From 8385a19669870f253ee39fe5b679890490b96bb1 Mon Sep 17 00:00:00 2001
From: swan <swan.lin@lynxanalytics.com>
Date: Fri, 7 Jan 2022 15:38:15 +0800
Subject: [PATCH 1/5] add speech2text notebook example - in the context of
 transcription service in court trials

---
 data/speech_sample_output.csv                 |  299 ++
 .../sample-form-response-speech-to-text.json  |  301 ++
 examples/speech_to_text_example.ipynb         | 2640 +++++++++++++++++
 3 files changed, 3240 insertions(+)
 create mode 100644 data/speech_sample_output.csv
 create mode 100644 examples/sample-form-response-speech-to-text.json
 create mode 100644 examples/speech_to_text_example.ipynb

diff --git a/data/speech_sample_output.csv b/data/speech_sample_output.csv
new file mode 100644
index 0000000..ca86e97
--- /dev/null
+++ b/data/speech_sample_output.csv
@@ -0,0 +1,299 @@
+sex,race,truth_count,match_count
+F,CHINESE,80,60
+F,CHINESE,89,64
+M,CHINESE,82,53
+F,INDIAN,85,53
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+M,MALAY,82,56
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+F,CHINESE,79,43
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+F,CHINESE,89,60
+M,CHINESE,89,62
+M,CHINESE,81,47
+F,CHINESE,94,48
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+M,CHINESE,82,55
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+M,CHINESE,67,47
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+M,MALAY,72,52
+F,CHINESE,85,60
+F,CHINESE,82,52
+M,CHINESE,85,57
diff --git a/examples/sample-form-response-speech-to-text.json b/examples/sample-form-response-speech-to-text.json
new file mode 100644
index 0000000..1136ba1
--- /dev/null
+++ b/examples/sample-form-response-speech-to-text.json
@@ -0,0 +1,301 @@
+{
+  "eventId": "4ceb5c3b-2878-4546-ad3e-9dab1147ba06",
+  "eventType": "FORM_RESPONSE",
+  "createdAt": "2021-09-26T07:14:49.410Z",
+  "data": {
+    "responseId": "100e0116-86e9-4625-8f80-dd539392104c",
+    "respondentId": "mZoZ90",
+    "formId": "mR4Nlw",
+    "formName": "Model Ops Card Builder",
+    "createdAt": "2021-09-26T07:14:49.000Z",
+    "fields": [
+      {
+        "key": "question_3yXOqx",
+        "label": "Name",
+        "type": "INPUT_TEXT",
+        "value": "Speech to Text Model"
+      },
+      {
+        "key": "question_waO1Wy",
+        "label": "Overview",
+        "type": "TEXTAREA",
+        "value": "The government want to create an automated transcription tool used in trials for evidence purposes. The tool should be able to capture the different spoken accents and transcribe into text accurately so as not to bias against the defendant."
+      },
+      {
+        "key": "question_3X5jBY",
+        "label": "Intended Users",
+        "type": "INPUT_TEXT",
+        "value": "Judical System and Defendant"
+      },
+      {
+        "key": "question_w8N9pP",
+        "label": "Intended Use Cases",
+        "type": "TEXTAREA",
+        "value": "Automate transcriptions and reduce human error in making court decisions"
+      },
+      {
+        "key": "question_m6DYq5",
+        "label": "Version",
+        "type": "INPUT_TEXT",
+        "value": "v1"
+      },
+      {
+        "key": "question_wbZMaE",
+        "label": "Product Owner(s)",
+        "type": "INPUT_TEXT",
+        "value": "Timothy"
+      },
+      {
+        "key": "question_wArYkN",
+        "label": "Model Developer(s)",
+        "type": "INPUT_TEXT",
+        "value": "Swan"
+      },
+      {
+        "key": "question_mBEYvY",
+        "label": "Reviewer(s)",
+        "type": "INPUT_TEXT",
+        "value": "Jason"
+      },
+      {
+        "key": "question_wzEBxk",
+        "label": "Please select any regulatory guidelines which the model should be in compliance with",
+        "type": "CHECKBOXES",
+        "value": [
+          "1f033a0c-1fa7-44a8-9309-0fe858283844",
+          "0a574b19-1a88-42e6-b4ba-b288b3fec5a4"
+        ],
+        "options": [
+          {
+            "id": "0a574b19-1a88-42e6-b4ba-b288b3fec5a4",
+            "text": "MAS Fairness, Ethics, Accountability and Transparency (FEAT) principles"
+          },
+          {
+            "id": "0e0e9512-6ea8-496e-9ee4-83dc1a23a5a7",
+            "text": "HKMA AI principles"
+          },
+          {
+            "id": "5fcd3e36-042c-4c71-b76a-e9ec325f6205",
+            "text": "Bank of England and FCA's guideline"
+          }
+        ]
+      },
+      {
+        "key": "question_wzEBxk_0a574b19-1a88-42e6-b4ba-b288b3fec5a4",
+        "label": "Please select any regulatory guidelines which the model should be in compliance with (MAS Fairness, Ethics, Accountability and Transparency (FEAT) principles)",
+        "type": "CHECKBOXES",
+        "value": true
+      },
+      {
+        "key": "question_wzEBxk_0e0e9512-6ea8-496e-9ee4-83dc1a23a5a7",
+        "label": "Please select any regulatory guidelines which the model should be in compliance with (HKMA AI principles)",
+        "type": "CHECKBOXES",
+        "value": false
+      },
+      {
+        "key": "question_wzEBxk_5fcd3e36-042c-4c71-b76a-e9ec325f6205",
+        "label": "Please select any regulatory guidelines which the model should be in compliance with (Bank of England and FCA's guideline)",
+        "type": "CHECKBOXES",
+        "value": false
+      },
+      {
+        "key": "question_wzEBxk_1f033a0c-1fa7-44a8-9309-0fe858283844",
+        "label": "Please select any regulatory guidelines which the model should be in compliance with (Not applicable)",
+        "type": "CHECKBOXES",
+        "value": true
+      },
+      {
+        "key": "question_n0V4jj",
+        "label": "Name of dataset",
+        "type": "INPUT_TEXT",
+        "value": "National Speech Corpus Dataset"
+      },
+      {
+        "key": "question_wM10jE",
+        "label": "Description of dataset",
+        "type": "TEXTAREA",
+        "value": "Dataset taken from https://www.imda.gov.sg/programme-listing/digital-services-lab/national-speech-corpus"
+      },
+      {
+        "key": "question_wzEVQ0",
+        "label": "Protected attributes in dataset",
+        "type": "TEXTAREA",
+        "value": "gender, race"
+      },
+      {
+        "key": "question_w5XNqE",
+        "label": "Protected attributes in production",
+        "type": "TEXTAREA",
+        "value": "gender, race"
+      },
+      {
+        "key": "question_3Eqpo2",
+        "label": "Justification of use of protected attributes",
+        "type": "TEXTAREA",
+        "value": "Potential improvement in model performance with protected attributes used as features in model"
+      },
+      {
+        "key": "question_wdb72K",
+        "label": "Name of dataset 2",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_mJ1yEz",
+        "label": "Description of dataset 2",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_mYj2zJ",
+        "label": "Protected attributes in dataset 2",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_mDqLvZ",
+        "label": "Protected attributes in production (dataset 2)",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_3jeK8Y",
+        "label": "Justification of use of protected attributes (dataset 2)",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_3laplv",
+        "label": "Name of dataset 3",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_wgbB8M",
+        "label": "Description of dataset 3",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_mRWOBj",
+        "label": "Protected attributes in dataset 3",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_wo9JVx",
+        "label": "Protected attributes in production (dataset 3)",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_3yXO86",
+        "label": "Justification of use of protected attributes (dataset 3)",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_3je4aQ",
+        "label": "What is the key metric used in evaluating the model's performance?",
+        "type": "INPUT_TEXT",
+        "value": "Mean Squared Error"
+      },
+      {
+        "key": "question_mOQRBg",
+        "label": "2nd metric (if applicable)",
+        "type": "INPUT_TEXT",
+        "value": "Mean Absolute Error"
+      },
+      {
+        "key": "question_mVpr1y",
+        "label": "3rd metric (if applicable)",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_mKp4dz",
+        "label": "Who are the individuals and groups that are considered to be at-risk of being systematically disadvantaged by the system?",
+        "type": "TEXTAREA",
+        "value": "gender and race"
+      },
+      {
+        "key": "question_31WYq4",
+        "label": "Expected Benefits",
+        "type": "TEXTAREA",
+        "value": "Reduce manpower and human error on transcription services. A more accurate model than current system will aid the judicial system in making better court decisions"
+      },
+      {
+        "key": "question_nGeMBk",
+        "label": "Expected Harms",
+        "type": "TEXTAREA",
+        "value": "Incorrectly transcribed/interpreted texts can impact on the defendant/ruling negatively."
+      },
+      {
+        "key": "question_mJ1r5z",
+        "label": "Mitigation strategies",
+        "type": "TEXTAREA",
+        "value": "Improve upon the existing model by further training and enhancements"
+      },
+      {
+        "key": "question_nPRoBe",
+        "label": "Type of fairness analysis conducted",
+        "type": "INPUT_TEXT",
+        "value": "Equal mean squared error"
+      },
+      {
+        "key": "question_3Eqpbr",
+        "label": "Segment of analysis",
+        "type": "INPUT_TEXT",
+        "value": "Gender and race"
+      },
+      {
+        "key": "question_nrDXrR",
+        "label": "Description of fairness analysis",
+        "type": "TEXTAREA",
+        "value": "Disparity ratio of MSE of any 2 bins in the respective attribute should not be more than a factor of 1.1"
+      },
+      {
+        "key": "question_w4aZNY",
+        "label": "Type of fairness analysis conducted 2",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_3jeK94",
+        "label": "Segment of analysis 2",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_w2j2x9",
+        "label": "Description of fairness analysis 2",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_3xVGN5",
+        "label": "Segment of analysis 3",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_mZ9xRB",
+        "label": "Type of fairness analysis conducted 3",
+        "type": "INPUT_TEXT",
+        "value": null
+      },
+      {
+        "key": "question_3NqzBj",
+        "label": "Description of fairness analysis 3",
+        "type": "TEXTAREA",
+        "value": null
+      },
+      {
+        "key": "question_n0V0Q9",
+        "label": "Please enter your email",
+        "type": "INPUT_EMAIL",
+        "value": "timothy.lin@cylynx.io"
+      }
+    ]
+  }
+}
\ No newline at end of file
diff --git a/examples/speech_to_text_example.ipynb b/examples/speech_to_text_example.ipynb
new file mode 100644
index 0000000..b3bfd9c
--- /dev/null
+++ b/examples/speech_to_text_example.ipynb
@@ -0,0 +1,2640 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import json\n",
+    "from IPython import display\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
+    "\n",
+    "# parent directory to work with dev\n",
+    "sys.path.insert(0, '..')\n",
+    "import verifyml.model_card_toolkit as mctlib\n",
+    "from verifyml.model_card_toolkit import model_card_pb2, ModelCard\n",
+    "from verifyml.model_card_toolkit.utils.tally_form import tally_form_to_mc\n",
+    "from verifyml.model_tests.utils import plot_to_str\n",
+    "from verifyml.model_tests.FEAT import (\n",
+    "    SubgroupDisparity,\n",
+    "    MinMaxMetricThreshold,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read output with truth and prediction columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read output\n",
+    "# truth column -> total no. of words spoken by the individual\n",
+    "# prediction column -> total no. of words correctly transcribed by google's speech to text model model\n",
+    "\n",
+    "output = pd.read_csv(\"../data/speech_sample_output.csv\")\n",
+    "output.rename(columns={'truth_count':'truth', 'match_count':'prediction'},inplace=True)\n",
+    "mse_test = round(mean_squared_error(output.truth, output.prediction),3)\n",
+    "mae_test = round(mean_absolute_error(output.truth, output.prediction), 3)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "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>sex</th>\n",
+       "      <th>race</th>\n",
+       "      <th>truth</th>\n",
+       "      <th>prediction</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>80</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>89</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>82</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>F</td>\n",
+       "      <td>INDIAN</td>\n",
+       "      <td>85</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>73</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>80</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>M</td>\n",
+       "      <td>MALAY</td>\n",
+       "      <td>72</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>85</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>82</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>85</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>298 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    sex     race  truth  prediction\n",
+       "0     F  CHINESE     80          60\n",
+       "1     F  CHINESE     89          64\n",
+       "2     M  CHINESE     82          53\n",
+       "3     F   INDIAN     85          53\n",
+       "4     M  CHINESE     73          47\n",
+       "..   ..      ...    ...         ...\n",
+       "293   M  CHINESE     80          50\n",
+       "294   M    MALAY     72          52\n",
+       "295   F  CHINESE     85          60\n",
+       "296   F  CHINESE     82          52\n",
+       "297   M  CHINESE     85          57\n",
+       "\n",
+       "[298 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Word count plot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(output.truth, output.prediction)\n",
+    "plt.xlabel('# of spoken words')\n",
+    "plt.ylabel('# of words transcribed correctly')\n",
+    "plt.title('No. of spoken words vs No. of words transcribed correctly')\n",
+    "word_match_plot = plot_to_str()\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Analysing model perfomance"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# ROC/Min Max Threshold Test\n",
+    "\n",
+    "threshold_test_gender = MinMaxMetricThreshold(\n",
+    "    #test_name=\"\",        # Default test name and description will be used accordingly if not specified\n",
+    "    #test_desc=\"\",\n",
+    "    attr=\"sex\",\n",
+    "    metric=\"mae\",\n",
+    "    threshold=30,\n",
+    ")\n",
+    "threshold_test_gender.run(df_test_with_output=output)\n",
+    "threshold_test_gender.plot(alpha=0.05)\n",
+    "\n",
+    "threshold_test_race = MinMaxMetricThreshold(\n",
+    "    attr=\"race\",\n",
+    "    metric=\"mae\",\n",
+    "    threshold=30,\n",
+    ")\n",
+    "threshold_test_race.run(df_test_with_output=output)\n",
+    "threshold_test_race.plot(alpha=0.05)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SbQIAwEFnt4G5u9+e5DNLms9IcuE8fWGSxyy0X9zdN3f31Um2Jzmlqo5Jcvfufkd3d5JXLawDAAAHrX0dw3x0d1+fJPPzveb2TUmuXei3Y27bNE8vbV9WVZ1VVduqatvOnTv3sUQAAFi9/X3T33LjknvQvqzuvqC7N3f35o0bN+634gAAYG/ta2D+5DzMIvPzjXP7jiTHLfQ7Nsl1c/uxy7QDAMBBbV8D86VJzpynz0zypoX2rVV1aFWdkOnmvnfPwzY+X1UPmz8d40kL6wAAwEFrw+46VNVFSbYkOaqqdiR5XpIXJrmkqp6a5Jokj02S7r6iqi5J8sEktyQ5u7tvnTf19EyfuHF4kjfPDwAAOKjtNjB39+NWWHTqCv3PS3LeMu3bkjx4r6oDAIB15pv+AABgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAICBDetdwMHq+HP+93qXwEHoho9+Oon3Byv72Asftd4lALCfucIMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADvukPgAPCN2KyEt+aysjB+I2prjADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADKwqMFfVz1fVFVX1N1V1UVUdVlVHVtVlVfXh+fmIhf7nVtX2qrqqqk5bffkAAHBg7XNgrqpNSX42yebufnCSQ5JsTXJOksu7+8Qkl8/zqaqT5uUnJzk9yflVdcjqygcAgANrtUMyNiQ5vKo2JLlzkuuSnJHkwnn5hUkeM0+fkeTi7r65u69Osj3JKavcPwAAHFD7HJi7+xNJXpTkmiTXJ/m77n5rkqO7+/q5z/VJ7jWvsinJtQub2DG33UZVnVVV26pq286dO/e1RAAAWLXVDMk4ItNV4xOS3CfJXarqx0arLNPWy3Xs7gu6e3N3b964ceO+lggAAKu2miEZj0hydXfv7O4vJ/nDJN+Z5JNVdUySzM83zv13JDluYf1jMw3hAACAg9ZqAvM1SR5WVXeuqkpyapIrk1ya5My5z5lJ3jRPX5pka1UdWlUnJDkxybtXsX8AADjgNuzrit39rqp6fZL3JrklyV8nuSDJXZNcUlVPzRSqHzv3v6KqLknywbn/2d196yrrBwCAA2qfA3OSdPfzkjxvSfPNma42L9f/vCTnrWafAACwlnzTHwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwAAAMCMwAADGxY7wLg9uTej3/hepcAAKwxV5gBAGBAYAYAgAGBGQAABgRmAAAYcNMfALCm3EDN7Y0rzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAysKjBX1T2r6vVV9aGqurKqHl5VR1bVZVX14fn5iIX+51bV9qq6qqpOW335AABwYK32CvNvJXlLd39jkm9OcmWSc5Jc3t0nJrl8nk9VnZRka5KTk5ye5PyqOmSV+wcAgANqnwNzVd09yfckeVmSdPeXuvumJGckuXDudmGSx8zTZyS5uLtv7u6rk2xPcsq+7h8AANbCaq4w3z/JziSvqKq/rqrfq6q7JDm6u69Pkvn5XnP/TUmuXVh/x9wGAAAHrdUE5g1JvjXJS7v7W5J8MfPwixXUMm29bMeqs6pqW1Vt27lz5ypKBACA1VlNYN6RZEd3v2uef32mAP3JqjomSebnGxf6H7ew/rFJrltuw919QXdv7u7NGzduXEWJAACwOvscmLv7hiTXVtUD56ZTk3wwyaVJzpzbzkzypnn60iRbq+rQqjohyYlJ3r2v+wcAgLWwYZXr/0yS11bVnZJ8NMmPZwrhl1TVU5Nck+SxSdLdV1TVJZlC9S1Jzu7uW1e5fwAAOKBWFZi7+31JNi+z6NQV+p+X5LzV7BMAANaSb/oDAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgYNWBuaoOqaq/rqo/muePrKrLqurD8/MRC33PrartVXVVVZ222n0DAMCBtj+uMP9ckisX5s9Jcnl3n5jk8nk+VXVSkq1JTk5yepLzq+qQ/bB/AAA4YFYVmKvq2CSPSvJ7C81nJLlwnr4wyWMW2i/u7pu7++ok25Ocspr9AwDAgbbaK8wvTvKLSf5poe3o7r4+Sebne83tm5Jcu9Bvx9x2G1V1VlVtq6ptO3fuXGWJAACw7/Y5MFfVo5Pc2N3v2dNVlmnr5Tp29wXdvbm7N2/cuHFfSwQAgFXbsIp1vyvJD1XVI5McluTuVfWaJJ+sqmO6+/qqOibJjXP/HUmOW1j/2CTXrWL/AABwwO3zFebuPre7j+3u4zPdzPdn3f1jSS5Ncubc7cwkb5qnL02ytaoOraoTkpyY5N37XDkAAKyB1VxhXskLk1xSVU9Nck2SxyZJd19RVZck+WCSW5Kc3d23HoD9AwDAfrNfAnN3vy3J2+bpTyc5dYV+5yU5b3/sEwAA1oJv+gMAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBgnwNzVR1XVf+nqq6sqiuq6ufm9iOr6rKq+vD8fMTCOudW1faquqqqTtsfLwAAAA6k1VxhviXJM7v7QUkeluTsqjopyTlJLu/uE5NcPs9nXrY1yclJTk9yflUdspriAQDgQNvnwNzd13f3e+fpzye5MsmmJGckuXDudmGSx8zTZyS5uLtv7u6rk2xPcsq+7h8AANbCfhnDXFXHJ/mWJO9KcnR3X59MoTrJveZum5Jcu7Dajrltue2dVVXbqmrbzp0790eJAACwT1YdmKvqrkn+Z5JndPfnRl2XaevlOnb3Bd29ubs3b9y4cbUlAgDAPltVYK6qO2YKy6/t7j+cmz9ZVcfMy49JcuPcviPJcQurH5vkutXsHwAADrTVfEpGJXlZkiu7+78uLLo0yZnz9JlJ3rTQvrWqDq2qE5KcmOTd+7p/AABYCxtWse53JXlikg9U1fvmtmcneWGSS6rqqUmuSfLYJOnuK6rqkiQfzPQJG2d3962r2D8AABxw+xyYu/svsvy45CQ5dYV1zkty3r7uEwAA1ppv+gMAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYWPPAXFWnV9VVVbW9qs5Z6/0DAMDeWNPAXFWHJPnvSX4gyUlJHldVJ61lDQAAsDfW+grzKUm2d/dHu/tLSS5OcsYa1wAAAHtsrQPzpiTXLszvmNsAAOCgtGGN91fLtPVtOlWdleSsefYLVXXVAa0K9s5RST613kVwcKpfW+8K4HbDv6Usax3/Hb3fSgvWOjDvSHLcwvyxSa5b2qm7L0hywVoVBXujqrZ19+b1rgPg9sy/pdyerPWQjL9KcmJVnVBVd0qyNcmla1wDAADssTW9wtzdt1TVTyf5kySHJHl5d1+xljUAAMDeWOshGenuP07yx2u9X9iPDBcCWD3/lnK7Ud23uecOAACY+WpsAAAYEJhhL1TVrVX1voXH8etdE8DtQVV1Vb16YX5DVe2sqj9az7pgT6z5GGa4nfuH7n7oehcBcDv0xSQPrqrDu/sfknx/kk+sc02wR1xhBgDWypuTPGqeflySi9axFthjAjPsncMXhmO8Yb2LAbiduTjJ1qo6LMlDkrxrneuBPWJIBuwdQzIA9lF3v3++9+Nx8RGz3I4IzADAWro0yYuSbEnydetbCuwZgRkAWEsvT/J33f2BqtqyzrXAHhGYAYA10907kvzWetcBe8M3/QEAwIBPyQAAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGDg/wMWx1xl0btnUQAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Subgroup Disparity Test\n",
+    "\n",
+    "sgd_test_gender = SubgroupDisparity(\n",
+    "    attr='sex',\n",
+    "    metric='mse',\n",
+    "    method='ratio',\n",
+    "    threshold=1.1,\n",
+    ")\n",
+    "sgd_test_gender.run(output)\n",
+    "sgd_test_gender.plot(alpha=0.05)     # default alpha argument shows 95% C.I bands\n",
+    "\n",
+    "sgd_test_race = SubgroupDisparity(\n",
+    "    attr='race',\n",
+    "    metric='mse',\n",
+    "    method='ratio',\n",
+    "    threshold=1.1,\n",
+    ")\n",
+    "sgd_test_race.run(output)\n",
+    "sgd_test_race.plot(alpha=0.05)    # default alpha argument shows 95% C.I bands"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Bootstrap model card from tally form and scaffold assets\n",
+    "We can add the quantitative analysis, explainability analysis and fairness analysis sections to a bootstrap model card for convenience. In this example, we use an existing model card which we created from the tally form response.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Convert form response to model card protobuf\n",
+    "pb = tally_form_to_mc(\"sample-form-response-speech-to-text.json\")\n",
+    "\n",
+    "# Initialize the mct and scaffold using the existing protobuf\n",
+    "mct = mctlib.ModelCardToolkit(output_dir = \"model_card_output\", file_name=\"speech_to_text_example\")\n",
+    "mc = mct.scaffold_assets(proto=pb)\n",
+    "mc.model_details.name = \"Speech to Text Model\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Convert test objects to a model-card-compatible format"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# init model card test objects\n",
+    "mc_threshold_test_gender = mctlib.Test()\n",
+    "mc_threshold_test_race = mctlib.Test()\n",
+    "mc_sgd_test_race = mctlib.Test()\n",
+    "mc_sgd_test_gender = mctlib.Test()\n",
+    "\n",
+    "\n",
+    "# assign tests to them\n",
+    "mc_threshold_test_gender.read_model_test(threshold_test_gender)\n",
+    "mc_threshold_test_race.read_model_test(threshold_test_race)\n",
+    "mc_sgd_test_race.read_model_test(sgd_test_race)\n",
+    "mc_sgd_test_gender.read_model_test(sgd_test_gender)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "scrolled": true,
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "# Add quantitative analysis\n",
+    "\n",
+    "# Create 3 PerformanceMetric to store our results\n",
+    "mc.quantitative_analysis.performance_metrics = [mctlib.PerformanceMetric() for i in range(0, 3)]\n",
+    "mc.quantitative_analysis.performance_metrics[0].type = \"MSE\"\n",
+    "mc.quantitative_analysis.performance_metrics[0].value = str(mse_test)\n",
+    "mc.quantitative_analysis.performance_metrics[0].slice = \"Test Set\"\n",
+    "\n",
+    "mc.quantitative_analysis.performance_metrics[1].type = \"MAE\"\n",
+    "mc.quantitative_analysis.performance_metrics[1].value = str(mae_test)\n",
+    "mc.quantitative_analysis.performance_metrics[1].slice = \"Test Set\"\n",
+    "mc.quantitative_analysis.performance_metrics[1].graphics.description = (\n",
+    "  'About 65% of the words are correctly transcribed')\n",
+    "mc.quantitative_analysis.performance_metrics[1].graphics.collection = [\n",
+    "    mctlib.Graphic(image=word_match_plot)\n",
+    "]\n",
+    "\n",
+    "mc.quantitative_analysis.performance_metrics[2].type = \"MSE across sensitive groups\"\n",
+    "mc.quantitative_analysis.performance_metrics[2].tests = [mc_threshold_test_gender, mc_threshold_test_race]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add the fairness requirement for subgroup disparity\n",
+    "mc.fairness_analysis.fairness_reports[0].tests = [mc_sgd_test_race, mc_sgd_test_gender]\n",
+    "\n",
+    "\n",
+    "mct.update_model_card(mc)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Model Card Display"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<!DOCTYPE html>\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "<html lang=\"en\">\n",
+       "\n",
+       "<head>\n",
+       "\n",
+       "<style>\n",
+       "\n",
+       "  h1 {text-align: center;}\n",
+       "\n",
+       "  .row {\n",
+       "\n",
+       "    display: flex;\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  .col {\n",
+       "\n",
+       "    flex: 1;\n",
+       "\n",
+       "    align-items: center; \n",
+       "\n",
+       "    justify-content: center;\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  .card {\n",
+       "\n",
+       "    padding: 1em;\n",
+       "\n",
+       "    border: 1px solid #DADCE0;\n",
+       "\n",
+       "    margin: 10px;\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  .card .card {\n",
+       "\n",
+       "    padding: 0.5em;\n",
+       "\n",
+       "    padding-bottom: 0em;\n",
+       "\n",
+       "    border: none;\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  .img-container {\n",
+       "\n",
+       "    display: flex;\n",
+       "\n",
+       "    flex-wrap: wrap;\n",
+       "\n",
+       "    justify-content: space-around;\n",
+       "\n",
+       "    text-align: center;\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  .img-item {\n",
+       "\n",
+       "    flex: 1;\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  .center {\n",
+       "\n",
+       "    margin-left: auto;\n",
+       "\n",
+       "    margin-right: auto;\n",
+       "\n",
+       "    text-align: center;\n",
+       "\n",
+       "  }\n",
+       "\n",
+       "  table, th, td { border: 1px solid black; }\n",
+       "\n",
+       "  th, td { border: 1px solid #CCC; height: 30px; }\n",
+       "\n",
+       "  caption { font-weight: bold; }\n",
+       "\n",
+       "</style>\n",
+       "\n",
+       "<title>\n",
+       "\n",
+       "  Model Card for Speech to Text Model\n",
+       "\n",
+       "</title>\n",
+       "\n",
+       "</head>\n",
+       "\n",
+       "<body>\n",
+       "\n",
+       "  <h1>\n",
+       "\n",
+       "    Model Card for Speech to Text Model\n",
+       "\n",
+       "  </h1>\n",
+       "\n",
+       "    <div class=\"row\">\n",
+       "\n",
+       "      <div class=\"col card\">\n",
+       "\n",
+       "        <h2>Model Details</h2>\n",
+       "\n",
+       "          <h3>Overview</h3>\n",
+       "\n",
+       "              The government want to create an automated transcription tool used in trials for evidence purposes. The tool should be able to capture the different spoken accents and transcribe into text accurately so as not to bias against the defendant.\n",
+       "\n",
+       "          <h3>Version</h3>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div>name: v1</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "              \n",
+       "\n",
+       "  \n",
+       "\n",
+       "\n",
+       "\n",
+       "              \n",
+       "\n",
+       "  \n",
+       "\n",
+       "\n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          <h3>Owners</h3>\n",
+       "\n",
+       "            \n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <li>Timothy, Product Owner(s)</li>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <li>Swan, Model Developer(s)</li>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <li>Jason, Reviewer(s)</li>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "            \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          <h3>Regulatory requirements</h3>\n",
+       "\n",
+       "              MAS Fairness, Ethics, Accountability and Transparency (FEAT) principles\n",
+       "\n",
+       "        </div>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <div class=\"col card\">\n",
+       "\n",
+       "        <h2>Considerations</h2>\n",
+       "\n",
+       "          \n",
+       "\n",
+       "            <h3>Intended Users</h3>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <ul>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <li>Judical System and Defendant</li>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </ul>\n",
+       "\n",
+       "\n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "            <h3>Use Cases</h3>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <ul>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <li>Automate transcriptions and reduce human error in making court decisions</li>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </ul>\n",
+       "\n",
+       "\n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "          \n",
+       "\n",
+       "            <h3>Fairness Considerations</h3>\n",
+       "\n",
+       "              <ul>\n",
+       "\n",
+       "                <li>\n",
+       "\n",
+       "                  <div><b>Group at risk:</b> gender and race</div>\n",
+       "\n",
+       "                  <div><b>Benefits:</b> Reduce manpower and human error on transcription services. A more accurate model than current system will aid the judicial system in making better court decisions</div>\n",
+       "\n",
+       "                  <div><b>Harms:</b> Incorrectly transcribed/interpreted texts can impact on the defendant/ruling negatively.</div>\n",
+       "\n",
+       "                  <div><b>Mitigation Strategy:</b> Improve upon the existing model by further training and enhancements</div>\n",
+       "\n",
+       "                </li> </ul>\n",
+       "\n",
+       "          \n",
+       "\n",
+       "      </div>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"col card\">\n",
+       "\n",
+       "    <h2>Datasets</h2>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <div class=\"row\">\n",
+       "\n",
+       "        <div class=\"col card\">\n",
+       "\n",
+       "          <h3>National Speech Corpus Dataset</h3>\n",
+       "\n",
+       "            <p>Dataset taken from https://www.imda.gov.sg/programme-listing/digital-services-lab/national-speech-corpus</p>\n",
+       "\n",
+       "            \n",
+       "\n",
+       "              <h4>Sensitive data</h4>\n",
+       "\n",
+       "              <ul>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <li>gender</li>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <li> race</li>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "              </ul>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <h4>Sensitive data used in model</h4>\n",
+       "\n",
+       "                <ul>\n",
+       "\n",
+       "                \n",
+       "\n",
+       "                  <li>gender</li>\n",
+       "\n",
+       "                \n",
+       "\n",
+       "                  <li> race</li>\n",
+       "\n",
+       "                \n",
+       "\n",
+       "                </ul>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "              \n",
+       "\n",
+       "                <h4>Justification</h4>\n",
+       "\n",
+       "                <p> Potential improvement in model performance with protected attributes used as features in model</p>\n",
+       "\n",
+       "              \n",
+       "\n",
+       "            \n",
+       "\n",
+       "            \n",
+       "\n",
+       "            \n",
+       "\n",
+       "        </div>\n",
+       "\n",
+       "      </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h2>Quantitative Analysis</h2>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h3>MSE - 890.221 (Test Set)</h3>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h3>MAE - 29.047 (Test Set)</h3>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  <div class=\"row\">\n",
+       "\n",
+       "    <div class=\"col\">\n",
+       "\n",
+       "      \n",
+       "\n",
+       "        <div class=\"center\"><i>About 65% of the words are correctly transcribed</i></div>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"img-container\">\n",
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+       "\n",
+       "  \n",
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+       "    <div class=\"img-item\">\n",
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+       "t2b261rb4ji1xoeMHMdxHMDvEBzHcZyA3yE4juM4gDsEx3EcJ+AOwXEcxwHcITiO4zgBdwiO4zgO\n",
+       "AP8fO8cJ+XlZ2bAAAAAASUVORK5CYII=\n",
+       "' alt='None' />\n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h3>MSE across sensitive groups  </h3>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <h4> Min Max Threshold Test </h4>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Description: </b>\n",
+       "\n",
+       "    Test if the mae of the subgroups within sex \n",
+       "is lower than the threshold of 30.\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Threshold: </b>\n",
+       "\n",
+       "    30 \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Result: </b>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td></td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>mae</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>passed\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>sex_F</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>29.391</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>True\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>sex_M</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>28.77</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>True\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "   \n",
+       "\n",
+       "    <div><b style=\"color:green;\"> Passed</b></div> \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  <div class=\"row\">\n",
+       "\n",
+       "    <div class=\"col\">\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"img-container\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div class=\"img-item\">\n",
+       "\n",
+       "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAr8AAAF2CAYAAABnFvAPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
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+       "YQi/AAAMQ/gFAGAYwi8AAMMQfgEAGIbwCwDAMIRfAACGIfwCADAM4RcAgGEIvwAADEP4BQBgGNsN\n",
+       "v1X16qr6WlV9asG0a1XViVX1+fnffde2TAAAWLnl9Pwek+TgRdOemOR93X3TJO+bXwMAwG5tu+G3\n",
+       "uz+Q5LxFkw9Ncuz8/Ngkv7W6ZQEAwOrb2TG/1+3us5Nk/vc6q1cSAACsjTW/4K2qjqyqjVW1cdOm\n",
+       "TWu9OQAA2KqdDb/nVNX+STL/+7WtzdjdR3f3hu7esG7dup3cHAAArNzOht93JDlifn5EkrevTjkA\n",
+       "ALB2lnOrs+OSfCTJzavqzKp6RJJnJ/n1qvp8kl+fXwMAwG5tr+3N0N2HbaXpbqtcCwAArCm/8AYA\n",
+       "wDCEXwAAhiH8AgAwDOEXAIBhCL8AAAxD+AUAYBjCLwAAwxB+AQAYhvALAMAwhF8AAIYh/AIAMAzh\n",
+       "FwCAYQi/AAAMQ/gFAGAYwi8AAMMQfgEAGIbwCwDAMIRfAACGIfwCADAM4RcAgGEIvwAADEP4BQBg\n",
+       "GMIvAADDEH4BABiG8AsAwDCEXwAAhiH8AgAwDOEXAIBhCL8AAAxD+AUAYBjCLwAAwxB+AQAYhvAL\n",
+       "AMAwhF8AAIYh/AIAMAzhFwCAYQi/AAAMQ/gFAGAYe61k4ao6PckFSS5O8sPu3rAaRQEAwFpYUfid\n",
+       "/Wp3n7sK6wEAgDVl2AMAAMNYafjtJO+pqlOq6silZqiqI6tqY1Vt3LRp0wo3BwAAO2+l4fcu3f0L\n",
+       "Se6R5A+q6q6LZ+juo7t7Q3dvWLdu3Qo3BwAAO29F4be7z5r//VqStyW5/WoUBQAAa2Gnw29VXbWq\n",
+       "rr75eZLfSPKp1SoMAABW20ru9nDdJG+rqs3reUN3//OqVAUAAGtgp8Nvd38xyW1WsRYAAFhTbnUG\n",
+       "AMAwhF8AAIYh/AIAMAzhFwCAYQi/AAAMQ/gFAGAYwi8AAMMQfgEAGIbwCwDAMIRfAACGIfwCADAM\n",
+       "4RcAgGEIvwAADEP4BQBgGMIvAADDEH4BABiG8AsAwDCEXwAAhiH8AgAwDOEXAIBhCL8AAAxD+AUA\n",
+       "YBjCLwAAwxB+AQAYhvALAMAwhF8AAIYh/AIAMAzhFwCAYQi/AAAMQ/gFAGAYwi8AAMMQfgEAGIbw\n",
+       "CwDAMIRfAACGIfwCADAM4RcAgGEIvwAADGNF4beqDq6qz1bVF6rqiatVFAAArIWdDr9VdfkkL01y\n",
+       "jyS3THJYVd1ytQoDAIDVtpKe39sn+UJ3f7G7f5DkH5IcujplAQDA6ltJ+L1BkjMWvD5zngYAALul\n",
+       "vVawbC0xrbeYqerIJEfOLy+sqs+uYJuwFvZLcu6uLoLdTz1nV1cAewyfoyxpF3+O3nipiSsJv2cm\n",
+       "OWDB6xsmOWvxTN19dJKjV7AdWFNVtbG7N+zqOgD2VD5H2ZOsZNjDx5LctKp+qqqukORBSd6xOmUB\n",
+       "AMDq2+me3+7+YVU9Jsm/JLl8kld396dXrTIAAFhlKxn2kO4+IckJq1QL7CqG5QCsjM9R9hjVvcU1\n",
+       "agAAcJnk540BABiG8AsAwDCEX9iOqjq9qj5ZVafOjzvv6poA9hTzZ+gHF007tao+tatqYmwruuAN\n",
+       "BvKr3e0G7gA75+pVdUB3n1FVt9jVxTA2Pb9cZlTVVavqXVX1n1X1qap6YFXdrqr+rapOqap/qar9\n",
+       "q+qaVfXZqrr5vNxxVfXIXV0/wK60xp+hb0rywPn5YUmOW8v3Atsi/HJZcnCSs7r7Nt196yT/nORv\n",
+       "ktyvu2+X5NVJntHd30zymCTHVNWDkuzb3a/czrrfP39Nd/JavgGAXWgtP0PfnOS+8/N7J3nnmrwD\n",
+       "WAbDHrgs+WSS51bVc5Icn+T8JLdOcmJVJdOPsZydJN19YlXdP8lLk9xmGes27AG4rFvLz9Dzkpw/\n",
+       "h+XTknxn9cuH5RF+uczo7s9V1e2SHJLkWUlOTPLp7r7T4nmr6nJJbpHku0muleTMS7NWgN3NpfAZ\n",
+       "+sZMYfnhq1Uz7AzDHrjMqKrrJ/lOd78uyXOT3CHJuqq609y+d1Xdap79jzL1PhyW5NVVtfeuqBlg\n",
+       "d3EpfIa+Lcn/SfIvq1487AA9v1yW/FySv66qHyW5KMmjk/wwyYur6pqZzvcXVtVFSX43ye27+4Kq\n",
+       "+kCSJyd5yi6qG2B3sKafod19QZLnJMk8jAJ2CT9vDADAMAx7AABgGIY9wGy+jdkVF00+vLs/uSvq\n",
+       "AdiT+AxlT2HYAwAAwzDsAQCAYQi/AAAMQ/gFAGAYwi8AAMMQfgEAGMb/BzqxsbHcdrODAAAAAElF\n",
+       "TkSuQmCC\n",
+       "' alt='Mean Absolute Error across sex subgroups' />\n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <h4> Min Max Threshold Test </h4>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Description: </b>\n",
+       "\n",
+       "    Test if the mae of the subgroups within race \n",
+       "is lower than the threshold of 30.\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Threshold: </b>\n",
+       "\n",
+       "    30 \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Result: </b>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td></td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>mae</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>passed\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>race_CHINESE</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>30.475</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>False\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>race_INDIAN</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>27.99</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>True\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>race_MALAY</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>28.68</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>True\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div><b style=\"color:red;\"> Failed</b></div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  <div class=\"row\">\n",
+       "\n",
+       "    <div class=\"col\">\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"img-container\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div class=\"img-item\">\n",
+       "\n",
+       "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAr8AAAF2CAYAAABnFvAPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
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+       "/RbYctxc0b/UUlU/rqpd6KYZ/BfdqD9VdUNVHVhVD6Ibff+nad7K/8s2+2kxbwR2BzaoqvsBf5hB\n",
+       "/bPaT1X1zap6Jt3o6AXAkX3XdK8PmPj6vJXuOLqCbjrO2GNZh24azqSPlen39VxqGTtWZrsPJM2Q\n",
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+       "u6r6bhL6fwankyna9wR2rao7knyR7i3eT8xxG9Iorczj7Bt00xKupAvUf5Hk8cDSqrokyWXAp5Ns\n",
+       "MM27kHvRBWr6n3sDX+xvfwJYp6ouXO6j10gZfu/a/giQ7kNgrwceX1XX9gF0bbo/gJOdyDnAa6vq\n",
+       "mzPYxnnA3wynXKB76+jNjJtTNZWquoUuKH89yZXAC+hGgaVVZWUcd1N5D9283NumWqAfHXsUcP64\n",
+       "9kfThYBTundluSfdfMdPjFvFtNuQVoKVdpxV1Z+TnAkcCDwCeN5A917Aw5Jc3N9eD3gh8O+Tras/\n",
+       "/l4IPD/JP/f1bJRk3aq6Abijv2gVcs7v3cN6dL8o/pBkE2DHvv0CYLP+P1f6+VBr0n0l9auSrNW3\n",
+       "PzRTT+L/HPCkJM8da0jynCSPmkuhVXUrcChwwHTLJnlcks366/eg+zDPJXPZrjQCozzuJlVV3wI2\n",
+       "ACbM5+3XuRbwPuDSqjp7XPdewDuqan5/2QzYPMlWs9mGtJKtrOPsQ8Abq+qasYb+786L6D50Pb+q\n",
+       "5gO7MHG+/KAdgLOqasv+PlsBJ9IN3Gg14cjv3UBVnZXkp3SjqRcB3+/b/5xkD+BjSdahmw+1A91/\n",
+       "rPOBn/QT85cyxYFZVTcl2Rn4SJKPALcCZwOvAzaaY8mfovvAzaDxc35fTfdL78h+3hbAj4DB088M\n",
+       "zvk9u6peNsd6pFkb5XE3jfcw8WwOn01yC3Av4FS6P9Dj7cmdwWHMl/r2M2awDWmlW1nHWVWdx8R3\n",
+       "JJ8GXF5Vlw+0fQfYNsmm/e1P9n8bAS4FLqQ7rgadCLyK7p1PrQb8emNJkiQ1w2kPkiRJaobTHgRA\n",
+       "P4d3/Fsyt1TVE1ZFPVILPO6k0fM403hOe5AkSVIznPYgSZKkZhh+JUmS1AzDryRJkpph+JUkSVIz\n",
+       "DL+SJElqxv8H6i/VME7aXSsAAAAASUVORK5CYII=\n",
+       "' alt='Mean Absolute Error across race subgroups' />\n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h2>Fairness Analysis</h2>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  <h3>Equal mean squared error  </h3>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Segment: </b>\n",
+       "\n",
+       "    Gender and race \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Description: </b>\n",
+       "\n",
+       "    Disparity ratio of MSE of any 2 bins in the respective attribute should not be more than a factor of 1.1 \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <h4> Subgroup Disparity Test </h4>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Description: </b>\n",
+       "\n",
+       "    Test if the maximum ratio of the mean squared error of any 2\n",
+       "groups within race attribute exceeds 1.1. To\n",
+       "pass, this value cannot exceed the threshold. \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Threshold: </b>\n",
+       "\n",
+       "    1.1 \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Result: </b>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td></td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>race_mse_max_ratio\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>0</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>1.177\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div><b style=\"color:red;\"> Failed</b></div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  <div class=\"row\">\n",
+       "\n",
+       "    <div class=\"col\">\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"img-container\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div class=\"img-item\">\n",
+       "\n",
+       "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAswAAAF1CAYAAAD8/Lw6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
+       "bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsT\n",
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+       "N4cz95k5Z86Zcfx8rmtfe6173Wute++19sz33Ptea1d3BwAAWN599ncDAADgQCYwAwDAgMAMAAAD\n",
+       "AjMAAAwIzAAAMCAwAwDAgMAMMFBVr6yqX9/f7WDjVdWzq+od+7sdwP4nMMNBqKpuqqqvVtVRS8rf\n",
+       "V1VdVVv2Q5teVFU3VtWXq2pHVb1+o9uwr82B6u75NS0+Hr6/2wbAviMww8HrxiTn7Jqpqr+T5AH7\n",
+       "oyFVdW6SH0vy1O5+cJKtSa7aD+04dB02+87ufvCSx617su+9bc86tX/dtz3Y5yEbvc/9ZX+8v8C+\n",
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+       "/6rqS1X11qU92guekOS/d/fHkqS7b+/uSxa2dUJV/b/zdq6sqt+uqlfPy7ZV1Y4l7b6pqp46T59a\n",
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+       "z9k62Y+fs0fubsFGB+YdSY5fmN+c5Nallbr7kiSXbFSj2FhVtb27t+7vdsDBymcM1p/P2TeXjR6S\n",
+       "8f8nOamqTqiq+yU5O8kVG9wGAADYYxvaw9zdd1XVzyT570kOSfKK7r5uI9sAAAB7Y6OHZKS735Tk\n",
+       "TRu9Xw4ohtvA+vIZg/Xnc/ZNpLrvdc0dAAAw89PYAAAwIDCzoqp6WFVdVlUfq6oPVtWbqurbq+ov\n",
+       "l9R7aVX90jz9yqp6+jx9dVVtX6i3taqunqe3VdUXqup9C4+nzst+paquq6r3z+VPXNje9Qv137BB\n",
+       "bwVsiKr68vy8paq6qn52YdlvV9Wz5+lXVtWNVfW/quojVfWqqjpuoe5NVXXUwvw/nrf3HQtlw33A\n",
+       "wWo+7/9oYf7QqtpZVX+2pN4bq+qdS8r+9v+7Zba7qaq+VlX/fJ4/r6pev7D8ofP/pyfs21fEehKY\n",
+       "GaqqSvInSa7u7kd198lJXpTkmL3c1NFV9YO7WfY/uvtxC4+3VdWTk/xQksd393cleWru+aM3P7JQ\n",
+       "/+l72Rb4RnJHkp+f7yy0nBd092OTPDrJe5P8xaDuOUnekekORXuzDzgYfSXJY6rqAfP8P0jyycUK\n",
+       "VXV4kscnOXwvAu4zklyT6fOWJL+XZPOuzqAkv5rppgc3rqHtbDCBmZU8JcnXuvt3dxV09/tyz/C6\n",
+       "J34jyYv3ov6xST7d3XfO+/x0d9/rnt3wTWBnkquSnDuq1JPfSnJ7knv9cVpVD07yvUmek3sH5j3a\n",
+       "BxyE3pzkafP0OUlet2T5P0nyX5Nclnt/bnbnnCTPzxSSj+vpYrHnJnlZVW1Nclqm/xP5BiIws5LH\n",
+       "JHn3bpY9anEoRZKfGmznnUnurKqnLLPs7y0ZkvGoJG9Ncvz8NfPFVfX3l6zzmoX6/uHhYHdRkudX\n",
+       "1SF7UPc9Sb5jmfKzkryluz+S5LNV9fg17AMOFpclObuq7p/ku5K8a8nyXSH6dfl6j/FuVdXxSR7W\n",
+       "3dcmuTzJM5Oku9+f6Za6VyX5ue7+6j57BWwIgZm1+NjiUIokv7tC/V/P8r3MS4dkfKy7v5zkezL9\n",
+       "RPrOJK9fMqZycUjGC/bBa4ED1vzV7bVJnrUH1Ws35edkCgeZn+/xn/9e7gMOCnOQ3ZLp83CPW95W\n",
+       "1TFJTkzyjvkPzbuq6jErbPLsTEE5uffn7HeSfLK7/2IfNJ0NJjCzkusyBdc16+4/T3L/JE/aw/p3\n",
+       "d/fV3f2SJD+T6asx+Gb1r5K8MCv/u/3dST60WFBV35rkB5L8flXdlOQFSZ45X6Owmn3AweSKJL+Z\n",
+       "ew/HeGaSI5LcOH9utmTlYRnnJHn2XP+KJI+tqpPmZX8zP/gG5B9FVvLnSQ6rqp/cVVBVT0jyyFVu\n",
+       "78Ikv7xSpap69MI/MknyuCSfWOU+4Rted384yQczXQx7LzX5uUzj/9+yZPHTk7yqux/Z3Vu6+/gk\n",
+       "Nyb5vr3ZBxykXpHkV7v7A0vKz0lyxvyZ2ZKp82i3gbmqHp3kQd193MI6/3q0Dt84BGaG5osV/nGS\n",
+       "fzDfBue6JC9NsqoL8OZfety5pHjpGOanJ3lwkktruo3d+5OcPO93l8UxzG9bTVvgG9CFSTYvKfuN\n",
+       "qvpfST6S5AlJnrLM+MhzMt3tZtF/yfLDL5bbBxy0untHd//7xbKq2pLkEZnudrGr3o1JvrjrFqdJ\n",
+       "XlxVO3Y9svvP2Ypjnznw+aU/AAAY0MMMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAA\n",
+       "MCAwAwDAwP8BbSTAWhj1u30AAAAASUVORK5CYII=\n",
+       "' alt='Mean Squared Error across race subgroups' />\n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<div class=\"col card\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <h4> Subgroup Disparity Test </h4>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Description: </b>\n",
+       "\n",
+       "    Test if the maximum ratio of the mean squared error of any 2\n",
+       "groups within sex attribute exceeds 1.1. To\n",
+       "pass, this value cannot exceed the threshold. \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Threshold: </b>\n",
+       "\n",
+       "    1.1 \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "  <div>\n",
+       "\n",
+       "    <b> Result: </b>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      \n",
+       "\n",
+       "<table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td></td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>sex_mse_max_ratio\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "      <tr>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>0</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      <td>1.045\r",
+       "</td>\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      </tr>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</table>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "   \n",
+       "\n",
+       "    <div><b style=\"color:green;\"> Passed</b></div> \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    \n",
+       "\n",
+       "  <div class=\"row\">\n",
+       "\n",
+       "    <div class=\"col\">\n",
+       "\n",
+       "      \n",
+       "\n",
+       "      \n",
+       "\n",
+       "  <div class=\"img-container\">\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  \n",
+       "\n",
+       "    <div class=\"img-item\">\n",
+       "\n",
+       "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAswAAAF1CAYAAAD8/Lw6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
+       "bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsT\n",
+       "AAALEwEAmpwYAAAcbElEQVR4nO3de7QlZ1kn4N9LGpJwT0wTQieQIBFJEFHbCOo4rcFJBDQsHZYN\n",
+       "CEFwIhgvODiSIAyoZAaVGZFxwhjlEi4mREYg4wgS4yDjyMUGWWAIkYZA0iQhzSVyUQOJ7/xR1bg9\n",
+       "Oefry+k+pwPPs9Zeu+qrr6reXbVP9+/U+Wrv6u4AAADLu8N6FwAAAAczgRkAAAYEZgAAGBCYAQBg\n",
+       "QGAGAIABgRkAAAYEZoA9VFWvrKoXrHcdHDhVdXxVdVVtWO9agIOHwAxf5arqY1X1pao6akn7++Zg\n",
+       "cPw61PTsqrq6qr5QVTuq6nVrXcP+VlVPrqpb59e0+LjPetcGwOoIzPC14eokj9s1U1XflOTw9Sik\n",
+       "qs5M8sQkj+juuybZnOTydajjQFxBfEd333XJ47o92ffe1nMgr4C6urp/OI7w1UNghq8Nr07ypIX5\n",
+       "M5O8arFDVR1aVS+qqmuq6pNV9T+q6vB52RFV9UdVtbOqPjtPH7uw7tuq6ler6v9V1eer6q1Lr2gv\n",
+       "+PYkf9LdH0mS7r6huy9Y2NYJVfXn83Yuq6rfrqrXzMu2VNWOJXV/rKoeMU+fUlXvqKqbqur6ed07\n",
+       "LfTtqjq7qj6c5MNz26Pnq+03VdVfVtVDFvp/S1W9d67ldUkO2+MjvsRc57Oq6v1JvlhVD5jreWpV\n",
+       "XZPkz6rqDlX1nKr6eFXdWFWvqqp7zOsfv7T/MvvY3Xk6sqpeUVXXzcvfuHhc5/puSPKK+f3w4rnv\n",
+       "dfP0oXP/o+Zt31RVn6mq/1tVd5iXPauqPjEfs6uq6tQVjscjq+qDc79PVNUvLCxb9pxU1Y9W1Uer\n",
+       "6u7z/A9U1Q1VtXGZ7R9WVa+pqk/P2/mrqjp64Vw8YqHv83e9xxY8ZX7d11fVMxf6Hl5VF87H78qq\n",
+       "+sXF9+Qy53lDVf1QVV0x1/G2qnrQQv+uqgcszH9l2M/CeXl2VX1q3vYT9uQYAvuXwAxfG96Z5O5V\n",
+       "9aCqOiTJjyZZGhB+Lck3JHlokgck2ZTkP87L7pDkFUnul+S+Sf4hyW8vWf/xSX48yb2S3CnJSv95\n",
+       "vzPJk6rqP1TV5rmeRb+f5D1Jjkryq5nC/Z66NcnPz+s+PMmpSX5qSZ/HJPmOJCdV1bcmeXmSn0zy\n",
+       "dUl+J8mlc1i8U5I3Zvpl48gkf5DkR/ailuU8LsmjktwzyS1z279O8qAkpyV58vz43iT3T3LX3PY4\n",
+       "L/Zfanfn6dVJ7pzk5Ezn6TcXlt070+u8X5KzkvxSkodlej98c5JTkjxn7vvMJDuSbExydJJnJ+mq\n",
+       "emCSn07y7d19t7nGj61wLF6W5Cfnfg/O/AvA6Jx09+uSvCPJS6rq6+Zt/ER371xm+2cmuUeS4+bt\n",
+       "PG0+Hnvqe5OcmOTfJDlnIWA/L8nxmc7P9yf5sWXWXTzP909yUZJnZDpef5zkf9XCL3K7ce9M7+dN\n",
+       "82u6YD7OyQrHEDgAutvDw+Or+JEpsDwiU9j5z0lOT3JZkg1JOtN//pXki0m+fmG9hye5eoVtPjTJ\n",
+       "Zxfm35bkOQvzP5XkLYOanpDkT+d9fjrJOXP7fTMFybss9P39JK+Zp7ck2bHc61thP89I8oaF+U7y\n",
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+       "eT5Xt+m/B+f+K+cpyTFJ/inJEcv025LkS0kOW2j7SJJHLsyfluRj8/SvJHlTkgcs2c4Dktw4v9/u\n",
+       "uJvarskUiu++pH3FczJP33Ne9wNJfmew/afM5+shK/1MLMw/f+E9tus4f+PC8l9P8rJ5+qNJTltY\n",
+       "9hOL78llzvNzk1yyMH+HJJ9IsmXhPfmAheWv3PUem8/L0p+HS5I8d3QMPTw89v/DFWb42vHqTFeB\n",
+       "n5wlwzEyXfm6c5L3zH82vinJW+b2VNWdq+p35qECn0vy9iT3XHJ1+IaF6b/PdHV0Wd392u5+RKbw\n",
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+       "D5E4J0m6e3umX1Ken+TGqrq4Vr7p8UeSPDLJx2sagvPwuX10TtLdN2W62v/gJP9lpWOR6f3+J0ku\n",
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+       "Lln8qUx/rj55Iezdo6eb8pLpT/APTPId3X33TFdfk+nK9Gpq+nJ3/0GS92cKQNcnOaKq7rLQ7b4L\n",
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+       "uyXJJ3ezjV1G5+naJEdW1T33sLblarkuSbr78939zO6+f5IfTPLvd41V7u7f7+7vntftTEN9bruz\n",
+       "7r/q7jMyDQ15Y6Yrp8n4nKSqHprp6vFFSV6y0oGY31u/3N0nJfnOJI/OP4/j/xfvo0zDHpY6brnX\n",
+       "nul9cewK/b6y+4Xpf3Ec5/fTcZmuMifTL5ejWpb7edh1HlY6hsB+JjDD15anZhqSsHjFatdVr99N\n",
+       "8ptVda8kqapN81XfJLlbpkB9U1UdmWkc5z6p6ePXHlVVd6vpJrcfyDSm9l1zqN+W5Jer6k5V9d2Z\n",
+       "Atkuf5vksHn9O2YaZnLowvK7Jflcki9U1TcmefpuyvndJE+rqu+oyV121ZZprOwtSX52vnHrhzON\n",
+       "4z2QLkry8zXd+HjXTFfIX9fdt+xmvV1WPE/dfX2SNyc5v6abA+9YVd+zwnZ21fKcqtpY0w2c/zHz\n",
+       "uPeabsp7wBz+Ppdp7PitVfXAqvq+mm4O/Me5lluXbng+t0+oqnt095cXtpEMzklVHTbX8OxM4+U3\n",
+       "VdXSMeq79vG9VfVN8y9Vn8s0tGXXPt6XZOt8DDYn+bfLbOK58xX7k+d97frow0uSnDsfw02ZxmyP\n",
+       "XJLkUVV16vyefWaSmzMNF9lVy+Or6pCqOj3TcKCldv08/KtMwf8PdnMMgf1MYIavId39ke7etsLi\n",
+       "Z2X6M/s75z/n/2mmq5VJ8uJMH0P3qUw37b1lFWV8LlPguSbTON9fT/L07v6LefnjM92U95lMge8r\n",
+       "w0e6++8yjY/+vUxX6L6Y6eazXX5hXv/zmYLX8POd52Px7zLdGPfZTK//yfOyLyX54Xn+s5lulFx6\n",
+       "ZX6ph9dtP4f523ezzqKXZxpK8PZMfw34xyQ/sxfrvzjj8/TETMHxQ5nGGj9jsK0XZPrl5f2Zxgu/\n",
+       "d25Lppvh/jTJFzL9YnF+d78t0y8vL5z3f0OmK5/PXmH7T0zysfm99rTMN8+NzkmmMfg7uvul3X3z\n",
+       "vM4LqurEZbZ/7ySvz/R+uzLJn+efb3R9bpKvn7f/y5nGyS/15/O+L0/you5+69z+K5nec1fPx+D1\n",
+       "mQLwsrr7qrnO/5bpuPxgkh+c319J8nNz202Zxva/cckmbpjrvC7Ja5M8rbs/NC9b9hgC+1/9y+F5\n",
+       "AAeXqnp+ppuihAEOOlX19CRbu3u5K8Or3faWTDcjHrubrsAB5gozAOyhqjqmqr5rHk70wExDLN6w\n",
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+       "v95lAADwVew973nPp7p743LLDvrAfPzxx2fbtpU+NhYAAFavqj6+0jJDMgAAYEBgBgCAAYEZAAAG\n",
+       "BGYAABjYbWCuqpdX1Y1V9TcLbb9RVR+qqvdX1Ruq6p4Ly86tqu1VdVVVnbbQ/m1V9YF52Uuqqvb7\n",
+       "qwEAgP1sT64wvzLJ6UvaLkvy4O5+SJK/TXJuklTVSUm2Jjl5Xuf8qjpkXuelSc5KcuL8WLpNAAA4\n",
+       "6Ow2MHf325N8ZknbW7v7lnn2nUmOnafPSHJxd9/c3Vcn2Z7klKo6Jsndu/sdPX0X96uSPGY/vQYA\n",
+       "ADhg9scY5qckefM8vSnJtQvLdsxtm+bppe0AAHBQW1VgrqpfSnJLktfualqmWw/aV9ruWVW1raq2\n",
+       "7dy5czUlAgDAquxzYK6qM5M8OskT5mEWyXTl+LiFbscmuW5uP3aZ9mV19wXdvbm7N2/cuOw3FAIA\n",
+       "wJrYp8BcVacneVaSH+ruv19YdGmSrVV1aFWdkOnmvnd39/VJPl9VD5s/HeNJSd60ytoBAOCA27C7\n",
+       "DlV1UZItSY6qqh1JnpfpUzEOTXLZ/Olw7+zup3X3FVV1SZIPZhqqcXZ33zpv6umZPnHj8Exjnt8c\n",
+       "AAA4yNU/j6Y4OG3evLm3bdu23mUAAPBVrKre092bl1vmm/5gL2zZsiVbtmxZ7zIAgDUkMAMAwIDA\n",
+       "DAAAAwIzAAAMCMwAADAgMAMAwIDADAAAAwIzAAAMCMwAADAgMAMAwIDADAAAAwIzAAAMCMwAADAg\n",
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+       "5DNLms9IcuE8fWGSxyy0X9zdN3f31Um2Jzmlqo5Jcvfufkd3d5JXLawDAAAHrX0dw3x0d1+fJPPz\n",
+       "veb2TUmuXei3Y27bNE8vbV9WVZ1VVduqatvOnTv3sUQAAFi9/X3T33LjknvQvqzuvqC7N3f35o0b\n",
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+       "aFWdkOnmvnfPwzY+X1UPmz8d40kL6wAAwEFrw+46VNVFSbYkOaqqdiR5XpIXJrmkqp6a5Jokj02S\n",
+       "7r6iqi5J8sEktyQ5u7tvnTf19EyfuHF4kjfPDwAAOKjtNjB39+NWWHTqCv3PS3LeMu3bkjx4r6oD\n",
+       "AIB15pv+AABgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAIAB\n",
+       "gRkAAAYEZgAAGBCYAQBgQGAGAIABgRkAAAYEZgAAGBCYAQBgQGAGAICBDetdwMHq+HP+93qXwEHo\n",
+       "ho9+Oon3Byv72Asftd4lALCfucIMAAADAjMAAAwIzAAAMCAwAwDAgMAMAAADAjMAAAwIzAAAMCAw\n",
+       "AwDAgMAMAAADvukPgAPCN2KyEt+aysjB+I2prjADAMCAwAwAAAMCMwAADAjMAAAwIDADAMCAwAwA\n",
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+       "qj9az7pgT6z5GGa4nfuH7n7oehcBcDv0xSQPrqrDu/sfknx/kk+sc02wR1xhBgDWypuTPGqeflyS\n",
+       "i9axFthjAjPsncMXhmO8Yb2LAbiduTjJ1qo6LMlDkrxrneuBPWJIBuwdQzIA9lF3v3++9+Nx8RGz\n",
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+       "/QEAwIBPyQAAgAGBGQAABgRmAAAYEJgBAGBAYAYAgAGBGQAABgRmAAAYEJgBAGDg/wMWx1xl0btn\n",
+       "UQAAAABJRU5ErkJggg==\n",
+       "' alt='Mean Squared Error across sex subgroups' />\n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  \n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "    \n",
+       "\n",
+       "    </div>\n",
+       "\n",
+       "  </div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "  \n",
+       "\n",
+       "</div>\n",
+       "\n",
+       "\n",
+       "\n",
+       "    \n",
+       "\n",
+       "</body>\n",
+       "\n",
+       "</html>\n"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Export to html\n",
+    "html = mct.export_format(output_file=\"speech_to_text_example.html\")\n",
+    "display.display(display.HTML(html))"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "veritas",
+   "language": "python",
+   "name": "veritas"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  },
+  "widgets": {
+   "application/vnd.jupyter.widget-state+json": {
+    "state": {},
+    "version_major": 2,
+    "version_minor": 0
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ab4b2c27fb9708bbb92fc47f33fc69a86980b509 Mon Sep 17 00:00:00 2001
From: swan <swan.lin@lynxanalytics.com>
Date: Fri, 7 Jan 2022 15:40:19 +0800
Subject: [PATCH 2/5] card outputs

---
 .../data/speech_to_text_example.proto         |  Bin 0 -> 66082 bytes
 .../model_cards/speech_to_text_example.html   | 2132 +++++++++++++++++
 2 files changed, 2132 insertions(+)
 create mode 100644 examples/model_card_output/data/speech_to_text_example.proto
 create mode 100644 examples/model_card_output/model_cards/speech_to_text_example.html

diff --git a/examples/model_card_output/data/speech_to_text_example.proto b/examples/model_card_output/data/speech_to_text_example.proto
new file mode 100644
index 0000000000000000000000000000000000000000..f9d94adfbcfce592e99a36f771d69ec2b1d8e2f5
GIT binary patch
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diff --git a/examples/model_card_output/model_cards/speech_to_text_example.html b/examples/model_card_output/model_cards/speech_to_text_example.html
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@@ -0,0 +1,2132 @@
+<!DOCTYPE html>
+
+
+
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+
+
+<html lang="en">
+
+<head>
+
+<style>
+
+  h1 {text-align: center;}
+
+  .row {
+
+    display: flex;
+
+  }
+
+  .col {
+
+    flex: 1;
+
+    align-items: center; 
+
+    justify-content: center;
+
+  }
+
+  .card {
+
+    padding: 1em;
+
+    border: 1px solid #DADCE0;
+
+    margin: 10px;
+
+  }
+
+  .card .card {
+
+    padding: 0.5em;
+
+    padding-bottom: 0em;
+
+    border: none;
+
+  }
+
+  .img-container {
+
+    display: flex;
+
+    flex-wrap: wrap;
+
+    justify-content: space-around;
+
+    text-align: center;
+
+  }
+
+  .img-item {
+
+    flex: 1;
+
+  }
+
+  .center {
+
+    margin-left: auto;
+
+    margin-right: auto;
+
+    text-align: center;
+
+  }
+
+  table, th, td { border: 1px solid black; }
+
+  th, td { border: 1px solid #CCC; height: 30px; }
+
+  caption { font-weight: bold; }
+
+</style>
+
+<title>
+
+  Model Card for Speech to Text Model
+
+</title>
+
+</head>
+
+<body>
+
+  <h1>
+
+    Model Card for Speech to Text Model
+
+  </h1>
+
+    <div class="row">
+
+      <div class="col card">
+
+        <h2>Model Details</h2>
+
+          <h3>Overview</h3>
+
+              The government want to create an automated transcription tool used in trials for evidence purposes. The tool should be able to capture the different spoken accents and transcribe into text accurately so as not to bias against the defendant.
+
+          <h3>Version</h3>
+
+              
+
+  
+
+    <div>name: v1</div>
+
+
+
+              
+
+  
+
+
+
+              
+
+  
+
+
+
+          
+
+          
+
+          <h3>Owners</h3>
+
+            
+
+              
+
+                <li>Timothy, Product Owner(s)</li>
+
+              
+
+                <li>Swan, Model Developer(s)</li>
+
+              
+
+                <li>Jason, Reviewer(s)</li>
+
+              
+
+            
+
+          
+
+          
+
+          
+
+          
+
+          <h3>Regulatory requirements</h3>
+
+              MAS Fairness, Ethics, Accountability and Transparency (FEAT) principles
+
+        </div>
+
+      
+
+      
+
+      <div class="col card">
+
+        <h2>Considerations</h2>
+
+          
+
+            <h3>Intended Users</h3>
+
+              
+
+  
+
+  <ul>
+
+  
+
+    <li>Judical System and Defendant</li>
+
+  
+
+  </ul>
+
+
+
+          
+
+          
+
+            <h3>Use Cases</h3>
+
+              
+
+  
+
+  <ul>
+
+  
+
+    <li>Automate transcriptions and reduce human error in making court decisions</li>
+
+  
+
+  </ul>
+
+
+
+          
+
+          
+
+          
+
+          
+
+          
+
+            <h3>Fairness Considerations</h3>
+
+              <ul>
+
+                <li>
+
+                  <div><b>Group at risk:</b> gender and race</div>
+
+                  <div><b>Benefits:</b> Reduce manpower and human error on transcription services. A more accurate model than current system will aid the judicial system in making better court decisions</div>
+
+                  <div><b>Harms:</b> Incorrectly transcribed/interpreted texts can impact on the defendant/ruling negatively.</div>
+
+                  <div><b>Mitigation Strategy:</b> Improve upon the existing model by further training and enhancements</div>
+
+                </li> </ul>
+
+          
+
+      </div>
+
+      
+
+    </div>
+
+    
+
+      
+
+  <div class="col card">
+
+    <h2>Datasets</h2>
+
+    
+
+      <div class="row">
+
+        <div class="col card">
+
+          <h3>National Speech Corpus Dataset</h3>
+
+            <p>Dataset taken from https://www.imda.gov.sg/programme-listing/digital-services-lab/national-speech-corpus</p>
+
+            
+
+              <h4>Sensitive data</h4>
+
+              <ul>
+
+              
+
+                <li>gender</li>
+
+              
+
+                <li> race</li>
+
+              
+
+              </ul>
+
+              
+
+                <h4>Sensitive data used in model</h4>
+
+                <ul>
+
+                
+
+                  <li>gender</li>
+
+                
+
+                  <li> race</li>
+
+                
+
+                </ul>
+
+              
+
+              
+
+                <h4>Justification</h4>
+
+                <p> Potential improvement in model performance with protected attributes used as features in model</p>
+
+              
+
+            
+
+            
+
+            
+
+        </div>
+
+      </div>
+
+    
+
+  </div>
+
+
+
+    
+
+    
+
+      
+
+<div class="col card">
+
+  <h2>Quantitative Analysis</h2>
+
+  
+
+    
+
+      
+
+<div class="col card">
+
+  <h3>MSE - 890.221 (Test Set)</h3>
+
+  
+
+  
+
+  
+
+  
+
+</div>
+
+
+
+    
+
+      
+
+<div class="col card">
+
+  <h3>MAE - 29.047 (Test Set)</h3>
+
+  
+
+  
+
+  
+
+    
+
+  <div class="row">
+
+    <div class="col">
+
+      
+
+        <div class="center"><i>About 65% of the words are correctly transcribed</i></div>
+
+      
+
+      
+
+  <div class="img-container">
+
+  
+
+  
+
+    <div class="img-item">
+
+      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90
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+' alt='None' />
+
+    </div>
+
+  
+
+  </div>
+
+    
+
+    </div>
+
+  </div>
+
+
+
+  
+
+  
+
+</div>
+
+
+
+    
+
+      
+
+<div class="col card">
+
+  <h3>MSE across sensitive groups  </h3>
+
+  
+
+  
+
+  
+
+  
+
+    
+
+      
+
+<div class="col card">
+
+  
+
+  <h4> Min Max Threshold Test </h4>
+
+  
+
+  
+
+  <div>
+
+    <b> Description: </b>
+
+    Test if the mae of the subgroups within sex 
+is lower than the threshold of 30.
+  
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Threshold: </b>
+
+    30 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Result: </b>
+
+    
+
+    
+
+      
+
+<table>
+
+
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td></td>
+
+      
+
+      <td>mae</td>
+
+      
+
+      <td>passed
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>sex_F</td>
+
+      
+
+      <td>29.391</td>
+
+      
+
+      <td>True
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>sex_M</td>
+
+      
+
+      <td>28.77</td>
+
+      
+
+      <td>True
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+  
+
+</table>
+
+
+
+    
+
+  </div>
+
+  
+
+   
+
+    <div><b style="color:green;"> Passed</b></div> 
+
+    
+
+  
+
+    
+
+  <div class="row">
+
+    <div class="col">
+
+      
+
+      
+
+  <div class="img-container">
+
+  
+
+  
+
+    <div class="img-item">
+
+      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAr8AAAF2CAYAAABnFvAPAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90
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+TkSuQmCC
+' alt='Mean Absolute Error across sex subgroups' />
+
+    </div>
+
+  
+
+  </div>
+
+    
+
+    </div>
+
+  </div>
+
+
+
+  
+
+</div>
+
+
+
+    
+
+      
+
+<div class="col card">
+
+  
+
+  <h4> Min Max Threshold Test </h4>
+
+  
+
+  
+
+  <div>
+
+    <b> Description: </b>
+
+    Test if the mae of the subgroups within race 
+is lower than the threshold of 30.
+  
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Threshold: </b>
+
+    30 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Result: </b>
+
+    
+
+    
+
+      
+
+<table>
+
+
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td></td>
+
+      
+
+      <td>mae</td>
+
+      
+
+      <td>passed
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>race_CHINESE</td>
+
+      
+
+      <td>30.475</td>
+
+      
+
+      <td>False
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>race_INDIAN</td>
+
+      
+
+      <td>27.99</td>
+
+      
+
+      <td>True
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>race_MALAY</td>
+
+      
+
+      <td>28.68</td>
+
+      
+
+      <td>True
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+  
+
+</table>
+
+
+
+    
+
+  </div>
+
+  
+
+  
+
+    <div><b style="color:red;"> Failed</b></div>
+
+  
+
+  
+
+    
+
+  <div class="row">
+
+    <div class="col">
+
+      
+
+      
+
+  <div class="img-container">
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+DL+SJElqxv8H6i/VME7aXSsAAAAASUVORK5CYII=
+' alt='Mean Absolute Error across race subgroups' />
+
+    </div>
+
+  
+
+  </div>
+
+    
+
+    </div>
+
+  </div>
+
+
+
+  
+
+</div>
+
+
+
+    
+
+  
+
+</div>
+
+
+
+    
+
+  
+
+  
+
+</div>
+
+
+
+    
+
+    
+
+    
+
+      
+
+<div class="col card">
+
+  <h2>Fairness Analysis</h2>
+
+  
+
+    
+
+      
+
+<div class="col card">
+
+  <h3>Equal mean squared error  </h3>
+
+  
+
+  <div>
+
+    <b> Segment: </b>
+
+    Gender and race 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Description: </b>
+
+    Disparity ratio of MSE of any 2 bins in the respective attribute should not be more than a factor of 1.1 
+
+  </div>
+
+  
+
+  
+
+  
+
+    
+
+      
+
+<div class="col card">
+
+  
+
+  <h4> Subgroup Disparity Test </h4>
+
+  
+
+  
+
+  <div>
+
+    <b> Description: </b>
+
+    Test if the maximum ratio of the mean squared error of any 2
+groups within race attribute exceeds 1.1. To
+pass, this value cannot exceed the threshold. 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Threshold: </b>
+
+    1.1 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Result: </b>
+
+    
+
+    
+
+      
+
+<table>
+
+
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td></td>
+
+      
+
+      <td>race_mse_max_ratio
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>0</td>
+
+      
+
+      <td>1.177
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+  
+
+</table>
+
+
+
+    
+
+  </div>
+
+  
+
+  
+
+    <div><b style="color:red;"> Failed</b></div>
+
+  
+
+  
+
+    
+
+  <div class="row">
+
+    <div class="col">
+
+      
+
+      
+
+  <div class="img-container">
+
+  
+
+  
+
+    <div class="img-item">
+
+      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAswAAAF1CAYAAAD8/Lw6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90
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+MCAwAwDAwP8BbSTAWhj1u30AAAAASUVORK5CYII=
+' alt='Mean Squared Error across race subgroups' />
+
+    </div>
+
+  
+
+  </div>
+
+    
+
+    </div>
+
+  </div>
+
+
+
+  
+
+</div>
+
+
+
+    
+
+      
+
+<div class="col card">
+
+  
+
+  <h4> Subgroup Disparity Test </h4>
+
+  
+
+  
+
+  <div>
+
+    <b> Description: </b>
+
+    Test if the maximum ratio of the mean squared error of any 2
+groups within sex attribute exceeds 1.1. To
+pass, this value cannot exceed the threshold. 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Threshold: </b>
+
+    1.1 
+
+  </div>
+
+  
+
+  
+
+  <div>
+
+    <b> Result: </b>
+
+    
+
+    
+
+      
+
+<table>
+
+
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td></td>
+
+      
+
+      <td>sex_mse_max_ratio
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+      <tr>
+
+      
+
+      
+
+      <td>0</td>
+
+      
+
+      <td>1.045
</td>
+
+      
+
+      </tr>
+
+    
+
+  
+
+    
+
+  
+
+</table>
+
+
+
+    
+
+  </div>
+
+  
+
+   
+
+    <div><b style="color:green;"> Passed</b></div> 
+
+    
+
+  
+
+    
+
+  <div class="row">
+
+    <div class="col">
+
+      
+
+      
+
+  <div class="img-container">
+
+  
+
+  
+
+    <div class="img-item">
+
+      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAswAAAF1CAYAAAD8/Lw6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90
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From 7c8dd0b8a264634dbd7d3640673aec580260d1d4 Mon Sep 17 00:00:00 2001
From: swan <swan.lin@lynxanalytics.com>
Date: Fri, 7 Jan 2022 16:00:09 +0800
Subject: [PATCH 3/5] upload notebook to get the outputs from google's model

---
 data/speaker.csv                              | 1032 +++++++++++++++++
 .../get_output_from_google_speech_model.ipynb |  661 +++++++++++
 2 files changed, 1693 insertions(+)
 create mode 100644 data/speaker.csv
 create mode 100644 examples/get_output_from_google_speech_model.ipynb

diff --git a/data/speaker.csv b/data/speaker.csv
new file mode 100644
index 0000000..b6f40fc
--- /dev/null
+++ b/data/speaker.csv
@@ -0,0 +1,1032 @@
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diff --git a/examples/get_output_from_google_speech_model.ipynb b/examples/get_output_from_google_speech_model.ipynb
new file mode 100644
index 0000000..8c6496c
--- /dev/null
+++ b/examples/get_output_from_google_speech_model.ipynb
@@ -0,0 +1,661 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Input .wav files from gs into google's speech to text model "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import pandas as pd\n",
+    "import sys \n",
+    "\n",
+    "sys.path.insert(0, '..')\n",
+    "# Add google app crendtials\n",
+    "pd.set_option('max_colwidth', 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read participants df\n",
+    "df=pd.read_csv(\"../data/speaker.csv\")\n",
+    "\n",
+    "# Sample 100 IDs each from each race\n",
+    "id_lst=list(pd.concat(\n",
+    "    [\n",
+    "        df[df.race=='CHINESE'].sample(100, random_state=99),\n",
+    "        df[df.race=='MALAY'].sample(100, random_state=99),\n",
+    "        df[df.race=='INDIAN'].sample(100, random_state=99)\n",
+    "    ]\n",
+    ").id)\n",
+    "\n",
+    "# Remove 2 errors id\n",
+    "id_lst.remove(2888)\n",
+    "id_lst.remove(3151)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id</th>\n",
+       "      <th>sex</th>\n",
+       "      <th>race</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2852</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2670</td>\n",
+       "      <td>F</td>\n",
+       "      <td>MALAY</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3064</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2862</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2861</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1026</th>\n",
+       "      <td>2026</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1027</th>\n",
+       "      <td>2068</td>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1028</th>\n",
+       "      <td>2173</td>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1029</th>\n",
+       "      <td>2216</td>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1030</th>\n",
+       "      <td>2650</td>\n",
+       "      <td>F</td>\n",
+       "      <td>INDIAN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1031 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        id sex     race\n",
+       "0     2852   F  CHINESE\n",
+       "1     2670   F    MALAY\n",
+       "2     3064   F  CHINESE\n",
+       "3     2862   F  CHINESE\n",
+       "4     2861   F  CHINESE\n",
+       "...    ...  ..      ...\n",
+       "1026  2026   F  CHINESE\n",
+       "1027  2068   M  CHINESE\n",
+       "1028  2173   F  CHINESE\n",
+       "1029  2216   M  CHINESE\n",
+       "1030  2650   F   INDIAN\n",
+       "\n",
+       "[1031 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "# Imports the Google Cloud libraries\n",
+    "from google.cloud import speech\n",
+    "from google.cloud import storage\n",
+    "\n",
+    "\n",
+    "# Instantiates a client\n",
+    "client = speech.SpeechClient()\n",
+    "\n",
+    "# Get the first 10 text outputs of each sampled IDs using google speech model\n",
+    "for p in id_lst:\n",
+    "    lst=[]\n",
+    "    i=0\n",
+    "    o=1\n",
+    "    # Speech file numbers can be missing inbetween\n",
+    "    while i!=10:\n",
+    "        if o>300:\n",
+    "            break\n",
+    "        name = f\"out/0{p}{o:04d}.WAV\"   \n",
+    "        storage_client = storage.Client()\n",
+    "        bucket_name = 'national_speech_corpus'\n",
+    "        bucket = storage_client.bucket(bucket_name)\n",
+    "        \n",
+    "        # Check if file exists\n",
+    "        if storage.Blob(bucket=bucket, name=name).exists(storage_client):\n",
+    "            gcs_uri = f\"gs://national_speech_corpus/out/0{p}00{o:02d}.WAV\"\n",
+    "            audio = speech.RecognitionAudio(uri=gcs_uri)\n",
+    "            config = speech.RecognitionConfig(\n",
+    "                language_code=\"en-SG\",\n",
+    "                enable_automatic_punctuation=False,\n",
+    "            )\n",
+    "            # Detects speech in the audio file\n",
+    "            response = client.recognize(config=config, audio=audio)\n",
+    "            \n",
+    "            # Check if there's output from the model\n",
+    "            if len(response.results) > 0:\n",
+    "                sentence = response.results[0].alternatives[0].transcript\n",
+    "\n",
+    "                # Replace digits into words\n",
+    "                my_dict = {'0': 'zero ', '1': 'one ', '2': 'two ', '3': 'three ', '4': 'four ', '5': 'five ', '6': 'six ', '7': 'seven ', '8': 'eight ', '9': 'nine '}\n",
+    "                for item in sentence:\n",
+    "                    if item in my_dict.keys():\n",
+    "                        sentence=sentence.replace(item, my_dict[item])\n",
+    "                lst.append(sentence)\n",
+    "            else:\n",
+    "                lst.append('')\n",
+    "            i+=1\n",
+    "        o+=1\n",
+    "    \n",
+    "    # Read transcript (truth) file for a particular participant\n",
+    "    # Drop even index, first column and keep only first 10 sentences \n",
+    "    output_df = pd.read_csv(f\"script/0{p}0.txt\", sep=\"\\t\", header=None).iloc[1:20:2].drop(0,axis=1).reset_index(drop=True)\n",
+    "    output_df.columns=['truth']\n",
+    "    output_df['output']=lst\n",
+    "    output_df.to_csv(f\"output/{p}.csv\",index=False)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Add accuracy metrics to the output df and sampled speaker df\n",
+    "df_speaker=df[df.id.isin(id_lst)]\n",
+    "df_speaker['truth_count']=0\n",
+    "df_speaker['match_count']=0\n",
+    "for p in id_lst:\n",
+    "    output_df = pd.read_csv(f'output/{p}.csv')\n",
+    "    output_df['truth_split'] = output_df.truth.apply(lambda x: re.split(\"[\\W]+\",str(x).lower()))\n",
+    "    output_df['output_split'] = output_df.output.apply(lambda x: re.split(\"[\\W]+\",str(x).lower()))\n",
+    "    output_df['truth_count']=output_df.truth_split.apply(lambda x: len(x))\n",
+    "    output_df['match_count']=output_df.apply(lambda x:len(set(x.truth_split).intersection(set(x.output_split))),axis=1) \n",
+    "    output_df.to_csv(f\"output/{p}.csv\",index=False)\n",
+    "    \n",
+    "    df_speaker.loc[df_speaker.id==p,'truth_count']=output_df.truth_count.sum()\n",
+    "    df_speaker.loc[df_speaker.id==p,'match_count']=output_df.match_count.sum()\n",
+    "\n",
+    "df_speaker.reset_index().to_csv('speech_sample_output.csv',index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "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>truth</th>\n",
+       "      <th>output</th>\n",
+       "      <th>truth_split</th>\n",
+       "      <th>output_split</th>\n",
+       "      <th>truth_count</th>\n",
+       "      <th>match_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>nine</td>\n",
+       "      <td>nine</td>\n",
+       "      <td>['nine']</td>\n",
+       "      <td>['nine', '']</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>De Fabio Factory and Apple</td>\n",
+       "      <td>disable Factory in apple</td>\n",
+       "      <td>['de', 'fabio', 'factory', 'and', 'apple']</td>\n",
+       "      <td>['disable', 'factory', 'in', 'apple']</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Oyakodon Yakizakana and Carrot Halwa</td>\n",
+       "      <td>oil Condon and carrot halwa</td>\n",
+       "      <td>['oyakodon', 'yakizakana', 'and', 'carrot', 'halwa']</td>\n",
+       "      <td>['oil', 'condon', 'and', 'carrot', 'halwa']</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>where can I find the best Red ruby</td>\n",
+       "      <td>where can I find the best way to be</td>\n",
+       "      <td>['where', 'can', 'i', 'find', 'the', 'best', 'red', 'ruby']</td>\n",
+       "      <td>['where', 'can', 'i', 'find', 'the', 'best', 'way', 'to', 'be']</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Kuih Kaswi waffle and Coconut Kuih</td>\n",
+       "      <td>Quaker sweet waffle in Coconut Creek</td>\n",
+       "      <td>['kuih', 'kaswi', 'waffle', 'and', 'coconut', 'kuih']</td>\n",
+       "      <td>['quaker', 'sweet', 'waffle', 'in', 'coconut', 'creek']</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>please tell me how to cook Satay Babat</td>\n",
+       "      <td>please tell me how to cook saute Bobbitt</td>\n",
+       "      <td>['please', 'tell', 'me', 'how', 'to', 'cook', 'satay', 'babat']</td>\n",
+       "      <td>['please', 'tell', 'me', 'how', 'to', 'cook', 'saute', 'bobbitt']</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>six five two three nine seven nine three</td>\n",
+       "      <td>six five  to three nine seven nine three</td>\n",
+       "      <td>['six', 'five', 'two', 'three', 'nine', 'seven', 'nine', 'three']</td>\n",
+       "      <td>['six', 'five', 'to', 'three', 'nine', 'seven', 'nine', 'three', '']</td>\n",
+       "      <td>8</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Denis D Cotta Paine Eric and Chong Tze Chien</td>\n",
+       "      <td>Dennis Dakota and Chong searching</td>\n",
+       "      <td>['denis', 'd', 'cotta', 'paine', 'eric', 'and', 'chong', 'tze', 'chien']</td>\n",
+       "      <td>['dennis', 'dakota', 'and', 'chong', 'searching']</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>I can't eat all of this Satay Babat</td>\n",
+       "      <td>I can't eat all of this Saturday Bobbitt</td>\n",
+       "      <td>['i', 'can', 't', 'eat', 'all', 'of', 'this', 'satay', 'babat']</td>\n",
+       "      <td>['i', 'can', 't', 'eat', 'all', 'of', 'this', 'saturday', 'bobbitt']</td>\n",
+       "      <td>9</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Maxi Cash Cadbury and Ajinomoto</td>\n",
+       "      <td>Mexican Inn Camp Bowie and Angie nomoto</td>\n",
+       "      <td>['maxi', 'cash', 'cadbury', 'and', 'ajinomoto']</td>\n",
+       "      <td>['mexican', 'inn', 'camp', 'bowie', 'and', 'angie', 'nomoto']</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          truth  \\\n",
+       "0                                          nine   \n",
+       "1                    De Fabio Factory and Apple   \n",
+       "2          Oyakodon Yakizakana and Carrot Halwa   \n",
+       "3            where can I find the best Red ruby   \n",
+       "4            Kuih Kaswi waffle and Coconut Kuih   \n",
+       "5        please tell me how to cook Satay Babat   \n",
+       "6      six five two three nine seven nine three   \n",
+       "7  Denis D Cotta Paine Eric and Chong Tze Chien   \n",
+       "8           I can't eat all of this Satay Babat   \n",
+       "9               Maxi Cash Cadbury and Ajinomoto   \n",
+       "\n",
+       "                                      output  \\\n",
+       "0                                      nine    \n",
+       "1                   disable Factory in apple   \n",
+       "2                oil Condon and carrot halwa   \n",
+       "3        where can I find the best way to be   \n",
+       "4       Quaker sweet waffle in Coconut Creek   \n",
+       "5   please tell me how to cook saute Bobbitt   \n",
+       "6  six five  to three nine seven nine three    \n",
+       "7          Dennis Dakota and Chong searching   \n",
+       "8   I can't eat all of this Saturday Bobbitt   \n",
+       "9    Mexican Inn Camp Bowie and Angie nomoto   \n",
+       "\n",
+       "                                                                truth_split  \\\n",
+       "0                                                                  ['nine']   \n",
+       "1                                ['de', 'fabio', 'factory', 'and', 'apple']   \n",
+       "2                      ['oyakodon', 'yakizakana', 'and', 'carrot', 'halwa']   \n",
+       "3               ['where', 'can', 'i', 'find', 'the', 'best', 'red', 'ruby']   \n",
+       "4                     ['kuih', 'kaswi', 'waffle', 'and', 'coconut', 'kuih']   \n",
+       "5           ['please', 'tell', 'me', 'how', 'to', 'cook', 'satay', 'babat']   \n",
+       "6         ['six', 'five', 'two', 'three', 'nine', 'seven', 'nine', 'three']   \n",
+       "7  ['denis', 'd', 'cotta', 'paine', 'eric', 'and', 'chong', 'tze', 'chien']   \n",
+       "8           ['i', 'can', 't', 'eat', 'all', 'of', 'this', 'satay', 'babat']   \n",
+       "9                           ['maxi', 'cash', 'cadbury', 'and', 'ajinomoto']   \n",
+       "\n",
+       "                                                           output_split  \\\n",
+       "0                                                          ['nine', '']   \n",
+       "1                                 ['disable', 'factory', 'in', 'apple']   \n",
+       "2                           ['oil', 'condon', 'and', 'carrot', 'halwa']   \n",
+       "3       ['where', 'can', 'i', 'find', 'the', 'best', 'way', 'to', 'be']   \n",
+       "4               ['quaker', 'sweet', 'waffle', 'in', 'coconut', 'creek']   \n",
+       "5     ['please', 'tell', 'me', 'how', 'to', 'cook', 'saute', 'bobbitt']   \n",
+       "6  ['six', 'five', 'to', 'three', 'nine', 'seven', 'nine', 'three', '']   \n",
+       "7                     ['dennis', 'dakota', 'and', 'chong', 'searching']   \n",
+       "8  ['i', 'can', 't', 'eat', 'all', 'of', 'this', 'saturday', 'bobbitt']   \n",
+       "9         ['mexican', 'inn', 'camp', 'bowie', 'and', 'angie', 'nomoto']   \n",
+       "\n",
+       "   truth_count  match_count  \n",
+       "0            1            1  \n",
+       "1            5            2  \n",
+       "2            5            3  \n",
+       "3            8            6  \n",
+       "4            6            2  \n",
+       "5            8            6  \n",
+       "6            8            5  \n",
+       "7            9            2  \n",
+       "8            9            7  \n",
+       "9            5            1  "
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sample transcribed text vs truth\n",
+    "speaker = pd.read_csv(f\"output/2002.csv\")\n",
+    "speaker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "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>sex</th>\n",
+       "      <th>race</th>\n",
+       "      <th>truth_count</th>\n",
+       "      <th>match_count</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>80</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>89</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>82</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>F</td>\n",
+       "      <td>INDIAN</td>\n",
+       "      <td>85</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>73</td>\n",
+       "      <td>47</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>293</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>80</td>\n",
+       "      <td>50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>294</th>\n",
+       "      <td>M</td>\n",
+       "      <td>MALAY</td>\n",
+       "      <td>72</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>295</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>85</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>296</th>\n",
+       "      <td>F</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>82</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>297</th>\n",
+       "      <td>M</td>\n",
+       "      <td>CHINESE</td>\n",
+       "      <td>85</td>\n",
+       "      <td>57</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>298 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    sex     race  truth_count  match_count\n",
+       "0     F  CHINESE           80           60\n",
+       "1     F  CHINESE           89           64\n",
+       "2     M  CHINESE           82           53\n",
+       "3     F   INDIAN           85           53\n",
+       "4     M  CHINESE           73           47\n",
+       "..   ..      ...          ...          ...\n",
+       "293   M  CHINESE           80           50\n",
+       "294   M    MALAY           72           52\n",
+       "295   F  CHINESE           85           60\n",
+       "296   F  CHINESE           82           52\n",
+       "297   M  CHINESE           85           57\n",
+       "\n",
+       "[298 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# output to be passed into feat tests (subgroup disparity and min/max threshold)\n",
+    "df_speaker=pd.read_csv('../data/speech_sample_output.csv')\n",
+    "df_speaker"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "race\n",
+       "CHINESE    0.648744\n",
+       "INDIAN     0.673310\n",
+       "MALAY      0.659702\n",
+       "Name: rate, dtype: float64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Sample accuracy stats across race\n",
+    "\n",
+    "tmp=df_speaker.copy()\n",
+    "tmp['rate']=tmp.match_count/tmp.truth_count\n",
+    "tmp.groupby('race').rate.mean()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "veritas",
+   "language": "python",
+   "name": "veritas"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

From 2d6a4b34172a1adc50c9af7bdf170fde452f6bf3 Mon Sep 17 00:00:00 2001
From: swan <swan.lin@lynxanalytics.com>
Date: Fri, 7 Jan 2022 18:04:31 +0800
Subject: [PATCH 4/5] notebook edits-remove empty string, unique count fix,
 rerun

---
 data/speech_sample_output.csv                 |  574 +++---
 .../get_output_from_google_speech_model.ipynb |  155 +-
 .../data/speech_to_text_example.proto         |  Bin 66082 -> 63777 bytes
 .../model_cards/speech_to_text_example.html   | 1615 ++++++++--------
 examples/speech_to_text_example.ipynb         | 1673 ++++++++---------
 5 files changed, 1992 insertions(+), 2025 deletions(-)

diff --git a/data/speech_sample_output.csv b/data/speech_sample_output.csv
index ca86e97..2d65eac 100644
--- a/data/speech_sample_output.csv
+++ b/data/speech_sample_output.csv
@@ -1,299 +1,299 @@
 sex,race,truth_count,match_count
-F,CHINESE,80,60
-F,CHINESE,89,64
-M,CHINESE,82,53
-F,INDIAN,85,53
-M,CHINESE,73,47
-F,CHINESE,84,50
-M,CHINESE,81,56
-M,CHINESE,78,52
-F,CHINESE,79,50
-F,CHINESE,81,55
-M,MALAY,82,56
-F,CHINESE,87,46
-M,CHINESE,83,52
-F,INDIAN,85,59
-F,MALAY,88,52
+F,CHINESE,77,60
+F,CHINESE,84,64
+M,CHINESE,80,53
+F,INDIAN,80,53
+M,CHINESE,70,45
+F,CHINESE,81,50
+M,CHINESE,78,56
+M,CHINESE,76,52
+F,CHINESE,76,50
+F,CHINESE,77,53
+M,MALAY,77,53
+F,CHINESE,82,46
+M,CHINESE,79,52
+F,INDIAN,82,59
+F,MALAY,85,50
 M,MALAY,72,55
-F,CHINESE,85,58
-F,INDIAN,82,57
-F,MALAY,79,46
-M,MALAY,76,52
-F,MALAY,74,49
-F,MALAY,80,48
-F,MALAY,89,52
-F,MALAY,80,53
-F,MALAY,76,51
-M,MALAY,90,48
-F,INDIAN,80,64
-F,INDIAN,86,64
+F,CHINESE,80,58
+F,INDIAN,79,56
+F,MALAY,74,46
+M,MALAY,75,52
+F,MALAY,70,48
+F,MALAY,77,48
+F,MALAY,84,52
+F,MALAY,75,53
+F,MALAY,75,51
+M,MALAY,85,48
+F,INDIAN,79,63
+F,INDIAN,82,64
 F,INDIAN,76,51
-M,MALAY,77,47
-F,INDIAN,86,55
-F,INDIAN,79,59
-F,MALAY,82,57
-M,MALAY,79,51
-F,MALAY,74,49
-F,INDIAN,83,61
-M,INDIAN,80,59
-F,INDIAN,89,66
-F,MALAY,95,60
-M,MALAY,84,59
-M,CHINESE,83,60
-M,MALAY,89,66
-F,INDIAN,86,65
-M,INDIAN,89,53
+M,MALAY,75,47
+F,INDIAN,81,54
+F,INDIAN,77,59
+F,MALAY,80,57
+M,MALAY,78,51
+F,MALAY,73,49
+F,INDIAN,81,60
+M,INDIAN,79,59
+F,INDIAN,86,66
+F,MALAY,92,60
+M,MALAY,80,59
+M,CHINESE,83,59
+M,MALAY,85,66
+F,INDIAN,83,64
+M,INDIAN,85,50
 M,INDIAN,80,57
-M,MALAY,83,56
-M,MALAY,90,52
-M,MALAY,92,55
-F,INDIAN,86,58
-M,MALAY,85,56
-M,MALAY,82,51
-M,MALAY,81,56
-M,MALAY,93,56
-F,MALAY,81,54
-M,INDIAN,82,53
-F,INDIAN,78,63
-M,CHINESE,80,54
-M,INDIAN,80,61
-M,INDIAN,82,53
-M,INDIAN,78,60
-F,INDIAN,91,59
-M,INDIAN,91,57
-M,CHINESE,94,66
-M,INDIAN,76,57
-F,INDIAN,79,55
-M,INDIAN,84,62
-M,CHINESE,79,59
+M,MALAY,81,55
+M,MALAY,86,51
+M,MALAY,86,55
+F,INDIAN,81,55
+M,MALAY,82,53
+M,MALAY,79,49
+M,MALAY,78,55
+M,MALAY,85,55
+F,MALAY,79,54
+M,INDIAN,81,52
+F,INDIAN,76,62
+M,CHINESE,78,52
+M,INDIAN,79,59
+M,INDIAN,80,52
+M,INDIAN,77,57
+F,INDIAN,89,59
+M,INDIAN,87,53
+M,CHINESE,90,66
+M,INDIAN,72,57
+F,INDIAN,76,54
+M,INDIAN,82,58
+M,CHINESE,77,56
 M,INDIAN,78,47
-M,INDIAN,77,53
-F,INDIAN,83,58
-F,INDIAN,82,60
-M,INDIAN,78,56
-M,INDIAN,81,62
-M,INDIAN,79,57
-M,CHINESE,81,55
-F,INDIAN,79,56
+M,INDIAN,75,50
+F,INDIAN,81,58
+F,INDIAN,80,60
+M,INDIAN,77,56
 M,INDIAN,80,60
-F,INDIAN,86,69
-M,MALAY,66,49
-M,MALAY,73,55
-F,INDIAN,74,45
-M,MALAY,68,56
-F,CHINESE,78,49
-F,CHINESE,86,57
-F,CHINESE,95,63
-F,CHINESE,89,56
-M,INDIAN,80,51
-M,CHINESE,83,53
-F,CHINESE,84,60
-F,MALAY,80,53
-F,MALAY,79,54
-F,MALAY,82,58
-M,MALAY,89,56
-M,MALAY,79,61
-F,CHINESE,90,51
-F,INDIAN,80,53
-M,CHINESE,80,56
-F,INDIAN,88,52
-M,CHINESE,91,59
-F,CHINESE,84,58
-M,MALAY,89,53
-M,INDIAN,84,48
-M,MALAY,80,43
+M,INDIAN,77,55
+M,CHINESE,79,54
+F,INDIAN,75,56
+M,INDIAN,78,60
+F,INDIAN,83,66
+M,MALAY,65,49
+M,MALAY,71,54
+F,INDIAN,70,45
+M,MALAY,67,56
+F,CHINESE,75,49
+F,CHINESE,84,55
+F,CHINESE,88,62
+F,CHINESE,88,54
+M,INDIAN,79,51
+M,CHINESE,80,51
+F,CHINESE,83,58
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+F,CHINESE,79,50
+F,INDIAN,77,53
+M,CHINESE,75,56
+F,INDIAN,82,51
+M,CHINESE,86,59
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 M,MALAY,81,53
-M,INDIAN,86,55
-M,INDIAN,90,48
-M,MALAY,82,51
-M,INDIAN,90,61
-M,MALAY,91,65
-M,INDIAN,85,55
-M,INDIAN,96,56
-M,INDIAN,102,78
-M,MALAY,79,50
-M,MALAY,76,57
-F,CHINESE,78,52
-F,CHINESE,84,56
-M,CHINESE,98,52
-F,CHINESE,107,68
-F,CHINESE,81,57
-F,CHINESE,86,52
-M,CHINESE,84,56
-F,MALAY,86,55
-M,CHINESE,79,49
-M,CHINESE,81,51
-M,CHINESE,89,59
-M,MALAY,79,56
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-M,MALAY,75,55
+M,INDIAN,78,47
+M,MALAY,77,43
+M,MALAY,74,51
+M,INDIAN,81,55
+M,INDIAN,86,48
 M,MALAY,81,51
-M,MALAY,81,49
-M,MALAY,88,61
-M,INDIAN,89,60
-M,MALAY,88,57
-M,MALAY,76,52
-M,MALAY,91,62
-M,MALAY,96,71
-M,INDIAN,88,69
-M,MALAY,97,56
-M,MALAY,81,60
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-M,MALAY,84,52
-M,INDIAN,97,60
-M,INDIAN,94,62
-F,INDIAN,88,62
-M,INDIAN,93,65
-M,MALAY,97,67
-M,INDIAN,78,56
-F,INDIAN,86,53
-F,INDIAN,80,60
-M,INDIAN,90,56
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-M,INDIAN,79,58
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-M,INDIAN,82,59
-M,INDIAN,84,43
-M,INDIAN,97,57
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-F,MALAY,87,56
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-F,CHINESE,82,60
-F,INDIAN,64,35
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-M,MALAY,81,58
-F,CHINESE,97,61
-F,MALAY,83,58
-F,CHINESE,91,53
-F,CHINESE,93,65
-M,INDIAN,100,62
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-F,INDIAN,79,52
-M,CHINESE,86,51
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+M,INDIAN,85,63
+F,MALAY,76,42
+F,CHINESE,78,49
+F,INDIAN,88,64
+F,INDIAN,80,56
+M,CHINESE,65,47
+M,CHINESE,74,50
+M,MALAY,68,52
+F,CHINESE,82,58
+F,CHINESE,79,52
+M,CHINESE,82,57
diff --git a/examples/get_output_from_google_speech_model.ipynb b/examples/get_output_from_google_speech_model.ipynb
index 8c6496c..99d17dc 100644
--- a/examples/get_output_from_google_speech_model.ipynb
+++ b/examples/get_output_from_google_speech_model.ipynb
@@ -9,22 +9,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "import os\n",
     "import pandas as pd\n",
     "import sys \n",
+    "import re\n",
     "\n",
     "sys.path.insert(0, '..')\n",
+    "\n",
     "# Add google app crendtials\n",
+    "#os.environ[\"GOOGLE_APPLICATION_CREDENTIALS\"]=\"\"\n",
+    "\n",
     "pd.set_option('max_colwidth', 1000)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,7 +51,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 20,
    "metadata": {
     "scrolled": true
    },
@@ -167,7 +171,7 @@
        "[1031 rows x 3 columns]"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -241,9 +245,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 49,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\Clare\\AppData\\Local\\Temp/ipykernel_6324/1510333972.py:3: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_speaker['truth_count']=0\n",
+      "C:\\Users\\Clare\\AppData\\Local\\Temp/ipykernel_6324/1510333972.py:4: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  df_speaker['match_count']=0\n",
+      "C:\\Users\\Clare\\AppData\\Roaming\\Python\\Python38\\site-packages\\pandas\\core\\indexing.py:1817: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  self._setitem_single_column(loc, value, pi)\n"
+     ]
+    }
+   ],
    "source": [
     "# Add accuracy metrics to the output df and sampled speaker df\n",
     "df_speaker=df[df.id.isin(id_lst)]\n",
@@ -251,9 +280,9 @@
     "df_speaker['match_count']=0\n",
     "for p in id_lst:\n",
     "    output_df = pd.read_csv(f'output/{p}.csv')\n",
-    "    output_df['truth_split'] = output_df.truth.apply(lambda x: re.split(\"[\\W]+\",str(x).lower()))\n",
-    "    output_df['output_split'] = output_df.output.apply(lambda x: re.split(\"[\\W]+\",str(x).lower()))\n",
-    "    output_df['truth_count']=output_df.truth_split.apply(lambda x: len(x))\n",
+    "    output_df['truth_split'] = output_df.truth.apply(lambda x: str(x).lower().split())\n",
+    "    output_df['output_split'] = output_df.output.apply(lambda x: str(x).lower().split())\n",
+    "    output_df['truth_count']=output_df.truth_split.apply(lambda x: len(set(x)))\n",
     "    output_df['match_count']=output_df.apply(lambda x:len(set(x.truth_split).intersection(set(x.output_split))),axis=1) \n",
     "    output_df.to_csv(f\"output/{p}.csv\",index=False)\n",
     "    \n",
@@ -265,7 +294,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -303,7 +332,7 @@
        "      <td>nine</td>\n",
        "      <td>nine</td>\n",
        "      <td>['nine']</td>\n",
-       "      <td>['nine', '']</td>\n",
+       "      <td>['nine']</td>\n",
        "      <td>1</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
@@ -340,7 +369,7 @@
        "      <td>Quaker sweet waffle in Coconut Creek</td>\n",
        "      <td>['kuih', 'kaswi', 'waffle', 'and', 'coconut', 'kuih']</td>\n",
        "      <td>['quaker', 'sweet', 'waffle', 'in', 'coconut', 'creek']</td>\n",
-       "      <td>6</td>\n",
+       "      <td>5</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -357,8 +386,8 @@
        "      <td>six five two three nine seven nine three</td>\n",
        "      <td>six five  to three nine seven nine three</td>\n",
        "      <td>['six', 'five', 'two', 'three', 'nine', 'seven', 'nine', 'three']</td>\n",
-       "      <td>['six', 'five', 'to', 'three', 'nine', 'seven', 'nine', 'three', '']</td>\n",
-       "      <td>8</td>\n",
+       "      <td>['six', 'five', 'to', 'three', 'nine', 'seven', 'nine', 'three']</td>\n",
+       "      <td>6</td>\n",
        "      <td>5</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -374,10 +403,10 @@
        "      <th>8</th>\n",
        "      <td>I can't eat all of this Satay Babat</td>\n",
        "      <td>I can't eat all of this Saturday Bobbitt</td>\n",
-       "      <td>['i', 'can', 't', 'eat', 'all', 'of', 'this', 'satay', 'babat']</td>\n",
-       "      <td>['i', 'can', 't', 'eat', 'all', 'of', 'this', 'saturday', 'bobbitt']</td>\n",
-       "      <td>9</td>\n",
-       "      <td>7</td>\n",
+       "      <td>['i', \"can't\", 'eat', 'all', 'of', 'this', 'satay', 'babat']</td>\n",
+       "      <td>['i', \"can't\", 'eat', 'all', 'of', 'this', 'saturday', 'bobbitt']</td>\n",
+       "      <td>8</td>\n",
+       "      <td>6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
@@ -426,35 +455,35 @@
        "5           ['please', 'tell', 'me', 'how', 'to', 'cook', 'satay', 'babat']   \n",
        "6         ['six', 'five', 'two', 'three', 'nine', 'seven', 'nine', 'three']   \n",
        "7  ['denis', 'd', 'cotta', 'paine', 'eric', 'and', 'chong', 'tze', 'chien']   \n",
-       "8           ['i', 'can', 't', 'eat', 'all', 'of', 'this', 'satay', 'babat']   \n",
+       "8              ['i', \"can't\", 'eat', 'all', 'of', 'this', 'satay', 'babat']   \n",
        "9                           ['maxi', 'cash', 'cadbury', 'and', 'ajinomoto']   \n",
        "\n",
-       "                                                           output_split  \\\n",
-       "0                                                          ['nine', '']   \n",
-       "1                                 ['disable', 'factory', 'in', 'apple']   \n",
-       "2                           ['oil', 'condon', 'and', 'carrot', 'halwa']   \n",
-       "3       ['where', 'can', 'i', 'find', 'the', 'best', 'way', 'to', 'be']   \n",
-       "4               ['quaker', 'sweet', 'waffle', 'in', 'coconut', 'creek']   \n",
-       "5     ['please', 'tell', 'me', 'how', 'to', 'cook', 'saute', 'bobbitt']   \n",
-       "6  ['six', 'five', 'to', 'three', 'nine', 'seven', 'nine', 'three', '']   \n",
-       "7                     ['dennis', 'dakota', 'and', 'chong', 'searching']   \n",
-       "8  ['i', 'can', 't', 'eat', 'all', 'of', 'this', 'saturday', 'bobbitt']   \n",
-       "9         ['mexican', 'inn', 'camp', 'bowie', 'and', 'angie', 'nomoto']   \n",
+       "                                                        output_split  \\\n",
+       "0                                                           ['nine']   \n",
+       "1                              ['disable', 'factory', 'in', 'apple']   \n",
+       "2                        ['oil', 'condon', 'and', 'carrot', 'halwa']   \n",
+       "3    ['where', 'can', 'i', 'find', 'the', 'best', 'way', 'to', 'be']   \n",
+       "4            ['quaker', 'sweet', 'waffle', 'in', 'coconut', 'creek']   \n",
+       "5  ['please', 'tell', 'me', 'how', 'to', 'cook', 'saute', 'bobbitt']   \n",
+       "6   ['six', 'five', 'to', 'three', 'nine', 'seven', 'nine', 'three']   \n",
+       "7                  ['dennis', 'dakota', 'and', 'chong', 'searching']   \n",
+       "8  ['i', \"can't\", 'eat', 'all', 'of', 'this', 'saturday', 'bobbitt']   \n",
+       "9      ['mexican', 'inn', 'camp', 'bowie', 'and', 'angie', 'nomoto']   \n",
        "\n",
        "   truth_count  match_count  \n",
        "0            1            1  \n",
        "1            5            2  \n",
        "2            5            3  \n",
        "3            8            6  \n",
-       "4            6            2  \n",
+       "4            5            2  \n",
        "5            8            6  \n",
-       "6            8            5  \n",
+       "6            6            5  \n",
        "7            9            2  \n",
-       "8            9            7  \n",
+       "8            8            6  \n",
        "9            5            1  "
       ]
      },
-     "execution_count": 27,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -467,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -502,36 +531,36 @@
        "      <th>0</th>\n",
        "      <td>F</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>80</td>\n",
+       "      <td>77</td>\n",
        "      <td>60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>F</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>89</td>\n",
+       "      <td>84</td>\n",
        "      <td>64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>M</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>82</td>\n",
+       "      <td>80</td>\n",
        "      <td>53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>F</td>\n",
        "      <td>INDIAN</td>\n",
-       "      <td>85</td>\n",
+       "      <td>80</td>\n",
        "      <td>53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>M</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>73</td>\n",
-       "      <td>47</td>\n",
+       "      <td>70</td>\n",
+       "      <td>45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -544,35 +573,35 @@
        "      <th>293</th>\n",
        "      <td>M</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>80</td>\n",
+       "      <td>74</td>\n",
        "      <td>50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>294</th>\n",
        "      <td>M</td>\n",
        "      <td>MALAY</td>\n",
-       "      <td>72</td>\n",
+       "      <td>68</td>\n",
        "      <td>52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>295</th>\n",
        "      <td>F</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>85</td>\n",
-       "      <td>60</td>\n",
+       "      <td>82</td>\n",
+       "      <td>58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>296</th>\n",
        "      <td>F</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>82</td>\n",
+       "      <td>79</td>\n",
        "      <td>52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>297</th>\n",
        "      <td>M</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>85</td>\n",
+       "      <td>82</td>\n",
        "      <td>57</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -582,22 +611,22 @@
       ],
       "text/plain": [
        "    sex     race  truth_count  match_count\n",
-       "0     F  CHINESE           80           60\n",
-       "1     F  CHINESE           89           64\n",
-       "2     M  CHINESE           82           53\n",
-       "3     F   INDIAN           85           53\n",
-       "4     M  CHINESE           73           47\n",
+       "0     F  CHINESE           77           60\n",
+       "1     F  CHINESE           84           64\n",
+       "2     M  CHINESE           80           53\n",
+       "3     F   INDIAN           80           53\n",
+       "4     M  CHINESE           70           45\n",
        "..   ..      ...          ...          ...\n",
-       "293   M  CHINESE           80           50\n",
-       "294   M    MALAY           72           52\n",
-       "295   F  CHINESE           85           60\n",
-       "296   F  CHINESE           82           52\n",
-       "297   M  CHINESE           85           57\n",
+       "293   M  CHINESE           74           50\n",
+       "294   M    MALAY           68           52\n",
+       "295   F  CHINESE           82           58\n",
+       "296   F  CHINESE           79           52\n",
+       "297   M  CHINESE           82           57\n",
        "\n",
        "[298 rows x 4 columns]"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -610,20 +639,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
        "race\n",
-       "CHINESE    0.648744\n",
-       "INDIAN     0.673310\n",
-       "MALAY      0.659702\n",
+       "CHINESE    0.671469\n",
+       "INDIAN     0.690566\n",
+       "MALAY      0.683185\n",
        "Name: rate, dtype: float64"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/examples/model_card_output/data/speech_to_text_example.proto b/examples/model_card_output/data/speech_to_text_example.proto
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diff --git a/examples/model_card_output/model_cards/speech_to_text_example.html b/examples/model_card_output/model_cards/speech_to_text_example.html
index 645adf5..7cc45e4 100644
--- a/examples/model_card_output/model_cards/speech_to_text_example.html
+++ b/examples/model_card_output/model_cards/speech_to_text_example.html
@@ -380,7 +380,7 @@ <h2>Quantitative Analysis</h2>
 
 <div class="col card">
 
-  <h3>MSE - 890.221 (Test Set)</h3>
+  <h3>MSE - 712.513 (Test Set)</h3>
 
   
 
@@ -400,7 +400,7 @@ <h3>MSE - 890.221 (Test Set)</h3>
 
 <div class="col card">
 
-  <h3>MAE - 29.047 (Test Set)</h3>
+  <h3>MAE - 26.003 (Test Set)</h3>
 
   
 
@@ -430,263 +430,262 @@ <h3>MAE - 29.047 (Test Set)</h3>
 
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 ' alt='None' />
 
     </div>
@@ -746,7 +745,7 @@ <h4> Min Max Threshold Test </h4>
     <b> Description: </b>
 
     Test if the mae of the subgroups within sex 
-is lower than the threshold of 30.
+is lower than the threshold of 27.
   
 
   </div>
@@ -759,7 +758,7 @@ <h4> Min Max Threshold Test </h4>
 
     <b> Threshold: </b>
 
-    30 
+    27 
 
   </div>
 
@@ -821,7 +820,7 @@ <h4> Min Max Threshold Test </h4>
 
       
 
-      <td>29.391</td>
+      <td>26.143</td>
 
       
 
@@ -847,7 +846,7 @@ <h4> Min Max Threshold Test </h4>
 
       
 
-      <td>28.77</td>
+      <td>25.891</td>
 
       
 
@@ -903,138 +902,134 @@ <h4> Min Max Threshold Test </h4>
 
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 TkSuQmCC
 ' alt='Mean Absolute Error across sex subgroups' />
 
@@ -1077,7 +1072,7 @@ <h4> Min Max Threshold Test </h4>
     <b> Description: </b>
 
     Test if the mae of the subgroups within race 
-is lower than the threshold of 30.
+is lower than the threshold of 27.
   
 
   </div>
@@ -1090,7 +1085,7 @@ <h4> Min Max Threshold Test </h4>
 
     <b> Threshold: </b>
 
-    30 
+    27 
 
   </div>
 
@@ -1152,7 +1147,7 @@ <h4> Min Max Threshold Test </h4>
 
       
 
-      <td>30.475</td>
+      <td>27.141</td>
 
       
 
@@ -1178,7 +1173,7 @@ <h4> Min Max Threshold Test </h4>
 
       
 
-      <td>27.99</td>
+      <td>25.404</td>
 
       
 
@@ -1204,7 +1199,7 @@ <h4> Min Max Threshold Test </h4>
 
       
 
-      <td>28.68</td>
+      <td>25.47</td>
 
       
 
@@ -1260,163 +1255,159 @@ <h4> Min Max Threshold Test </h4>
 
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     </div>
@@ -1597,7 +1588,7 @@ <h4> Subgroup Disparity Test </h4>
 
       
 
-      <td>1.177
</td>
+      <td>1.136
</td>
 
       
 
@@ -1649,151 +1640,144 @@ <h4> Subgroup Disparity Test </h4>
 
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 ' alt='Mean Squared Error across race subgroups' />
 
     </div>
@@ -1906,7 +1890,7 @@ <h4> Subgroup Disparity Test </h4>
 
       
 
-      <td>1.045
</td>
+      <td>1.023
</td>
 
       
 
@@ -1956,137 +1940,122 @@ <h4> Subgroup Disparity Test </h4>
 
     <div class="img-item">
 
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 ' alt='Mean Squared Error across sex subgroups' />
 
     </div>
diff --git a/examples/speech_to_text_example.ipynb b/examples/speech_to_text_example.ipynb
index b3bfd9c..b176f85 100644
--- a/examples/speech_to_text_example.ipynb
+++ b/examples/speech_to_text_example.ipynb
@@ -88,36 +88,36 @@
        "      <th>0</th>\n",
        "      <td>F</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>80</td>\n",
+       "      <td>77</td>\n",
        "      <td>60</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>F</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>89</td>\n",
+       "      <td>84</td>\n",
        "      <td>64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>M</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>82</td>\n",
+       "      <td>80</td>\n",
        "      <td>53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>F</td>\n",
        "      <td>INDIAN</td>\n",
-       "      <td>85</td>\n",
+       "      <td>80</td>\n",
        "      <td>53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>M</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>73</td>\n",
-       "      <td>47</td>\n",
+       "      <td>70</td>\n",
+       "      <td>45</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -130,35 +130,35 @@
        "      <th>293</th>\n",
        "      <td>M</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>80</td>\n",
+       "      <td>74</td>\n",
        "      <td>50</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>294</th>\n",
        "      <td>M</td>\n",
        "      <td>MALAY</td>\n",
-       "      <td>72</td>\n",
+       "      <td>68</td>\n",
        "      <td>52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>295</th>\n",
        "      <td>F</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>85</td>\n",
-       "      <td>60</td>\n",
+       "      <td>82</td>\n",
+       "      <td>58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>296</th>\n",
        "      <td>F</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>82</td>\n",
+       "      <td>79</td>\n",
        "      <td>52</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>297</th>\n",
        "      <td>M</td>\n",
        "      <td>CHINESE</td>\n",
-       "      <td>85</td>\n",
+       "      <td>82</td>\n",
        "      <td>57</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
@@ -168,17 +168,17 @@
       ],
       "text/plain": [
        "    sex     race  truth  prediction\n",
-       "0     F  CHINESE     80          60\n",
-       "1     F  CHINESE     89          64\n",
-       "2     M  CHINESE     82          53\n",
-       "3     F   INDIAN     85          53\n",
-       "4     M  CHINESE     73          47\n",
+       "0     F  CHINESE     77          60\n",
+       "1     F  CHINESE     84          64\n",
+       "2     M  CHINESE     80          53\n",
+       "3     F   INDIAN     80          53\n",
+       "4     M  CHINESE     70          45\n",
        "..   ..      ...    ...         ...\n",
-       "293   M  CHINESE     80          50\n",
-       "294   M    MALAY     72          52\n",
-       "295   F  CHINESE     85          60\n",
-       "296   F  CHINESE     82          52\n",
-       "297   M  CHINESE     85          57\n",
+       "293   M  CHINESE     74          50\n",
+       "294   M    MALAY     68          52\n",
+       "295   F  CHINESE     82          58\n",
+       "296   F  CHINESE     79          52\n",
+       "297   M  CHINESE     82          57\n",
        "\n",
        "[298 rows x 4 columns]"
       ]
@@ -206,7 +206,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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ynOaitwO8M6dOYva1S3cYNmrt38LMqZMKO6bT+CQ6BElt4eX3K7YBMLMNZvZwcaY5TnNQCvCWLs6lAC9QmFMo1euzjJwsVLtDWAQYEDeryIDxhVjkOE1GtQBvkRfoafuPcgfgZCLRIZjZuN40xHGaFQ/wOo1CGmHarWZ2VFdljuPEM3JwK2tiLv4jB7fmEltwAZqTF4mzjCTtImkosKekIZLawmMsMLLXLHScBmfm1Em09m/Zoay1fwtH7j2sx+IxF6A5eVJt2ukngIXA3kTxhNLjOuDHxZvmOM3BtP1Hcd5x+zFqcCsCRg1u5bzj9uP2R9b1WDzmAjQnT6rFEH4A/EDSp83sR71ok+M0HXEB3s9dtSR23yyxBY9POHmSRqm8TdLg0kYYPvpUcSY5Tt8gD/GYC9CcPEnjEE4zs42ljZD19LSuPiRpkqQlZY/nJX02xCFukfRoeB7SA/sdp2FJii1kEY/lUUeJeYvXMOX82xg36wamnH+bxyH6IGkcQr/yDKeSWoABXX3IzJaZ2WQzm0yU5mIT8BtgFnCrmU0Ebg3bjtPnSIotZJkhlEcd4MFpJ0JR3roqO0hzgLHAhUSCtE8Cq83s86kPIr0LOMfMpkhaBhxhZmsl7QXcYWZV/850dHTYwoUL0x7OcZyMTDn/ttipsaMGt3L3rHfUwCInDyQtMrOOtPunyWX0RaIZR/9KpFq+GfhZRrs+DMwNr0eY2VqA4BSGx31A0unA6QDt7e0ZD+c4ThY8OO1ACocQkthdAtxmZpnnskkaABwLzM7yOTO7CLgIojuErMd1HCc91cRzTt8hjVL5WGAOUdxgnKTJwNfM7NiUx3gPcL+ZPRW2n5K0V9mQ0dPdsNtxuiSrgrdIxW9edRdlo2dHdSDdkNE5wMHAHQBmtiSoldMyg9eHiwCuB04Gzg/P12Woy3FSkTXDaJEZSfOqu0gbPTuqA+mCygvM7BBJi81s/1D2oJm9ucvKpYHAamC8mT0XyoYCVwPtwCpgupltqFaPB5WdrGQNkhYZVM2rbg/8OlkpIqj8kKSPAC2SJgJnAn9MU7mZbQKGVpStBzwxnlMoWYOkRQZV86rbA79O0aTRIXwa2Ad4BbgSeA74bIE2OU6PyargLVLxm1fdrkpOJg9RnQvzunAIQYR2vZl92cwOCo+zzezlXrLPcbpFVgVvnorfouou0sZGJg9RnQvzIqo6BDPbCmyStEcv2eM4uZBVwZuX4rfIuou0sZHJI+OrZ42NSBNUvho4FLgFeKlUbmZnFmva63hQ2XGcJMbNuoG4q5iAx89/b6/VUY8UEVS+ITwcx3HqjjxEdS7Mi6jqEEIM4Z/N7OhessdxmpJaLHPZV5bWzENU58K8iKoOwcy2StokaY+SjsBxnGwUKSirp2PWijxEdS7Mi/AYguMUTC0EZS5ic8BjCI5Td9RCUOYiNqc7pMl2emnIWPrGULTMzLYUa5bjFEeWsfWz5y1l7oLVbDWjRWLGIaM5d9p+mY7XnYBlT8f/eyNI2ldiFH2JLpXKko4AHgV+DPwE+IuktxVrluMUQxYB0tnzlnL5/FVsDcOqW824fP4qzp63NNMxswrK8hBJFS1icyFXc5ImdcV3gHeZ2dvN7G3AVOB7xZrlOMWQRYA0d8Hq2DqSypPIKijLQyRVtIjNhVzNSZoYQv/yhXHM7C+S+hdok+MURpax9a0JEy6Syqsxbf9RqS/GeY3/ZzlmVjxG0ZykuUNYKOnnko4Ij4uBRUUb5jhFkCVBXIsUu29SeV40QhK7RrDRyU6aO4R/Bc4gSnst4E6iWILj1DVxQc8sAqQZh4zm8vmrYsuTAqpZA61xQetGEElltdED0I1BGh3CIODlkOiupF7eOax10Cu4DsHJSqUwC6IL1nnHRTOEejLLqGNMW2zdxx84iv9ZtCb2mHH1l4LWlZx0aDsdY9rq/gKa9iJf7VzUW5uajaw6hDQOYT5wtJm9GLZ3BW42s7f2yNIMuENwslKLFdBapNj4QtIxJ8y+MXb/FonHzjumRzbWEy6Sqx1ZHUKaGMIuJWcAEF4P7I5xjtNb1GIFtKRgc9b9uxO0rmc8AN04pHEIL0k6oLQh6UDAz6RT19RiBbSkYHPW/YsOWvc2HoBuHNIElT8LXCPpybC9F3BCYRY5DU29BA9nTp3EzF8/wJatr//b7t+izIHZLEHfpBjCzKmTYvulO0HreiKtjY0QJHci0qSuuE/S3sAkollGj3jqCieOusuwWTnyknEkpjLoW1IqA5x33H6xF8O4YDAQ2y/HHziKln5i67bXDWvpp8T9oX4ylWY5155JtHHoMqhcD3hQuTGop+BhHrbkFfTNGoTOGpyuBfV0rp1kiggqO04q6il4mIcteQV98woq11MQtp7OtZMfaWIITh8my1h2PS1DWM2WtBlMq/2Dz5IFNcmWrHcIIwe3xp4PyDYck4eorp7OtZMfiXcIkg6o9uhNI53akDWjZdEZNrOQZMvYoa2pM5jOOGR0bN3jhw3MlAU1yZYZh4zOVH7k3sM6nY+Z1zzAzF8/kPocJZ3Ts+ctbdhz7eRHtSGj74THj4EFwEXAxeH1D4s3zak1WTNaFp1hMwtJtsxf/mzs/nEZTM+dth8nHdq+fRpoi8RJh7azfF28SD8pC2qSLedO2y9T+e2PrOt0PrZssx1mUkH1c5R0TucuWN2w59rJjzRK5V8B3zCzpWF7X+DfzeyU4s2L8KBybRg364bYiTkCHj//vb1tTi6MnZW8+N+KlG3Ko47ukHQ+4kg6R1nqqFaP0xgUEVTeu+QMAMzsIWByN2xzGoxmFBTlIQartyyoWfbNS1TnNCdpgsoPS/oZcDnRTO6TgIcLtcqpC/IUFOUhtMqjjqxiMOgcsO0NQVlSptazrlrCtop9+7doh2Gjaudo5tRJzLzmAbaUaR/69xMnHDw6k6iuO0Frp/5JM2S0C1EK7NKymXcC/2VmLxds23Z8yKh25HUh72m2yzwzZqbNYNq/n0B0utge0L4Hdz+2oVO9Uya0cf+q53psY1Jbqx13xfrNqc7RvMVrYhXccz74FqCz8wMy9btnNq0vcs92GiptBdrLV07rTdwhNDZ5iJiKFkIl1Z8HWW3MaksWoVzWfix6f6dYco8hSDoWWAL8LmxPlnR9ty10+hx5iJiKFkIVKajKWnfW/bMI5bL2Y9HlTn2RJoZwDnAwcAeAmS2RNLZAm5wGIIu4qZqIKW7/hSs3dBrSyVMIlcXGPMhqY1ZbkgLCWc9FFlvy2t9JphaxmDSzjF4zs+cKtcJpKLKKm47ce1hqodVZVy+JFX2NHdqaixAqyfaxQxNm3/Tb8WLb2r+FicMHxe47cfigXGxMEn1NmdAWu3+cgC6pnQMHxP/kk9qfVYDmgrV8yCoKzYs0DuEhSR8BWiRNlPQj4I+FWuXUNVnFTbc/si610GpbwujH/OXP5iKESrI9SbC22847dTrmplcr5/lEbHp1Wy42Jom+rjjtsFihXFzKjKR2Pvr0S7HHTGp/VgGaC9byIasoNC/SzDIaCHwZeBeRTuUm4Os+y6jvkpe4KWs9eYi+8rC9EQR7WdsJxYrqnGzk9R3LPahsZpvM7MvAUcCRZvbl3nQGTv2Rl7gpy7hyXqKvPGxvBMFeVluabZW2RqdW37E0dwgHAf8N7BaKngM+amaLuqxcGgz8DNiXSNT2UWAZcBUwFlgBfMjM4u9XA36HUDuSxFpJK4bFCba+f8JkIH6O+1lXL0kcJirnpEPbYxefyZqlM2mefNJqZ28YsssOwyxTJrQxvaM9ca59XDuz2liNtOI5IFZvcPDYIbFahqShJ6c25KXnyF2HIOlB4AwzuytsHw78xMzenMKYS4G7zOxnkgYAA4EvARvM7HxJs4AhZvbFavW4Q6gN1b6U0PkidM3CVbEXm4nDB/HEsy93ebFNop/gI4e0x16wky7k1X44aWdIDRzQL9a+klPI4iiz2phkd1rx3PEHjuKqe1d3UiTPmf6W2Flc7gzqjzz+RBThEO42syldlcV8bnfgAWC8lR1E0jLgCDNbK2kv4A4zqzoFwR1CbcgqMqqW9K2n1GJ1saxJ7LKujFakYK0RVl1ziierQ0jUIZSteXCvpJ8Cc4mGfU4gaBK6YDywDviFpLcAi4DPACPMbC1AcArDE45/OnA6QHt7e6rGOPlSTyKjRlhdLOvKaEUK1uqpX5zGoZow7TsV2+eUvU4zgWEn4ADg02a2QNIPgFlpDTOzi4jWYKCjo6P+F35uQupJZNSd1cV6m6wroxUpWKunfnEah0SHYGZH9rDuJ4AnzGxB2P41kUN4StJeZUNGT/fwOE5BVMt2euLF9+wQL5gyoY0pE9oSYwgrntnUaTx77J4DU8UQStk4r7pvdacg6QkHxZfnIYRKas+UCW2xCfJmTp3E5695gK1l7WzpJ2ZUySSahbjzkTWG0OgCsUbIpNoINiZRbcjoJDO7XNJZce+b2XerVWxmf5O0WtKkkBTvKODP4XEycH54vq7b1juFUvoSpwke3/3YhkQF7yHjh7JifcUqY4rK4xxCP1UI1EozIiv/8FoX5T1kekd7rEMAdphNVVJTL1i+fgdnAGzfPu+4/Xp8kUg6H0llV91XsYJbg88srQyql9S7QN1ccBvBxmokBpUlfcLMfirpnLj3zeyrXVYuTSaadjoAWA6cSqR9uBpoB1YB080s/lcX8KByfZE1eJx1uCePOvIInuaVATVLNtK8aMaso43QpnqzMbegcnAGLcDzZva97hhjZkuAOGOO6k59TmOSNSCcRx15BE/zCsBmaWde1NOEgLxohDY1go3VqJrt1My2hvTX3XIITuNQ5LhnLe4QRg5ujR3nzzLfPq8MqC1Sr48r19OEgLxohDY1go3VSJPc7o+SLpD0j5IOKD0Kt8zpNbJmVkyKFYzYbUBspstDxw+J3X/8sIGpbRyUkKUzqY6BA/rFZk09e97S2P3jOHLvYbHl1bKdxnHo+CG9nrmyGbOONkKbGsHGaqRxCG8F9gG+RjQV9TvAt4s0yuldsmZWTMr2uVNLS2ymyxXr4/9lL1+3KbY8judf2RpbnlRH0uyluQtWx5bHcfsj62LLN726LTbr6C1nHRFbvmL95l7PXNmMWUcboU2NYGM1Ui2hWWs8qFwsWTMr5rV/rUib1TOvjJONkB3VaU6KWELzmyFJXWl7iKRzu2mfU4dkzayYV3keGTaz1pFl/7wyTjZCdlTHgXS5jBab2f4VZfebWa/FEfwOIT/iAq0dY9oyJbGD+Eyacz4YnzitY0xb7P4nHDQ6NjtqHEkJ8o4/cBRXzl9F+SBWP+CwBFFZlqypR+49LDEpXVy/TNt/VOb+LXIooehAdiMLsPoKRWU7PcjMXgnbrcBCM9unR5ZmwB1CPpw9b2nsBTjpIgnJ2TvjVLAHj4tPrTxlQhv3Pv5sp/3bBvXnqRde7dLufoLvfmgy0PkivHDlhtg2TZnQxj3LN+wgcOtO1tTjDxzF7Y+sS9UvB7TvkdkJFUVe6ZNrVb+TD0U4hC8AxwK/4PU1Da43s2/1xNAsuEPIhwmzb0ycuhknnMqavbNIkoQ9SW1KIg+BW1bBWjMK0+pNgOXEk5swrYSZfSvcJRxNFAf7upnd1AMbnRqRl7irnoRWWW3Jow+yiozqqb/yEkg1ugDLiadLhyBpEHCzmf1O0iRgkqT+ZralePPql3oaP01rS7V/x3F1ZM3eWSRJQrOstmS9Qxg4oGX7XUjpmFkFa9UC2Xl8j7Kcu7wC2Y0uwHLiSaNDuBPYRdIo4P+I8hFdUqRR9U5WIVe92DLjkNGxdSQJpwYmiMGSRGK779ySqXynDBOEkoRm/RO+wbu0xFeeJGRLKn/p1a2djpnULyN2GxBbniTMy+N7lFTH2KHxF+YksV1WGl2A5cSTxiHIzDYBxwE/MrMPAG8q1qz6JquQq15sOXfafpmEU0niriSRWNby1zLcZCTZ8vLW+EqSypOEbFlEckm2PPNi/E1zkjAvj+9RUh3zl8cvU54ktstKowuwnHi6HDICJOkw4ETgYxk+17TU0/hpVlvOnbZfp3w+4wpc+rLeyCPRXta6s56jLN+joldpq8a0/Ue5A2gy0twhfAaYDfzGzP4kaTxwe7Fm1Tf1JDTKw5a+NO6bNJ5fpEguLyFfln2z2uI4kMIhmNmdZnasmf1n2F5uZmcWb1r9UvT46bzFa5hy/m2Mm3UDU86/bfuYclx5NVuS6knbnikT2mL3T0riNmVCW7SCVxn9+ymxnrj9+xGJ1tLakjRuP3H4ICqqpp+iOEqcjTMOGd2pD1oqKyizO66/4uqodi7y+B4l1VHNFif5N9bXSZO64o2SLpJ0s6TbSo/eMK5eKXL8NClIePa8pbHlQKwtQOqAZVJ7rjjtsNiYwxlHTuz0xekHjBu2a+dVubr6413xfktQMFfaMr2jvdMFuqWfeOc+f5dYdcXiZWwzWLB8fayNHWPaOvXBd6a/Jbb9V5x2WGx/nTttv0znAuLPXZbvUdK5S7LFh3jqa1JIvZFGmPYAcCGwCNgevTKzRcWa9jp9SZiWVQyWJAQqUjhUtGAtixisyGPmhYu46ou+dD5yF6YBr5nZf/XAJicDeQUJiwx8Fy1YyyIGK/KYeVFPkxAcPx/VSBNU/l9Jn5K0l6S20qNwy/ooeQUJiwx8F5m9NKn+WhwzL+ppEoLj56MaaRzCycBM4I9Ew0aLgL4xflMD8goSFhn4TqojSfhWLaic1sZq/RIXJK62qllvB1u7cy486FkcLqpLJk0uo3G9YYgTUQr6xaUzyJIxs1o9PeWahfEpq69PuGg9/cIr9BOdMo9O72hnekc6G5PaA3DVfRWroAnOOHIi1yxctUP20SkT2rjitMN6Pe1I1nNRmUm0PAjtQeGeU+Rvo9FJtWKapH2J1Mm7lMrM7JcF2rUDfSmo3AiMzUnIVmSAu5EDhM3YJqc25B5UlnQOcASRQ7gReA/wB6DXHILTnBQZ4G7kAGEztslpDNLMMvog8BZgsZmdKmkE8LNizXKKIi5j6LnT9qtJ9taRg1s58eJ7Og3rTO9oT21LtaybWdsU1zePr3sxdtgpiTz60TOJOrUiTVB5s5ltA16TtDvwNDC+WLOcIiitmFaZvfPEi+/JJNRJChInkTQP6LWtWzutMHb3Yxs46+olqW1Jyt45dmhrpjYl9U2cfSdefE9sHXkJnjzo6dSKNA5hoaTBwMVEM4zuB+4t0iinGOYuWB1bfvdjGzJl3bzitMM6OYVqTiIpSpW0fGalwriaLUnZO+cvfzZTm5L6Jo64ZTIhvyy4nknUqRVVh4wkCTjPzDYCF0r6HbC7mT3YG8Y5+ZJVxFVtzDpu2CSvYHMWW/IS8uUhcMtz7N8ziTq1oOodgkVTkOaVba9wZ9C4ZBVx1dOYdVYxUVYhXx4CNxc8OY1OmqDyfEkHmdl9hVvjAMUtzznjkNFcPr+zhmDKhDbuX/XcDsMdpTHrLEHoicMHxS4eM2K3Aax74VW2lZX1AyYk7C92HGbq36LtGUMrjzlz6qQd5uyXbD/+wFFcdd9qtpQtlFOqJ0vfxDFlQluiLTOveYAtZWNe/fslH7NW1NPyr059kSaGcCRwj6THJD0oaakkv0soiFpkYhw3bNfYMeuFKzdkCkKfceTETgrhicMH8c59/m4HZwCwDRi+286dUlSLmCC0wcKVGzJlDO0Y09Y5eFFlVOjxdS920UuvM27YrrG2LFy5IXu2117GM3061UiT7XRMXLmZrSzEohj6kjCtSFFSabH4SlokHjvvmNT7J5FkY9Z64ig622uW+EeSLVltrAUueutbZBWmpblDONfMVpY/gHO7b6JTjSJFSVmXj8wrCN2oy1NmtaU3lq3sKS56c6qRJoawT/mGpBbgwGLMaRyKGoctUpRU7Z9tlv2TSBKD5bFmQVIdScesVT8m2VgvuOjNqUbiHYKk2ZJeAN4s6fnweIFImHZdr1lYhxQ5DlukKGnPXftnKk/KXpqUSTRJDJZU/y4t6QbYW/u3cOj4IZmOOXZo/AUuSciWVmxXzZZDxw+pe0GZi96caiQ6BDM7z8x2A+aY2e7hsZuZDTWz2b1oY92RlwApjiJFSUlCsKTyc6ftF7uE5KZXK0PEEUlisKT6X94af9fQT3Rq/4r18UMaScecv/zZ2P2ThGxJYrvvnzA5tS0r1m+ue0GZi96caqTKdlpr6i2oPG7WDbETVgQ8fv57e9uc1FQLnK7IYHdS+/Ok0p68jpnHOWrU8+/0PYoIKjsV9HUBUtGrl8XVk/WYWYVpWejr599pXgp1CJJWBN3CEkkLQ1mbpFskPRqe4wdk65iix2GzrpaVdv9qK5dlIa8V05JiEYeOH9KpPTOnTuqkWegnEleSy7rCXBZ8HN5pVrp0CJImSNo5vD5C0pkh2V1ajjSzyWW3LbOAW81sInBr2G4oihyHzRqwzrL/9I72WN3U9I72TDZmXTENiI1F3HLWEZ3KS6rpyvZcs3BVp6R3pe24c3HutP0KO0c+Du80K2mEaUuADmAscBNwPTDJzDormTp/dgXQYWbPlJUtA44ws7WS9gLuMLOqf63qLYZQJFmFQ1n2z0uU1J0kdmljFEk2JpEkqnMcp5gYwjYzew34APB9M/scsFfK+g24WdIiSaeHshFmthYgPA+P+6Ck0yUtlLRw3br4mSHNSF5Cq7jyRhAlZbUlD9Gb4zgRaRzCFkkzgJOB34ay+InlnZliZgcQLbt5hqS3pTXMzC4ysw4z6xg2LH7ueDOSNWCZpbwRgqFZbckrkO04TjqHcCpwGPANM3tc0jjg8jSVm9mT4flp4DfAwcBTYaiI8Px0dwzPm6yB3KLIGrCcOXVSbFwgbv/uBEPj+iUpSLz7zi2x5aXsoGn6d+bUSfSviB7376fEYyYFsvPi7HlLmTD7RsbOuoEJs2/k7HlRQr16+b44Tp506RDM7M9mdqaZzQ3bj5vZ+V19TtIgSbuVXgPvAh4iikGcHHY7mTpQPddTBsisActrFq6KTeoZF/jNWndSv4wbtmusE/ratP1ixV3TO9qz9W9M5dM72mMD0+dO2y++jhzIa8lRx2kUEoPKkpZSJWGwmb25asXSeKK7AohyJl1pZt+QNBS4GmgHVgHTzSx+TcJA0UHlRs4AmZfYLI6kfiky82g9nYu8sr06Tq3IGlSultzufeH5jPB8WXg+EdjUVcVmthx4S0z5euCotAb2Bo0QbK0FeS1P2aiB7zyXHHWcRiDRIZTWO5A0xcymlL01S9LdwNeKNq638AyQ8ST1S7WsnnErrGXp32r7Jq3eVhTdyfaaF76qmVML0gSVB0k6vLQh6a1AvMS0QWlk5Wle6uM4kvolSQU8dmhr7Jj7a1t3TD5XIi4jadIxk+ouBXmLIClgPWK3AbHlSRlWs1JPMS2nb5HGIXwU+HFIQ/E48JNQ1jQ0svI0KUvnFacd1uO6k/olSQWclGE0Kdtp3P5Jx0yqe+6C1d1sXdckZXt95sUtsfsn2ZiVIrPpOk41qiqVw2I4Z5rZ9yTtHvZ/rtesC/QlpXIjU6SCucjgeVaKtsWzqTp5katS2cy2Au8Pr5+vhTNwGoesIrEs+2fNalokRdvSCAJCpzlJs4Tm3ZIuAK4CXioVmtn9hVlVRxQZ3Mtad9L+ediYVEeWQO6MQ0Zz+fzO+oeJwwfx6NMvxe6flqS6ixam1cKWmVMnMfvapTsMGzVKTMtpbNI4hLeG5/JZRQY0/YTrUnCv9MMsBfeAHjuFrHUn7b9w5Qb+Z9GaHtmYVPc1C1dx92OvS0RKgVwg1imUyiodCBDrELKQVHeRs4yS6BjTxtx7V7O1LP1qSz/RMabngXx4/bz5LCOnt/EV06pQpEgqr6ymWUViWWxJImuG0SSBV6NmKq0n8ZzjVCP3bKeS9pD03VLmUUnfkbRHz8xsDIoUSeWV1TSrSKyn+1Y7Ztb9GzVTaT2J5xwnT9JMO/1v4AXgQ+HxPPCLIo2qF4oM7uWV1TSPpSK7k2E0S3K3egoIQ88T03nQ12lW0jiECWZ2jpktD4+vAuOLNqweKFKw1p2spkUtFZlUd5K47dDxQzIJp5KCrbUICOch+mpkIaPjVCONQ9hcoVSeAvSJe+MiBWtZ684qEstiY1LdV5x2WKwwa8X6zZmEU0kCr1oEhPMQfTWykNFxqpFmCc3JwKXAHkTamA3AyWb2YOHWBVyYVl80snCqkW13nKzkme0UADNbArwlKJUxs+e7b57TDDRyMsBGtt1xiqZLhyDpMWA+cBdwJ/Dnoo1ystHbmTFnTp3EzGseYEvZPPz+/cTMqZPqKktnnC0u+nKcZNLEEN4E/BQYCnxb0nJJv+niM04vUbPMmDGrmi1cuaFusnQm9Qvg4/+Ok0AapfJWYEt43gY8RZ2sg+xUD5IWdZGbc9MytmzdcSR+y1bbriLuTVuSqNYvd896hzsAx4khjUN4HlgKfBe4OKx45tQJtRBJFSmSywsXjzlOdtIMGc0gih18CviVpK9KqqslMPsytRBJFSmSywsXjzlOdrp0CGZ2nZnNBD4B3AicAvy2YLuclGQVSfVUpVvtmHmI5PLCxWOOk500s4z+B5gM/JVoptG/AAuKNctJS5bMmHllb612zI4xbXUxy8gzhjpOdtII0w4C7g+L5dQEF6blg2fpdJy+RRHCtPt6ZpJTL3ig1XGcaqSZZeQ0CXmqdPNavS3L/vUkenOcZiTRIUiaYmZ3S9rZzF7pTaOcYshLpZvX6m1ZYhpFrl7nOE5EtVlGPwzP9/SGIU7x5JWlM0n0NXfB6kyZRLNkHs0jS6njONWpNmS0RdIvgFGSflj5ppmdWZxZTlFM239Uj/9R5yVMy1Lu8Q/HKZ5qdwjvA24CXgYWxTycPkpewrQs5S40c5ziSXQIZvaMmf0KONbMLq189KKNTp2RlzAti3jMhWaOUzxpZhmtD9lNpwAG/AH4jJk9UahlTt2SlzAti3jMhWaOUzxphGm3AFcCl4Wik4ATzeydBdu2HRemOY7jZCerMC1NcrvhZvYLM3stPC4BhnXbQsdxHKcuSTNktE7SScDcsD0D6DMpsF0M5ThOXyHNHcJHgQ8BfwPWAh8MZU1PzVYjcxzHqQFpchmtAo7tBVvqjlqsRuY4jlMr0twh9FlcDOU4Tl/CHUIVXAzlOE5fonCHIKlF0mJJvw3bbZJukfRoeB5StA3dxcVQjuP0Jbp0CJLOLnu9czeO8Rng4bLtWcCtZjYRuDVs1yV5JYNzHMdpBKqlv/4C0ZKZHwTODcX3AAekrVzSG4D3At8AzgrF7weOCK8vBe4AvpjB5l4lj2RwjuM4jUC1O4RlwHRgvKS7JF0EDJWUZbzk+8AXgG1lZSPMbC1AeB4e90FJp0taKGnhunXrMhzScRzH6Q7VHMKzwJeAvxL9oy+lwJ4l6Y9dVSzpfcDTZtatzKhmdpGZdZhZx7BhLox2HMcpmmo6hHcD5wATgO8CDwAvmdmpKeueAhwr6RhgF2B3SZcDT0nay8zWStoLeLr75juO4zh5US399ZfM7ChgBXA5kfMYJukPkv63q4rNbLaZvcHMxgIfBm4zs5OA64GTw24nA9f1rAmO4zhOHqTJZXSTmd0H3CfpX83scEl79uCY5wNXS/oYsIooTuE4juPUmDSpK75QtnlKKHsmy0HM7A6i2USY2XrgqCyfdxzHcYonkzDNzB4oyhDHcRyntnjqCsdxHAdwh+A4juME3CE4juM4gDsEx3EcJ+AOwXEcxwHcITiO4zgBdwiO4zgO4A7BcRzHCbhDcBzHcQB3CI7jOE7AHYLjOI4DuENwHMdxAu4QHMdxHCDdeggNybzFa5hz0zKe3LiZkYNbmTl1EtP2H1VrsxzHceqWpnQI8xavYfa1S9m8ZSsAazZuZva1SwHcKTiO4yTQlENGc25att0ZlNi8ZStzblpWI4scx3Hqn6Z0CE9u3Jyp3HEcx2lShzBycGumcsdxHKdJHcLMqZNo7d+yQ1lr/xZmTp1UI4scx3Hqn6YMKpcCxz7LyHEcJz1N6RAgcgruABzHcdLTlENGjuM4TnbcITiO4ziAOwTHcRwn4A7BcRzHAdwhOI7jOAGZWa1t6BJJ64CVtbYjB/YEnqm1Eb1EX2lrX2kn9J22NlM7x5jZsLQ7N4RDaBYkLTSzjlrb0Rv0lbb2lXZC32lrX2lnHD5k5DiO4wDuEBzHcZyAO4Te5aJaG9CL9JW29pV2Qt9pa19pZyc8huA4juMAfofgOI7jBNwhOI7jOIA7hMKQNEnSkrLH85I+K6lN0i2SHg3PQ2pta0+R9DlJf5L0kKS5knZpxnYCSPpMaOefJH02lDV8WyX9t6SnJT1UVpbYLkmzJf1V0jJJU2tjdfdIaOv0cE63Seqo2L9h25oVdwgFYWbLzGyymU0GDgQ2Ab8BZgG3mtlE4Naw3bBIGgWcCXSY2b5AC/BhmqydAJL2BU4DDgbeArxP0kSao62XAO+uKIttl6Q3EZ3jfcJnfiKphcbhEjq39SHgOODO8sImaGsm3CH0DkcBj5nZSuD9wKWh/FJgWq2MypGdgFZJOwEDgSdpznb+AzDfzDaZ2WvA74EP0ARtNbM7gQ0VxUntej/wKzN7xcweB/5K5CQbgri2mtnDZrYsZveGbmtW3CH0Dh8G5obXI8xsLUB4Hl4zq3LAzNYA3wZWAWuB58zsZpqsnYGHgLdJGippIHAMMJrmbCskt2sUsLpsvydCWTPSl9rqDqFoJA0AjgWuqbUtRRDGld8PjANGAoMknVRbq4rBzB4G/hO4Bfgd8ADwWk2Nqg2KKWvW+et9qa3uEHqB9wD3m9lTYfspSXsBhOena2ZZPhwNPG5m68xsC3At8Faar50AmNnPzewAM3sb0bDDozRpW0lu1xNEd0Yl3kA0TNiM9KW2ukPoBWbw+nARwPXAyeH1ycB1vW5RvqwCDpU0UJKI4iUP03ztBEDS8PDcThSEnEuTtpXkdl0PfFjSzpLGAROBe2tgX2/Ql9oKZuaPgh5EAdb1wB5lZUOJZmw8Gp7bam1nDu38KvAI0Rj7ZcDOzdjO0Na7gD8TDRcd1SznlMixrQW2EP0r/li1dgFfBh4DlgHvqbX9ObT1A+H1K8BTwE3N0NasD09d4TiO4wA+ZOQ4juME3CE4juM4gDsEx3EcJ+AOwXEcxwHcITiO4zgBdwhO3SHpPElHSJomKVOiOEnDJC2QtFjSPxZg2ymSLsi73iKQdImkD9baDqdxcIfg1COHAAuAtxPN+8/CUcAjZra/mWX9bMPSzBk4nd7DHYJTN0iaI+lB4CDgHuDjwH9J+o+YfcdIulXSg+G5XdJk4FvAMWENitaKz5wv6c/hM98OZZdIulDSXZL+Iul9oXwXSb+QtDTcbRwZY8N7Jd0jaU9J7wqv75d0jaRdwz4rJH01lC+VtHdMPTdKenN4vbjUXklfl/RxRcwJ6zAslXRCeP8ISbdLuhJYGva7ILTxBsqS7MW13XE6UWtlnD/8Uf4gSi38I6A/cHeV/f4XODm8/igwL7w+BbggZv82IqVpSYw5ODxfQpSorh9RWoIngF2AzwO/CPvsTZSiY5dS/UTK1ruAIcCeRHn0B4X9vwj8R3i9Avh0eP0p4Gcxts0CzgB2B+4jqGSB24FJwPFECfVagBHBlr2AI4CXgHFh/+PK9hsJbAQ+mNR2f/ij8uF3CE69sT+whOgi/Ocq+x0GXBleXwYc3kW9zwMvAz+TdBzRgkUlrjazbWb2KLA8HPvwUC9m9giwEnhj2P9Ioov+e83sWeBQ4E3A3ZKWEOX9GVNW/7XheREwNsa2u4C3hWPeAOwa0muPtShH/+HAXDPbalGSxN8T3UUB3GtRnn5CHaX9ngRuS9F2x9nOTrU2wHEAwnDPJUTZJJ8hygOlcIE9zMw2d1FF1RwsZvaapIOJYgwfBv4NeEfCZ434tMcllgPjiRzEwrDvLWY2I2H/V8LzVuJ/c/cBHaHeW4juOE4jciB0YctLMbbvWFC97Y6zHb9DcOoCM1ti0XKjfyH6t30bMNWiZUjjnMEfiS5uACcCf6hWfxjT38PMbgQ+C0wue3u6pH6SJhBd6JcRDQGdGD77RqA9lEN0t3Ac8EtJ+wDzgSmS/j7sPzB8Jm3bXyVahOVDoa67gH/n9YD6ncAJklokDSO6E4jLuHknUWbOlpCu+sgUbXec7fgdglM3hIvds2a2TdLeZlZtyOhM4L8lzQTWAad2Uf1uwHWSdiH6x/25sveWEQ3DjAA+aWYvS/oJcKGkpUSL4JxiZq9EGb6jNbMlnUi08NE/EcUW5kraOdR5NpFzS8tdRNlTN0m6i+hOqeQQfkM0RPYA0R3AF8zsbzEB6t8Q/fNfGo79+xRtd5zteLZTp08j6RLgt2b261rb4ji1xoeMHMdxHMDvEBzHcZyA3yE4juM4gDsEx3EcJ+AOwXEcxwHcITiO4zgBdwiO4zgOAP8fO8cJ+XlZ2bAAAAAASUVORK5CYII=\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -241,7 +241,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -253,7 +253,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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94GTec/oRbHbjlYv1X7nmBhz48rcAcPB3P8PjbvntYv0/W+dxfGDrvQH45Lc/xnp33LRY//kbPJl/fdFeABz+jYNZ487bF+s/a5Nn8JktdwPgyBPey8r3/Hmx/jMevwVfeM7/AeC4rx4w4rE56ckv4JhnvYKV776LI//zfSP6v/b0rfna07dmjT/9nsP/68Mj+o955rac9JQXst7ti/jkSR8f0f+FLXbgjCc8Z8rPvb8+7vPwvOfBj38MBx44op9DD4V58+D00+GDHxzZ//nPw5OeBN/+Nnx8ZH0cfTRstBEcfzwcfvjI/q99DdZeG448srsMd8op8MhHwmGHwQknjOw/88zu58c+BiedtHjfKqvAd77TLR90EJxxxuL9a60FJ57YLb/znXD22Yv3b7ghHHNMt7zffnDBBYv3P/GJcET/mO69N/z854v3z5vXPX4Ar3sdXHvt4v3PfS58uD/mr3kN3Hzz4v1bbQXvfne3vM02cOedi/dvtx287W3d8vDfeTCp33uHDt23we1M4Pcer3xl9zt3n31G9r/rXbD11t3jtt9+I/sPPtjnHjT/3Jvqa+6D7bm32Dnmc69bfig+9wY48itJkqRmpKqW2s7mz59fCxcuXGr7GzL3gJOX+j5b8Lt+NPWx/eiyZtZVh7xiWZeg5cCCfgTlzKERJUkzynPsoSvJeVU1f3i7I7+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGasuKwLkCRJ45t7wMnLuoSHpN9deTPg4ztbrjrkFcu6hBHGDb9JNgL+A3gscB9wRFV9Ksn7gDcCi/pVD6yqU2arUEnLN184ZocvzLNreXxhljS7JjLyew+wf1Wdn2RV4Lwkp/V9n6yqj81eeZIkSdLMGTf8VtX1wPX98h1JLgM2mO3CJEmSpJk2qQ+8JZkLPBM4p2/aN8lFSb6UZI2ZLk6SJEmaSRMOv0keDZwI7FdVtwOHA48H5tGNDH98jNvtnWRhkoWLFi0abRVJkiRpqZhQ+E2yEl3w/UpVfR2gqm6oqnur6j7gC8AWo922qo6oqvlVNX/OnDkzVbckSZI0aeOG3yQBvghcVlWfGGhfb2C1HYBLZr48SZIkaeZM5NsetgT2AC5OckHfdiCwW5J5QAFXAfvMQn2SJEnSjJnItz38CMgoXX6nryRJkh5U/PfGkiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1IyJfM+vNKrH7n7Isi5BkiRpUhz5lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZqy4rAuQJI3tsbsfsqxLkB7SPMfa48ivJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzxg2/STZK8v0klyW5NMlb+/Y1k5yW5Bf9zzVmv1xJkiRp6iYy8nsPsH9VPQX4a+AfkmwGHACcUVWbAmf01yVJkqTl1rjht6qur6rz++U7gMuADYDtgaP61Y4CXj1LNUqSJEkzYlJzfpPMBZ4JnAOsW1XXQxeQgXVmvDpJkiRpBk04/CZ5NHAisF9V3T6J2+2dZGGShYsWLZpKjZIkSdKMmFD4TbISXfD9SlV9vW++Icl6ff96wI2j3baqjqiq+VU1f86cOTNRsyRJkjQlE/m2hwBfBC6rqk8MdH0L2LNf3hP45syXJ0mSJM2cFSewzpbAHsDFSS7o2w4EDgFOSPIG4DfATrNSoSRJkjRDxg2/VfUjIGN0bzWz5UiSJEmzx//wJkmSpGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmjBt+k3wpyY1JLhloe1+S3ya5oL9sO7tlSpIkSdM3kZHfI4GXj9L+yaqa119OmdmyJEmSpJk3bvitqh8AtyyFWiRJkqRZNZ05v/smuaifFrHGWCsl2TvJwiQLFy1aNI3dSZIkSdMz1fB7OPB4YB5wPfDxsVasqiOqan5VzZ8zZ84UdydJkiRN35TCb1XdUFX3VtV9wBeALWa2LEmSJGnmTSn8Jllv4OoOwCVjrStJkiQtL1Ycb4UkxwILgLWTXAu8F1iQZB5QwFXAPrNXoiRJkjQzxg2/VbXbKM1fnIVaJEmSpFnlf3iTJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmSmjFu+E3ypSQ3JrlkoG3NJKcl+UX/c43ZLVOSJEmavomM/B4JvHxY2wHAGVW1KXBGf12SJElaro0bfqvqB8Atw5q3B47ql48CXj2zZUmSJEkzb6pzftetqusB+p/rjLVikr2TLEyycNGiRVPcnSRJkjR9s/6Bt6o6oqrmV9X8OXPmzPbuJEmSpDFNNfzekGQ9gP7njTNXkiRJkjQ7php+vwXs2S/vCXxzZsqRJEmSZs9EvursWOBs4ElJrk3yBuAQ4CVJfgG8pL8uSZIkLddWHG+FqtptjK6tZrgWSZIkaVb5H94kSZLUDMOvJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZqw4nRsnuQq4A7gXuKeq5s9EUZIkSdJsmFb47b24qm6age1IkiRJs8ppD5IkSWrGdMNvAacmOS/J3jNRkCRJkjRbpjvtYcuqui7JOsBpSS6vqh8MrtCH4r0BNt5442nuTpIkSZq6aY38VtV1/c8bgW8AW4yyzhFVNb+q5s+ZM2c6u5MkSZKmZcrhN8mjkqw6tAy8FLhkpgqTJEmSZtp0pj2sC3wjydB2vlpV352RqiRJkqRZMOXwW1VXAs+YwVokSZKkWeVXnUmSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNMPxKkiSpGYZfSZIkNcPwK0mSpGYYfiVJktQMw68kSZKaYfiVJElSMwy/kiRJaobhV5IkSc0w/EqSJKkZhl9JkiQ1w/ArSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+SJElqhuFXkiRJzTD8SpIkqRmGX0mSJDXD8CtJkqRmGH4lSZLUDMOvJEmSmmH4lSRJUjMMv5IkSWqG4VeSJEnNmFb4TfLyJFck+WWSA2aqKEmSJGk2TDn8JlkB+BywDbAZsFuSzWaqMEmSJGmmTWfkdwvgl1V1ZVX9BTgO2H5mypIkSZJm3nTC7wbANQPXr+3bJEmSpOXSitO4bUZpqxErJXsDe/dX/5DkimnsU8uftYGblnURD0X5yLKuQMsRz7NZ4nmmnufYLFnG59gmozVOJ/xeC2w0cH1D4LrhK1XVEcAR09iPlmNJFlbV/GVdh/RQ5nkmzS7PsbZMZ9rDucCmSf4qycOBXYFvzUxZkiRJ0syb8shvVd2TZF/ge8AKwJeq6tIZq0ySJEmaYdOZ9kBVnQKcMkO16MHJKS3S7PM8k2aX51hDUjXiM2qSJEnSQ5L/3liSJEnNMPxKkiSpGYZfjSvJE5OckuSXSS5LckKSdZMsSHLSsHWPTLJjv3xmkvn98lVJThxYb8ckR/bLeyVZlOSCgctmSR6W5NNJLklycZJzk/zVwPYuHlj/00vtAZFmWZK5SS7plxckqSSvHOg/KcmCfvnMJFckuSjJ5Uk+m+QxA+v+Ydi2/ynJXUlWH2hb4j6kh6L+PKskBw20rZ3k7iSfHbbuhUmOHdZ2/+vdKNt+Zr/tl/XXD04e+MbbJJskuXLwXNXSY/h9CEhnVo5lkpWBk4HDq+oJVfUU4HBgzhQ2Nz/JU8foO76q5g1cfgbsAqwPbF5VTwd2AG4buM2LB9b/xynUI03ZbJ53o7gW+Jcl9L+2qjYHNgf+DHxzCevuRvdVlTtMch/SUrcUzrMrge0Gru8ELPbNVUmeQpeXXpjkURPc7m7Aj/qfAAcB2/fbAvgU8O6qum2KdWsaDL8PUv1frJclOQw4H/hikoVJLk3y/oH1np3kx/1frT9JsmqSFZJ8tB9JvSjJPkvY1e7A2VX17aGGqvp+VV0yhbI/Bhw4ifXXA66vqvv6/V5bVbdOYb/SjFiK591wFwK/T/KSJa1UVX8B3g5snOQZo9T/eODRwLt44EV5UvuQZttSPs/uBC5L/y4l3aDLCcPW2R04GjgVeNUE6g+wI7AX8NIkK1fVncA/A4cl2QZYtaq+Mu6DoVkxra860zL3JOBvq+rNSdasqluSrACckWRz4HLgeGCXqjo3yWp0J/obgN9X1bOTPAI4K8mpVfXrUfbxNOC8JdTwgiQXDFzfGDhpjHVPAN6c5Amj9O2S5PkD15/br/+jJC8AzgCOqaqfDqzz/ST39stHVdUnl1CnNFOWxnk3mg/2l9OWtFJV3ZvkQuDJdIF20G7AscAPgSclWaeqbpzsPqSlYGmeZ8cBuyb5HXAv3X+rXX+gfxfgJX1N+9KdQ0uyJfDrqvpVkjOBbYGvV9UpSd4A/Afw/CVtQLPL8PvgdnVV/W+/vHOSvemO6XrAZkDRjZyeC1BVtwMkeSmw+cBcpdWBTYGJvggP+mFV3f+WUfp5vGO4F/go8E7gO8P6jq+qfYe1XZvkScDf9JczkuxUVWf0/S+uKv8Xu5a2ZXLeVdUPk9D/MTiejNG+K7BDVd2X5Ot0b/F+bor7kGbT0jzPvks3LeEGukB9vyTPBhZV1dVJrgW+lGSNcd6F3I0uUNP/3AP4en/9c8AqVXXFEu+9ZpXh98HtjwDpPgT2NuDZVXVrH0BXpnsBHO2LnAO8paq+N4F9XAq8aGbKBbq3jt7JsDlVY6mqP9MF5e8kuQF4Nd0osLSsLI3zbiwfopuXe89YK/SjY08HLhvWvjldCDite1eWh9PNd/zcsE2Muw9pKVhq51lV/SXJecD+wFOBVw507wY8OclV/fXVgNcA/z7atvrz7zXAq5L8S1/PWklWrao7gPv6i5Yh5/w+NKxG94vi90nWBbbp2y8H1u//cqWfD7Ui3b+kflOSlfr2J2bsSfxfBZ6X5BVDDUlenuTpUym0qu4GPgnsN966SZ6VZP1++WF0H+a5eir7lWbBbJ53o6qqU4E1gBHzefttrgR8GLimqi4a1r0b8L6qmttf1gc2SLLJZPYhLWVL6zz7OPCOqrp5qKF/3dmJ7kPXc6tqLrA9I+fLD9oauLCqNupvswlwIt3AjZYTjvw+BFTVhUl+SjeaeiVwVt/+lyS7AJ9JsgrdfKit6f5inQuc30/MX8QYJ2ZV3ZlkO+DQJIcCdwMXAW8F1ppiyV+k+8DNoOFzft9M90vvC/28LYCfAINfPzM45/eiqnr9FOuRJm02z7txfIiR3+bwlSR/Bh4BnE73Aj3crjwQHIZ8o28/ZwL7kJa6pXWeVdWljHxH8oXAb6vqtwNtPwA2S7Jef/3z/WsjwDXAFXTn1aATgTfRvfOp5YD/3liSJEnNcNqDJEmSmuG0BwHQz+Ed/pbMn6vqOcuiHqkFnnfS7PM803BOe5AkSVIznPYgSZKkZhh+JUmS1AzDryRJkpph+JUkSVIzDL+SJElqxv8HBJeBc1T/ZxYAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -272,7 +272,7 @@
     "    #test_desc=\"\",\n",
     "    attr=\"sex\",\n",
     "    metric=\"mae\",\n",
-    "    threshold=30,\n",
+    "    threshold=27,\n",
     ")\n",
     "threshold_test_gender.run(df_test_with_output=output)\n",
     "threshold_test_gender.plot(alpha=0.05)\n",
@@ -280,7 +280,7 @@
     "threshold_test_race = MinMaxMetricThreshold(\n",
     "    attr=\"race\",\n",
     "    metric=\"mae\",\n",
-    "    threshold=30,\n",
+    "    threshold=27,\n",
     ")\n",
     "threshold_test_race.run(df_test_with_output=output)\n",
     "threshold_test_race.plot(alpha=0.05)"
@@ -295,7 +295,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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\n",
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JxgAAUAnGAABQCcYAAFAJxgAAUNX557qAc+2SK//1uS6BO6F3vfm3K/cHJ/fWFz/tXJcAwBHQYwwAAAnGAABQCcYAAFAJxgAAUHn4DoCz5CFVTsZDzJzOnfUhZj3GAACQYAwAAJVgDAAAlWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAJVgDAAAlWAMAADVAYPxzDxoZl4+M785M2+YmSfOzENm5pUz86bt64P32r9gZm6YmTfOzFOOrnwAADgcB+0x/qfVz6y1Prn6c9Ubqiur69Zal1bXbfPNzOOqy6vHV0+tXjIz5x124QAAcJjOGIxn5oHV51bfV7XW+tBa69bq6dVLt2YvrZ6xTT+9unqt9cG11luqG6onHG7ZAABwuA7SY/zo6pbqB2bm12bme2fm/tXD11o3V21fH7a1v7B6x972N27LPsrMPGdmrp+Z62+55ZazehMAAHC2DhKMz6/+fPXda63PqD7QNmziFOYky9ZtFqx11VrrsrXWZRdccMGBigUAgKNykGB8Y3XjWuvV2/zL2wXld8/MI6q2r+/Za3/x3vYXVTcdTrkAAHA0zhiM11rvqt4xM4/dFj2pen11bXXFtuyK6hXb9LXV5TNz75l5VHVp9cuHWjUAAByy8w/Y7u9WPzwz96reXP3NdqH6mpl5dvX26kuq1lqvm5lr2oXnD1fPW2t95NArBwCAQ3SgYLzWem112UlWPekU7V9UvehPXxYAANyx/OU7AABIMAYAgOrgY4zhvyof/6UvPtclAAB3MMEYADh0Ohi4KzKUAgAAEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKASjAEAoBKMAQCgEowBAKC6HcF4Zs6bmV+bmZ/a5h8yM6+cmTdtXx+81/YFM3PDzLxxZp5yFIUDAMBhuj09xl9TvWFv/srqurXWpdV123wz87jq8urx1VOrl8zMeYdTLgAAHI0DBeOZuah6WvW9e4ufXr10m35p9Yy95VevtT641npLdUP1hEOpFgAAjshBe4y/q/oH1R/vLXv4Wuvmqu3rw7blF1bv2Gt347YMAADutM4YjGfmi6r3rLVec8B9zkmWrZPs9zkzc/3MXH/LLbcccNcAAHA0DtJj/DnVX5mZt1ZXV58/Mz9UvXtmHlG1fX3P1v7G6uK97S+qbjpxp2utq9Zal621LrvgggvO4i0AAMDZO2MwXmu9YK110VrrknYP1f3btdaXV9dWV2zNrqhesU1fW10+M/eemUdVl1a/fOiVAwDAITr/LLZ9cXXNzDy7env1JVVrrdfNzDXV66sPV89ba33krCsFAIAjdLuC8VrrVdWrtunfrp50inYvql50lrUBAMAdxl++AwCABGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoBGMAAKgEYwAAqARjAACoDhCMZ+bimfl3M/OGmXndzHzNtvwhM/PKmXnT9vXBe9u8YGZumJk3zsxTjvINAADAYThIj/GHq+evtT6l+uzqeTPzuOrK6rq11qXVddt827rLq8dXT61eMjPnHUXxAABwWM4YjNdaN6+1fnWb/r3qDdWF1dOrl27NXlo9Y5t+enX1WuuDa623VDdUTzjkugEA4FDdrjHGM3NJ9RnVq6uHr7Vurl14rh62NbuwesfeZjduy07c13Nm5vqZuf6WW275U5QOAACH58DBeGYeUP1f1deutd5/uqYnWbZus2Ctq9Zal621LrvgggsOWgYAAByJAwXjmblnu1D8w2utH98Wv3tmHrGtf0T1nm35jdXFe5tfVN10OOUCAMDROMinUkz1fdUb1lr/eG/VtdUV2/QV1Sv2ll8+M/eemUdVl1a/fHglAwDA4Tv/AG0+p/qK6jdm5rXbshdWL66umZlnV2+vvqRqrfW6mbmmen27T7R43lrrI4ddOAAAHKYzBuO11n/s5OOGq550im1eVL3oLOoCAIA7lL98BwAACcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUAnGAABQCcYAAFAJxgAAUB1hMJ6Zp87MG2fmhpm58qiOAwAAh+FIgvHMnFf9H9UXVI+rnjkzjzuKYwEAwGE4qh7jJ1Q3rLXevNb6UHV19fQjOhYAAJy1owrGF1bv2Ju/cVsGAAB3Sucf0X7nJMvWRzWYeU71nG3292fmjUdUC/xpPbR677kugjuf+UfnugK4y/DvKCd1jv8d/cRTrTiqYHxjdfHe/EXVTfsN1lpXVVcd0fHhrM3M9Wuty851HQB3Vf4d5a7mqIZS/Ep16cw8ambuVV1eXXtExwIAgLN2JD3Ga60Pz8zfqX62Oq/6/rXW647iWAAAcBiOaihFa62frn76qPYPdwBDfQDOjn9HuUuZtdaZWwEAwN2cPwkNAAAJxnAbM/ORmXnt3uuSc10TwF3FzKyZ+cG9+fNn5paZ+alzWRccxJGNMYa7sD9Ya336uS4C4C7qA9Wnzsx911p/UP2l6p3nuCY4ED3GAMBh+zfV07bpZ1YvO4e1wIEJxnBb990bRvET57oYgLugq6vLZ+Y+1adVrz7H9cCBGEoBt2UoBcBZWGv9+vZ8xjPz0a3chQjGAMBRuLb6zupY9XHnthQ4GMEYADgK31/97lrrN2bm2DmuBQ5EMAYADt1a68bqn57rOuD28JfvAAAgn0oBAACVYAwAAJVgDAAAlWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAFX9/862TVD3XhCzAAAAAElFTkSuQmCC\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -307,7 +307,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 864x432 with 1 Axes>"
       ]
@@ -832,7 +832,7 @@
        "\n",
        "<div class=\"col card\">\n",
        "\n",
-       "  <h3>MSE - 890.221 (Test Set)</h3>\n",
+       "  <h3>MSE - 712.513 (Test Set)</h3>\n",
        "\n",
        "  \n",
        "\n",
@@ -852,7 +852,7 @@
        "\n",
        "<div class=\"col card\">\n",
        "\n",
-       "  <h3>MAE - 29.047 (Test Set)</h3>\n",
+       "  <h3>MAE - 26.003 (Test Set)</h3>\n",
        "\n",
        "  \n",
        "\n",
@@ -882,263 +882,262 @@
        "\n",
        "    <div class=\"img-item\">\n",
        "\n",
-       "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAYQAAAEWCAYAAABmE+CbAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
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+       "/iJpT0lviT/fJelSSUPiY5ZL+mZcvkTSlIR6rpH0L/Hnu0v9lfRtSR9VxJmK8iUskfS+eP+Rkm6S\n",
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+       "Hf6R6GkK4A6L4uQT11E67nHgxgx9d5zt2KXRAjgOQDz1cz5RFMeniOI4Kb7JHm5mmypUkRqvxcxe\n",
+       "kfQ6IpvD+4FPAUcFzjWSQw6XWEaUu+PVRHG2BFxvZscHjn8pft9C8m/uTqA7rvd6oiePU4iUCBVk\n",
+       "eSFB9u0L0vvuONvhTwpOU2Bm91iUSvRvRP+6bwRmWJRiNEkh/JnoBgdwAvCntPrjOf49zOwa4LNE\n",
+       "gRNLHCepn6SJRDf7pUTTQSfE574aGBOXQ/TUcCzwS0mvAW4Hpkp6VXz8oPicrH1/mSgRynvjum4F\n",
+       "vsg2I/stwPskdUjqInoiSIp0eQtRRMyOOHz0tAx9d5zt8CcFp2mIb3jrzWyrpClmljZ9dBrwv5Jm\n",
+       "EWXb+1CF6ncDrpQ0kOif9+fK9i0lmpLZG/i4mb0o6afA2ZKWECWsOdnMXooic0c5sSWdQJTM6J1E\n",
+       "tob5kgbEdc4lUnBZuZUouulGSbcSPTGVlMIVRNNl9xI9CXzJzP6RYLS+gugJYEnc9h8z9N1xtsOj\n",
+       "pDptjaTzgf9nZpc1WhbHaQZ8+shxHMfpwZ8UHMdxnB78ScFxHMfpwZWC4ziO04MrBcdxHKcHVwqO\n",
+       "4zhOD64UHMdxnB7+fw3bq5A6Kp1AAAAAAElFTkSuQmCC\n",
        "' alt='None' />\n",
        "\n",
        "    </div>\n",
@@ -1198,7 +1197,7 @@
        "    <b> Description: </b>\n",
        "\n",
        "    Test if the mae of the subgroups within sex \n",
-       "is lower than the threshold of 30.\n",
+       "is lower than the threshold of 27.\n",
        "  \n",
        "\n",
        "  </div>\n",
@@ -1211,7 +1210,7 @@
        "\n",
        "    <b> Threshold: </b>\n",
        "\n",
-       "    30 \n",
+       "    27 \n",
        "\n",
        "  </div>\n",
        "\n",
@@ -1274,7 +1273,7 @@
        "\n",
        "      \n",
        "\n",
-       "      <td>29.391</td>\n",
+       "      <td>26.143</td>\n",
        "\n",
        "      \n",
        "\n",
@@ -1301,7 +1300,7 @@
        "\n",
        "      \n",
        "\n",
-       "      <td>28.77</td>\n",
+       "      <td>25.891</td>\n",
        "\n",
        "      \n",
        "\n",
@@ -1358,138 +1357,134 @@
        "\n",
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+       "wHYw7IGrko8neUFVPT/JyUkuTXKHJKdWVTL9GMsFSdLdp1bVg5P8VZI7bse6DXsArupW8zX0kiSX\n",
+       "zmH57CTfWvnyYfsIv1xldPdnqupOSQ5L8twkpyb5ZHffdfGyVXW1JLdN8u0kN0xy3pVZK8Du5kp4\n",
+       "DX1jprD86JWqGXaGYQ9cZVTVzZJ8q7tfl+QFSe6cZE1V3XVu37uqbj8v/nuZeh+OSHJ8Ve29K2oG\n",
+       "2F1cCa+hb0vyf5L804oXDztAzy9XJT+d5C+q6gdJLk/y+CTfT/LSqrp+pvP92Kq6PMlvJzmkuy+r\n",
+       "qvcneVqSp++iugF2B6v6GtrdlyV5fpLMwyhgl/DzxgAADMOwBwAAhmHYA8zmy5hdc9HsI7v747ui\n",
+       "HoA9iddQ9hSGPQAAMAzDHgAAGIbwCwDAMIRfAACGIfwCADAM4RcAgGH8fxUhV4DqMB3wAAAAAElF\n",
        "TkSuQmCC\n",
        "' alt='Mean Absolute Error across sex subgroups' />\n",
        "\n",
@@ -1532,7 +1527,7 @@
        "    <b> Description: </b>\n",
        "\n",
        "    Test if the mae of the subgroups within race \n",
-       "is lower than the threshold of 30.\n",
+       "is lower than the threshold of 27.\n",
        "  \n",
        "\n",
        "  </div>\n",
@@ -1545,7 +1540,7 @@
        "\n",
        "    <b> Threshold: </b>\n",
        "\n",
-       "    30 \n",
+       "    27 \n",
        "\n",
        "  </div>\n",
        "\n",
@@ -1608,7 +1603,7 @@
        "\n",
        "      \n",
        "\n",
-       "      <td>30.475</td>\n",
+       "      <td>27.141</td>\n",
        "\n",
        "      \n",
        "\n",
@@ -1635,7 +1630,7 @@
        "\n",
        "      \n",
        "\n",
-       "      <td>27.99</td>\n",
+       "      <td>25.404</td>\n",
        "\n",
        "      \n",
        "\n",
@@ -1662,7 +1657,7 @@
        "\n",
        "      \n",
        "\n",
-       "      <td>28.68</td>\n",
+       "      <td>25.47</td>\n",
        "\n",
        "      \n",
        "\n",
@@ -1719,163 +1714,159 @@
        "\n",
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-       "PzRTT+L/HPCkJM8da0jynCSPmkuhVXUrcChwwHTLJnlcks366/eg+zDPJXPZrjQCozzuJlVV3wI2\n",
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-       "rPOBn/QT85cyxYFZVTcl2Rn4SJKPALcCZwOvAzaaY8mfovvAzaDxc35fTfdL78h+3hbAj4DB088M\n",
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-       "JJ8GXF5Vlw+0fQfYNsmm/e1P9n8bAS4FLqQ7rgadCLyK7p1PrQb8emNJkiQ1w2kPkiRJaobTHgRA\n",
-       "P4d3/Fsyt1TVE1ZFPVILPO6k0fM403hOe5AkSVIznPYgSZKkZhh+JUmS1AzDryRJkpph+JUkSVIz\n",
-       "DL+SJElqxv8H6i/VME7aXSsAAAAASUVORK5CYII=\n",
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+       "DbAZsFuSzWaqMEmSJGmmTWfkdwvgl1V1ZVX9BTgO2H5mypIkSZJm3nTC7wbANQPXr+3bJEmSpOXS\n",
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+       "51hDUjXiM2qSJEnSQ5L/3liSJEnNMPxKkiSpGYZfjSvJE5OckuSXSS5LckKSdZMsSHLSsHWPTLJj\n",
+       "v3xmkvn98lVJThxYb8ckR/bLeyVZlOSCgctmSR6W5NNJLklycZJzk/zVwPYuHlj/00vtAZFmWZK5\n",
+       "SS7plxckqSSvHOg/KcmCfvnMJFckuSjJ5Uk+m+QxA+v+Ydi2/ynJXUlWH2hb4j6kh6L+PKskBw20\n",
+       "rZ3k7iSfHbbuhUmOHdZ2/+vdKNt+Zr/tl/XXD04e+MbbJJskuXLwXNXSY/h9CEhnVo5lkpWBk4HD\n",
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+       "kjOBbYGvV9UpSd4A/Afw/CVtQLPL8PvgdnVV/W+/vHOSvemO6XrAZkDRjZyeC1BVtwMkeSmw+cBc\n",
+       "pdWBTYGJvggP+mFV3f+WUfp5vGO4F/go8E7gO8P6jq+qfYe1XZvkScDf9JczkuxUVWf0/S+uKv8X\n",
+       "u5a2ZXLeVdUPk9D/MTiejNG+K7BDVd2X5Ot0b/F+bor7kGbT0jzPvks3LeEGukB9vyTPBhZV1dVJ\n",
+       "rgW+lGSNcd6F3I0uUNP/3AP4en/9c8AqVXXFEu+9ZpXh98HtjwDpPgT2NuDZVXVrH0BXpnsBHO2L\n",
+       "nAO8paq+N4F9XAq8aGbKBbq3jt7JsDlVY6mqP9MF5e8kuQF4Nd0osLSsLI3zbiwfopuXe89YK/Sj\n",
+       "Y08HLhvWvjldCDite1eWh9PNd/zcsE2Muw9pKVhq51lV/SXJecD+wFOBVw507wY8OclV/fXVgNcA\n",
+       "/z7atvrz7zXAq5L8S1/PWklWrao7gPv6i5Yh5/w+NKxG94vi90nWBbbp2y8H1u//cqWfD7Ui3b+k\n",
+       "flOSlfr2J2bsSfxfBZ6X5BVDDUlenuTpUym0qu4GPgnsN966SZ6VZP1++WF0H+a5eir7lWbBbJ53\n",
+       "o6qqU4E1gBHzefttrgR8GLimqi4a1r0b8L6qmttf1gc2SLLJZPYhLWVL6zz7OPCOqrp5qKF/3dmJ\n",
+       "7kPXc6tqLrA9I+fLD9oauLCqNupvswlwIt3AjZYTjvw+BFTVhUl+SjeaeiVwVt/+lyS7AJ9Jsgrd\n",
+       "fKit6f5inQuc30/MX8QYJ2ZV3ZlkO+DQJIcCdwMXAW8F1ppiyV+k+8DNoOFzft9M90vvC/28LYCf\n",
+       "AINfPzM45/eiqnr9FOuRJm02z7txfIiR3+bwlSR/Bh4BnE73Aj3crjwQHIZ8o28/ZwL7kJa6pXWe\n",
+       "VdWljHxH8oXAb6vqtwNtPwA2S7Jef/3z/WsjwDXAFXTn1aATgTfRvfOp5YD/3liSJEnNcNqDJEmS\n",
+       "muG0BwHQz+Ed/pbMn6vqOcuiHqkFnnfS7PM803BOe5AkSVIznPYgSZKkZhh+JUmS1AzDryRJkpph\n",
+       "+JUkSVIzDL+SJElqxv8HBJeBc1T/ZxYAAAAASUVORK5CYII=\n",
        "' alt='Mean Absolute Error across race subgroups' />\n",
        "\n",
        "    </div>\n",
@@ -2057,7 +2048,7 @@
        "\n",
        "      \n",
        "\n",
-       "      <td>1.177\r",
+       "      <td>1.136\r",
        "</td>\n",
        "\n",
        "      \n",
@@ -2110,151 +2101,144 @@
        "\n",
        "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAswAAAF1CAYAAAD8/Lw6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
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-       "SpKnVNXTquq9VfXFqrqlql66ZJ3vm4/R5+flz57Ld3vuLrPfE+fz6wtV9en5uC6+v4cu1L26qv7Z\n",
-       "PVev/ziv++HF1zaft2+fX/fbqup3Fs7bex27FY71Suf5S6vqDVX1+nl/76mqxy7U3aNjAKyOwAwH\n",
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-       "8qIkXVWPTvIzSZ7Q3Q+Z23jT4P14VpILkzwkyTuSfCXTH3aHZ3qfnltVZyVJVT0iyZuT/Md5v49L\n",
-       "8r55O6Nzd6lfS/LWJEck2Txvb089McnHM51fL0nyx1V15LzstUmuzXQevTTTtyhL7e2xHjkz0zl5\n",
-       "5LzvP62q+67iGAB7q7s9PDwOskem/yyfmins/OskZyS5MsmhSTrJliSVKaw8amG9Jye5cTfbfFyS\n",
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+       "s8ZrFObSBiwnFyf542w9HeNZSfZKsnF836zN7NMyTkhy0lj/4iSPq6qDxnXfHW/8APKPIrP5UJJd\n",
+       "q+p5Wwqq6klJHjnH/Z2V5Hdmq1RVB0/8I5Mkj0/ypTm2CT/wWmufT/K5DBfDbqUGL8ww//99U1Y/\n",
+       "M8n5rbVHttbWttbWJNmY5Gnb0wYsU29M8srW2menlJ+Q5KjxPbM2w+DRjIG5qg5O8qDW2qqJbf6g\n",
+       "tw0/OARmusaLFX4+yU+PX4NzRZIzk8zpArzxlx43TymeOof5mUn2SHJeDV9j95kkh4ztbjE5h/mD\n",
+       "c+kL/AA6K8nqKWV/VFX/kuQLSZ6U5OnTzI88IcO33Uz6m0w//WK6NmDZaq1taq39yWRZVa1N8ogM\n",
+       "33axpd7GJLdv+YrTJC+vqk1bbpn5fTbr3Gd2fn7pDwAAOowwAwBAh8AMAAAdAjMAAHQIzAAA0CEw\n",
+       "AwBAh8AMAAAdAjMAAHQIzAAA0PH/AdC9P09wBgiuAAAAAElFTkSuQmCC\n",
        "' alt='Mean Squared Error across race subgroups' />\n",
        "\n",
        "    </div>\n",
@@ -2368,7 +2352,7 @@
        "\n",
        "      \n",
        "\n",
-       "      <td>1.045\r",
+       "      <td>1.023\r",
        "</td>\n",
        "\n",
        "      \n",
@@ -2419,137 +2403,122 @@
        "\n",
        "    <div class=\"img-item\">\n",
        "\n",
-       "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAswAAAF1CAYAAAD8/Lw6AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
+       "      <img src='data:image/jpeg;base64,iVBORw0KGgoAAAANSUhEUgAAAsYAAAF1CAYAAADr3izzAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90\n",
        "bGliIHZlcnNpb24zLjUuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8/fFQqAAAACXBIWXMAAAsT\n",
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-       "Oefry+k+pwPPs9Zeu+qrr6reXbVP9+/U+Wrv6u4AAADLu8N6FwAAAAczgRkAAAYEZgAAGBCYAQBg\n",
-       "QGAGAIABgRkAAAYEZoA9VFWvrKoXrHcdHDhVdXxVdVVtWO9agIOHwAxf5arqY1X1pao6akn7++Zg\n",
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-       "+PYkf9LdH0mS7r6huy9Y2NYJVfXn83Yuq6rfrqrXzMu2VNWOJXV/rKoeMU+fUlXvqKqbqur6ed07\n",
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+       "wN2cPwkNAAAJxnAbM/ORmXnt3uuSc10TwF3FzKyZ+cG9+fNn5paZ+alzWRccxJGNMYa7sD9Ya336\n",
+       "uS4C4C7qA9Wnzsx911p/UP2l6p3nuCY4ED3GAMBh+zfV07bpZ1YvO4e1wIEJxnBb990bRvET57oY\n",
+       "gLugq6vLZ+Y+1adVrz7H9cCBGEoBt2UoBcBZWGv9+vZ8xjPz0a3chQjGAMBRuLb6zupY9XHnthQ4\n",
+       "GMEYADgK31/97lrrN2bm2DmuBQ5EMAYADt1a68bqn57rOuD28JfvAAAgn0oBAACVYAwAAJVgDAAA\n",
+       "lWAMAACVYAwAAJVgDAAAlWAMAACVYAwAAFX9/862TVD3XhCzAAAAAElFTkSuQmCC\n",
        "' alt='Mean Squared Error across sex subgroups' />\n",
        "\n",
        "    </div>\n",

From 2cfdab15b27d7e2de3e802284a5e54b50e7b861a Mon Sep 17 00:00:00 2001
From: swan <swan.lin@lynxanalytics.com>
Date: Mon, 10 Jan 2022 14:18:50 +0800
Subject: [PATCH 5/5] edits in response, rerun

---
 .../data/speech_to_text_example.proto         | Bin 63777 -> 63905 bytes
 .../model_cards/speech_to_text_example.html   |  12 ++++--------
 .../sample-form-response-speech-to-text.json  |   8 ++++----
 examples/speech_to_text_example.ipynb         |  12 ++++--------
 4 files changed, 12 insertions(+), 20 deletions(-)

diff --git a/examples/model_card_output/data/speech_to_text_example.proto b/examples/model_card_output/data/speech_to_text_example.proto
index 9cbe283abc84c9fe597e71690ff4d81d6e3e1a10..3528fab93de3f55018d652deb3a197255b5f19d7 100644
GIT binary patch
delta 413
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zu?(cM@f~~yOF_^Va1T@vYz78qe*cfzhd=xDYr#_~*g78)g$Y=OF5Or-<A`MS&hHta
z;^{GT4!|Y^F^@>Al$3=ry5Y{PMrA@R`UIFof?*&e8PF86jwk}5+=)PGV89w^BqI0i
z_Gxxh8KD-Ond#zk0$qo<%o4(c4Jk-S6O4tF;K)tpSkkV6|7<!a$_%X&V+9C?Eb!ou
zoBl1d&<a*4e-NjvhnZ>AcY;b+r1_?IUw-$hZ(hFeYS&l6YD_939h5<1NY!SmangXj
zwq{FVp@i0@>SxwPMQ2p8D?_7D--%rq^V_H`8cmBv_qR(0XxUy-58g}uJg@r~K`(!u
SAFiQ_AVoFgF)cS9*E?T5h>|P-

delta 278
zcmXxeu}T9$5C&lGjtFjHaaf7P#1|0KS=)#pC<bgqSa;`g3%T86ZZ5IX7ZBv!2M8&|
z>+=Yb%9jy+0Y{_F!YuR8|1H0=<!5%JUNg0OStFVfIKUY17|w#lY5tLnON2?7A^8eD
z!(7WFJBCm*c0<^Qwm}Qd1C!I!28uv{GiT8o?4C#4))fVkWV`q4xJVX*r!;>|<8eBC
zxK~>f^cG1CfV9E<Z_GF2-pzSn3CdY;RZXJ!HAq+Wqk|er+A1wgFn3&n>@XS@k7nWA
lwv5fjrv)bBmgM6<zj{mkOR|om{vbP5C-JI3T*ZFp;u}BJWeNZQ

diff --git a/examples/model_card_output/model_cards/speech_to_text_example.html b/examples/model_card_output/model_cards/speech_to_text_example.html
index 7cc45e4..0a29f35 100644
--- a/examples/model_card_output/model_cards/speech_to_text_example.html
+++ b/examples/model_card_output/model_cards/speech_to_text_example.html
@@ -136,7 +136,7 @@ <h2>Model Details</h2>
 
           <h3>Overview</h3>
 
-              The government want to create an automated transcription tool used in trials for evidence purposes. The tool should be able to capture the different spoken accents and transcribe into text accurately so as not to bias against the defendant.
+              Using an automated transcription tool, the government want to transcribe audio files recorded in trials for evidential purposes. The tool should be able to capture the various spoken accents and transcribe into text accurately so as not to bias against the defendant. The tool chosen here will be Google&#39;s Speech to Text Model evaluated on a set of audio files by National Speech Corpus.
 
           <h3>Version</h3>
 
@@ -240,7 +240,7 @@ <h3>Use Cases</h3>
 
   
 
-    <li>Automate transcriptions and reduce human error in making court decisions</li>
+    <li>Automate transcriptions and reduce human error and manpower in making court decisions</li>
 
   
 
@@ -326,11 +326,7 @@ <h4>Sensitive data used in model</h4>
 
                 
 
-                  <li>gender</li>
-
-                
-
-                  <li> race</li>
+                  <li>N.A. (Protected attributes are not trained in the speech to text model)</li>
 
                 
 
@@ -342,7 +338,7 @@ <h4>Sensitive data used in model</h4>
 
                 <h4>Justification</h4>
 
-                <p> Potential improvement in model performance with protected attributes used as features in model</p>
+                <p> N.A.</p>
 
               
 
diff --git a/examples/sample-form-response-speech-to-text.json b/examples/sample-form-response-speech-to-text.json
index 1136ba1..9ef78a9 100644
--- a/examples/sample-form-response-speech-to-text.json
+++ b/examples/sample-form-response-speech-to-text.json
@@ -19,7 +19,7 @@
         "key": "question_waO1Wy",
         "label": "Overview",
         "type": "TEXTAREA",
-        "value": "The government want to create an automated transcription tool used in trials for evidence purposes. The tool should be able to capture the different spoken accents and transcribe into text accurately so as not to bias against the defendant."
+        "value": "Using an automated transcription tool, the government want to transcribe audio files recorded in trials for evidential purposes. The tool should be able to capture the various spoken accents and transcribe into text accurately so as not to bias against the defendant. The tool chosen here will be Google's Speech to Text Model evaluated on a set of audio files by National Speech Corpus."
       },
       {
         "key": "question_3X5jBY",
@@ -31,7 +31,7 @@
         "key": "question_w8N9pP",
         "label": "Intended Use Cases",
         "type": "TEXTAREA",
-        "value": "Automate transcriptions and reduce human error in making court decisions"
+        "value": "Automate transcriptions and reduce human error and manpower in making court decisions"
       },
       {
         "key": "question_m6DYq5",
@@ -126,13 +126,13 @@
         "key": "question_w5XNqE",
         "label": "Protected attributes in production",
         "type": "TEXTAREA",
-        "value": "gender, race"
+        "value": "N.A. (Protected attributes are not trained in the speech to text model)" 
       },
       {
         "key": "question_3Eqpo2",
         "label": "Justification of use of protected attributes",
         "type": "TEXTAREA",
-        "value": "Potential improvement in model performance with protected attributes used as features in model"
+        "value": "N.A."
       },
       {
         "key": "question_wdb72K",
diff --git a/examples/speech_to_text_example.ipynb b/examples/speech_to_text_example.ipynb
index b176f85..dbcc0d5 100644
--- a/examples/speech_to_text_example.ipynb
+++ b/examples/speech_to_text_example.ipynb
@@ -588,7 +588,7 @@
        "\n",
        "          <h3>Overview</h3>\n",
        "\n",
-       "              The government want to create an automated transcription tool used in trials for evidence purposes. The tool should be able to capture the different spoken accents and transcribe into text accurately so as not to bias against the defendant.\n",
+       "              Using an automated transcription tool, the government want to transcribe audio files recorded in trials for evidential purposes. The tool should be able to capture the various spoken accents and transcribe into text accurately so as not to bias against the defendant. The tool chosen here will be Google&#39;s Speech to Text Model evaluated on a set of audio files by National Speech Corpus.\n",
        "\n",
        "          <h3>Version</h3>\n",
        "\n",
@@ -692,7 +692,7 @@
        "\n",
        "  \n",
        "\n",
-       "    <li>Automate transcriptions and reduce human error in making court decisions</li>\n",
+       "    <li>Automate transcriptions and reduce human error and manpower in making court decisions</li>\n",
        "\n",
        "  \n",
        "\n",
@@ -778,11 +778,7 @@
        "\n",
        "                \n",
        "\n",
-       "                  <li>gender</li>\n",
-       "\n",
-       "                \n",
-       "\n",
-       "                  <li> race</li>\n",
+       "                  <li>N.A. (Protected attributes are not trained in the speech to text model)</li>\n",
        "\n",
        "                \n",
        "\n",
@@ -794,7 +790,7 @@
        "\n",
        "                <h4>Justification</h4>\n",
        "\n",
-       "                <p> Potential improvement in model performance with protected attributes used as features in model</p>\n",
+       "                <p> N.A.</p>\n",
        "\n",
        "              \n",
        "\n",