5_Transfer_Learning__NLP_Transformers.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Curso Transfer Learning\n",
        "\n",
        "<img src=\"https://yaelmanuel.com/wp-content/uploads/2021/12/platzi-banner-logo-matematicas.png\" width=\"500px\">\n",
        "\n",
        "---"
      ],
      "metadata": {
        "id": "E4OEtECGjKtp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Creando nuestro propio Clasificador de Reseñas 📦 🔍\n",
        "\n",
        "En este lab aprenderás:\n",
        "\n",
        "* [Transformers](https://huggingface.co/docs/transformers/v4.17.0/en/index)\n",
        "* Descargar un dataset, prepararlo, entrenarlo, realizar finetuning y guardarlo.\n",
        "* Manejo de plataformas como [Hugging Face Hub](https://huggingface.co/) y [Weights & Biases](https://wandb.ai/)."
      ],
      "metadata": {
        "id": "p_ny9c9fjbLP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 1) Descarga del dataset 🤓\n",
        "\n",
        "Utilizaremos un conjunto de reseña de productos de Amazon [Amazon Reviews Dataset](https://www.kaggle.com/datasets/mexwell/amazon-reviews-multi)."
      ],
      "metadata": {
        "id": "fxgR3AqoTxQP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install --upgrade --force-reinstall --no-deps kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5R1N7hZuzBt3",
        "outputId": "b457dfae-6bbb-4bea-d60b-3176498fd0d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting kaggle\n",
            "  Downloading kaggle-1.6.17.tar.gz (82 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/82.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m82.7/82.7 kB\u001b[0m \u001b[31m3.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: kaggle\n",
            "  Building wheel for kaggle (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for kaggle: filename=kaggle-1.6.17-py3-none-any.whl size=105786 sha256=dfeee9b9419babc416070998bb821e1081b52a3776a4815a2205762d4f306511\n",
            "  Stored in directory: /root/.cache/pip/wheels/9f/af/22/bf406f913dc7506a485e60dce8143741abd0a92a19337d83a3\n",
            "Successfully built kaggle\n",
            "Installing collected packages: kaggle\n",
            "  Attempting uninstall: kaggle\n",
            "    Found existing installation: kaggle 1.6.17\n",
            "    Uninstalling kaggle-1.6.17:\n",
            "      Successfully uninstalled kaggle-1.6.17\n",
            "Successfully installed kaggle-1.6.17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 92
        },
        "id": "AyekOFW-zEm7",
        "outputId": "4fbfcf17-a9d8-41e3-c177-46dc8178b803"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3aecf265-fc7b-4652-9d2b-b1aa31a03a74\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-3aecf265-fc7b-4652-9d2b-b1aa31a03a74\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
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              "\n",
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              "  cancel.textContent = 'Cancel upload';\n",
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              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
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              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
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              "          100 :\n",
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              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'kaggle.json': b'{\"username\":\"carlosbustillo\",\"key\":\"60aea5be8b60f01d6ee908af46ecd025\"}'}"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 /root/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "5SfH8NkpzIhz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets list -s amazon-reviews-multi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IhjGsdD-zKtV",
        "outputId": "1006059e-b3f8-4c82-ced8-3f606adc6763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ref                                                       title                                                size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
            "--------------------------------------------------------  --------------------------------------------------  -----  -------------------  -------------  ---------  ---------------  \n",
            "mexwell/amazon-reviews-multi                              ✏️ Amazon Reviews Multi                             131MB  2023-07-25 11:35:14           1428         14  1.0              \n",
            "karkavelrajaj/amazon-sales-dataset                        Amazon Sales Dataset                                  2MB  2023-01-17 06:21:15         129979        791  1.0              \n",
            "jeromeblanchet/multidomain-sentiment-analysis-dataset     Johns Hopkins Multi-Domain Sentiment Dataset ∑∞       2GB  2020-01-14 15:36:51            382          9  0.5625           \n",
            "dataup1/ogbn-products                                     OGBN-Products (Processed for PyG)                     3GB  2021-02-27 07:50:34            285          5  0.7058824        \n",
            "fiodarryzhykau/employee-review                            Employee Review                                     306KB  2021-02-02 03:59:47           2271         10  1.0              \n",
            "thebuzz/ground-truth-labels-amazon-movie-reviews-dataset  Ground truth labels - Amazon movie reviews dataset    7MB  2017-07-08 18:46:36            333          6  0.9411765        \n",
            "lokeshbaviskar/amazon-rating-prediction-based-on-reviews  Amazon Rating Prediction based on Reviews           757KB  2022-12-18 06:25:33             72          2  0.5882353        \n",
            "shashwnk/amazon-project-eda                               Amazon-Project-EDA                                    4MB  2024-02-05 13:28:48             34          1  0.47058824       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d mexwell/amazon-reviews-multi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1fVsLJUHzZls",
        "outputId": "6f4b05ac-32da-45b0-8782-fe3f57d7f186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset URL: https://www.kaggle.com/datasets/mexwell/amazon-reviews-multi\n",
            "License(s): other\n",
            "Downloading amazon-reviews-multi.zip to /content\n",
            "100% 131M/131M [00:03<00:00, 60.2MB/s]\n",
            "100% 131M/131M [00:03<00:00, 41.7MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip '/content/amazon-reviews-multi.zip'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Utdg3XSSzgg1",
        "outputId": "deeb43a9-e1be-469d-e20e-f04d4de95f60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/amazon-reviews-multi.zip\n",
            "  inflating: test.csv                \n",
            "  inflating: train.csv               \n",
            "  inflating: validation.csv          \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2) Preparación de la data 👌"
      ],
      "metadata": {
        "id": "HX8Q_QXsTts3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.1) Instalamos las dependencias 🙌"
      ],
      "metadata": {
        "id": "BYa0Tjnv74mE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers datasets evaluate"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rpApC-1N_lt9",
        "outputId": "7ef38d62-3177-4632-fe3a-9cda1c66d431"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.47.1)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (3.2.0)\n",
            "Requirement already satisfied: evaluate in /usr/local/lib/python3.10/dist-packages (0.4.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.24.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.27.0)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.11.6)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.22,>=0.21 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.21.0)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.67.1)\n",
            "Requirement already satisfied: pyarrow>=15.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (17.0.0)\n",
            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.3.8)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (2.2.2)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets) (3.5.0)\n",
            "Requirement already satisfied: multiprocess<0.70.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (0.70.16)\n",
            "Requirement already satisfied: fsspec<=2024.9.0,>=2023.1.0 in /usr/local/lib/python3.10/dist-packages (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets) (2024.9.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.11.10)\n",
            "Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.4.4)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.2)\n",
            "Requirement already satisfied: async-timeout<6.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.3)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (24.3.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.5.0)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.1.0)\n",
            "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (0.2.1)\n",
            "Requirement already satisfied: yarl<2.0,>=1.17.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.18.3)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.24.0->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.12.14)\n",
            "Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
            "Requirement already satisfied: tzdata>=2022.7 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.17.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import evaluate\n",
        "\n",
        "from transformers import AutoTokenizer, AutoModelForSequenceClassification, TrainingArguments, Trainer\n",
        "\n",
        "from datasets import Dataset, DatasetDict"
      ],
      "metadata": {
        "id": "00hvceE4zrjh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.2) Explorar dataset 👀"
      ],
      "metadata": {
        "id": "LghPjoil8Njr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train = pd.read_csv('/content/train.csv', sep=\",\", index_col=0)\n",
        "df_test = pd.read_csv('/content/test.csv', sep=\",\", index_col=0)\n",
        "df_val = pd.read_csv('/content/validation.csv', sep=\",\", index_col=0)"
      ],
      "metadata": {
        "id": "DvYiKcMazwnv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 230
        },
        "id": "UKMYJcXa2a-l",
        "outputId": "991f4a46-0de6-4d65-8049-19b93476e639"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    review_id          product_id          reviewer_id  stars  \\\n",
              "0  de_0203609  product_de_0865382  reviewer_de_0267719      1   \n",
              "1  de_0559494  product_de_0678997  reviewer_de_0783625      1   \n",
              "2  de_0238777  product_de_0372235  reviewer_de_0911426      1   \n",
              "\n",
              "                                         review_body  \\\n",
              "0     Armband ist leider nach 1 Jahr kaputt gegangen   \n",
              "1                 In der Lieferung war nur Ein Akku!   \n",
              "2  Ein Stern, weil gar keine geht nicht. Es hande...   \n",
              "\n",
              "                review_title language  product_category  \n",
              "0  Leider nach 1 Jahr kaputt       de            sports  \n",
              "1   EINS statt ZWEI Akkus!!!       de  home_improvement  \n",
              "2            Achtung Abzocke       de         drugstore  "
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              "        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 id=\"df-7bbe43b5-e554-43a3-8c5b-459a9b308d47\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7bbe43b5-e554-43a3-8c5b-459a9b308d47')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7bbe43b5-e554-43a3-8c5b-459a9b308d47 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos a quedarnos exclusivamente con las reseñas en español, es decir `language = es`."
      ],
      "metadata": {
        "id": "71KvqJva3LBe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train_es = df_train[df_train['language'] == 'es']\n",
        "df_test_es = df_test[df_test['language'] == 'es']\n",
        "df_val_es = df_val[df_val['language'] == 'es']"
      ],
      "metadata": {
        "id": "wCJ5wYDe3nxb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train_es.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "bolVMvlq3ps3",
        "outputId": "bb9a02b2-faca-4659-8a33-8b8a00deaf51"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         review_id          product_id          reviewer_id  stars  \\\n",
              "400000  es_0491108  product_es_0296024  reviewer_es_0999081      1   \n",
              "400001  es_0869872  product_es_0922286  reviewer_es_0216771      1   \n",
              "400002  es_0811721  product_es_0474543  reviewer_es_0929213      1   \n",
              "\n",
              "                                              review_body  \\\n",
              "400000  Nada bueno se me fue ka pantalla en menos de 8...   \n",
              "400001  Horrible, nos tuvimos que comprar otro porque ...   \n",
              "400002  Te obligan a comprar dos unidades y te llega s...   \n",
              "\n",
              "                                             review_title language  \\\n",
              "400000                                   television Nevir       es   \n",
              "400001          Dinero tirado a la basura con esta compra       es   \n",
              "400002  solo llega una unidad cuando te obligan a comp...       es   \n",
              "\n",
              "       product_category  \n",
              "400000      electronics  \n",
              "400001      electronics  \n",
              "400002        drugstore  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dcd8c289-4c0d-4ec0-8879-e9e8d19b14cd\" 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>review_id</th>\n",
              "      <th>product_id</th>\n",
              "      <th>reviewer_id</th>\n",
              "      <th>stars</th>\n",
              "      <th>review_body</th>\n",
              "      <th>review_title</th>\n",
              "      <th>language</th>\n",
              "      <th>product_category</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>400000</th>\n",
              "      <td>es_0491108</td>\n",
              "      <td>product_es_0296024</td>\n",
              "      <td>reviewer_es_0999081</td>\n",
              "      <td>1</td>\n",
              "      <td>Nada bueno se me fue ka pantalla en menos de 8...</td>\n",
              "      <td>television Nevir</td>\n",
              "      <td>es</td>\n",
              "      <td>electronics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>400001</th>\n",
              "      <td>es_0869872</td>\n",
              "      <td>product_es_0922286</td>\n",
              "      <td>reviewer_es_0216771</td>\n",
              "      <td>1</td>\n",
              "      <td>Horrible, nos tuvimos que comprar otro porque ...</td>\n",
              "      <td>Dinero tirado a la basura con esta compra</td>\n",
              "      <td>es</td>\n",
              "      <td>electronics</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>400002</th>\n",
              "      <td>es_0811721</td>\n",
              "      <td>product_es_0474543</td>\n",
              "      <td>reviewer_es_0929213</td>\n",
              "      <td>1</td>\n",
              "      <td>Te obligan a comprar dos unidades y te llega s...</td>\n",
              "      <td>solo llega una unidad cuando te obligan a comp...</td>\n",
              "      <td>es</td>\n",
              "      <td>drugstore</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-dcd8c289-4c0d-4ec0-8879-e9e8d19b14cd')\"\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",
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              "  </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-dcd8c289-4c0d-4ec0-8879-e9e8d19b14cd 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-dcd8c289-4c0d-4ec0-8879-e9e8d19b14cd');\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 id=\"df-ca8d78f9-9905-4a69-8538-e203480405f0\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ca8d78f9-9905-4a69-8538-e203480405f0')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ca8d78f9-9905-4a69-8538-e203480405f0 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train_es"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reseteamos el índice"
      ],
      "metadata": {
        "id": "Z5wyYeKF5g3u"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train_es = df_train_es.reset_index(drop=True)\n",
        "df_test_es = df_test_es.reset_index(drop=True)\n",
        "df_val_es = df_val_es.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "Eb5JdCzi5kcl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train_es.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "aA_qGUME5wEC",
        "outputId": "85b18d70-a444-4ad4-c5cf-6ca5e94dceea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    review_id          product_id          reviewer_id  stars  \\\n",
              "0  es_0491108  product_es_0296024  reviewer_es_0999081      1   \n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Exploramos la distribución de los productos y estrellas"
      ],
      "metadata": {
        "id": "YXPnGkbb51DV"
      }
    },
    {
      "cell_type": "code",
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        "df_train_es[\"product_category\"].value_counts()"
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          "height": 1000
        },
        "id": "_AV7pmGD5TvR",
        "outputId": "239d403b-ee04-471b-efbe-56188643bfa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product_category\n",
              "home                        26962\n",
              "wireless                    25886\n",
              "toy                         13647\n",
              "sports                      13189\n",
              "pc                          11191\n",
              "home_improvement            10879\n",
              "electronics                 10385\n",
              "beauty                       7337\n",
              "automotive                   7143\n",
              "kitchen                      6695\n",
              "apparel                      5737\n",
              "drugstore                    5513\n",
              "book                         5264\n",
              "furniture                    5229\n",
              "baby_product                 4881\n",
              "office_product               4771\n",
              "lawn_and_garden              4237\n",
              "other                        3937\n",
              "pet_products                 3713\n",
              "personal_care_appliances     3573\n",
              "luggage                      3328\n",
              "camera                       3029\n",
              "shoes                        2754\n",
              "digital_ebook_purchase       1843\n",
              "video_games                  1733\n",
              "jewelry                      1598\n",
              "musical_instruments          1530\n",
              "watch                        1490\n",
              "industrial_supplies          1482\n",
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              "Name: count, dtype: int64"
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              "      <td>4881</td>\n",
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              "      <td>3713</td>\n",
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              "      <td>3573</td>\n",
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              "      <th>luggage</th>\n",
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              "      <td>3029</td>\n",
              "    </tr>\n",
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              "      <td>2754</td>\n",
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              "      <td>1843</td>\n",
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              "      <td>1733</td>\n",
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              "      <td>1044</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
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    {
      "cell_type": "code",
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        "df_train_es[\"stars\"].value_counts()"
      ],
      "metadata": {
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        "id": "J2Xpf-_v5aI8",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
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            "text/plain": [
              "stars\n",
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              "2    40000\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>40000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> int64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.2) Acondicionar columnas 🔍"
      ],
      "metadata": {
        "id": "Ull0vjyr9Ihl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Vamos a asignar una etiqueta basado en la cantidad de estrellas:\n",
        "- Si el número de estrellas es mayor o igual a 3, le asignamos una buena calificación (valor 1).\n",
        "- Caso contrario es una mala calificación (valor 0)."
      ],
      "metadata": {
        "id": "Pt-Ccg3r6ZIz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_train_es['labels'] = df_train_es['stars'].apply(lambda x: 1 if x >= 3 else 0)\n",
        "df_test_es['labels'] = df_test_es['stars'].apply(lambda x: 1 if x >= 3 else 0)\n",
        "df_val_es['labels'] = df_val_es['stars'].apply(lambda x: 1 if x >= 3 else 0)"
      ],
      "metadata": {
        "id": "m1FKNA5n6eth"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_train_es.head(2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 236
        },
        "id": "rByfAHUz7qtt",
        "outputId": "ae93fe33-56ca-4d80-836c-a8471c3c1101"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    review_id          product_id          reviewer_id  stars  \\\n",
              "0  es_0491108  product_es_0296024  reviewer_es_0999081      1   \n",
              "1  es_0869872  product_es_0922286  reviewer_es_0216771      1   \n",
              "\n",
              "                                         review_body  \\\n",
              "0  Nada bueno se me fue ka pantalla en menos de 8...   \n",
              "1  Horrible, nos tuvimos que comprar otro porque ...   \n",
              "\n",
              "                                review_title language product_category  labels  \n",
              "0                           television Nevir       es      electronics       0  \n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_train_es"
            }
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          "metadata": {},
          "execution_count": 21
        }
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    {
      "cell_type": "code",
      "source": [
        "df_train_es.tail(2)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 219
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        "id": "AWGmUS3Q7gZc",
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "         review_id          product_id          reviewer_id  stars  \\\n",
              "199998  es_0930141  product_es_0788855  reviewer_es_0694290      5   \n",
              "199999  es_0859809  product_es_0378794  reviewer_es_0141717      5   \n",
              "\n",
              "                                              review_body review_title  \\\n",
              "199998                 Perfecto para el cumple de mi hijo  Recomendado   \n",
              "199999  Súper bien! Las brochas son buenas, no sé meno...    Preciosas   \n",
              "\n",
              "       language product_category  labels  \n",
              "199998       es              toy       1  \n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8b50f7ab-f1cb-4eef-8179-65d9ad42c66a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_train_es\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"review_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"es_0859809\",\n          \"es_0930141\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"product_es_0378794\",\n          \"product_es_0788855\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"reviewer_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"reviewer_es_0141717\",\n          \"reviewer_es_0694290\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"stars\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_body\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"S\\u00faper bien! Las brochas son buenas, no s\\u00e9 menos han ca\\u00eddo pelitos, quiz\\u00e1s pesan un poco pero con los chulas que son da igual xD\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"review_title\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Preciosas\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"language\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"es\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"home\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"labels\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### 2.3) Adaptar formato del dataset 🔧"
      ],
      "metadata": {
        "id": "Ty_NQsJkBYUC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Convertir los DataFrames en objetos Dataset de la librería datasets\n",
        "train_dataset = Dataset.from_pandas(df_train_es)\n",
        "test_dataset = Dataset.from_pandas(df_test_es)\n",
        "val_dataset = Dataset.from_pandas(df_val_es)\n",
        "\n",
        "# Crear un DatasetDict con los conjuntos de datos\n",
        "dataset = DatasetDict({\n",
        "    'train': train_dataset,\n",
        "    'validation': val_dataset,\n",
        "    'test': test_dataset\n",
        "})\n",
        "\n",
        "# Ver la estructura\n",
        "print(dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tUsIjjMiBbfP",
        "outputId": "40849d77-f692-4ca2-a396-aca079e3c46b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category', 'labels'],\n",
            "        num_rows: 200000\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category', 'labels'],\n",
            "        num_rows: 5000\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['review_id', 'product_id', 'reviewer_id', 'stars', 'review_body', 'review_title', 'language', 'product_category', 'labels'],\n",
            "        num_rows: 5000\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 3) Tokenización 📊"
      ],
      "metadata": {
        "id": "z5CReMaiYSDt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = \"BSC-TeMU/roberta-base-bne\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)"
      ],
      "metadata": {
        "id": "WVuggvwh8eo6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Podemos visualizar el número total de tokens únicos que el tokenizador puede reconocer y procesar.\n",
        "\n",
        "El vocabulario incluye todas las palabras, subpalabras y símbolos que el modelo ha aprendido a reconocer."
      ],
      "metadata": {
        "id": "GvrvfZ7n9NnW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.vocab_size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wxyp3nx38yG_",
        "outputId": "90782ea9-cfc8-48f7-b8c6-6f1c18164856"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50262"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"¡Aprendiendo sobre Transfer Learning en Platzi!\"\n",
        "tokenized_text = tokenizer.encode(text)\n",
        "\n",
        "for token in tokenized_text:\n",
        "    print(token, tokenizer.decode([token]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iROFPbIh81W_",
        "outputId": "60340858-bd31-4280-c16f-5b7152562b75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 <s>\n",
            "1465 ¡\n",
            "13327 Aprend\n",
            "772 iendo\n",
            "588  sobre\n",
            "5269  Trans\n",
            "1238 fer\n",
            "1165  Le\n",
            "25150 arning\n",
            "334  en\n",
            "2447  Pla\n",
            "36422 tzi\n",
            "55 !\n",
            "2 </s>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "encoded_text = tokenizer(text, return_tensors=\"pt\")\n",
        "print(encoded_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XerD8XU19bfX",
        "outputId": "924ee207-5b69-4fab-f85c-802e6d3cfebd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'input_ids': tensor([[    0,  1465, 13327,   772,   588,  5269,  1238,  1165, 25150,   334,\n",
            "          2447, 36422,    55,     2]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tokenize_reviews(examples):\n",
        "    return tokenizer(examples[\"review_body\"], truncation=True)"
      ],
      "metadata": {
        "id": "szNKFldgCIvK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns = dataset[\"train\"].column_names\n",
        "columns.remove(\"labels\")\n",
        "encoded_dataset = dataset.map(tokenize_reviews, batched=True, remove_columns=columns)\n",
        "print(encoded_dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367,
          "referenced_widgets": [
            "8d91b9c092cd42b68c5ab89d5da6dd65",
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            "d5bc151052804ee4820bdbd96268b205",
            "a0c7fa0335684a86ae1116e908bfe466",
            "ef77784e27c444f7b6cbb169e0026239",
            "ba4781ab5dee4010ba2e5b46c0d5ea2c",
            "3da5dfa668e3457488af03b7cf2395a3",
            "3fa4ceb705304409889f7e43f25fba4b",
            "e4c87b0a8f0644a28f9bdb4bb5199a52",
            "fb0237f4e96740c1878b2d47fab0c675",
            "084a17873e0d4a058715e0fc059cf4ad",
            "66141848e99e4e278e1844177dd597aa",
            "b1230fd1a80a45b39db8d3431c100d1e",
            "9c840d3ccb7f440986afa73fd070e2f5",
            "7c56006bc3144bb68ee6e40836cc5c55",
            "35028ca7f75844ffa6875fb3ecca27d3",
            "b11778d34fa543ceb50d1345f08ae9cf",
            "744627848d6f46c4b144ceaeb4cbe93c",
            "3af02586750c44db9d6f9273dab3bde4",
            "0db7b99fdf644c66bce3c981a1f97f09",
            "23dca3b98ac74ee38e913652f5728916",
            "8e2bf87c63334ea3a1af36192d97257d",
            "3e739af8869b4491a67622b57ec43e6c"
          ]
        },
        "id": "ro-lqt9wCNJb",
        "outputId": "bc6ef615-2aa3-468d-b84e-493ee064bcc6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/200000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d91b9c092cd42b68c5ab89d5da6dd65"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1c5f45f0770d4493816b302a2dcfb7e5"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b1230fd1a80a45b39db8d3431c100d1e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['labels', 'input_ids', 'attention_mask'],\n",
            "        num_rows: 200000\n",
            "    })\n",
            "    validation: Dataset({\n",
            "        features: ['labels', 'input_ids', 'attention_mask'],\n",
            "        num_rows: 5000\n",
            "    })\n",
            "    test: Dataset({\n",
            "        features: ['labels', 'input_ids', 'attention_mask'],\n",
            "        num_rows: 5000\n",
            "    })\n",
            "})\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 4) Finetuning de la convnet 😨"
      ],
      "metadata": {
        "id": "NdRZS4ZactJU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "num_labels = 2\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWCAfvykCp-r",
        "outputId": "45a6396f-0e1b-4d1b-b09f-9b6fe6eea4d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of RobertaForSequenceClassification were not initialized from the model checkpoint at BSC-TeMU/roberta-base-bne and are newly initialized: ['classifier.dense.bias', 'classifier.dense.weight', 'classifier.out_proj.bias', 'classifier.out_proj.weight']\n",
            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Input IDs -> hidden states -> outputs**\n",
        "\n",
        "Resumen:\n",
        "- Input IDs son la representación numérica del texto.\n",
        "- El modelo recibe estos input IDs y calcula los hidden states, que son las representaciones internas del texto procesado.\n",
        "- outputs almacena los hidden states y otras salidas del modelo."
      ],
      "metadata": {
        "id": "zhonmAfXDC_r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "outputs = model(**encoded_text)\n",
        "print(outputs)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jw8sxM7MDRTr",
        "outputId": "84715a18-4fb0-4b56-bafa-4997b4348130"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SequenceClassifierOutput(loss=None, logits=tensor([[ 0.0219, -0.0384]], grad_fn=<AddmmBackward0>), hidden_states=None, attentions=None)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Vamos a evaluar el accuracy como métrica de rendimiento**"
      ],
      "metadata": {
        "id": "OJYEx1nlFqT3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "metric = evaluate.load(\"accuracy\")\n",
        "print(metric)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9bfwMOpsFuP4",
        "outputId": "6bfcf8ad-da62-41c3-ee1a-f49db075689f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "EvaluationModule(name: \"accuracy\", module_type: \"metric\", features: {'predictions': Value(dtype='int32', id=None), 'references': Value(dtype='int32', id=None)}, 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)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "\n",
        "    return metric.compute(predictions=predictions, references=labels)"
      ],
      "metadata": {
        "id": "U7kM0k-gF5jb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 5) Hugging Face Hub 🤗"
      ],
      "metadata": {
        "id": "801ek5pQH9PN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "El modelo entrenado lo vamos a subir a Hugging Face Hub así lo podemos compartir con el mundo 😎"
      ],
      "metadata": {
        "id": "DIvohdjCGt9o"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Importante:** La nueva credencial que vamos a crear debe tener permisos de escritura (write)."
      ],
      "metadata": {
        "id": "x2Nu52oMJYxO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oArrbI7G3BH",
        "outputId": "f08249c0-140e-47b4-cd68-dc68a0fba0ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    To log in, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Enter your token (input will not be visible): \n",
            "Add token as git credential? (Y/n) y\n",
            "Token is valid (permission: write).\n",
            "The token `proyecto-platzi` has been saved to /root/.cache/huggingface/stored_tokens\n",
            "\u001b[1m\u001b[31mCannot authenticate through git-credential as no helper is defined on your machine.\n",
            "You might have to re-authenticate when pushing to the Hugging Face Hub.\n",
            "Run the following command in your terminal in case you want to set the 'store' credential helper as default.\n",
            "\n",
            "git config --global credential.helper store\n",
            "\n",
            "Read https://git-scm.com/book/en/v2/Git-Tools-Credential-Storage for more details.\u001b[0m\n",
            "Token has not been saved to git credential helper.\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful.\n",
            "The current active token is: `proyecto-platzi`\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global credential.helper store"
      ],
      "metadata": {
        "id": "d2URSeAsH5tj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 6) Entrenamiento 💪"
      ],
      "metadata": {
        "id": "2N6Izd0TEV7X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = model_checkpoint.split(\"/\")[-1]"
      ],
      "metadata": {
        "id": "F9nyoJdIGTYa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HaTc4-QLhxOs",
        "outputId": "9c07b0c6-515f-4af7-9f11-0bc95e26354b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "roberta-base-bne\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 8\n",
        "num_train_epochs=2\n",
        "num_train_samples = 20_000\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)"
      ],
      "metadata": {
        "id": "BBuT7MeDGUxG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "training_args = TrainingArguments(\n",
        "    output_dir=\"results\",\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    learning_rate=2e-5,\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=0.01,\n",
        "    eval_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    logging_steps=logging_steps,\n",
        "    push_to_hub=True,\n",
        "    hub_model_id=f\"{model_name}-platzi-project-nlp\"\n",
        ")"
      ],
      "metadata": {
        "id": "5rmX_vSzEdnw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=encoded_dataset[\"validation\"],\n",
        "    processing_class=tokenizer,\n",
        ")"
      ],
      "metadata": {
        "id": "DqM9zQCBIuAM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Para este paso es necesario crearse una cuenta gratuita en [Weights & Biases](https://wandb.ai/home), porque el entrenamiento y las métricas se harán ahí.**"
      ],
      "metadata": {
        "id": "6cCBuU6gMBm5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        },
        "id": "AL_-Ucv1J0dj",
        "outputId": "444ecc49-4df9-46a5-8310-88cf04464e12"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m The `run_name` is currently set to the same value as `TrainingArguments.output_dir`. If this was not intended, please specify a different run name by setting the `TrainingArguments.run_name` parameter.\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend.  Please refer to https://wandb.me/wandb-core for more information.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "        window._wandbApiKey = new Promise((resolve, reject) => {\n",
              "            function loadScript(url) {\n",
              "            return new Promise(function(resolve, reject) {\n",
              "                let newScript = document.createElement(\"script\");\n",
              "                newScript.onerror = reject;\n",
              "                newScript.onload = resolve;\n",
              "                document.body.appendChild(newScript);\n",
              "                newScript.src = url;\n",
              "            });\n",
              "            }\n",
              "            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n",
              "            const iframe = document.createElement('iframe')\n",
              "            iframe.style.cssText = \"width:0;height:0;border:none\"\n",
              "            document.body.appendChild(iframe)\n",
              "            const handshake = new Postmate({\n",
              "                container: iframe,\n",
              "                url: 'https://wandb.ai/authorize'\n",
              "            });\n",
              "            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n",
              "            handshake.then(function(child) {\n",
              "                child.on('authorize', data => {\n",
              "                    clearTimeout(timeout)\n",
              "                    resolve(data)\n",
              "                });\n",
              "            });\n",
              "            })\n",
              "        });\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Tracking run with wandb version 0.19.1"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Run data is saved locally in <code>/content/wandb/run-20250115_152216-8dyigzgh</code>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/cabustillo13/huggingface/runs/8dyigzgh' target=\"_blank\">results</a></strong> to <a href='https://wandb.ai/cabustillo13/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/developer-guide' target=\"_blank\">docs</a>)<br>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View project at <a href='https://wandb.ai/cabustillo13/huggingface' target=\"_blank\">https://wandb.ai/cabustillo13/huggingface</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              " View run at <a href='https://wandb.ai/cabustillo13/huggingface/runs/8dyigzgh' target=\"_blank\">https://wandb.ai/cabustillo13/huggingface/runs/8dyigzgh</a>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='5000' max='5000' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [5000/5000 14:52, Epoch 2/2]\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>0.360500</td>\n",
              "      <td>0.381132</td>\n",
              "      <td>0.848800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>2</td>\n",
              "      <td>0.256100</td>\n",
              "      <td>0.467120</td>\n",
              "      <td>0.854600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "TrainOutput(global_step=5000, training_loss=0.3246493255615234, metrics={'train_runtime': 959.1194, 'train_samples_per_second': 41.705, 'train_steps_per_second': 5.213, 'total_flos': 1730826744362880.0, 'train_loss': 0.3246493255615234, 'epoch': 2.0})"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 7) Guardar el modelo 💾"
      ],
      "metadata": {
        "id": "F2MHfLfbxrtc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para eso vamos a hacer un push a Hugging Face Hub"
      ],
      "metadata": {
        "id": "WU43fbEIMn33"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140,
          "referenced_widgets": [
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            "581363d5d40b4d99bc53842bca153195",
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            "1a613953d56147d780960206434715fa"
          ]
        },
        "id": "jKZWJavUMtG2",
        "outputId": "c5812f04-91db-4264-f2c3-2456fe191487"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "events.out.tfevents.1736914196.09aa6524aa6c.2160.0:   0%|          | 0.00/7.80k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fdfafd3f50f148919c3874cd522801f7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/cabustillo13/roberta-base-bne-platzi-project-nlp/commit/d1d1f304cf5f207e39eb89694ae54b895a87f727', commit_message='End of training', commit_description='', oid='d1d1f304cf5f207e39eb89694ae54b895a87f727', pr_url=None, repo_url=RepoUrl('https://huggingface.co/cabustillo13/roberta-base-bne-platzi-project-nlp', endpoint='https://huggingface.co', repo_type='model', repo_id='cabustillo13/roberta-base-bne-platzi-project-nlp'), pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 8) Hacer Predicciones en Producción 🤙"
      ],
      "metadata": {
        "id": "79swAfE-ny4f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline"
      ],
      "metadata": {
        "id": "HZSt1UpDNf7M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cargar el modelo una vez (al inicio de la aplicación)"
      ],
      "metadata": {
        "id": "AUEDyzPisUvi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_checkpoint = \"cabustillo13/roberta-base-bne-platzi-project-nlp\"\n",
        "pipe = pipeline(\"sentiment-analysis\", model=model_checkpoint)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 259,
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        "outputId": "71377393-d3d8-4bbe-c111-f2776b1db00f"
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      "execution_count": null,
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        {
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Device set to use cuda:0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ejemplos de uso"
      ],
      "metadata": {
        "id": "KVRKmC25oQ0V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pipe(\"me encanto el pantalon!!!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JolSBxn4pySc",
        "outputId": "da7aa76a-2d52-46ff-b47f-d763f7c2fabb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'LABEL_1', 'score': 0.9975569248199463}]"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe(\"Te obligan a comprar dos unidades\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5rKQcuLwpz4I",
        "outputId": "75ed6aad-4c43-433a-ef8d-f8857dfab257"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'LABEL_0', 'score': 0.9499565958976746}]"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pipe(\"la peor compra de mi vida!!! no recomiendo!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HWt2y660p1TY",
        "outputId": "40e04f52-3010-4998-a5b2-bdc80703471f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'label': 'LABEL_0', 'score': 0.9918383955955505}]"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 9) Conclusiones\n",
        "\n",
        "- Aprender sobre los distintos objetos y métodos que nos ofrece Transformers.\n",
        "\n",
        "- Realizar el proceso completo de clasificación de reseñas con Transformers.\n",
        "\n",
        "- Aprender tips sobre implementación con el uso de la GPU, Hugging Face Hub y Weights & Biases.\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "<br>"
      ],
      "metadata": {
        "id": "Wl7v6oyFkREF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "---\n",
        "\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "\n",
        "<img src=\"https://static.platzi.com/media/avatars/platziteam_8cfe6fc7-1246-4c9a-9f5d-d10d467443ee.png\" width=\"100px\">\n",
        "\n",
        "[Platzi](https://platzi.com/) 🚀\n"
      ],
      "metadata": {
        "id": "0sQ95LlzkUZN"
      }
    }
  ]
}