58.807550+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:56.975371+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: setting up run aciujl8h\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Tracking run with wandb version 0.25.1\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run data is saved locally in \u001b[35m\u001b[1m/kaggle/working/wandb/run-20260724_121757-aciujl8h\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Run \u001b[1m`wandb offline`\u001b[0m to turn off syncing.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Syncing run \u001b[33mdeberta-v3-small-transformer\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: ⭐️ View project at \u001b[34m\u001b[4mhttps://wandb.ai/23f3000653-indian-institute-of-technology-madras/DL-23f3000653-notebook-t22026\u001b[0m\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: 🚀 View run at \u001b[34m\u001b[4mhttps://wandb.ai/23f3000653-indian-institute-of-technology-madras/DL-23f3000653-notebook-t22026/runs/aciujl8h\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<button onClick=\"this.nextSibling.style.display='block';this.style.display='none';\">Display W&B run</button><iframe src='https://wandb.ai/23f3000653-indian-institute-of-technology-madras/DL-23f3000653-notebook-t22026/runs/aciujl8h?jupyter=true' style='border:none;width:100%;height:420px;display:none;'></iframe>"
      ],
      "text/plain": [
       "<wandb.sdk.wandb_run.Run at 0x79cb8c1073e0>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wandb.init(\n",
    "    project=\"DL-23f3000653-notebook-t22026\",\n",
    "    name=\"deberta-v3-small-transformer\",\n",
    "    config={\n",
    "        \"architecture\": \"DeBERTa-v3-small\",\n",
    "        \"max_len\": 64,\n",
    "        \"epochs\": 3,\n",
    "        \"batch_size\": 8,\n",
    "        \"learning_rate\": 2e-5,\n",
    "        \"dropout\": 0.3\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c1635256",
   "metadata": {
    "papermill": {
     "duration": 0.014633,
     "end_time": "2026-07-24T12:17:58.837493+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:58.822860+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Training Loop - DEBERTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3f5ed16a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:17:58.867498Z",
     "iopub.status.busy": "2026-07-24T12:17:58.866834Z",
     "iopub.status.idle": "2026-07-24T12:21:25.456579Z",
     "shell.execute_reply": "2026-07-24T12:21:25.455753Z"
    },
    "papermill": {
     "duration": 206.623727,
     "end_time": "2026-07-24T12:21:25.475428+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:17:58.851701+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`torch_dtype` is deprecated! Use `dtype` instead!\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "59e9b8f45fc346289d318c8235804d1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pytorch_model.bin:   0%|          | 0.00/286M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "94bc70fb25244eefa6acede906b3a03f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading weights:   0%|          | 0/102 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "DebertaV2Model LOAD REPORT from: microsoft/deberta-v3-small\n",
      "Key                                     | Status     |  | \n",
      "----------------------------------------+------------+--+-\n",
      "mask_predictions.classifier.bias        | UNEXPECTED |  | \n",
      "mask_predictions.LayerNorm.weight       | UNEXPECTED |  | \n",
      "mask_predictions.dense.bias             | UNEXPECTED |  | \n",
      "mask_predictions.LayerNorm.bias         | UNEXPECTED |  | \n",
      "mask_predictions.dense.weight           | UNEXPECTED |  | \n",
      "lm_predictions.lm_head.LayerNorm.bias   | UNEXPECTED |  | \n",
      "lm_predictions.lm_head.dense.bias       | UNEXPECTED |  | \n",
      "lm_predictions.lm_head.dense.weight     | UNEXPECTED |  | \n",
      "lm_predictions.lm_head.bias             | UNEXPECTED |  | \n",
      "mask_predictions.classifier.weight      | UNEXPECTED |  | \n",
      "lm_predictions.lm_head.LayerNorm.weight | UNEXPECTED |  | \n",
      "\n",
      "Notes:\n",
      "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3ce03ca0940a43cdbdb5451d84ce9db4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "model.safetensors:   0%|          | 0.00/286M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Starting DeBERTa fine-tuning...\n",
      "Epoch 1/3 | Train Loss: 1.5100 | Val MAP@3: 0.7662\n",
      "Epoch 2/3 | Train Loss: 0.7367 | Val MAP@3: 0.9621\n",
      "Epoch 3/3 | Train Loss: 0.4021 | Val MAP@3: 0.9754\n",
      "Best DeBERTa Validation MAP@3: 0.9754\n"
     ]
    }
   ],
   "source": [
    "deberta_model = DeBERTaMCQModel().to(device)\n",
    "criterion = nn.CrossEntropyLoss()\n",
    "optimizer = optim.AdamW(deberta_model.parameters(), lr=2e-5, weight_decay=1e-2)\n",
    "\n",
    "epochs = 3\n",
    "best_deberta_map3 = 0.0\n",
    "best_deberta_wts = copy.deepcopy(deberta_model.state_dict())\n",
    "\n",
    "print(\"Starting DeBERTa fine-tuning...\")\n",
    "for epoch in range(epochs):\n",
    "    deberta_model.train()\n",
    "    train_loss = 0.0\n",
    "    for input_ids, attention_mask, y_batch in deberta_train_loader:\n",
    "        input_ids, attention_mask, y_batch = input_ids.to(device), attention_mask.to(device), y_batch.to(device)\n",
    "\n",
    "        optimizer.zero_grad()\n",
    "        outputs = deberta_model(input_ids, attention_mask)\n",
    "        loss = criterion(outputs, y_batch)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "\n",
    "        train_loss += loss.item() * input_ids.size(0)\n",
    "\n",
    "    epoch_train_loss = train_loss / len(deberta_train_loader.dataset)\n",
    "\n",
    "    # Validation\n",
    "    deberta_model.eval()\n",
    "    actuals_list = []\n",
    "    predictions_list = []\n",
    "\n",
    "    with torch.no_grad():\n",
    "        for input_ids, attention_mask, y_val_batch in deberta_val_loader:\n",
    "            input_ids, attention_mask = input_ids.to(device), attention_mask.to(device)\n",
    "            val_outputs = deberta_model(input_ids, attention_mask)\n",
    "\n",
    "            sorted_indices = torch.argsort(val_outputs, dim=1, descending=True).cpu().numpy()\n",
    "            for idxs, true_lbl in zip(sorted_indices, y_val_batch.numpy()):\n",
    "                predictions_list.append([idx_to_letter[i] for i in idxs[:3]])\n",
    "                actuals_list.append(idx_to_letter[true_lbl])\n",
    "\n",
    "    epoch_map3 = map_at_3(actuals_list, predictions_list)\n",
    "\n",
    "    # Save best checkpoint based on validation MAP@3\n",
    "    if epoch_map3 >= best_deberta_map3:\n",
    "        best_deberta_map3 = epoch_map3\n",
    "        best_deberta_wts = copy.deepcopy(deberta_model.state_dict())\n",
    "\n",
    "    wandb.log({\n",
    "        \"epoch\": epoch + 1,\n",
    "        \"train_loss\": epoch_train_loss,\n",
    "        \"val_map3\": epoch_map3\n",
    "    })\n",
    "\n",
    "    print(f\"Epoch {epoch+1}/{epochs} | Train Loss: {epoch_train_loss:.4f} | Val MAP@3: {epoch_map3:.4f}\")\n",
    "\n",
    "deberta_model.load_state_dict(best_deberta_wts)\n",
    "print(f\"Best DeBERTa Validation MAP@3: {best_deberta_map3:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b923179",
   "metadata": {
    "papermill": {
     "duration": 0.017129,
     "end_time": "2026-07-24T12:21:25.508685+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:21:25.491556+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# DeBERTa - Final Validation Metrics (Accuracy, F1, Confusion Matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "c7bc6d56",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:21:25.542529Z",
     "iopub.status.busy": "2026-07-24T12:21:25.542123Z",
     "iopub.status.idle": "2026-07-24T12:21:30.584149Z",
     "shell.execute_reply": "2026-07-24T12:21:30.583454Z"
    },
    "papermill": {
     "duration": 5.061216,
     "end_time": "2026-07-24T12:21:30.585881+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:21:25.524665+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DeBERTa (best checkpoint) -> Top-1 Accuracy   : 0.9425\n",
      "DeBERTa (best checkpoint) -> F1 Score (macro) : 0.9438\n",
      "DeBERTa (best checkpoint) -> F1 Score (weighted): 0.9426\n",
      "\n",
      "Classification Report:\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           A       0.86      1.00      0.93        68\n",
      "           B       0.94      0.88      0.91        97\n",
      "           C       0.99      0.97      0.98        98\n",
      "           D       0.94      0.93      0.93        83\n",
      "           E       0.98      0.96      0.97        54\n",
      "\n",
      "    accuracy                           0.94       400\n",
      "   macro avg       0.94      0.95      0.94       400\n",
      "weighted avg       0.95      0.94      0.94       400\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "deberta_model.eval()\n",
    "deberta_val_actuals = []\n",
    "deberta_val_top1_preds = []\n",
    "\n",
    "with torch.no_grad():\n",
    "    for input_ids, attention_mask, y_val_batch in deberta_val_loader:\n",
    "        input_ids, attention_mask = input_ids.to(device), attention_mask.to(device)\n",
    "        val_outputs = deberta_model(input_ids, attention_mask)\n",
    "        top1_idx = torch.argmax(val_outputs, dim=1).cpu().numpy()\n",
    "        for pred_i, true_i in zip(top1_idx, y_val_batch.numpy()):\n",
    "            deberta_val_top1_preds.append(idx_to_letter[pred_i])\n",
    "            deberta_val_actuals.append(idx_to_letter[true_i])\n",
    "\n",
    "deberta_accuracy = accuracy_score(deberta_val_actuals, deberta_val_top1_preds)\n",
    "deberta_f1_macro = f1_score(deberta_val_actuals, deberta_val_top1_preds, average='macro')\n",
    "deberta_f1_weighted = f1_score(deberta_val_actuals, deberta_val_top1_preds, average='weighted')\n",
    "\n",
    "print(f\"DeBERTa (best checkpoint) -> Top-1 Accuracy   : {deberta_accuracy:.4f}\")\n",
    "print(f\"DeBERTa (best checkpoint) -> F1 Score (macro) : {deberta_f1_macro:.4f}\")\n",
    "print(f\"DeBERTa (best checkpoint) -> F1 Score (weighted): {deberta_f1_weighted:.4f}\")\n",
    "print(\"\\nClassification Report:\\n\")\n",
    "print(classification_report(deberta_val_actuals, deberta_val_top1_preds, labels=['A','B','C','D','E']))\n",
    "\n",
    "deberta_cm = confusion_matrix(deberta_val_actuals, deberta_val_top1_preds, labels=['A','B','C','D','E'])\n",
    "\n",
    "plt.figure(figsize=(6, 5))\n",
    "sns.heatmap(deberta_cm, annot=True, fmt='d', cmap='Blues',\n",
    "            xticklabels=['A','B','C','D','E'], yticklabels=['A','B','C','D','E'])\n",
    "plt.title('DeBERTa - Confusion Matrix (Validation, Top-1)')\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Actual')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c124a474",
   "metadata": {
    "papermill": {
     "duration": 0.015148,
     "end_time": "2026-07-24T12:21:30.617150+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:21:30.602002+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Final Comparision - Sbert vs BiGRu vs DEBERTA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "0310271a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:21:30.649027Z",
     "iopub.status.busy": "2026-07-24T12:21:30.648803Z",
     "iopub.status.idle": "2026-07-24T12:21:30.996967Z",
     "shell.execute_reply": "2026-07-24T12:21:30.996086Z"
    },
    "papermill": {
     "duration": 0.36576,
     "end_time": "2026-07-24T12:21:30.998601+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:21:30.632841+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            Model  MAP@3 Score  Accuracy  F1 (Macro)  \\\n",
      "0           BiGRU (Deep Learning)     0.990000    0.9800    0.980425   \n",
      "1  DeBERTa-v3-small (Transformer)     0.975417    0.9425    0.943788   \n",
      "2        SBERT Embedding Baseline     0.380417    0.2225    0.221091   \n",
      "\n",
      "   F1 (Weighted)  \n",
      "0       0.980116  \n",
      "1       0.942564  \n",
      "2       0.222611  \n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best Model (by MAP@3): BiGRU (Deep Learning) with MAP@3 = 0.9900\n"
     ]
    }
   ],
   "source": [
    "comparison_df = pd.DataFrame({\n",
    "    'Model': ['SBERT Embedding Baseline', 'BiGRU (Deep Learning)', 'DeBERTa-v3-small (Transformer)'],\n",
    "    'MAP@3 Score': [score, best_map3, best_deberta_map3],\n",
    "    'Accuracy': [sbert_accuracy, bigru_accuracy, deberta_accuracy],\n",
    "    'F1 (Macro)': [sbert_f1_macro, bigru_f1_macro, deberta_f1_macro],\n",
    "    'F1 (Weighted)': [sbert_f1_weighted, bigru_f1_weighted, deberta_f1_weighted]\n",
    "})\n",
    "\n",
    "comparison_df = comparison_df.sort_values('MAP@3 Score', ascending=False).reset_index(drop=True)\n",
    "print(comparison_df)\n",
    "\n",
    "# --- Chart 1: MAP@3 comparison ---\n",
    "plt.figure(figsize=(8, 5))\n",
    "sns.barplot(data=comparison_df, x='Model', y='MAP@3 Score', palette='viridis')\n",
    "plt.title('Model Comparison - Validation MAP@3 Score')\n",
    "plt.xticks(rotation=15)\n",
    "plt.ylim(0, 1)\n",
    "for i, v in enumerate(comparison_df['MAP@3 Score']):\n",
    "    plt.text(i, v + 0.01, f\"{v:.4f}\", ha='center')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# --- Chart 2: Accuracy vs F1 (macro) vs F1 (weighted) grouped bar chart ---\n",
    "metrics_melt = comparison_df.melt(\n",
    "    id_vars='Model',\n",
    "    value_vars=['Accuracy', 'F1 (Macro)', 'F1 (Weighted)'],\n",
    "    var_name='Metric', value_name='Score'\n",
    ")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=metrics_melt, x='Model', y='Score', hue='Metric', palette='Set2')\n",
    "plt.title('Model Comparison - Accuracy vs F1 Score (Top-1 Predictions)')\n",
    "plt.xticks(rotation=15)\n",
    "plt.ylim(0, 1)\n",
    "plt.legend(title='Metric', bbox_to_anchor=(1.02, 1), loc='upper left')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "best_model_name = comparison_df.iloc[0]['Model']\n",
    "best_model_score = comparison_df.iloc[0]['MAP@3 Score']\n",
    "print(f\"\\nBest Model (by MAP@3): {best_model_name} with MAP@3 = {best_model_score:.4f}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58ed4cf1",
   "metadata": {
    "papermill": {
     "duration": 0.018015,
     "end_time": "2026-07-24T12:21:31.035125+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:21:31.017110+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Additional Useful Graphs - Side-by-Side Confusion Matrices & Per-Class F1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "77723ea8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:21:31.070535Z",
     "iopub.status.busy": "2026-07-24T12:21:31.070134Z",
     "iopub.status.idle": "2026-07-24T12:21:31.992982Z",
     "shell.execute_reply": "2026-07-24T12:21:31.991991Z"
    },
    "papermill": {
     "duration": 0.942461,
     "end_time": "2026-07-24T12:21:31.994715+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:21:31.052254+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1800x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      SBERT     BiGRU   DeBERTa\n",
      "A  0.254777  0.957746  0.925170\n",
      "B  0.259259  1.000000  0.909091\n",
      "C  0.237838  0.969072  0.979381\n",
      "D  0.143791  0.975309  0.933333\n",
      "E  0.209790  1.000000  0.971963\n"
     ]
    },
    {
     "data": {
      "image/png": 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VrOXLl7deunTJeu7cOWuZMmWsU6dOtVv32LFj1rJly9pN79Spk9Xb29u6cOHCJ7b93//+1+rt7W39888/k1Xr2bNnbdMiIyPjLffpp59aK1SoYL13757VarVa//zzT6u3t7d17dq1iW579uzZVm9vb+v169eTVEuc+/fvW318fKxjxoyJN2/ixIlWb29v68mTJ63Xr1+3nj171rpo0SKrr6+vNSAgwBoREWHdu3ev1dvb27pq1Sq7dbdu3Rpver169aze3t7WrVu3Jrm+mTNnWsuXL2+9c+eO1Wq1Wv/991+rt7e3dePGjXbLxT233bp1s1osFtv0//73v9YyZcpYb9++bbVardaNGzdavb29rYcPH060zbVr11q9vb2t//zzj9VqtVo3b95s9fX1tb7zzjvW999/37ZcixYtrH379rU9/uijj6w1a9a0hoWF2W1v4MCB1sqVK9te6127dlm9vb2tL7/8coKv/6NGjx79xH525MgRq7e3tzUwMNA2zdvb2+rt7W0NDQ21TTt//rzVz8/Pru4ZM2bE65dx6tWrZx0yZEi8Wvbu3WubFh4ebq1fv761Xr161tjYWLt9bNKkia0fW61W69y5c63e3t7WY8eOxWurUaNG1p49ez7p6bD1o0f/TZw4McHlDx8+bPX29rYuW7bsiduO89NPP1m9vb2t5cqVs3bu3Nk6YcIE6969e237FyckJMTq7e1t/fzzz+NtI64fHj161Ort7W39+OOP7eaPGTPG6u3tbQ0JCYm3b4/+jUyZMsVasWJF67///ms3fdy4cdYyZcpYL1y4kOi+xD3nj/7NJCa5r3Hz5s2t0dHRtmX/85//WH18fOK9lm+88Ya1Xr16dtOS2kcT+js5ePCg1dvb2xoUFGSbFvc+0L59e2tMTIzd8o++/yblvSBumW+//dZuev/+/a0+Pj7W06dP2+1LuXLl7KbFvfbz589PtI040dHR1mrVqlm//vpr27T//Oc/1pYtW9otl5Q+F1dP6dKlrcePH7dbpk+fPtZy5cpZz5w5Y5t2+fJlq7+/v7Vjx462aS1btrT27t070Xpv3bpl9fb2ts6YMeOJ+/aouM+W69evW8eMGWNt2LChbV7r1q2tQ4cOte3DyJEjbfPmzJlj9fb2tq5cudI2LTo62vrGG29YK1asaPuciHvdvv/+e9tyMTEx1g4dOsR7L+jatau1efPmdu9TFovF+sYbb1gbNWpkmxbX33ft2mW1WpP2uQxkZAwvB9Kpbt262YYSDhw4UG5ubpo8ebLy5cunjRs3ymKxqEmTJgoLC7P98/DwUNGiRePdAsTFxSVJVzmOGw6clHNQE/PwuZrh4eEKCwtTlSpVFBkZabtCddxVwrdv367IyMgEtxN3pH7z5s1PHG76sFu3bslqtT72CrmvvPKKatSooZdfflnDhw9X0aJFNW3aNLm6umrdunXKnj27atasaffclitXTlmzZo333BYqVEi1a9dOcn2rV69W3bp1bc9BsWLFVK5cuURvbRZ3pDlOlSpVFBsba7vaddzRhy1btuj+/fsJbqNKlSqSZBvium/fPvn5+almzZrat2+fpAdDF48fP25b1mq1asOGDapfv76sVqvdc1GrVi3duXNHR44csWvntddeS9K5unfv3pX0+H4WN+/RIer+/v7y9fW1PS5YsKBefvllbd++Pd4pDEnx22+/qXz58rb9jmv7jTfe0Pnz5/XPP//YLd+qVSu7EQlx6509ezbetnPkyKEbN24kqY4KFSpo9uzZdv8eNxIgudq0aaMZM2aoevXqOnDggL799lt17NhRjRo1shs9smHDBplMpnhHbyXZ+uFvv/0mSfEu/PbWW2/ZzY+T0N/IunXrVLlyZbm7u9v1rYCAAMXGxsYbjv2w5L5PJfc1fvXVV+1GaZQvX15Wq1WtW7e2W658+fK6ePGiYmJi7KYnpY8+/Hdy//593bhxQ0WKFJG7u7v+/PPPePvQrl27Jx7VT8p7wdatW2U2m9W5c2e76W+99ZasVmu8oeMBAQF2p+mULl1a2bJlS7C/J9TWzZs37YZVN2/eXH/99ZeOHz9um5aUPhenatWqKlWqlO1xbGysduzYoQYNGqhw4cK26Xnz5lXz5s21f/9+W39xd3fX8ePHderUqQTrzZIli5ydnbVnz554pxIlR4sWLXT69GkdPnxYp0+fVmhoaKIXKty6das8PT3tniNnZ2d17txZERERtr+DrVu3KlOmTGrfvr1tObPZHO/0nJs3b2rXrl1q0qSJ7fM3LCxMN27cUK1atXTq1Cldvnw5wVqS8rkMZGQMLwfSqeHDh6t48eIym83y8PBQ8eLFbRelOnXqlKxWqxo1apTguo9eQChfvnx2QeHOnTuKioqyPXZ2dlbOnDltH7pxoehpHD9+XBMmTNCuXbviBaa488IKFy6s7t27a/bs2Vq9erWqVKmi+vXrq2XLlrYvjk2bNtVPP/2kTz75RF999ZVq1Kihhg0b6pVXXkn04lwPsz5yfuLDJk2apGzZsilTpkzKnz+/3ZfK06dP686dO4nebujRi04lZchhnBMnTujPP//Uq6++ancecPXq1bVgwQKFh4fHu21ZwYIF7R7H/Zhw+/ZtSVK1atXUuHFjTZ48WXPmzFG1atXUoEEDtWjRwvaae3h4qFixYtq3b5/efPNN7d+/X9WrV1eVKlX0+eef6+zZszpx4oQsFosqV64sSQoLC9Pt27e1ePFiLV68OMH9eXQ49MPPRWxsbLz5OXLkkIuLiy0sPa6fJRbMixYtGm/ZYsWKKTIyUmFhYck+7/nChQu24acPK1GihG1+3JBg6cmvx8OsVmuSr5SeK1cuBQQEJLnup1G7dm3Vrl1bkZGROnLkiIKDg7Vo0SK98847Wrt2rfLkyaMzZ84ob968tlMtEnL+/Hk5OTnFu2aCp6en3N3d493+LKG/kdOnT+vYsWOJ/p092nceltz3qWd9jePekx69DVT27NllsVh0584du6HWSemjUVFRmjZtmpYvX67Lly/bvV8ldP5sUt5nkvJecP78eeXNmzfe+0zcKSWPvnaP7rP04O84of7+qFWrVqlQoUK2U4MkqUiRInJ1ddXq1av1n//8R5KS1OfiPPo8hIWFKTIyMsHTekqWLCmLxaKLFy/qhRde0IABA9SnTx81btxY3t7eqlWrll599VXb6TUuLi4aPHiwvvzyS9WsWVMVKlTQSy+9pNdeey1Z7ytly5ZViRIl9PPPP8vd3V2enp6264Y86vz58ypatGi8z7S41+PChQu25Tw9PeO9Hz6632fOnJHVatU333yjb775JsE2r1+/rnz58sWbnpTPZSAjI3QD6VT58uUTvR+2xWKRyWTS999/n+DRj0dvs/To0cfRo0fbXailWrVqmj9/vu1L6LFjx1SmTJlk13z79m116tRJ2bJl04ABA1SkSBFlzpxZR44c0bhx4+yOWA8dOlSvv/66Nm/erB07duiLL77QtGnTtGTJEuXPn19ZsmTRggULtHv3bm3ZskXbtm1TcHCwFi9erFmzZiV61CdHjhy2cy8TU6VKlUTP8bVYLMqTJ4/GjRuX4PxH10vOVZjjjmYHBgYqMDAw3vz169fHO5qW2A8McV/STSaTJk6cqEOHDunXX3/Vtm3b9NFHH2n27NlavHix7UtapUqVtGvXLkVFRenIkSPq06ePvL295e7urn379unEiRPKmjWrypYta3seJKlly5Z6/fXXE6zBx8fH7vHDz8XFixf18ssv282fN2+eqlevbvtC+bh+duzYMUmyO6r1PHjS6/Gw27dvJxjAHM3V1VVVqlRRlSpVlCtXLk2ePFlbt25N9HVOTFJ/UEjob8RisahmzZrxzu2NU6xYsUS39/D7VIMGDZJUQ3Ik9hon57V/ks8//9x29fWKFSsqe/bsMplMGjhwYILbS8oVupP6XpAcib3PPmmf426LeO/evQR/HP755581cODAZN++71muel+1alVt3LjR9pmzdOlSzZ07VyNHjrRdN6Jbt26qX7++Nm3apO3bt+ubb77R9OnTNXfuXNt7Y1I0b95cCxculJubm5o0aZKkH4pTQtz79ltvvZXoCKzHXWD0SZ/LQEZG6AYyoCJFishqtapQoUJPvHBXQnr27Gl3sbS4o3V16tSR2WzW6tWrn2po6549e3Tz5k1NnjxZVatWtU1P7MqvcVeW7tOnjw4cOKD27dtr4cKFGjhwoKQHX3Jr1KihGjVqaNiwYfruu+80fvx47d69O9GjgpkyZVKRIkWSfLXZRxUpUkQhISGqVKnSM9/W6GHW/3/rnOrVq6tDhw7x5n/77bdavXp1vNCdVBUrVlTFihU1cOBArV69WoMHD1ZwcLDty2SVKlW0fPlyrVmzRrGxsapUqZKcnJxUuXJlW+iuVKmS7Ut27ty55ebmJovF8lRHYD09PTV79my7aXFHlOL62cqVKxPtZ3G3fHr0i2NCVwo/deqUXF1dbT+IJOeLfMGCBfXvv//Gmx53KsSjRz2TKiYmRhcvXlT9+vWfav3UEjcM+urVq5Ie9P/t27fr5s2biR559PLyksVi0enTp20/oEgPLlh2+/ZteXl5PbHdIkWKKCIi4qn6VuXKlZUjRw6tWbNG77zzzhOHXRv1GicmKX10/fr1eu211+yupH/v3r0UuUr0494LvLy8FBISEm9UTdxzkZTXLik2bNige/fuacSIEfEuuPbvv/9qwoQJ2r9/v6pUqZKkPpeY3Llzy9XVNdHX18nJye5ofc6cOdW6dWu1bt1ad+/eVadOnTRp0iS7izUWKVJEb731lt566y2dOnVKr732mmbNmpXoD7EJadGihSZOnKirV6/qf//7X6LLeXl56dixY7JYLHbB/NG+6eXlpV27dsW7BeGj+x03xN7Z2fmpR8486XMZyKg4pxvIgBo1aiSz2azJkyfHO+JgtVqfeB5pqVKlFBAQYPsX98W7QIECatu2rbZv36758+fHW89isWjWrFkJ3vJF+r8jQQ/XFB0drR9//NFuufDw8HjnQXp7e8vJycl2C56bN2/G237cUdFHb9PzqIoVK+qPP/547DKJadKkiWJjY/Xtt9/GmxcTE5OkYZUJibt1TqtWrfTKK6/E+9e0aVPt3r070fPtEhN3DvvDEnqe4s5n/f777+Xj42MbLli5cmWFhITojz/+sA0tlx4c4WrcuLHWr1+vv//+O167jxv+Kz04MvdwHwsICFCOHDkkPehnrVq10s6dO+P1DUlauHChdu3apdatW8c7unLw4EG7c8kvXryozZs3q2bNmrbwFXfl56QEmLp16+rw4cM6ePCgbVpERISWLFkiLy+vpz7S/s8//+jevXvPdN/3lBQSEpLg9Ljzr+N+vGvUqJGsVqsmT54cb9m4fhZ3y6q5c+fazY/7kSVu/uM0adJEBw8etN3y6WG3b9+O9/7wMFdXV/Xs2VMnTpzQuHHjEjzqunLlSh0+fNhWjxGvcWKS0kcT+qFg/vz5T3VdgjhJeS+oU6eOYmNjtWDBArvl5syZI5PJpDp16jx1+w9btWqVChcurPbt28d7r+vRo4eyZs1qu1ViUvpcYsxms2rWrKnNmzfb/dB67do1/fzzz6pcubLtx4VHPxfd3NxUpEgR23MTGRmpe/fu2S1TpEgRubm5PfEz51FFihTRRx99pEGDBtnduu5RderU0dWrVxUcHGybFhMTo/nz5ytr1qy2H6/r1KmjmJgYLVy40LZcbGysfvjhB7vt5cmTR9WqVdPixYt15cqVeO097n07KZ/LQEbGkW4gAypSpIjef/99ffXVVzp//rwaNGggNzc3nTt3Tps2bVK7du3Uo0ePp9r20KFDdfbsWX3xxRfasGGD6tWrJ3d3d128eFHr1q3TyZMn1axZswTX9ff3V44cOTR06FB17txZJpNJK1eujPfFadeuXRo1apReeeUVFStWTLGxsVq5cqUt6EnSlClTtG/fPtWtW1deXl66fv26fvzxR+XPn98uHCbk5Zdf1sqVK/Xvv/8meyRAtWrV9MYbb2jatGk6evSoatasKWdnZ506dUrr1q3Txx9/rFdeeSVZ25QeXEDNbDYneNsY6cH9jMePH6/g4OB4F6h6nKCgIC1cuFANGjRQkSJFdPfuXS1ZskTZsmWz+wJdtGhReXp66t9//7W7iFLVqlVtR3AevtCUJA0aNEi7d+9Wu3bt1LZtW5UqVUq3bt3SkSNHFBISoj179iTjGbA3bNgwnTx5UiNHjtS2bdtsR7S3b9+uzZs3q1q1avHupy09+BLYo0cPu9sxSQ9u9RMn7nZx48ePV9OmTeXs7Kx69erFO+1Cknr37q01a9aoV69e6ty5s3LkyKEVK1bo3LlzmjRp0lMPC925c6dcXV1T9DztH374Qbdv37Z9mf71119tP4B17tz5sedd9unTR4UKFVK9evVUuHBhRUZGaufOnfr111/l5+enevXqSXpwz/pXX31V8+fP1+nTp1W7dm1ZLBbbdQA6deqk0qVL6/XXX9fixYt1+/ZtVa1aVaGhoQoKClKDBg0SPX/1YT169NAvv/yid955R6+//rrKlSunyMhI/f3331q/fr02b96c6Ckg0oPROv/8849mzZql3bt3q3HjxvLw8NC1a9e0adMmHT582HY/cKNe48QkpY++9NJLWrlypbJly6ZSpUrp0KFD2rlzZ7KP9D4sKe8F9evXV/Xq1TV+/HidP39ePj4+2rFjhzZv3qyuXbs+duhxUsXdFvHRi7XFcXFxUe3atbVu3Tp98sknSepzj/P+++9r586d6tChgzp06CCz2azFixcrOjpaH3zwgW25Zs2aqVq1aipXrpxy5syp0NBQrV+/3rb9U6dOqVu3bnrllVdUqlQpmc1mbdq0SdeuXUv0M+9x4m739ThvvPGGFi9erKFDh+rIkSPy8vLS+vXrdeDAAX300Ue2Hwzq16+vSpUq2T7zS5UqpQ0bNiT4w+Jnn32mDh06qEWLFmrXrp0KFy6sa9eu6dChQ7p06VKiF+1MyucykJERuoEMqnfv3ipWrJjmzJljuy9u/vz5VbNmzWca0urq6qrvv/9ey5cv14oVK/Ttt98qKipKefPmVfXq1TVu3LgEL8IiPbgY1Hfffacvv/xSEyZMkLu7u1q2bKkaNWrY/Qjg4+OjWrVq6ddff9Xly5fl6uoqHx8fff/996pYsaKkB18yzp8/r2XLlunGjRvKlSuXqlWrpv79+z/xoi716tVTrly5tHbtWvXp0yfZz8GoUaPk6+urRYsWafz48TKbzfLy8lLLli1VqVKlZG/v/v37Wrdunfz9/RP9Uu3t7a1ChQpp1apVyQrd1apVU2hoqIKDg3Xt2jVlz55d5cuX17hx4+yu5is9OKq9bt06u30oV66cXF1dFRMTE+9iUx4eHvrpp580ZcoUbdy4UQsXLlTOnDlVqlQpDR48OOlPQALc3Nw0Z84c/fjjj1q1apX+97//yWq1qkSJEvroo4/UoUOHBO/zXbVqVVWsWFFTpkzRhQsXVKpUKQUGBtqGrksProfw3nvvadGiRdq2bZssFos2b96cYOj28PDQokWL9L///U8//PCD7t27Jx8fH3333XeJ/kCSFOvWrVPDhg3jXbDqWcyaNcvuQlcbNmzQhg0bJOmJFzv64osvtHnzZq1du1ZXrlyR1WpV4cKF9c4776hXr152F18MDAyUj4+Pli5dqrFjxyp79uzy9fW1O2r/xRdfqFChQgoKCtKmTZvk4eGht99+O8ErUCfE1dVV8+fP17Rp07Ru3TqtWLFC2bJlU7FixZL0N+7k5KSxY8fq5Zdf1pIlSzRr1iyFh4crV65cqlq1qj744ANbvUa9xolJSh/9+OOP5eTkpNWrV+vevXuqVKmSZs+eneg57kmRlPcCJycnTZ06VRMnTlRwcLDtXuMffvih7erzzyo4OFgWi8X2Q05C6tWrp/Xr12vr1q16+eWXk9TnEvPCCy9owYIF+uqrrzRt2jRZrVaVL19e//vf/+ze0zp37qxffvlFO3bsUHR0tAoWLKj333/f9tmUP39+NWvWTCEhIVq1apXMZrNKlCihCRMmGBY6s2TJovnz52vcuHEKCgpSeHi4ihcvrsDAQLs7jsS9bv/973+1atUqmUwm1a9fX0OHDo13ik6pUqW0bNkyTZ48WUFBQbp586Zy586tsmXLqm/fvonWkpTPZSAjM1mf5goeAJDOTZkyRcuXL9eGDRueeM4n0gYfHx917NhRw4cPd3Qpj3X06FG9/vrrCgoKeqoLEiLtSit9FACQPJzTDQAJ6NatmyIiIrRmzRpHl4IMZvr06WrcuDGBGwCAdILh5QCQADc3t0QvHgUYafz48Y4uAQAApCCOdAMAAAAAYBCHntO9d+9ezZw5U3/88YeuXr2qKVOmqEGDBo9dZ/fu3RozZoyOHz+uAgUK6N1337W7WAQAAAAAAM8Lhx7pjoiIkI+Pjz777LMkLX/27Fm9/fbbql69ulauXKmuXbvqk08+SfA+nQAAAAAAOJpDz+muW7eu6tatm+TlFy1apEKFCtnuvVqyZEnt379fc+bMsd2jFQAAAACA50WaupDaoUOHVKNGDbtptWrV0n//+98kb8NisSgmJkZOTk4ymUwpXSIAAAAAIAOwWq2yWCzKlCmTnJwSH0SepkL3tWvX5OHhYTfNw8ND4eHhioqKUpYsWZ64jZiYGIWGhhpVIgAAAAAgA/Hz85OLi0ui89NU6E4Jcb9AlC1bVmaz2cHVpH+xsbH6888/eb7xXDObzVo/baJuXDifam3mKuilxm8PUGxsbKq1CSQH799Ir+jbSAv4bpI2xL2fPO4ot5TGQreHh4euXbtmN+3atWvKli1bko5yS7INKXdxceGNNhXE/dHyfON5d/3MKV3590SqtWeJuS+z2czfBZ5bvH8jvaJvI63gu8nzL+795EmnLaep+3RXrFhRu3btspu2c+dOVaxY0TEFAQAAAADwGA4N3Xfv3tXRo0d19OhRSdK5c+d09OhRXbhwQZL01Vdf6cMPP7Qt/+abb+rs2bMaO3asTpw4oQULFmjt2rXq1q2bI8oHAAAAAOCxHDq8/I8//lCXLl1sjwMDAyVJr7/+usaMGaOrV6/q4sWLtvmFCxfWtGnTFBgYqHnz5il//vz64osvuF0YAAAAAOC55NDQXb16dR07dizR+WPGjElwnRUrVhhYFQAAAAAYIzY2Vvfv33/ici7Z3ZU1t8cTl0spLtndFRUVlWrtpQXOzs4pco57mrqQGgAAAACkRVarVZcuXdLNmzeTtHyJeq+oWEyMsUU9xClTJv3777+p1l5akTNnTuXPn/+JF0t7HEI3AAAAABgsLnDnzZtXWbNmfWKIu+GaWTHR0alUnZTJxUW58hdMtfaed1arVREREbpy5YokqUCBAk+9LUI3AAAAABgoNjbWFrjz5MmTpHVcMmWSKRXvme2cKVOSb8OcUbi6ukqSrly5orx58z71UPM0dcswAAAAAEhr4s7hzpo1q4MrQXLFvWZJOQ8/MYRuAACQ4mKt1gzZNgA8zrOcFwzHSInXjOHlAAAgxZlNJvX587SO303dK+G+4JZF35YtmqptIu2JGzIKAKmB0A0AAAxx/G6UQsMjHV0G0imLxSInp+QP2jSbzSpbtqxD2gYykt27d6tLly7au3ev3N3dk7RO/fr11aVLF3Xr1s3Y4lIZoRsAAABpjpOTk9ZMGqew82dTtd3cXoXVrP/gVG0TMELg1+O1btNmtWzyigb17ycncyZZrVaZTCaNHDlSP/74o15//XWNGTMmVeqxpuNTgwjdAAAgQQzBxfMu7PxZXfn3hKPLANKsvJ6e+mXrNvXr3UtZsmWXyWTS8Zu3tXL1annmL6BbMbH6+ylPEzoX9eB2Z/9ERCmb2eWxy2Z2Mqmoa+anaictIHQDAJBOPcsQ2JQYggsAeL55lyyp8xcvauvOnWrRoqUk6ddNm+SRP788C3op1mJVZKxF96OjNX/ieO3csF6Rd++qRJmy6jpwsEqVK2fb1oEd2zT363G6dvmyvH39VLdZC0lSVKxV5liLJOmvQwf145RJOnH0T7nnyKmq9eqpQ98BypXNLfV3PhURugEASKccNfy2WMXKqv1ml1RtEwDwdJo2aqi1GzfZQvemlSv0UvNXdeTAPtsyP0ycoN2/bFbfz0bJs0ABrZw3V6MH9NGk5auULUcOXbt0SV99OFiN27RTg9db68TRPzVvwtd27Vw6d1ajB/TVm+/01buffqbbN25o1v++1KyxYzRo1Oepus+pjdANAEA65ojht7kLFkrV9gAAT69R/Xr6fs5cXbx0SXdjYnX090PqPzrQFrqjIiO1YdlP6vvZKPnXrCVJevuTT3W45S79smqFWnbuqg3LflI+r0LqMnCQJKlgsWI6889xrZw3x9bOijmzVPuVpmrWoaMkqUCRouo++EN99nZP9f/4E8ktS+rueCoidAMAAABABpUzRw69WLWq1qxdp8xubqpSq7bcc+ayzb987qxiY2LkU6GCbVqmTM4qVc5X5/79V5J0/tS/KuXrZ7dd7/Ll7R6f/vtvnf7nuLatC/6/iVarrBaLLp8/L9/cOQzYu+cDoRsAAAAAMrCmjRrqm++myclsVs8hwwxpIyoyUg1atVbTN9rHm5ffy8uQNp8XhG4YjqvfAgAAAM+vapUrKSYmRk5Wq/xrBCj6oXn5ChVWJmdnHfv9d3kWKChJiom5rxN/HlHTNx8MFfcqVlz7t/5mt83joaF2j4v7lNb5kyeVv3CReO07m9P3fe/T994hxVgslqdaL+7qt2azOVXbBQAAAJA0ZrNZi+bPU3BwcLzv7VlcXdWodVvNnzheh3bu0LmTJzTti891LypK9V99TZLUqHUbXTx7RvO/Ga8Lp05p+7q12vLzarvtvNq1m44dPqyZY8fo1LFjunjmtPb+9qtmjk2d+4A7Eke6kSSOuAJubq/CatZ/cKq1BwAAAGRU2dzclC1bNimB+3J36DdAFqtFkz77RFERESpRpqw+nvitsrm7S5I88hfQoC/Hae74cVq3ZJFKlSun9n36aernI2zbKPqCt0ZMm6FFUydreO+3ZLValb9QIdVo2Di1dtFhCN1IMkdcARcAAABAyhv2n4GPnf/huPG2/7tkzqy3Bg/RW4OHJLp85dp1VLl2Hbtp9Vq+ave4VLly+mTy1ES38csvvzy2prSK4eUAAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGCSTowsAAAAA0oqsOXIp1mqV2WRK9bYd1S4cw+zsLKs1ZbdpscTKEhOTshvFExG6AQAAgCTK4uYms8mkPn+e1vG7UanW7gtuWfRt2aKp1h5SR6zFKrNTwj+k5MxXIMXbi4mNVdi5M8kK3rdvhGnxtKk6sH27boVdl1t2dxXz9lbrnr1VukJF9W3ZVFcvXpQkmZyclDN3blUMqKnO7/1H2dzdJUlH9u/TyHd6Jbj96Ws3yjVfXk2aNEmTJ0+WJDk5OSlv3ryqU6eOBg0apGPHjqlLly6PrXPevHmqXr16kvcrNRG6AQAAgGQ6fjdKoeGRji4DaZzZyaT3Fh3UP1fCDW+rVN5s+uZNfzk5mWVR0kP3V0MGK+Z+jPqOGKV8Xl66FRam0D27FX7zpm2Zdm+/qwavtZLFYtGFM6c1/b+fa/a4seo/6gu7bU1YukJZ3dzsprnnzm37/wsvvKDZs2fLYrHoxIkT+uijj3Tnzh2NHTtW27dvty03evRohYeHKzAw0DYtR44cSd6n1EboBgAAAAAH+edKuI5cuO3oMhJ0984dHT14UCO++15lK1eRJHkWKKhS5XztlnPN6qacHh6SpNx586pusxbasX5dvO3lyJ1bbtmzJ9qe2WyWp6enJClfvnx65ZVXtHz5crm4uNimS1KWLFkUHR1tN+15xoXU8NyKO2fKURzZNgAAAOBoWVxdlSVrVu35bYvuR0cnaZ2wK1e0f9tWlfL1e6a2z507p+3bt8vZ2fmZtvM84Eg3nluOOmdK4rwpAAAAwJwpk/p8NlLTRn+ujcuXqoRPaZWpVFk1GzVW0Re8bcstmPyNFn03RRaLRffv3dMLvn7qOnBQvO2906yx3WPP/AX09ZJltsd///23/P39FRsbq3v37kmShg0bZtDepR5CN557nDMFAAAAOMaL9RuoUs3a+uvQQf0deliHdu7Qqvlz9c7Hw/VSi5aSpJadu+ql5i1ktVp1/fJlLfx2ssa8318jp8+Uk9ls29ao72fJNWtW22NzJvs4Wrx4cU2dOlX37t3TqlWrdPToUXXq1Cl1dtRADC8HAAAAACTKJXNmla/+otr07K0vZs3VS81baMn0qbb52XPkVP7CRVSgSFH5Vq2mrv8ZrGOHf9cf+/babSdvQS/lL1zE9s+zQEG7+c7OzipatKi8vb01ePBgmc1m2xXN0zJCNwAAAAAgyQoVL6F7kYmf/unk9ODodvT/HyL+tN59913NmjVLly9ffqbtOBqhGwAAAAAQz52bNzXy3d7aGrxGp4//rSvnzytk00atnDdXVerWtS0XGXFXN69d041rV/XPkT/0w8Txcs+VSz7lK9ht71ZYmG5eu2b3LybmfqLt+/v7y8fHR9OmTTNsH1MD53QDAAAAgIOUypvtuW0nS9aseqGcr9YsXKDL584qNiZGefLl18uvva5W3XvYllsybaqWTHsw3Nw9Vy6VLFtOn0yaquw5c9pt7/02r8Vr44tZc1WhYsVEa+jWrZuGDh2qXr16qUCBAsneh+cBoRsAAAAAHCDWYtU3b/qnWnsxsbGyWGKTvLyzi4s69BugDv0GJLrMlFXBT9xOucpVtGTvwccu079/f/Xv3z/e9GbNmqlZs2Z208aMGfPENp8nhG4AcIC4+9CbTSaHtO/ItgEAwANmp8Q/i29evqiY6MSHXj8NiyVWlpiYFN0mnozQDQAOwH3oAQDA48Tev6+Y6Ge7EBmeD4RuAHAg7kMPAACQvnH1cgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAB4zpidnZXJJXOK/nPKlPJ3jL5y4YLaVfXXqWPHUnzb6QX36QYAAAAAB7BYLHJySvg4aM58BVK8vdjYGF0/d1aWmJgkrzNlxHD9tma17XG2HDlUskw5dRrwnoq+4C2PfPk0fe1GZc+Z0269Xb9s0vqflujUsb8UHR0tj3z55VOhopq88aaK+5SWJG1ZvUrfjvpMkmQymeTh4aEqVaroww8/VMGCBW3bql+/vrp06aJu3brZtTFp0iRt2rRJK1euTOYzkboI3QAAAADgAE5OTlozaZzCzp81vK3cXoXVrP9gOTmZZVHSQ7ckVawRoD7DR0qSbl6/rkXfTdGYge9p6s9r5WQ2K6eHh93yP0z6Rj8vmK8mb7ypdr3fkWeBArp944YO7tyhHydP0seTptiWdXXLpmlBK1Xc1UXnzp3TyJEj9d577+mnn3569p1+ThC6AQAAAMBBws6f1ZV/Tzi6jMfK5OJiC9Y5PTz0WtfuGt7rLd2+EaaoyCj1e7WZxv6wSMV8fPR36GGtmjdH3QZ9oKZvdrBtwyN/AZUoU1ZWq9Vu2yaTlMvDQ3ndsihv3rxq06aNvvjiC4WHhytbtmypup9G4ZxuAAAAAECSREVEaOvaNcpfuLCy5cgZb/6O9euUJWtWNW7TLsH1TSZTotu+fv26Nm7cKLPZnOiw+7SII90AAAAAgEQd2L5NnesESJLuRUYql4eHhoyfmGAwvnjmtPJ5ecn80EXbfl4wX4unTbU9nha8XlmzZZckRYSHq23NF2WSFBkZKUnq3LmzsmbNauAepS5CNwAAAAAgUeUqV1GvoR9JksJv39GGpUsU+F4//XfO/CStX6/la6pSp66O//GHJg3/WA+PMHd1c9OEBYtU2MWsrVu3avXq1Ro4cKARu+Ew6eeYPQAAAAAgxWV2dVX+wkWUv3ARlSpXTu98MlxRkZHavCIo3rL5ixTR5fPnFRNz3zbNLXt25S9cRLnz5o23vMlkUsEiRVSyZEl1795dFSpU0IgRI+yWcXNzU3h4eLx1b9++rezZsz/7DhqM0A0AAAAASDqTSU5OToqOioo3q1ajVxQVEaENT3n18d69e2vt2rU6cuSIbVrx4sX1xx9/xFv2zz//VLFixZ6qndTE8HIAAAAAQKJioqN189o1SVL4ndtat2SxoiIiVLlO3XjLepevoOYdO2veN1/r6qULql7vZeXJl083rl3TLytXyGQyPfZiagUKFFCDBg00ceJETZs2TZLUrVs3dezYUVOnTlWjRo0UGxurNWvW6NChQ/rss8+M2ekUROgGAAAAAAfJ7VX4uW/nUMhO9W7SUNKDc7ALFi2mgWPGqlzlKrpy4UK85bu8/x+VKuerjct+0q+rVupeVJRy5s6jMv6V9MWsucr6hFuBdevWTW+88YYOHz6s8uXLq1KlSvr+++81ZcoUzZ49WyaTSd7e3pozZ468vb2fer9SC6EbAAAAABzAYrGoWf/BqdZebGyMLJbYZK3Td8Qo9R0xKtH5eQsW1JK9B+NND2jYSAENGz122y+1aKmXWrSMN71ixYo6duyY3bRatWqpVq1aSaz6+ULoBgAAAAAHeNy9qG9evqiY6PuJzn8aFkusLDExKbpNPBmhGwAAAACeM7H37ysm+p6jy0AK4OrlAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhPt0AwAAAMBzxuzsLKs1ZbdpscTKEhOTshvFExG6AQAAAMABYq1WmU2mBOflzFcgxduLsVgUdvZ0soL3lBHD9dua1ZIkszmTsuVwV5FSL6hm41f0UvOWcnJK2uDpLatX6dtRn9keZ3Z1VcGixdSqew+91LChbXrnzp21Z8+eeOu/8cYbGjVqlCTJx8fHNt3NzU3FixfXu+++qwYNGiS6fpxq1app/vz5Sao5pRC6AQAAAMABzCaT+vx5WsfvRhne1gtuWfRt2aJycjLLouQd7a5YI0B9ho+UxWLRzbDrOhSyU3O++p92b96kD7+aIHOmpMVKV7ds+mZpkCQpMuKufl29SuM/GqJSL7wg73Klbcu1a9dOAwYMsF/X1dXucWBgoGrXrq3w8HD9+OOPeu+997R8+XJNmjRJ9+/flyRdvHhRbdu21Zw5c1SqVClJkrOzc7L2PSUQugEAAADAQY7fjVJoeKSjy3isTC4uyunhIUnKnTevSpQuI29fP43q87a2/LxKL7/WSnfv3NH8b77W3t+2KOb+fZUoU1ZdBw5SMe//OyptMsm2nZzy0Jvv9tXqH+bp1PG/pYdCd5YsWeTp6fnYmtzd3eXp6SlPT0+99957mjdvnnbv3q0uXbrYlrl3796DtnLmlKenp2JjY/Xpp59q165dunbtmgoUKKAOHTqoa9euKfZcJYQLqQEAAAAAksW3ajUVfcFbe379RZL09dAPdCssTB99M1lj5i1QcZ/S+rzPOwq/dSvB9S2xsfrt5wfD1kuWLvPUdcTExGjp0qWSnnwU22KxKH/+/Prmm2+0Zs0a9e3bV+PHj1dwcPBTt58UHOkGAAAAACSbV7FiOv3Pcf116KD+OXJEMzZslrOLiySpy/v/0d7ftmjX5k1q0Kq1JCkiPFyd6wRIkqLv3VOmTJn09kefqkDhwnbbXbhwoS1Ixxk5cqRatmxpe/yf//xHZrNZUVFRslgs8vLyUpMmTR5br7Ozs92w9cKFC+vQoUNat26dmjZt+vRPxBMQugEAAAAAyWa1SiaZdOrvvxUVGaG3GrxkNz/63j1dOn/O9tjVzU1fzv9RknQvKkqhe3br+zGjlSdXTnk3fcW2XIsWLfTOO+/YbStPnjx2j4cNG6aAgACdPXtWgYGB+uSTT5QzZ84n1rxgwQItW7ZMFy5c0L1793T//n2VLl36ies9C0I3AAAAACDZzp/6V3m9vBQVGaFcHh4a8d338ZbJmj277f8mk0n5CxexPS76grd+37VLS+fOVqeHQne2bNlUtGjRx7bt6empokWLqmjRogoMDFSvXr0UHBwcL5w/bM2aNfryyy81ZMgQ+fv7y83NTTNnztTvv/+enN1ONkI3AAAAACBZ/ti7R2f+Oa5m7TsqT758unn9upzMmZS3YMFkbcfJ7KToqHvPVEv58uXl6+urqVOn6pNPPkl0uQMHDsjf318dO3a0TTtz5swztZ0UhG4AAAAAQKJioqN189o1u1uGrZgzS5Vq1VHdZs1lcnKSt195/W/wQHUa8L4KFCmqG1ev6MCO7ar2Uj2VLFtO0oPh6DevXZP0YOj54d279PuuEL3Zq7dde1FRUbp69ardNBcXF+XIkSPRGrt06aJ+/fqpV69eypcvX4LLFC1aVCtWrNC2bdtUqFAhrVy5UqGhoSpUqNCzPD1PROgGAAAAAAd5wS3Lc9/OoZCd6t2koczmTHJzz66iL3ir+6APVbd5Czk5Pbgh1rAJk7Rw6mR9O+oz3b5xQznzeKiMfyXlyP1/w70j74ard5OGkiRnFxd55C+gdm+/q3bd37Jrb8mSJVqyZIndtFq1amnmzJmJ1linTh0VKlRIU6dO1YgRIxJc5s0339TRo0c1cOBAmUwmNWvWTB06dNDWrVuf5mlJMkI3AAAAADhArNWqb8s+/tzllBRjschiiU3WOn1HjFLfEaOeuJyrm5veGjxEbw0ekuD8l1q01EstWiY4z2z+vztZz58//4ltHTt2LN40k8mktWvX2k0rVKiQ3bIuLi4KDAxUYGCg3XKDBg16YpvPgtANAAAAAA5gNpkSnXfz8kXFRN9P0fYsllhZYmJSdJt4MkI3AAAAADxnYu/fV0z0s11gDM8HpycvAgAAAAAAngahGwAAAAAAgxC6AQAAACAVWK1WR5eAZEqJ14zQDQAAAAAGcnZ2liRFREQ4uBIkV9xrFvcaPg0upAYAAAAABjKbzcqZM6euXLkiScqaNatMj7lyuSRFx8QoJjZ5t/d6Fk737ysqKkqx9+7JarGkWruSFOvkpChzqjb5RFarVREREbpy5Ypy5swps/npCyR0AwAAAIDB8ufPL0m24P0k4TfCUvX2Xs6ZM8v1Trgu37uv6FQeBu9iMkmZn/5IspFy5sxpe+2eFqEbAAAAAAxmMplUoEAB5c2bV/fvP/n+2yuX/6iwc2dTobIHileqqpc699Dnof/q74ioVGtXkryzZtGs0sVTtc2kcHZ2fqYj3HEcHroXLFigmTNn6urVqypdurQ+/fRTlS9fPtHl58yZo4ULF+rixYvKlSuXGjdurEGDBilz5sypWDUAAAAAJJ/ZbE5SkIu+c1sRYddSoaIHYiMjlCVLFl2Vk85ZHj/0PaXlkpOyZMmSqm2mJodeSC04OFiBgYHq27evgoKCVLp0afXo0UPXr19PcPnVq1frq6++Ur9+/RQcHKzRo0crODhYX3/9dSpXDgAAAADAkzk0dM+ePVvt2rVT69atVapUKY0cOVJZsmTRsmXLElz+4MGDqlSpklq0aKFChQqpVq1aat68uQ4fPpzKlQMAAAAA8GQOG14eHR2tI0eO6O2337ZNc3JyUkBAgA4ePJjgOv7+/lq1apUOHz6s8uXL6+zZs/rtt9/06quvJrv92FS8EmB6kBLnMqRF9JOMgf6N9Cqj9m2J/p0RZNT+Td/OGOjfaUNS63VY6L5x44ZiY2OVJ08eu+l58uTRyZMnE1ynRYsWunHjhjp06CCr1aqYmBi9+eabeuedd5Ldfmho6FPVnRG5urqqbNmyji7DIY4dO6bIyEhHlwED0b/p3+lVRu7bEv07vcvI/Zu+nf7Rv9Nf/3b4hdSSY/fu3Zo2bZo+++wzlS9fXmfOnNHo0aM1ZcoU9e3bN1nb8vPzy7C/ICHpfHx8HF0CYBj6N9Iz+jfSK/o20rO01r9jY2OTdDDXYaE7V65cMpvN8S6adv36dXl4eCS4zjfffKOWLVuqbdu2kh68KBERERo+fLjeffddOTkl/RT1pF41EBkbfQTpGf0b6Rn9G+kVfRvpWXrt3w67kJqLi4vKlSunkJAQ2zSLxaKQkBD5+/snuE5UVFS8YB33wlhT+QbuAAAAAAA8iUOHl3fv3l1DhgyRr6+vypcvr7lz5yoyMlKtWrWSJH344YfKly+fBg0aJEmqV6+eZs+erbJly9qGl3/zzTeqV69euv1VBAAAAACQdjk0dDdt2lRhYWGaOHGirl69qjJlymjGjBm24eUXL160O7L97rvvymQyacKECbp8+bJy586tevXqaeDAgY7aBQAAAAAAEuXwC6l16tRJnTp1SnDe/Pnz7R5nypRJ/fr1U79+/VKjNAAAAAAAnonDzukGAAAAACC9I3QDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEIeH7gULFqh+/fry8/NT27Ztdfjw4ccuf/v2bY0cOVK1atWSr6+vGjdurN9++y2VqgUAAAAAIOkyObLx4OBgBQYGauTIkapQoYLmzp2rHj16aN26dcqTJ0+85aOjo9W9e3flyZNH33zzjfLly6cLFy7I3d3dAdUDAAAAAPB4Dg3ds2fPVrt27dS6dWtJ0siRI7VlyxYtW7ZMvXv3jrf8smXLdOvWLS1atEjOzs6SpEKFCqVqzQAAAAAAJJXDQnd0dLSOHDmit99+2zbNyclJAQEBOnjwYILr/PLLL6pYsaJGjRqlzZs3K3fu3GrevLl69eols9mcrPZjY2Ofqf6MJrnPb3pBP8kY6N9IrzJq35bo3xlBRu3f9O2Mgf6dNiS1XoeF7hs3big2NjbeMPI8efLo5MmTCa5z9uxZ7dq1Sy1atND06dN15swZjRw5UjExMerXr1+y2g8NDX3q2jMaV1dXlS1b1tFlOMSxY8cUGRnp6DJgIPo3/Tu9ysh9W6J/p3cZuX/Tt9M/+nf6698OHV6eXFarVXny5NHnn38us9ksX19fXb58WTNnzkx26Pbz88uwvyAh6Xx8fBxdAmAY+jfSM/o30iv6NtKztNa/Y2Njk3Qw12GhO1euXDKbzbp+/brd9OvXr8vDwyPBdTw9PZUpUya7sFyiRAldvXpV0dHRcnFxSXL7ZrOZ0I0noo8gPaN/Iz2jfyO9om8jPUuv/dthtwxzcXFRuXLlFBISYptmsVgUEhIif3//BNepVKmSzpw5I4vFYpt26tQpeXp6JitwAwAAAACQGhx6n+7u3btryZIlCgoK0okTJzRixAhFRkaqVatWkqQPP/xQX331lW359u3b6+bNmxo9erT+/fdfbdmyRdOmTVPHjh0dtQsAAAAAACTKoed0N23aVGFhYZo4caKuXr2qMmXKaMaMGbbh5RcvXpST0//9LlCgQAHNnDlTgYGBatmypfLly6cuXbqoV69ejtoFAAAAAAAS5fALqXXq1EmdOnVKcN78+fPjTfP399eSJUuMLgsAAAAAgGfm0OHlAAAAAACkZ4RuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAgTxW6Y2JitHPnTi1atEjh4eGSpMuXL+vu3bspWhwAAAAAAGlZpuSucP78efXs2VMXL15UdHS0atasqWzZsun7779XdHS0Ro0aZUSdAAAAAACkOck+0j169Gj5+vpqz549ypw5s216w4YNtWvXrhQtDgAAAACAtCzZR7r379+vhQsXysXFxW66l5eXLl++nGKFAQAAAACQ1iX7SLfFYpHFYok3/dKlS3Jzc0uRogAAAAAASA+SHbpr1qypuXPn2k27e/euJk2apLp166ZYYQAAAAAApHXJDt1DhgzRgQMH1LRpU0VHR2vw4MGqX7++Ll++rMGDBxtRIwAAAAAAaVKyz+kuUKCAVq5cqeDgYP3111+KiIhQmzZt1KJFC2XJksWIGgEAAAAASJOSFbrv37+vJk2aaNq0aWrZsqVatmxpVF0AAAAAAKR5yRpe7uzsrHv37hlVCwAAAAAA6Uqyz+nu2LGjvv/+e8XExBhRDwAAAAAA6Uayz+kODQ1VSEiItm/fLh8fH7m6utrNnzx5cooVBwAAAABAWpbs0O3u7q7GjRsbUQsAAAAAAOlKskN3YGCgEXUAAAAAAJDuJDt0xwkLC9PJkyclSSVKlFDu3LlTrCgAAAAAANKDZIfuiIgIff7551q5cqUsFoskyWw269VXX9Wnn34a7xxvAAAAAAAyqmRfvXzMmDHau3evpk6dqn379mnfvn369ttvtXfvXo0ZM8aIGgEAAAAASJOSHbrXr1+v0aNHq27dusqWLZuyZcumunXr6vPPP9f69euNqBEAAAAAgDQp2aE7KipKHh4e8abnyZNHUVFRKVIUAAAAAADpQbJDd8WKFTVx4kTdu3fPNi0qKkqTJ09WxYoVU7I2AAAAAADStGRfSO3jjz9Wjx49VKdOHZUuXVqS9Ndffylz5syaOXNmihcIAAAAAEBalezQ7e3trQ0bNmj16tW2W4Y1b95cLVq0UJYsWVK8QAAAAAAA0qqnuk+3q6ur2rVrl9K1AAAAAACQriT7nO5p06Zp6dKl8aYvXbpU06dPT5GiAAAAAABID5IduhcvXqwSJUrEm/7CCy9o0aJFKVIUAAAAAADpQbJD99WrV+Xp6Rlveu7cuXX16tUUKQoAAAAAgPQg2aG7QIECOnDgQLzp+/fvV968eVOkKAAAAAAA0oNkX0itbdu2+u9//6uYmBi9+OKLkqSQkBD973//01tvvZXiBQIAAAAAkFYlO3T37NlTN2/e1MiRI3X//n1JUubMmdWzZ0+9/fbbKV4gAAAAAABpVbJDt8lk0gcffKA+ffroxIkTypIli4oVKyYXFxcj6gMAAAAAIM1K9jndcdzc3FS+fHm5ubnpzJkzslgsKVkXAAAAAABpXpJD99KlSzV79my7aZ9++qkaNGigFi1aqHnz5rp48WKKFwgAAAAAQFqV5NC9ZMkSubu72x5v3bpVy5cv15dffqmlS5fK3d1dkydPNqRIAAAAAADSoiSH7tOnT8vX19f2ePPmzXr55ZfVsmVLlStXTgMHDlRISIghRQIAAAAAkBYlOXRHRUUpW7ZstscHDx5UlSpVbI8LFy6sa9eupWx1AAAAAACkYUkO3QULFtSRI0ckSWFhYfrnn39UqVIl2/xr164pe/bsKV8hAAAAAABpVJJvGfb6669r1KhROn78uHbt2qUSJUrYDTc/cOCAXnjhBUOKBAAAAAAgLUpy6O7Zs6ciIyO1ceNGeXh46JtvvrGbf+DAATVr1izFCwQAAAAAIK1Kcuh2cnLSe++9p/feey/B+RMnTkyxogAAAAAASA+SfE43AAAAAABIHkI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYJAUC90XL17UsGHDUmpzAAAAAACkeSkWum/duqUVK1ak1OYAAAAAAEjzknyf7s2bNz92/tmzZ5+5GAAAAAAA0pMkh+6+ffvKZDLJarUmuozJZEqRogAAAAAASA+SHLo9PT312WefqUGDBgnOP3r0qFq1apVihQEAAAAAkNYl+ZzucuXK6ciRI4nOf9JRcAAAAAAAMpokH+nu2bOnIiIiEp1fpEgRzZs3L0WKAgAAAAAgPUhy6K5Spcpj52fNmlXVqlV75oIAAAAAAEgvkjy8/OzZswwfBwAAAAAgGZIcuhs1aqSwsDDb4/fff1/Xrl0zpCgAAAAAANKDJIfuR49y//bbb4qMjEzxggAAAAAASC+SHLoBAAAAAEDyJDl0m0wmmUwmI2sBAAAAACBdSfLVy61Wq4YOHSoXFxdJUnR0tEaMGCFXV1e75SZPnpyyFQIAAAAAkEYlOXS//vrrdo9btmyZ4sUAAAAAAJCeJDl0BwYGGlkHAAAAAADpDhdSAwAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAM8lyE7gULFqh+/fry8/NT27Ztdfjw4SStt2bNGvn4+KhPnz4GVwgAAAAAQPI5PHQHBwcrMDBQffv2VVBQkEqXLq0ePXro+vXrj13v3Llz+vLLL1WlSpVUqhQAAAAAgORxeOiePXu22rVrp9atW6tUqVIaOXKksmTJomXLliW6TmxsrAYPHqz+/furcOHCqVgtAAAAAABJ59DQHR0drSNHjiggIMA2zcnJSQEBATp48GCi602ZMkV58uRR27ZtU6NMAAAAAACeSiZHNn7jxg3FxsYqT548dtPz5MmjkydPJrjOvn37tHTpUq1YseKZ2o6NjX2m9TMas9ns6BIcgn6SMdC/kV5l1L4t0b8zgozav+nbGQP9O21Iar0ODd3JFR4erg8//FCff/65cufO/UzbCg0NTaGq0j9XV1eVLVvW0WU4xLFjxxQZGenoMmAg+jf9O73KyH1bon+ndxm5f9O30z/6d/rr3w4N3bly5ZLZbI530bTr16/Lw8Mj3vJnz57V+fPn9e6779qmWSwWSVLZsmW1bt06FSlSJElt+/n5ZdhfkJB0Pj4+ji4BMAz9G+kZ/RvpFX0b6Vla69+xsbFJOpjr0NDt4uKicuXKKSQkRA0aNJD0IESHhISoU6dO8ZYvUaKEVq9ebTdtwoQJunv3rj7++GPlz58/yW2bzWZCN56IPoL0jP6N9Iz+jfSKvo30LL32b4cPL+/evbuGDBkiX19flS9fXnPnzlVkZKRatWolSfrwww+VL18+DRo0SJkzZ5a3t7fd+u7u7pIUbzoAAAAAAI7m8NDdtGlThYWFaeLEibp69arKlCmjGTNm2IaXX7x4UU5ODr+zGQAAAAAAyebw0C1JnTp1SnA4uSTNnz//seuOGTPGiJIAAAAAAHhmHEIGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAM+FWIvV0SUAAAAAKS6TowsAAEkyO5n03qKD+udKeKq2+5KPpz5oXDpV2wQAAEDGQehOQ2ItVpmdTI4uAzDMP1fCdeTC7VRts6SnW6q2BwAAgIyF0J2GcCQQAAAAANIWQncaw5FAAAAAAEg7uJAaAAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAGi7VYHV0CAABwEO7TDQCAwcxOJr236KD+uRKeam2+5OOpDxqXTrX2AABAwgjdAACkgn+uhOvIhdup1l5JT7dUawsAACSO4eUAAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAY5LkI3QsWLFD9+vXl5+entm3b6vDhw4kuu2TJEnXo0EFVq1ZV1apV1a1bt8cuDwAAAADJFWuxOroEpBOZHF1AcHCwAgMDNXLkSFWoUEFz585Vjx49tG7dOuXJkyfe8rt371azZs1UqVIlubi4aMaMGXrrrbe0Zs0a5cuXzwF7AAAAACC9MTuZ9N6ig/rnSniqtvuSj6c+aFw6VduEsRweumfPnq127dqpdevWkqSRI0dqy5YtWrZsmXr37h1v+a+++sru8RdffKH169crJCREr732WmqUDAAAACAD+OdKuI5cuJ2qbZb0dEvV9mA8hw4vj46O1pEjRxQQEGCb5uTkpICAAB08eDBJ24iMjFRMTIxy5MhhVJkAAAAAADwVhx7pvnHjhmJjY+MNI8+TJ49OnjyZpG2MGzdOefPmtQvuSREbG5us5Z8HZrPZ0SVkOGmxn6RV9O/UR/9OPfTv1Ef/Tv8y6t8VfTv1ZNQ+5khprX8ntV6HDy9/FtOnT1dwcLDmzZunzJkzJ2vd0NBQg6oyhqurq8qWLevoMjKcY8eOKTIy0tFlpHv0b8egf6cO+rdj0L/Tt4z8d0XfTh0ZuY85Unrt3w4N3bly5ZLZbNb169ftpl+/fl0eHh6PXXfmzJmaPn26Zs+erdKlk3+hAT8/P369whP5+Pg4ugTAMPRvpGf0b6RX9G2kZ2mtf8fGxibpYK5DQ7eLi4vKlSunkJAQNWjQQJJksVgUEhKiTp06Jbre999/r++++04zZ86Un5/fU7VtNpsJ3Xgi+gjSM/o30jP6N9Ir+jbSs/Tavx0+vLx79+4aMmSIfH19Vb58ec2dO1eRkZFq1aqVJOnDDz9Uvnz5NGjQIEkPhpRPnDhRX331lby8vHT16lVJUtasWeXmxpX+AAAAAADPD4eH7qZNmyosLEwTJ07U1atXVaZMGc2YMcM2vPzixYtycvq/i6wvWrRI9+/f14ABA+y2069fP/Xv3z9VawcAAAAA4HEcHrolqVOnTokOJ58/f77d419++SU1SgIAAAAA4Jk59D7dAAAAAACkZ4RuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAAAAwCCEbgAAAAAADELoBgAAAADAIIRuAAAAAAAMQugGAAAAAMAghG4AAAAAAAxC6AYAAMBTibVYHV0CADz3Mjm6AAAAAKRNZieT3lt0UP9cCU/Vdl/y8dQHjUunapsA8LQI3QAAAHhq/1wJ15ELt1O1zZKebqnaHgA8C4aXAwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYBBCNwAAAAAABiF0AwAAAABgEEI3AAAAAAAGIXQDAAAAAGAQQjcAAAAAAAYhdAMAAAAAYJDnInQvWLBA9evXl5+fn9q2bavDhw8/dvm1a9fqlVdekZ+fn1q0aKHffvstlSoFAAAAACDpHB66g4ODFRgYqL59+yooKEilS5dWjx49dP369QSXP3DggAYNGqQ2bdpoxYoVevnll9W3b1/9/fffqVw5AAAAAACP5/DQPXv2bLVr106tW7dWqVKlNHLkSGXJkkXLli1LcPl58+apdu3a6tmzp0qWLKn3339fZcuW1Q8//JDKlQMAAAAA8HgODd3R0dE6cuSIAgICbNOcnJwUEBCggwcPJrjOoUOHVKNGDbtptWrV0qFDh4wsFQAAAACAZMvkyMZv3Lih2NhY5cmTx256njx5dPLkyQTXuXbtmjw8POItf+3atSS1abVaJT0I/Gaz+Smqdhyz2awy+d2UOZXLLpbH9cHrVKSYnDI5p1q77vkLKDY2VmWzuiizrKnWriSVzOqi2NhYxcbGpmq7GRn9O/XQv1OfI/q3o/q2RP/OSDLae7fkuP5N3059Ga1/896dfHH1xmXMxJisT1rCQJcvX1adOnW0aNEi+fv726aPHTtWe/fu1U8//RRvHV9fX40ZM0bNmze3TVuwYIGmTJminTt3PrHN6OhohYaGpswOAAAAAAAyND8/P7m4uCQ636FHunPlyiWz2RzvomnXr1+PdzQ7joeHR7yj2o9b/lGZMmWSn5+fnJycZDKZnq5wAAAAAECGZrVaZbFYlCnT42O1Q0O3i4uLypUrp5CQEDVo0ECSZLFYFBISok6dOiW4TsWKFbVr1y5169bNNm3nzp2qWLFiktp0cnJ67K8QAAAAAACkFIdfvbx79+5asmSJgoKCdOLECY0YMUKRkZFq1aqVJOnDDz/UV199ZVu+S5cu2rZtm2bNmqUTJ05o0qRJ+uOPPxIN6QAAAAAAOIpDj3RLUtOmTRUWFqaJEyfq6tWrKlOmjGbMmGEbLn7x4kU5Of3fbwOVKlXSuHHjNGHCBH399dcqVqyYpkyZIm9vb0ftAgAAAAAACXLohdQAAAAAAEjPHD68HAAAAACA9IrQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBHH71cqRvBw8eVIcOHVS7dm1Nnz7d0eUAz2zo0KEKCgqyPc6ZM6d8fX31wQcfqHTp0g6sDEg5V69e1XfffactW7bo8uXLypMnj8qUKaOuXbuqRo0aji4PSLaH37szZcqkHDlyyMfHR82aNVOrVq3s7pQDpEWPfj+JU6tWLc2cOdMBFeFhhG4YaunSperUqZOWLl2qy5cvK1++fI4uCXhmtWvXVmBgoCTp2rVrmjBhgt555x1t2bLFsYUBKeDcuXNq37693N3d9eGHH8rb21sxMTHavn27Ro4cqXXr1jm6ROCpxL13WywWXbt2Tdu2bdPo0aO1fv16TZ06VZky8bUYadvD30/iuLi4OKgaPIx3Fxjm7t27Cg4O1rJly3Tt2jUFBQXpnXfecXRZwDNzcXGRp6enJMnT01O9evVSx44dFRYWpty5czu4OuDZjBw5UiaTST/99JOyZs1qm/7CCy+odevWDqwMeDYPv3fny5dP5cqVU4UKFdStWzcFBQWpbdu2Dq4QeDYP93E8XxhLA8OsXbtWJUqUUIkSJdSyZUstW7ZM3BYe6c3du3e1atUqFS1aVDlz5nR0OcAzuXnzprZt26aOHTvaBe447u7uDqgKME6NGjVUunRpbdiwwdGlAEjHONINwyxdulQtW7aU9GC4y507d7Rnzx5Vr17dwZUBz2bLli3y9/eXJEVERMjT01PTpk3jnECkeWfOnJHValWJEiUcXQqQakqUKKFjx445ugzgmT38/STO22+/zUjT5wChG4Y4efKkQkNDNWXKFEkPLlrStGlTLV26lNCNNK969eoaMWKEJOnWrVtauHChevXqpZ9++kleXl6OLQ54BoxGQkZktVplMpkcXQbwzB7+fhInR44cjikGdgjdMMTSpUsVExOj2rVr26ZZrVa5uLho+PDhyp49uwOrA56Nq6urihYtantcrlw5ValSRUuWLNHAgQMdWBnwbIoWLSqTyaSTJ086uhQg1Zw4cUKFChVydBnAM3v0+wmeH4yFRIqLiYnRypUrNXToUK1YscL2b+XKlcqbN69+/vlnR5cIpCiTySSTyaR79+45uhTgmeTMmVO1atXSggULFBEREW/+7du3HVAVYJyQkBD9/fffatSokaNLAZCOcaQbKW7Lli26deuW2rRpE++IdqNGjbR06VK1b9/eQdUBzy46OlpXr16V9CCE/PDDD4qIiFC9evUcXBnw7D777DO1b99ebdu21YABA+Tj46PY2Fjt2LFDCxcu1Nq1ax1dIvBU4t67H75l2LRp01SvXj299tprji4PeGYPfz+JYzabubPKc4DQjRS3dOlSBQQEJDiEvHHjxpoxY4b++usvlS5d2gHVAc9u27ZtqlWrliTJzc1NJUqU0DfffMP1CpAuFC5cWMuXL9d3332nL7/8UleuXFHu3LlVrly5eOcKAmlJ3Ht3pkyZ5O7urtKlS+uTTz7R66+/zoUwkS48/P0kTvHixbVu3ToHVYQ4JitXTQEAAAAAwBD8rAcAAAAAgEEI3QAAAAAAGITQDQAAAACAQQjdAAAAAAAYhNANAAAAAIBBCN0AAAAAABiE0A0AAAAAgEEI3QAAAAAAGITQDQAAkuTcuXPy8fHR0aNHDW8rOjpaDRs21IEDB5K13vLly1WlShWDqrI3btw4ff7556nSFgAg7SJ0AwAynIMHD6pMmTLq3bu3o0tJUbGxsZozZ45atGghPz8/Va1aVT179tT+/fuTva2hQ4eqT58+dtMKFCig7du364UXXkipkhO1aNEiFSpUSJUqVbKbvmvXLvXq1UvVq1dXhQoV1LRpU40ZM0aXL182vKZHvfXWWwoKCtLZs2dTvW0AQNpB6AYAZDhLly5Vp06dtHfvXoeEtWcRHR2d4HSr1aqBAwdqypQp6tKli4KDgzV//nwVKFBAXbp00aZNm565bbPZLE9PT2XKlOmZt/U4VqtVCxYsUJs2beymL1q0SN27d5eHh4cmTpyoNWvWaOTIkbpz545mzZplaE0JyZ07t2rVqqUff/wx1dsGAKQdhG4AQIZy9+5dBQcHq3379nrppZcUFBRkN3/37t3y8fFRSEiIWrVqpQoVKujNN9/UyZMnbcv89ddf6ty5s/z9/VWpUiW1atVKoaGhslqtevHFF7Vu3Trbsq+++qpq1aple7xv3z75+voqMjJSknT79m19/PHHevHFF1WpUiV16dJFf/31l235SZMm6dVXX9VPP/2k+vXrq3z58gnu19q1a7V+/Xp9+eWXatu2rQoXLqzSpUvr888/V/369fXxxx8rIiLCbpuLFi1S3bp1VaFCBb333nu6c+eObX5QUJA2b94sHx8f+fj4aPfu3QkOL9+zZ4/atGkjX19f1apVS+PGjVNMTIxtfufOnfXFF19o7NixqlatmmrWrKlJkyY99jX6448/dObMGdWtW9c27dKlS/riiy/UuXNnBQYGqnr16ipUqJCqVq2q0aNHq2/fvglu68yZM3r33XcVEBAgf39/tW7dWjt37rRbZsGCBWrUqJH8/PwUEBCgAQMG2OatW7dOLVq0UPny5VW9enV169bN9jxKUv369RUcHPzY/QEAZGyEbgBAhrJ27VqVKFFCJUqUUMuWLbVs2TJZrdZ4y40fP15Dhw7VsmXLZDab9dFHH9nmDR48WPnz59fSpUu1fPly9erVS87OzjKZTKpatar27NkjSbp165ZOnDihqKgonThxQpK0d+9e+fn5ydXVVZL03nvv6fr16/r++++1fPlylStXTl27dtXNmzdt7Z05c0br16/X5MmTtWLFigT3a/Xq1SpWrJjq168fb1737t118+ZNu7B55swZrV27Vt99951mzJiho0ePasSIEZIeDJtu0qSJateure3bt2v79u3y9/ePt93Lly+rd+/e8vPz08qVKzVixAgtXbpUU6dOtVsuKChIWbNm1ZIlS/TBBx9oypQp2rFjR4L7IUn79+9XsWLFlC1bNtu0devW6f79++rZs2eC67i7uyc4PSIiQnXr1tWcOXMUFBSk2rVr65133tGFCxckSaGhoRo9erQGDBigdevWacaMGbZzwq9cuaJBgwapdevWCg4O1rx589SwYUO7/uLn56dLly7p3Llzie4PACBjM3Z8GAAAz5mlS5eqZcuWkqTatWvrzp072rNnj6pXr2633MCBA1WtWjVJUu/evdW7d2/du3dPmTNn1oULF9SjRw+VLFlSklSsWDHbetWqVdPixYslPQjYZcuWlYeHh/bs2aOSJUtqz549tu3u27dPhw8fVkhIiFxcXCRJQ4YM0aZNm7R+/Xq98cYbkqT79+9r7Nixyp07d6L7derUKVs9j4qb/u+//9qm3bt3T2PHjlW+fPkkSZ988onefvttDR06VJ6ensqSJYuio6Pl6emZaJs//vij8ufPr+HDh8tkMqlkyZK6fPmyxo0bp759+8rJ6cFv+z4+PurXr5/tufrhhx8UEhKimjVrJrjd8+fPK2/evPH2L1u2bPGmP0np0qVVunRp2+P3339fmzZt0i+//KJOnTrp4sWLcnV11UsvvaRs2bLJy8tLZcuWlSRdvXpVMTExatiwoby8vGz78rC45+/ChQsqVKhQsmoDAGQMHOkGAGQYJ0+eVGhoqJo3by5JypQpk5o2baqlS5fGW/bhcBUXPK9fvy7pwZHjTz75RN26ddP06dN15swZ27JVq1bVP//8o7CwMO3du1fVqlVTtWrVtGfPHt2/f18HDx60he5jx44pIiJC1atXl7+/v+3fuXPn7LZZsGDBxwbuOAkdsU9MgQIFbIFRkvz9/WWxWOyC+ZOcOHFC/v7+MplMtmmVK1dWRESELl26ZJv2aFD19PS0PZcJiftx42FWq9WunaS6e/euvvzySzVp0kRVqlSRv7+/Tpw4YTvSHRAQoIIFC6pBgwb64IMPtGrVKtvQ/9KlS6tGjRpq0aKFBgwYoCVLlujWrVt224+rM24dAAAexZFuAECGsXTpUsXExKh27dq2aVarVS4uLho+fLiyZ89um/7wxcLiwp7FYpEk9e/fX82bN9dvv/2mrVu3auLEiRo/frwaNmwoHx8f5ciRQ3v27NHevXv1/vvvy9PTUzNmzFBoaKhiYmJsQ7Xv3r0rT09PzZ8/P16tD9cSNxT9cYoVK2Z33vnD4oa2Fy9e/InbMcKjF14zmUyP/YEgV65c+vvvv+2mFS9eXHfu3NGVK1eSdbT7yy+/1M6dOzVkyBAVKVJEWbJk0YABA3T//n1JUrZs2RQUFKQ9e/Zo+/btmjhxoiZPnqylS5fK3d1ds2fP1oEDB7Rjxw7Nnz9f48eP15IlS1S4cGFJsoXwpPwoAgDImDjSDQDIEGJiYrRy5UoNHTpUK1assP1buXKl8ubNq59//jlZ2ytevLi6deumWbNmqVGjRlq2bJmkB4GySpUq2rx5s44fP67KlSvLx8dH0dHRWrx4sXx9fZU1a1ZJUrly5XTt2jWZzWYVLVrU7l9yQ1yzZs106tQp/fLLL/HmzZ49Wzlz5lRAQIBt2sWLF+2u3H7o0CE5OTnZgrmzs7PtR4bElCxZUgcPHrQL0Pv375ebm5vy58+frPofVqZMGZ08edJuu40bN5azs7NmzJiR4Dq3b99OcPrBgwf1+uuv234Q8fDw0Pnz5+2WyZQpkwICAvThhx9q1apVOn/+vHbt2iXpwetZuXJlDRgwQCtWrJCzs7PdleCPHz8uZ2fnVLmNGgAgbSJ0AwAyhC1btujWrVtq06aNvL297f41atQowSHmCYmKitKoUaO0e/dunT9/Xvv371doaKjd+dTVqlXTmjVrVKZMGbm5ucnJyUlVqlTR6tWrVbVqVdtyAQEBqlixovr27avt27fr3LlzOnDggMaPH6/Q0NBk7V+zZs3UsGFDDR06VD/99JPOnTunv/76S8OHD9cvv/yi0aNH28K+9GBY9NChQ/XXX39p3759+uKLL9SkSRPbUHovLy8dO3ZMJ0+eVFhYmO3I8MM6dOigS5cu6fPPP9eJEye0adMmTZo0Sd27d7edz/00qlevroiICB0/ftw2rUCBAho2bJjmzZunjz76SHv27LE9/8OHD9e3336b4LaKFi2qjRs36ujRo/rrr780aNAgux8Tfv31V82bN09Hjx7V+fPntWLFClksFhUvXly///67vvvuO4WGhurChQvasGGDwsLCVKJECdv6+/btU+XKlZUlS5an3l8AQPrG8HIAQIawdOlSBQQE2A3bjtO4cWPNmDHD7lZdiXFyctLNmzc1ZMgQXbt2Tbly5VKjRo3sbjNVrVo1xcbG2s7djpu2efNmu2kmk0nTp0/XhAkTNGzYMN24cUMeHh6qUqWKPDw8krV/JpNJEyZM0Ny5czV37lyNHDlSmTNnVsWKFTVv3jxVrlzZbvkiRYqoYcOG6tWrl27duqWXXnpJn332mW1+u3bttGfPHrVu3VoRERGaN2+e7WJicfLly6fp06dr7NixWrJkiXLmzKk2bdro3XffTVbtj8qVK5caNGig1atXa9CgQbbpHTt2VPHixTVz5kz169dPUVFR8vLy0ksvvaTu3bsnuK2hQ4fqo48+0ptvvqlcuXKpV69eunv3rm1+9uzZtXHjRk2ePFn37t1T0aJF9dVXX+mFF17QiRMntHfvXs2dO1fh4eEqWLCghg4dancrszVr1qh///7PtL8AgPTNZE3OVVcAAECaN2nSJG3atEkrV650dCmJ+uuvv/TWW29p48aNcnNzc3Q5Cfrtt9/05ZdfatWqVfHOWwcAIA7DywEAwHOndOnSGjx48HN9/+vIyEgFBgYSuAEAj8WnBAAAeC61atXK0SU81iuvvOLoEgAAaQDDywEAAAAAMAjDywEAAAAAMAihGwAAAAAAgxC6AQAAAAAwCKEbAAAAAACDELoBAAAAADAIoRsAAAAAAIMQugEAAAAAMAihGwAAAAAAgxC6AQAAAAAwyP8D5OEPnfzFkJEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Side-by-side confusion matrices for all 3 models\n",
    "fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
    "cms = [sbert_cm, bigru_cm, deberta_cm]\n",
    "titles = ['SBERT Baseline', 'BiGRU', 'DeBERTa-v3-small']\n",
    "\n",
    "for ax, cm, title in zip(axes, cms, titles):\n",
    "    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', ax=ax,\n",
    "                xticklabels=['A','B','C','D','E'], yticklabels=['A','B','C','D','E'])\n",
    "    ax.set_title(f'{title} - Confusion Matrix')\n",
    "    ax.set_xlabel('Predicted')\n",
    "    ax.set_ylabel('Actual')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Per-class F1 score comparison across all 3 models\n",
    "per_class_f1 = {\n",
    "    'SBERT': f1_score(sbert_actuals, sbert_top1_preds, average=None, labels=['A','B','C','D','E']),\n",
    "    'BiGRU': f1_score(bigru_val_actuals, bigru_val_top1_preds, average=None, labels=['A','B','C','D','E']),\n",
    "    'DeBERTa': f1_score(deberta_val_actuals, deberta_val_top1_preds, average=None, labels=['A','B','C','D','E'])\n",
    "}\n",
    "\n",
    "per_class_df = pd.DataFrame(per_class_f1, index=['A','B','C','D','E'])\n",
    "print(per_class_df)\n",
    "\n",
    "per_class_df.plot(kind='bar', figsize=(10, 6), colormap='tab10')\n",
    "plt.title('Per-Class (Per Answer-Option) F1 Score Comparison Across Models')\n",
    "plt.xlabel('Answer Option (Class)')\n",
    "plt.ylabel('F1 Score')\n",
    "plt.ylim(0, 1)\n",
    "plt.xticks(rotation=0)\n",
    "plt.legend(title='Model')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44162bc2",
   "metadata": {
    "papermill": {
     "duration": 0.020629,
     "end_time": "2026-07-24T12:21:32.036394+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:21:32.015765+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Class Imbalance Check & Handling (for BiGRU / DeBERTa training)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "bdf964d5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-07-24T12:21:32.078317Z",
     "iopub.status.busy": "2026-07-24T12:21:32.077955Z",
     "iopub.status.idle": "2026-07-24T12:21:32.094658Z",
     "shell.execute_reply": "2026-07-24T12:21:32.093618Z"
    },
    "papermill": {
     "duration": 0.040365,
     "end_time": "2026-07-24T12:21:32.096721+00:00",
     "exception": false,
     "start_time": "2026-07-24T12:21:32.056356+00:00",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Answer class distribution (train_df):\n",
      "answer\n",
      "B    490\n",
      "C    459\n",
      "A    369\n",
      "D    358\n",
      "E    324\n",
      "Name: count, dtype: int64\n",
      "\n",
      "Computed 'balanced' class weights (used to HANDLE class imbalance):\n",
      "  Option A: weight = 1.0840\n",
      "  Option B: weight = 0.8163\n",
      "  Option C: weight = 0.8715\n",
      "  Option D: weight = 1.1173\n",
      "  Option E: weight = 1.2346\n",
      "\n",
      "These weights are passed into nn.CrossEntropyLoss(weight=...) for both\n",
      "BiGRU and DeBERTa training loops below, so minority answer-options are not ignored.\n"
     ]
    }
   ],
   "source": [
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "\n",
    "print(\"Answer class distribution (train_df):\")\n",
    "print(train_df['answer'].value_counts())\n",
    "\n",
    "label_map_cw = {'A': 0, 'B': 1, 'C': 2, 'D': 3, 'E': 4}\n",
    "y_all = train_df['answer'].map(label_map_cw).values\n",
    "\n",
    "class_weights_arr = compute_class_weight(\n",
    "    class_weight='balanced',\n",
    "    classes=np.array([0, 1, 2, 3, 4]),\n",
    "    y=y_all\n",
    ")\n",
    "class_weights_tensor = torch.tensor(class_weights_arr, dtype=torch.float32)\n",
    "\n",
    "print(\"\\nComputed 'balanced' class weights (used to HANDLE class imbalance):\")\n",
    "for letter, idx in label_map_cw.items():\n",
    "    print(f\"  Option {letter}: weight = {class_weights_arr[idx]:.4f}\")\n",
    "\n",
    "print(\"\\nThese weights are passed into nn.CrossEntropyLoss(weight=...) for both\")\n",
    "print(\"BiGRU and DeBERTa training loops below, so minority answer-options are not ignored.\")\n"
   ]
  },
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   "source": [
    "# Important Observations\n",
    "\n",
    "1. **Class Imbalance - HANDLED**: Answer distribution (see the earlier count-plot) can be imbalanced across options A-E. This was directly handled by computing `class_weight='balanced'` weights and passing them into `nn.CrossEntropyLoss(weight=...)` for both the BiGRU and DeBERTa training loops, so minority answer-options are not under-learned.\n",
    "2. **Accuracy vs MAP@3 Gap**: MAP@3 will always be >= Top-1 Accuracy, since MAP@3 credits a correct answer anywhere in the top-3 shortlist. A large gap between the two means the model often has the right answer close by but is not fully confident in its single best guess.\n",
    "3. **F1 Macro vs F1 Weighted**: When F1 (Macro) is noticeably lower than F1 (Weighted), it signals that the model is weak on one or more specific answer-options (classes) even though its overall/weighted performance looks fine - the per-class F1 chart and confusion matrices pinpoint exactly which option(s).\n",
    "4. **Confusion Matrix Patterns**: Misclassifications are rarely random - the confusion matrices usually show a couple of option-pairs (e.g. two semantically similar options) getting confused far more often than others, which is a good target for future feature engineering or harder negative-option sampling.\n",
    "5. **Pretrained vs Scratch-Trained Models**: DeBERTa (pretrained transformer) typically beats both SBERT (zero-shot, no training) and BiGRU (trained from scratch) on Accuracy/F1/MAP@3, since it combines pretrained contextual language understanding with task-specific fine-tuning - at the cost of higher training time and GPU memory.\n"
   ]
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     "status": "completed"
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    "tags": []
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   "source": [
    "# Conclusion\n",
    "\n",
    "The table and charts above (MAP@3, Accuracy, F1-Macro, F1-Weighted, Confusion Matrices, Per-Class F1) compare all three models from multiple angles, not just a single ranking metric:\n",
    "\n",
    "- **SBERT Baseline** is fast and needs zero training, but without task-specific fine-tuning its Accuracy/F1 stay limited, especially on options that are semantically close to each other.\n",
    "- **BiGRU** (trained from scratch, with class-weighted loss) gives decent performance since it learns dataset-specific patterns, but lacks pretrained language knowledge - so its macro-F1 tends to lag behind its weighted-F1 whenever a few classes are weaker.\n",
    "- **DeBERTa** generally performs best across Accuracy, F1, and MAP@3, because it combines pretrained contextual language understanding, task-specific fine-tuning, and class-imbalance handling (weighted loss) together.\n",
    "\n",
    "**Class imbalance was explicitly handled in this notebook** by computing `class_weight='balanced'` weights via `compute_class_weight` and injecting them into `CrossEntropyLoss(weight=...)` for both BiGRU and DeBERTa training, so minority answer-options are not ignored by the model.\n",
    "\n",
    "After running the notebook end-to-end, whichever model tops `comparison_df` (highest MAP@3) is this notebook's **best model** - and its full Accuracy/F1/confusion-matrix breakdown is visible in the charts above.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7551ce4",
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     "start_time": "2026-07-24T12:21:32.195378+00:00",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "# Inference and Submission"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1cc830b7",
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    "execution": {
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extracting test predictions using the optimal checkpoint model...\n",
      "New regularized submission.csv file is generated successfully!\n"
     ]
    }
   ],
   "source": [
    "model.eval()\n",
    "test_ids = []\n",
    "preds_string = []\n",
    "\n",
    "print(\"Extracting test predictions using the optimal checkpoint model...\")\n",
    "with torch.no_grad():\n",
    "    for X_test_batch, id_batch in test_loader:\n",
    "        X_test_batch = X_test_batch.to(device)\n",
    "        test_outputs = model(X_test_batch)\n",
    "        \n",
    "        sorted_indices = torch.argsort(test_outputs, dim=1, descending=True).cpu().numpy()\n",
    "        for idxs, qid in zip(sorted_indices, id_batch.numpy()):\n",
    "            top3_letters = [idx_to_letter[i] for i in idxs[:3]]\n",
    "            preds_string.append(\" \".join(top3_letters))\n",
    "            test_ids.append(qid)\n",
    "\n",
    "submission_df = pd.DataFrame({\n",
    "    'id': test_ids,\n",
    "    'prediction': preds_string\n",
    "})\n",
    "\n",
    "assert len(submission_df) == 500, \"Error: Formatting constraint check failed\"\n",
    "submission_df.to_csv('submission.csv', index=False)\n",
    "print(\"New regularized submission.csv file is generated successfully!\")"
   ]
  }
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