googlecloudplatform--generative-ai/gemini/use-cases/entity-extraction/evaluate.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "948123f6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2025 Google LLC\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b784874a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import dotenv\n",
    "import os\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "import document_processing\n",
    "import evaluate\n",
    "\n",
    "# Reload modules to reflect recent edits.\n",
    "import importlib\n",
    "importlib.reload(document_processing)\n",
    "importlib.reload(evaluate)\n",
    "\n",
    "\n",
    "# Load environment variables.\n",
    "dotenv.load_dotenv(dotenv_path=\".env\", override=True)\n",
    "PROJECT_ID = os.environ.get(\"GEMINI_PROJECT_ID\")\n",
    "if not PROJECT_ID:\n",
    "    raise ValueError(\"GEMINI_PROJECT_ID environment variable must be set.\")\n",
    "LOCATION = os.environ.get(\"GEMINI_LOCATION\", \"global\")\n",
    "IMAGE_PATHS = os.environ.get(\"IMAGE_PATHS\", \"\")\n",
    "IMAGE_PREFIX = os.environ.get(\"IMAGE_PREFIX\", \"\")\n",
    "\n",
    "# Set evaluation parameters.\n",
    "EVAL_MODEL = \"gemini-2.5-flash\"\n",
    "RANDOM_STATE = 42\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a4468715",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(df, title='Confusion Matrix'):\n",
    "    # Create a confusion matrix that is ordered by the average exact match score\n",
    "    # for each class.\n",
    "\n",
    "    # Extract the ordered list of classes.\n",
    "    avg_df = df.groupby('reference')['exact_match'].mean().reset_index()\n",
    "    avg_df = avg_df.sort_values(by='exact_match', ascending=True)\n",
    "\n",
    "    ordered_classes = avg_df['reference'].tolist()\n",
    "    extra_preds = [cls for cls in df['response'].unique() if cls not in ordered_classes]\n",
    "    full_order = ordered_classes + extra_preds\n",
    "\n",
    "    # Create and order the confusion matrix.\n",
    "    cm = pd.crosstab(df['reference'], df['response'])\n",
    "    cm_ordered = cm.reindex(index=full_order, columns=full_order, fill_value=0)\n",
    "\n",
    "    # Plot the confusion matrix.\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    ax = sns.heatmap(cm_ordered, annot=True, fmt='d', cmap='Blues')\n",
    "\n",
    "    ax.xaxis.tick_top()\n",
    "    ax.xaxis.set_label_position('top')\n",
    "    plt.xticks(rotation=90)\n",
    "    plt.title(title, pad=20)\n",
    "    plt.ylabel('Actual (Reference)')\n",
    "    plt.xlabel('Predicted (Response)')\n",
    "\n",
    "    plt.show()    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "ee614cc6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Analyze the intent, visual layout, text content, and structural elements of the document.\n",
      "    Classify it into exactly one of the following classes based on its distinguishing features.\n",
      "    Output as JSON in the following format:\n",
      "    \n",
      "    \"reasoning\": \"Brief explanation of the key visual cues and keywords found that justify the class\",\n",
      "    \"class\": \"class_name\"\n",
      "    \n",
      "\n",
      "    Classes:\n",
      "\n",
      "    {\n",
      "    \"handwritten\": \"Document entirely handwritten. Process this before other classes\",\n",
      "    \"form_10_k\": \"Annual report of a company\",\n",
      "    \"form_10_q\": \"Quarter report of a company\",\n",
      "    \"resume\": \"CV or work history\",\n",
      "    \"file_folder\": \"Folder cover or label\",\n",
      "    \"budget\": \"Financial forecasts, spreadsheets, or internal expenditure authorizations. IF the document is a fill-in-the-blank form requesting or authorizing money (e.g., 'Contribution Request', 'Account Code', 'Amount: $'), classify as budget. EXCLUDE: Do NOT classify as invoice unless it is a final bill from an external vendor.\",\n",
      "    \"invoice\": \"Definitive demand for payment for goods/services already delivered. Look for 'Remit To', 'Net 30', 'Terms', 'Tax ID', or 'Please Pay'. If it is an external bill, it is an invoice, even if it has form-like boxes. EXCLUDE: Do NOT classify as budget.\",\n",
      "    \"scientific_publication\": \"Scholarly article\",\n",
      "    \"scientific_report\": \"Research findings document\",\n",
      "    \"specification\": \"Detailed technical, engineering, or architectural requirements. Look for section numbers (e.g., 1.1, 1.2), blueprints, materials lists, or strict technical constraints. IF it is a technical checklist or engineering form, classify as specification, NOT form.\",\n",
      "    \"questionnaire\": \"Survey instrument\",\n",
      "    \"email\": \"Digital message\",\n",
      "    \"memo\": \"Internal office correspondence. Look for a strict, block-style header at the top containing 'TO:', 'FROM:', 'DATE:', and 'SUBJECT:' (or 'RE:'). EXCLUDE: If the document starts with a conversational salutation like 'Dear [Name]', it is NOT a memo. Classify as letter instead.\",\n",
      "    \"presentation\": \"Official corporate communications or media press releases\",\n",
      "    \"news_article\": \"Clipping from a newspaper, magazine, or journal\",\n",
      "    \"advertisement\": \"Promotional content\",\n",
      "    \"letter\": \"Typed correspondence, usually external. Visually characterized by formal salutations ('Dear...', 'To Whom It May Concern') and sign-offs at the bottom ('Sincerely', 'Cordially', 'Best Regards'). EXCLUDE: If the document uses a rigid 'TO / FROM / SUBJECT' block header, classify as memo instead.\",\n",
      "    \"form\": \"Administrative data collection documents featuring blank lines, checkboxes, or fax cover sheets. EXTREME EXCLUSION: If the document contains prices, 'Account Code', authorizes spending, or contains technical engineering data, do NOT classify as form. Classify as budget, invoice, or specification instead.\",\n",
      "    \"other\": \"None of the above\"\n",
      "}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arieljassan/Documents/projects/generative-ai/gemini/use-cases/entity-extraction/evaluate.py:103: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  df = df.groupby(\"reference\", group_keys=False).apply(\n",
      "Gemini Inference: 100%|██████████| 120/120 [00:24<00:00,  4.88it/s]\n",
      "Computing Metrics for Evaluation Dataset: 100%|██████████| 120/120 [00:04<00:00, 24.97it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[AggregatedMetricResult(\n",
      "  mean_score=0.7916666666666666,\n",
      "  metric_name='exact_match',\n",
      "  num_cases_error=0,\n",
      "  num_cases_total=120,\n",
      "  num_cases_valid=120,\n",
      "  stdev_score=0.407819232762071\n",
      ")]\n"
     ]
    },
    {
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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run evaluation for document classification task. It will run on the specified \n",
    "# sample size and return the evaluation results and a DataFrame with detailed \n",
    "# information about each evaluated sample.\n",
    "importlib.reload(document_processing)\n",
    "importlib.reload(evaluate)\n",
    "\n",
    "SAMPLE_SIZE = 120\n",
    "\n",
    "\n",
    "result, df = (\n",
    "    evaluate.run_evaluation(\n",
    "        project_id=PROJECT_ID, \n",
    "        location=LOCATION, \n",
    "        csv_path=IMAGE_PATHS,\n",
    "        image_prefix=IMAGE_PREFIX,\n",
    "        eval_model=EVAL_MODEL,\n",
    "        sample_size=SAMPLE_SIZE,\n",
    "        random_state=RANDOM_STATE,\n",
    "        stratify=True,\n",
    "    )\n",
    ")\n",
    "\n",
    "print(result.summary_metrics)\n",
    "plot_confusion_matrix(df, title='Confusion Matrix for All Classes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "dff50ab7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    Analyze the intent, visual layout, text content, and structural elements of the document.\n",
      "    Classify it into exactly one of the following classes based on its distinguishing features.\n",
      "    Output as JSON in the following format:\n",
      "    \n",
      "    \"reasoning\": \"Brief explanation of the key visual cues and keywords found that justify the class\",\n",
      "    \"class\": \"class_name\"\n",
      "    \n",
      "\n",
      "    Classes:\n",
      "\n",
      "    {\n",
      "    \"budget\": \"Financial forecasts, spreadsheets, or internal expenditure authorizations. IF the document is a fill-in-the-blank form requesting or authorizing money (e.g., 'Contribution Request', 'Account Code', 'Amount: $'), classify as budget. EXCLUDE: Do NOT classify as invoice unless it is a final bill from an external vendor.\",\n",
      "    \"invoice\": \"Definitive demand for payment for goods/services already delivered. Look for 'Remit To', 'Net 30', 'Terms', 'Tax ID', or 'Please Pay'. If it is an external bill, it is an invoice, even if it has form-like boxes. EXCLUDE: Do NOT classify as budget.\",\n",
      "    \"specification\": \"Detailed technical, engineering, or architectural requirements. Look for section numbers (e.g., 1.1, 1.2), blueprints, materials lists, or strict technical constraints. IF it is a technical checklist or engineering form, classify as specification, NOT form.\",\n",
      "    \"form\": \"Administrative data collection documents featuring blank lines, checkboxes, or fax cover sheets. EXTREME EXCLUSION: If the document contains prices, 'Account Code', authorizes spending, or contains technical engineering data, do NOT classify as form. Classify as budget, invoice, or specification instead.\"\n",
      "}\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/arieljassan/Documents/projects/generative-ai/gemini/use-cases/entity-extraction/evaluate.py:103: FutureWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
      "  df = df.groupby(\"reference\", group_keys=False).apply(\n",
      "Gemini Inference: 100%|██████████| 60/60 [00:22<00:00,  2.70it/s]\n",
      "Computing Metrics for Evaluation Dataset: 100%|██████████| 60/60 [00:01<00:00, 39.18it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[AggregatedMetricResult(\n",
      "  mean_score=0.8,\n",
      "  metric_name='exact_match',\n",
      "  num_cases_error=0,\n",
      "  num_cases_total=60,\n",
      "  num_cases_valid=60,\n",
      "  stdev_score=0.4033755872716886\n",
      ")]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Run evaluation for a specified set of target classes. This will allow us to \n",
    "# focus the evaluation on a subset of classes that are of particular interest.\n",
    "importlib.reload(document_processing)\n",
    "importlib.reload(evaluate)\n",
    "\n",
    "SAMPLE_SIZE = 60\n",
    "\n",
    "\n",
    "target_classes = [\n",
    "    \"budget\", \n",
    "    \"specification\",\n",
    "    \"form\",\n",
    "    \"invoice\"\n",
    "]\n",
    "selected_result, selected_df = (\n",
    "    evaluate.run_evaluation(\n",
    "        project_id=PROJECT_ID, \n",
    "        location=LOCATION, \n",
    "        csv_path=IMAGE_PATHS,\n",
    "        image_prefix=IMAGE_PREFIX,\n",
    "        eval_model=EVAL_MODEL,\n",
    "        sample_size=SAMPLE_SIZE,\n",
    "        random_state=RANDOM_STATE,\n",
    "        stratify=True,\n",
    "        classes=target_classes,\n",
    "    )\n",
    ")\n",
    "\n",
    "print(selected_result.summary_metrics)\n",
    "plot_confusion_matrix(selected_df, title='Confusion Matrix for Selected Classes')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "cdf32ba3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>img_path</th>\n",
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       "      <td>budget</td>\n",
       "      <td>budget</td>\n",
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       "      <td>budget</td>\n",
       "      <td>budget</td>\n",
       "      <td>1.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/ti31139030.png</td>\n",
       "      <td>budget</td>\n",
       "      <td>budget</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/11003957.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>budget</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/tob07809.99.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>budget</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/24010475.png</td>\n",
       "      <td>form</td>\n",
       "      <td>budget</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/tcal0289181.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>budget</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/0000167320.png</td>\n",
       "      <td>budget</td>\n",
       "      <td>budget</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/tnwl0028229.png</td>\n",
       "      <td>form</td>\n",
       "      <td>budget</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/2045814412_4416.png</td>\n",
       "      <td>budget</td>\n",
       "      <td>budget</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/2076879244.png</td>\n",
       "      <td>budget</td>\n",
       "      <td>budget</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/524557796+-7813.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>form</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2074666677.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2074551212.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2072514136.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2029026843.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2064200721.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2024471389.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2077753550.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2028711777.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2084607707.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/509609620.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2501097632.png</td>\n",
       "      <td>budget</td>\n",
       "      <td>form</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2030772375.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/509189296_509189297.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>form</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/507038881.png</td>\n",
       "      <td>form</td>\n",
       "      <td>form</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/0001219658.png</td>\n",
       "      <td>budget</td>\n",
       "      <td>invoice</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2075573314.png</td>\n",
       "      <td>form</td>\n",
       "      <td>invoice</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2023921600.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2081562369_2371.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2080690649.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/506077664+-7664.png</td>\n",
       "      <td>budget</td>\n",
       "      <td>invoice</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2028714926.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2063321228.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/CTRCONTRACTS024759-4.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/88132033.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/00922266_00922271.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2074660865.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2028720753_2028720754.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2001207874.png</td>\n",
       "      <td>invoice</td>\n",
       "      <td>invoice</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/515618928+-8930.png</td>\n",
       "      <td>form</td>\n",
       "      <td>invoice</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2069720832.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2057163994_2057163995.png</td>\n",
       "      <td>form</td>\n",
       "      <td>specification</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2055275453.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2069730436.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/530542237+-2239.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2057410449_2057410451.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2057433890.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/0011572293.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/505957777.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2054772976_2054772982.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2069736584.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/504804068+-4068.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/0012229304.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/0000225206.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/512383380_512383389.png</td>\n",
       "      <td>specification</td>\n",
       "      <td>specification</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                                                            img_path  \\\n",
       "0                     gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/2045467868.png   \n",
       "1                gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/ti16352375_2377.png   \n",
       "2                    gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/tcal0288700.png   \n",
       "3                       gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/11006870.png   \n",
       "4                     gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/0000079198.png   \n",
       "5                     gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/2048693171.png   \n",
       "6                     gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/ti31139030.png   \n",
       "7                       gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/11003957.png   \n",
       "8                    gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/tob07809.99.png   \n",
       "9                       gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/24010475.png   \n",
       "10                   gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/tcal0289181.png   \n",
       "11                    gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/0000167320.png   \n",
       "12                   gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/tnwl0028229.png   \n",
       "13               gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/2045814412_4416.png   \n",
       "14                    gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/budget/2076879244.png   \n",
       "15                 gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/524557796+-7813.png   \n",
       "16                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2074666677.png   \n",
       "17                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2074551212.png   \n",
       "18                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2072514136.png   \n",
       "19                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2029026843.png   \n",
       "20                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2064200721.png   \n",
       "21                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2024471389.png   \n",
       "22                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2077753550.png   \n",
       "23                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2028711777.png   \n",
       "24                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2084607707.png   \n",
       "25                       gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/509609620.png   \n",
       "26                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2501097632.png   \n",
       "27                      gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/2030772375.png   \n",
       "28             gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/509189296_509189297.png   \n",
       "29                       gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/form/507038881.png   \n",
       "30                   gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/0001219658.png   \n",
       "31                   gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2075573314.png   \n",
       "32                   gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2023921600.png   \n",
       "33              gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2081562369_2371.png   \n",
       "34                   gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2080690649.png   \n",
       "35              gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/506077664+-7664.png   \n",
       "36                   gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2028714926.png   \n",
       "37                   gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2063321228.png   \n",
       "38         gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/CTRCONTRACTS024759-4.png   \n",
       "39                     gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/88132033.png   \n",
       "40            gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/00922266_00922271.png   \n",
       "41                   gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2074660865.png   \n",
       "42        gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2028720753_2028720754.png   \n",
       "43                   gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/2001207874.png   \n",
       "44              gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/invoice/515618928+-8930.png   \n",
       "45             gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2069720832.png   \n",
       "46  gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2057163994_2057163995.png   \n",
       "47             gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2055275453.png   \n",
       "48             gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2069730436.png   \n",
       "49        gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/530542237+-2239.png   \n",
       "50  gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2057410449_2057410451.png   \n",
       "51             gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2057433890.png   \n",
       "52             gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/0011572293.png   \n",
       "53              gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/505957777.png   \n",
       "54  gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2054772976_2054772982.png   \n",
       "55             gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/2069736584.png   \n",
       "56        gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/504804068+-4068.png   \n",
       "57             gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/0012229304.png   \n",
       "58             gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/0000225206.png   \n",
       "59    gs://github-repo/generative-ai/gemini/use-cases/entity-extraction/images/specification/512383380_512383389.png   \n",
       "\n",
       "         response      reference  exact_match  \n",
       "0          budget         budget          1.0  \n",
       "1   specification         budget          0.0  \n",
       "2         invoice         budget          0.0  \n",
       "3            form         budget          0.0  \n",
       "4          budget         budget          1.0  \n",
       "5          budget         budget          1.0  \n",
       "6          budget         budget          1.0  \n",
       "7         invoice         budget          0.0  \n",
       "8         invoice         budget          0.0  \n",
       "9            form         budget          0.0  \n",
       "10        invoice         budget          0.0  \n",
       "11         budget         budget          1.0  \n",
       "12           form         budget          0.0  \n",
       "13         budget         budget          1.0  \n",
       "14         budget         budget          1.0  \n",
       "15  specification           form          0.0  \n",
       "16           form           form          1.0  \n",
       "17           form           form          1.0  \n",
       "18           form           form          1.0  \n",
       "19           form           form          1.0  \n",
       "20           form           form          1.0  \n",
       "21           form           form          1.0  \n",
       "22           form           form          1.0  \n",
       "23           form           form          1.0  \n",
       "24           form           form          1.0  \n",
       "25           form           form          1.0  \n",
       "26         budget           form          0.0  \n",
       "27           form           form          1.0  \n",
       "28  specification           form          0.0  \n",
       "29           form           form          1.0  \n",
       "30         budget        invoice          0.0  \n",
       "31           form        invoice          0.0  \n",
       "32        invoice        invoice          1.0  \n",
       "33        invoice        invoice          1.0  \n",
       "34        invoice        invoice          1.0  \n",
       "35         budget        invoice          0.0  \n",
       "36        invoice        invoice          1.0  \n",
       "37        invoice        invoice          1.0  \n",
       "38        invoice        invoice          1.0  \n",
       "39        invoice        invoice          1.0  \n",
       "40        invoice        invoice          1.0  \n",
       "41        invoice        invoice          1.0  \n",
       "42        invoice        invoice          1.0  \n",
       "43        invoice        invoice          1.0  \n",
       "44           form        invoice          0.0  \n",
       "45  specification  specification          1.0  \n",
       "46           form  specification          0.0  \n",
       "47  specification  specification          1.0  \n",
       "48  specification  specification          1.0  \n",
       "49  specification  specification          1.0  \n",
       "50  specification  specification          1.0  \n",
       "51  specification  specification          1.0  \n",
       "52  specification  specification          1.0  \n",
       "53  specification  specification          1.0  \n",
       "54  specification  specification          1.0  \n",
       "55  specification  specification          1.0  \n",
       "56  specification  specification          1.0  \n",
       "57  specification  specification          1.0  \n",
       "58  specification  specification          1.0  \n",
       "59  specification  specification          1.0  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_colwidth', None)\n",
    "selected_df\n",
    "\n"
   ]
  }
 ],
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