evaluations/analyze_aragog.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "00660aae-32be-4ff8-8c03-9de0df9dc1d3",
   "metadata": {
    "id": "00660aae-32be-4ff8-8c03-9de0df9dc1d3"
   },
   "source": [
    "## After the process finished we can analyse de results. Let's read the aggregated scores into a single DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "013450e2-a49b-4111-8f04-6999376484f1",
   "metadata": {
    "id": "013450e2-a49b-4111-8f04-6999376484f1"
   },
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "def parse_results(f_name: str):\n",
    "    pattern = r\"score_report_(.*?)__top_k:(\\d+)__chunk_size:(\\d+)\\.csv\"\n",
    "    match = re.search(pattern, f_name)\n",
    "    if match:\n",
    "        embeddings_model = match.group(1)\n",
    "        top_k = int(match.group(2))\n",
    "        chunk_size = int(match.group(3))\n",
    "        return embeddings_model, top_k, chunk_size\n",
    "    else:\n",
    "        print(\"No match found\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67d86ea5-5d39-4cb8-90d2-470471571ac5",
   "metadata": {
    "id": "67d86ea5-5d39-4cb8-90d2-470471571ac5"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "\n",
    "def read_scores(path: str):\n",
    "    all_scores = []\n",
    "    for root, dirs, files in os.walk(path):\n",
    "        for f_name in files:\n",
    "            if not f_name.startswith(\"score_report\"):\n",
    "                continue\n",
    "\n",
    "            embeddings_model, top_k, chunk_size = parse_results(f_name)\n",
    "            df = pd.read_csv(path+\"/\"+f_name)\n",
    "            df_transposed = df.T\n",
    "            df_transposed.columns = df_transposed.iloc[0]\n",
    "            df_transposed = df_transposed[1:]\n",
    "\n",
    "            # Add new columns\n",
    "            df_transposed['embeddings'] = embeddings_model\n",
    "            df_transposed['top_k'] = top_k\n",
    "            df_transposed['chunk_size'] = chunk_size\n",
    "\n",
    "            all_scores.append(df_transposed)\n",
    "\n",
    "    df = pd.concat(all_scores)\n",
    "    df.reset_index(drop=True, inplace=True)\n",
    "    df.rename_axis(None, axis=1, inplace=True)\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a03966eb-658d-4e16-bce0-e198886eca35",
   "metadata": {
    "id": "a03966eb-658d-4e16-bce0-e198886eca35"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "df = read_scores('results/results_aragog_2024_06_12/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a018bfb3-755b-4a4f-9f2d-cf69201f9f6d",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 913
    },
    "id": "a018bfb3-755b-4a4f-9f2d-cf69201f9f6d",
    "outputId": "8ed45d7d-be8c-4187-9860-eb85ec9e016f"
   },
   "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>context_relevance</th>\n",
       "      <th>faithfulness</th>\n",
       "      <th>sas</th>\n",
       "      <th>embeddings</th>\n",
       "      <th>top_k</th>\n",
       "      <th>chunk_size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.791589</td>\n",
       "      <td>0.67757</td>\n",
       "      <td>0.480863</td>\n",
       "      <td>all-mpnet-base-v2</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.941952</td>\n",
       "      <td>0.91472</td>\n",
       "      <td>0.645992</td>\n",
       "      <td>all-mpnet-base-v2</td>\n",
       "      <td>3</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.946651</td>\n",
       "      <td>0.88785</td>\n",
       "      <td>0.639072</td>\n",
       "      <td>all-mpnet-base-v2</td>\n",
       "      <td>2</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.949844</td>\n",
       "      <td>0.866822</td>\n",
       "      <td>0.613355</td>\n",
       "      <td>msmarco-distilroberta-base-v2</td>\n",
       "      <td>1</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.933489</td>\n",
       "      <td>0.948598</td>\n",
       "      <td>0.65133</td>\n",
       "      <td>all-MiniLM-L6-v2</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.952544</td>\n",
       "      <td>0.893769</td>\n",
       "      <td>0.662694</td>\n",
       "      <td>msmarco-distilroberta-base-v2</td>\n",
       "      <td>2</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.867887</td>\n",
       "      <td>0.834112</td>\n",
       "      <td>0.560079</td>\n",
       "      <td>all-mpnet-base-v2</td>\n",
       "      <td>1</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.964182</td>\n",
       "      <td>0.943925</td>\n",
       "      <td>0.62854</td>\n",
       "      <td>msmarco-distilroberta-base-v2</td>\n",
       "      <td>3</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.88004</td>\n",
       "      <td>0.929907</td>\n",
       "      <td>0.600428</td>\n",
       "      <td>msmarco-distilroberta-base-v2</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.909813</td>\n",
       "      <td>0.738318</td>\n",
       "      <td>0.530884</td>\n",
       "      <td>msmarco-distilroberta-base-v2</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.843447</td>\n",
       "      <td>0.831776</td>\n",
       "      <td>0.555873</td>\n",
       "      <td>all-MiniLM-L6-v2</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.94463</td>\n",
       "      <td>0.928349</td>\n",
       "      <td>0.659311</td>\n",
       "      <td>all-MiniLM-L6-v2</td>\n",
       "      <td>3</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.912355</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.661135</td>\n",
       "      <td>all-MiniLM-L6-v2</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.82648</td>\n",
       "      <td>0.866044</td>\n",
       "      <td>0.584507</td>\n",
       "      <td>all-mpnet-base-v2</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.916422</td>\n",
       "      <td>0.892523</td>\n",
       "      <td>0.609728</td>\n",
       "      <td>all-mpnet-base-v2</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.885314</td>\n",
       "      <td>0.869159</td>\n",
       "      <td>0.587424</td>\n",
       "      <td>msmarco-distilroberta-base-v2</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.948038</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.643175</td>\n",
       "      <td>all-mpnet-base-v2</td>\n",
       "      <td>3</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.945016</td>\n",
       "      <td>0.936916</td>\n",
       "      <td>0.68591</td>\n",
       "      <td>msmarco-distilroberta-base-v2</td>\n",
       "      <td>3</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.901218</td>\n",
       "      <td>0.890654</td>\n",
       "      <td>0.611468</td>\n",
       "      <td>all-mpnet-base-v2</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.953649</td>\n",
       "      <td>0.919003</td>\n",
       "      <td>0.664224</td>\n",
       "      <td>msmarco-distilroberta-base-v2</td>\n",
       "      <td>2</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.897715</td>\n",
       "      <td>0.845794</td>\n",
       "      <td>0.538579</td>\n",
       "      <td>all-mpnet-base-v2</td>\n",
       "      <td>1</td>\n",
       "      <td>128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.834891</td>\n",
       "      <td>0.738318</td>\n",
       "      <td>0.524882</td>\n",
       "      <td>all-MiniLM-L6-v2</td>\n",
       "      <td>1</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.869485</td>\n",
       "      <td>0.895639</td>\n",
       "      <td>0.633806</td>\n",
       "      <td>all-MiniLM-L6-v2</td>\n",
       "      <td>2</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.912991</td>\n",
       "      <td>0.827103</td>\n",
       "      <td>0.574832</td>\n",
       "      <td>all-MiniLM-L6-v2</td>\n",
       "      <td>1</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.951702</td>\n",
       "      <td>0.925456</td>\n",
       "      <td>0.642837</td>\n",
       "      <td>all-MiniLM-L6-v2</td>\n",
       "      <td>2</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.918135</td>\n",
       "      <td>0.934579</td>\n",
       "      <td>0.67328</td>\n",
       "      <td>msmarco-distilroberta-base-v2</td>\n",
       "      <td>3</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.909638</td>\n",
       "      <td>0.932243</td>\n",
       "      <td>0.676347</td>\n",
       "      <td>all-MiniLM-L6-v2</td>\n",
       "      <td>3</td>\n",
       "      <td>256</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   context_relevance faithfulness       sas                     embeddings  \\\n",
       "0           0.791589      0.67757  0.480863              all-mpnet-base-v2   \n",
       "1           0.941952      0.91472  0.645992              all-mpnet-base-v2   \n",
       "2           0.946651      0.88785  0.639072              all-mpnet-base-v2   \n",
       "3           0.949844     0.866822  0.613355  msmarco-distilroberta-base-v2   \n",
       "4           0.933489     0.948598   0.65133               all-MiniLM-L6-v2   \n",
       "5           0.952544     0.893769  0.662694  msmarco-distilroberta-base-v2   \n",
       "6           0.867887     0.834112  0.560079              all-mpnet-base-v2   \n",
       "7           0.964182     0.943925   0.62854  msmarco-distilroberta-base-v2   \n",
       "8            0.88004     0.929907  0.600428  msmarco-distilroberta-base-v2   \n",
       "9           0.909813     0.738318  0.530884  msmarco-distilroberta-base-v2   \n",
       "10          0.843447     0.831776  0.555873               all-MiniLM-L6-v2   \n",
       "11           0.94463     0.928349  0.659311               all-MiniLM-L6-v2   \n",
       "12          0.912355          NaN  0.661135               all-MiniLM-L6-v2   \n",
       "13           0.82648     0.866044  0.584507              all-mpnet-base-v2   \n",
       "14          0.916422     0.892523  0.609728              all-mpnet-base-v2   \n",
       "15          0.885314     0.869159  0.587424  msmarco-distilroberta-base-v2   \n",
       "16          0.948038          NaN  0.643175              all-mpnet-base-v2   \n",
       "17          0.945016     0.936916   0.68591  msmarco-distilroberta-base-v2   \n",
       "18          0.901218     0.890654  0.611468              all-mpnet-base-v2   \n",
       "19          0.953649     0.919003  0.664224  msmarco-distilroberta-base-v2   \n",
       "20          0.897715     0.845794  0.538579              all-mpnet-base-v2   \n",
       "21          0.834891     0.738318  0.524882               all-MiniLM-L6-v2   \n",
       "22          0.869485     0.895639  0.633806               all-MiniLM-L6-v2   \n",
       "23          0.912991     0.827103  0.574832               all-MiniLM-L6-v2   \n",
       "24          0.951702     0.925456  0.642837               all-MiniLM-L6-v2   \n",
       "25          0.918135     0.934579   0.67328  msmarco-distilroberta-base-v2   \n",
       "26          0.909638     0.932243  0.676347               all-MiniLM-L6-v2   \n",
       "\n",
       "    top_k  chunk_size  \n",
       "0       1          64  \n",
       "1       3         256  \n",
       "2       2         256  \n",
       "3       1         256  \n",
       "4       3          64  \n",
       "5       2         256  \n",
       "6       1         256  \n",
       "7       3         256  \n",
       "8       2          64  \n",
       "9       1          64  \n",
       "10      1         128  \n",
       "11      3         128  \n",
       "12      2         128  \n",
       "13      2          64  \n",
       "14      2         128  \n",
       "15      1         128  \n",
       "16      3         128  \n",
       "17      3         128  \n",
       "18      3          64  \n",
       "19      2         128  \n",
       "20      1         128  \n",
       "21      1          64  \n",
       "22      2          64  \n",
       "23      1         256  \n",
       "24      2         256  \n",
       "25      3          64  \n",
       "26      3         256  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "16cd5072-5d3c-453c-b4f9-1c285a6afe68",
   "metadata": {
    "id": "16cd5072-5d3c-453c-b4f9-1c285a6afe68"
   },
   "source": [
    "## Let's see wich parameter configuration yielded the best Semantic Similarity Answer score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "44d1fce4-430d-4365-b27e-d6e862eabc75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "df.boxplot(column='sas', by='embeddings', ax=ax)\n",
    "\n",
    "plt.xlabel(\"Embeddings Model\")\n",
    "plt.ylabel(\"Semantic Answer Similarity Values\")\n",
    "plt.title(\"Boxplots of Semantic Answer Similarity Values Aggregated by Embeddings\")\n",
    "\n",
    "#plt.show()\n",
    "plt.savefig(\"bloxplot.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c2cffb97-c7ee-4dd8-8b22-f5efa48336db",
   "metadata": {},
   "source": [
    "## Let's see what are the `top_k` and `chunk_size` values for the outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "13dbc053-e4f4-4a59-9446-67c70ba45f74",
   "metadata": {},
   "source": [
    "### `all-mpnet-base-v2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8616c992-934a-414c-89e8-ea8ccad2408e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp = df[df['embeddings'] == 'all-mpnet-base-v2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b4328bc2-dccd-4a18-96d1-818df2d7e8d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "context_relevance             0.941952\n",
       "faithfulness                   0.91472\n",
       "sas                           0.645992\n",
       "embeddings           all-mpnet-base-v2\n",
       "top_k                                3\n",
       "chunk_size                         256\n",
       "Name: 1, dtype: object"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.loc[df_tmp['sas'].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "56d6327f-bec1-4fbe-a7fe-9ab4e51b4160",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "context_relevance             0.791589\n",
       "faithfulness                   0.67757\n",
       "sas                           0.480863\n",
       "embeddings           all-mpnet-base-v2\n",
       "top_k                                1\n",
       "chunk_size                          64\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.loc[df_tmp['sas'].idxmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c913276c-8030-459e-b65a-5b30e54b6715",
   "metadata": {},
   "source": [
    "## `msmarco-distilroberta-base-v2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "dbc6831a-3eca-461c-8243-a2e9659fb220",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp = df[df['embeddings'] == 'msmarco-distilroberta-base-v2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "2c9b5ef4-d141-4219-9af8-6b86d3fbbb62",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "context_relevance                         0.945016\n",
       "faithfulness                              0.936916\n",
       "sas                                        0.68591\n",
       "embeddings           msmarco-distilroberta-base-v2\n",
       "top_k                                            3\n",
       "chunk_size                                     128\n",
       "Name: 17, dtype: object"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.loc[df_tmp['sas'].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "f001f85f-2e95-43c9-b665-9a4aafa2b70f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "context_relevance                         0.909813\n",
       "faithfulness                              0.738318\n",
       "sas                                       0.530884\n",
       "embeddings           msmarco-distilroberta-base-v2\n",
       "top_k                                            1\n",
       "chunk_size                                      64\n",
       "Name: 9, dtype: object"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.loc[df_tmp['sas'].idxmin()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "875f20fa-1d05-4453-a6da-a607fa75cb55",
   "metadata": {},
   "source": [
    "## `all-MiniLM-L6-v2`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "d6105b7f-e1b3-4654-a39e-915797fa7c58",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_tmp = df[df['embeddings'] == 'all-MiniLM-L6-v2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "00d06d75-9f43-40fb-85ec-c4fea16cb1e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "context_relevance            0.909638\n",
       "faithfulness                 0.932243\n",
       "sas                          0.676347\n",
       "embeddings           all-MiniLM-L6-v2\n",
       "top_k                               3\n",
       "chunk_size                        256\n",
       "Name: 26, dtype: object"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.loc[df_tmp['sas'].idxmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6c6a51b5-6945-4832-b4dd-b273f8ee0fe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "context_relevance            0.834891\n",
       "faithfulness                 0.738318\n",
       "sas                          0.524882\n",
       "embeddings           all-MiniLM-L6-v2\n",
       "top_k                               1\n",
       "chunk_size                         64\n",
       "Name: 21, dtype: object"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_tmp.loc[df_tmp['sas'].idxmin()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a5bb992-867c-41a6-bf25-b69645ad19b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "68e4ed5e-5afe-4db2-a93e-f4232e733092",
   "metadata": {
    "id": "68e4ed5e-5afe-4db2-a93e-f4232e733092"
   },
   "outputs": [],
   "source": [
    "detailed_best_sas_df = pd.read_csv(\"results/results_aragog_2024_06_12/detailed_msmarco-distilroberta-base-v2__top_k:3__chunk_size:128.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "c7f425f4-ed35-4625-8824-f06e33622eac",
   "metadata": {
    "id": "c7f425f4-ed35-4625-8824-f06e33622eac",
    "outputId": "1cbde902-59e0-4551-d38e-11a91f65eb06"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "    .dataframe tbody tr th {\n",
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       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>questions</th>\n",
       "      <th>contexts</th>\n",
       "      <th>true_answers</th>\n",
       "      <th>predicted_answers</th>\n",
       "      <th>context_relevance</th>\n",
       "      <th>faithfulness</th>\n",
       "      <th>sas</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>What are the two main tasks BERT is pre-traine...</td>\n",
       "      <td>['steps in our\\nframework: pre-training and ﬁn...</td>\n",
       "      <td>Masked LM (MLM) and Next Sentence Prediction (...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.052841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>What model sizes are reported for BERT, and wh...</td>\n",
       "      <td>['on the 336M model, the modiﬁcations\\nin (b) ...</td>\n",
       "      <td>BERTBASE (L=12, H=768, A=12, Total Parameters=...</td>\n",
       "      <td>BERT BASE (L=12, H=768, A=12, Total Parameters...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.738328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>How does BERT's architecture facilitate the us...</td>\n",
       "      <td>['DistilBERT, a distilled version of BERT: sma...</td>\n",
       "      <td>BERT uses a multi-layer bidirectional Transfor...</td>\n",
       "      <td>BERT's architecture facilitates the use of a u...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.794824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Can you describe the modifications LLaMA makes...</td>\n",
       "      <td>['increasing computational\\nneeds. Our work ai...</td>\n",
       "      <td>LLaMA incorporates pre-normalization (using R...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.046518</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>How does LLaMA's approach to embedding layer o...</td>\n",
       "      <td>['embedding GEMM. How-\\never, in transformer l...</td>\n",
       "      <td>LLaMA introduces optimizations in its embeddin...</td>\n",
       "      <td>LLaMA's approach to embedding layer optimizati...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.814197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>How do the selected tasks in SuperGLUE reflect...</td>\n",
       "      <td>['high-level goals, the original version of\\nt...</td>\n",
       "      <td>The selected tasks, chosen for their difficult...</td>\n",
       "      <td>The selected tasks in SuperGLUE reflect the be...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.891120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>In the context of PAL, what role do meaningful...</td>\n",
       "      <td>['et al.\\n(2021); Madaan et al. (2022). We cre...</td>\n",
       "      <td>Meaningful variable names in PAL's generated c...</td>\n",
       "      <td>Meaningful variable names are important in the...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.588567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>How does PAL address the execution of complex ...</td>\n",
       "      <td>['intermediate\\nreasoning steps, but ofﬂoads t...</td>\n",
       "      <td>PAL addresses complex computations by generat...</td>\n",
       "      <td>PAL addresses the execution of complex computa...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.889795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>How does SuperGLUE's design ensure it presents...</td>\n",
       "      <td>['high-level goals, the original version of\\nt...</td>\n",
       "      <td>SuperGLUE introduces more challenging tasks re...</td>\n",
       "      <td>SuperGLUE's design ensures a more challenging ...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.932926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106</th>\n",
       "      <td>In what ways does SuperGLUE expand beyond GLU...</td>\n",
       "      <td>['in GLUE. The remaining tasks\\nwere identiﬁed...</td>\n",
       "      <td>By including coreference resolution and questi...</td>\n",
       "      <td>SuperGLUE expands beyond GLUE's task formats b...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.886653</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>107 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             questions  \\\n",
       "0    What are the two main tasks BERT is pre-traine...   \n",
       "1    What model sizes are reported for BERT, and wh...   \n",
       "2    How does BERT's architecture facilitate the us...   \n",
       "3    Can you describe the modifications LLaMA makes...   \n",
       "4    How does LLaMA's approach to embedding layer o...   \n",
       "..                                                 ...   \n",
       "102  How do the selected tasks in SuperGLUE reflect...   \n",
       "103  In the context of PAL, what role do meaningful...   \n",
       "104  How does PAL address the execution of complex ...   \n",
       "105  How does SuperGLUE's design ensure it presents...   \n",
       "106   In what ways does SuperGLUE expand beyond GLU...   \n",
       "\n",
       "                                              contexts  \\\n",
       "0    ['steps in our\\nframework: pre-training and ﬁn...   \n",
       "1    ['on the 336M model, the modiﬁcations\\nin (b) ...   \n",
       "2    ['DistilBERT, a distilled version of BERT: sma...   \n",
       "3    ['increasing computational\\nneeds. Our work ai...   \n",
       "4    ['embedding GEMM. How-\\never, in transformer l...   \n",
       "..                                                 ...   \n",
       "102  ['high-level goals, the original version of\\nt...   \n",
       "103  ['et al.\\n(2021); Madaan et al. (2022). We cre...   \n",
       "104  ['intermediate\\nreasoning steps, but ofﬂoads t...   \n",
       "105  ['high-level goals, the original version of\\nt...   \n",
       "106  ['in GLUE. The remaining tasks\\nwere identiﬁed...   \n",
       "\n",
       "                                          true_answers  \\\n",
       "0    Masked LM (MLM) and Next Sentence Prediction (...   \n",
       "1    BERTBASE (L=12, H=768, A=12, Total Parameters=...   \n",
       "2    BERT uses a multi-layer bidirectional Transfor...   \n",
       "3     LLaMA incorporates pre-normalization (using R...   \n",
       "4    LLaMA introduces optimizations in its embeddin...   \n",
       "..                                                 ...   \n",
       "102  The selected tasks, chosen for their difficult...   \n",
       "103  Meaningful variable names in PAL's generated c...   \n",
       "104   PAL addresses complex computations by generat...   \n",
       "105  SuperGLUE introduces more challenging tasks re...   \n",
       "106  By including coreference resolution and questi...   \n",
       "\n",
       "                                     predicted_answers  context_relevance  \\\n",
       "0                                                  NaN                1.0   \n",
       "1    BERT BASE (L=12, H=768, A=12, Total Parameters...                1.0   \n",
       "2    BERT's architecture facilitates the use of a u...                1.0   \n",
       "3                                                  NaN                1.0   \n",
       "4    LLaMA's approach to embedding layer optimizati...                1.0   \n",
       "..                                                 ...                ...   \n",
       "102  The selected tasks in SuperGLUE reflect the be...                1.0   \n",
       "103  Meaningful variable names are important in the...                1.0   \n",
       "104  PAL addresses the execution of complex computa...                1.0   \n",
       "105  SuperGLUE's design ensures a more challenging ...                1.0   \n",
       "106  SuperGLUE expands beyond GLUE's task formats b...                1.0   \n",
       "\n",
       "     faithfulness       sas  \n",
       "0             1.0  0.052841  \n",
       "1             1.0  0.738328  \n",
       "2             1.0  0.794824  \n",
       "3             1.0 -0.046518  \n",
       "4             1.0  0.814197  \n",
       "..            ...       ...  \n",
       "102           1.0  0.891120  \n",
       "103           1.0  0.588567  \n",
       "104           1.0  0.889795  \n",
       "105           1.0  0.932926  \n",
       "106           1.0  0.886653  \n",
       "\n",
       "[107 rows x 7 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "detailed_best_sas_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "0e2da651-7843-4dab-bc55-7f51d9965901",
   "metadata": {
    "id": "0e2da651-7843-4dab-bc55-7f51d9965901"
   },
   "outputs": [],
   "source": [
    "def inspect(idx):\n",
    "    print(\"Question: \")\n",
    "    print(detailed_best_sas_df.loc[idx]['questions'])\n",
    "    print(\"\\nTrue Answer:\")\n",
    "    print(detailed_best_sas_df.loc[idx]['true_answers'])\n",
    "    print()\n",
    "    print(\"Generated Answer:\")\n",
    "    print(detailed_best_sas_df.loc[idx]['predicted_answers'])\n",
    "    print()\n",
    "    print(f\"Context Relevance  : {detailed_best_sas_df.loc[idx]['context_relevance']}\")\n",
    "    print(f\"Faithfulness       : {detailed_best_sas_df.loc[idx]['faithfulness']}\")\n",
    "    print(f\"Semantic Similarity: {detailed_best_sas_df.loc[idx]['sas']}\")\n",
    "    print()\n",
    "    print(\"Contexts:\")\n",
    "    for context in eval(detailed_best_sas_df.loc[idx]['contexts']):\n",
    "        print(context)\n",
    "        print(\"---------\")\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "3d7e17ed-cbce-4dd6-a69d-ad217630fa23",
   "metadata": {
    "id": "3d7e17ed-cbce-4dd6-a69d-ad217630fa23",
    "outputId": "ba0a5d28-c52c-46b2-834b-2e11bbb15732"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question: \n",
      "What model sizes are reported for BERT, and what are their specifications?\n",
      "\n",
      "True Answer:\n",
      "BERTBASE (L=12, H=768, A=12, Total Parameters=110M) and BERTLARGE (L=24, H=1024, A=16, Total Parameters=340M).\n",
      "\n",
      "Generated Answer:\n",
      "BERT BASE (L=12, H=768, A=12, Total Parameters=110M) and BERT LARGE (L=24, H=1024, A=16, Total Parameters=340M)\n",
      "\n",
      "Context Relevance  : 1.0\n",
      "Faithfulness       : 1.0\n",
      "Semantic Similarity: 0.7383280992507935\n",
      "\n",
      "Contexts:\n",
      "on the 336M model, the modiﬁcations\n",
      "in (b) enable stable training with lower training loss.\n",
      "Using the architecture change in Figure 7(b), we consider\n",
      "three different cases as detailed in Table 4. The 336M model\n",
      "has the same size as BERT-large. The 1.3B is the same as\n",
      "the BERT-xlarge conﬁguration that was previously shown\n",
      "to get worse results than the 336M BERT-large model (Lan\n",
      "et al., 2019). We further scale the BERT model using both\n",
      "larger hidden size as well as more layers to arrive at the 3.9B\n",
      "parameter case. In all cases, the hidden size per attention\n",
      "head is kept constant at 64. 336M and 1.3B models are\n",
      "trained for 2 million iterations while the 3.9B model is\n",
      "trained for 1.5 million iterations and is still training.\n",
      "On a 3% held-out set, 336M, 1.3B, and 3.9B models achieve\n",
      "validation set perplexity \n",
      "---------\n",
      "- 85.6 - 85.8\n",
      "R.M. Reader (Ensemble) 81.2 87.9 82.3 88.5\n",
      "Ours\n",
      "BERT BASE (Single) 80.8 88.5 - -\n",
      "BERT LARGE (Single) 84.1 90.9 - -\n",
      "BERT LARGE (Ensemble) 85.8 91.8 - -\n",
      "BERT LARGE (Sgl.+TriviaQA) 84.2 91.1 85.1 91.8\n",
      "BERT LARGE (Ens.+TriviaQA) 86.2 92.2 87.4 93.2\n",
      "Table 2: SQuAD 1.1 results. The BERT ensemble\n",
      "is 7x systems which use different pre-training check-\n",
      "points and ﬁne-tuning seeds.\n",
      "System Dev Test\n",
      "EM F1 EM F1\n",
      "Top Leaderboard Systems (Dec 10th, 2018)\n",
      "Human 86.3 89.0 86.9 89.5\n",
      "#1 Single - MIR-MRC (F-Net) - - 74.8 78.0\n",
      "#2 Single - nlnet - - 74.2 77.1\n",
      "Published\n",
      "unet (Ensemble) - - 71.4 74.9\n",
      "SLQA+ (Single) - 71.4 74.4\n",
      "Ours\n",
      "BERT LARGE (Single) 78.7 81.9 80.0 83.1\n",
      "Table 3: SQuAD 2.0 results. We exclude entries that\n",
      "use BERT as one of their components.\n",
      "tuning data, we only lose 0.1-0.4 F1, still outper-\n",
      "forming all existing systems by a \n",
      "---------\n",
      "and our im-\n",
      "plementation is almost identical to the original,\n",
      "we will omit an exhaustive background descrip-\n",
      "tion of the model architecture and refer readers to\n",
      "Vaswani et al. (2017) as well as excellent guides\n",
      "such as “The Annotated Transformer.”2\n",
      "In this work, we denote the number of layers\n",
      "(i.e., Transformer blocks) as L, the hidden size as\n",
      "H, and the number of self-attention heads as A.3\n",
      "We primarily report results on two model sizes:\n",
      "BERT BASE (L=12, H=768, A=12, Total Param-\n",
      "eters=110M) and BERT LARGE (L=24, H=1024,\n",
      "A=16, Total Parameters=340M).\n",
      "BERT BASE was chosen to have the same model\n",
      "size as OpenAI GPT for comparison purposes.\n",
      "Critically, however, the BERT Transformer uses\n",
      "bidirectional self-attention, while the GPT Trans-\n",
      "former uses constrained self-attention where every\n",
      "token can only attend to context to its left.4\n",
      "1https://github.com/tensorﬂow/tensor2tensor\n",
      "2http:https://nlp.seas.harvard.edu/2018/04/03/attention.html\n",
      "3In all cases we set the feed-forward/ﬁlter size to be 4H,\n",
      "i.e., 3072 for \n",
      "---------\n",
      "\n"
     ]
    }
   ],
   "source": [
    "inspect(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "15a9808b-0a85-438a-a5b2-7e6b960391b8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Question: \n",
      "How should future language model benchmarks be structured to ensure a holistic assessment of models' capabilities and knowledge breadth?\n",
      "\n",
      "True Answer:\n",
      "Future benchmarks should integrate a broader spectrum of subjects and cognitive skills, emphasizing the inclusion of tasks that test models' ethical reasoning, understanding of human values, and ability to perform complex problem-solving, beyond the mere scale of data and parameters.\n",
      "\n",
      "Generated Answer:\n",
      "Future language model benchmarks should be structured to measure knowledge acquired during pretraining by evaluating models exclusively in zero-shot and few-shot settings across a diverse set of subjects that humans learn. The benchmark should cover a wide range of subjects across STEM, humanities, social sciences, and more, ranging in difficulty from elementary to advanced professional levels. It should test both world knowledge and problem-solving ability, ensuring a holistic assessment of models' capabilities and knowledge breadth.\n",
      "\n",
      "Context Relevance  : 0.6\n",
      "Faithfulness       : 1.0\n",
      "Semantic Similarity: 0.6483339071273804\n",
      "\n",
      "Contexts:\n",
      "learning model\n",
      "usage should be developed for guiding users to learn ‘Dos’\n",
      "and Dont’ in AI. Detailed policies could also be proposed\n",
      "to list all user’s responsibilities before the model access.\n",
      "C. Language Models Beyond ChatGPT\n",
      "The examination of ethical implications associated with\n",
      "language models necessitates a comprehensive examina-\n",
      "tion of the broader challenges that arise within the domain\f",
      "of language models, in light of recent advancements in\n",
      "the field of artificial intelligence. The last decade has seen\n",
      "a rapid evolution of AI techniques, characterized by an\n",
      "exponential increase in the size and complexity of AI\n",
      "models, and a concomitant scale-up of model parameters.\n",
      "The scaling laws that govern the development of language\n",
      "models,asdocumentedinrecentliterature[84,85],suggest\n",
      "thatwecanexpecttoencounterevenmoreexpansivemod-\n",
      "els that incorporate multiple modalities in the near future.\n",
      "Efforts to integrate multiple modalities into a single model\n",
      "are driven by the ultimate goal of realizing the concept of\n",
      "foundation models [86]. \n",
      "---------\n",
      "language models are\n",
      "at learning and applying knowledge from many domains.\n",
      "To bridge the gap between the wide-ranging knowledge that models see during pretraining and the\n",
      "existing measures of success, we introduce a new benchmark for assessing models across a diverse\n",
      "set of subjects that humans learn. We design the benchmark to measure knowledge acquired during\n",
      "pretraining by evaluating models exclusively in zero-shot and few-shot settings. This makes the\n",
      "benchmark more challenging and more similar to how we evaluate humans. The benchmark covers\n",
      "57subjects across STEM, the humanities, the social sciences, and more. It ranges in difﬁculty from\n",
      "an elementary level to an advanced professional level, and it tests both world knowledge and problem\n",
      "solving ability. Subjects range from traditional areas, such as mathematics and history, to more\n",
      "1arXiv:2009.03300v3 [cs.CY] 12 Jan 2021\f",
      "Published as a conference paper at \n",
      "---------\n",
      "a\n",
      "lack of access to the benefits of these models for people\n",
      "who speak different languages and can lead to biased or\n",
      "unfairpredictionsaboutthosegroups[14,15].Toovercome\n",
      "this, it is crucial to ensure that the training data contains\n",
      "a substantial proportion of diverse, high-quality corpora\n",
      "from various languages and cultures.\n",
      "b) Robustness: Another major ethical consideration\n",
      "in the design and implementation of language models is\n",
      "their robustness. Robustness refers to a model’s ability\n",
      "to maintain its performance when given input that is\n",
      "semantically or syntactically different from the input it\n",
      "was trained on.\n",
      "Semantic Perturbation: Semantic perturbation is a type\n",
      "of input that can cause a language model to fail [40, 41].\n",
      "This input has different syntax but is semantically similar\n",
      "to the input used for training the model. To address this,\n",
      "it is crucial to ensure that the training data is diverse and\n",
      "representative of the population it will \n",
      "---------\n",
      "\n"
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