LLM_with_noisy_labels.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Fine-Tuning OpenAI Language Models with Noisily and Labeled Data\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/cmauck10/notebooks/blob/master/LLM_with_noisy_labels.ipynb)\n",
        "\n",
        "This notebook demonstrates how data-centric AI tools like cleanlab can improve a fine-tuned Large Language Model (LLM; a.k.a. Foundation Model). Such tools optimize the dataset itself rather than altering the model architecture/hyperparameters — running the exact same fine-tuning code on the improved dataset boosts test-set performance by 37% on a politeness classification task studied here. We achieve similar accuracy gains via the same data-centric AI process across 3 state-of-the-art LLM models one can fine-tune via the OpenAI API: Davinci, Ada, and Curie. These are variants of the base LLM underpinning GPT-3 & ChatGPT.\n",
        "\n",
        "![Improving OpenAI Davinci LLM with Cleanlab (1).png](data:image/png;base64,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)\n",
        "\n",
        "Labeled data powers AI/ML in the enterprise, but real-world datasets have been found to contain between 7-50% annotation errors. Imperfectly-labeled text data hampers the training (and evaluation of) ML models across tasks like intent recognition, entity recognition, and sequence generation. Although pretrained LLMs are equipped with a lot of world knowledge, their performance is adversely affected by noisy training data (as noted by OpenAI).  Here we illustrate data-centric techniques to mitigate the effect of label noise without changing any code related to model architecture, hyperparameters, or training. These data quality improvement techniques should thus remain applicable even for future advanced LLMs like GPT-10."
      ],
      "metadata": {
        "id": "-2oiAi43n1AF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup and Import Data\n",
        "\n"
      ],
      "metadata": {
        "id": "p6U9pHG3n7aw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oDTnuDe4DQfQ"
      },
      "outputs": [],
      "source": [
        "!pip install openai\n",
        "!pip install cleanlab\n",
        "\n",
        "import openai\n",
        "import pandas as pd\n",
        "pd.set_option('max_colwidth', None)\n",
        "import numpy as np\n",
        "import os\n",
        "from cleanlab.filter import find_label_issues\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from openai.embeddings_utils import get_embedding\n",
        "\n",
        "# Import train and test data.\n",
        "train = pd.read_csv(\"https://s.cleanlab.ai/stanford-politeness/fine-tuning/train.csv\")\n",
        "test = pd.read_csv(\"https://s.cleanlab.ai/stanford-politeness/fine-tuning/test.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# You need to set your OpenAI API keys here\n",
        "%env OPENAI_API_KEY = {key}\n",
        "openai.api_key = '{key}'"
      ],
      "metadata": {
        "id": "mX0YYCGuqDN7"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Our Data\n",
        "Here we consider a 3-class variant of the Stanford Politeness Dataset, which has text phrases labeled as: impolite, neutral, or polite. Annotated by human raters, some of these labels are naturally low-quality.\n",
        "\n",
        "Our training dataset has 1916 examples each labeled by a single human annotator, and thus some may be unreliable.\n",
        "\n",
        "The test dataset has 480 examples each labeled by 5 annotators, and we use their consensus label as a high-quality approximation of the true politeness (measuring test accuracy against these consensus labels). To ensure a fair comparison, this test dataset remains fixed throughout our experiments (all label cleaning / dataset modification is only done in the training set). We reformat these CSV files into the jsonl file type required by OpenAI’s fine-tuning API."
      ],
      "metadata": {
        "id": "7EWy8tzVZpsJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "m9gB7s5XnAfI",
        "outputId": "88e6844a-7751-4df7-cc9a-70272956fd87"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                         prompt  \\\n",
              "0  Question: how do we move the content from the existing page to the new page?  How do we reference the space to the new page?   \n",
              "1                                           Why would you doubt that. And for that matter why wouldn't you rewrite it yourself?   \n",
              "2                                 Thank you for closing out this debate.  Can you please restore the history for GFDL purposes?   \n",
              "3                             You may want to take a look at <url>.  Can you be specific on what \"spamming\" is in this context?   \n",
              "4                               The consequences apparently have no sliding scale? It's either \"you're blocked,\" or you're not?   \n",
              "\n",
              "  completion  \n",
              "0    neutral  \n",
              "1   impolite  \n",
              "2    neutral  \n",
              "3    neutral  \n",
              "4   impolite  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9ab3cc9-9f26-4169-9ddb-64a7a32c7341\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>prompt</th>\n",
              "      <th>completion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Question: how do we move the content from the existing page to the new page?  How do we reference the space to the new page?</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Why would you doubt that. And for that matter why wouldn't you rewrite it yourself?</td>\n",
              "      <td>impolite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Thank you for closing out this debate.  Can you please restore the history for GFDL purposes?</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>You may want to take a look at &lt;url&gt;.  Can you be specific on what \"spamming\" is in this context?</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>The consequences apparently have no sliding scale? It's either \"you're blocked,\" or you're not?</td>\n",
              "      <td>impolite</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9ab3cc9-9f26-4169-9ddb-64a7a32c7341')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-e9ab3cc9-9f26-4169-9ddb-64a7a32c7341 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-e9ab3cc9-9f26-4169-9ddb-64a7a32c7341');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 250
        },
        "id": "T8QmoIduZ72P",
        "outputId": "1405c1bf-6fc6-46c6-b159-31c066759687"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                         prompt  \\\n",
              "0                                                                 Excuse me? What are you accusing me of doing?   \n",
              "1                                                                                    I don't understand Tasc0??   \n",
              "2                                                 Well, you leaved me also curious. Why was it not appropriate?   \n",
              "3  Hi, I meant to do this a long time ago, but I guess I forgot.  You are the \"Sandpiper\" on the HP wiki right?   \n",
              "4                                      DONE. Do you think everything is now sorted so I can beep Brain Boulton?   \n",
              "\n",
              "  completion  \n",
              "0   impolite  \n",
              "1   impolite  \n",
              "2   impolite  \n",
              "3    neutral  \n",
              "4    neutral  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-21c30b2e-fddd-4b4a-98d0-be645b3df243\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>prompt</th>\n",
              "      <th>completion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Excuse me? What are you accusing me of doing?</td>\n",
              "      <td>impolite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>I don't understand Tasc0??</td>\n",
              "      <td>impolite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Well, you leaved me also curious. Why was it not appropriate?</td>\n",
              "      <td>impolite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Hi, I meant to do this a long time ago, but I guess I forgot.  You are the \"Sandpiper\" on the HP wiki right?</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DONE. Do you think everything is now sorted so I can beep Brain Boulton?</td>\n",
              "      <td>neutral</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-21c30b2e-fddd-4b4a-98d0-be645b3df243')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-21c30b2e-fddd-4b4a-98d0-be645b3df243 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-21c30b2e-fddd-4b4a-98d0-be645b3df243');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Prepare Data"
      ],
      "metadata": {
        "id": "r3rQ88BXoJjt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Helper method to format data according to OpenAI requirements.\n",
        "# Saves the resulting formatted jsonl file to fname.\n",
        "def format_data(df, fname):\n",
        "  # Save to jsonl file with name fname.\n",
        "  df.to_json(fname, orient='records', lines=True)\n",
        "  # printf \\\"y\\nn\\ny\\n\\\" lets us pipe input arguments into format_data command\n",
        "  # the \\\"y\\nn\\ny\\n\\\" is inputing:\n",
        "  # - y (yes) to add whitespace before completion\n",
        "  # - n (no) to split into train/test\n",
        "  # - y (yes) to save as jsonl file\n",
        "  sys_cmd = f\"printf \\\"y\\nn\\ny\\n\\\" | openai tools fine_tunes.prepare_data -f {fname}\"\n",
        "  # Execute command.\n",
        "  os.system(sys_cmd)\n",
        "  return"
      ],
      "metadata": {
        "id": "hG3bTf13gKye"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Format test and train data.\n",
        "format_data(train, 'train.jsonl')\n",
        "format_data(test, 'test.jsonl')"
      ],
      "metadata": {
        "id": "0narWL2_gK27"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Overview\n",
        "\n",
        "This notebook walks through the following steps:\n",
        "\n",
        "- Use the original data to fine-tune different state-of-the-art LLMs via the OpenAI API: Davinci, Ada, and Curie.\n",
        "- Establish the baseline accuracy of each fine-tuned model on a test set with high-quality labels (established via consensus and high-agreement amongst many human annotators who rated each test example).\n",
        "- Use the `find_label_issues()` method from the cleanlab package to automatically identify hundreds of mislabeled examples.\n",
        "- Remove the data with automatically-flagged label issues from the dataset, and then fine-tune the exact same LLMs on the auto-filtered dataset. **This simple step reduces the error in Davinci model predictions by 8%!**\n",
        "- Use a **no-code** solution to efficiently fix the label errors in the dataset, and then fine-tune the exact same LLM on the fixed dataset. **This reduces the error in Davinci model predictions by 37%!**\n",
        "\n",
        "Similar gains are achieved via these same processes for the Ada and Curie models — in all cases, nothing was changed about the model nor the fine-tuning code!"
      ],
      "metadata": {
        "id": "5x8YYsslZcQ8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fine-Tune and Evaluate OpenAI Model\n",
        "\n",
        "Here’s how our Python code looks to fine-tune the Davinci LLM for 3-class classification and evaluate its test accuracy."
      ],
      "metadata": {
        "id": "tXb5EiDLpGW3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Starts a fine-tuning job using the davinci model.\n",
        "!openai api fine_tunes.create -t \"train_prepared.jsonl\" -v \"test_prepared.jsonl\" --compute_classification_metrics --classification_n_classes 3 -m davinci --suffix \"baseline\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLVKsd_r-zJx",
        "outputId": "5a33042a-21ed-4406-821e-fb5dbf184c64"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found potentially duplicated files with name 'train_prepared.jsonl', purpose 'fine-tune' and size 336697 bytes\n",
            "file-vqt0WzPQIrfrfUIMGLIVPJw3\n",
            "file-g29hvLzawW2NRoAgMJVLf0Jc\n",
            "file-gMn6EF6jG0YZQbOC5Osoe98s\n",
            "Enter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: \n",
            "Upload progress: 100% 337k/337k [00:00<00:00, 388Mit/s]\n",
            "Uploaded file from train_prepared.jsonl: file-BWyYtPvUOj3bEciNpn9PnXH4\n",
            "Found potentially duplicated files with name 'test_prepared.jsonl', purpose 'fine-tune' and size 86248 bytes\n",
            "file-kQ9nvpgBZ4ymSwZTSHrcathS\n",
            "file-A8UDyjjGn77nDSTnQVpKNwZR\n",
            "file-Gn7Xe3h8RzjpBG2bkNt1ZpdU\n",
            "file-ij3XWzohVe7DHe1MfGeOmlIK\n",
            "file-JPRgknZ0XXyJpHVTslOVH5GP\n",
            "file-XMpLsORHGXPHIsPpuEGhCLjT\n",
            "Enter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: \n",
            "Upload progress: 100% 86.2k/86.2k [00:00<00:00, 125Mit/s]\n",
            "Uploaded file from test_prepared.jsonl: file-47roCLnANxzRtxbfZjc0Wta3\n",
            "Created fine-tune: ft-9800F2gcVNzyMdTLKcMqAtJ5\n",
            "Streaming events until fine-tuning is complete...\n",
            "\n",
            "(Ctrl-C will interrupt the stream, but not cancel the fine-tune)\n",
            "[2023-04-03 16:38:08] Created fine-tune: ft-9800F2gcVNzyMdTLKcMqAtJ5\n",
            "\n",
            "Stream interrupted. Job is still pending.\n",
            "To resume the stream, run:\n",
            "\n",
            "  openai api fine_tunes.follow -i ft-9800F2gcVNzyMdTLKcMqAtJ5\n",
            "\n",
            "To cancel your job, run:\n",
            "\n",
            "  openai api fine_tunes.cancel -i ft-9800F2gcVNzyMdTLKcMqAtJ5\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Once the job completes, we query a fine_tunes.results endpoint to see the test accuracy achieved when fine-tuning this LLM on the original training dataset."
      ],
      "metadata": {
        "id": "t3_YPGRebG09"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Query API for results and save them to CSV that we can view.\n",
        "# You will need to change the fine-tune ID from the above cell.\n",
        "!openai api fine_tunes.results -i ft-9800F2gcVNzyMdTLKcMqAtJ5 > baseline-davinci.csv\n",
        "base_df = pd.read_csv('baseline-davinci.csv')\n",
        "baseline_acc = base_df.iloc[-1]['classification/accuracy']\n",
        "print(f\"Fine-tuning Accuracy: {baseline_acc:.1%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C2dUDbsa_G7J",
        "outputId": "67498753-4ab5-4549-bdf0-88a2a28d952a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuning Accuracy: 63.1%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our baseline Davinci LLM achieves a test accuracy of 63% when fine-tuned on the raw training data with possibly noisy labels. Even a state-of-the-art LLM like the Davinci model produces lackluster results for this classification task, is it because the data labels are noisy?"
      ],
      "metadata": {
        "id": "PD6zkVTubI2q"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Automatically Find Label Issues\n",
        "\n",
        "The [cleanlab](https://github.com/cleanlab/cleanlab) Python package employs Confident Learning algorithms to estimate which data are mislabeled in a classification dataset. These algorithms require **out-of-sample** predicted class probabilities for all of our training examples and apply a novel form of calibration to determine when to trust the model over the given label in the data.   \n",
        "\n",
        "To obtain these predicted probabilities we:\n",
        "\n",
        "1. Use the OpenAI API to compute embeddings from the Davinci model for all of our training examples. You can download the embeddings [here](https://cleanlab-public.s3.amazonaws.com/Datasets/stanford-politeness/LLM-blog/train_embed_davinci.npy).\n",
        "2. Fit a logistic regression model on the embeddings and labels in the original data. We use 10-fold cross-validation which allows us to produce out-of-sample predicted class probabilities for every example in the training dataset."
      ],
      "metadata": {
        "id": "AXYcWbg1bOqz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Get Embeddings"
      ],
      "metadata": {
        "id": "0LcB8cTuuWUJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# This model is expensive ($15) so we will load in the embeddings from AWS instead of re-computing them.\n",
        "embedding_model = \"text-similarity-davinci-001\"\n",
        "\n",
        "# You can also use this model which is very cheap (few cents) with slightly less quality.\n",
        "# embedding_model = \"text-embedding-ada-002\"\n",
        "\n",
        "# Get embeddings using the OpenAI embedding API.\n",
        "embedding_df = train.copy()\n",
        "embedding_df[\"embedding\"] = embedding_df.prompt.apply(lambda x: get_embedding(x, engine=embedding_model))"
      ],
      "metadata": {
        "id": "HXlI_W4GgLIJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Load in embeddings computed with Davinci model.\n",
        "!wget -nc https://cleanlab-public.s3.amazonaws.com/Datasets/stanford-politeness/LLM-blog/train_embed_davinci.npy"
      ],
      "metadata": {
        "id": "Sngz6QlSgLL-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Find Label Issues\n",
        "\n",
        "With just one line of code, cleanlab estimates which examples have label issues in our training dataset."
      ],
      "metadata": {
        "id": "esccH4QE7ObZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "embeddings = np.load('train_embed_davinci.npy')\n",
        "model = LogisticRegression()\n",
        "\n",
        "label_to_num={'impolite':0, 'neutral':1, 'polite':2}\n",
        "labels = train.completion\n",
        "labels_num = [label_to_num[l] for l in labels]\n",
        "\n",
        "# Generate out-of-sample predicted probabilities for all of the training data with 10-fold cross validation.\n",
        "pred_probs = cross_val_predict(estimator=model, X=embeddings, y=labels, cv=10, method=\"predict_proba\")\n",
        "\n",
        "# Returns list of indices of label issues, sorted by self_confidence.\n",
        "issue_idx = find_label_issues(labels_num, pred_probs, return_indices_ranked_by='self_confidence')\n",
        "\n",
        "# Show some examples!\n",
        "train.iloc[issue_idx].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "2YyPukPRgLYu",
        "outputId": "50b76883-1fb7-4933-c932-42b2af1cbabf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                                                                                                            prompt  \\\n",
              "633   I don't understand how you find it appropriate to talk about your illegal drug use on your user page.  Also, how do you have the right to ban other people's usernames with a name like \"'''BONG'''warrior\"?   \n",
              "592                                        You see how time consuming and annoying it is when a person keeps on telling you to provide a source. Why do you want ''Michael'' to be just another compilation album?   \n",
              "1089                                                          <url> is one of the three fundamental Wikipedia content policies; why is it a \"shame\" you have to follow it? What did you mean by \"people like you\"?   \n",
              "838                                                                                                                   I'll take a look at getLogEntries when I have time. Would you mind adding me as a committer?   \n",
              "1272                                                     One last thought on groups claiming to be the FI - don't the <url>s claim to be the Fourth International?  Or perhaps it's just *a* Fourth International?   \n",
              "\n",
              "     completion  \n",
              "633      polite  \n",
              "592      polite  \n",
              "1089     polite  \n",
              "838    impolite  \n",
              "1272     polite  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-33e3c8e4-4ef5-461a-beec-a1da019f60ea\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>prompt</th>\n",
              "      <th>completion</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>633</th>\n",
              "      <td>I don't understand how you find it appropriate to talk about your illegal drug use on your user page.  Also, how do you have the right to ban other people's usernames with a name like \"'''BONG'''warrior\"?</td>\n",
              "      <td>polite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>592</th>\n",
              "      <td>You see how time consuming and annoying it is when a person keeps on telling you to provide a source. Why do you want ''Michael'' to be just another compilation album?</td>\n",
              "      <td>polite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1089</th>\n",
              "      <td>&lt;url&gt; is one of the three fundamental Wikipedia content policies; why is it a \"shame\" you have to follow it? What did you mean by \"people like you\"?</td>\n",
              "      <td>polite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>838</th>\n",
              "      <td>I'll take a look at getLogEntries when I have time. Would you mind adding me as a committer?</td>\n",
              "      <td>impolite</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1272</th>\n",
              "      <td>One last thought on groups claiming to be the FI - don't the &lt;url&gt;s claim to be the Fourth International?  Or perhaps it's just *a* Fourth International?</td>\n",
              "      <td>polite</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33e3c8e4-4ef5-461a-beec-a1da019f60ea')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [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-33e3c8e4-4ef5-461a-beec-a1da019f60ea 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-33e3c8e4-4ef5-461a-beec-a1da019f60ea');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let’s take a look at a few of the label issues automatically identified in our dataset. Here’s one example that is clearly mislabeled:\n",
        "\n",
        "- Phrase: *I'll take a look at getLogEntries when I have time. Would you mind adding me as a committer?*\n",
        "- Label: *impolite*\n",
        "\n",
        "Labeling errors like this are why we might be seeing poor model results.\n",
        "\n",
        "**Note: `find_label_issues`is able to determine which of the original `labels` are potentially incorrect given only out-of-sample `pred_probs` from a trained model.**"
      ],
      "metadata": {
        "id": "RUEl1-S-bZzU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Filter label issues and fine-tune a more robust LLM\n",
        "\n",
        "Now that we have the indices of potentially mislabeled examples (identified via automated techniques), let’s remove these 471 examples from our training dataset. Fine-tuning the exact same Davinci LLM on the filtered dataset achieves a test accuracy of 66% (on the same test data where our original Davinci LLM achieved 63% accuracy). We reduced the error-rate of the model by 8% using less but better quality training data!"
      ],
      "metadata": {
        "id": "s0FJWH-S_9zr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove the label errors found by cleanlab.\n",
        "train_dropped = train.drop(issue_idx).reset_index(drop=True)\n",
        "format_data(train_dropped, 'train_dropped.jsonl')"
      ],
      "metadata": {
        "id": "TJ07YO6L7Kvt"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a more robust classifier with less erroneous data.\n",
        "!openai api fine_tunes.create -t \"train_dropped_prepared.jsonl\" -v \"test_prepared.jsonl\" --compute_classification_metrics --classification_n_classes 3 -m davinci --suffix \"dropped\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4xdCW6u6FTpV",
        "outputId": "5ce1c197-79cb-4738-e917-eaf1173340f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found potentially duplicated files with name 'train_dropped_prepared.jsonl', purpose 'fine-tune' and size 252414 bytes\n",
            "file-ITAYOMWmqKTGDm4agNcNl837\n",
            "file-LjY86Rtqf3Xyxa1C99RecG18\n",
            "file-X8gdrPtDD5JR0TcVayhPFHFW\n",
            "file-nipvjQRxN36018PcMYQ5R0vZ\n",
            "Enter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: \n",
            "Upload progress: 100% 252k/252k [00:00<00:00, 230Mit/s]\n",
            "Uploaded file from train_dropped_prepared.jsonl: file-3eaJp9rH3HpLYPZTIoGdJxJz\n",
            "Found potentially duplicated files with name 'test_prepared.jsonl', purpose 'fine-tune' and size 86248 bytes\n",
            "file-A8UDyjjGn77nDSTnQVpKNwZR\n",
            "file-kQ9nvpgBZ4ymSwZTSHrcathS\n",
            "file-Gn7Xe3h8RzjpBG2bkNt1ZpdU\n",
            "file-ij3XWzohVe7DHe1MfGeOmlIK\n",
            "file-JPRgknZ0XXyJpHVTslOVH5GP\n",
            "file-XMpLsORHGXPHIsPpuEGhCLjT\n",
            "file-47roCLnANxzRtxbfZjc0Wta3\n",
            "file-tGOmIEgWSMDsp1JFMoz7dRSX\n",
            "file-MDeMFZCtCSYpzSI7UhgGXDM7\n",
            "file-4ZLDinbfZ2A8ej5x0KsZL0li\n",
            "file-0RePynbyMb1Z6TeRPt3ZneR3\n",
            "file-qwi0VypvDVTG1Py3jspBQ3Fr\n",
            "Enter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: \n",
            "Upload progress: 100% 86.2k/86.2k [00:00<00:00, 103Mit/s]\n",
            "Uploaded file from test_prepared.jsonl: file-muZIcc3lud7FvUqMQJSuKYco\n",
            "Created fine-tune: ft-InhTRQGu11gIDlVJUt0LYbEx\n",
            "Streaming events until fine-tuning is complete...\n",
            "\n",
            "(Ctrl-C will interrupt the stream, but not cancel the fine-tune)\n",
            "[2023-04-03 21:45:59] Created fine-tune: ft-InhTRQGu11gIDlVJUt0LYbEx\n",
            "\n",
            "Stream interrupted. Job is still pending.\n",
            "To resume the stream, run:\n",
            "\n",
            "  openai api fine_tunes.follow -i ft-InhTRQGu11gIDlVJUt0LYbEx\n",
            "\n",
            "To cancel your job, run:\n",
            "\n",
            "  openai api fine_tunes.cancel -i ft-InhTRQGu11gIDlVJUt0LYbEx\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Query API for results and save them to CSV that we can view.\n",
        "# You will need to change the fine-tune ID from the above cell.\n",
        "!openai api fine_tunes.results -i ft-InhTRQGu11gIDlVJUt0LYbEx > dropped-davinci.csv\n",
        "dropped_df = pd.read_csv('dropped-davinci.csv')\n",
        "dropped_acc = dropped_df.iloc[-1]['classification/accuracy']\n",
        "print(f\"Fine-tuning Accuracy: {dropped_acc:.1%}\")"
      ],
      "metadata": {
        "id": "Ks0ZWqeCFb5s",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc6a2e70-2bc3-4f3c-c819-5383d34a37d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuning Accuracy: 66.0%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "PDJsN374cQhi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Fixing the Label Errors\n",
        "\n",
        "Instead of just dropping the potential label issues, the smarter (yet more complex) way to improve our dataset would be to correct the label issues by hand. This simultaneously removes a noisy data point and adds an accurate one, but making such corrections manually is cumbersome.\n",
        "\n",
        "[Cleanlab Studio](https://cleanlab.ai/studio/) provides a user-friendly interface to make these changes **without writing a single line of code.** Simply upload your dataset and Studio computes everything we just did above via provided AI and data-quality algorithms, so you can spend more time *fixing* the issues instead of just *finding* them.\n",
        "\n",
        "![Screen Shot 2023-04-03 at 1.06.50 PM-min.png](data:image/png;base64,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)\n",
        "\n",
        "Studio automatically flags examples it thinks are likely mislabeled and provides suggested label corrections for the relevant data such that a dataset can be quickly improved. Here, we use the auto-fix feature on this dataset and replace the Studio-found label issues with the automatically-suggested label. From data upload to data export, the whole process took only 5 minutes.\n",
        "\n",
        "We then fine-tune the exact same Davinci LLM on the dataset corrected with Cleanlab Studio. The resulting model achieves 77**%** accuracy on the same test dataset as before, which is a **37% reduction in error** from our original version of this model.\n",
        "\n",
        "**Note: throughout this entire process, we never changed any code related to model architecture/hyperparameters, training, or data preprocessing!**  All improvement strictly comes from increasing the quality of our training data, which leaves room for additional optimizations on the modeling side."
      ],
      "metadata": {
        "id": "Fp84Tbe9BIzd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Load in and format data improved with Studio.\n",
        "train_fixed = pd.read_csv('https://s.cleanlab.ai/stanford-politeness/fine-tuning/train_fixed.csv')\n",
        "format_data(train_fixed, 'train_fixed.jsonl')"
      ],
      "metadata": {
        "id": "eluwtfYQ7K0K"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Train a more robust classifier with less erroneous data.\n",
        "!openai api fine_tunes.create -t \"train_fixed_prepared.jsonl\" -v \"test_prepared.jsonl\" --compute_classification_metrics --classification_n_classes 3 -m davinci --suffix \"fixed\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jsTQRHb9_xfp",
        "outputId": "6aecbdba-b4f9-440e-8dcb-3c484099c1f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found potentially duplicated files with name 'train_fixed_prepared.jsonl', purpose 'fine-tune' and size 336831 bytes\n",
            "file-VzAGx7KBPBpxIDC34Tywq3of\n",
            "file-yua1qZd1yc3AOwUFdlZfiLgU\n",
            "file-AxfSD90pmYHJpnULJHHPZt1c\n",
            "Enter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: \n",
            "Upload progress: 100% 337k/337k [00:00<00:00, 398Mit/s]\n",
            "Uploaded file from train_fixed_prepared.jsonl: file-sO6pCkt1etcfbCvIEwoHOayX\n",
            "Found potentially duplicated files with name 'test_prepared.jsonl', purpose 'fine-tune' and size 86248 bytes\n",
            "file-A8UDyjjGn77nDSTnQVpKNwZR\n",
            "file-kQ9nvpgBZ4ymSwZTSHrcathS\n",
            "file-Gn7Xe3h8RzjpBG2bkNt1ZpdU\n",
            "file-ij3XWzohVe7DHe1MfGeOmlIK\n",
            "file-JPRgknZ0XXyJpHVTslOVH5GP\n",
            "file-XMpLsORHGXPHIsPpuEGhCLjT\n",
            "file-47roCLnANxzRtxbfZjc0Wta3\n",
            "file-MDeMFZCtCSYpzSI7UhgGXDM7\n",
            "file-tGOmIEgWSMDsp1JFMoz7dRSX\n",
            "Enter file ID to reuse an already uploaded file, or an empty string to upload this file anyway: \n",
            "Upload progress: 100% 86.2k/86.2k [00:00<00:00, 133Mit/s]\n",
            "Uploaded file from test_prepared.jsonl: file-4ZLDinbfZ2A8ej5x0KsZL0li\n",
            "Created fine-tune: ft-MQbaduYd8UGD2EWBmfpoQpkQ\n",
            "Streaming events until fine-tuning is complete...\n",
            "\n",
            "(Ctrl-C will interrupt the stream, but not cancel the fine-tune)\n",
            "[2023-04-03 21:40:57] Created fine-tune: ft-MQbaduYd8UGD2EWBmfpoQpkQ\n",
            "\n",
            "Stream interrupted. Job is still pending.\n",
            "To resume the stream, run:\n",
            "\n",
            "  openai api fine_tunes.follow -i ft-MQbaduYd8UGD2EWBmfpoQpkQ\n",
            "\n",
            "To cancel your job, run:\n",
            "\n",
            "  openai api fine_tunes.cancel -i ft-MQbaduYd8UGD2EWBmfpoQpkQ\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Query API for results and save them to CSV that we can view.\n",
        "# You will need to change the fine-tune ID from the above cell.\n",
        "!openai api fine_tunes.results -i ft-MQbaduYd8UGD2EWBmfpoQpkQ > fixed-davinci.csv\n",
        "fixed_df = pd.read_csv('fixed-davinci.csv')\n",
        "fixed_acc = fixed_df.iloc[-1]['classification/accuracy']\n",
        "print(f\"Fine-tuning Accuracy: {fixed_acc:.1%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yiLn8exB_93l",
        "outputId": "fc7efad6-7a40-4681-834a-3b10441ae607"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuning Accuracy: 77.3%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluating other LLMs\n",
        "\n",
        "We repeated this same experiment with two other recent LLM models OpenAI offers for fine-tuning: Ada and Curie. The resulting improvements look similar to those achieved for the Davinci model.\n",
        "\n",
        "![Improving OpenAI Models with Cleanlab.png](data:image/png;base64,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)"
      ],
      "metadata": {
        "id": "C_dQVbMde7d8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Curie LLM"
      ],
      "metadata": {
        "id": "SI2XKwZ9famD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fine-tune on original data using the curie model.\n",
        "!openai api fine_tunes.create -t \"train_prepared.jsonl\" -v \"test_prepared.jsonl\" --compute_classification_metrics --classification_n_classes 3 -m curie --suffix \"baseline\""
      ],
      "metadata": {
        "id": "FEIABtkFaYAR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get baseline accuracy.\n",
        "!openai api fine_tunes.results -i ft-5oLnoHQci5uKWrEpzdMtA6JP > baseline-curie.csv\n",
        "base_df = pd.read_csv('baseline-curie.csv')\n",
        "baseline_acc = base_df.iloc[-1]['classification/accuracy']\n",
        "print(f\"Fine-tuning Accuracy: {baseline_acc:.1%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5039gep6ai2C",
        "outputId": "6ec0644a-5ea1-47c1-b64e-fbeff540c3b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuning Accuracy: 66.3%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fine-tune on cleanlab auto-filtered data using the curie model.\n",
        "!openai api fine_tunes.create -t \"train_dropped_prepared.jsonl\" -v \"test_prepared.jsonl\" --compute_classification_metrics --classification_n_classes 3 -m curie --suffix \"dropped\""
      ],
      "metadata": {
        "id": "SpgaxS8TEV1i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get cleanlab auto-filtered accuracy.\n",
        "!openai api fine_tunes.results -i ft-pn9HDPSie8C7Q1nTkvWtvoXZ > dropped-curie.csv\n",
        "dropped_df = pd.read_csv('dropped-curie.csv')\n",
        "dropped_acc = dropped_df.iloc[-1]['classification/accuracy']\n",
        "print(f\"Fine-tuning Accuracy: {dropped_acc:.1%}\")"
      ],
      "metadata": {
        "id": "9SiyvYyAFMw9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a5785bd-b094-403b-d6be-614d6235b368"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuning Accuracy: 65.8%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fine-tune on Studio fixed data using the curie model.\n",
        "!openai api fine_tunes.create -t \"train_fixed_prepared.jsonl\" -v \"test_prepared.jsonl\" --compute_classification_metrics --classification_n_classes 3 -m curie --suffix \"fixed\""
      ],
      "metadata": {
        "id": "Dtvine_o_lad"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get Studio fixed accuracy.\n",
        "!openai api fine_tunes.results -i ft-PbfdxV4pVKNmpxbQYFLlcKY1 > fixed-curie.csv\n",
        "fixed_df = pd.read_csv('fixed-curie.csv')\n",
        "fixed_acc = fixed_df.iloc[-1]['classification/accuracy']\n",
        "print(f\"Fine-tuning Accuracy: {fixed_acc:.1%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MbalwbUYACPi",
        "outputId": "f6681182-d562-475c-9e80-8dba9742f5bc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuning Accuracy: 75.8%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Ada LLM"
      ],
      "metadata": {
        "id": "NvWZk_mBgsxt"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7QgzQqFkcJjX"
      },
      "outputs": [],
      "source": [
        "# Fine-tune on original data using the Ada model.\n",
        "!openai api fine_tunes.create -t \"train_prepared.jsonl\" -v \"test_prepared.jsonl\" --compute_classification_metrics --classification_n_classes 3 -m ada --suffix \"baseline\""
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get baseline accuracy.\n",
        "!openai api fine_tunes.results -i ft-B5njNShNPFMsP1UFH9DMYjFh > baseline-ada.csv\n",
        "base_df = pd.read_csv('baseline-ada.csv')\n",
        "baseline_acc = base_df.iloc[-1]['classification/accuracy']\n",
        "print(f\"Fine-tuning Accuracy: {baseline_acc:.1%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ggyMEd_jgLDp",
        "outputId": "2d069d86-0d23-40c3-e11d-4902d78e66eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuning Accuracy: 61.0%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fine-tune on cleanlab auto-filtered data using the Ada model.\n",
        "!openai api fine_tunes.create -t \"train_dropped_prepared.jsonl\" -v \"test_prepared.jsonl\" --compute_classification_metrics --classification_n_classes 3 -m ada --suffix \"dropped\""
      ],
      "metadata": {
        "id": "cHOW6pe5TwN3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get cleanlab auto-filtered accuracy.\n",
        "!openai api fine_tunes.results -i ft-CETg5biYzyfzjc6lFTdZmTpq > dropped.csv\n",
        "dropped_df = pd.read_csv('dropped.csv')\n",
        "dropped_acc = dropped_df.iloc[-1]['classification/accuracy']\n",
        "print(f\"Fine-tuning Accuracy: {dropped_acc:.1%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yifJi17xAB84",
        "outputId": "35cbad50-bce8-453e-a026-b7b6bf51a6c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuning Accuracy: 67.1%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fine-tune on Studio fixed data using the Ada model.\n",
        "!openai api fine_tunes.create -t \"train_fixed_prepared.jsonl\" -v \"test_prepared.jsonl\" --compute_classification_metrics --classification_n_classes 3 -m ada --suffix \"fixed\""
      ],
      "metadata": {
        "id": "CB6hP1Zr7K4D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get Studio fixed accuracy.\n",
        "!openai api fine_tunes.results -i ft-lmwAPtXHCzBDkB154rVtWWL5 > fixed-ada.csv\n",
        "fixed_df = pd.read_csv('fixed-ada.csv')\n",
        "fixed_acc = fixed_df.iloc[-1]['classification/accuracy']\n",
        "print(f\"Fine-tuning Accuracy: {fixed_acc:.1%}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sGRjFLf9BpkE",
        "outputId": "ac57f473-cdf5-490f-edeb-cc2973812c57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fine-tuning Accuracy: 72.7%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Conclusion\n",
        "\n",
        "Data-centric AI is a powerful paradigm for handling noisy data via AI/automated techniques rather than the tedious manual effort data scientists often dread. Tools like [Cleanlab](https://cleanlab.ai/studio/) help you efficiently find and fix data and label issues that can be used to improve any ML model (not just LLMs) for most types of data (not just text, but also images, audio, tabular data, etc).  [Open-source versions](https://github.com/cleanlab/cleanlab) of such tools can *utilize* any ML model to diagnose/fix issues in the data and then improve the data *for* any other ML model. [No-code platforms](https://cleanlab.ai/studio) come with good ML models built-in and interfaces to quickly correct data issues.\n",
        "\n",
        "These sorts of tools will still remain applicable with future advances in ML models like GPT-10, and will only become better at identifying issues when used with more accurate models!  Practice data-centric AI to systematically engineer better data via AI/automation. This frees you to capitalize on your unique domain knowledge rather than fixing general data issues like label errors.\n",
        "\n",
        "# Next Steps\n",
        "\n",
        "- Star the [github](https://github.com/cleanlab/cleanlab) repo to support open-source Data-centric AI development.\n",
        "- Join the [Community Slack](https://cleanlab.ai/slack/) to discuss how you’re practicing data-centric AI."
      ],
      "metadata": {
        "id": "tdOyRKMMhcGK"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}