Bias_Bounty_1_Tutorial_for_Beginners.ipynb

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        "<a href=\"https://colab.research.google.com/github/KristiArbo/biasbounty1_humaneintelligence/blob/main/Bias_Bounty_1_Tutorial_for_Beginners.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "markdown",
      "source": [
        "# Bias Bounty 1 Tutorial for Beginners\n",
        "\n",
        "The overview of the challenge can be found on our website [here](https://www.humane-intelligence.org/bounty1).\n",
        "\n",
        "The datasets for this challenge can be found on GitHub [here](https://github.com/humane-intelligence/bias-bounty-data).\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "### **Notebook Overview**\n",
        "Click on the 'Table of contents' icon on the left hand pane.\n"
      ],
      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "# Documentation References"
      ],
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      "cell_type": "markdown",
      "source": [
        "Throughout the notebook, I'll be referencing different Python libraries and modules and providing documentation links for so you can easily access the information when the specific code is being used.\n",
        "\n",
        "But I also wanted to compile all of links I'll be sharing in one place, so here you go:\n",
        "\n",
        "\n",
        "\n",
        "*   [Pandas](https://pandas.pydata.org/docs/)\n",
        "*   [Requests](https://requests.readthedocs.io/en/latest/)\n",
        "*   [Zipfile](https://docs.python.org/3/library/zipfile.html)\n",
        "*   [io](https://docs.python.org/3/library/io.html)\n"
      ],
      "metadata": {
        "id": "uPhYNatOh-ut"
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    {
      "cell_type": "markdown",
      "source": [
        "# Video 1 - Downloading the Data"
      ],
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    {
      "cell_type": "markdown",
      "source": [
        "## **Step One** - Overview of Datasets\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "Go to [GitHub](https://github.com/humane-intelligence/bias-bounty-data) and review the Readme document to learn about each of the three datasets:\n",
        "- what each category represents (factuality, bias, or misdirection)\n",
        "- the number of observations\n",
        "- the number of variables and what each of those variables are (type, description)"
      ],
      "metadata": {
        "id": "5WZXdVcG3WHn"
      }
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    {
      "cell_type": "markdown",
      "source": [
        "## **Step Two** - Importing Additional Python Library\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "Import the necessary Python library called Pandas to enable data analysis by running ▶ this code.\n",
        "\n",
        "Check out the [Pandas documentation](https://pandas.pydata.org/docs/) if you'd like to learn more about all its functionality and capabilities."
      ],
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      "source": [
        "import pandas as pd"
      ]
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      "cell_type": "markdown",
      "source": [
        "\n",
        "## **Step Three** - Accessing the Dataset\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Access the dataset for the category you want to investigate for this challenge. You have two options to do so:\n",
        "\n",
        "\n",
        "**3.1.** Download the zip file from GitHub, add to your Google Drive, and connect to the file in the Notebook\n",
        "\n",
        "**OR** *(do not run both)*\n",
        "\n",
        "**3.2.** Connect to the dataset via its URL and access the contents of the zip file using the following Python library and modules:"
      ],
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      "cell_type": "markdown",
      "source": [
        "### Step 3.1 - Downloading the zip file\n",
        "\n",
        "\n",
        "\n",
        "Downloading the zip file to your computer, unzip it and add to your Google Drive, then access your Google Drive in the Notebook"
      ],
      "metadata": {
        "id": "VunLQpI53kdX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "KY69Bwc37sgq",
        "outputId": "0fee928d-ed76-4d42-e3fd-0145e0a19890"
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now that the Notebook has access to your Google Drive, you need to tell it where your file is in the drive. Click on the three dots next to the file name and copy the file path.\n",
        "\n",
        "Then you'll use the following Pandas function to read the csv file and store that as a Pandas DataFrame (df) variable."
      ],
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        "id": "I1hpJpGT7liZ"
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    },
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      "cell_type": "code",
      "source": [
        "factuality_df = pd.read_csv('/content/drive/MyDrive/factuality.csv')"
      ],
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        "id": "2K7TZkcv3y6a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see if that worked and that we're able to access the dataset that we have now named with the variable 'factuality_df'. Here's a useful Python function to do so:"
      ],
      "metadata": {
        "id": "Yd2neIunt2DZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# The .head() function inspects the first few rows (and column headings) of the dataset\n",
        "factuality_df.head()"
      ],
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        "### Step 3.2 - Connecting to the dataset via URL\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "First, you need to import a few extra Python modules and a library to be able to use a URL to access the dataset.\n"
      ],
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        "id": "tcaV7-p32elv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "import zipfile\n",
        "from io import BytesIO"
      ],
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        "id": "5KqyVDKtxXZ1"
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      "execution_count": null,
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      "source": [
        "To understand what functions we'll be using from the library and modules and what they can do, review the following:\n",
        "\n",
        "- Documentation for [requests](https://requests.readthedocs.io/en/latest/)\n",
        "\n",
        "- Documentation for [zipfile](https://docs.python.org/3/library/zipfile.html)\n",
        "\n",
        "- Documentation for [io](https://docs.python.org/3/library/io.html)\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "Now you understand what the following methods do:"
      ],
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        "# URL of the Factuality Dataset on GitHub\n",
        "url = \"https://github.com/humane-intelligence/bias-bounty-data/raw/main/factuality.csv.zip\"\n",
        "\n",
        "# Download the file\n",
        "response = requests.get(url)\n",
        "with zipfile.ZipFile(BytesIO(response.content)) as factualityzip:\n",
        "    with factualityzip.open('factuality.csv') as csvfile:\n",
        "        factuality_df = pd.read_csv(csvfile)"
      ],
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        "Let's see if that worked and that we're able to access the dataset that we have now named with the variable 'factuality_df'. Here's a useful Python function to do so:"
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "factuality_df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "EjpMicH4_weg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Video 2 - Inspecting the Dataset"
      ],
      "metadata": {
        "id": "eadCFHhtuNdQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Step Four** - Inspecting the Data\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "In this section, we will walk through some basic data inspection functions, as well as aggregate functions and comparisons.\n"
      ],
      "metadata": {
        "id": "u9pBbBbpD9qq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4.1 Basic data inspection functions"
      ],
      "metadata": {
        "id": "Q70tTNvprNTl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each of the column names represent one of the variables from the dataset, which were explained in detail in the README documentation on GitHub [here](https://github.com/humane-intelligence/bias-bounty-data?tab=readme-ov-file#dataset-variables)."
      ],
      "metadata": {
        "id": "Si-SN_WZm-VR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect the first few rows\n",
        "factuality_df.head()"
      ],
      "metadata": {
        "id": "09K7yquOFmg2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Inspect the last few rows\n",
        "display(factuality_df.tail())"
      ],
      "metadata": {
        "id": "wWTR68eYIEc8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get basic information such as column names, the total number of rows in that\n",
        "# column that have data in them (non-null) and what data type is in each column\n",
        "factuality_df.info()"
      ],
      "metadata": {
        "id": "OYPE2nKJGSsj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eaa80795-239b-4f22-e4e4-83e00585f654"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 16205 entries, 0 to 16204\n",
            "Data columns (total 17 columns):\n",
            " #   Column               Non-Null Count  Dtype \n",
            "---  ------               --------------  ----- \n",
            " 0   Unnamed: 0           16205 non-null  int64 \n",
            " 1   conversation_id      16205 non-null  int64 \n",
            " 2   turn_number          16205 non-null  int64 \n",
            " 3   role_number          16205 non-null  int64 \n",
            " 4   system               1822 non-null   object\n",
            " 5   user                 7166 non-null   object\n",
            " 6   assistant            7070 non-null   object\n",
            " 7   bias_bounty_labels   16205 non-null  object\n",
            " 8   category_name        16205 non-null  object\n",
            " 9   challenges_name      16205 non-null  object\n",
            " 10  contestant_message   16205 non-null  object\n",
            " 11  conversation         16205 non-null  object\n",
            " 12  submission_message   16205 non-null  object\n",
            " 13  user_justification   16205 non-null  object\n",
            " 14  submission_grade     16205 non-null  object\n",
            " 15  conversation_length  16205 non-null  int64 \n",
            " 16  unique_id            16205 non-null  int64 \n",
            "dtypes: int64(6), object(11)\n",
            "memory usage: 2.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The Dtype shows us what type of data is in each column. 'Int64' stands for Integer and those are numerical values; 'object' in this case stands for text.\n",
        "\n",
        "So you can see that almost all of the columns have data (aka non-null values) in them in all 16,205 rows, except for columns 'system', 'user', and 'assistant'. Let's double check and calculate how many 'null' aka blank cells there are in our dataframe."
      ],
      "metadata": {
        "id": "U6l97RR9B0-8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Check for missing values aka blank cells\n",
        "factuality_df.isnull().sum()"
      ],
      "metadata": {
        "id": "UYE8roNvGlz4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# View the shape (how many columns and rows) of the DataFrame\n",
        "factuality_df.shape"
      ],
      "metadata": {
        "id": "iz9H_loeHVew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# View a random sample of rows\n",
        "factuality_df.sample(5)"
      ],
      "metadata": {
        "id": "dbjtpSBSVgVc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "JNG54TKT_rlN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4.2 Math Functions and Filtering"
      ],
      "metadata": {
        "id": "MKt7dgiIIIMX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's check some basic math (like max, min, mean, etc.) on the numerical data in our dataframe. But we know from our README documentation that some of these columns with numerical values aren't really useful for us to calculate math on, such as the ID number for a specific conversation. So let's only run our formula on the column with useful numerical data for math, 'conversation_length'."
      ],
      "metadata": {
        "id": "iSzOG5t1GJbf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get descriptive statistics for columns with numerical data in them\n",
        "factuality_df['conversation_length'].describe()"
      ],
      "metadata": {
        "id": "aGjIwijcK4S1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a07701d6-3169-4e60-8231-7e78d7ab61a3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    16205.000000\n",
              "mean        19.361493\n",
              "std         17.810337\n",
              "min          2.000000\n",
              "25%          7.000000\n",
              "50%         13.000000\n",
              "75%         25.000000\n",
              "max         93.000000\n",
              "Name: conversation_length, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The dataset categorized the Large Language Model conversations into two different category types:\n",
        "\n",
        "\n",
        "1.   **Category Name**: the classification for type of A.I. Bill of Rights harm/risk\n",
        "2.   **Challenges Name**: the classification for the defcon challenge type\n",
        "\n",
        "The **Challenges Name** for the Factuality dataset can be found on page 19 of the [2024 Generative AI Red Teaming Transparency Report](https://drive.google.com/file/d/1JqpbIP6DNomkb32umLoiEPombK2-0Rc-/view?usp=sharing).\n",
        "\n",
        "For the purposes of the Bias Bounty Challenge #1, we will primarily be focusing only on the Challenges Name.\n",
        "\n"
      ],
      "metadata": {
        "id": "S046xb5GKLvS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# challenges_name\t= the classification for the defcon challenge type\n",
        "# Step 1: Get unique 'challenges_name' and their counts in the 'challenges_name' column\n",
        "\n",
        "total_count_challenges_names = factuality_df['challenges_name'].value_counts()\n",
        "\n",
        "total_count_challenges_names"
      ],
      "metadata": {
        "id": "pWzPTeZIWrdz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4ba9cb0-1a36-45b1-831e-b193f881b11f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "challenges_name\n",
              "Political misinformation    6642\n",
              "Defamatory information      5187\n",
              "Economic misinformation     4376\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 2: Create a new column 'accepted' based on the 'submission_grade' column where submissions were 'accepted'\n",
        "factuality_df['accepted'] = factuality_df['submission_grade'] == 'accepted'\n",
        "\n",
        "# Step 3: Group by 'challenges_name' and count the number of accepted submissions\n",
        "accepted_challenges_counts = factuality_df.groupby('challenges_name')['accepted'].sum()\n",
        "\n",
        "# Step 4: Show the results\n",
        "accepted_challenges_counts\n"
      ],
      "metadata": {
        "id": "GEDQfaA-xQ8i",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3d3f7312-c2d3-433d-a6e8-d2ca13a52c7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "challenges_name\n",
              "Defamatory information      439\n",
              "Economic misinformation     595\n",
              "Political misinformation    839\n",
              "Name: accepted, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Of all of the the prompts that we have in our dataset, whether they were submitted or not, the following shows what percent of the prompts were accepted out of all of the prompts, based on the 'challenges_name':"
      ],
      "metadata": {
        "id": "6Xxdn5I_fufq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Calculate the percentage and print the results in a readable format\n",
        "percent = (accepted_challenges_counts / total_count_challenges_names) * 100\n",
        "percent_df = percent.reset_index().rename(columns={0: 'percentage_accepted'})\n",
        "percent_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "_TIH_8vDe8wr",
        "outputId": "0d2eb169-e7f7-43f7-86cb-df34ee96809c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            challenges_name  percentage_accepted\n",
              "0    Defamatory information             8.463466\n",
              "1   Economic misinformation            13.596892\n",
              "2  Political misinformation            12.631737"
            ],
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "percent_df",
              "summary": "{\n  \"name\": \"percent_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"challenges_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Defamatory information\",\n          \"Economic misinformation\",\n          \"Political misinformation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"percentage_accepted\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.728188274807825,\n        \"min\": 8.4634663582032,\n        \"max\": 13.59689213893967,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          8.4634663582032,\n          13.59689213893967,\n          12.631737428485396\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "o_NK3tYRAMMq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4.3 Filtering by Multiple Columns with Different Criteria"
      ],
      "metadata": {
        "id": "6Sggn8NmJ7yc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "How can we analyze the average conversation length it took for a specific challenges_name type to have the conversation accepted?"
      ],
      "metadata": {
        "id": "BEptFQRc-5jc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Define the list of challenges_name categories\n",
        "categories = ['Defamatory information', 'Economic misinformation', 'Political misinformation']\n",
        "\n",
        "# Step 2: Create a dictionary to store the results from the code below\n",
        "results = {}\n",
        "\n",
        "# Step 3: Iterate over each category and calculate the average conversation length\n",
        "for category in categories:\n",
        "    # Step 3.1: Filter rows where challenges_name is the current category and submission_grade is 'accepted'\n",
        "    filtered_factuality_df = factuality_df[(factuality_df['challenges_name'] == category) & (factuality_df['submission_grade'] == 'accepted')]\n",
        "\n",
        "    # Step 3.2: Calculate the average conversation length for the current category\n",
        "    average_conversation_length = filtered_factuality_df['conversation_length'].mean()\n",
        "\n",
        "# Step 4: Store the result in the dictionary\n",
        "    results[category] = average_conversation_length\n",
        "\n",
        "# Step 5: Print the results\n",
        "for category, avg_length in results.items():\n",
        "    print(f\"Average conversation length for acceppted submissions in '{category}': {avg_length:.2f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8lhg16mXDdOy",
        "outputId": "a65a6d9a-80a2-4848-9f16-f890392273bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Average conversation length for acceppted submissions in 'Defamatory information': 13.00\n",
            "Average conversation length for acceppted submissions in 'Economic misinformation': 15.54\n",
            "Average conversation length for acceppted submissions in 'Political misinformation': 20.17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see how many short (less than or equal to 3 turns) conversations were accepted around the topic of economic misinformation."
      ],
      "metadata": {
        "id": "tsTo8ExPJp04"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter rows where challenges_name is 'Economic misinformation', submission_grade is 'accepted', and conversation_length is less than 3\n",
        "\n",
        "# Store the filtered results as a variable, econ_filtered_factuality_df\n",
        "econ_filtered_factuality_df = factuality_df[(factuality_df['challenges_name'] == 'Economic misinformation') &\n",
        "                 (factuality_df['submission_grade'] == 'accepted') &\n",
        "                 (factuality_df['conversation_length'] <= 3)]\n",
        "\n",
        "# Let's create a new variable, economic_accepted_count, where we store the count (.shape[0]), of the filtered dataset\n",
        "economic_accepted_count = econ_filtered_factuality_df.shape[0]\n",
        "\n",
        "print(f\"Number of accepted submissions for 'Economic misinformation' with conversation length less than 3: {economic_accepted_count}\")\n"
      ],
      "metadata": {
        "id": "sNJd8pzNJ6bb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c605f2b9-e3fc-4c65-8273-804a4d0c8de1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of accepted submissions for 'Economic misinformation' with conversation length less than 3: 123\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see how many short (less than or equal to 3 turns) conversations were accepted around the topic of political misinformation."
      ],
      "metadata": {
        "id": "5Wsrrxm6IFnu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter rows where challenges_name is 'Political misinformation', submission_grade is 'accepted', and conversation_length is less than 3\n",
        "\n",
        "# Store the filtered results as a variable, poli_filtered_factuality_df\n",
        "poli_filtered_factuality_df = factuality_df[(factuality_df['challenges_name'] == 'Political misinformation') &\n",
        "                 (factuality_df['submission_grade'] == 'accepted') &\n",
        "                 (factuality_df['conversation_length'] <= 3)]\n",
        "\n",
        "# Let's create a new variable, political_accepted_count, where we store the count (.shape[0]), of the filtered dataset\n",
        "political_accepted_count = poli_filtered_factuality_df.shape[0]\n",
        "\n",
        "print(f\"Number of accepted submissions for 'Political misinformation' with conversation length less than 3: {political_accepted_count}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pg32Cq_vL0Ou",
        "outputId": "c46cf689-d590-44f7-ae65-4ae3c08fea4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of accepted submissions for 'Political misinformation' with conversation length less than 3: 114\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see how many short (less than or equal to 3 turns) conversations were accepted around the topic of defamatory information.\n",
        "\n",
        "**Note**: The challenges_name is called 'Defamatory information' not 'Defamatory misinformation'"
      ],
      "metadata": {
        "id": "MkcP1dMOI_dj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter rows where challenges_name is 'Defamatory information', submission_grade is 'accepted', and conversation_length is less than 3\n",
        "\n",
        "# Store the filtered results as a variable, defam_filtered_factuality_df\n",
        "defam_filtered_factuality_df = factuality_df[\n",
        "    (factuality_df['challenges_name'] == 'Defamatory information') &\n",
        "    (factuality_df['submission_grade'] == 'accepted') &\n",
        "    (factuality_df['conversation_length'] <= 3)]\n",
        "\n",
        "# Let's create a new variable, defamatory_accepted_count, where we store the count (.shape[0]), of the filtered dataset\n",
        "defamatory_accepted_count = defam_filtered_factuality_df.shape[0]\n",
        "\n",
        "print(f\"Number of accepted submissions for 'Defamatory information' with conversation length less than 3: {defamatory_accepted_count}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8yEHI7FLNHgi",
        "outputId": "f2cd73ce-34d4-4e89-b38a-a0fb8edffb41"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of accepted submissions for 'Defamatory information' with conversation length less than 3: 81\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4.4 Hugging Face: An Easier Way to Read the Conversations"
      ],
      "metadata": {
        "id": "kU7KdGlmJohE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "When we run our code to view only the 'Defamatory information' conversations under 3 turns that were 'accepted', we see that it's really difficult to read and view easily in Jupyter Notebooks."
      ],
      "metadata": {
        "id": "P7LkiGWh5Eaq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "defam_filtered_factuality_df.head()"
      ],
      "metadata": {
        "id": "DPo96SVfIIvJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "If we instead use Hugging Face to view our dataset, it is much easier for us to read the flow of the conversation.\n",
        "\n",
        "Let's log in to our Hugging Face account (which we'll need to create anyways for the challenge submission) and then view the [dataset](https://huggingface.co/datasets/jinnovation/generative-ai-red-teaming).\n",
        "\n",
        "Follow along in the tutorial video to find out how to use the Data Viewer."
      ],
      "metadata": {
        "id": "oyCPRjLv5o1d"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "MiAMem1A-wfO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Video 3 - Creating New Categories"
      ],
      "metadata": {
        "id": "zVTVp1fJ_udg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Step Five** - Analyzing the Conversations\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "In this section, we'll be diving into text analysis using spaCy. This will enable us to get a quicker overview of how successful prompts were constructed and what topics were covered. We will be using the DataFrames that we constructed in Step 4.3."
      ],
      "metadata": {
        "id": "qcN2BgPpJF8M"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 5.1 Setting Up and Configuring spaCy to Analyze Text"
      ],
      "metadata": {
        "id": "NA3zPTrP-3mx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We're going to be using **spaCy**, which is a free, open-source library for advanced Natural Language Processing (NLP) in Python.\n",
        "\n",
        "\n",
        "If you’re working with a lot of text, you’ll eventually want to know more about it. For example, what’s it about? What do the words mean in context? Who is doing what to whom? What companies and products are mentioned? Which texts are similar to each other?\n",
        "\n",
        "Here's the [spaCy 101 documentation](https://spacy.io/usage/spacy-101) to get us started."
      ],
      "metadata": {
        "id": "RvWfYk82_Eus"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import spacy\n"
      ],
      "metadata": {
        "id": "AIYtCV68F_Im"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we need to load spaCy's trained pipeline for the English language.\n",
        "\n",
        "What is a trained pipeline?\n",
        "It's basically a pipe for you to push your text through that has lots of code built into it that processing your text based on large datasets that the pipeline was pre-trained on."
      ],
      "metadata": {
        "id": "uiugnPT__QgQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the spaCy model\n",
        "nlp = spacy.load(\"en_core_web_sm\")"
      ],
      "metadata": {
        "id": "hBjAaFbMGCgh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we know that we already have three unique DataFrame variables that we created in Step 4.3:\n",
        "\n",
        "\n",
        "\n",
        "*   econ_filtered_factuality_df\n",
        "*   poli_filtered_factuality_df\n",
        "*   defam_filtered_factuality_df\n",
        "\n",
        "\n",
        "\n",
        "Based on what we did in Step 4.3, each of these variables **only** contains rows of data for the following columns when that column has been filtered by the following criteria:\n",
        "\n",
        "\n",
        "1.   One type of challenges_name (either economic misinformation, political misinformation, or defamatory information)\n",
        "2.   Only prompts that received a 'submission_grade' of 'accepted'\n",
        "3. Only conversation_lengths that were equal to or less than 3\n",
        "\n",
        "If we want to do text analysis on our filtered DataFrame, we need to use .copy() to create a new DataFrame that is a copy of the filtered results. This avoids the SettingWithCopyWarning that we'd get when trying to do text analysis using spaCy. I found this out the hard way.\n",
        "\n",
        "\n",
        "*Remember: Each of the column names represent one of the variables from the dataset, which were explained in detail in the README documentation on GitHub [here](https://github.com/humane-intelligence/bias-bounty-data?tab=readme-ov-file#dataset-variables)*\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "UllNDRVaANos"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filter the DataFrames and make a copy\n",
        "econ_filtered_factuality_df = factuality_df[\n",
        "    (factuality_df['challenges_name'] == 'Economic misinformation') &\n",
        "    (factuality_df['submission_grade'] == 'accepted') &\n",
        "    (factuality_df['conversation_length'] <= 3)].copy()\n",
        "\n",
        "\n",
        "poli_filtered_factuality_df = factuality_df[\n",
        "    (factuality_df['challenges_name'] == 'Political misinformation') &\n",
        "    (factuality_df['submission_grade'] == 'accepted') &\n",
        "    (factuality_df['conversation_length'] <= 3)].copy()\n",
        "\n",
        "defam_filtered_factuality_df = factuality_df[\n",
        "    (factuality_df['challenges_name'] == 'Defamatory information') &\n",
        "    (factuality_df['submission_grade'] == 'accepted') &\n",
        "    (factuality_df['conversation_length'] <= 3)].copy()"
      ],
      "metadata": {
        "id": "_V3grLUCCRAy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ok, great now that we have copies of these filtered DataFrames, let's use spaCy to do some analysis.\n",
        "\n",
        "During processing, when we .apply( ) the 'trained pipeline' that we stored as variable 'nlp', spaCy tokenizes the text, i.e. segments it into words, punctuation and so on.\n",
        "\n",
        "Then it stores the tokenized text into a 'doc'. Each 'doc' consists of individual tokens, and we can iterate over them.\n",
        "\n"
      ],
      "metadata": {
        "id": "zVK4a698C9kW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply the trained pipeline (nlp) to the 'conversation' column in our Econ Misinformation Filtered DataFrame and store it in a spaCy 'doc'\n",
        "econ_filtered_factuality_df['doc'] = econ_filtered_factuality_df['conversation'].apply(nlp)\n",
        "\n",
        "# Apply the trained pipeline (nlp) to the 'conversation' column in our Political Misinformation Filtered DataFrame and store it in a spaCy 'doc'\n",
        "poli_filtered_factuality_df['doc'] = poli_filtered_factuality_df['conversation'].apply(nlp)\n",
        "\n",
        "# Apply the trained pipeline (nlp) to the 'conversation' column in our Defamatory Information Filtered DataFrame and store it in a spaCy 'doc'\n",
        "defam_filtered_factuality_df['doc'] = defam_filtered_factuality_df['conversation'].apply(nlp)\n"
      ],
      "metadata": {
        "id": "cd748HQUaayG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "5GtB93ZR-kXI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 5.2 Extracting Named Entities from our Conversations"
      ],
      "metadata": {
        "id": "emg1rLQb94AT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "According to the spaCy documentation, we can analyze a variety of things in our text.\n",
        "\n",
        "Named Entities, in particular, are useful as this can extract objects, persons, geographic locations, and more from the text. See the full list of spaCy's [named entities' categories](https://github.com/explosion/spaCy/discussions/9147).\n",
        "\n",
        "Keep in mind this is an imperfect model (as are most), so the categorizing of entities might not always be accurate."
      ],
      "metadata": {
        "id": "WBsri9Foa1lV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Economic Misinformation Named Entities**"
      ],
      "metadata": {
        "id": "mPOFEsG_TNns"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to remove stop words (small and commonly used words, e.g. 'a', 'the') from a doc\n",
        "def remove_stop_words(doc):\n",
        "    return [token for token in doc if not token.is_stop]\n",
        "\n",
        "# Remove stop words and create a new column with the cleaned docs\n",
        "econ_filtered_factuality_df['cleaned_doc'] = econ_filtered_factuality_df['doc'].apply(remove_stop_words)\n",
        "\n",
        "# Convert cleaned docs back to string if needed for further processing or visualization\n",
        "econ_filtered_factuality_df['cleaned_text'] = econ_filtered_factuality_df['cleaned_doc'].apply(lambda tokens: \" \".join([token.text for token in tokens]))\n",
        "\n",
        "# Extract named entities and create a DataFrame\n",
        "entities = []\n",
        "for doc in econ_filtered_factuality_df['doc']:\n",
        "    for ent in doc.ents:\n",
        "        entities.append((ent.text, ent.label_))\n",
        "\n",
        "econ_entities_df = pd.DataFrame(entities, columns=['Entity', 'Category'])\n",
        "\n",
        "# Display the DataFrame with unique entities\n",
        "econ_unique_entities_df = econ_entities_df.drop_duplicates().sort_values(by='Category')\n",
        "econ_unique_entities_df.reset_index(drop=True, inplace=True)\n",
        "econ_unique_entities_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705
        },
        "id": "bjBGzZCFSAWN",
        "outputId": "4239125a-4d1c-4eb7-f64a-e3ad29e74cd0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                             Entity     Category\n",
              "0                       2:\\n[Sketch     CARDINAL\n",
              "1                       1:\\n[Sketch     CARDINAL\n",
              "2                               221     CARDINAL\n",
              "3                             62.36     CARDINAL\n",
              "4                  9.4%.\\n\\nOverall     CARDINAL\n",
              "..                              ...          ...\n",
              "206              more than 12 hours         TIME\n",
              "207                         Tonight         TIME\n",
              "208                         evening         TIME\n",
              "209                      late-night         TIME\n",
              "210  Political Mishaps & Tech Tales  WORK_OF_ART\n",
              "\n",
              "[211 rows x 2 columns]"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "econ_unique_entities_df",
              "summary": "{\n  \"name\": \"econ_unique_entities_df\",\n  \"rows\": 211,\n  \"fields\": [\n    {\n      \"column\": \"Entity\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 206,\n        \"samples\": [\n          \"a year\",\n          \"10.7%.\\\\n\\\\nThe\",\n          \"more than 12 hours\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"ORG\",\n          \"PERSON\",\n          \"CARDINAL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No charts were generated by quickchart\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Political Misinformation Named Entities**"
      ],
      "metadata": {
        "id": "5mAuEin7RIMu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to remove stop words (small and commonly used words, e.g. 'a', 'the') from a doc\n",
        "def remove_stop_words(doc):\n",
        "    return [token for token in doc if not token.is_stop]\n",
        "\n",
        "# Remove stop words and create a new column with the cleaned docs\n",
        "poli_filtered_factuality_df['cleaned_doc'] = poli_filtered_factuality_df['doc'].apply(remove_stop_words)\n",
        "\n",
        "# Convert cleaned docs back to string if needed for further processing or visualization\n",
        "poli_filtered_factuality_df['cleaned_text'] = poli_filtered_factuality_df['cleaned_doc'].apply(lambda tokens: \" \".join([token.text for token in tokens]))\n",
        "\n",
        "# Extract named entities and add them to a dictionary called 'entities'\n",
        "entities = []\n",
        "for doc in poli_filtered_factuality_df['doc']:\n",
        "    for ent in doc.ents:\n",
        "        entities.append((ent.text, ent.label_))\n",
        "\n",
        "#  Create a new DataFrame with just the entities and their categories\n",
        "poli_entities_df = pd.DataFrame(entities, columns=['Entity', 'Category'])\n",
        "\n",
        "# Display the DataFrame with only unique entities in more readable way\n",
        "poli_unique_entities_df = poli_entities_df.drop_duplicates().sort_values(by='Category')\n",
        "poli_unique_entities_df.reset_index(drop=True, inplace=True)\n",
        "poli_unique_entities_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 687
        },
        "id": "aXAP5Ih1lJyw",
        "outputId": "437b894b-ce3c-4924-cd10-600887a9e6e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                      Entity     Category\n",
              "0        more than 1 million     CARDINAL\n",
              "1                         35     CARDINAL\n",
              "2                        200     CARDINAL\n",
              "3                      2,600     CARDINAL\n",
              "4                        One     CARDINAL\n",
              "..                       ...          ...\n",
              "276                  Beretta      PRODUCT\n",
              "277                  beretta      PRODUCT\n",
              "278                 the Game  WORK_OF_ART\n",
              "279        Black Wall Street  WORK_OF_ART\n",
              "280  The Tulsa Race Massacre  WORK_OF_ART\n",
              "\n",
              "[281 rows x 2 columns]"
            ],
            "text/html": [
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              "      <th>3</th>\n",
              "      <td>2,600</td>\n",
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              "      <th>4</th>\n",
              "      <td>One</td>\n",
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              "    <tr>\n",
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              "      <td>Beretta</td>\n",
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              "      <th>277</th>\n",
              "      <td>beretta</td>\n",
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              "      <th>278</th>\n",
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              "      <th>279</th>\n",
              "      <td>Black Wall Street</td>\n",
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              "    <tr>\n",
              "      <th>280</th>\n",
              "      <td>The Tulsa Race Massacre</td>\n",
              "      <td>WORK_OF_ART</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "<p>281 rows × 2 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "poli_unique_entities_df",
              "summary": "{\n  \"name\": \"poli_unique_entities_df\",\n  \"rows\": 281,\n  \"fields\": [\n    {\n      \"column\": \"Entity\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 276,\n        \"samples\": [\n          \"May 30 to June 23\",\n          \"Tulsa\",\n          \"the Communist Party of Germany\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 13,\n        \"samples\": [\n          \"PRODUCT\",\n          \"ORG\",\n          \"CARDINAL\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Defamatory Information Named Entities**"
      ],
      "metadata": {
        "id": "GlVYUHqVZ95R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to remove stop words (small and commonly used words, e.g. 'a', 'the') from a doc\n",
        "def remove_stop_words(doc):\n",
        "    return [token for token in doc if not token.is_stop]\n",
        "\n",
        "# Remove stop words and create a new column with the cleaned docs\n",
        "defam_filtered_factuality_df['cleaned_doc'] = defam_filtered_factuality_df['doc'].apply(remove_stop_words)\n",
        "\n",
        "# Convert cleaned docs back to string if needed for further processing or visualization\n",
        "defam_filtered_factuality_df['cleaned_text'] = defam_filtered_factuality_df['cleaned_doc'].apply(lambda tokens: \" \".join([token.text for token in tokens]))\n",
        "\n",
        "# Extract named entities and create a DataFrame\n",
        "entities = []\n",
        "for doc in defam_filtered_factuality_df['doc']:\n",
        "    for ent in doc.ents:\n",
        "        entities.append((ent.text, ent.label_))\n",
        "\n",
        "defam_entities_df = pd.DataFrame(entities, columns=['Entity', 'Category'])\n",
        "\n",
        "# Display the DataFrame with unique entities\n",
        "defam_unique_entities_df = defam_entities_df.drop_duplicates().sort_values(by='Category')\n",
        "defam_unique_entities_df.reset_index(drop=True, inplace=True)\n",
        "defam_unique_entities_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 687
        },
        "id": "Vglzd5BhaBk_",
        "outputId": "a4dd7398-9fd1-4079-b783-858abf495ac9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Entity     Category\n",
              "0                                                   17     CARDINAL\n",
              "1                                                   10     CARDINAL\n",
              "2                                                 four     CARDINAL\n",
              "3                                                   11     CARDINAL\n",
              "4                                                   15     CARDINAL\n",
              "..                                                 ...          ...\n",
              "230                                            tonight         TIME\n",
              "231                                    The Babylon Bee  WORK_OF_ART\n",
              "232                              Alex Stamos Convicted  WORK_OF_ART\n",
              "233  Subject: Important Notice: Staff Update\\n\\nDear *  WORK_OF_ART\n",
              "234                                          Elon Musk  WORK_OF_ART\n",
              "\n",
              "[235 rows x 2 columns]"
            ],
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              "      <td>The Babylon Bee</td>\n",
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              "      <td>Alex Stamos Convicted</td>\n",
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              "      <th>233</th>\n",
              "      <td>Subject: Important Notice: Staff Update\\n\\nDear *</td>\n",
              "      <td>WORK_OF_ART</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>234</th>\n",
              "      <td>Elon Musk</td>\n",
              "      <td>WORK_OF_ART</td>\n",
              "    </tr>\n",
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              "<p>235 rows × 2 columns</p>\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "defam_unique_entities_df",
              "summary": "{\n  \"name\": \"defam_unique_entities_df\",\n  \"rows\": 235,\n  \"fields\": [\n    {\n      \"column\": \"Entity\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 228,\n        \"samples\": [\n          \"Diego Maradona\",\n          \"the exact year\",\n          \"more than two dozen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"CARDINAL\",\n          \"DATE\",\n          \"LAW\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "EixrPRTiAinP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step Six - Creating New Challenge Name Sub-Categories"
      ],
      "metadata": {
        "id": "G9d8JzcAA4i_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 6.1 Grading Document and Submissions Template\n",
        "\n"
      ],
      "metadata": {
        "id": "e8zV4KJnA_qw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We’re going to start on the Bias Bounty webpage down at [The Dates section](https://www.humane-intelligence.org/bounty1). Click on the link for the instructions.\n",
        "\n",
        "This opens the [Public Description of Grading Mechanisms / Equations](https://docs.google.com/document/d/1i3iQJLfC0wOuE9Ykg1WpSy09l6Gi27vMi70ALya4JuI/edit#heading=h.uzwgu3pc73f9)\n",
        "\n",
        "Then open the [template for beginner submissions](https://docs.google.com/spreadsheets/d/1rxD6mhan7B9lxHmfqF4kXYGtk1vvXMOFqSCdCvlo3Ns/edit?gid=0#gid=0) and make your own copy.\n"
      ],
      "metadata": {
        "id": "6gQ1UsDEwd7t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 6.2 Inputting Your New Sub-Categories"
      ],
      "metadata": {
        "id": "GBvhXz4cxNll"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Then let’s update our ‘bias_bounty_category’ to ‘factuality’, since that’s the dataset we’re working with. Under challenges_name, we’ll put each of the three challenges_name. Under incompleteness_label is where we will add in our sub-categories within each of the categories of the ‘challenges_name’.\n",
        "\n",
        "So spend some time reviewing the named entities from each DataFrame and start brainstorming your ideas!"
      ],
      "metadata": {
        "id": "iYSRhYLVxWHs"
      }
    }
  ]
}