5_Escuela_Verano_Modelos_de_Lenguaje.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/orlandxrf/escuela-verano/blob/main/5_Escuela_Verano_Modelos_de_Lenguaje.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/img/logos.png\" height=\"100\" />\n",
        "\n",
        "<h1>Escuela de verano del Laboratorio de Estructuras y Dinámicas Sociales</h1>\n",
        "Escuela de Verano, 25-28 de Junio de 2024\n",
        "<hr>\n",
        "<h2><strong>Descubre el poder del PLN: aprende a pre-procesar, analizar y extraer información de textos<strong></h2>\n",
        "<hr>"
      ],
      "metadata": {
        "id": "oHT5goyLgg7X"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelos de lenguaje"
      ],
      "metadata": {
        "id": "cYWRBhBnvw8i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Los modelos de lenguaje son sistemas de inteligencia artificial que pueden procesar y generar texto. Están entrenados en grandes cantidades de datos de texto para aprender las relaciones estadísticas entre las palabras y frases. Esto les permite realizar tareas como:\n",
        "\n",
        "* **Traducción automática**: Traducir texto de un idioma a otro.\n",
        "* **Resumen de texto**: Generar un resumen de un texto largo.\n",
        "* **Respuesta a preguntas**: Responder preguntas sobre un texto.\n",
        "* **Generación de texto**: Crear texto nuevo, como poemas, historias o guiones."
      ],
      "metadata": {
        "id": "wzRUJyliv4VS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelos pre-entrenados\n",
        "\n",
        "Los modelos pre-entrenados son modelos de lenguaje que ya han sido entrenados en un conjunto de datos general de texto y código. Esto les permite aprender las características básicas del lenguaje y pueden ser ajustados para realizar tareas específicas con solo una pequeña cantidad de datos adicionales.\n",
        "\n",
        "Algunos ejemplos populares de modelos pre-entrenados incluyen:\n",
        "\n",
        "* **BERT**: Bidirectional Encoder Representations from Transformers\n",
        "* **GPT-3**: Generative Pre-training Transformer 3"
      ],
      "metadata": {
        "id": "DnRR3nH2G_5V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Obtener archivos"
      ],
      "metadata": {
        "id": "5-pNTFBiGXoQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para este taller se almacenan los archivos en [Github](https://github.com/orlandxrf/escuela-verano) adicional a todo el material usado.\n",
        "\n",
        "Para evitar subir los archivos a Google Colab cada vez que se quiera trabajar con el Notebook, se realizará el proceso de forma automática. Para la descarga de los archivos se utiliza el comando `wget`\n",
        "\n",
        "**Wget**, que significa \"World Wide Web GET\", es una utilidad de línea de comandos en sistemas operativos tipo Unix, como Linux, que se utiliza para descargar archivos de la web. Es una herramienta poderosa y versátil que permite descargar una amplia variedad de contenido.\n",
        "\n",
        "La sintaxis para descargar un archivo es la siguiente:\n",
        "\n",
        "```bash\n",
        "!wget -P data/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/El_maravilloso_Mago_de_Oz.pdf\n",
        "```\n",
        "\n",
        "El resultado esperado por cada archivo descargado es similar al siguiente\n",
        "\n",
        "```bash\n",
        "--2024-06-22 18:35:06--  https://github.com/orlandxrf/escuela-verano/blob/main/data/El_maravilloso_Mago_de_Oz.pdf\n",
        "Resolving github.com (github.com)... 140.82.121.3\n",
        "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n",
        "HTTP request sent, awaiting response... 200 OK\n",
        "Length: unspecified [text/html]\n",
        "Saving to: ‘data/El_maravilloso_Mago_de_Oz.pdf’\n",
        "\n",
        "El_maravilloso_Mago     [ <=>                ] 241.79K  --.-KB/s    in 0.03s   \n",
        "\n",
        "2024-06-22 18:35:06 (8.05 MB/s) - ‘data/El_maravilloso_Mago_de_Oz.pdf’ saved [247596]\n",
        "```\n",
        "\n",
        "Es importante señalar que si la carpeta donde se desea almacenar el archivo no existe, se crea. En caso de descargar el archivo cuando previamente existe, lo que ocurre es que se sobre escribe el archivo descargado, es decir el archivo descargado reemplaza al previo, siempre y cuando tengan el mismo nombre."
      ],
      "metadata": {
        "id": "zldabsuBdNr1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# descargar los archivos utilizando \"wget\"\n",
        "\n",
        "#!wget -P data/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/Escuela_de_verano.txt\n",
        "#!wget -P data/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/Los_Caudillos_de_1830.txt\n",
        "!wget -P data/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/Los_Caudillos_de_1830.csv\n",
        "!wget -P data/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/ia_tweets.csv\n",
        "#!wget -P data/fake_news/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/fake_news/dev.xlsx\n",
        "#!wget -P data/fake_news/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/fake_news/train.xlsx\n",
        "#!wget -P data/fake_news/ https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/fake_news/test.xlsx\n",
        "#!wget -P data/ https://github.com/orlandxrf/escuela-verano/blob/main/data/El_maravilloso_Mago_de_Oz.pdf"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0PTIygDWJdVN",
        "outputId": "f80a4bc3-66c5-401b-c1c5-2f65caf91589"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-06-27 15:16:20--  https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/Los_Caudillos_de_1830.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.110.133, 185.199.109.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 234389 (229K) [text/plain]\n",
            "Saving to: ‘data/Los_Caudillos_de_1830.csv’\n",
            "\n",
            "Los_Caudillos_de_18 100%[===================>] 228.90K  --.-KB/s    in 0.01s   \n",
            "\n",
            "2024-06-27 15:16:20 (17.0 MB/s) - ‘data/Los_Caudillos_de_1830.csv’ saved [234389/234389]\n",
            "\n",
            "--2024-06-27 15:16:20--  https://raw.githubusercontent.com/orlandxrf/escuela-verano/main/data/ia_tweets.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 918376 (897K) [text/plain]\n",
            "Saving to: ‘data/ia_tweets.csv’\n",
            "\n",
            "ia_tweets.csv       100%[===================>] 896.85K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2024-06-27 15:16:20 (42.6 MB/s) - ‘data/ia_tweets.csv’ saved [918376/918376]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# instalar modelo en español de Spacy para extraer las oraciones del texto\n",
        "!python -m spacy download es_core_news_sm"
      ],
      "metadata": {
        "id": "h1TOCCYyREpJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "992cfb88-0e46-4906-ff46-5c4801e52aad"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting es-core-news-sm==3.7.0\n",
            "  Downloading https://github.com/explosion/spacy-models/releases/download/es_core_news_sm-3.7.0/es_core_news_sm-3.7.0-py3-none-any.whl (12.9 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.9/12.9 MB\u001b[0m \u001b[31m52.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: spacy<3.8.0,>=3.7.0 in /usr/local/lib/python3.10/dist-packages (from es-core-news-sm==3.7.0) (3.7.5)\n",
            "Requirement already satisfied: spacy-legacy<3.1.0,>=3.0.11 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (3.0.12)\n",
            "Requirement already satisfied: spacy-loggers<2.0.0,>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (1.0.5)\n",
            "Requirement already satisfied: murmurhash<1.1.0,>=0.28.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (1.0.10)\n",
            "Requirement already satisfied: cymem<2.1.0,>=2.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2.0.8)\n",
            "Requirement already satisfied: preshed<3.1.0,>=3.0.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (3.0.9)\n",
            "Requirement already satisfied: thinc<8.3.0,>=8.2.2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (8.2.5)\n",
            "Requirement already satisfied: wasabi<1.2.0,>=0.9.1 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (1.1.3)\n",
            "Requirement already satisfied: srsly<3.0.0,>=2.4.3 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2.4.8)\n",
            "Requirement already satisfied: catalogue<2.1.0,>=2.0.6 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2.0.10)\n",
            "Requirement already satisfied: weasel<0.5.0,>=0.1.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (0.4.1)\n",
            "Requirement already satisfied: typer<1.0.0,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (0.12.3)\n",
            "Requirement already satisfied: tqdm<5.0.0,>=4.38.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (4.66.4)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2.31.0)\n",
            "Requirement already satisfied: pydantic!=1.8,!=1.8.1,<3.0.0,>=1.7.4 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2.7.4)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (3.1.4)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (67.7.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (24.1)\n",
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            "Requirement already satisfied: numpy>=1.19.0 in /usr/local/lib/python3.10/dist-packages (from spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (1.25.2)\n",
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            "Requirement already satisfied: shellingham>=1.3.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (1.5.4)\n",
            "Requirement already satisfied: rich>=10.11.0 in /usr/local/lib/python3.10/dist-packages (from typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (13.7.1)\n",
            "Requirement already satisfied: cloudpathlib<1.0.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (0.18.1)\n",
            "Requirement already satisfied: smart-open<8.0.0,>=5.2.1 in /usr/local/lib/python3.10/dist-packages (from weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (7.0.4)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2.1.5)\n",
            "Requirement already satisfied: marisa-trie>=0.7.7 in /usr/local/lib/python3.10/dist-packages (from language-data>=1.2->langcodes<4.0.0,>=3.2.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (1.2.0)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (2.16.1)\n",
            "Requirement already satisfied: wrapt in /usr/local/lib/python3.10/dist-packages (from smart-open<8.0.0,>=5.2.1->weasel<0.5.0,>=0.1.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (1.14.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=10.11.0->typer<1.0.0,>=0.3.0->spacy<3.8.0,>=3.7.0->es-core-news-sm==3.7.0) (0.1.2)\n",
            "Installing collected packages: es-core-news-sm\n",
            "Successfully installed es-core-news-sm-3.7.0\n",
            "\u001b[38;5;2m✔ Download and installation successful\u001b[0m\n",
            "You can now load the package via spacy.load('es_core_news_sm')\n",
            "\u001b[38;5;3m⚠ Restart to reload dependencies\u001b[0m\n",
            "If you are in a Jupyter or Colab notebook, you may need to restart Python in\n",
            "order to load all the package's dependencies. You can do this by selecting the\n",
            "'Restart kernel' or 'Restart runtime' option.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tqdm.auto import tqdm\n",
        "import pandas as pd\n",
        "import spacy\n",
        "\n",
        "nlp = spacy.load(\"es_core_news_sm\")\n",
        "\n",
        "ruta_archivo = \"data/Los_Caudillos_de_1830.csv\"\n",
        "\n",
        "# Leer el archivo .csv\n",
        "df = pd.read_csv(ruta_archivo)\n",
        "\n",
        "oraciones = []\n",
        "\n",
        "for texto in tqdm(df['texto'], total=df.shape[0], desc=\"Obteniendo oraciones\"):\n",
        "    doc = nlp(texto, disable=[\"ner\"])\n",
        "    for sent in doc.sents:\n",
        "        tokens = [token.text for token in sent]\n",
        "        if len(tokens) > 1:\n",
        "          oraciones.append(sent.text)\n",
        "\n",
        "print(f\"Cantidad de oraciones: {len(oraciones):,}\")\n",
        "\n",
        "# longitud máxima de oraciones\n",
        "max_len = max(len(oracion) for oracion in oraciones)\n",
        "\n",
        "print(f\"Longitud máxima de oraciones: {max_len}\")\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
            "1568a529ad604d5187523d62cefa41fe",
            "d0c6488acf664bfa9ae1c9d000ee27dd",
            "654e8a47eb304d6ebe2f20a70a4d062f",
            "0c3351f457e3458eaf34369814e3c1c0",
            "9e3f106b7b30471e88c66dcd10ea5774",
            "0d04fcbef4b444b89263c6cacac26da5",
            "12253e5eaf314638b09611826f656f31",
            "f6894db3f8c749ef8190fc657e3947d1",
            "a10dc3231b7c40f7b07b514d2ab98878",
            "50d667d9836342b585f164b359e84df4",
            "8c9a9af469d9424a95108da61457b67b"
          ]
        },
        "id": "K0Mj35T8ROEG",
        "outputId": "35c0f135-e8c7-44f6-c186-79eddd4b18b9"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Obteniendo oraciones:   0%|          | 0/1747 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1568a529ad604d5187523d62cefa41fe"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cantidad de oraciones: 2,778\n",
            "Longitud máxima de oraciones: 450\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# limpiar tweets\n",
        "import re\n",
        "\n",
        "def limpiar_tweet(tweet):\n",
        "    # Eliminar URLs\n",
        "    tweet = re.sub(r'http\\S+|www\\S+|https\\S+', '', tweet, flags=re.MULTILINE)\n",
        "\n",
        "    # Eliminar menciones (@usuario)\n",
        "    tweet = re.sub(r'@\\w+', '', tweet)\n",
        "\n",
        "    # Eliminar hashtags (#hashtag)\n",
        "    tweet = re.sub(r'#\\w+', '', tweet)\n",
        "\n",
        "    # Eliminar caracteres especiales y puntuación\n",
        "    tweet = re.sub(r'[^\\w\\s]', '', tweet)\n",
        "\n",
        "    # Eliminar números\n",
        "    tweet = re.sub(r'\\d+', '', tweet)\n",
        "\n",
        "    # Eliminar espacios adicionales\n",
        "    tweet = re.sub(r'\\s+', ' ', tweet).strip()\n",
        "\n",
        "    return tweet\n",
        "\n",
        "df = pd.read_csv(\"data/ia_tweets.csv\")\n",
        "\n",
        "# aplicar la función a cada renglón del DataFrame\n",
        "tweets_df = pd.DataFrame(df[\"text\"].apply(limpiar_tweet))\n",
        "\n",
        "print(f\"Cantidad de tweets: {len(tweets_df):,}\")\n",
        "\n",
        "# eliminar renglones vacíos despues de la limpieza\n",
        "tweets_df = tweets_df[tweets_df[\"text\"] != \"\"]\n",
        "\n",
        "print(f\"Cantidad de tweets sin renglones vacíos: {len(tweets_df):,}\")\n",
        "\n",
        "# agregar la columna 'polarity' del DataFrame original al DataFrame tweets_df\n",
        "tweets_df[\"polarity\"] = df[\"polarity\"]\n",
        "\n",
        "# mostrar el DataFrame\n",
        "tweets_df\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "LSqpv6jyX3dc",
        "outputId": "91a28982-f163-4786-de05-689009957dfb"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cantidad de tweets: 4,038\n",
            "Cantidad de tweets sin renglones vacíos: 3,925\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   text polarity\n",
              "0     Comentaba en una charla sobre IA que el proble...        N\n",
              "1     Eso es imposible Y sí va a generar un montón d...        N\n",
              "2     Lo disruptivo es que tras años de decir que la...      NEU\n",
              "3     Prohibición del uso comercial de las imágenes ...        N\n",
              "4     Cómo se está estudiando prohibir chatGPT en Eu...      NEU\n",
              "...                                                 ...      ...\n",
              "4033  INNOVACIÓN Puede un chatbot predicar mejor que...      NEU\n",
              "4034           Sería mejor por que no abusaría de niños        N\n",
              "4035  Ejemplo el uso estratégico de tener datos está...      NEU\n",
              "4036  X estás mal y ngmi porque esto es el futuro Yo...      NEU\n",
              "4037  X Por qué publicas esto Yo Solo quiero compart...      NEU\n",
              "\n",
              "[3925 rows x 2 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f4fb7156-5c9c-41f4-a70e-01e3f379eaa0\" 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",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>text</th>\n",
              "      <th>polarity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Comentaba en una charla sobre IA que el proble...</td>\n",
              "      <td>N</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Eso es imposible Y sí va a generar un montón d...</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Lo disruptivo es que tras años de decir que la...</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Prohibición del uso comercial de las imágenes ...</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Cómo se está estudiando prohibir chatGPT en Eu...</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4033</th>\n",
              "      <td>INNOVACIÓN Puede un chatbot predicar mejor que...</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4034</th>\n",
              "      <td>Sería mejor por que no abusaría de niños</td>\n",
              "      <td>N</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4035</th>\n",
              "      <td>Ejemplo el uso estratégico de tener datos está...</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4036</th>\n",
              "      <td>X estás mal y ngmi porque esto es el futuro Yo...</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4037</th>\n",
              "      <td>X Por qué publicas esto Yo Solo quiero compart...</td>\n",
              "      <td>NEU</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3925 rows × 2 columns</p>\n",
              "</div>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4fb7156-5c9c-41f4-a70e-01e3f379eaa0')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "    </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",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-f4fb7156-5c9c-41f4-a70e-01e3f379eaa0 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-f4fb7156-5c9c-41f4-a70e-01e3f379eaa0');\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",
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              "            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",
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              "    </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",
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              "      --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",
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              "      --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",
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              "      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-ff9a9a20-d675-4d7c-aa4f-4704dad930e4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_75a47718-46ee-47e3-81d9-cec1e1e4e1ed\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('tweets_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_75a47718-46ee-47e3-81d9-cec1e1e4e1ed button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('tweets_df');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tweets_df",
              "summary": "{\n  \"name\": \"tweets_df\",\n  \"rows\": 3925,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3823,\n        \"samples\": [\n          \"El auge de la inteligencia artificial acelera las emisiones contaminantes La falta de transparencia de las grandes empresas tecnol\\u00f3gicas dificulta calibrar la huella de carbono de unos sistemas que requieren de cada vez m\\u00e1s recursos como energ\\u00eda o agua\",\n          \"Conoc\\u00e9 el an\\u00e1lisis completo en este enlace\",\n          \"para que no hagan new fakes soloque lo del papa con el abrigojajajaja\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"polarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"N\",\n          \"NEU\",\n          \"P\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Extracción de información\n",
        "\n",
        "La extracción de información (EI) es la tarea de identificar y extraer información específica de un texto. Por ejemplo, un sistema de EI podría usarse para extraer los nombres de las personas y las organizaciones de un artículo de noticias.\n",
        "\n",
        "Los modelos de lenguaje pre-entrenados se pueden usar para la EI de varias maneras. Por ejemplo, se pueden usar para entrenar un clasificador que pueda identificar entidades nombradas en el texto."
      ],
      "metadata": {
        "id": "nwC8GWFYHg-L"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ejemplo práctico"
      ],
      "metadata": {
        "id": "l2nESVKCHrV5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Instalar las librerías necesarias\n",
        "#!pip install transformers torch"
      ],
      "metadata": {
        "id": "1lqDHYxoIThK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Importar las bibliotecas requeridas\n",
        "from transformers import AutoTokenizer, AutoModelForTokenClassification\n",
        "from transformers import pipeline\n",
        "from collections import defaultdict\n",
        "import torch\n",
        "\n",
        "# model_name = \"mrm8488/bert-spanish-cased-finetuned-ner\"\n",
        "model_name = \"Babelscape/wikineural-multilingual-ner\"\n",
        "\n",
        "# Comprobar si hay una GPU disponible y definir el dispositivo\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "# Cargar el tokenizer y el modelo\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForTokenClassification.from_pretrained(model_name)\n",
        "\n",
        "# Mover el modelo a la GPU si está disponible\n",
        "model.to(device)\n",
        "\n",
        "# Crear la tubería de NER con CPU\n",
        "# nlp = pipeline(\"ner\", model=model, tokenizer=tokenizer)\n",
        "\n",
        "# Crear la tubería de NER con el dispositivo especificado\n",
        "nlp = pipeline(\"ner\", model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)"
      ],
      "metadata": {
        "id": "jEGyzAN7HpXs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Definir el texto en español\n",
        "texto = \"El Sr. López compró un iPhone 15 en la tienda Apple de la Ciudad de México.\"\n",
        "# texto = \"Barack Obama nació en Honolulu, Hawaii, y fue el 44.º presidente de los Estados Unidos.\"\n",
        "\n",
        "# Realizar la predicción de NER\n",
        "entidades = nlp(texto)\n",
        "\n",
        "# Procesar las entidades para obtener las entidades completas y sus etiquetas\n",
        "entidades_completas = []\n",
        "entidad_actual = \"\"\n",
        "etiqueta_actual = \"\"\n",
        "\n",
        "for entidad in entidades:\n",
        "    if entidad['entity'].startswith('B-'):\n",
        "        if entidad_actual:\n",
        "            entidades_completas.append((entidad_actual, etiqueta_actual))\n",
        "        entidad_actual = entidad['word']\n",
        "        etiqueta_actual = entidad['entity'][2:]  # Remove the B- or I- prefix\n",
        "    elif entidad['entity'].startswith('I-') and entidad['entity'][2:] == etiqueta_actual:\n",
        "        # Remove the '##' if it's a sub-word token\n",
        "        if entidad['word'].startswith('##'):\n",
        "            entidad_actual += entidad['word'][2:]\n",
        "        else:\n",
        "            entidad_actual += \" \" + entidad['word']\n",
        "\n",
        "# Añadir la última entidad si existe\n",
        "if entidad_actual:\n",
        "    entidades_completas.append((entidad_actual, etiqueta_actual))\n",
        "\n",
        "print(entidades_completas)\n",
        "\n",
        "# Imprimir las entidades completas\n",
        "for entidad, etiqueta in entidades_completas:\n",
        "    print(f\"{entidad}: {etiqueta}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SgyQKJeW_o6q",
        "outputId": "a2bae95e-ff40-424a-c870-a4c7a1d7dc35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[('iPhone 15', 'MISC'), ('Apple', 'ORG'), ('Ciudad de México', 'LOC')]\n",
            "iPhone 15: MISC\n",
            "Apple: ORG\n",
            "Ciudad de México: LOC\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reconocimiento de entidades nombradas"
      ],
      "metadata": {
        "id": "DJjx5bh8L8Q1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reconocer entidades en libro"
      ],
      "metadata": {
        "id": "DhcWiTatBDXz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Leer el archivo de \"Los Caudillos de 1838\" para identificar todas las posibles entidades en el texto."
      ],
      "metadata": {
        "id": "PkRLvUZDMIxM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Función para procesar un texto y obtener las entidades completas\n",
        "def obtener_entidades_completas(texto):\n",
        "    max_length = 512\n",
        "    tokens = tokenizer.tokenize(texto)\n",
        "    if len(tokens) > max_length:\n",
        "        tokens = tokens[:max_length]\n",
        "        texto = tokenizer.convert_tokens_to_string(tokens)\n",
        "\n",
        "    entidades = nlp(texto)\n",
        "    entidades_completas = []\n",
        "    entidad_actual = \"\"\n",
        "    etiqueta_actual = \"\"\n",
        "\n",
        "    for entidad in entidades:\n",
        "        if entidad['entity'].startswith('B-'):\n",
        "            if entidad_actual:\n",
        "                entidades_completas.append((entidad_actual, etiqueta_actual))\n",
        "            entidad_actual = entidad['word']\n",
        "            etiqueta_actual = entidad['entity'][2:]  # Remove the B- or I- prefix\n",
        "        elif entidad['entity'].startswith('I-') and entidad['entity'][2:] == etiqueta_actual:\n",
        "            # Remove the '##' if it's a sub-word token\n",
        "            if entidad['word'].startswith('##'):\n",
        "                entidad_actual += entidad['word'][2:]\n",
        "            else:\n",
        "                entidad_actual += \" \" + entidad['word']\n",
        "\n",
        "    # Añadir la última entidad si existe\n",
        "    if entidad_actual:\n",
        "        entidades_completas.append((entidad_actual, etiqueta_actual))\n",
        "\n",
        "    return entidades_completas\n",
        "\n",
        "# Aplicar la tarea de NER a cada renglón del archivo\n",
        "resultados = []\n",
        "for oracion in tqdm(oraciones, desc=\"Identificando entidades...\", total=len(oraciones)):\n",
        "    entidades_completas = obtener_entidades_completas(oracion)\n",
        "    if len(entidades_completas) > 0:\n",
        "      resultados.append(entidades_completas)\n",
        "\n",
        "print(f\"{len(resultados):,} entidades identificadas\")\n",
        "\n",
        "tmp_entidades = {\n",
        "    \"entidad\": [],\n",
        "    \"etiqueta\": [],\n",
        "}\n",
        "\n",
        "for entity_list in resultados:\n",
        "  for entidad, etiqueta in entity_list:\n",
        "    tmp_entidades[\"entidad\"].append(entidad)\n",
        "    tmp_entidades[\"etiqueta\"].append(etiqueta)\n",
        "\n",
        "# Crear un DataFrame con los resultados\n",
        "resultados_df = pd.DataFrame(tmp_entidades)\n",
        "\n",
        "# Guardar los resultados en un nuevo archivo .csv\n",
        "# resultados_df.to_csv(\"data/resultados_ner.csv\", index=False)\n",
        "\n",
        "# Mostrar una muestra de los resultados\n",
        "# print(resultados_df.head())\n",
        "\n",
        "resultados_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473,
          "referenced_widgets": [
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            "c0bc8fe516a042bf9774d1c3846a1643"
          ]
        },
        "id": "UfPt2DvLA7xM",
        "outputId": "7c0f26fc-db86-41a8-e997-6fd898f3c4a0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Identificando entidades...:   0%|          | 0/2778 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fcf7b92af2e84ebb897a532fc1b97d3d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2,116 entidades identificadas\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                         entidad etiqueta\n",
              "0                     PÍO BAROJA      PER\n",
              "1                  LOS CAUDILLOS     MISC\n",
              "2                         NOVELA     MISC\n",
              "3     Calle de Ventura Rodríguez      LOC\n",
              "4                  LIBRO PRIMERO     MISC\n",
              "...                          ...      ...\n",
              "3489           Nacer vivir morir     MISC\n",
              "3490                    Gastizar      PER\n",
              "3491                      Madrid      LOC\n",
              "3492                     Febrero     MISC\n",
              "3493                        1918     MISC\n",
              "\n",
              "[3494 rows x 2 columns]"
            ],
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              "      <td>PÍO BAROJA</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LOS CAUDILLOS</td>\n",
              "      <td>MISC</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NOVELA</td>\n",
              "      <td>MISC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Calle de Ventura Rodríguez</td>\n",
              "      <td>LOC</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LIBRO PRIMERO</td>\n",
              "      <td>MISC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3489</th>\n",
              "      <td>Nacer vivir morir</td>\n",
              "      <td>MISC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3490</th>\n",
              "      <td>Gastizar</td>\n",
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              "    <tr>\n",
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              "      <td>Madrid</td>\n",
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              "      <th>3492</th>\n",
              "      <td>Febrero</td>\n",
              "      <td>MISC</td>\n",
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              "    <tr>\n",
              "      <th>3493</th>\n",
              "      <td>1918</td>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "resultados_df",
              "summary": "{\n  \"name\": \"resultados_df\",\n  \"rows\": 3494,\n  \"fields\": [\n    {\n      \"column\": \"entidad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1178,\n        \"samples\": [\n          \"No me choca\",\n          \"Manuel Ruiz\",\n          \"Burgos\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"etiqueta\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"MISC\",\n          \"ORG\",\n          \"PER\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Reconocer entidades en tweets"
      ],
      "metadata": {
        "id": "BU7-Yp7eA8nd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Función para procesar un texto y obtener las entidades completas\n",
        "def obtener_entidades_completas(texto):\n",
        "    max_length = 512\n",
        "    tokens = tokenizer.tokenize(texto)\n",
        "    if len(tokens) > max_length:\n",
        "        tokens = tokens[:max_length]\n",
        "        texto = tokenizer.convert_tokens_to_string(tokens)\n",
        "\n",
        "    entidades = nlp(texto)\n",
        "    entidades_completas = []\n",
        "    entidad_actual = \"\"\n",
        "    etiqueta_actual = \"\"\n",
        "\n",
        "    for entidad in entidades:\n",
        "        if entidad['entity'].startswith('B-'):\n",
        "            if entidad_actual:\n",
        "                entidades_completas.append((entidad_actual, etiqueta_actual))\n",
        "            entidad_actual = entidad['word']\n",
        "            etiqueta_actual = entidad['entity'][2:]  # Remove the B- or I- prefix\n",
        "        elif entidad['entity'].startswith('I-') and entidad['entity'][2:] == etiqueta_actual:\n",
        "            # Remove the '##' if it's a sub-word token\n",
        "            if entidad['word'].startswith('##'):\n",
        "                entidad_actual += entidad['word'][2:]\n",
        "            else:\n",
        "                entidad_actual += \" \" + entidad['word']\n",
        "\n",
        "    # Añadir la última entidad si existe\n",
        "    if entidad_actual:\n",
        "        entidades_completas.append((entidad_actual, etiqueta_actual))\n",
        "\n",
        "    return entidades_completas\n",
        "\n",
        "# Aplicar la tarea de NER a cada renglón del archivo\n",
        "resultados = []\n",
        "for idx, row in tqdm(tweets_df.iterrows(), desc=\"Identificando entidades...\", total=tweets_df.shape[0]):\n",
        "    entidades_completas = obtener_entidades_completas(row[\"text\"])\n",
        "    if len(entidades_completas) > 0:\n",
        "      resultados.append(entidades_completas)\n",
        "\n",
        "print(f\"{len(resultados):,} entidades identificadas\")\n",
        "\n",
        "tmp_entidades = {\n",
        "    \"entidad\": [],\n",
        "    \"etiqueta\": [],\n",
        "}\n",
        "\n",
        "for entity_list in resultados:\n",
        "  for entidad, etiqueta in entity_list:\n",
        "    tmp_entidades[\"entidad\"].append(entidad)\n",
        "    tmp_entidades[\"etiqueta\"].append(etiqueta)\n",
        "\n",
        "# Crear un DataFrame con los resultados\n",
        "resultados_tweets_df = pd.DataFrame(tmp_entidades)\n",
        "\n",
        "# Guardar los resultados en un nuevo archivo .csv\n",
        "# resultados_tweets_df.to_csv(\"data/resultados_ner.csv\", index=False)\n",
        "\n",
        "# Mostrar una muestra de los resultados\n",
        "# print(resultados_tweets_df.head())\n",
        "\n",
        "resultados_tweets_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 736,
          "referenced_widgets": [
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        "id": "sSsyBaVVMTDj",
        "outputId": "755c87d2-8c23-48a9-cc89-58014cd5a6bf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Identificando entidades...:   0%|          | 0/3925 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "199acd0ddecb43f6be5cbe3d50cc88c3"
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          "text": [
            "3,079 entidades identificadas\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             entidad etiqueta\n",
              "0                 IA     MISC\n",
              "1                 IA     MISC\n",
              "2           Chat GPT     MISC\n",
              "3               auto     MISC\n",
              "4                 IA     MISC\n",
              "...              ...      ...\n",
              "4674              lu     MISC\n",
              "4675        Sorpresa     MISC\n",
              "4676  Pastor ChatGPT     MISC\n",
              "4677         chatGPT     MISC\n",
              "4678              Yo     MISC\n",
              "\n",
              "[4679 rows x 2 columns]"
            ],
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              "      <th>entidad</th>\n",
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              "      <th>2</th>\n",
              "      <td>Chat GPT</td>\n",
              "      <td>MISC</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>auto</td>\n",
              "      <td>MISC</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>IA</td>\n",
              "      <td>MISC</td>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4674</th>\n",
              "      <td>lu</td>\n",
              "      <td>MISC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4675</th>\n",
              "      <td>Sorpresa</td>\n",
              "      <td>MISC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4676</th>\n",
              "      <td>Pastor ChatGPT</td>\n",
              "      <td>MISC</td>\n",
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              "    <tr>\n",
              "      <th>4677</th>\n",
              "      <td>chatGPT</td>\n",
              "      <td>MISC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4678</th>\n",
              "      <td>Yo</td>\n",
              "      <td>MISC</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4679 rows × 2 columns</p>\n",
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              "    async function quickchart(key) {\n",
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              "\n",
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              "\n",
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              "\n",
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              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('resultados_tweets_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('resultados_tweets_df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "resultados_tweets_df",
              "summary": "{\n  \"name\": \"resultados_tweets_df\",\n  \"rows\": 4679,\n  \"fields\": [\n    {\n      \"column\": \"entidad\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2216,\n        \"samples\": [\n          \"Comit\\u00e9 Europeo de Protecci\\u00f3n de Datos\",\n          \"SIUEME\",\n          \"Felon Musk\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"etiqueta\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"LOC\",\n          \"PER\",\n          \"MISC\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Análisis de Sentimientos"
      ],
      "metadata": {
        "id": "nc8zWaosWtF1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "El análisis de sentimientos es la tarea de determinar la opinión o el sentimiento expresado en un texto. Por ejemplo, un sistema de análisis de sentimientos podría usarse para determinar si una reseña de producto es positiva o negativa.\n",
        "\n",
        "Los modelos de lenguaje pre-entrenados se pueden usar para el análisis de sentimientos de varias maneras. Por ejemplo, se pueden usar para entrenar un clasificador que pueda clasificar el texto como positivo, negativo o neutral."
      ],
      "metadata": {
        "id": "sTmDJyQkWwaa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Ejemplo práctico"
      ],
      "metadata": {
        "id": "_oz6p9QdXl1B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importamos las bibliotecas necesarias\n",
        "import pandas as pd\n",
        "from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification\n",
        "\n",
        "# Verificamos si tenemos una GPU disponible\n",
        "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
        "print(f'Usando dispositivo: {device}')\n",
        "\n",
        "# Cargamos el modelo y el tokenizador pre-entrenados\n",
        "# model_name = 'nlptown/bert-base-multilingual-uncased-sentiment'  # Puedes cambiar el modelo si es necesario\n",
        "model_name = 'edumunozsala/roberta_bne_sentiment_analysis_es'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
        "model.to(device)\n",
        "print(\"Modelo cargado!\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t1dzcj3YXjR0",
        "outputId": "598592ef-85a2-4d0e-8466-1b64ed01b9af"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Usando dispositivo: cuda\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RobertaForSequenceClassification(\n",
              "  (roberta): RobertaModel(\n",
              "    (embeddings): RobertaEmbeddings(\n",
              "      (word_embeddings): Embedding(50262, 768, padding_idx=1)\n",
              "      (position_embeddings): Embedding(514, 768, padding_idx=1)\n",
              "      (token_type_embeddings): Embedding(1, 768)\n",
              "      (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "      (dropout): Dropout(p=0.0, inplace=False)\n",
              "    )\n",
              "    (encoder): RobertaEncoder(\n",
              "      (layer): ModuleList(\n",
              "        (0-11): 12 x RobertaLayer(\n",
              "          (attention): RobertaAttention(\n",
              "            (self): RobertaSelfAttention(\n",
              "              (query): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (key): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (value): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "            (output): RobertaSelfOutput(\n",
              "              (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "              (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "              (dropout): Dropout(p=0.0, inplace=False)\n",
              "            )\n",
              "          )\n",
              "          (intermediate): RobertaIntermediate(\n",
              "            (dense): Linear(in_features=768, out_features=3072, bias=True)\n",
              "            (intermediate_act_fn): GELUActivation()\n",
              "          )\n",
              "          (output): RobertaOutput(\n",
              "            (dense): Linear(in_features=3072, out_features=768, bias=True)\n",
              "            (LayerNorm): LayerNorm((768,), eps=1e-05, elementwise_affine=True)\n",
              "            (dropout): Dropout(p=0.0, inplace=False)\n",
              "          )\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "  )\n",
              "  (classifier): RobertaClassificationHead(\n",
              "    (dense): Linear(in_features=768, out_features=768, bias=True)\n",
              "    (dropout): Dropout(p=0.0, inplace=False)\n",
              "    (out_proj): Linear(in_features=768, out_features=2, bias=True)\n",
              "  )\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# conservar tweets distintos a la etiqueta \"NEU\" de la columna \"polarity\"\n",
        "tweets_df = tweets_df[tweets_df[\"polarity\"] != \"NEU\"]"
      ],
      "metadata": {
        "id": "n-vCSGU5EYHf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Crear el pipeline de análisis de sentimientos\n",
        "sentiment_analysis = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer, device=0 if torch.cuda.is_available() else -1)\n",
        "\n",
        "# Aplicamos el análisis de sentimientos a los tweets\n",
        "tweets_df['sentiment'] = tweets_df['text'].apply(lambda x: sentiment_analysis(x)[0]['label'])\n",
        "\n",
        "# Guardamos los resultados en un nuevo archivo CSV\n",
        "# output_file_path = 'ruta/al/nuevo_archivo.csv'  # Reemplaza esto con la ruta donde quieres guardar el archivo\n",
        "# tweets_df.to_csv(output_file_path, index=False)\n",
        "\n",
        "# print(f'Análisis de sentimientos completado. Los resultados se han guardado en {output_file_path}')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LO2goyhHDGEu",
        "outputId": "78b13b69-7d0c-43f9-e241-d8f67e98823e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-29-b3ae0a35c69f>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  tweets_df['sentiment'] = tweets_df['text'].apply(lambda x: sentiment_analysis(x)[0]['label'])\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        "tweets_df"
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      "execution_count": null,
      "outputs": [
        {
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              "                                                   text polarity sentiment\n",
              "0     Comentaba en una charla sobre IA que el proble...        N  Positivo\n",
              "1     Eso es imposible Y sí va a generar un montón d...        N  Negativo\n",
              "3     Prohibición del uso comercial de las imágenes ...        N  Negativo\n",
              "6     Esta transición va a estar bien difícil El pla...        N  Negativo\n",
              "7     Va a costar mucho adaptarse a los nuevos cambi...        N  Negativo\n",
              "...                                                 ...      ...       ...\n",
              "4014  Estoy valorando el uso de ChatGPT de Inteligen...        P  Positivo\n",
              "4015  Análisis de ChatGPT Noticias emocionantes Espe...        P  Positivo\n",
              "4024  No se si se han dado cuenta pero el chatgpt sa...        P  Positivo\n",
              "4026  Hoy aprobé el curso introductorio de de y Es i...        P  Positivo\n",
              "4034           Sería mejor por que no abusaría de niños        N  Negativo\n",
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              "[1331 rows x 3 columns]"
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tweets_df",
              "summary": "{\n  \"name\": \"tweets_df\",\n  \"rows\": 1331,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1270,\n        \"samples\": [\n          \"Y cuando OpenAI les corte o modifique el acceso a la API se termin\\u00f3 el negocio Armar sobre la base de la infraestructura de otro como le pas\\u00f3 a los que desarrollaban productos para Twitter no suele terminar bien\",\n          \"Mexico no tiene desarrollo ah\\u00ed por lo que legislar algo que aun esta muy lejos de que aqu\\u00ed tenga amplia utilizaci\\u00f3n es un tiempo perdido es tema actual en otros pa\\u00edses Aqu\\u00ed aun estamos con alto indice de analfabetismo digital Es teatro electorerohay otros temas relevantes\",\n          \"Me encant\\u00f3 el remate al final Schopen Espero que estas frases te hayan sacado una sonrisa aunque sea ir\\u00f3nica Recuerda la vida es un chiste pero no siempre es gracioso\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"polarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"P\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Negativo\",\n          \"Positivo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# cambiar etiqueta \"Negativo\" y \"Positivo\" de la columna \"sentiment\" a \"N\" y \"P\" respectivamente\n",
        "tweets_df[\"sentiment\"] = tweets_df[\"sentiment\"].replace({\"Negativo\": \"N\", \"Positivo\": \"P\"})\n",
        "\n",
        "tweets_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "hrk4EeqdEEH9",
        "outputId": "9e02d37f-715f-40f6-bfbe-fcdd32e7a088"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-31-bb61c5836ff4>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  tweets_df[\"sentiment\"] = tweets_df[\"sentiment\"].replace({\"Negativo\": \"N\", \"Positivo\": \"P\"})\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   text polarity sentiment\n",
              "0     Comentaba en una charla sobre IA que el proble...        N         P\n",
              "1     Eso es imposible Y sí va a generar un montón d...        N         N\n",
              "3     Prohibición del uso comercial de las imágenes ...        N         N\n",
              "6     Esta transición va a estar bien difícil El pla...        N         N\n",
              "7     Va a costar mucho adaptarse a los nuevos cambi...        N         N\n",
              "...                                                 ...      ...       ...\n",
              "4014  Estoy valorando el uso de ChatGPT de Inteligen...        P         P\n",
              "4015  Análisis de ChatGPT Noticias emocionantes Espe...        P         P\n",
              "4024  No se si se han dado cuenta pero el chatgpt sa...        P         P\n",
              "4026  Hoy aprobé el curso introductorio de de y Es i...        P         P\n",
              "4034           Sería mejor por que no abusaría de niños        N         N\n",
              "\n",
              "[1331 rows x 3 columns]"
            ],
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              "    <tr>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    border-radius: 50%;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
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              "      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",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "      [theme=dark] .colab-df-generate {\n",
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              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('tweets_df')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('tweets_df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tweets_df",
              "summary": "{\n  \"name\": \"tweets_df\",\n  \"rows\": 1331,\n  \"fields\": [\n    {\n      \"column\": \"text\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 1270,\n        \"samples\": [\n          \"Y cuando OpenAI les corte o modifique el acceso a la API se termin\\u00f3 el negocio Armar sobre la base de la infraestructura de otro como le pas\\u00f3 a los que desarrollaban productos para Twitter no suele terminar bien\",\n          \"Mexico no tiene desarrollo ah\\u00ed por lo que legislar algo que aun esta muy lejos de que aqu\\u00ed tenga amplia utilizaci\\u00f3n es un tiempo perdido es tema actual en otros pa\\u00edses Aqu\\u00ed aun estamos con alto indice de analfabetismo digital Es teatro electorerohay otros temas relevantes\",\n          \"Me encant\\u00f3 el remate al final Schopen Espero que estas frases te hayan sacado una sonrisa aunque sea ir\\u00f3nica Recuerda la vida es un chiste pero no siempre es gracioso\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"polarity\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"P\",\n          \"N\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sentiment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"N\",\n          \"P\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluar las predicciones obtenidas por el modelo columns \"sentiment\" con las anotadas manualmente columna \"polarity\"\n",
        "from sklearn.metrics import classification_report\n",
        "\n",
        "print(classification_report(tweets_df[\"polarity\"], tweets_df[\"sentiment\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xZodqR4BZnbI",
        "outputId": "b615772d-7982-4995-c59e-57d69728ed48"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           N       0.77      0.83      0.80       590\n",
            "           P       0.85      0.81      0.83       741\n",
            "\n",
            "    accuracy                           0.82      1331\n",
            "   macro avg       0.81      0.82      0.81      1331\n",
            "weighted avg       0.82      0.82      0.82      1331\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Traducción automática"
      ],
      "metadata": {
        "id": "WH-dhmVMFTEu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import MarianMTModel, MarianTokenizer\n",
        "\n",
        "# Cargar el modelo y el tokenizador\n",
        "model_name = 'Helsinki-NLP/opus-mt-en-es'  # Modelo de inglés a español\n",
        "# model_name = 'Helsinki-NLP/opus-mt-es-en'  # Modelo de español a inglés\n",
        "model = MarianMTModel.from_pretrained(model_name)\n",
        "tokenizer = MarianTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "id": "3oIqWBByFWiV",
        "outputId": "e78a219f-4661-4bd7-9ee8-5194c276b57c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 400,
          "referenced_widgets": [
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      "execution_count": 1,
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          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
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              "config.json:   0%|          | 0.00/1.44k [00:00<?, ?B/s]"
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          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/models/marian/tokenization_marian.py:175: UserWarning: Recommended: pip install sacremoses.\n",
            "  warnings.warn(\"Recommended: pip install sacremoses.\")\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Función para traducir texto\n",
        "def translate(text, model, tokenizer):\n",
        "    # Tokenizar el texto\n",
        "    inputs = tokenizer(text, return_tensors=\"pt\", padding=True)\n",
        "    # Generar la traducción\n",
        "    translated = model.generate(**inputs)\n",
        "    # Decodificar la traducción\n",
        "    translated_text = tokenizer.batch_decode(translated, skip_special_tokens=True)\n",
        "    return translated_text[0]\n",
        "\n",
        "# Texto a traducir\n",
        "text_to_translate = \"Hello, how are you?\"\n",
        "# text_to_translate = \"Hola, ¿cómo estás?\"\n",
        "\n",
        "\n",
        "# Traducir el texto\n",
        "translated_text = translate(text_to_translate, model, tokenizer)\n",
        "\n",
        "print(f'Texto original: {text_to_translate}')\n",
        "print(f'Traducción: {translated_text}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8s2IFIMVQH95",
        "outputId": "bd7897a0-ebb9-4504-ee8d-ace69c0cc95c"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Texto original: Hola, ¿cómo estás?\n",
            "Traducción: Hey, how are you?\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# traducir del español al inglés de todas las \"oraciones\"\n",
        "traducciones_es_en = []\n",
        "n_oraciones = 50 # len(oraciones)\n",
        "for i, oracion in tqdm(enumerate(oraciones), desc=\"Tradución...\", total=len(oraciones)):\n",
        "  if i == n_oraciones:\n",
        "    print(f\"{i:,} oraciones traducidas\")\n",
        "    break\n",
        "  traducciones_es_en.append(translate(oracion, model, tokenizer))\n",
        "\n",
        "print(f\"{len(traducciones_es_en):,} oraciones traducidas\")\n",
        "\n",
        "# almacenar texto original y el traducido en un DataFrame\n",
        "traducciones_es_en_df = pd.DataFrame({\"oracion\": oraciones[:n_oraciones], \"traduccion\": traducciones_es_en})\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 84,
          "referenced_widgets": [
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        },
        "id": "H46a32ewRG4m",
        "outputId": "95b29a4f-3156-4b37-8fc2-d1878ef80e01"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Traducido...:   0%|          | 0/2778 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "51 oraciones traducidas\n",
            "50 oraciones traducidas\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "traducciones_es_en_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xGEM9ZsiTEpf",
        "outputId": "657b8d46-0dc3-4268-a3c4-5c8be770ca69"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                              oracion  \\\n",
              "0                                         PÍO BAROJA.   \n",
              "1                              LOS CAUDILLOS DE 1830.   \n",
              "2                                             NOVELA.   \n",
              "3   RAFAEL CARO RAGGIO: EDITOR Calle de Ventura Ro...   \n",
              "4                                      LIBRO PRIMERO.   \n",
              "5                              EL ETERNO CONSPIRADOR.   \n",
              "6                                        DON EUGENIO.   \n",
              "7   UN día, al anochecer, apareció en la fonda de ...   \n",
              "8   Era un tipo seco, amojamado, con la cara y las...   \n",
              "9   Tenía el aire de cansancio de los que vienen d...   \n",
              "10  Vestía redingot negro, pantalón con trabillas,...   \n",
              "11  --¿Quién es este hombre?--preguntaron Lacy y O...   \n",
              "12         --Es un vascongado que viene de la Habana.   \n",
              "13                                Ahí está su nombre.   \n",
              "14  Los dos jóvenes leyeron el nombre: Eugenio de ...   \n",
              "15            --¿Es de los nuestros?--preguntó Ochoa.   \n",
              "16  --Yo le he conocido aquí en 1824--dijo Iturri-...   \n",
              "17  El recién llegado escribió unas cuantas cartas...   \n",
              "18  Al día siguiente preguntaron por él dos o tres...   \n",
              "19  Por lo que dijo Iturri, Aviraneta traía pasapo...   \n",
              "20  A la hora de almorzar Iturri sentó a la misma ...   \n",
              "21               Aviraneta les hizo alguna impresión.   \n",
              "22  Tenía marcada tendencia por la frase amarga y ...   \n",
              "23  --¿Ha tenido usted larga conferencia con Aguad...   \n",
              "24                                              --Sí.   \n",
              "25                                       --¿Qué dice?   \n",
              "26                                       --Poca cosa.   \n",
              "27  --¿No está contento de la marcha de los aconte...   \n",
              "28                                      --Eso parece.   \n",
              "29           --¿Y el general Mina no tiene confianza?   \n",
              "30                                        --Muy poca.   \n",
              "31  Por lo que he podido traslucir no está content...   \n",
              "32  Se me figura que va arrastrado por la fogosida...   \n",
              "33  --Es que el general está viejo, enfermo y natu...   \n",
              "34     Ya verá usted como todo sale bien--dijo Ochoa.   \n",
              "35                            --Mejor, mejor; ¡ojalá!   \n",
              "36  Aviraneta contó sus viajes, y estaba hablando ...   \n",
              "37  El italiano era un hombrecito calvo, de unos c...   \n",
              "38  Vestía un traje raído y sin brillo y llevaba l...   \n",
              "39  El señor Pagani, así se llamaba, era al parece...   \n",
              "40  --Me ha explicado mi amigo Iturri su situación...   \n",
              "41  --Tengo el pasaporte del capitán general de la...   \n",
              "42                       --¿Quiere usted enseñármelo?   \n",
              "43                              --Ahora vengo con él.   \n",
              "44  Aviraneta entró en su cuarto y volvió poco des...   \n",
              "45  --He salido de la Habana con mi pasaporte pens...   \n",
              "46  --¿Usted ha tenido que ver algo en política?--...   \n",
              "47  --Sí, en parte--murmuró Aviraneta--yo fuí mili...   \n",
              "48  El italiano contempló con desconfianza a su in...   \n",
              "49                                     --Está bien...   \n",
              "\n",
              "                                           traduccion  \n",
              "0                                         PIO BAROJA.  \n",
              "1                              THE CAUBILLES OF 1830.  \n",
              "2                                             NOVELA.  \n",
              "3   RAFAEL CARO RAGGIO: EDITOR Calle de Ventura Ro...  \n",
              "4                                         BOOK FIRST.  \n",
              "5                            THE ETERNAL CONSPIRATOR.  \n",
              "6                                        DON EUGENIO.  \n",
              "7   One day, at dusk, a man appeared in Iturri's f...  \n",
              "8   He was a dry guy, embroidered, with his face a...  \n",
              "9   He had the air of tiredness of those who come ...  \n",
              "10  He was wearing black redingot, pants with loop...  \n",
              "11    \"Who is this man?\" Lacy and Ochoa asked Iturri.  \n",
              "12            - He's a vascongado coming from Havana.  \n",
              "13                                  There's his name.  \n",
              "14  The two young men read the name: Eugenio de Av...  \n",
              "15                    \"Is he one of us?\" asked Ochoa.  \n",
              "16  \"I met him here in 1824,\" said Iturri, \"I thin...  \n",
              "17  The newcomer wrote a few letters and got into ...  \n",
              "18  The next day two or three people asked for him...  \n",
              "19  From what Iturri said, Aviraneta was carrying ...  \n",
              "20  At lunch time Iturri sat at the same table whe...  \n",
              "21            Aviraneta made some impression on them.  \n",
              "22  He had a marked tendency for the bitter phrase...  \n",
              "23  \"Have you had a long lecture with Aguado?\" Och...  \n",
              "24                                            - Yeah.  \n",
              "25                                - What does it say?  \n",
              "26                                    - Little thing.  \n",
              "27   \"Are you not pleased with the course of events?\"  \n",
              "28                                     - It seems so.  \n",
              "29              - And General Mina has no confidence?  \n",
              "30                                     - Very little.  \n",
              "31  As far as I can see, he's not happy with the o...  \n",
              "32  It seems to me that it is carried away by the ...  \n",
              "33  \"The general is old, sick, and naturally distr...  \n",
              "34  You'll see how everything works out,\" Ochoa said.  \n",
              "35                          - Better, better, better.  \n",
              "36  Aviraneta recounted his travels, and was talki...  \n",
              "37  The Italian was a bald little man, about forty...  \n",
              "38  He was wearing a shaved and shiny suit and wea...  \n",
              "39  Mr Pagani, that was his name, was apparently i...  \n",
              "40  \"My friend Iturri has explained to me your sit...  \n",
              "41  \"I have the passport of the captain general of...  \n",
              "42                       - Would you like to show me?  \n",
              "43                         - I'm coming with him now.  \n",
              "44  Aviraneta entered his room and returned shortl...  \n",
              "45  \"I have left Havana with my passport thinking ...  \n",
              "46  \"Have you had anything to do with politics?\" a...  \n",
              "47  \"Yes, in part,\" said Aviraneta, \"I was a milit...  \n",
              "48  The Italian looked suspiciously at his interlo...  \n",
              "49                                     - It's okay...  "
            ],
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              "      <th>0</th>\n",
              "      <td>PÍO BAROJA.</td>\n",
              "      <td>PIO BAROJA.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>LOS CAUDILLOS DE 1830.</td>\n",
              "      <td>THE CAUBILLES OF 1830.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>NOVELA.</td>\n",
              "      <td>NOVELA.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RAFAEL CARO RAGGIO: EDITOR Calle de Ventura Ro...</td>\n",
              "      <td>RAFAEL CARO RAGGIO: EDITOR Calle de Ventura Ro...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>LIBRO PRIMERO.</td>\n",
              "      <td>BOOK FIRST.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>EL ETERNO CONSPIRADOR.</td>\n",
              "      <td>THE ETERNAL CONSPIRATOR.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>DON EUGENIO.</td>\n",
              "      <td>DON EUGENIO.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>UN día, al anochecer, apareció en la fonda de ...</td>\n",
              "      <td>One day, at dusk, a man appeared in Iturri's f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
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              "      <td>He was a dry guy, embroidered, with his face a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Tenía el aire de cansancio de los que vienen d...</td>\n",
              "      <td>He had the air of tiredness of those who come ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Vestía redingot negro, pantalón con trabillas,...</td>\n",
              "      <td>He was wearing black redingot, pants with loop...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>--¿Quién es este hombre?--preguntaron Lacy y O...</td>\n",
              "      <td>\"Who is this man?\" Lacy and Ochoa asked Iturri.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>--Es un vascongado que viene de la Habana.</td>\n",
              "      <td>- He's a vascongado coming from Havana.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Ahí está su nombre.</td>\n",
              "      <td>There's his name.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Los dos jóvenes leyeron el nombre: Eugenio de ...</td>\n",
              "      <td>The two young men read the name: Eugenio de Av...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>--¿Es de los nuestros?--preguntó Ochoa.</td>\n",
              "      <td>\"Is he one of us?\" asked Ochoa.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>--Yo le he conocido aquí en 1824--dijo Iturri-...</td>\n",
              "      <td>\"I met him here in 1824,\" said Iturri, \"I thin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>El recién llegado escribió unas cuantas cartas...</td>\n",
              "      <td>The newcomer wrote a few letters and got into ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Al día siguiente preguntaron por él dos o tres...</td>\n",
              "      <td>The next day two or three people asked for him...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Por lo que dijo Iturri, Aviraneta traía pasapo...</td>\n",
              "      <td>From what Iturri said, Aviraneta was carrying ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>A la hora de almorzar Iturri sentó a la misma ...</td>\n",
              "      <td>At lunch time Iturri sat at the same table whe...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Aviraneta les hizo alguna impresión.</td>\n",
              "      <td>Aviraneta made some impression on them.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Tenía marcada tendencia por la frase amarga y ...</td>\n",
              "      <td>He had a marked tendency for the bitter phrase...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>--¿Ha tenido usted larga conferencia con Aguad...</td>\n",
              "      <td>\"Have you had a long lecture with Aguado?\" Och...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>--Sí.</td>\n",
              "      <td>- Yeah.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>--¿Qué dice?</td>\n",
              "      <td>- What does it say?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>--Poca cosa.</td>\n",
              "      <td>- Little thing.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>--¿No está contento de la marcha de los aconte...</td>\n",
              "      <td>\"Are you not pleased with the course of events?\"</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>--Eso parece.</td>\n",
              "      <td>- It seems so.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>--¿Y el general Mina no tiene confianza?</td>\n",
              "      <td>- And General Mina has no confidence?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>--Muy poca.</td>\n",
              "      <td>- Very little.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Por lo que he podido traslucir no está content...</td>\n",
              "      <td>As far as I can see, he's not happy with the o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Se me figura que va arrastrado por la fogosida...</td>\n",
              "      <td>It seems to me that it is carried away by the ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>--Es que el general está viejo, enfermo y natu...</td>\n",
              "      <td>\"The general is old, sick, and naturally distr...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Ya verá usted como todo sale bien--dijo Ochoa.</td>\n",
              "      <td>You'll see how everything works out,\" Ochoa said.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>--Mejor, mejor; ¡ojalá!</td>\n",
              "      <td>- Better, better, better.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Aviraneta contó sus viajes, y estaba hablando ...</td>\n",
              "      <td>Aviraneta recounted his travels, and was talki...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>El italiano era un hombrecito calvo, de unos c...</td>\n",
              "      <td>The Italian was a bald little man, about forty...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>Vestía un traje raído y sin brillo y llevaba l...</td>\n",
              "      <td>He was wearing a shaved and shiny suit and wea...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>El señor Pagani, así se llamaba, era al parece...</td>\n",
              "      <td>Mr Pagani, that was his name, was apparently i...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>--Me ha explicado mi amigo Iturri su situación...</td>\n",
              "      <td>\"My friend Iturri has explained to me your sit...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>--Tengo el pasaporte del capitán general de la...</td>\n",
              "      <td>\"I have the passport of the captain general of...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>--¿Quiere usted enseñármelo?</td>\n",
              "      <td>- Would you like to show me?</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>--Ahora vengo con él.</td>\n",
              "      <td>- I'm coming with him now.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>Aviraneta entró en su cuarto y volvió poco des...</td>\n",
              "      <td>Aviraneta entered his room and returned shortl...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>45</th>\n",
              "      <td>--He salido de la Habana con mi pasaporte pens...</td>\n",
              "      <td>\"I have left Havana with my passport thinking ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>--¿Usted ha tenido que ver algo en política?--...</td>\n",
              "      <td>\"Have you had anything to do with politics?\" a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>--Sí, en parte--murmuró Aviraneta--yo fuí mili...</td>\n",
              "      <td>\"Yes, in part,\" said Aviraneta, \"I was a milit...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>El italiano contempló con desconfianza a su in...</td>\n",
              "      <td>The Italian looked suspiciously at his interlo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>--Está bien...</td>\n",
              "      <td>- It's okay...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "type": "dataframe",
              "variable_name": "traducciones_es_en_df",
              "summary": "{\n  \"name\": \"traducciones_es_en_df\",\n  \"rows\": 50,\n  \"fields\": [\n    {\n      \"column\": \"oracion\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"Ah\\u00ed est\\u00e1 su nombre.\",\n          \"El se\\u00f1or Pagani, as\\u00ed se llamaba, era al parecer, insustitu\\u00edble en su oficina; sab\\u00eda cuatro o cinco idiomas a la perfecci\\u00f3n, trabajaba constantemente y ganaba poco.\",\n          \"--Muy poca.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"traduccion\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 50,\n        \"samples\": [\n          \"There's his name.\",\n          \"Mr Pagani, that was his name, was apparently irreplaceable in his office; he knew four or five languages perfectly, worked constantly and earned little.\",\n          \"- Very little.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "traducciones_es_en_df"
      ],
      "metadata": {
        "id": "NsyeSjg2SB75"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}