MeIA_Analisis_de_Sentimientos_ipynb_のコピー.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LoiueFragosoUwUr/Sentiment-analysis-for-tourist-reviews-AI/blob/main/MeIA_Analisis_de_Sentimientos_ipynb_%E3%81%AE%E3%82%B3%E3%83%94%E3%83%BC.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Configuración**\n",
        "\n",
        "Si está ejecutando este notebook en Google Colab, ejecute la siguiente celda para instalar las bibliotecas que necesitamos:"
      ],
      "metadata": {
        "id": "SAvVrV7GAADz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#!pip install transformers datasets\n",
        "!pip install transformers==4.28.0 datasets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1hQ_BF_P7xaJ",
        "outputId": "32d84c9d-e05d-4ae2-a020-05f58b7bc0a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers==4.28.0\n",
            "  Downloading transformers-4.28.0-py3-none-any.whl (7.0 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.0/7.0 MB\u001b[0m \u001b[31m59.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting datasets\n",
            "  Downloading datasets-2.13.1-py3-none-any.whl (486 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.2/486.2 kB\u001b[0m \u001b[31m39.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0) (3.12.2)\n",
            "Collecting huggingface-hub<1.0,>=0.11.0 (from transformers==4.28.0)\n",
            "  Downloading huggingface_hub-0.15.1-py3-none-any.whl (236 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m236.8/236.8 kB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0) (1.22.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0) (23.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0) (6.0)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0) (2022.10.31)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0) (2.27.1)\n",
            "Collecting tokenizers!=0.11.3,<0.14,>=0.11.1 (from transformers==4.28.0)\n",
            "  Downloading tokenizers-0.13.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (7.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m104.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.28.0) (4.65.0)\n",
            "Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n",
            "Collecting dill<0.3.7,>=0.3.0 (from datasets)\n",
            "  Downloading dill-0.3.6-py3-none-any.whl (110 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m15.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) (1.5.3)\n",
            "Collecting xxhash (from datasets)\n",
            "  Downloading xxhash-3.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (212 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m24.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting multiprocess (from datasets)\n",
            "  Downloading multiprocess-0.70.14-py310-none-any.whl (134 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m18.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: fsspec[http]>=2021.11.1 in /usr/local/lib/python3.10/dist-packages (from datasets) (2023.6.0)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets) (3.8.4)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (23.1.0)\n",
            "Requirement already satisfied: charset-normalizer<4.0,>=2.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (2.0.12)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (6.0.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0.0a3 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (4.0.2)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.9.2)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets) (1.3.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.11.0->transformers==4.28.0) (4.6.3)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.28.0) (1.26.16)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.28.0) (2023.5.7)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.28.0) (3.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 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) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: tokenizers, xxhash, dill, multiprocess, huggingface-hub, transformers, datasets\n",
            "Successfully installed datasets-2.13.1 dill-0.3.6 huggingface-hub-0.15.1 multiprocess-0.70.14 tokenizers-0.13.3 transformers-4.28.0 xxhash-3.2.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cargar dataset**"
      ],
      "metadata": {
        "id": "DzLU5d3A1ZH1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1tmwwMpvl8dc"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "data = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/Tutoriales_MeIA/MeIA_Rest_Mex_Sentiment_Analysis_2023_Train.xlsx', index_col=0)\n",
        "#data.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FW-AsAZo121u",
        "outputId": "0a9efb5c-b577-4dd6-b999-ba96b2ab668c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Análizis de datos del conjunto de entrenamiento original."
      ],
      "metadata": {
        "id": "XAPnsa722Rva"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Número de instacias: \",len(data))\n",
        "print(\"Distribución de instancias por clase:\")\n",
        "print(data[\"Class\"].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDpbYNw106Nj",
        "outputId": "e81d9e19-4b31-4a1e-e5a5-475211f73623"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Número de instacias:  17500\n",
            "Distribución de instancias por clase:\n",
            "5    5250\n",
            "4    4375\n",
            "3    3500\n",
            "2    2625\n",
            "1    1750\n",
            "Name: Class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 767
        },
        "id": "w0Xv88v11exY",
        "outputId": "6bc32803-8434-4e55-85b4-e2e2756f8bf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    Class\n",
              "Review                                                   \n",
              "Dar mantenimiento adecuado y aclarar jurisdicci...      1\n",
              "yo estaba tan emocionada de este viaje, pero tr...      1\n",
              "Este hotel es un engaño. De cuatro estrellas na...      1\n",
              "La ducha se inundo completamente  a los 5 min d...      1\n",
              "Decidimos alquilar un taxi por un día para ir a...      1"
            ],
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>Class</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Review</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Dar mantenimiento adecuado y aclarar jurisdicción de quién es. De uno de los puentes que lleva a las cascadas cayó una turista 100 m hacia abajo en abril 2021 (obviamente falleció al caer sobre las rocas) y nunca se aclaró si la responsabilidad fue de CFE o del Municipio.</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>yo estaba tan emocionada de este viaje, pero tras el 2-1 2 horas de autobús /viaje y 1 parada de un parque ecológico con un Cenote - me di cuenta este viaje era más de una trampa para turistas que un sitio histórico. Las ruinas en sí tienen más vendedores ladrando y haciendo ruido con sus mercancías que puede debilitar un palo. que siga usted y su guía y tratar de vender a usted mientras usted está tratando de escuchar al guía sobre lo que estás viendo. Hacía calor, y el poquito de sombra disponible fue tomado por los vendedores. La única cosa que he oído en ese viaje fue \"oferta! oferta! más barato que Wal-Mart! Casi gratis!\" que necesitan para mantener a los vendedores en una zona y para permitir a los visitantes que se acerquen a ellos, no sentimos acosados y seguido por ellos. Y por amor de Dios, acondicionar algunos lugares sombreados para sentarse allí!!! Había basura en las ruinas. . Nunca recomendaría este viaje a cualquiera. Fue un día entero y $ nunca volver. Una de las \"maravillas\" del mundo? Realmente? sólo me\" maravilla\" ¿por qué me dejaron lo que podría ser un sitio guay conseguir así crapped!.</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Este hotel es un engaño. De cuatro estrellas nada. Una estrella siendo generoso. Carece de higiene básica, el servicio pésimo, las habitaciones viejas, cama rota, polvo en todos lados, el desayuno impresentable, manteles sucios, sin servilletas, un desastre. Nunca más.</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>La ducha se inundo completamente  a los 5 min de usarla por una coladera sucia y el agua se transfirio al resto del baño. No habia toallas tampoco. Lo que habla de la poca higiene y poco cuidado a los detalles. El personal ni siquiera se disculpo por el incidente. Los ruidos en el pasillo se oyen muchísimo a la habitación.  Mala experiencia y una noche por hab. Estandar costo casi 2000 pesos mexicanos, La decoracion se ve vieja y anticuada.</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Decidimos alquilar un taxi por un día para ir a Habana. El taxista recomendó y nos llevó a este restaurante para el almuerzo. Creo que él hizo esto como un montón de grupos de turistas ir allí. Para dos adultos y un niño de 7...y mi hijo de 9 años pagamos casi $150.00 canadiense para el almuerzo! No nos dimos cuenta los \"lados\" eran extra. La camarera Camino venta sugestiva ... sabiendo que los chicos es probable que se llena en el plato principal. Además, el 10% de propina era incluidos. La carne rallado que los niños lo estaba bien y abundante, pero no vale la pena los 18 dólares un plato. No podía comer la porción de langosta de mi comida como la langosta probado ... skunky ... creo que era un tipo diferente de langosta más barato que era asqueroso. Este fue un servicio de almuerzo, así que no había música en vivo ... quizás leer otras críticas, es donde este lugar es bueno para. La única cosa era la sabrosa bebida ... caña de azúcar y zumo de piña ... lo que estoy seguro de que podríamos conseguir por la mitad del precio en otro sitio. Fuera de todo nuestro viaje a Cuba, este restaurante fue la única decepción y empeoramiento del viaje! Casi pagado tanto para el almuerzo como nosotros pagamos el taxista que nos llevó desde Valedaro para todo el día!Más</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**División estratificada**\n",
        "\n",
        "Se divide estratificadamente el dataset original para formar un conjunto de entrenamiento y pruebas."
      ],
      "metadata": {
        "id": "fWyGFO033E6B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "a,b = train_test_split(data, test_size=0.30, stratify = data['Class'])\n",
        "print(\"Longitud del conjunto de entrenamiento (70%): \",len(a))\n",
        "print(\"Longitud del conjunto de pruebas (30%): \",len(b))\n",
        "print(len(a)+len(b))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tWAXnp-M3ILP",
        "outputId": "733fd8e1-11a1-4b73-e81c-4ddfca871aae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Longitud del conjunto de entrenamiento (70%):  12250\n",
            "Longitud del conjunto de pruebas (30%):  5250\n",
            "17500\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Distribución de instancias del conjunto de entrenamiento por clase:\")\n",
        "print(a[\"Class\"].value_counts())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jf6DLe575iyF",
        "outputId": "6ee8f67b-5d0c-42c5-cf77-a75a76ced458"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Distribución de instancias del conjunto de entrenamiento por clase:\n",
            "5    3675\n",
            "4    3063\n",
            "3    2450\n",
            "2    1837\n",
            "1    1225\n",
            "Name: Class, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import Dataset, concatenate_datasets, DatasetDict\n",
        "\n",
        "ds_train = Dataset.from_pandas(a)\n",
        "ds_test = Dataset.from_pandas(b)\n",
        "ds_dict = {'train' : ds_train, 'test':ds_test}\n",
        "dataset = DatasetDict(ds_dict)\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-2sH1oIO7UEK",
        "outputId": "d16aa5d6-17ab-4093-a7cf-3f4903b96d0f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['Class', 'Review'],\n",
              "        num_rows: 12250\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['Class', 'Review'],\n",
              "        num_rows: 5250\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pre-procesamiento del dataset**"
      ],
      "metadata": {
        "id": "WaA-BUrbAQXC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cambiar el nombre de la columana que contiene las clases a predecir por \"labels\""
      ],
      "metadata": {
        "id": "4Uo_QcBJBiH5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.rename_column(\"Class\", \"labels\")"
      ],
      "metadata": {
        "id": "Mae37pSfApsG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P20xunQ5CTD6",
        "outputId": "4a17c38d-9202-40de-ed84-0838f2e8140e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['labels', 'Review'],\n",
              "        num_rows: 12250\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['labels', 'Review'],\n",
              "        num_rows: 5250\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Las etiquetas (labels) en HF se inicializan a partir del numeró 0, por lo cuál, las etiquetas de polaridad se deben modifican de la siguiente manera: label 1 = 0, label 2 = 1, label 3 = 2, label 4 = 3 y label 5 = 4."
      ],
      "metadata": {
        "id": "wLyZ-_KHCoAH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def polarity_init(example):\n",
        "  example[\"labels\"] = example[\"labels\"] - 1\n",
        "  return example\n",
        "\n",
        "dataset = dataset.map(polarity_init)\n",
        "\n",
        "dataset.set_format(\"pandas\")\n",
        "df = dataset[\"train\"][:]\n",
        "print(df[\"labels\"].value_counts())\n",
        "dataset.reset_format()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127,
          "referenced_widgets": [
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          ]
        },
        "id": "btRilKBQDE6_",
        "outputId": "93c25730-e163-4d27-dab6-526b552bd591"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/12250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b7eb174dddfe4c4494ca774b91f34da4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5250 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "501af06969c340508a8b4aa53d5d37a8"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4    3675\n",
            "3    3063\n",
            "2    2450\n",
            "1    1837\n",
            "0    1225\n",
            "Name: labels, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Login en Hugging Face para compartir el modelo**"
      ],
      "metadata": {
        "id": "ikm3pIy1ccBw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Esto sólo funciona en Google Colab! Para los notebooks normales, es necesario ejecutar esto en el terminal\n",
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y1AF50-UcbXh",
        "outputId": "8c0ae8f8-2ba0-4418-b5a4-01d085acc3ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "    \n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) y\n",
            "Token is valid (permission: write).\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"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Si no tienes instalado Git LFS, puedes hacerlo descomentando y ejecutando la celda de abajo:"
      ],
      "metadata": {
        "id": "NDQhPRoNclDh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!apt install git-lfs\n",
        "!git config --global user.email \"vg055@hotmail.com\"\n",
        "!git config --global user.name \"Victor055\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ua4N6M-GcmT5",
        "outputId": "8ab1228d-fd36-47ce-afbf-55f85d5f96e1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree       \n",
            "Reading state information... Done\n",
            "git-lfs is already the newest version (2.9.2-1).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 13 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Tokenizar las reseñas**"
      ],
      "metadata": {
        "id": "7OhBj1TzY2xt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer\n",
        "model_checkpoint = \"distilbert-base-multilingual-cased\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)\n",
        "tokenizer.vocab_size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 163,
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      "execution_count": null,
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/29.0 [00:00<?, ?B/s]"
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            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/466 [00:00<?, ?B/s]"
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              "Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/996k [00:00<?, ?B/s]"
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.96M [00:00<?, ?B/s]"
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          "metadata": {},
          "execution_count": 17
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      "cell_type": "code",
      "source": [
        "def tokenize_reviews(examples):\n",
        "    return tokenizer(examples[\"Review\"], truncation=True)\n",
        "\n",
        "columns = dataset[\"train\"].column_names\n",
        "columns.remove(\"labels\")\n",
        "encoded_dataset = dataset.map(tokenize_reviews, batched=True, remove_columns=columns)\n",
        "encoded_dataset"
      ],
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        "colab": {
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/12250 [00:00<?, ? examples/s]"
            ],
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/5250 [00:00<?, ? examples/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "9080cc0edb004950bb5c4392cd7864f4"
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          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['labels', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 12250\n",
              "    })\n",
              "    test: Dataset({\n",
              "        features: ['labels', 'input_ids', 'attention_mask'],\n",
              "        num_rows: 5250\n",
              "    })\n",
              "})"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
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      "cell_type": "code",
      "source": [
        "encoded_dataset[\"train\"][0]"
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      "metadata": {
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        "id": "lXB6w8E5amBh",
        "outputId": "ea063ecb-3374-44f7-ac44-d90668c1b493"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "  1]}"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Cargar el modelo pre-entrenado**"
      ],
      "metadata": {
        "id": "Ixw6P62BauC_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "\n",
        "num_labels = 5\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_checkpoint, num_labels=num_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 160,
          "referenced_widgets": [
            "4220bf23d9e74bc3b6ba4a4a4d3bbaf3",
            "aa8dcd1248ae48608cc00d2d9d040e59",
            "7efcbf81c8c34eeb8e3e08e7fb0fba53",
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            "997bbfd5af624a65b1ba3fbbc74a285e",
            "24db658335a341c2bb7906ea86c64442",
            "1571a4422f7c4a57aa18603c2a6e1f60",
            "caf37497b4f14a4eb352db7bb46aceb6",
            "ed1a9b41a69a46ef9056b346496ab5e7",
            "ae97b6c709d64dada58ef1b8ef6c0609"
          ]
        },
        "id": "zVdLhUG2a2aX",
        "outputId": "876b3c0a-9c69-4958-e25f-9ed40ededbbe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/542M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "4220bf23d9e74bc3b6ba4a4a4d3bbaf3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Some weights of the model checkpoint at distilbert-base-multilingual-cased were not used when initializing DistilBertForSequenceClassification: ['vocab_layer_norm.bias', 'vocab_transform.weight', 'vocab_projector.weight', 'vocab_layer_norm.weight', 'vocab_transform.bias', 'vocab_projector.bias']\n",
            "- This IS expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
            "- This IS NOT expected if you are initializing DistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
            "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-multilingual-cased and are newly initialized: ['pre_classifier.bias', 'pre_classifier.weight', 'classifier.bias', 'classifier.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": [
        "**Definir metríca de evaluación**"
      ],
      "metadata": {
        "id": "zqzJPa6zbIZu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from datasets import load_metric\n",
        "\n",
        "metric = load_metric(\"f1\")\n",
        "metric"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "982433bf72db43ce913a00a75af0c964",
            "501057793be2404db346cf20fc409cf7",
            "af1128628ef9472b89164ee43492ee70",
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            "29b23b9664714d8db35b5812f066a7a4"
          ]
        },
        "id": "6cvCg9qtbOMg",
        "outputId": "d1e8e76d-ede2-4f8e-922f-06cc2fd07079"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-21-ee0dc6f5e028>:3: FutureWarning: load_metric is deprecated and will be removed in the next major version of datasets. Use 'evaluate.load' instead, from the new library 🤗 Evaluate: https://huggingface.co/docs/evaluate\n",
            "  metric = load_metric(\"f1\")\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading builder script:   0%|          | 0.00/2.32k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "982433bf72db43ce913a00a75af0c964"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Metric(name: \"f1\", 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",
              "    labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.\n",
              "    pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.\n",
              "    average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.\n",
              "\n",
              "        - 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.\n",
              "        - 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.\n",
              "        - 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.\n",
              "        - 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.\n",
              "        - 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).\n",
              "    sample_weight (`list` of `float`): Sample weights Defaults to None.\n",
              "\n",
              "Returns:\n",
              "    f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.\n",
              "\n",
              "Examples:\n",
              "\n",
              "    Example 1-A simple binary example\n",
              "        >>> f1_metric = datasets.load_metric(\"f1\")\n",
              "        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])\n",
              "        >>> print(results)\n",
              "        {'f1': 0.5}\n",
              "\n",
              "    Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.\n",
              "        >>> f1_metric = datasets.load_metric(\"f1\")\n",
              "        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)\n",
              "        >>> print(round(results['f1'], 2))\n",
              "        0.67\n",
              "\n",
              "    Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.\n",
              "        >>> f1_metric = datasets.load_metric(\"f1\")\n",
              "        >>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])\n",
              "        >>> print(round(results['f1'], 2))\n",
              "        0.35\n",
              "\n",
              "    Example 4-A multiclass example, with different values for the `average` input.\n",
              "        >>> predictions = [0, 2, 1, 0, 0, 1]\n",
              "        >>> references = [0, 1, 2, 0, 1, 2]\n",
              "        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")\n",
              "        >>> print(round(results['f1'], 2))\n",
              "        0.27\n",
              "        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")\n",
              "        >>> print(round(results['f1'], 2))\n",
              "        0.33\n",
              "        >>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")\n",
              "        >>> print(round(results['f1'], 2))\n",
              "        0.27\n",
              "        >>> results = f1_metric.compute(predictions=predictions, references=references, average=None)\n",
              "        >>> print(results)\n",
              "        {'f1': array([0.8, 0. , 0. ])}\n",
              "\"\"\", stored examples: 0)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "def compute_metrics(eval_pred):\n",
        "    predictions, labels = eval_pred\n",
        "    predictions = np.argmax(predictions, axis=1)\n",
        "    return metric.compute(predictions=predictions, references=labels, average=\"weighted\")"
      ],
      "metadata": {
        "id": "o53gGCMPbSUm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Afinar el modelo pre-entrenado (Fine-Tuning)**"
      ],
      "metadata": {
        "id": "qf5hhxgLbY_a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments\n",
        "\n",
        "model_name = model_checkpoint.split(\"/\")[-1]\n",
        "\n",
        "batch_size = 16\n",
        "num_train_epochs=2\n",
        "train_dataset = encoded_dataset[\"train\"]\n",
        "logging_steps = len(train_dataset) // (2 * batch_size * num_train_epochs)\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=\"results\",\n",
        "    num_train_epochs=num_train_epochs,\n",
        "    learning_rate=2e-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=\"f1\",\n",
        "    weight_decay=0.01,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    logging_steps=logging_steps,\n",
        "    push_to_hub=True,\n",
        "    push_to_hub_model_id=f\"{model_name}-finetuned-MeIA-AnalisisDeSentimientos\"\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IJ_2W5Yfba2g",
        "outputId": "56661b25-7ec1-4636-ff34-dc65537b8953"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1469: FutureWarning: `--push_to_hub_model_id` is deprecated and will be removed in version 5 of 🤗 Transformers. Use `--hub_model_id` instead and pass the full repo name to this argument (in this case vg055/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos).\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    compute_metrics=compute_metrics,\n",
        "    train_dataset=train_dataset,\n",
        "    eval_dataset=encoded_dataset[\"test\"],\n",
        "    tokenizer=tokenizer\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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        },
        "id": "FvwronSodxiu",
        "outputId": "a765c5f8-0cfd-4865-e206-d06ac05b5a72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Cloning https://huggingface.co/vg055/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos into local empty directory.\n",
            "WARNING:huggingface_hub.repository:Cloning https://huggingface.co/vg055/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos into local empty directory.\n"
          ]
        },
        {
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        {
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              "Download file runs/Jun29_01-28-49_d48e365ce337/1688002262.950781/events.out.tfevents.1688002262.d48e365ce337.2…"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "3158ece1432b42d7ba9a41d4b29d7683"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Clean file runs/Jun29_01-28-49_d48e365ce337/events.out.tfevents.1688002262.d48e365ce337.200.0:  16%|#5        …"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "21ed2235387948eba00f9334c0f7121b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Download file runs/Jun29_01-28-49_d48e365ce337/events.out.tfevents.1688003201.d48e365ce337.200.2: 100%|#######…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "edd67f71b026493589885ae7965c21ce"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Clean file runs/Jun29_01-28-49_d48e365ce337/1688002262.950781/events.out.tfevents.1688002262.d48e365ce337.200.…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8671bb6a69f2458fb07acab94eaa89fa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Clean file runs/Jun29_01-28-49_d48e365ce337/events.out.tfevents.1688003201.d48e365ce337.200.2: 100%|##########…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "638fbdce9b67429fae2d79156483eaac"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Clean file pytorch_model.bin:   0%|          | 1.00k/516M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9e2335d3223f43d7bd32d9a542a4c77f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "ERROR:root:Internal Python error in the inspect module.\n",
            "Below is the traceback from this internal error.\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 3553, in run_code\n",
            "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
            "  File \"<ipython-input-24-8c1e32eec3ac>\", line 3, in <cell line: 3>\n",
            "    trainer = Trainer(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\", line 550, in __init__\n",
            "    self.init_git_repo(at_init=True)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\", line 3497, in init_git_repo\n",
            "    self.repo = Repository(self.args.output_dir, clone_from=repo_name, token=self.args.hub_token)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\n",
            "    return fn(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/repository.py\", line 516, in __init__\n",
            "    self.clone_from(repo_url=clone_from)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\", line 118, in _inner_fn\n",
            "    return fn(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/repository.py\", line 669, in clone_from\n",
            "    run_subprocess(\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_subprocess.py\", line 83, in run_subprocess\n",
            "    return subprocess.run(\n",
            "  File \"/usr/lib/python3.10/subprocess.py\", line 505, in run\n",
            "    stdout, stderr = process.communicate(input, timeout=timeout)\n",
            "  File \"/usr/lib/python3.10/subprocess.py\", line 1154, in communicate\n",
            "    stdout, stderr = self._communicate(input, endtime, timeout)\n",
            "  File \"/usr/lib/python3.10/subprocess.py\", line 2021, in _communicate\n",
            "    ready = selector.select(timeout)\n",
            "  File \"/usr/lib/python3.10/selectors.py\", line 416, in select\n",
            "    fd_event_list = self._selector.poll(timeout)\n",
            "KeyboardInterrupt\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\", line 2099, in showtraceback\n",
            "    stb = value._render_traceback_()\n",
            "AttributeError: 'KeyboardInterrupt' object has no attribute '_render_traceback_'\n",
            "\n",
            "During handling of the above exception, another exception occurred:\n",
            "\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 1101, in get_records\n",
            "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 248, in wrapped\n",
            "    return f(*args, **kwargs)\n",
            "  File \"/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\", line 281, in _fixed_getinnerframes\n",
            "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 1662, in getinnerframes\n",
            "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 1620, in getframeinfo\n",
            "    filename = getsourcefile(frame) or getfile(frame)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 829, in getsourcefile\n",
            "    module = getmodule(object, filename)\n",
            "  File \"/usr/lib/python3.10/inspect.py\", line 878, in getmodule\n",
            "    os.path.realpath(f)] = module.__name__\n",
            "  File \"/usr/lib/python3.10/posixpath.py\", line 396, in realpath\n",
            "    path, ok = _joinrealpath(filename[:0], filename, strict, {})\n",
            "  File \"/usr/lib/python3.10/posixpath.py\", line 431, in _joinrealpath\n",
            "    st = os.lstat(newpath)\n",
            "KeyboardInterrupt\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
            "\u001b[0;32m<ipython-input-24-8c1e32eec3ac>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m trainer = Trainer(\n\u001b[0m\u001b[1;32m      4\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, model, args, data_collator, train_dataset, eval_dataset, tokenizer, model_init, compute_metrics, callbacks, optimizers, preprocess_logits_for_metrics)\u001b[0m\n\u001b[1;32m    549\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpush_to_hub\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 550\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minit_git_repo\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mat_init\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    551\u001b[0m             \u001b[0;31m# In case of pull, we need to make sure every process has the latest.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36minit_git_repo\u001b[0;34m(self, at_init)\u001b[0m\n\u001b[1;32m   3496\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3497\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrepo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRepository\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moutput_dir\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mclone_from\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrepo_name\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtoken\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhub_token\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3498\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mEnvironmentError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/repository.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, local_dir, clone_from, repo_type, token, git_user, git_email, revision, skip_lfs_files, client)\u001b[0m\n\u001b[1;32m    515\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mclone_from\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 516\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclone_from\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrepo_url\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mclone_from\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    517\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_validators.py\u001b[0m in \u001b[0;36m_inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    117\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 118\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    119\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/repository.py\u001b[0m in \u001b[0;36mclone_from\u001b[0;34m(self, repo_url, token)\u001b[0m\n\u001b[1;32m    668\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 669\u001b[0;31m                     run_subprocess(\n\u001b[0m\u001b[1;32m    670\u001b[0m                         \u001b[0;31m# 'git lfs clone' is deprecated (will display a warning in the terminal)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_subprocess.py\u001b[0m in \u001b[0;36mrun_subprocess\u001b[0;34m(command, folder, check, **kwargs)\u001b[0m\n\u001b[1;32m     82\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 83\u001b[0;31m     return subprocess.run(\n\u001b[0m\u001b[1;32m     84\u001b[0m         \u001b[0mcommand\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/subprocess.py\u001b[0m in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    504\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 505\u001b[0;31m             \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommunicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    506\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mTimeoutExpired\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/subprocess.py\u001b[0m in \u001b[0;36mcommunicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m   1153\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1154\u001b[0;31m                 \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_communicate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mendtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1155\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyboardInterrupt\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/subprocess.py\u001b[0m in \u001b[0;36m_communicate\u001b[0;34m(self, input, endtime, orig_timeout)\u001b[0m\n\u001b[1;32m   2020\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2021\u001b[0;31m                     \u001b[0mready\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mselector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mselect\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2022\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_timeout\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendtime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morig_timeout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstdout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstderr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.10/selectors.py\u001b[0m in \u001b[0;36mselect\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    415\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 416\u001b[0;31m             \u001b[0mfd_event_list\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_selector\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpoll\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtimeout\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    417\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mInterruptedError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2098\u001b[0m                         \u001b[0;31m# in the engines. This should return a list of strings.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2099\u001b[0;31m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'KeyboardInterrupt' object has no attribute '_render_traceback_'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "    \u001b[0;31m[... skipping hidden 1 frame]\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mshowtraceback\u001b[0;34m(self, exc_tuple, filename, tb_offset, exception_only, running_compiled_code)\u001b[0m\n\u001b[1;32m   2099\u001b[0m                         \u001b[0mstb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_render_traceback_\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2100\u001b[0m                     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2101\u001b[0;31m                         stb = self.InteractiveTB.structured_traceback(etype,\n\u001b[0m\u001b[1;32m   2102\u001b[0m                                             value, tb, tb_offset=tb_offset)\n\u001b[1;32m   2103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1365\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m         return FormattedTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1368\u001b[0m             self, etype, value, tb, tb_offset, number_of_lines_of_context)\n\u001b[1;32m   1369\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, value, tb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1265\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mverbose_modes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1266\u001b[0m             \u001b[0;31m# Verbose modes need a full traceback\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1267\u001b[0;31m             return VerboseTB.structured_traceback(\n\u001b[0m\u001b[1;32m   1268\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtb_offset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnumber_of_lines_of_context\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1269\u001b[0m             )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mstructured_traceback\u001b[0;34m(self, etype, evalue, etb, tb_offset, number_of_lines_of_context)\u001b[0m\n\u001b[1;32m   1122\u001b[0m         \u001b[0;34m\"\"\"Return a nice text document describing the traceback.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1123\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1124\u001b[0;31m         formatted_exception = self.format_exception_as_a_whole(etype, evalue, etb, number_of_lines_of_context,\n\u001b[0m\u001b[1;32m   1125\u001b[0m                                                                tb_offset)\n\u001b[1;32m   1126\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mformat_exception_as_a_whole\u001b[0;34m(self, etype, evalue, etb, number_of_lines_of_context, tb_offset)\u001b[0m\n\u001b[1;32m   1080\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1081\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1082\u001b[0;31m         \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfind_recursion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morig_etype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1083\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1084\u001b[0m         \u001b[0mframes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat_records\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlast_unique\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecursion_repeat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/IPython/core/ultratb.py\u001b[0m in \u001b[0;36mfind_recursion\u001b[0;34m(etype, value, records)\u001b[0m\n\u001b[1;32m    380\u001b[0m     \u001b[0;31m# first frame (from in to out) that looks different.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    381\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_recursion_error\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0metype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 382\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrecords\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    383\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    384\u001b[0m     \u001b[0;31m# Select filename, lineno, func_name to track frames with\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: object of type 'NoneType' has no len()"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.train()"
      ],
      "metadata": {
        "id": "O1NY3ohnd31F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Evaluación del modelo**"
      ],
      "metadata": {
        "id": "O4vzjmoXd8RU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "eval_results = trainer.evaluate()\n",
        "print(eval_results)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "xCHqLz4Ed52W",
        "outputId": "e1d8d1e9-cb5e-4890-faa2-a76c6a98c780"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='329' max='329' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [329/329 00:49]\n",
              "    </div>\n",
              "    "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'eval_loss': 1.0305339097976685, 'eval_f1': 0.5444967854287687, 'eval_runtime': 49.5333, 'eval_samples_per_second': 105.989, 'eval_steps_per_second': 6.642, 'epoch': 2.0}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Subir el modelo a Hugging Face**"
      ],
      "metadata": {
        "id": "FJGo0BttfND8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.push_to_hub()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308,
          "referenced_widgets": [
            "d39c5c6d958e4e2795b88ba4085ad3ee",
            "177b4ba0e47a4439a7265ce27f9ca10c",
            "8808043515404716853e843cd1904123",
            "f928137cadc148c2842dbdd106a2eb29",
            "50bff2843cb840cfb752d1420c1063b7",
            "9191d8ad322b49d4a5aaeadea1455609",
            "e67fe6d3321b4ffdb8066e568d4725a2",
            "64d689219df340248c1f3f4c4400b293",
            "70b6dd0b3a8c43ccb6acc34ae6aa2d8e",
            "09da555be944480bb0abd042de61c95f",
            "daba860c1ae04117b4c65e6f519ac9b8",
            "246b6b1542b540ec9a00379ec02ca53d",
            "dedd19ca5e694927a90eee74ad5284de",
            "dfc7a806cf154847b7928e4d5fae848e",
            "549a4502e6a14737b0e8289f2febc954",
            "cdfbd230d44c4427a484ef628b4c4049",
            "8e35ed9ba25d4f499d02ce613dc8a51e",
            "f785ebd2f86d4f9783c6168eba19397d",
            "e91bf68a7f6d478abb7e57ad7bfae2a9",
            "a4d25cbfe9f14407aef0d9db94689f0f",
            "95a708c011f44ff9bd2638966aed0f93",
            "bcdafeb0b0224b14bc8cbbd9a3bc8e69"
          ]
        },
        "id": "xm_h3J3VfRr_",
        "outputId": "8528b548-75b1-4152-96ef-8e891e9bf1b9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/Jun16_23-13-19_cfce56d978d8/events.out.tfevents.1686957215.cfce56d978d8.1137.0: 100%|########…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d39c5c6d958e4e2795b88ba4085ad3ee"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload file runs/Jun16_23-13-19_cfce56d978d8/events.out.tfevents.1686958139.cfce56d978d8.1137.2: 100%|########…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "246b6b1542b540ec9a00379ec02ca53d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "To https://huggingface.co/vg055/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos\n",
            "   5e131dc..ef3afdb  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/vg055/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos\n",
            "   5e131dc..ef3afdb  main -> main\n",
            "\n",
            "To https://huggingface.co/vg055/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos\n",
            "   ef3afdb..74094e1  main -> main\n",
            "\n",
            "WARNING:huggingface_hub.repository:To https://huggingface.co/vg055/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos\n",
            "   ef3afdb..74094e1  main -> main\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'https://huggingface.co/vg055/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos/commit/ef3afdb209025b778e44058cda3dd009a13caed4'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Guarda modelo y utilizarlo desde disco local**"
      ],
      "metadata": {
        "id": "7ErJCY0n1i03"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainer.save_model(\"/content/drive/MyDrive/Colab Notebooks/Tutoriales_MeIA\")"
      ],
      "metadata": {
        "id": "xcpAkv4I1lQs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoModelForSequenceClassification\n",
        "model2 = AutoModelForSequenceClassification.from_pretrained(\"/content/drive/MyDrive/Colab Notebooks/Tutoriales_MeIA/\")"
      ],
      "metadata": {
        "id": "VUTYPyes1nXT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(model2)"
      ],
      "metadata": {
        "id": "tCiIEWgc3KbA",
        "outputId": "2dd041cb-d9ed-4622-d0b3-e1c32443632c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DistilBertForSequenceClassification(\n",
            "  (distilbert): DistilBertModel(\n",
            "    (embeddings): Embeddings(\n",
            "      (word_embeddings): Embedding(119547, 768, padding_idx=0)\n",
            "      (position_embeddings): Embedding(512, 768)\n",
            "      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "      (dropout): Dropout(p=0.1, inplace=False)\n",
            "    )\n",
            "    (transformer): Transformer(\n",
            "      (layer): ModuleList(\n",
            "        (0-5): 6 x TransformerBlock(\n",
            "          (attention): MultiHeadSelfAttention(\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "            (q_lin): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (k_lin): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (v_lin): Linear(in_features=768, out_features=768, bias=True)\n",
            "            (out_lin): Linear(in_features=768, out_features=768, bias=True)\n",
            "          )\n",
            "          (sa_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "          (ffn): FFN(\n",
            "            (dropout): Dropout(p=0.1, inplace=False)\n",
            "            (lin1): Linear(in_features=768, out_features=3072, bias=True)\n",
            "            (lin2): Linear(in_features=3072, out_features=768, bias=True)\n",
            "            (activation): GELUActivation()\n",
            "          )\n",
            "          (output_layer_norm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (pre_classifier): Linear(in_features=768, out_features=768, bias=True)\n",
            "  (classifier): Linear(in_features=768, out_features=5, bias=True)\n",
            "  (dropout): Dropout(p=0.2, inplace=False)\n",
            ")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "tx = pipeline(\"text-classification\", model=model2, tokenizer=tokenizer)\n",
        "texto = \"Bueno es un. Hotel que ya es necesario un mantenimiento y una modernización urgente porque con el nivel que esta presentando a hora no le alcanza ni par un 3 estrella sus ascensores son deplorable  y tiene un mal servicio de su personal\"\n",
        "tx(texto)"
      ],
      "metadata": {
        "id": "R32uayLh1o8U"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Pruebas**"
      ],
      "metadata": {
        "id": "16HacI2puebH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cargar conjunto de datos de pruebas"
      ],
      "metadata": {
        "id": "kQ_HGFyCu5if"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "data = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/Tutoriales_MeIA/MeIA_Rest_Mex_Sentiment_Analysis_2023_Test.xlsx', index_col=0)"
      ],
      "metadata": {
        "id": "52Rd5AWKuaEW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Cargar pipeline del modelo entrenado"
      ],
      "metadata": {
        "id": "RUbzDBFlu8q3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline\n",
        "pi = pipeline(\"text-classification\", model=\"vg055/distilbert-base-multilingual-cased-finetuned-MeIA-AnalisisDeSentimientos\", max_length=512, truncation=True)"
      ],
      "metadata": {
        "id": "hzBZQ6ZVukoK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Ejemplo de prueba**"
      ],
      "metadata": {
        "id": "zipufSFpuqWq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "text = \"Bueno es un. Hotel que ya es necesario un mantenimiento y una modernización urgente porque con el nivel que esta presentando a hora no le alcanza ni par un 3 estrella sus ascensores son deplorable  y tiene un mal servicio de su personal\"\n",
        "res = pi(text)\n",
        "print(res)"
      ],
      "metadata": {
        "id": "Hk5z_GiButLO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conjunto de pruebas y generción de archivo de texto para reto MeIA**"
      ],
      "metadata": {
        "id": "zPwIrdrruwWJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Abrir archivo de texto en modo escritura para almacenar los resultados para el reto\n",
        "with open('Resultados_MeIATeam_ModeloBase.txt', 'w') as f:\n",
        "  for i in range(len(data)):\n",
        "      print(i,\" - \",data.iloc[i]['Review'])\n",
        "      res = pi(data.iloc[i]['Review'])\n",
        "      label = res[0]['label']\n",
        "      #Establecer la etiqueta correspondiente\n",
        "      if label == 'LABEL_0':\n",
        "        label = \"1\"\n",
        "      elif label == 'LABEL_1':\n",
        "        label = \"2\"\n",
        "      elif label == 'LABEL_2':\n",
        "        label = \"3\"\n",
        "      elif label == 'LABEL_3':\n",
        "        label = \"4\"\n",
        "      elif label == 'LABEL_4':\n",
        "        label = \"5\"\n",
        "      #Escribir predicción del sentimiento en el archivo de texto con el formaro correspondiente\n",
        "      f.write(str(i)+\"\\t\"+label+\"\\n\")\n",
        "      #Imprimir en pantalla lo que se escribio en el archivo txt\n",
        "      print(str(i)+\"\\t\"+label+\"\\n\")\n",
        "      #Descomente si solo quiere probar este bloque de codigo para las primeras tres instancias de pruebas\n",
        "      #if i ==2:\n",
        "        #break"
      ],
      "metadata": {
        "id": "nHu8Wi0_uy_r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib as plt\n",
        "#initialize\n",
        "# a list\n",
        "List1=[]\n",
        "\n",
        "num=1\n",
        "numcube= num**3\n",
        "\n",
        "while numcube <100:\n",
        "    #We can add elements to a list by using append\n",
        "    List1.append(numcube)\n",
        "    num += 1\n",
        "    numcube = num**3\n",
        "\n",
        "print (List1)\n",
        "\n",
        "print(List1[0],List1[-1])\n",
        "\n",
        "List2=[]\n",
        "\n",
        "for num in List1:\n",
        "    num-=50\n",
        "    List2.append(num)\n",
        "print(List2)\n",
        "#we can create an array from an already existing list\n",
        "array1 = np.array(List1)\n",
        "#their visualization is slightly different\n",
        "\n",
        "print(\"array representation\",array1)\n",
        "print(\"list representation\",List1)\n",
        "#numpy functions can take lists or array as inputs      list as input and array as outputs\n",
        "# but the will return an array as output\n",
        "\n",
        "res1= np.sin(List1)\n",
        "res2= np.sin(array1)\n",
        "\n",
        "print(res1)\n",
        "print(res2)\n",
        "#create a matrix with a numpy array and lists of lists\n",
        "list3=[[1,0],[0,-1]]\n",
        "mat1= np.array(list3)\n",
        "print(mat1)\n",
        "print(list3)\n",
        "#arrays behave like vectors\n",
        "#np.dot allows us to do matrix multiplication\n",
        "vector1= np.array([.6,-.3])\n",
        "vector2= np.dot(mat1,vector1)\n",
        "\n",
        "print(vector2)\n",
        "print(np.dot(vector2,vector1))\n",
        "\n",
        "xOR= 10\n",
        "xOB=-10\n",
        "xO =[xOR,xOB]\n",
        "\n",
        "vOR=0\n",
        "vOB=1.8\n",
        "#we will pack them with a numpy array\n",
        "#this will help us with the matrix vector multiplication\n",
        "vO= np.array([vOR,vOB])\n",
        "#mR = redmarblemass\n",
        "#mB = bluemarblemass\n",
        "#extra variables\n",
        "mR = 3\n",
        "mB = 3\n",
        "msum=mR+mB\n",
        "mdiff= mR-mB\n",
        "Mass_matrix= np.array([[mdiff/msum,2*mB/msum],[2*mR/msum,-mdiff/msum]])\n",
        "vT= np.dot(Mass_matrix,vO)\n",
        "\n",
        "\n",
        "print(\"velocity of the red marble after the collision\",vT[0])\n",
        "print(\"velocity of the blue marble after the collision\",vT[1])\n",
        "\n",
        "coll_time =(xO[0]-xO[1])/(vO[1]-vO[0])\n",
        "print(\"Collision at time =\", coll_time,\" sec\")\n",
        "#use the function to calculate the position as a function of time\n",
        "\n",
        "def position(xOO,vOO,t):\n",
        "    #determine the position t seconds after being at xOO,\n",
        "    #while traveling with velocity vOO\n",
        "    return xOO+vOO*t\n",
        "coll_pos = position(xO,vO,coll_time)\n",
        "coll_pos\n",
        "Tsteps=100\n",
        "time = np.linspace(0,2*coll_time,Tsteps)\n",
        "red_pos=[]\n",
        "blue_pos=[]\n",
        "# loop iterating time value\n",
        "for t in time:\n",
        "    #before collision we use initial values of position and velocity\n",
        "    if t< coll_time:\n",
        "        red_pos=position(xOR,vOR,t)\n",
        "        blue_pos=position(xOR,vOB,t)\n",
        "#after collision we use the calculated values of position and velocity\n",
        "#time is now the difference between total and the collision time\n",
        "    else:\n",
        "        red_pos_t=position(coll_pos[0],vT[0],t-coll_time)\n",
        "        blue_pos_t= position(coll_pos[1],vT[1],t-coll_time)\n",
        "    red_pos.append(red_pos_t)\n",
        "    blue_pos.append(blue_pos_t)\n",
        "plt.plot(time,red_pos,'r')\n",
        "plt.plot(time,blue_pos,'r')\n",
        "plt.title('One dimensional collision')\n",
        "plt.xlabel('time(seconds)')\n",
        "plt.ylabel('position(cm)')\n",
        "plt.legend(['Red marble','Blue marble'])\n",
        "plt.tight_layout(pad=0.4,v_pad=6.5,h_pad=1.0)"
      ],
      "metadata": {
        "id": "6nR_nWEG3BVj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "outputId": "51d021bf-5b99-4ae4-d497-024cab9b0662"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 8, 27, 64]\n",
            "1 64\n",
            "[-49, -42, -23, 14]\n",
            "array representation [ 1  8 27 64]\n",
            "list representation [1, 8, 27, 64]\n",
            "[0.84147098 0.98935825 0.95637593 0.92002604]\n",
            "[0.84147098 0.98935825 0.95637593 0.92002604]\n",
            "[[ 1  0]\n",
            " [ 0 -1]]\n",
            "[[1, 0], [0, -1]]\n",
            "[0.6 0.3]\n",
            "0.27\n",
            "velocity of the red marble after the collision 1.8\n",
            "velocity of the blue marble after the collision 0.0\n",
            "Collision at time = 11.11111111111111  sec\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-1e972d9cfc1d>\u001b[0m in \u001b[0;36m<cell line: 91>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     99\u001b[0m         \u001b[0mred_pos_t\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mposition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoll_pos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mcoll_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m         \u001b[0mblue_pos_t\u001b[0m\u001b[0;34m=\u001b[0m \u001b[0mposition\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcoll_pos\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvT\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mcoll_time\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m     \u001b[0mred_pos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mred_pos_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m     \u001b[0mblue_pos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mblue_pos_t\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mred_pos\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.float64' object has no attribute 'append'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "K-5Fswen3W5M"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JOVatn8_3Lgc"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}