curate_dolly_es_with_argilla.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/mariagrandury/devfest-cloud-madrid/blob/main/curate_dolly_es_with_argilla.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_5kaFb4dOPD"
      },
      "source": [
        "# 🖼️ Curate an instruction dataset for supervised fine-tuning\n",
        "\n",
        "- YouTube tutorial: https://www.youtube.com/watch?v=0gvpsMfNzVc&t=175s\n",
        "- Blog tutorial: https://docs.argilla.io/en/latest/guides/llms/examples/curating-feedback-instructiondataset.html\n",
        "- Original colab: https://colab.research.google.com/github/argilla-io/argilla/blob/main/docs/_source/guides/llms/examples/curating-feedback-instructiondataset.ipynb\n",
        "\n",
        "---\n",
        "\n",
        "The internet is flooding with open-source datasets for fine-tuning LLMs, some created by humans, others generated with generative models. However, these datasets often have many problematic and low-quality examples. By curating them, we can make the fine-tuning step more efficient.\n",
        "\n",
        "In this example, we will demonstrate how to replicate the [Databricks' Dolly dataset](https://huggingface.co/datasets/argilla/databricks-dolly-15k-curated-multilingual) cleaning effort started by the Argilla community. This will show how you can build an instruction dataset for your fine-tuning projects by cleaning a public dataset using Argilla's Feedback Task.\n",
        "\n",
        "Let's get started!\n",
        "\n",
        "<img src=\"https://github.com/argilla-io/argilla/blob/main/docs/_source/_static/images/llms/curating-feedback-instructiondataset/snapshot_dolly_curation.png?raw=1\" alt=\"A Feedback Task setting for the curation of Databricks' Dolly dataset\" style=\"width: 1100px;\">\n",
        "\n",
        "<div class=\"alert alert-info\">\n",
        "\n",
        "Note\n",
        "\n",
        "This tutorial is a Jupyter Notebook. There are two options to run it:\n",
        "\n",
        "- Use the Open in Colab button at the top of this page. This option allows you to run the notebook directly on Google Colab. Don't forget to change the runtime type to GPU for faster model training and inference.\n",
        "- Download the .ipynb file by clicking on the View source link at the top of the page. This option allows you to download the notebook and run it on your local machine or on a Jupyter notebook tool of your choice.\n",
        "\n",
        "You can also watch a simplified version of this tutorial in video format:\n",
        "\n",
        "<iframe width=\"100%\" height=\"450\" src=\"https://www.youtube.com/embed/0gvpsMfNzVc\" title=\"YouTube video player\" frameborder=\"0\" allow=\"accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share\" allowfullscreen></iframe>\n",
        "\n",
        "</div>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AW3DexKWdOPG"
      },
      "source": [
        "## Setup\n",
        "\n",
        "For this tutorial, you will need to have an Argilla server running. If you don't have one already, check out our [Quickstart](../../../getting_started/quickstart.md) or [Installation](../../../getting_started/installation/installation.md) pages. Once you do, complete the following steps:\n",
        "\n",
        "1. Install the Argilla client and the required third party libraries using `pip`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j7IiXgekdOPH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac7a7204-7223-4504-e216-d8b112ddcd7c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.6/2.6 MB\u001b[0m \u001b[31m6.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.6/69.6 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m51.1/51.1 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m58.3/58.3 kB\u001b[0m \u001b[31m7.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install --upgrade argilla datasets pandas plotly -qqq"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "djBxK2eNdOPI"
      },
      "source": [
        "2. Let's make the necessary imports:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QTrQuWUcdOPJ"
      },
      "outputs": [],
      "source": [
        "import argilla as rg\n",
        "from datasets import Dataset, load_dataset\n",
        "import pandas as pd\n",
        "import random\n",
        "from collections import defaultdict\n",
        "import plotly.express as px"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ndnra_-ZdOPJ"
      },
      "source": [
        "3. If you are running Argilla using the Docker quickstart image or Hugging Face Spaces, you need to init the Argilla client with the `URL` and `API_KEY`:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aZqVrHHrdOPK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b15ee0a2-9e2c-4a44-cd57-51c5969792f4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/argilla/client/client.py:153: UserWarning: No workspace configuration was detected. To work with Argilla  datasets, specify a valid workspace name on `rg.init` or set it up through the `rg.set_workspace` function.\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "# Replace api_url with the url to your HF Spaces URL if using Spaces\n",
        "# Replace api_key if you configured a custom API key\n",
        "rg.init(\n",
        "    api_url=\"https://mariagrandury-curate-dataset-using-argilla.hf.space/\",\n",
        "    api_key=\"admin.apikey\"\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Log into Hugging Face"
      ],
      "metadata": {
        "id": "qv_wCpyAYCcs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xVUru4fOYJPD",
        "outputId": "97701932-c4e2-430b-8ec5-04257f0cab6f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.16.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\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) (4.7.1)\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) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.5.7)\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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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            "2b970bb2feea4e4f837398155898be15",
            "eb0155fe83814107a331dc11af2d6200",
            "f4087f0ddc514c15bd2805087571af5e",
            "94a2f49a632d4e9a8f646dc3af90ea04",
            "b29105fd55204025b994d343cceeb9a0",
            "dc0c520ca68740558b9c554a320a9075",
            "61664386d8a249b599e7e10e337cc2b1",
            "c0eca3d840f74237ba54980f20dca708",
            "c4221e332898463494a1c1b2f82cd2b1",
            "180fee7a33ce4fcba7d85caaae130b21",
            "1775163cb0b84f3aaf40e58cd46e06e2",
            "bbbd2368a6e445fb81e87dd33cee29b9",
            "bb171df1678c4e28879c2468b2dce3f4",
            "86f52516cc05456f833347292b3ef9cc",
            "a9e1e77d39a148429e86642bd67b71b0",
            "91e63939b4974996ba022962e49e9a7b",
            "8c7dc39bde6e4a1cbeddaee82d5b8b1c",
            "67fb3aa214774eefba29f0ef22cf2138",
            "d0d7f7a812c047668030d9842a930497",
            "b47b9ccd9c0640f6b1678aceb77cd78a",
            "40b97dbf71604d1ebbaaf3fb80ed0ea2"
          ]
        },
        "id": "0NsWB7LwYn5x",
        "outputId": "829968ce-8cab-49eb-d503-2a20df264e02"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "84212c3ae5f340ef89c4358e5c31c68d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TGGJQsusdOPK"
      },
      "source": [
        "## Define the project\n",
        "As a first step, let's load the dataset and quickly explore the data:"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data = load_dataset(\"argilla/databricks-dolly-15k-curated-multilingual\", split=\"es\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PnIaApvKbRha",
        "outputId": "e1d4482c-1f49-4667-b004-b934af389dcb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:datasets.builder:Found cached dataset parquet (/root/.cache/huggingface/datasets/argilla___parquet/argilla--databricks-dolly-15k-curated-multilingual-689355e8f2754010/0.0.0/14a00e99c0d15a23649d0db8944380ac81082d4b021f398733dd84f3a6c569a7)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-Y7hB1MtdOPL",
        "outputId": "341e4907-0e41-40c9-909b-296210363a35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 745
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                             instruction  \\\n",
              "0              ¿Cuándo empezó a operar Virgin Australia?   \n",
              "1             ¿Cuál es una especie de pez? Topo o cuerda   \n",
              "2      ¿Por qué los camellos pueden sobrevivir mucho ...   \n",
              "3      Los padres de Alice tienen tres hijas: Amy, Je...   \n",
              "4                        ¿Cuándo nació Tomoaki Komorida?   \n",
              "...                                                  ...   \n",
              "15010                            ¿Cómo acepto el cambio?   \n",
              "15011                  ¿Qué es un láser y quién lo creó?   \n",
              "15012  ¿Cuál es la diferencia entre una bicicleta de ...   \n",
              "15013  ¿Cómo ayuda el SIG en el sector de la inversió...   \n",
              "15014                             ¿Qué son los Maestros?   \n",
              "\n",
              "                                                 context  \\\n",
              "0      Virgin Australia, nombre comercial de Virgin A...   \n",
              "1                                                          \n",
              "2                                                          \n",
              "3                                                          \n",
              "4      Komorida nació en la prefectura de Kumamoto el...   \n",
              "...                                                  ...   \n",
              "15010                                                      \n",
              "15011  Un láser es un dispositivo que emite luz media...   \n",
              "15012                                                      \n",
              "15013                                                      \n",
              "15014                                                      \n",
              "\n",
              "                                                response        category  \\\n",
              "0      Virgin Australia inició sus servicios el 31 de...       closed_qa   \n",
              "1                                                   Tope  classification   \n",
              "2      Los camellos utilizan la grasa de sus jorobas ...         open_qa   \n",
              "3                 El nombre de la tercera hija es Alicia         open_qa   \n",
              "4         Tomoaki Komorida nació el 10 de julio de 1981.       closed_qa   \n",
              "...                                                  ...             ...   \n",
              "15010             Acepta el cambio y verás la diferencia   brainstorming   \n",
              "15011  Un láser es un dispositivo que emite luz a par...   summarization   \n",
              "15012  Las bicicletas de carretera están diseñadas pa...         open_qa   \n",
              "15013  Los inversores inmobiliarios dependen de una i...      general_qa   \n",
              "15014  El Torneo de Maestros es un torneo de golf que...      general_qa   \n",
              "\n",
              "                                 instruction_original_en  \\\n",
              "0             When did Virgin Australia start operating?   \n",
              "1               Which is a species of fish? Tope or Rope   \n",
              "2         Why can camels survive for long without water?   \n",
              "3      Alice's parents have three daughters: Amy, Jes...   \n",
              "4                        When was Tomoaki Komorida born?   \n",
              "...                                                  ...   \n",
              "15010                         How do i accept the change   \n",
              "15011                What is a laser and who created it?   \n",
              "15012  What is the difference between a road bike and...   \n",
              "15013  How does GIS help in the real estate investmen...   \n",
              "15014                               What is the Masters?   \n",
              "\n",
              "                                     context_original_en  \\\n",
              "0      Virgin Australia, the trading name of Virgin A...   \n",
              "1                                                          \n",
              "2                                                          \n",
              "3                                                          \n",
              "4      Komorida was born in Kumamoto Prefecture on Ju...   \n",
              "...                                                  ...   \n",
              "15010                                                      \n",
              "15011  A laser is a device that emits light through a...   \n",
              "15012                                                      \n",
              "15013                                                      \n",
              "15014                                                      \n",
              "\n",
              "                                    response_original_en     id  \n",
              "0      Virgin Australia commenced services on 31 Augu...      0  \n",
              "1                                                   Tope      1  \n",
              "2      Camels use the fat in their humps to keep them...      2  \n",
              "3                The name of the third daughter is Alice      3  \n",
              "4             Tomoaki Komorida was born on July 10,1981.      4  \n",
              "...                                                  ...    ...  \n",
              "15010          Embrace the change and see the difference  15010  \n",
              "15011  A laser is a device that emits light from an e...  15011  \n",
              "15012  Road bikes are built to be ridden on asphalt a...  15012  \n",
              "15013  Real estate investors depend on precise, accur...  15013  \n",
              "15014  The Masters Tournament is a golf tournament he...  15014  \n",
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              "      <td>¿Cuándo empezó a operar Virgin Australia?</td>\n",
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              "      <td>3</td>\n",
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              "      <th>4</th>\n",
              "      <td>¿Cuándo nació Tomoaki Komorida?</td>\n",
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              "      <td>Tomoaki Komorida was born on July 10,1981.</td>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>Acepta el cambio y verás la diferencia</td>\n",
              "      <td>brainstorming</td>\n",
              "      <td>How do i accept the change</td>\n",
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              "      <td>15011</td>\n",
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              "      <td>open_qa</td>\n",
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              "      <td>15012</td>\n",
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              "      <th>15013</th>\n",
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              "      <td></td>\n",
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              "      <td>general_qa</td>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
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              "            '<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",
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            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "df = data.to_pandas()\n",
        "df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UVMKwkK3dOPM"
      },
      "source": [
        "For our project, we would like to make sure that the `instruction`, the `context` and the `response` are formulated clearly and concisely, and that they provide correct information. We will add those fields to our records. The `category` will help our annotators know what these fields should look like, so we will add it too. We can also keep the `id` so we can identify the records easily on post-processing."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hxLwxcA6dOPM"
      },
      "outputs": [],
      "source": [
        "# format the data as Argilla records\n",
        "records = [rg.FeedbackRecord(fields={\"category\": record[\"category\"], \"instruction\": record[\"instruction\"], \"response\": record[\"response\"], \"context\": record[\"context\"]}, external_id=record['id']) for record in data]\n",
        "\n",
        "# list of fields that we will use later for our dataset settings\n",
        "fields = [\n",
        "    rg.TextField(name=\"category\", title=\"Task category\"),\n",
        "    rg.TextField(name=\"instruction\"),\n",
        "    rg.TextField(name=\"context\", title=\"Input\", required=False),\n",
        "    rg.TextField(name=\"response\")\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3zRJfsR8dOPM"
      },
      "source": [
        "Now we can think of the questions that we would like to ask about these records and we will provide some guidelines for the annotators."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cPkzWcjldOPM"
      },
      "outputs": [],
      "source": [
        "# list of questions to display in the feedback form\n",
        "questions =[\n",
        "    rg.TextQuestion(\n",
        "        name=\"new-instruction\",\n",
        "        title=\"Final instruction:\",\n",
        "        description=\"Write the final version of the instruction, making sure that it matches the task category. If the original instruction is ok, copy and paste it here.\",\n",
        "        required=True\n",
        "    ),\n",
        "    rg.TextQuestion(\n",
        "        name=\"new-input\",\n",
        "        title=\"Final input:\",\n",
        "        description=\"Write the final version of the input, making sure that it makes sense with the task category. If the original input is ok, copy and paste it here. If an input is not needed, leave this empty.\",\n",
        "        required=False\n",
        "    ),\n",
        "    rg.TextQuestion(\n",
        "        name=\"new-response\",\n",
        "        title=\"Final response:\",\n",
        "        description=\"Write the final version of the response, making sure that it matches the task category and makes sense for the instruction (and input) provided. If the original response is ok, copy and paste it here.\",\n",
        "        required=True\n",
        "    )\n",
        "]\n",
        "\n",
        "guidelines = \"In this dataset, you will find a collection of records that show a category, an instruction, an input and a response to that instruction. The aim of the project is to correct the instructions, intput and responses to make sure they are of the highest quality and that they match the task category that they belong to. All three texts should be clear and include real information. In addition, the response should be as complete but concise as possible.\\n\\nTo curate the dataset, you will need to provide an answer to the following text fields:\\n\\n1 - Final instruction:\\nThe final version of the instruction field. You may copy it using the copy icon in the instruction field. Leave it as it is if it's ok or apply any necessary corrections. Remember to change the instruction if it doesn't represent well the task category of the record.\\n\\n2 - Final input:\\nThe final version of the instruction field. You may copy it using the copy icon in the input field. Leave it as it is if it's ok or apply any necessary corrections. If the task category and instruction don't need of an input to be completed, leave this question blank.\\n\\n3 - Final response:\\nThe final version of the response field. You may copy it using the copy icon in the response field. Leave it as it is if it's ok or apply any necessary corrections. Check that the response makes sense given all the fields above.\\n\\nYou will need to provide at least an instruction and a response for all records. If you are not sure about a record and you prefer not to provide a response, click Discard.\""
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Push dataset to Argilla\n",
        "\n",
        "Run the cell below if you don't want to split the workload. Otherwise, check the section \"Split the workload and import to Argilla\"."
      ],
      "metadata": {
        "id": "qRhLO7lIe-i5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = rg.FeedbackDataset(\n",
        "    guidelines=guidelines,\n",
        "    fields=fields,\n",
        "    questions=questions\n",
        ")\n",
        "dataset.add_records(records)\n",
        "dataset.push_to_argilla(name='databricks-dolly-15k-curated-es', workspace=\"admin\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kmHtHINifChb",
        "outputId": "782277c3-417b-4d3e-fbb7-a226b768b077"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Pushing records to Argilla...: 100%|██████████| 250/250 [00:00<00:00, 39153.73it/s]\n",
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "... annotate"
      ],
      "metadata": {
        "id": "Z_Ftz9HfhT7m"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Publish the results"
      ],
      "metadata": {
        "id": "c6WxcAdohHGa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "curated_dataset = rg.FeedbackDataset.from_argilla(\"databricks-dolly-15k-curated-es\", workspace=\"admin\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "u3HtmoWThWLD",
        "outputId": "fbb80f27-2d58-48b5-c188-14aa7f274665"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/argilla/client/feedback/dataset.py:363: UserWarning: `user_id` not provided, so it will be set to `None`. Which is not an issue, unless you're planning to log the response in Argilla, as  it will be automatically set to the active `user_id`.\n",
            "  self.__records = parse_obj_as(List[FeedbackRecord], first_batch.items)\n",
            "Fetching records from Argilla: 1it [00:00, ?it/s]/usr/local/lib/python3.10/dist-packages/argilla/client/feedback/dataset.py:377: UserWarning: `user_id` not provided, so it will be set to `None`. Which is not an issue, unless you're planning to log the response in Argilla, as  it will be automatically set to the active `user_id`.\n",
            "  records = parse_obj_as(List[FeedbackRecord], batch.items)\n",
            "Fetching records from Argilla: 61it [01:03,  1.06s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.push_to_huggingface(\"mariagrandury/databricks-dolly-15k-curated-es\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "9839659ee1f647ef8786854b80d08e18",
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            "abd2143716224949a81aadf6065e53ca",
            "86632a7fdeca433ebe9b3c3c840495db",
            "4eac34de94124051af6a6ee156152f56",
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            "d6bb516e1c6e425c9f64d5b46c8f00f9",
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            "f1cbc16739514f0f9281ca67056b53c3",
            "e9aba57967d84a66a52c2d132781592d",
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            "83ae288cbbb947f3ab9599285e924ad7",
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            "2b4365a7d0454bb7986a8389e222a0a1"
          ]
        },
        "id": "8R87bA_phkcM",
        "outputId": "00a22152-185b-4a12-99ef-90b6b58c8fce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Pushing dataset shards to the dataset hub:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9839659ee1f647ef8786854b80d08e18"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Creating parquet from Arrow format:   0%|          | 0/16 [00:00<?, ?ba/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f1cbc16739514f0f9281ca67056b53c3"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UXRfr1rIdOPM"
      },
      "source": [
        "\n",
        "## Split the workload and import to Argilla\n",
        "For this specific project, we don't want any overlap between our annotation team, as we only want one unique version of each record. We'll assume that the annotations of our team have the desired quality to work as demonstration data for our instruction-following model.\n",
        "\n",
        ".. tip:: For extra quality assurance, you can make a new dataset where annotators rate the quality of the human annotated dataset.\n",
        "\n",
        "To avoid having multiple responses for a record, we will split the workload between all of our annotators and import the records assigned to them in a dataset in their personal workspace.\n",
        "\n",
        "First, let's get the list of users that will be annotating this data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Xh5Yv5bpdOPM"
      },
      "outputs": [],
      "source": [
        "users = [user for user in rg.User.list() if user.role =='annotator']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uv3tux5vdOPM"
      },
      "source": [
        "When we're happy with the list of users, we can move on to do the assignments:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YtVu9pCydOPM"
      },
      "outputs": [],
      "source": [
        "# shuffle the records to get a random assignment\n",
        "random.shuffle(records)\n",
        "\n",
        "# build a dictionary where the key is the username and the value is the list of records assigned to them\n",
        "assignments = defaultdict(list)\n",
        "\n",
        "# divide your records in chunks of the same length as the users list and make the assignments\n",
        "# you will need to follow the instructions to create and push a dataset for each of the key-value pairs in this dictionary\n",
        "n = len(users)\n",
        "chunked_records = [records[i:i + n] for i in range(0, len(records), n)]\n",
        "for chunk in chunked_records:\n",
        "    for idx, record in enumerate(chunk):\n",
        "        assignments[users[idx].username].append(record)\n",
        "\n",
        "for username,records in assignments.items():\n",
        "    # check that the user has a personal workspace and create it if not\n",
        "    try:\n",
        "        workspace = rg.Workspace.from_name(username)\n",
        "    except:\n",
        "        workspace = rg.Workspace.create(username)\n",
        "        user = rg.User.from_name(username)\n",
        "        workspace.add_user(user.id)\n",
        "\n",
        "    # create a dataset for each annotator and push it to their personal workspace\n",
        "    dataset = rg.FeedbackDataset(\n",
        "        guidelines=guidelines,\n",
        "        fields=fields,\n",
        "        questions=questions\n",
        "    )\n",
        "    dataset.add_records(records)\n",
        "    dataset.push_to_argilla(name='curate_dolly', workspace=workspace.name)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zvpD61hJdOPN"
      },
      "source": [
        "## Collect feedback\n",
        "\n",
        "At this point, the datasets are ready to start the annotation. Once the annotations are done, we will collect all the feedback from our team and combine it in a single dataset."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j8PbhmTudOPN"
      },
      "outputs": [],
      "source": [
        "feedback = []\n",
        "for username in assignments.keys():\n",
        "    feedback.extend(rg.FeedbackDataset.from_argilla('curate_dolly', workspace=username))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dEgXNdRfdOPN"
      },
      "source": [
        "Let's explore the dataset a bit so we can draw some conclusions about it:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pZAj6-lKdOPN"
      },
      "outputs": [],
      "source": [
        "responses = []\n",
        "\n",
        "for record in feedback:\n",
        "    if record.responses is None or len(record.responses) == 0:\n",
        "        continue\n",
        "\n",
        "    # we should only have 1 response per record, so we can safely use the first one only\n",
        "    response = record.responses[0]\n",
        "\n",
        "    if response.status != 'submitted':\n",
        "        changes = []\n",
        "    else:\n",
        "        changes = []\n",
        "        if response.values['new-instruction'].value != record.fields['instruction']:\n",
        "            changes.append('instruction')\n",
        "        if response.values['new-input'].value != record.fields['context']:\n",
        "            changes.append('input')\n",
        "        if response.values['new-response'].value != record.fields['response']:\n",
        "            changes.append('response')\n",
        "\n",
        "    responses.append({'status': response.status, 'category': record.fields['category'], 'changes': ','.join(changes)})\n",
        "\n",
        "responses_df = pd.DataFrame(responses)\n",
        "responses_df = responses_df.replace('', 'None')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "knjvRPoLdOPN"
      },
      "outputs": [],
      "source": [
        "fig = px.histogram(responses_df, x='status')\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Muea5uJkdOPN"
      },
      "source": [
        "![Plot showing the number of submitted and discarded responses](https://github.com/argilla-io/argilla/blob/main/docs/_source/_static/images/llms/curating-feedback-instructiondataset/plot_submitted_discarded.png?raw=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1EHGoIxVdOPN"
      },
      "source": [
        "We can see that the majority of the records have submitted responses. That means that we are not losing too much data during the annotation project.\n",
        "\n",
        ".. tip:: If an important percentage of the records have a discarded response, you can take all the discarded records and serve them to a different annotator as long as you are using the `Discard` button as a way for your annotation team to skip records.\n",
        "\n",
        "Now, let's check how many of our submitted responses proposed modifications to the original text:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Itcc4JH3dOPN"
      },
      "outputs": [],
      "source": [
        "fig = px.histogram(responses_df.loc[responses_df['status']=='submitted'], x='changes')\n",
        "fig.update_xaxes(categoryorder='total descending')\n",
        "fig.update_layout(bargap=0.2)\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y9gxUe4jdOPN"
      },
      "source": [
        "![Plot showing the fields that were modified](https://github.com/argilla-io/argilla/blob/main/docs/_source/_static/images/llms/curating-feedback-instructiondataset/plot_modified_fields.png?raw=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVD8z_8OdOPN"
      },
      "source": [
        "As we can see here, an important percentage of the submitted responses considered that the records needed at least one modification and that the response is the field most likely to be modified."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ha1k3atwdOPO"
      },
      "source": [
        "We could publish the dataset as it is now, but for this example we'll do a little post-processing to simplify the fields and substitute the old instruction and response with the new version provided by our annotators. That way, we have a dataset that's fully ready for fine-tuning."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "su2NfkZDdOPO"
      },
      "outputs": [],
      "source": [
        "new_records = []\n",
        "for record in feedback:\n",
        "    if record.responses:\n",
        "        continue\n",
        "    # we should only have 1 response per record, so we can safely use the first one only\n",
        "    response = record.responses[0]\n",
        "    # we will skip records where our annotators didn't submit their feedback\n",
        "    if response.status != 'submitted':\n",
        "        continue\n",
        "\n",
        "    record.fields['instruction'] = response.values['new-instruction'].value\n",
        "    record.fields['context'] = response.values['new-input'].value\n",
        "    record.fields['response'] = response.values['new-response'].value\n",
        "\n",
        "    new_records.append(record.fields)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NdRSVRkWdOPO"
      },
      "source": [
        "Let's check how it looks:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "amXTmyJGdOPO",
        "outputId": "c29e1c98-bf49-4ad4-df0e-8d32923f4bad"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>instruction</th>\n",
              "      <th>context</th>\n",
              "      <th>response</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>open_qa</td>\n",
              "      <td>How do I get rid of mosquitos in my house?</td>\n",
              "      <td></td>\n",
              "      <td>You can get rid of mosquitos in your house by ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>classification</td>\n",
              "      <td>Classify each country as \"African\" or \"Europea...</td>\n",
              "      <td></td>\n",
              "      <td>Nigeria: African\\nRwanda: African\\nPortugal: E...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>information_extraction</td>\n",
              "      <td>Extract the unique names of composers from the...</td>\n",
              "      <td>To some extent, European and the US traditions...</td>\n",
              "      <td>Pierre Boulez, Luigi Nono, Karlheinz Stockhaus...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>general_qa</td>\n",
              "      <td>Should investors time the market?</td>\n",
              "      <td></td>\n",
              "      <td>Timing the market is based on predictions of t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>classification</td>\n",
              "      <td>I am looking at items on my desk right now, te...</td>\n",
              "      <td></td>\n",
              "      <td>Trident mint, Macbook Pro, Pixel 3, Apple Airp...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>summarization</td>\n",
              "      <td>How was basketball was invented?</td>\n",
              "      <td>Basketball began with its invention in 1891 in...</td>\n",
              "      <td>Basketball was invented as a less injury-prone...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>general_qa</td>\n",
              "      <td>Is there an answer to the universe?</td>\n",
              "      <td></td>\n",
              "      <td>According to the novel Hitchhikers Guide to th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>information_extraction</td>\n",
              "      <td>From the passage note down the name of the cou...</td>\n",
              "      <td>The World Bank is an international financial i...</td>\n",
              "      <td>U.S., Japan, China, Germany, U.K.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>open_qa</td>\n",
              "      <td>Give me the list of top 10 movies of all time,...</td>\n",
              "      <td></td>\n",
              "      <td>These are the top 10 movies based on their IMD...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>creative_writing</td>\n",
              "      <td>Is social media good for you ?</td>\n",
              "      <td></td>\n",
              "      <td>There are conflicting views on the impact of s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>open_qa</td>\n",
              "      <td>What is the difference between a pomelo and a ...</td>\n",
              "      <td></td>\n",
              "      <td>Both a pomelo and a grapefruit have similar ci...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>classification</td>\n",
              "      <td>Identify the bird from the list: Barn owl, Pyt...</td>\n",
              "      <td></td>\n",
              "      <td>Barn owl</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>creative_writing</td>\n",
              "      <td>Create a 5 day itinerary for a trip to Japan. ...</td>\n",
              "      <td></td>\n",
              "      <td>Day 1: Arrive at Osaka, in the center of Japan...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>brainstorming</td>\n",
              "      <td>What are some ways to improve the value of you...</td>\n",
              "      <td></td>\n",
              "      <td>There are several things you do can to increas...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>summarization</td>\n",
              "      <td>Give me a summary about St Paul's Cathedral</td>\n",
              "      <td>St Paul's Cathedral is an Anglican cathedral i...</td>\n",
              "      <td>St Paul's Cathedral, an Anglican cathedral in ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                  category                                        instruction  \\\n",
              "0                  open_qa         How do I get rid of mosquitos in my house?   \n",
              "1           classification  Classify each country as \"African\" or \"Europea...   \n",
              "2   information_extraction  Extract the unique names of composers from the...   \n",
              "3               general_qa                  Should investors time the market?   \n",
              "4           classification  I am looking at items on my desk right now, te...   \n",
              "5            summarization                   How was basketball was invented?   \n",
              "6               general_qa                Is there an answer to the universe?   \n",
              "7   information_extraction  From the passage note down the name of the cou...   \n",
              "8                  open_qa  Give me the list of top 10 movies of all time,...   \n",
              "9         creative_writing                     Is social media good for you ?   \n",
              "10                 open_qa  What is the difference between a pomelo and a ...   \n",
              "11          classification  Identify the bird from the list: Barn owl, Pyt...   \n",
              "12        creative_writing  Create a 5 day itinerary for a trip to Japan. ...   \n",
              "13           brainstorming  What are some ways to improve the value of you...   \n",
              "14           summarization        Give me a summary about St Paul's Cathedral   \n",
              "\n",
              "                                              context  \\\n",
              "0                                                       \n",
              "1                                                       \n",
              "2   To some extent, European and the US traditions...   \n",
              "3                                                       \n",
              "4                                                       \n",
              "5   Basketball began with its invention in 1891 in...   \n",
              "6                                                       \n",
              "7   The World Bank is an international financial i...   \n",
              "8                                                       \n",
              "9                                                       \n",
              "10                                                      \n",
              "11                                                      \n",
              "12                                                      \n",
              "13                                                      \n",
              "14  St Paul's Cathedral is an Anglican cathedral i...   \n",
              "\n",
              "                                             response  \n",
              "0   You can get rid of mosquitos in your house by ...  \n",
              "1   Nigeria: African\\nRwanda: African\\nPortugal: E...  \n",
              "2   Pierre Boulez, Luigi Nono, Karlheinz Stockhaus...  \n",
              "3   Timing the market is based on predictions of t...  \n",
              "4   Trident mint, Macbook Pro, Pixel 3, Apple Airp...  \n",
              "5   Basketball was invented as a less injury-prone...  \n",
              "6   According to the novel Hitchhikers Guide to th...  \n",
              "7                   U.S., Japan, China, Germany, U.K.  \n",
              "8   These are the top 10 movies based on their IMD...  \n",
              "9   There are conflicting views on the impact of s...  \n",
              "10  Both a pomelo and a grapefruit have similar ci...  \n",
              "11                                           Barn owl  \n",
              "12  Day 1: Arrive at Osaka, in the center of Japan...  \n",
              "13  There are several things you do can to increas...  \n",
              "14  St Paul's Cathedral, an Anglican cathedral in ...  "
            ]
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          "execution_count": 56,
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        "new_df = pd.DataFrame(new_records)\n",
        "new_df"
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        "Now we're happy with the result, we can publish it in the Hugging Face Hub, so the whole open-source community can benefit from it."
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      "source": [
        "#push to hub\n",
        "new_dataset = Dataset(new_records)\n",
        "new_dataset.push_to_hub(\".../curated_databricks-dolly-15k\")"
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