{
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   "id": "81bb23ad",
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   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>query</th>\n",
       "      <th>semantic_similarity</th>\n",
       "      <th>rougeL_f1</th>\n",
       "      <th>status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>What is the Berry Export Summary 2028 and what...</td>\n",
       "      <td>0.8763</td>\n",
       "      <td>0.3206</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>What are some of the benefits reported from ha...</td>\n",
       "      <td>0.9655</td>\n",
       "      <td>0.6016</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>What are the unique features of the Coolands f...</td>\n",
       "      <td>0.7942</td>\n",
       "      <td>0.2519</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>What is the main difference between the Nation...</td>\n",
       "      <td>0.9024</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>How did Gunnar Nelson win the fight against Za...</td>\n",
       "      <td>0.8510</td>\n",
       "      <td>0.3101</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>What are some of the features of Fabiana Filip...</td>\n",
       "      <td>0.9099</td>\n",
       "      <td>0.2963</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>How did Dan Foley feel about his portrayal on ...</td>\n",
       "      <td>0.9170</td>\n",
       "      <td>0.4444</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>What is the reason for the closure of the comm...</td>\n",
       "      <td>0.8298</td>\n",
       "      <td>0.3636</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>What are the five love and relationship podcas...</td>\n",
       "      <td>0.7104</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>FAIL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Which two teams dropped out of the Primal Ques...</td>\n",
       "      <td>0.9701</td>\n",
       "      <td>0.4258</td>\n",
       "      <td>PASS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               query  semantic_similarity  \\\n",
       "0  What is the Berry Export Summary 2028 and what...               0.8763   \n",
       "1  What are some of the benefits reported from ha...               0.9655   \n",
       "2  What are the unique features of the Coolands f...               0.7942   \n",
       "3  What is the main difference between the Nation...               0.9024   \n",
       "4  How did Gunnar Nelson win the fight against Za...               0.8510   \n",
       "5  What are some of the features of Fabiana Filip...               0.9099   \n",
       "6  How did Dan Foley feel about his portrayal on ...               0.9170   \n",
       "7  What is the reason for the closure of the comm...               0.8298   \n",
       "8  What are the five love and relationship podcas...               0.7104   \n",
       "9  Which two teams dropped out of the Primal Ques...               0.9701   \n",
       "\n",
       "   rougeL_f1 status  \n",
       "0     0.3206   PASS  \n",
       "1     0.6016   PASS  \n",
       "2     0.2519   PASS  \n",
       "3     0.2597   PASS  \n",
       "4     0.3101   PASS  \n",
       "5     0.2963   PASS  \n",
       "6     0.4444   PASS  \n",
       "7     0.3636   PASS  \n",
       "8     0.1860   FAIL  \n",
       "9     0.4258   PASS  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load scores from CSV\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv(\"rag_scores.csv\")\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "58e0482f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>semantic_similarity</th>\n",
       "      <th>rougeL_f1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>75.000000</td>\n",
       "      <td>75.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.852692</td>\n",
       "      <td>0.395061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.088759</td>\n",
       "      <td>0.216511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.591500</td>\n",
       "      <td>0.098600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.794600</td>\n",
       "      <td>0.251600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.873200</td>\n",
       "      <td>0.325600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.918150</td>\n",
       "      <td>0.495100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       semantic_similarity  rougeL_f1\n",
       "count            75.000000  75.000000\n",
       "mean              0.852692   0.395061\n",
       "std               0.088759   0.216511\n",
       "min               0.591500   0.098600\n",
       "25%               0.794600   0.251600\n",
       "50%               0.873200   0.325600\n",
       "75%               0.918150   0.495100\n",
       "max               1.000000   1.000000"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a9073d52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "status\n",
       "PASS    64\n",
       "FAIL    11\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['status'].value_counts()"
   ]
  }
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