content_filter_example.ipynb

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  "metadata": {
    "colab": {
      "name": "content_filter_example.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPrfCiqd1UV10Jl0zMSix7m",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/rafajak/gpt3_examples/blob/master/content_filter_example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hF5RFj871vO8",
        "outputId": "3a7f11f6-f398-4933-e087-723219ff3095",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        }
      },
      "source": [
        "!pip install openai\n",
        "!pip install transformers\n",
        "\n",
        "from google.colab import files\n",
        "import openai\n",
        "from transformers import GPT2Tokenizer\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import json"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: openai in /usr/local/lib/python3.6/dist-packages (0.2.4)\n",
            "Requirement already satisfied: requests>=2.20; python_version >= \"3.0\" in /usr/local/lib/python3.6/dist-packages (from openai) (2.23.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20; python_version >= \"3.0\"->openai) (2020.6.20)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20; python_version >= \"3.0\"->openai) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20; python_version >= \"3.0\"->openai) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20; python_version >= \"3.0\"->openai) (2.10)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.6/dist-packages (3.2.0)\n",
            "Requirement already satisfied: sentencepiece!=0.1.92 in /usr/local/lib/python3.6/dist-packages (from transformers) (0.1.91)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.6/dist-packages (from transformers) (20.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.6/dist-packages (from transformers) (3.0.12)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.6/dist-packages (from transformers) (2019.12.20)\n",
            "Requirement already satisfied: tokenizers==0.8.1.rc2 in /usr/local/lib/python3.6/dist-packages (from transformers) (0.8.1rc2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.6/dist-packages (from transformers) (4.41.1)\n",
            "Requirement already satisfied: dataclasses; python_version < \"3.7\" in /usr/local/lib/python3.6/dist-packages (from transformers) (0.7)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from transformers) (1.18.5)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.6/dist-packages (from transformers) (2.23.0)\n",
            "Requirement already satisfied: sacremoses in /usr/local/lib/python3.6/dist-packages (from transformers) (0.0.43)\n",
            "Requirement already satisfied: pyparsing>=2.0.2 in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (2.4.7)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from packaging->transformers) (1.15.0)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2020.6.20)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (1.24.3)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests->transformers) (2.10)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (0.16.0)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.6/dist-packages (from sacremoses->transformers) (7.1.2)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "id": "UAIVY9921yK_",
        "outputId": "eb8ab5fd-fbe7-4863-d9e0-39315d7a9ade",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "# To keep your secret API key secure - create a api_key.json file containing the key and upload it with the function below. \n",
        "# My config file looks like this: {\"api_key\": \"heresmysecretkey\"}\n",
        "\n",
        "upload = files.upload()\n",
        "openai.api_key = json.load(open(\"api_key.json\", \"r\"))[\"api_key\"]"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-30e25c5a-f7dc-4dbb-9993-2006eebf8422\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-30e25c5a-f7dc-4dbb-9993-2006eebf8422\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving api_key.json to api_key (1).json\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ysk-P-bk11tg"
      },
      "source": [
        "#arguments to send the API\n",
        "text_to_classify = \"Dang, this falafel is freaking fantastic\"\n",
        "\n",
        "kwargs = {\"engine\":\"content-filter-alpha-c4\",\n",
        "          \"temperature\":0,\n",
        "          \"max_tokens\":1,\n",
        "          \"top_p\":0,\n",
        "          \"logprobs\":3,\n",
        "          \"prompt\":f\"<|endoftext|>{text_to_classify}\\n--\\nLabel:\"\n",
        "          }\n",
        "\n",
        "r = openai.Completion.create(**kwargs)"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qx3NhzP2_Xqm"
      },
      "source": [
        "\n",
        "    0 - The text is safe.\n",
        "\n",
        "    1 - This text is sensitive. This means that the text could be talking \n",
        "        about a sensitive topic, something political, religious, or talking\n",
        "        about a protected class such as race or nationality.\n",
        "        \n",
        "    2 - This text is unsafe. This means that the text contains profane\n",
        "        language, prejudiced or hateful language, something that could be \n",
        "        NSFW, or text that portrays certain groups/people in a harmful manner.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u2Xx5beM5_AB",
        "outputId": "08aba68c-656c-4c5e-c07b-78c5c0a9f965",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        }
      },
      "source": [
        "#Let's see how did the content classifier label the text (text_to_classify)\n",
        "outputs = pd.DataFrame(r[\"choices\"][0][\"logprobs\"][\"top_logprobs\"]).T\n",
        "outputs.index = [\"safe_0\",\"sensitive_1\",\"unsafe_2\"]\n",
        "outputs"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>safe_0</th>\n",
              "      <td>-0.424900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sensitive_1</th>\n",
              "      <td>-3.065525</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unsafe_2</th>\n",
              "      <td>-1.206150</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                    0\n",
              "safe_0      -0.424900\n",
              "sensitive_1 -3.065525\n",
              "unsafe_2    -1.206150"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EvaEUxvXAMEd",
        "outputId": "f21e27bd-6293-4493-9a2c-4636a9f98073",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "print(f\"Text '{text_to_classify}' was classified as {outputs.idxmax()[0]}\")"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Text 'Dang, this falafel is freaking fantastic' was classified as safe_0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E79wAK_4-VZe",
        "outputId": "3f48ba13-88c0-4365-df51-6cd4311226f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        }
      },
      "source": [
        "#View as probabilities:\n",
        "outputs = outputs.T\n",
        "outputs = outputs.apply(lambda x: np.round(np.e**x,2))\n",
        "outputs"
      ],
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>safe_0</th>\n",
              "      <th>sensitive_1</th>\n",
              "      <th>unsafe_2</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.65</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   safe_0  sensitive_1  unsafe_2\n",
              "0    0.65         0.05       0.3"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MUd2IgPiAA-u"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}