challenge/Challenge_part2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R5FASbjKM1a8"
      },
      "source": [
        "# Investigate Bias in the System\n",
        "\n",
        "\n",
        "* Identify biased sample with help of evaluation instruments\n",
        "* Mitigate the bias changing the data or the system\n",
        "* Explain why the system may be biased\n",
        "* Regulate the use of a hate speech detection system\n",
        "* O anything else that may be missing!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WFXwojUKJ2Gm"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0sugtYdaJ9Sf",
        "outputId": "10321df7-ef6e-457b-8a34-3b4656b4646e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A0A5cpIVJtOJ",
        "outputId": "23ca3451-3e4b-4239-caab-bd79e494c7b5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(93819, 131)\n",
            "(26508, 66)\n"
          ]
        }
      ],
      "source": [
        "# Import training data\n",
        "PATH_TO_FOLDER = '/content/gdrive/MyDrive/path_to_folder/challenge'\n",
        "d_train = pd.read_csv(os.path.join(PATH_TO_FOLDER, 'data', 'train.csv'))\n",
        "print(d_train.shape)\n",
        "\n",
        "d_train_aggr = pd.read_csv(os.path.join(PATH_TO_FOLDER, 'data', 'train_aggregate.csv'))\n",
        "print(d_train_aggr.shape)\n",
        "\n",
        "# Import evaluation data\n",
        "PATH_TO_PREP = '/content/gdrive/MyDrive/path_to_folder/preparation/Adversifier/datathon_results'\n",
        "aaa_scores = pd.read_csv(os.path.join(PATH_TO_FOLDER, 'answers', 'team_1', 'results.tsv'), sep='\\t')\n",
        "hatecheck =  pd.read_csv(os.path.join(PATH_TO_FOLDER, 'answers', 'hatecheck.csv'))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hTDKUynKGI3p"
      },
      "source": [
        "# Example hate speech classifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFgvXXCnZVQ1",
        "outputId": "336fb9b8-dc4e-4f24-d16f-b06126b1ec1b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting huggingface_hub\n",
            "  Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.0)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.4.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.27.1)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.65.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.5.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.1)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (1.26.15)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2022.12.7)\n",
            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.12)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n",
            "Installing collected packages: huggingface_hub\n",
            "Successfully installed huggingface_hub-0.15.1\n"
          ]
        }
      ],
      "source": [
        "! pip install huggingface_hub"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PXnDVRk5M_k_"
      },
      "source": [
        "### Load the system"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 298,
          "referenced_widgets": [
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          ]
        },
        "id": "606WEVbJLgGQ",
        "outputId": "15f53a12-30aa-437b-f33b-579a67eda20d"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "config.json not found in HuggingFace Hub.\n",
            "WARNING:huggingface_hub.hub_mixin:config.json not found in HuggingFace Hub.\n"
          ]
        },
        {
          "data": {
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              "model_id": "2bb885fee4a54e5d87a86a7502fb12fb",
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        {
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              "model_id": "ac4a60b038414bf59877a9fb8f3ac42c",
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            "text/plain": [
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            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "182215bf772843a39ad9b10a328e1595",
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          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        }
      ],
      "source": [
        "# Example classifier: on full data (no sirve para investigar weaknesses)\n",
        "\n",
        "from huggingface_hub import from_pretrained_keras\n",
        "\n",
        "model = from_pretrained_keras(\"ucberkeley-dlab/hate-measure-roberta-base\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sR7fBY0KMWte",
        "outputId": "0340c874-98e7-44f7-c805-efbb5147f705"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "/content/gdrive/MyDrive/project\n"
          ]
        }
      ],
      "source": [
        "!echo $PATH_TO_FOLDER"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5tNBW6kZIeCb"
      },
      "outputs": [],
      "source": [
        "# Or clone the full repo to your drive\n",
        "!git clone https://github.com/dlab-projects/hate_measure.git $PATH_TO_FOLDER"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7LXY_5eOr7_"
      },
      "source": [
        "### Example functions to use the system"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPo4spVrzB3o",
        "outputId": "2ae3eb28-315e-4bfc-e78b-a267bebfd895"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.29.2-py3-none-any.whl (7.1 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m52.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.12.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.1)\n",
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            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
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            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4)\n",
            "Installing collected packages: tokenizers, transformers\n",
            "Successfully installed tokenizers-0.13.3 transformers-4.29.2\n"
          ]
        }
      ],
      "source": [
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        {
          "name": "stderr",
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          "text": [
            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
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              "0it [00:00, ?it/s]"
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            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"
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        {
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          "text": [
            "409/409 [==============================] - 214s 513ms/step\n"
          ]
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              "                                                    text  gtruth  pred  \\\n",
              "0                                White people are trash.       0     1   \n",
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              "2      First off you look cool as fuck! Anyway if we ...       1     1   \n",
              "3      Guranteed at least one of these guys has raped...       1     0   \n",
              "4                                     eat my fuck, bitch       1     1   \n",
              "...                                                  ...     ...   ...   \n",
              "13052  1:20 If the violence continues, it'll lead to ...       0     1   \n",
              "13053  AOC is a Puerto Rican of mostly Spanish descen...       0     0   \n",
              "13054  Is that Barney the purple dinosaur. Oh wait a ...       0     0   \n",
              "13055  Go to streets of the USA and our kids and vets...       1     1   \n",
              "13056  Yikes!!! Bad enough she'shard to look at.every...       1     1   \n",
              "\n",
              "       new_predictions  \n",
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              "4             1.209472  \n",
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              "13054        -0.128040  \n",
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              "\n",
              "[13057 rows x 4 columns]"
            ]
          },
          "execution_count": 8,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Examples predict on a dataframe\n",
        "import transformers\n",
        "\n",
        "d_test = pd.read_csv(os.path.join(PATH_TO_FOLDER,'results','team_X','f1_o.tsv'), sep='\\t', names=['text', 'gtruth', 'pred'])\n",
        "\n",
        "x = d_test['text'].values\n",
        "tokenizer = transformers.RobertaTokenizer.from_pretrained('roberta-base')\n",
        "tokens = tokenizer(x.tolist(), return_tensors='np', max_length=247,truncation=True, padding='max_length')\n",
        "inputs = [tokens['input_ids'], tokens['attention_mask']]\n",
        "d_test['new_predictions'] = model.predict(inputs)\n",
        "d_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "base_uri": "https://localhost:8080/"
        },
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "You've caught the big gay, it's known to be airborne via dildos.\n",
            "1/1 [==============================] - 0s 51ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[0.40414003]], dtype=float32)"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Examples predict on a text\n",
        "\n",
        "text = d_train_aggr.loc[0, 'text']\n",
        "print(text)\n",
        "\n",
        "tokens = tokenizer([text], return_tensors='np', max_length=247,truncation=True, padding='max_length')\n",
        "inputs = [tokens['input_ids'], tokens['attention_mask']]\n",
        "prediction = model.predict(inputs)\n",
        "prediction\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uFtWooyc1Ffw"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "from typing import List\n",
        "\n",
        "def example_predictions(input_texts: List):\n",
        "  model = from_pretrained_keras(\"ucberkeley-dlab/hate-measure-roberta-base\")\n",
        "  tokenizer = transformers.RobertaTokenizer.from_pretrained('roberta-base')\n",
        "  tokens = tokenizer(input_texts, return_tensors='np', max_length=247,truncation=True, padding='max_length')\n",
        "  inputs = [tokens['input_ids'], tokens['attention_mask']]\n",
        "  outputs = model.predict(inputs)\n",
        "  # Can apply thresholding\n",
        "  # outputs_b = np.where(outputs >= 0.5, 1, 0)\n",
        "  outputs_b = (outputs >= 0.5)\n",
        "  return outputs_b\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7FQ7rVmBGNPX"
      },
      "source": [
        "# Inspection instruments"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1l3zJ7ecq7jm"
      },
      "outputs": [],
      "source": [
        "# AAA link to repo (use with Binder)\n",
        "\n",
        "# ... file format for using a different test set: text label\n",
        "aaa_training_file = pd.read_csv(os.path.join(PATH_TO_FOLDER, 'results', 'aaa_files', 'aaa_train.csv'))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oUOv2pRbjo8v"
      },
      "source": [
        "https://github.com/Ago3/Adversifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
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        "id": "ob6X12Zfq-if",
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              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-6ad13149-23ca-4ed3-8b44-2b0babd6c2ed button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-6ad13149-23ca-4ed3-8b44-2b0babd6c2ed');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Unnamed: 0      functionality  case_id              test_case label_gold  \\\n",
              "0           0  derog_neg_emote_h        1         I hate women.     hateful   \n",
              "1           1  derog_neg_emote_h        2  I hate trans people.     hateful   \n",
              "\n",
              "   target_ident direction focus_words focus_lemma  ref_case_id  ref_templ_id  \\\n",
              "0         women   general        hate        hate          NaN           NaN   \n",
              "1  trans people   general        hate        hate          NaN           NaN   \n",
              "\n",
              "   templ_id            case_templ  pred_team_X pred_team_Y  \n",
              "0         1  I hate [IDENTITY_P].  non-hateful     hateful  \n",
              "1         1  I hate [IDENTITY_P].      hateful     hateful  "
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# HateCheck: load from results/hatecheck.csv\n",
        "hatecheck.head(2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V5kB_vvPkqj5"
      },
      "source": [
        "https://huggingface.co/datasets/Paul/hatecheck"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350,
          "referenced_widgets": [
            "734562a0c1cf4914924f551a13ca70aa",
            "2aaea023d3c04eafbab3d2fe66431c6e",
            "b4ed82f02495440f8ab2d1cf5bc19967",
            "279a941024b6433081833f9bcdc732a5",
            "12638aa3aa5b406cb203b981e380d6a4",
            "c42f0fbb77f94d7a92a2f1b4c9be70f8",
            "7910f223e296420698ca88f0917bb56c",
            "acac76ff6130464581011f82201e1008",
            "d8824ddc1289433f8eef8b134393dc8c",
            "bf7dc22239804f17a90a3704a2c265fc",
            "d6e9153d264c480f862b1cefed493324"
          ]
        },
        "id": "vApd37y6kukn",
        "outputId": "e1255290-3069-4c79-9ab1-c0cc803025c2"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "config.json not found in HuggingFace Hub.\n",
            "WARNING:huggingface_hub.hub_mixin:config.json not found in HuggingFace Hub.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "734562a0c1cf4914924f551a13ca70aa",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Fetching 5 files:   0%|          | 0/5 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:SavedModel saved prior to TF 2.5 detected when loading Keras model. Please ensure that you are saving the model with model.save() or tf.keras.models.save_model(), *NOT* tf.saved_model.save(). To confirm, there should be a file named \"keras_metadata.pb\" in the SavedModel directory.\n",
            "WARNING:tensorflow:No training configuration found in save file, so the model was *not* compiled. Compile it manually.\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "117/117 [==============================] - 58s 493ms/step\n"
          ]
        },
        {
          "data": {
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>functionality</th>\n",
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              "      <th>focus_lemma</th>\n",
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              "      <th>0</th>\n",
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              "      <td>derog_neg_emote_h</td>\n",
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              "      <td>I hate women.</td>\n",
              "      <td>hateful</td>\n",
              "      <td>women</td>\n",
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              "      <td>hate</td>\n",
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              "      <td>trans people</td>\n",
              "      <td>general</td>\n",
              "      <td>hate</td>\n",
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              "      <td>hateful</td>\n",
              "      <td>hateful</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-48d1fd3a-4aa3-4f72-be3c-2b4be0d6b97a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "    .colab-df-container {\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-48d1fd3a-4aa3-4f72-be3c-2b4be0d6b97a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-48d1fd3a-4aa3-4f72-be3c-2b4be0d6b97a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  "
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            "text/plain": [
              "   Unnamed: 0      functionality  case_id              test_case label_gold  \\\n",
              "0           0  derog_neg_emote_h        1         I hate women.     hateful   \n",
              "1           1  derog_neg_emote_h        2  I hate trans people.     hateful   \n",
              "\n",
              "   target_ident direction focus_words focus_lemma  ref_case_id  ref_templ_id  \\\n",
              "0         women   general        hate        hate          NaN           NaN   \n",
              "1  trans people   general        hate        hate          NaN           NaN   \n",
              "\n",
              "   templ_id            case_templ  pred_team_X pred_team_Y pred_model_1  \n",
              "0         1  I hate [IDENTITY_P].  non-hateful     hateful      hateful  \n",
              "1         1  I hate [IDENTITY_P].      hateful     hateful      hateful  "
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Create hatecheck table with new models\n",
        "\n",
        "# ... add model predictions\n",
        "models = ['model_1']\n",
        "\n",
        "for m in models:\n",
        "  hatecheck[f'pred_{m}'] = example_predictions(hatecheck['test_case'].values.tolist())\n",
        "  hatecheck[f'pred_{m}'].replace(to_replace={1:'hateful', 0: 'non-hateful'}, inplace=True)\n",
        "hatecheck.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 958
        },
        "id": "3oyBr2rKmSf9",
        "outputId": "354772a1-1603-41c5-86f8-9b355c668c20"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-34ea8636-4ad9-4bc8-8289-8d185f80ca8f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "\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>functionality</th>\n",
              "      <th>model_1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>derog_neg_emote_h</td>\n",
              "      <td>40.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>derog_neg_attrib_h</td>\n",
              "      <td>41.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>derog_dehum_h</td>\n",
              "      <td>70.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>derog_impl_h</td>\n",
              "      <td>18.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>threat_dir_h</td>\n",
              "      <td>82.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>threat_norm_h</td>\n",
              "      <td>73.6%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>slur_h</td>\n",
              "      <td>72.2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>slur_homonym_nh</td>\n",
              "      <td>63.3%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>slur_reclaimed_nh</td>\n",
              "      <td>80.2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>profanity_h</td>\n",
              "      <td>50.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>profanity_nh</td>\n",
              "      <td>98.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>ref_subs_clause_h</td>\n",
              "      <td>50.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>ref_subs_sent_h</td>\n",
              "      <td>47.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>negate_pos_h</td>\n",
              "      <td>15.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>negate_neg_nh</td>\n",
              "      <td>96.2%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>phrase_question_h</td>\n",
              "      <td>52.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>phrase_opinion_h</td>\n",
              "      <td>65.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>ident_neutral_nh</td>\n",
              "      <td>100.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>ident_pos_nh</td>\n",
              "      <td>100.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>counter_quote_nh</td>\n",
              "      <td>69.9%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>counter_ref_nh</td>\n",
              "      <td>100.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>target_obj_nh</td>\n",
              "      <td>95.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>target_indiv_nh</td>\n",
              "      <td>63.1%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>target_group_nh</td>\n",
              "      <td>59.7%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>spell_char_swap_h</td>\n",
              "      <td>18.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>spell_char_del_h</td>\n",
              "      <td>36.4%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>spell_space_del_h</td>\n",
              "      <td>55.3%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>spell_space_add_h</td>\n",
              "      <td>11.0%</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>spell_leet_h</td>\n",
              "      <td>35.3%</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
              "        \n",
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              "  "
            ],
            "text/plain": [
              "         functionality model_1\n",
              "0    derog_neg_emote_h   40.7%\n",
              "1   derog_neg_attrib_h   41.4%\n",
              "2        derog_dehum_h   70.7%\n",
              "3         derog_impl_h   18.6%\n",
              "4         threat_dir_h   82.7%\n",
              "5        threat_norm_h   73.6%\n",
              "6               slur_h   72.2%\n",
              "7      slur_homonym_nh   63.3%\n",
              "8    slur_reclaimed_nh   80.2%\n",
              "9          profanity_h   50.0%\n",
              "10        profanity_nh   98.0%\n",
              "11   ref_subs_clause_h   50.7%\n",
              "12     ref_subs_sent_h   47.4%\n",
              "13        negate_pos_h   15.7%\n",
              "14       negate_neg_nh   96.2%\n",
              "15   phrase_question_h   52.1%\n",
              "16    phrase_opinion_h   65.4%\n",
              "17    ident_neutral_nh  100.0%\n",
              "18        ident_pos_nh  100.0%\n",
              "19    counter_quote_nh   69.9%\n",
              "20      counter_ref_nh  100.0%\n",
              "21       target_obj_nh   95.4%\n",
              "22     target_indiv_nh   63.1%\n",
              "23     target_group_nh   59.7%\n",
              "24   spell_char_swap_h   18.0%\n",
              "25    spell_char_del_h   36.4%\n",
              "26   spell_space_del_h   55.3%\n",
              "27   spell_space_add_h   11.0%\n",
              "28        spell_leet_h   35.3%"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# ... compute accuracy\n",
        "func_accuracy_dict = {}\n",
        "for m in models:\n",
        "    func_accuracy_dict[m] = []\n",
        "    for func in pd.unique(hatecheck.functionality):\n",
        "        n_cases = hatecheck[hatecheck.functionality==func].shape[0]\n",
        "        n_correct = hatecheck[(hatecheck.functionality==func)&(hatecheck['label_gold']==hatecheck['pred_{}'.format(m)])].shape[0]\n",
        "        func_accuracy_dict[m].append('{:.1%}'.format(n_correct/n_cases))\n",
        "\n",
        "    # convert list to series\n",
        "    func_accuracy_dict[m] = pd.Series(func_accuracy_dict[m])\n",
        "    func_accuracy_dict[m].name = m\n",
        "\n",
        "# create df from dict\n",
        "func_accuracy_df = pd.Series(pd.unique(hatecheck.functionality))\n",
        "func_accuracy_df.name = 'functionality'\n",
        "\n",
        "for arc_data in func_accuracy_dict:\n",
        "    func_accuracy_df = pd.concat([func_accuracy_df, pd.Series(func_accuracy_dict[arc_data])], axis =1)\n",
        "func_accuracy_df"
      ]
    }
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