{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "b6545b65-5394-4375-826e-94953c55e76b",
      "metadata": {
        "id": "b6545b65-5394-4375-826e-94953c55e76b"
      },
      "source": [
        "# Clasificación de textos con 🤗 Transformers\n",
        "\n",
        "> Clasificar los tweets del TASS."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d0e4c917-165d-43b9-924e-47782adbd6c7",
      "metadata": {
        "id": "d0e4c917-165d-43b9-924e-47782adbd6c7"
      },
      "source": [
        "## Configuración"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bc05ee00-b4ec-4ae5-8b49-bf2531f3d666",
      "metadata": {
        "id": "bc05ee00-b4ec-4ae5-8b49-bf2531f3d666"
      },
      "source": [
        "Si está ejecutando este notebook en Google Colab, ejecute la siguiente celda para instalar las bibliotecas que necesitamos:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "cc194002-5d32-4b46-a8c8-471d3a8af1ef",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cc194002-5d32-4b46-a8c8-471d3a8af1ef",
        "outputId": "b30c67d7-78c0-4923-838c-a8836b3dd4c6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Downloading evaluate-0.4.6-py3-none-any.whl (84 kB)\n",
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            "\u001b[?25hInstalling collected packages: evaluate\n",
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          ]
        }
      ],
      "source": [
        "!pip install datasets\n",
        "!pip install evaluate\n",
        "!pip install transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c865ba89-9b87-4b91-bad3-97f5f4a66aa1",
      "metadata": {
        "id": "c865ba89-9b87-4b91-bad3-97f5f4a66aa1"
      },
      "source": [
        "Para compartir tu modelo con la comunidad, primero crea una cuenta en el [Hugging Face Hub](https://huggingface.co/join). A continuación, ejecute la siguiente celda y proporcione su nombre de usuario y contraseña para generar un token de autenticación:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "59c65f87-8ad0-48e6-a208-833896599c90",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59c65f87-8ad0-48e6-a208-833896599c90",
        "outputId": "25aa5674-f99c-4cb5-df38-69f7fcef8135"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/bin/bash: line 1: huggingface-cli: command not found\n"
          ]
        }
      ],
      "source": [
        "# Esto sólo funciona en Google Colab! Para los notebooks normales, es necesario ejecutar esto en el terminal\n",
        "!huggingface-cli login"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2dbb0a7a-2c4d-4c66-b626-8233f406fc1c",
      "metadata": {
        "id": "2dbb0a7a-2c4d-4c66-b626-8233f406fc1c"
      },
      "source": [
        "Si no tienes instalado [Git LFS](https://git-lfs.github.com), puedes hacerlo descomentando y ejecutando la celda de abajo:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "52ced30e-6759-44e5-b8cd-838f208c0ea6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52ced30e-6759-44e5-b8cd-838f208c0ea6",
        "outputId": "4320ecb8-847a-4c10-e089-1bbb75b4a0e2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (3.0.2-1ubuntu0.3).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 37 not upgraded.\n"
          ]
        }
      ],
      "source": [
        "!apt install git-lfs\n",
        "!git config --global user.email \"helena.adorno@gmail.com\"\n",
        "!git config --global user.name \"helenpy\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "tsWUks3y0RU2",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tsWUks3y0RU2",
        "outputId": "337c8806-ddd6-4608-d7b0-6d097859c717"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ],
      "source": [
        "#si usamos colab\n",
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "749879c3-fd1d-4f52-ac2b-d967a6c64c60",
      "metadata": {
        "id": "749879c3-fd1d-4f52-ac2b-d967a6c64c60",
        "tags": []
      },
      "source": [
        "## Cargar y explorar los datos"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a737aa78-c1e4-4a47-8424-35d69b5f7394",
      "metadata": {
        "id": "a737aa78-c1e4-4a47-8424-35d69b5f7394"
      },
      "source": [
        "Utilizaremos 🤗 Datasets para cargar y procesar nuestro conjunto de datos."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "id": "79e2afa1-ce7d-4ff5-88ff-5c455bf0e5d5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "79e2afa1-ce7d-4ff5-88ff-5c455bf0e5d5",
        "outputId": "42830fb4-28a4-4623-e52e-9f0b08ece137"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 4802\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['text', 'label'],\n",
              "        num_rows: 2443\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "from datasets import load_dataset, Dataset, Features, ClassLabel, Sequence, Value, DatasetDict\n",
        "import pandas as pd\n",
        "\n",
        "# Lectura del archivo donde se encuentran los datos de entrenamiento y validación\n",
        "data = pd.read_csv('/content/gdrive/MyDrive/PLNavanzado/Notebook/corpusTASS-2020/train.tsv', sep='\\t')\n",
        "data_dev = pd.read_csv('/content/gdrive/MyDrive/PLNavanzado/Notebook/corpusTASS-2020/dev.tsv', sep='\\t')\n",
        "\n",
        "data = data.drop(columns=['id', 'pais'])\n",
        "data_dev = data_dev.drop(columns=['id', 'pais'])\n",
        "\n",
        "# Diccionario de mapeo de etiquetas\n",
        "mapeo_etiquetas = {'N': 0, 'NEU': 1, 'P': 2}\n",
        "id2label = {0: \"N\", 1: \"NEU\", 2: \"P\"}\n",
        "\n",
        "# Transformación de la columna \"etiqueta\"\n",
        "#data['etiqueta_num'] = data['etiqueta'].map(mapeo_etiquetas)\n",
        "#data_dev['etiqueta_num'] = data_dev['etiqueta'].map(mapeo_etiquetas)\n",
        "\n",
        "#definimos la estructura del dataset de Huggingface\n",
        "features = Features({\n",
        "    'texto': Value('string'),\n",
        "    'etiqueta': ClassLabel(num_classes=3, names=['N', 'NEU', 'P'])\n",
        "})\n",
        "\n",
        "#transformamos de dataframe a dataset\n",
        "dataset_train = Dataset.from_pandas(data, features=features)\n",
        "dataset_dev = Dataset.from_pandas(data_dev, features=features)\n",
        "\n",
        "dataset_train = dataset_train.rename_column(\"texto\", \"text\")\n",
        "dataset_train = dataset_train.rename_column(\"etiqueta\", \"label\")\n",
        "dataset_dev = dataset_dev.rename_column(\"texto\", \"text\")\n",
        "dataset_dev = dataset_dev.rename_column(\"etiqueta\", \"label\")\n",
        "\n",
        "#creamos diccionario de dataset\n",
        "dataset = DatasetDict({'train': dataset_train, 'test': dataset_dev})\n",
        "dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "id": "375c2cd6-7118-400f-a8a1-f3bbd1ff6f81",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "375c2cd6-7118-400f-a8a1-f3bbd1ff6f81",
        "outputId": "33c8205c-c8b3-4beb-91d7-3c21e940c075"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Necesito ir a mi hogar y dormir con mis dos hijos peludos y Kabek y su gripe</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Mae eso si es cierto, ahora nadie (la mayoría) busca tener algo serio</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>y pues bueno seguiremos mirando pelis de terror</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>@GrupoPandora y son 30...30 años de acompañarnos,30 es mi edad y he crecido de la mano con ustedes MIL GRACIAS, atte: Ale</td>\n",
              "      <td>P</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Pues yo digo que todos aquí son felices, cada que les pregunto como están dicen: \"Bien\" en vez de \"De La Verga\" Para mí es suficiente</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>@sbarreiro1985 como puede ver en el gif, fue una pelea limpia, sin armas, cualquiera de los dos podía ganar!</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>@federicod @diego_vallarino Ud. vaya a lo tecnológico que es lo suyo Dejá el vino para mí...</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>@ChanqueteFest porq si no es posible ir con ellos necesito encontrar otro alojamiento por mi cuenta. ¿Podríais contestar por favor? Thks</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Increíble pero cierto, hoy hay 2º vídeo en el 1º canal  poco a poco volvemos a la actividad, después de un descanso tengo muchas ganas</td>\n",
              "      <td>P</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>@sancarlena77 @frankgrs nunca dije lo contrario</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "import random\n",
        "import pandas as pd\n",
        "from datasets import ClassLabel\n",
        "from IPython.display import display, HTML\n",
        "\n",
        "def show_random_elements(dataset, num_examples=10):\n",
        "    \"Taken from https://github.com/huggingface/notebooks/blob/master/examples/text_classification.ipynb\"\n",
        "\n",
        "    assert num_examples <= len(dataset), \"Can't pick more elements than there are in the dataset.\"\n",
        "    picks = []\n",
        "    for _ in range(num_examples):\n",
        "        pick = random.randint(0, len(dataset)-1)\n",
        "        while pick in picks:\n",
        "            pick = random.randint(0, len(dataset)-1)\n",
        "        picks.append(pick)\n",
        "\n",
        "    df = pd.DataFrame(dataset[picks])\n",
        "    for column, typ in dataset.features.items():\n",
        "        if isinstance(typ, ClassLabel):\n",
        "            df[column] = df[column].transform(lambda i: typ.names[i])\n",
        "    display(HTML(df.to_html()))\n",
        "\n",
        "show_random_elements(dataset['train'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "1653924b-93d3-4e4f-a5a9-d116d65d73cd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1653924b-93d3-4e4f-a5a9-d116d65d73cd",
        "outputId": "c86f413a-9c23-41f1-d37d-edb6f0237317"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                text  label\n",
              "0  @morbosaborealis jajajaja... eso es verdad... ...      0\n",
              "1  @Adriansoler espero y deseo que el interior te...      1\n",
              "2  comprendo que te molen mis tattoos, pero no te...      1\n",
              "3  Mi última partida jugada, con Sona support. La...      2\n",
              "4  Tranquilos que con el.dinero de Camacho seguro...      2"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2f95e6b8-ca3c-4447-a217-b79748486a56\" 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>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>@morbosaborealis jajajaja... eso es verdad... ...</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>@Adriansoler espero y deseo que el interior te...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>comprendo que te molen mis tattoos, pero no te...</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Mi última partida jugada, con Sona support. La...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Tranquilos que con el.dinero de Camacho seguro...</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2f95e6b8-ca3c-4447-a217-b79748486a56')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-2f95e6b8-ca3c-4447-a217-b79748486a56 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-2f95e6b8-ca3c-4447-a217-b79748486a56');\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",
              "\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4802,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4799,\n        \"samples\": [\n          \"Desde que tengo uso de raz\\u00f3n todos mis fin de a\\u00f1o han sido en casa de mi abuelita, este a\\u00f1o ser\\u00e1 distinto y triste, te extra\\u00f1o tanto abu\",\n          \"Pero soy tan pobre que \",\n          \"El post que comparti\\u00f3 @LuisLauretto no voy a poder estar tranquila hasta que anuncie qui\\u00e9n viene. Y ya estoy pobre #GnR #Metallica\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"label\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 42
        }
      ],
      "source": [
        "dataset.set_format(\"pandas\")\n",
        "df = dataset['train'][:]\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "00b2a622-e698-42cf-b964-2f2588c3fd29",
      "metadata": {
        "id": "00b2a622-e698-42cf-b964-2f2588c3fd29"
      },
      "outputs": [],
      "source": [
        "dataset.reset_format()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "id": "f5cfb477-a6cc-41bf-becc-b7bbfcaf6aea",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "f5cfb477-a6cc-41bf-becc-b7bbfcaf6aea",
        "outputId": "5d4b73a0-ac0b-40ee-a3da-e948aa6bda51"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>@Fantastiqvs Que pases unas lindas fiestas de fin de año y que el próximo sea mucho mejor para ti y los robots ¡Éxitos en todo Aldo!</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>@RPPNoticias @MidisPeru una buena noticia! No más frazadas. Sí más casas calientes</td>\n",
              "      <td>P</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Ojala yo presentándome para el concurso de shawn pero como vergonzosa se nace y  seguro que no me dejan participar pues aqui estoy</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>"
            ]
          },
          "metadata": {}
        }
      ],
      "source": [
        "show_random_elements(dataset['test'], num_examples=3)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "adfe2083-e959-446a-a831-90139c8ebbe4",
      "metadata": {
        "id": "adfe2083-e959-446a-a831-90139c8ebbe4",
        "tags": []
      },
      "source": [
        "## Tokenizar las reseñas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "8aa254bb-162f-44e4-9b40-595a90ad220c",
      "metadata": {
        "id": "8aa254bb-162f-44e4-9b40-595a90ad220c"
      },
      "outputs": [],
      "source": [
        "from transformers import AutoTokenizer, DataCollatorWithPadding\n",
        "\n",
        "model_checkpoint = \"distilbert/distilbert-base-multilingual-cased\"\n",
        "model_checkpointRob = \"roberta-base\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
        "tokenizerRob = AutoTokenizer.from_pretrained(model_checkpointRob)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "b19dba8c-c4ec-4f5a-a2b5-90ae13dc4415",
      "metadata": {
        "id": "b19dba8c-c4ec-4f5a-a2b5-90ae13dc4415"
      },
      "source": [
        "## Tokenización de las reseñas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "55cfde4b-6b30-4942-b664-fdb86af23ac8",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55cfde4b-6b30-4942-b664-fdb86af23ac8",
        "outputId": "fd18a406-28ca-43e8-c8ca-4fee9b1ff89c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "119547\n",
            "50265\n"
          ]
        }
      ],
      "source": [
        "print(tokenizer.vocab_size)\n",
        "print(tokenizerRob.vocab_size)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "8edd6d92-bc62-4bfc-bebf-0ce4713dfa8b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8edd6d92-bc62-4bfc-bebf-0ce4713dfa8b",
        "outputId": "acdce50c-44fb-4de2-aac4-27e1698a474f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "101 [CLS]\n",
            "199 ¡\n",
            "110516 hol\n",
            "10113 ##a\n",
            "117 ,\n",
            "11504 esta\n",
            "13386 ##mos\n",
            "13436 muy\n",
            "13077 fel\n",
            "39801 ##ices\n",
            "56309 prac\n",
            "13640 ##tica\n",
            "10605 ##ndo\n",
            "10109 la\n",
            "18436 tok\n",
            "18687 ##eni\n",
            "104679 ##zaci\n",
            "10263 ##on\n",
            "106 !\n",
            "102 [SEP]\n",
            "20\n"
          ]
        }
      ],
      "source": [
        "text = \"¡hola, estamos muy felices practicando la tokenizacion!\"\n",
        "tokenized_text = tokenizer.encode(text)\n",
        "\n",
        "for token in tokenized_text:\n",
        "    print(token, tokenizer.decode([token]))\n",
        "print(len(tokenized_text))"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"¡hola, estamos muy felices practicando la tokenizacion!\"\n",
        "tokenized_textRob = tokenizerRob.encode(text)\n",
        "\n",
        "for token in tokenized_textRob:\n",
        "    print(token, tokenizerRob.decode([token]))\n",
        "print(len(tokenized_textRob))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MwaSdRry9U25",
        "outputId": "ec4e0aee-b63b-4861-af46-2891d087238b"
      },
      "id": "MwaSdRry9U25",
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 <s>\n",
            "4056 �\n",
            "5543 �\n",
            "9649 hol\n",
            "102 a\n",
            "6 ,\n",
            "3304  est\n",
            "424 am\n",
            "366 os\n",
            "475  m\n",
            "5781 uy\n",
            "14383  fel\n",
            "6355 ices\n",
            "21886  pract\n",
            "636 ic\n",
            "5502 ando\n",
            "897  la\n",
            "19233  token\n",
            "1210 iz\n",
            "8647 acion\n",
            "328 !\n",
            "2 </s>\n",
            "22\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "523904b0-c3a8-4b1a-8947-309c27f61372",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "523904b0-c3a8-4b1a-8947-309c27f61372",
        "outputId": "39eb9228-5409-4eb8-9e0a-7bb10baf5290"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'input_ids': tensor([[   101,    199, 110516,  10113,    117,  11504,  13386,  13436,  13077,\n",
              "          39801,  56309,  13640,  10605,  10109,  18436,  18687, 104679,  10263,\n",
              "            106,    102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "encoded_text = tokenizer(text, return_tensors=\"pt\")\n",
        "encoded_text"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "f5ab78b5-f8d7-4701-98d5-42a7bb9cc677",
      "metadata": {
        "id": "f5ab78b5-f8d7-4701-98d5-42a7bb9cc677"
      },
      "outputs": [],
      "source": [
        "def tokenize_reviews(examples):\n",
        "    return tokenizer(examples[\"text\"], truncation=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "48c07922-100f-40b5-8b45-87448bde2f0f",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265,
          "referenced_widgets": [
            "d78beb6a20a44fde9cae53420d25c9df",
            "a4750f9a3eac4d1480233bf0f70520e5",
            "1a0ad2e2e5b2463da3c89090f160b084",
            "3c42641452f241d3bb707164d4e69cc1",
            "4e10728b57ac4113b769823b153d5431",
            "a1be665d8cc94dfd8542969c07f4cf33",
            "a4af71a6838c4f24a5d0f96ac48632a1",
            "24aaf744c6774e1e93a33f338f879c77",
            "d8f209313e274c639601b8e0b882a6e2",
            "38908949b549407e867a375ed16fd463",
            "27e9a12b38134b6ea86649ddbf63aea7",
            "77883a534f054f869cbaa784469b17bf",
            "d867a78eecaa45b4b355a0eeee6bcfae",
            "7a0d4ee021ad4d9695c62b6df7bc21be",
            "ce4bfe2558154ddc9bcb5ab672052f60",
            "e29b6e559a1f4dbf862c69c37bed0a5b",
            "163d058785784417a102df8c165aee99",
            "7bd51fb559dd47d7b3269b9420076d91",
            "0c1ef1a423a249a191075e0995efa758",
            "5bd471b2c3b146e6b9fde95323aa122a",
            "fb5ceab88f3642bb9efee80331351474",
            "e53f6cce090e46658afd4b2cce670761"
          ]
        },
        "id": "48c07922-100f-40b5-8b45-87448bde2f0f",
        "outputId": "ed0c7833-91d7-4dd7-c5ff-0ac798112c3a"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/4802 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d78beb6a20a44fde9cae53420d25c9df"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2443 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77883a534f054f869cbaa784469b17bf"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 4802\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['label', 'input_ids', 'token_type_ids', 'attention_mask'],\n",
              "        num_rows: 2443\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "columns = dataset['train'].column_names\n",
        "columns.remove(\"label\")\n",
        "encoded_dataset = dataset.map(tokenize_reviews, batched=True, remove_columns=columns)\n",
        "encoded_dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "0c02f46d-da3a-4198-a689-3936ece6948d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0c02f46d-da3a-4198-a689-3936ece6948d",
        "outputId": "ef5c252b-b332-45c1-c3f5-9e641c1113aa"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'label': 0,\n",
              " 'input_ids': [101,\n",
              "  137,\n",
              "  24984,\n",
              "  19804,\n",
              "  107956,\n",
              "  33269,\n",
              "  10201,\n",
              "  10320,\n",
              "  10320,\n",
              "  10320,\n",
              "  119,\n",
              "  119,\n",
              "  119,\n",
              "  36584,\n",
              "  10196,\n",
              "  79381,\n",
              "  119,\n",
              "  119,\n",
              "  119,\n",
              "  36579,\n",
              "  10192,\n",
              "  13605,\n",
              "  11381,\n",
              "  10854,\n",
              "  13819,\n",
              "  10133,\n",
              "  102],\n",
              " 'token_type_ids': [0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0,\n",
              "  0],\n",
              " 'attention_mask': [1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1,\n",
              "  1]}"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ],
      "source": [
        "encoded_dataset['train'][0]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# implementa una función de colación que: normaliza longitudes dentro de batch, preserva máscaras de atención, optimiza throughput en entrenamiento de Transformers\n",
        "data_collator = DataCollatorWithPadding(tokenizer=tokenizer)"
      ],
      "metadata": {
        "id": "J4x40Vg8f47c"
      },
      "id": "J4x40Vg8f47c",
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "631e602b-deb5-4d3b-b01a-c5934a37f573",
      "metadata": {
        "id": "631e602b-deb5-4d3b-b01a-c5934a37f573",
        "tags": []
      },
      "source": [
        "## Cargar el modelo preentrenado"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "ff98a5b1-6415-4d7e-ba47-7d89ce7ebfe4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343,
          "referenced_widgets": [
            "3dbef06099a4487a98818cd94f7d8789",
            "296b3997affd474fa34c75015b915a73",
            "b07a55f1e3d34aa3bd6fa4493fbd02dc",
            "83975d40a0fa40548512ad59ceae22d9",
            "45af666fb02c4e78892be403fa758c82",
            "37293a48668d46a68e3ae505ac13a01a",
            "cb462613aa0d4388a5854cc56f4c6fe9",
            "54d84636c0ba4c20b2063c15217d74a9",
            "20ee25bb0d0d4adf85580ec6bee212a3",
            "9afb31cc4c0340778003fad400250d71",
            "2488a4c9984744cd826bee9eec4fcea8"
          ]
        },
        "id": "ff98a5b1-6415-4d7e-ba47-7d89ce7ebfe4",
        "outputId": "a500ae25-5e74-4d32-b95b-1b05d7b80ff8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/100 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3dbef06099a4487a98818cd94f7d8789"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "DistilBertForSequenceClassification LOAD REPORT from: distilbert/distilbert-base-multilingual-cased\n",
            "Key                     | Status     | \n",
            "------------------------+------------+-\n",
            "vocab_transform.bias    | UNEXPECTED | \n",
            "vocab_layer_norm.bias   | UNEXPECTED | \n",
            "vocab_layer_norm.weight | UNEXPECTED | \n",
            "vocab_projector.bias    | UNEXPECTED | \n",
            "vocab_transform.weight  | UNEXPECTED | \n",
            "pre_classifier.bias     | MISSING    | \n",
            "pre_classifier.weight   | MISSING    | \n",
            "classifier.bias         | MISSING    | \n",
            "classifier.weight       | MISSING    | \n",
            "\n",
            "Notes:\n",
            "- UNEXPECTED\t:can be ignored when loading from different task/architecture; not ok if you expect identical arch.\n",
            "- MISSING\t:those params were newly initialized because missing from the checkpoint. Consider training on your downstream task.\n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "num_labels = 3\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5bda30da-6725-4a50-b905-e13456bfde52",
      "metadata": {
        "id": "5bda30da-6725-4a50-b905-e13456bfde52"
      },
      "source": [
        "### De las input IDs a los hidden states"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "ee527ed1-9ea6-48fe-a0fc-79594c5388c6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ee527ed1-9ea6-48fe-a0fc-79594c5388c6",
        "outputId": "f1f81237-47a7-449a-bb30-d693724ffd7a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SequenceClassifierOutput(loss=None, logits=tensor([[ 0.2089, -0.1513, -0.0368]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "outputs = model(**encoded_text)\n",
        "outputs"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "7c58d614-c1bf-45f0-8a45-858edac56d4c",
      "metadata": {
        "id": "7c58d614-c1bf-45f0-8a45-858edac56d4c"
      },
      "source": [
        "## Definir las métricas de rendimiento"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "195cb452-a01c-4804-9266-4054c50f89f3",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "195cb452-a01c-4804-9266-4054c50f89f3",
        "outputId": "edd76413-a411-4aa0-ae09-8740204b10c6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EvaluationModule(name: \"accuracy\", module_type: \"metric\", features: {'predictions': Value('int32'), 'references': Value('int32')}, usage: \"\"\"\n",
              "Args:\n",
              "    predictions (`list` of `int`): Predicted labels.\n",
              "    references (`list` of `int`): Ground truth labels.\n",
              "    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n",
              "    sample_weight (`list` of `float`): Sample weights Defaults to None.\n",
              "\n",
              "Returns:\n",
              "    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n",
              "\n",
              "Examples:\n",
              "\n",
              "    Example 1-A simple example\n",
              "        >>> accuracy_metric = evaluate.load(\"accuracy\")\n",
              "        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n",
              "        >>> print(results)\n",
              "        {'accuracy': 0.5}\n",
              "\n",
              "    Example 2-The same as Example 1, except with `normalize` set to `False`.\n",
              "        >>> accuracy_metric = evaluate.load(\"accuracy\")\n",
              "        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n",
              "        >>> print(results)\n",
              "        {'accuracy': 3.0}\n",
              "\n",
              "    Example 3-The same as Example 1, except with `sample_weight` set.\n",
              "        >>> accuracy_metric = evaluate.load(\"accuracy\")\n",
              "        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n",
              "        >>> print(results)\n",
              "        {'accuracy': 0.8778625954198473}\n",
              "\"\"\", stored examples: 0)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "import evaluate\n",
        "\n",
        "metric = evaluate.load(\"accuracy\")\n",
        "metric"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "c4ebab1c-893c-4eb9-8f51-9e55c6596463",
      "metadata": {
        "id": "c4ebab1c-893c-4eb9-8f51-9e55c6596463"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "107be5cd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "107be5cd",
        "outputId": "a9797b78-a521-4070-8f06-6bc28e3ac446"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'5.0.0'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "import transformers\n",
        "transformers.__version__"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9b77a005-0f56-42cb-a1e3-c231fd851305",
      "metadata": {
        "id": "9b77a005-0f56-42cb-a1e3-c231fd851305"
      },
      "source": [
        "## Afinar el modelo preentrenado"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "75fc4131-7a48-42bb-b9a7-f8df40a05aaf",
      "metadata": {
        "id": "75fc4131-7a48-42bb-b9a7-f8df40a05aaf"
      },
      "outputs": [],
      "source": [
        "from transformers import TrainingArguments\n",
        "from transformers.training_args import IntervalStrategy\n",
        "\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "\n",
        "batch_size = 16\n",
        "num_train_epochs=4\n",
        "train_dataset = encoded_dataset[\"train\"].shuffle(seed=42)#.select(range(num_train_samples))\n",
        "logging_steps = len(train_dataset) // (2 * batch_size * num_train_epochs)\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"results\",\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    learning_rate=2e-4,\n",
        "    per_device_train_batch_size=batch_size,\n",
        "    per_device_eval_batch_size=batch_size,\n",
        "    load_best_model_at_end=True,\n",
        "    metric_for_best_model=\"accuracy\",\n",
        "    weight_decay=0.1,\n",
        "    eval_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    logging_steps=logging_steps\n",
        "    #push_to_hub=True,\n",
        "    #push_to_hub_model_id=f\"{model_name}-finetuned-tass\"\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "d2fdc4a2-4653-49fe-b7aa-660c5d72077d",
      "metadata": {
        "id": "d2fdc4a2-4653-49fe-b7aa-660c5d72077d"
      },
      "outputs": [],
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "test_dataset = encoded_dataset[\"test\"]\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=test_dataset,\n",
        "    processing_class=tokenizer,\n",
        "    data_collator=data_collator,\n",
        "    compute_metrics=compute_metrics\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9c8a17d4-7939-4ac8-9ab6-ed5be0a00ba6",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331,
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            "c36a7ed9489d4c71a75c0c49d886e9c1",
            "cf6c33c303244dd5ba367b052fb9e6ea",
            "b4534a83117c43cfb3d338d4e3a815bb"
          ]
        },
        "id": "9c8a17d4-7939-4ac8-9ab6-ed5be0a00ba6",
        "outputId": "115698a0-bd57-406e-95c6-1e6e7b9a257f"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='1205' max='1204' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [1204/1204 03:11, Epoch 4/4]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Epoch</th>\n",
              "      <th>Training Loss</th>\n",
              "      <th>Validation Loss</th>\n",
              "      <th>Accuracy</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>1</td>\n",
              "      <td>0.606220</td>\n",
              "      <td>1.198696</td>\n",
              "      <td>0.535817</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.469720</td>\n",
              "      <td>1.334787</td>\n",
              "      <td>0.543594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>3</td>\n",
              "      <td>0.360733</td>\n",
              "      <td>1.519288</td>\n",
              "      <td>0.549325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>4</td>\n",
              "      <td>0.195522</td>\n",
              "      <td>2.131011</td>\n",
              "      <td>0.535407</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c56a9e25ca69481ba431f57c004c5dc7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "368c5cbe165244069fa989a11cb1bd48"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "189f15ad70ac468c93450631fe37efd1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Writing model shards:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7ecdf316421041c5b919a9fc8d0c9e05"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "trainer.train()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1b33bc90-1bce-4eae-9919-4e89da560293",
      "metadata": {
        "id": "1b33bc90-1bce-4eae-9919-4e89da560293"
      },
      "source": [
        "## OPCIONAL: Empuje hacia el Hugging Face Hub"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bbc6984b",
      "metadata": {
        "id": "bbc6984b"
      },
      "source": [
        "Si te conectaste a tu cuenta de Huggingface y en el trainer activaste los parámetros de push_to_hub y push_to_hub_model_id, entonces puedes subir tu modelo al repositorio de Huggingface, realizando lo siguiente"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d30b5e20-fda5-4805-bb26-e8f87e7dde00",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362,
          "referenced_widgets": [
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          ]
        },
        "id": "d30b5e20-fda5-4805-bb26-e8f87e7dde00",
        "outputId": "e08e7452-c93f-418c-80eb-5eeebd999f10"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Several commits (2) will be pushed upstream.\n",
            "WARNING:huggingface_hub.repository:Several commits (2) will be pushed upstream.\n",
            "The progress bars may be unreliable.\n",
            "WARNING:huggingface_hub.repository:The progress bars may be unreliable.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "29f1899279974dbfb3f01de565f0be51",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file pytorch_model.bin:   0%|          | 1.00/255M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "86a7c669efd34f2caaca39397c516dbf",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Upload file runs/Jun21_14-41-16_73a0e079d595/events.out.tfevents.1687358491.73a0e079d595.282.0:   0%|         …"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "To https://huggingface.co/helenpy/distilbert-base-uncased-finetuned-tass\n",
            "   63f8492..f759890  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/helenpy/distilbert-base-uncased-finetuned-tass\n",
            "   63f8492..f759890  main -> main\n",
            "\n",
            "To https://huggingface.co/helenpy/distilbert-base-uncased-finetuned-tass\n",
            "   f759890..2dc7f05  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/helenpy/distilbert-base-uncased-finetuned-tass\n",
            "   f759890..2dc7f05  main -> main\n",
            "\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'https://huggingface.co/helenpy/distilbert-base-uncased-finetuned-tass/commit/f75989096eb8fa1984e6d4c28a92e6127a81a908'"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "47f15c05-c94b-4dfd-8282-3ff41b7ebaef",
      "metadata": {
        "id": "47f15c05-c94b-4dfd-8282-3ff41b7ebaef"
      },
      "source": [
        "## Descargue el modelo desde el Hub"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4d4afd85-b413-4058-8e86-2b936b13e544",
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        },
        "id": "4d4afd85-b413-4058-8e86-2b936b13e544",
        "outputId": "2b2cc3cf-6b76-4f2c-f56e-0c31fbec2438"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a88e374036c44d8ea76afd43471dfefb",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/769 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "59ba469952f44027bd51958f8e877549",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/268M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "e0b76a3745034b90a3de76cb81c897a3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/320 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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              "model_id": "d5f8139087e24ac4a7fde671ac080e27",
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            },
            "text/plain": [
              "Downloading (…)solve/main/vocab.txt: 0.00B [00:00, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
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              "model_id": "2877f4297ab74883ac25ef0b31c8f52a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)/main/tokenizer.json: 0.00B [00:00, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
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        "from transformers import pipeline\n",
        "\n",
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        "pipe = pipeline(\"sentiment-analysis\", model=model_checkpoint)"
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