Tratamiento de Lenguaje Natural (PLN)/Cuadernos/nlp_FastText_Intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "ed579373-0237-47c7-bac0-df327165f0fb",
   "metadata": {
    "tags": []
   },
   "source": [
    "# <span style=\"color:green\"><center>Diplomado en Inteligencia Artificial y Aprendizaje Profundo</center></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "067f9282-4c82-4d4b-98f9-93b2028b6535",
   "metadata": {
    "tags": []
   },
   "source": [
    "# <span style=\"color:red\"><center>Introducción a FastText</center></span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8995cbdb-7711-4846-b7e7-ef6feb846a0c",
   "metadata": {},
   "source": [
    "<center>Library for efficient text classification and representation learning</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "26d4fa8b-e43d-476c-9eb3-60e229fccaa1",
   "metadata": {},
   "source": [
    "##   <span style=\"color:blue\">Profesores</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1d35956-c081-4d50-8b47-b1516900712c",
   "metadata": {},
   "source": [
    "1. Alvaro Mauricio Montenegro Díaz, ammontenegrod@unal.edu.co\n",
    "2. Daniel Mauricio Montenegro Reyes, dextronomo@gmail.com \n",
    "3. Campo Elías Pardo Turriago, cepardot@unal.edu.co "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7d8c5e8-1302-4fa6-bd10-bea8064d8680",
   "metadata": {},
   "source": [
    "##   <span style=\"color:blue\">Asesora Medios y Marketing digital</span>\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc912a37-b8a8-47f5-9dcd-5e764b8d17eb",
   "metadata": {},
   "source": [
    "4. Maria del Pilar Montenegro, pmontenegro88@gmail.com "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71796636-0712-4c57-8490-2bfffdbd8e92",
   "metadata": {},
   "source": [
    "## <span style=\"color:blue\">Asistentes</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8078ddb6-7dfd-47f8-a2a0-c1d62d4beca8",
   "metadata": {},
   "source": [
    "5. Oleg Jarma, ojarmam@unal.edu.co \n",
    "6. Laura Lizarazo, ljlizarazore@unal.edu.co "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "092a515e-0050-4a43-a94b-696c331fd8b2",
   "metadata": {},
   "source": [
    "## <span style=\"color:blue\">Referencias</span> "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8328e410-0067-4af5-8906-60b1658db2c6",
   "metadata": {},
   "source": [
    "1. P. Bojanowski*, E. Grave*, A. Joulin, T. Mikolov, [Enriching Word Vectors with Subword Information](https://arxiv.org/pdf/1607.04606.pdf)\n",
    "2. A. Joulin, E. Grave, P. Bojanowski, T. Mikolov, [Bag of Tricks for Efficient Text Classification](https://arxiv.org/pdf/1607.01759.pdf)\n",
    "3. A. Joulin, E. Grave, P. Bojanowski, M. Douze, H. Jégou, T. Mikolov, [FastText.zip: Compressing text classification models](https://arxiv.org/pdf/1612.03651.pdf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6dd985de-295f-43a5-84ae-976f47a40f2b",
   "metadata": {},
   "source": [
    "## <span style=\"color:blue\">Contenido</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48a52b8a-4b57-45b2-92be-3eb2ab1babbd",
   "metadata": {},
   "source": [
    "* [Introducción](#Introducción)\n",
    "* [Instalación](#Instalación)\n",
    "* [Modelo No Supervisado](#Modelo-No-Suervisado)\n",
    "    * [Uso del Modelo (Toy Example)](#Uso-del-Modelo-\\(Toy-Example\\))\n",
    "        * [Jugando con los Parámetros](#Jugando-con-los-Parámetros)\n",
    "        * [Palabras vecinas más cercanas](#Palabras-vecinas-más-cercanas)\n",
    "    * [Uso del Modelo (Wikipedia)](#Uso-del-Modelo-(Wikipedia))\n",
    "        * [Preprocesamiento](#Preprocesamiento)\n",
    "        * [Entrenando el Modelo](#Entrenando-el-Modelo)\n",
    "        * [Palabras más cercanas](#Palabras-más-cercanas)\n",
    "        * [Curiosidades](#Curiosidades)\n",
    "        * [Analogía de Palabras](#Analogía-de-Palabras)\n",
    "* [Modelo Supervisado](#Modelo-Supervisado)\n",
    "    * [Uso del Modelo](#Uso-del-Modelo)\n",
    "    * [Mejorando el Modelo](#Mejorando-el-Modelo)\n",
    "    * [Autotuning de Parámtros](#Autotuning-de-Parámetros)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "116cd36d-400d-4d95-a4d2-2f75f440b48f",
   "metadata": {},
   "source": [
    "## <span style=\"color:blue\">Introducción</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7aed1d13-ba1b-4290-b47f-7ccd527a182f",
   "metadata": {},
   "source": [
    "El modelo [FastText](https://fasttext.cc/) fue introducido por primera vez por Facebook en 2016 como una extensión y supuestamente una mejora del modelo vainilla de Word2Vec. \n",
    "\n",
    "Está basado en el artículo original titulado [Enriching Word Vectors with Subword Information](https://arxiv.org/pdf/1607.04606.pdf) de Mikolov et al. que es una lectura excelente para obtener una comprensión profunda de cómo funciona este modelo. En general, FastText es un marco para el aprendizaje de representaciones de palabras y también para realizar una clasificación de texto sólida, rápida y precisa. \n",
    "\n",
    "El marco es de código abierto de Facebook en GitHub y afirma tener lo siguiente.\n",
    "\n",
    "1. Vectores de palabras en inglés de última generación.\n",
    "2. Vectores de palabras para 157 idiomas entrenados en Wikipedia y rastreo.\n",
    "3. Modelos para identificación de idiomas y diversas tareas supervisadas.\n",
    "\n",
    "De acuedo con los autores,  en general, los modelos predictivos como el modelo *Word2Vec* suelen considerar cada palabra como una entidad distinta (por ejemplo, dónde) y generan una incrustación densa para la palabra. \n",
    "\n",
    "Sin embargo, esto representa una seria limitación con los idiomas que tienen un vocabulario masivo y muchas palabras raras que pueden no aparecer mucho en diferentes corpus. El modelo Word2Vec normalmente ignora la estructura morfológica de cada palabra y considera una palabra como una sola entidad. \n",
    "\n",
    "El modelo **FastText** considera cada palabra como una bolsa de n-gramas de caracteres. Esto también se denomina modelo de subpalabras en el documento.\n",
    "\n",
    "\n",
    "Se agregan símbolos de límites especiales <y> al principio y al final de las palabras. Esto  permite distinguir prefijos y sufijos de otras secuencias de caracteres. También incluiyen la propia palabra *w* en el conjunto de sus n-gramas, para aprender una representación de cada palabra (además de su carácter n-gramas). \n",
    "    \n",
    "    \n",
    "Tomando la palabra *where* y n = 3 (tri-gramas) como ejemplo, estará representada por el carácter n-gramas: <wh, whe, her, ere, re> y la secuencia especial <where> que representa la palabra completa . \n",
    "    \n",
    " Tenga en cuenta que la secuencia, correspondiente a la palabra <her> es diferente del trigrama ella de la palabra where.\n",
    "    \n",
    "En la práctica, el artículo recomienda extraer todos los n-gramas para $3\\le n \\le 6$ Este es un enfoque muy simple, y se podrían considerar diferentes conjuntos de n-gramas, por ejemplo, tomando todos los prefijos y sufijos. \n",
    "    \n",
    "Normalmente asociamos una representación vectorial (incrustación) a cada n-grama de una palabra. Por tanto, podemos representar una palabra mediante la suma de las representaciones vectoriales de sus n-gramas o el promedio de la incrustación de estos n-gramas. \n",
    "    \n",
    "Según los autores, debido a este efecto de aprovechar los n-gramas de palabras individuales basadas en sus caracteres, existe una mayor probabilidad de que las palabras raras obtengan una buena representación, ya que sus n-gramas basados en caracteres deben aparecer en otras palabras del corpus.\n",
    "    \n",
    "Vamos a la práctica."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14794e07-4318-4ec9-b6b3-b66c644699fe",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "debc2c13-e6f0-4eeb-be52-fa1ffd82aeba",
   "metadata": {},
   "source": [
    "## <span style=\"color:blue\">Instalación</span>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "799b181b-4dac-4c0e-ab23-f94d30d49425",
   "metadata": {},
   "source": [
    "`$ git clone https://github.com/facebookresearch/fastText.git`\n",
    "\n",
    "`$ cd fastText`\n",
    "\n",
    "`$ sudo pip install .`\n",
    "\n",
    "`$ # or :`\n",
    "\n",
    "`$ sudo python setup.py install`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ace4727b-3cbe-4032-ba2e-54c3c28c09eb",
   "metadata": {},
   "source": [
    "Si todo va bien, el siguiente comando debería funcionar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "6c6ae491-f39c-4961-b113-da6e7f762f3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fasttext as ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8af209fc-d056-4592-b42d-c048fe45f544",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on module fasttext.FastText in fasttext:\n",
      "\n",
      "NAME\n",
      "    fasttext.FastText\n",
      "\n",
      "DESCRIPTION\n",
      "    # Copyright (c) 2017-present, Facebook, Inc.\n",
      "    # All rights reserved.\n",
      "    #\n",
      "    # This source code is licensed under the MIT license found in the\n",
      "    # LICENSE file in the root directory of this source tree.\n",
      "\n",
      "FUNCTIONS\n",
      "    cbow(*kargs, **kwargs)\n",
      "    \n",
      "    load_model(path)\n",
      "        Load a model given a filepath and return a model object.\n",
      "    \n",
      "    read_args(arg_list, arg_dict, arg_names, default_values)\n",
      "    \n",
      "    skipgram(*kargs, **kwargs)\n",
      "    \n",
      "    supervised(*kargs, **kwargs)\n",
      "    \n",
      "    tokenize(text)\n",
      "        Given a string of text, tokenize it and return a list of tokens\n",
      "    \n",
      "    train_supervised(*kargs, **kwargs)\n",
      "        Train a supervised model and return a model object.\n",
      "        \n",
      "        input must be a filepath. The input text does not need to be tokenized\n",
      "        as per the tokenize function, but it must be preprocessed and encoded\n",
      "        as UTF-8. You might want to consult standard preprocessing scripts such\n",
      "        as tokenizer.perl mentioned here: http://www.statmt.org/wmt07/baseline.html\n",
      "        \n",
      "        The input file must must contain at least one label per line. For an\n",
      "        example consult the example datasets which are part of the fastText\n",
      "        repository such as the dataset pulled by classification-example.sh.\n",
      "    \n",
      "    train_unsupervised(*kargs, **kwargs)\n",
      "        Train an unsupervised model and return a model object.\n",
      "        \n",
      "        input must be a filepath. The input text does not need to be tokenized\n",
      "        as per the tokenize function, but it must be preprocessed and encoded\n",
      "        as UTF-8. You might want to consult standard preprocessing scripts such\n",
      "        as tokenizer.perl mentioned here: http://www.statmt.org/wmt07/baseline.html\n",
      "        \n",
      "        The input field must not contain any labels or use the specified label prefix\n",
      "        unless it is ok for those words to be ignored. For an example consult the\n",
      "        dataset pulled by the example script word-vector-example.sh, which is\n",
      "        part of the fastText repository.\n",
      "\n",
      "DATA\n",
      "    BOW = '<'\n",
      "    EOS = '</s>'\n",
      "    EOW = '>'\n",
      "    absolute_import = _Feature((2, 5, 0, 'alpha', 1), (3, 0, 0, 'alpha', 0...\n",
      "    displayed_errors = {}\n",
      "    division = _Feature((2, 2, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0), 1310...\n",
      "    print_function = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', 0)...\n",
      "    unicode_literals = _Feature((2, 6, 0, 'alpha', 2), (3, 0, 0, 'alpha', ...\n",
      "    unsupervised_default = {'autotuneDuration': 300, 'autotuneMetric': 'f1...\n",
      "\n",
      "FILE\n",
      "    /Users/moury/miniforge3/envs/impertinencias/lib/python3.10/site-packages/fasttext-0.9.2-py3.10-macosx-11.0-arm64.egg/fasttext/FastText.py\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(ft.FastText)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "24a512bd-4a33-4d46-ab5f-d0614584be60",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['BOW',\n",
       " 'EOS',\n",
       " 'EOW',\n",
       " 'FastText',\n",
       " '__builtins__',\n",
       " '__cached__',\n",
       " '__doc__',\n",
       " '__file__',\n",
       " '__loader__',\n",
       " '__name__',\n",
       " '__package__',\n",
       " '__path__',\n",
       " '__spec__',\n",
       " 'absolute_import',\n",
       " 'cbow',\n",
       " 'division',\n",
       " 'load_model',\n",
       " 'print_function',\n",
       " 'skipgram',\n",
       " 'supervised',\n",
       " 'tokenize',\n",
       " 'train_supervised',\n",
       " 'train_unsupervised',\n",
       " 'unicode_literals']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(ft)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ea770254-4538-4833-b3d5-aacc32289670",
   "metadata": {},
   "source": [
    "Como podemos observar, contamos con dos modelos: **unsupervised** y **supervised**.\n",
    "\n",
    "Veamos cómo funcionan."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aba0f416-2aa7-4e89-992d-73a7241c1b48",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7a0a7033-485e-4466-b954-e0f8303aed4c",
   "metadata": {},
   "source": [
    "## Modelo No Supervisado"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5977c7fb-b8a6-4a41-817a-5b7c22b5067d",
   "metadata": {},
   "source": [
    "Para este ejemplo de juguete, usemos los poemas de Daniel."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a9f3f4-9374-45bb-8658-d09174a549ac",
   "metadata": {},
   "source": [
    "## Uso del Modelo (Toy Example)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28ea8200-868e-4886-af1a-33f230aee5d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Read 0M words\n",
      "Number of words:  184\n",
      "Number of labels: 0\n",
      "Progress: 100.0% words/sec/thread:   65429 lr:  0.000000 avg.loss:  4.118722 ETA:   0h 0m 0s\n"
     ]
    }
   ],
   "source": [
    "model = ft.train_unsupervised('Poemas_Todo.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "593a0f9d-71f7-4c0f-9b7e-cc9c4fe2b378",
   "metadata": {},
   "source": [
    "Veamos qué tiene el modelo por dentro:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dad67c80-bdbe-43fb-b3a0-da1b9d0b5007",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__contains__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__getitem__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_labels',\n",
       " '_words',\n",
       " 'bucket',\n",
       " 'dim',\n",
       " 'epoch',\n",
       " 'f',\n",
       " 'get_analogies',\n",
       " 'get_dimension',\n",
       " 'get_input_matrix',\n",
       " 'get_input_vector',\n",
       " 'get_label_id',\n",
       " 'get_labels',\n",
       " 'get_line',\n",
       " 'get_meter',\n",
       " 'get_nearest_neighbors',\n",
       " 'get_output_matrix',\n",
       " 'get_sentence_vector',\n",
       " 'get_subword_id',\n",
       " 'get_subwords',\n",
       " 'get_word_id',\n",
       " 'get_word_vector',\n",
       " 'get_words',\n",
       " 'is_quantized',\n",
       " 'label',\n",
       " 'labels',\n",
       " 'loss',\n",
       " 'lr',\n",
       " 'lrUpdateRate',\n",
       " 'maxn',\n",
       " 'minCount',\n",
       " 'minCountLabel',\n",
       " 'minn',\n",
       " 'neg',\n",
       " 'predict',\n",
       " 'pretrainedVectors',\n",
       " 'quantize',\n",
       " 'save_model',\n",
       " 'set_args',\n",
       " 'set_matrices',\n",
       " 't',\n",
       " 'test',\n",
       " 'test_label',\n",
       " 'thread',\n",
       " 'verbose',\n",
       " 'wordNgrams',\n",
       " 'words',\n",
       " 'ws']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1c5dea40-2f7d-46f9-a6f4-8b5e2cafc71d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['</s>', 'y', 'de', 'que', 'la', 'el', 'en', 'las', 'con', 'los', 'por', 'un', 'se', 'tu', 'a', 'del', 'una', 'no', 'mi', 'es', 'te', 'me', 'como', 'su', 'mis', 'para', 'sin', 'tus', 'entre', 'porque', 'lo', 'más', 'sus', 'nos', 'noche', 'ese', 'cada', 'pero', 'al', 'hasta', 'todo', 'manos', 'cielo', 'Y', 'día', 'cuerpo', 'ojos', 'sueño', 'ni', 'son', 'cuando', 'ser', 'qué', 'Me', 'palabras', 'le', 'labios', 'Te', 'ya', 'o', 'cosas', 'mundo', 'si', 'mujer', 'sobre', 'cuerpos', 'vida', 'eres', 'luna', 'esa', 'este', 'toda', 'piel,', 'En', 'sólo', 'jamás', 'donde', 'amor', 'sueños', 'va', 'hay', 'Ese', 'siempre', 'e', 'Es', 'frío', 'La', 'puedo', 'tan', 'deseo', 'lleno', 'rostro', 'visto', 'he', 'dos', 'sueños,', 'quien', 'ha', 'noche,', 'imagen', 'Yo', 'nuestra', 'gusta', 'contra', 'He', 'nubes', 'pasión', 'mar', 'aire', 'esperar', 'volando', 'misma', 'dulce', 'vez', 'lejos', 'tiempo', 'brisa', 'aún', 'El', 'dónde', 'abismo', 'veces', 'música', 'ésta', 'piel', 'hacia', 'letras', 'ante', 'ojos,', 'besos', 'mirando', 'nuestro', 'así', 'somos', 'colores', 'apenas', 'éste', 'mismo', 'entonces', 'cara', 'formas', 'alma', 'profundo', 'desde', 'yo', 'soy', 'bajo', 'busco', 'tí,', 'Un', 'ideas', 'lugar', 'seres', 'brazos,', 'polvo', 'veo', 'No', 'ver', 'Vivo', 'A', 'sirve', 'sol', 'agua', 'casi', 'algún', 'tí', 'mientras', 'frase', 'mano', 'hace', 'dame', 'colores,', 'luces', 'luz', 'mí', 'esperando', 'emociones', 'caricias', 'permanente', 'Déjame', 'quiero', 'fue', 'suaves', 'sonido']\n"
     ]
    }
   ],
   "source": [
    "print(model.words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "da5a5320-6c21-4f56-9cbe-a5376958174c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "184"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(model.words)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0bd75fcc-d16a-44bb-8113-04e3ae579552",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.70543962e-03,  8.37066956e-03, -5.50515437e-03, -1.24249607e-04,\n",
       "        2.42939778e-03,  1.55407120e-03, -1.08765345e-03, -3.81100061e-03,\n",
       "        1.01086097e-02,  5.31225419e-03, -4.43909131e-03,  2.28441597e-04,\n",
       "       -7.25109538e-04,  4.66341013e-03, -3.68466972e-05, -1.87909952e-03,\n",
       "        9.05735709e-04,  3.71828605e-03, -2.06560642e-03, -6.25834754e-03,\n",
       "       -1.03769416e-03,  4.45846061e-04,  4.55524353e-03, -1.50300446e-03,\n",
       "        3.41521646e-03, -3.51131149e-03,  5.83332847e-04, -8.67802883e-04,\n",
       "       -2.16151448e-03,  2.36369530e-03, -9.19947214e-03, -2.07637064e-03,\n",
       "        8.52814002e-04, -2.41027959e-03, -1.15303211e-02, -7.64431199e-03,\n",
       "       -1.18330074e-03,  1.62962254e-03, -4.67444956e-03, -9.65831615e-03,\n",
       "        1.58754108e-03,  7.66551169e-03,  2.37135566e-03,  4.65557165e-03,\n",
       "       -4.28355881e-04, -5.12372050e-03,  6.79135206e-04, -1.44966587e-03,\n",
       "       -3.05020204e-03,  4.13821964e-03, -5.80588216e-03,  5.08889370e-03,\n",
       "       -1.52134825e-03,  4.22689086e-03,  1.25596335e-03, -4.01819032e-03,\n",
       "       -3.68661713e-03, -5.99279359e-04,  1.99793535e-03,  9.25289374e-03,\n",
       "       -3.55801685e-03,  6.36295415e-03,  1.08154844e-02,  2.25974480e-03,\n",
       "       -2.79875984e-03,  3.24897794e-03, -4.76871058e-03,  4.56008827e-03,\n",
       "        4.89121163e-03,  3.16283759e-03, -4.36968636e-03,  8.28509964e-03,\n",
       "        6.42280374e-03,  1.31064875e-03, -6.09720591e-03,  1.22815336e-03,\n",
       "        1.13256108e-02,  4.69434587e-03,  1.29487587e-03, -3.97395669e-03,\n",
       "       -5.74968196e-03,  1.95662375e-03,  8.50631949e-03, -4.85884957e-03,\n",
       "        2.64058565e-03, -4.64042928e-03, -5.40790730e-04,  5.33305760e-03,\n",
       "       -4.38782573e-03, -3.52200051e-03,  1.38505865e-02,  6.44145627e-03,\n",
       "        1.21967522e-02, -5.53641422e-03,  1.61109504e-03, -6.33485196e-03,\n",
       "        6.36053970e-03,  3.65184573e-03, -1.82133971e-03, -9.00177518e-04],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soy_vector = model.get_word_vector(\"soy\")\n",
    "soy_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c07ed896-c93e-4e36-ad25-1f1a82c608ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100,)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soy_vector.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "23e7dd18-88e8-4270-9fd5-a5156560688b",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save_model(\"Poemas.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "89f7fb4e-0834-4e1f-b024-0f8e9ac9c17c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ft.load_model(\"Poemas.bin\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "007e969f-e4d4-47fa-b5c6-4fdb0e62a0b4",
   "metadata": {},
   "source": [
    "Por defecto, el modelo entrenado es skipgram, pero también tenemos disponible la arquitectura cbow.\n",
    "\n",
    "![ft](https://fasttext.cc/img/cbo_vs_skipgram.png)\n",
    "\n",
    "Fuente: [FastText, Word Representations](https://fasttext.cc/docs/en/unsupervised-tutorial.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9b18a727-05e4-489d-94a9-ad10191dbf05",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model_cbow = fasttext.train_unsupervised('../Datos/Poemas_Todo.txt', \"cbow\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8567ad9d-265c-4547-b8ab-daa9d5fffad2",
   "metadata": {},
   "source": [
    "En palabras de Facebook,\n",
    "\n",
    "***In practice, we observe that skipgram models works better with subword information than cbow.***"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97e576af-a6f9-4f0e-87bb-65f6ca567e67",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f2fddb-a296-487e-b4f2-979387ca43ed",
   "metadata": {},
   "source": [
    "### Jugando con los Parámetros "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f09f0c5-f2cc-4de4-9052-f88541bfa0f0",
   "metadata": {},
   "source": [
    "Dependiendo del problema, puede ser que los parámetros por defecto no sean los más adecuados.\n",
    "\n",
    "Para conocer todos los parámetros de FastText, podemos ingresar [aquí](https://fasttext.cc/docs/en/python-module.html#train_unsupervised-parameters)\n",
    "\n",
    "Por ejemplo, modifiquemos los parámetros **mínimos** y **máximos** de lo n-gramas permitidos, la **dimensión** del vector, las **epochs** y la frecuencia mínima de palabras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ddba46b5-f09c-4a9e-871c-d007359879df",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fasttext as ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "172df83d-30c9-4212-98f1-f9ce4d9e7cc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Read 0M words\n",
      "Number of words:  846\n",
      "Number of labels: 0\n",
      "Progress: 100.0% words/sec/thread:   32835 lr:  0.000000 avg.loss:  3.358345 ETA:   0h 0m 0s\n"
     ]
    }
   ],
   "source": [
    "model = ft.train_unsupervised('Poemas_Todo.txt', minCount=2, minn=2, maxn=5, dim=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd52a9ea-2b7a-4ddc-a007-57e8338c8a47",
   "metadata": {},
   "source": [
    "Verifiquemos la longitud de las palabras:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dd4f71e5-5220-4283-aae0-176f056a3647",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(300,)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_word_vector(\"soy\").shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ae236b-6b61-48f3-90f6-b38982d5b14d",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77a92f92-2b66-4adb-87fd-d424558379bf",
   "metadata": {},
   "source": [
    "### Palabras vecinas más cercanas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba27aa9c-16e0-48c6-9cf4-767c0a191323",
   "metadata": {},
   "source": [
    "Como cada palabra en el corpus tiene un vector asociado, podemos obtener palabras cercanas usando la similaridad de coseno:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2b11bc88-da13-4066-9d20-6e629c022b46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.9999974966049194, 'sueños,'),\n",
       " (0.9999974966049194, 'sueño,'),\n",
       " (0.9999974966049194, 'sueños'),\n",
       " (0.9999962449073792, 'pequeño'),\n",
       " (0.9999961853027344, 'suelo'),\n",
       " (0.9999958276748657, 'pensamiento'),\n",
       " (0.9999958276748657, 'suelo,'),\n",
       " (0.9999954700469971, 'pensamiento,'),\n",
       " (0.9999954700469971, 'esta'),\n",
       " (0.9999953508377075, 'colores,'),\n",
       " (0.9999953508377075, 'suerte'),\n",
       " (0.9999953508377075, 'siento'),\n",
       " (0.9999952912330627, 'contaminadas'),\n",
       " (0.9999952912330627, 'aliento'),\n",
       " (0.9999951720237732, 'entonces,'),\n",
       " (0.9999951720237732, 'pequeñas'),\n",
       " (0.9999951720237732, 'pensamientos'),\n",
       " (0.9999951720237732, 'queriendo'),\n",
       " (0.9999951124191284, 'pequeños'),\n",
       " (0.9999951124191284, 'aquello')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " model.get_nearest_neighbors('sueño',k=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "91401eda-3484-4ba6-8c3e-8f382a349943",
   "metadata": {},
   "source": [
    "### Visualización de Embeddings de Palabras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "b5b603df-f16d-4994-8d32-f90408dae67d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "p = model.get_nearest_neighbors('sueño',k=15)\n",
    "similar_words = [x[1] for x in p]\n",
    "wvs = np.array([model.get_word_vector(sw) for sw in similar_words])\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "P = pca.fit_transform(wvs)\n",
    "labels = similar_words\n",
    "\n",
    "plt.figure(figsize=(18, 7))\n",
    "plt.scatter(P[:, 0], P[:, 1], c='lightgreen', edgecolors='b')\n",
    "plt.title('Visualización con PCA')\n",
    "\n",
    "eps = 0.5e12*np.finfo(float).eps\n",
    "for label, x, y in zip(labels, P[:, 0], P[:, 1]):\n",
    "    plt.annotate(label, xy=(x+eps, y+eps), xytext=(0, 0), textcoords='offset points')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "466fcaea-5b9f-4d7e-bbca-4b99dc73776d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moury/miniforge3/envs/impertinencias/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:982: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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D1qmaEUIojDFmHVRfQwhJkiRJOvakpEQe+PBRUlJ3VbSVltRhSJNbKC0NNVjZno6UOo9lXySE8HEMSZIkSToGndm6hJWzm+7RtnJ2U85sXVJDFVUtyTqLi4vJy8ur9uPq/zGEkCRJkqRj0IjhqUy+PYei/GaUltShKL8Zk2/PYcTw1JoubQ9J1blz505DiATUrekCJEmSJEnJy80NQH3uG3YJK5alcmbrEh4amVreXnvsr87LLtvKRRd9jzVr1lBaWso999zD0KFDKSgooFGjRhQUFHDXXXcxY8YMtmzZwq233srixYvZuXMn9957L5dddhnjx4/nlVdeYfv27WzZsoWtW7fy7rvvkpmZycCBA7ntttu4++67mTFjBp999hk333wzN954Y01/JEc0QwhJkiRJOkbl5gZyc3dP7lh7J3msqs4pU6bStGlTXnnlFaBsec6hQ4dWuf/IkSO54IILGDduHBs2bKBLly5ceOGFAMyaNYtFixZx6qmnMmPGDB588EFefvllAB5//HEaNmzI3Llz+eyzzzj33HPp1asXZ5xxxuG94KOYj2NIkiRJko446enpTJ8+naFDh5Kfn0/Dhg3323fatGmMHj2azMxMsrOz2b59O6tXrwagZ8+enHrqqfvd76mnniIzM5OuXbvy6aefUlRUdFiu51jhSAhJkiRJ0hGnZcuWFBYW8uqrrzJs2DB69epF3bp12bWrbBWN7du3V/SNMTJlyhRatWq1xzHefvttTjjhhP2eI8bI2LFj6d279+G5iGOQIyEkSZIkSbVGXl6kVdsdpKSUfc/Li1X2W7t2LfXr16dfv37cddddzJs3j7S0NAoLCwGYMmVKRd/evXszduxYYiw71vz586s85kknncSmTZv22O+xxx6jpKRsJY733nuPLVu2VMt1HqscCSFJkiRJqhXy8iJ3Dt9K30emcmO3tayc3ZQ7b88B6u8zYebixYsZMmQIderUITU1lccee4xt27Zx3XXXcf/999O1a9eKvvfccw933HEHGRkZxBhJS0urmPehsoyMDOrWrUv79u0ZNGgQt99+O8XFxXTs2JEYI40bN+b5558/zJ/C0S3sToJqu6ysrFhQUFDTZUiSJEmSDpNWbXfQc9RLtDhvTUVbUX4zXht2CcuX1t6JM491IYTCGGPWwfT1cQxJkiRJUq2wYlkqzbut3aOtebe1rFiWWkMVqboZQkiSJEmSaoUzW5ewcnbTPdpWzm7Kma1LaqgiVTdDCEmSJElSrTBieCqTb8+hKL8ZpSV1KMpvxuTbcxgx3JEQRwsnppQkSZIk1Qplk0/W575hl7BiWSpnti7hoZGp+0xKeSSYMWMGDz74YJUTYB7LDCEkSZIkSbVGbm4gN3f3JJRORnm08XEMSZIkSZIq2bJlCxdddBHt27enXbt2TJo0ibS0NNatWwdAQUEB2dnZFX0HDx5M586d6dChAy+88MI+x1u/fj2XX345GRkZdOvWjUWLFiV5ObWKIYQkSZIkSZVMnTqVpk2bsnDhQpYsWUJOTs5++44cOZILLriAuXPn8uabbzJkyBC2bNmyR58RI0bQoUMHFi1axP3338+AAQMO9yXUWoYQkiRJkiRVkp6ezvTp0xk6dCj5+fk0bNhwv32nTZvG6NGjyczMJDs7m+3bt7N69eo9+sycOZP+/fsDcMEFF/Dpp5+ycePGw3oNtZVzQkiSJEmSVEnLli0pLCzk1VdfZdiwYfTq1Yu6deuya9cuALZv317RN8bIlClTaNWq1R7H+Oijj/bos7cQjrzJNquDIyEkSZIkSceEvLxIq7Y7SEkp+56Xt284ALB27Vrq169Pv379uOuuu5g3bx5paWkUFhYCMGXKlIq+vXv3ZuzYsRVBw/z58/c53re//W0mTJgAlK2a0ahRIxo0aFDdl3dEcCSEJEmSJOmol5cXuXP4Vvo+MpUbu61l5eym3Hl7DlB/nyVAFy9ezJAhQ6hTpw6pqak89thjbNu2jeuuu47777+frl27VvS95557uOOOO8jIyCDGSFpa2j7Lct57771ce+21ZGRkUL9+fZ588skkLrlWClUNC6mNsrKyYkFBQU2XIUmSJEk6ArVqu4Oeo16ixXlrKtqK8pvx2rBLWL7UpUAPRQihMMaYdTB9fRxDkiRJknTUW7Eslebd1u7R1rzbWlYsS62hio5NhhCSJEmSpKPema1LWDm76R5tK2c35czWJTVU0bHJEEKSJEmSdNQbMTyVybfnUJTfjNKSOhTlN2Py7TmMGJ78SIjrr7+ed95550vtO2PGDP7+979Xc0XJcWJKSZIkSdJRr2zyyfrcN+wSVixL5czWJTw0MnWfSSmT8Pvf//5L7ztjxgxOPPFEzjnnnGqsKDmOhJAkSZIkHRNycwPLlx5HaWnZ9yQCiC1btnDRRRfRvn172rVrx6RJk8jOzmb3wgvTpk3j7LPPpmPHjlx55ZVs3rwZgLS0NEaMGEHHjh1JT09n2bJlFBcX87vf/Y6HH36YzMxM8vPzWbVqFT169CAjI4MePXqwevXqw35Nh8IQQpIkSZKkw2Tq1Kk0bdqUhQsXsmTJEnJyciq2rVu3jl/+8pdMnz6defPmkZWVxUMPPVSxvVGjRsybN48f/vCHPPjgg6SlpXHTTTfx4x//mAULFnDeeedxyy23MGDAABYtWsQ111zDbbfdVhOXedAMISRJkiRJOkzS09OZPn06Q4cOJT8/n4YNG1Zsmz17Nu+88w7nnnsumZmZPPnkk6xatapie58+fQDo1KkTxcXFVR5/1qxZ5ObmAtC/f39mzpx5+C6mGjgnhCRJkiRJh0nLli0pLCzk1VdfZdiwYfTq1atiW4yRnj17MnHixCr3rVevHgApKSns3LnzoM4XQvJzXHwRjoSQJEmSJOkLysuLtGq7g5SUsu95ebHKfmvXrqV+/fr069ePu+66i3nz5lVs69atG3/7299YsWIFAFu3buW999474HlPOukkNm3aVPH+nHPO4ZlnngFgwoQJfOtb3zrUSzusDCEkSZIkSfoC8vIidw7fSs9RL/HAh4/Sc9RL3Dl8a5VBxOLFi+nSpQuZmZmMHDmSn/3sZxXbGjduzPjx47n66qvJyMigW7duLFu27IDnvuSSS3juuecqJqYcM2YMTzzxBBkZGfzxj3/kkUceqfbrrU4hxqrTmtomKysr7p49VJIkSZKkmtKq7Q56jnqJFuetqWgrym/Ga8MuYfnS42qwspoRQiiMMWYdTF9HQkiSJEmS9AWsWJZK825r92hr3m0tK5al1lBFRw5DCEmSJEmSvoAzW5ewcnbTPdpWzm7Kma1LaqiiI4chhCRJkiRJX8CI4alMvj2HovxmlJbUoSi/GZNvz2HE8NozEmL8+PGsXbv28zsmzCU6JUmSJEn6AnJzA1Cf+4ZdwoplqZzZuoSHRqaWt9cO48ePp127djRt2vTzOyfIiSklSZIkSTpCPP3004wZM4YdO3bQtWtXfvvb33LddddRUFBACIHBgwdz2mmnMWjQIL7xjW9w/PHHM2vWLB544AFeeukltm3bxjnnnMN//ud/EkL1hCZOTClJkiRJ0lHm3XffZdKkSfztb39jwYIFpKSk8Mtf/pIPPviAJUuWsHjxYq699lr69u1LVlYWEyZMYMGCBRx//PHccsstzJ07lyVLlrBt2zZefvnlGrkGQwhJkiRJko4Ar7/+OoWFhXTu3JnMzExef/111q9fz8qVK7n11luZOnUqDRo0qHLfN998k65du5Kens4bb7zB0qVLE66+jCGEJEmSJElHgBgjAwcOZMGCBSxYsIDly5fzyCOPsHDhQrKzs/nNb37D9ddfv89+27dv50c/+hGTJ09m8eLF/OAHP2D79u01cAWGEJIkSZIk1ai8vEirtjtISSn7npdX9dyNPXr0YPLkyXz88ccArF+/nlWrVrFr1y6++93v8otf/IJ58+YBcNJJJ7Fp0yaAisChUaNGbN68mcmTJydwVVVzdQxJkiRJkmpIXl7kzuFb6fvIVG7stpaVs5ty5+05QP19Vtto06YNv/zlL+nVqxe7du0iNTWVhx56iCuuuIJdu3YBMGrUKAAGDRrETTfdVDEx5Q9+8APS09NJS0ujc+fOSV9mBVfHkCRJkiSphrRqu4Oeo16ixXlrKtqK8pvx2rBLWL70uBqs7OC5OoYkSZIkSUeAFctSad5t7R5tzbutZcWy1Bqq6PAyhJAkSZIkqYac2bqElbOb7tG2cnZTzmxdUkMVHV6GEJIkSZIk1ZARw1OZfHsORfnNKC2pQ1F+MybfnsOI4UfnSAgnppQkSZIkqYaUTT5Zn/uGXcKKZamc2bqEh0am7jMp5dHCEEKSJEmSpBqUmxvIzd09CeWRMRnll+XjGJIkSZIkKRGGEJIkSZIkKRGGEJIkSZIkKRGGEJIkSZIkKRGGEJIkSZIkKRGGEJIkSZIkKRGGEJIkSZIkKRGGEJIkSZIkKRGGEJIkSZIkKRHVEkKEEMaFED4OISyp1HZqCOG1EEJR+fdTKm0bFkJYEUJYHkLoXR01SJIkSZKk2q26RkKMB3L2arsbeD3G2AJ4vfw9IYQ2wFVA2/J9fhtCSKmmOiRJkiRJUi1VLSFEjPEtYP1ezZcBT5a/fhK4vFL7MzHGz2KM/wBWAF2qow5JkiRJklR7Hc45Ib4eY/wQoPz718rbvwG8X6nfmvK2fYQQbgghFIQQCj755JPDWKokSZIkSTrcamJiylBFW6yqY4zx8RhjVowxq3Hjxoe5LEmSJEmSdDgdzhDioxBCE4Dy7x+Xt68BTqvUrxmw9jDWIUmSJEmSaoHDGUK8CAwsfz0QeKFS+1UhhHohhDOAFsCcw1iHJEmSJEmqBepWx0FCCBOBbKBRCGENMAIYDTwbQrgOWA1cCRBjXBpCeBZ4B9gJ3BxjLK2OOiRJkiRJUu1VLSFEjPHq/WzqsZ/+I4GR1XFuSZIkSZJ0ZKiJiSklSZIkSdIxyBBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBCkiRJkiQlwhBC1eJPf/oT3bp1o0+fPrz66qs1XY4kSZIkqRYKMcaaruGgZGVlxYKCgpouQ5IkSZIkVRJCKIwxZh1MX0dCHOO2bNnCRRddRPv27WnXrh2TJk0iLS2NdevWAVBQUEB2dnZF38GDB9O5c2c6dOjACy+8AMD48ePp06cPOTk5tGjRgp/85CcVx584cSLp6em0a9eOoUOHAlBaWsqgQYNo164d6enpPPzww8letCRJkiSpRtSt6QJUs6ZOnUrTpk155ZVXANi4cWNFWLC3kSNHcsEFFzBu3Dg2bNhAly5duPDCCwFYsGAB8+fPp169erRq1Ypbb72VlJQUhg4dSmFhIaeccgq9evXi+eef57TTTuODDz5gyZIlAGzYsCGRa5UkSZIk1SxHQhzj0tPTmT59OkOHDiU/P5+GDRvut++0adMYPXo0mZmZZGdns337dlavXg1Ajx49aNiwIV/5yldo06YNq1atYu7cuWRnZ9O4cWPq1q3LNddcw1tvvUXz5s1ZuXIlt956K1OnTqVBgwZJXa4kSZIkqQYZQhylzjnnnIPq17JlSwoLC0lPT2fYsGH8/Oc/p27duuzatQuAf/zjH8ydOxeAGCNTpkxhwYIFLFiwgNWrV3PWWWcBUK9evYpjpqSksHPnTvY338gpp5zCwoULyc7O5je/+Q3XX3/9oVyqJEmSJOkIYQhxlCktLQXgllv+Rqu2O0hJibRqu4O8vKoDgbVr11K/fn369evHXXfdxbx580hLS6OwsBCAP//5zxV9e/fuzdixYyvChfnz5x+wlq5du/LXv/6VdevWUVpaysSJE+nevTvr1q1j165dfPe73+UXv/gF8+bNA+DRRx/l0UcfPeTPQJIkSZJUOxlC1LCRI0fSqlUrLrzwQq6++moefPBBsrOz2b0SyLp160hLSwPKAoYhQ4bQuXNnMjIy+M///E8AZsyYwfnnn09ubi7p6enk5UX69TuJnqNe4oEPH+Wrna9n4LVZnH56BiNGjACguLiYs846i+uuu46TTz6Zk046iV/84hf87Gc/46qrruLyyy+nQYMGLF68uKLWIUOG8Nprr3H88cdz/PHH88Mf/vCA19akSRNGjRrF+eefT/v27enYsSOXXXYZH3zwAdnZ2WRmZjJo0CBGjRoFwLJly/jqV79a3R+xJEmSJKmWcGLKGlRYWMgzzzzD/Pnz2blzJx07dqRTp0777f+HP/yBhg0bMnfuXD777DPOPfdcevXqBcCcOXNYsmQJZ5xxRtkIiHqltDhvDcveWAZxBTc8O4zXhl1MYWFf3nrrLU4//XSKioqYOHEimZmZfO973+PSSy8lKyuLwYMHM23aNLp3786QIUPYtm0bAOPGjeOcc85h+fLlLFu2jF69erF9+3YGDRrEoEGDKup8+eWXK17n5uaSm5u7x3W0b9++YvRDZcXFxTz00EOH8pFKkiRJkmoxR0LUoPz8fK644grq169PgwYNuPTSSw/Yf9q0aTz11FNkZmbStWtXPv30U4qKigDo0qULZ5xxBgArlqVSp07ZnA7L31zOsjeX8fzwOyh6txvLli2r2OeMM84gMzMTgE6dOlFcXMzGjRvZsGED3bt3B6B///4V5585c2bF+9atW/Nv//ZvvPfee9X2ebz88sscd9xx1XY8SZIkSVLt4kiIGhZC2Ket8sSQ27dvr2iPMTJ27Fh69+69R/8ZM2ZwwgknVLw/s3UJK1fWqdjnwh9fyNe++X1eG3YJy5eW/ZJfXFy8z2SS27ZtI8ZYZU27jyVJkiRJ0pdVYyMhQgg5IYTlIYQVIYS7a6qOwyEvLx7UpJDf/va3ee6559i2bRubNm3ipZdeAthjYsjJkydX9O/duzePPfYYJSUlALz33nts2bJln+OOGJ5K6WcpFOU3o2X3s/jrY/N59tZvMWJ4Kh988AEff/zxfms/+eSTadiwITNnzgRgwoQJe9S7+/17773H6tWradWq1Rf5aCRJkiRJx7AaCSFCCCnAb4D/C7QBrg4htKmJWqpbXl7kzuFbKyaF7DnqJe4cvrXKIKJjx458//vfJzMzk+9+97ucd955ANx111089thjnHPOOaxbt66i//XXX0+bNm3o2LEj7dq148Ybb2Tnzp37HDc3N3DccfDasEv4/dW/If7rJurtupBRozLo27cvmzZtOuA1PPHEE9x8882cffbZHH/88RXtP/rRjygtLSU9PZ3vf//7jB8/vmI0xYwZM7j44ou/1GcmSZIkSTo2hJoYYh9COBu4N8bYu/z9MIAY46j97ZOVlRV3rxhRm7Vqu4Oeo16ixXlrKtqK8pvt8SjE/tx7772ceOKJ3HXXXYe7zGo3Y8YMHnzwwT0mpZQkSZIkHf1CCIUxxqyD6VtTj2N8A3i/0vs15W17CCHcEEIoCCEUfPLJJ4kVdyhWLEulebe1e7Q177aWFctSa6iiz7dlyxYuuugi2rdvT7t27Zg0aRJpaWkVozAKCgrIzs6u6Dt48GA6d+5Mhw4deOGFF/Y53vr167n88svJyMigW7duLFq0KMnLkSRJkiTVUjUVQlQ18+E+QzJijI/HGLNijFmNGzdOoKxDd2brElbObrpH28rZTTmzdcnn7nvvvffWyCiIqVOn0rRpUxYuXMiSJUvIycnZb9+RI0dywQUXMHfuXN58802GDBmyz7wUI0aMoEOHDixatIj777+fAQMGHO5LkCRJkiQdAWoqhFgDnFbpfTNg7X76HlFGDE9l8u05FOU3o7SkDkX5zZh8ew4jhtfekRDp6elMnz6doUOHkp+fT8OGDffbd9q0aYwePZrMzEyys7PZvn07q1ev3qNP5aU8L7jgAj799FM2btx4WK9BkiRJklT71dQSnXOBFiGEM4APgKuA3BqqpVrl5gagPvcNu4QVy1I5s3UJD41MLW+vnVq2bElhYSGvvvoqw4YNo1evXgdcJnTKlCn7rIrx0Ucf7dFnb/tb9lOSJEmSdOyokZEQMcadwC3AX4B3gWdjjEtropbDITc3sHzpcZSWln2vqQDiYJcKXbt2LfXr16dfv37cddddzJs3b49lQqdMmVLRt3fv3owdO7YiaJg/f/4+x6u8lOeMGTNo1KgRDRo0YM6cOT6aIUmSJEnHsJoaCUGM8VXg1Zo6/9Fu91KhfR+Zyo3d1rJydlPuvD0HqL9PKLJ48WKGDBlCnTp1SE1N5bHHHmPbtm1cd9113H///XTt2rWi7z333MMdd9xBRkYGMUbS0tL2WRHj3nvv5dprryUjI4P69evz5JNPArB69eo9lvyUJEmSJB1bamSJzi/jSFmis7Y4lKVCD5chQ4bQv39/MjIyauT8kiRJkqTq90WW6KyxkRA6vFYsS+XGWrZU6AMPPFBj55YkSZIk1byaWh1Dh9mhLBUqSZIkSdLhYAhxlDoSlwqVJEmSJB3dfBzjKHUkLhUqSZIkSTq6GUIcxXJzA7m5uyehrJnJKCVJkiRJ2s3HMSRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMISRJkiRJUiIMIaRK7r33Xh588MGaLkOSJEmSjkqGENIh2LlzZ02XIEmSJElHDEMIHROeeuopMjIyaN++Pf3792fVqlX06NGDjIwMevTowerVq/fZZ8GCBXTr1o2MjAyuuOIK/vnPfwKQnZ3NT3/6U7p3784jjzxCYWEh3bt3p1OnTvTu3ZsPP/wQgDFjxtCmTRsyMjK46qqrEr1eSZIkSaqN6tZ0AdLhtnTpUkaOHMnf/vY3GjVqxPr16xk4cCADBgxg4MCBjBs3jttuu43nn39+j/0GDBjA2LFj6d69O//+7//Offfdx69//WsANmzYwF//+ldKSkro3r07L7zwAo0bN2bSpEkMHz6ccePGMXr0aP7xj39Qr149NmzYkPh1S5IkSVJtYwiho94bb7xB3759adSoEQCnnnoqs2bN4k9/+hMA/fv35yc/+cke+2zcuJENGzbQvXt3AAYOHMiVV15Zsf373/8+AMuXL2fJkiX07NkTgNLSUpo0aQJARkYG11xzDZdffjmXX375Yb1GSZIkSToSGELoqBdjJIRwwD6ft31vJ5xwQsWx27Zty6xZs/bp88orr/DWW2/x4osv8otf/IKlS5dSt67/y0mSJEk6djknhI5YeXmRVm13kJJS9j0vL1bZr0ePHjz77LN8+umnAKxfv55zzjmHZ555BoAJEybwrW99a499GjZsyCmnnEJ+fj4Af/zjHytGRVTWqlUrPvnkk4oQoqSkhKVLl7Jr1y7ef/99zj//fH71q1+xYcMGNm/ezJw5cxgwYEC1fQaSJEmSdCTxn2V1RMrLi9w5fCt9H5nKjd3WsnJ2U+68PQeoT27unqMa2rZty/Dhw+nevTspKSl06NCBMWPGMHjwYB544AEaN27ME088sc85nnzySW666Sa2bt1K8+bNq+xz3HHHMXnyZG677TY2btzIzp07ueOOO2jZsiX9+vVj48aNxBjp2bMnOTk5xBg59dRTD9fHIkmSJEm1Woix6n89rm2ysrJiQUFBTZehWqJV2x30HPUSLc5bU9FWlN+M14ZdwvKlx9VgZQc2ZMgQ+vfvT0ZGRk2XIkmSJEnVIoRQGGPMOpi+Po6hI9KKZak077Z2j7bm3dayYllqtZ5ny5YtXHTRRbRv35527doxadIk0tLSWLduHQAFBQVkZ2dX9B08eDCdO3emQ4cOvPDCCwCMHz+ePn36kJOTw/PPP8/TTz9dcfyJEyeSnp5Ou3btGDp0aLXWLkmSJEm1jSGEjkhnti5h5eyme7StnN2UM1uXVOt5pk6dStOmTVm4cCFLliwhJydnv31HjhzJBRdcwNy5c3nzzTcZMmQIW7ZsAWDBggVMmjSJxYsXM2nSJN5//33Wrl3L0KFDeeONN1iwYAFz587dZ5lQSZIkSTqaGELoiDRieCqTb8+hKL8ZpSV1KMpvxuTbcxgxvHpHQqSnpzN9+nSGDh1Kfn4+DRs23G/fadOmMXr0aDIzM8nOzmb79u2sXr0aKJscs2HDhnzlK1+hTZs2rFq1irlz55KdnU3jxo2pW7cu11xzDW+99Va11i9JkiRJtYkTU+qIVDb5ZH3uG3YJK5alcmbrEh4ambrPpJSHqmXLlhQWFvLqq68ybNgwevXqRd26ddm1axcA27dvr+gbY2TKlCm0atVqj2O8/fbb1KtXr+J9SkoKO3fu5EiZj0WSJEmSqosjIXTEys0NLF96HKWlZd+/SABxsMt7rl27lvr169OvXz/uuusu5s2bR1paGoWFhQBMmTKlom/v3r0ZO3ZsRbgwf/78A9bQtWtX/vrXv7Ju3TpKS0uZOHFixTKgAwYMYM6cOQd9PZIkSZJ0JHAkhI45X2R5z8WLFzNkyBDq1KlDamoqjz32GNu2beO6667j/vvvp2vXrhV977nnHu644w4yMjKIMZKWlsbLL7+83zqaNGnCqFGjOP/884kx8p3vfIfLLrsMgEWLFtGkSZPDcv2SJEmSVFNcolPHnNq+vOe//vUvrrvuOv77v/+7pkuRJEmSpM/lEp3SASS1vOeX1aBBAwMISZIkSUclQwgdc5Ja3lOSJEmStCdDCB1zklreU5IkSZK0Jyem1DEnqeU9JUmSJEl7MoTQMSk3N5Cbu3sSypqfjFKSJEmSjgU+jiFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJhCCFJkiRJkhJxSCFECOHKEMLSEMKuEELWXtuGhRBWhBCWhxB6V2rvFEJYXL5tTAghHEoNkiRJkiTpyHCoIyGWAH2Atyo3hhDaAFcBbYEc4LchhJTyzY8BNwAtyr9yDrEGSZIkSZJ0BDikECLG+G6McXkVmy4DnokxfhZj/AewAugSQmgCNIgxzooxRuAp4PJDqUGSJEmSJB0ZDtecEN8A3q/0fk152zfKX+/dXqUQwg0hhIIQQsEnn3xyWAqVJEmSJEnJqPt5HUII04H/U8Wm4THGF/a3WxVt8QDtVYoxPg48DpCVlbXffpIkSZIkqfb73BAixnjhlzjuGuC0Su+bAWvL25tV0S5JkiRJko5yh+txjBeBq0II9UIIZ1A2AeWcGOOHwKYQQrfyVTEGAPsbTSFJkiRJko4ih7pE5xUhhDXA2cArIYS/AMQYlwLPAu8AU4GbY4yl5bv9EPg9ZZNV/g/w50OpQZIkSZIkHRlC2SIVtV9WVlYsKCio6TIkSZIkSVIlIYTCGGPWwfQ9XI9jSJIkSZIk7cEQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJeKQQogQwgMhhGUhhEUhhOdCCCdX2jYshLAihLA8hNC7UnunEMLi8m1jQgjhUGqQJEmSJElHhkMdCfEa0C7GmAG8BwwDCCG0Aa4C2gI5wG9DCCnl+zwG3AC0KP/KOcQaJEmSJEnSEeCQQogY47QY487yt7OBZuWvLwOeiTF+FmP8B7AC6BJCaAI0iDHOijFG4Cng8kOpQZIkSZIkHRmqc06IwcCfy19/A3i/0rY15W3fKH+9d3uVQgg3hBAKQggFn3zySTWWKkmSJEmSklb38zqEEKYD/6eKTcNjjC+U9xkO7AQm7N6tiv7xAO1VijE+DjwOkJWVtd9+kiRJkiSp9vvcECLGeOGBtocQBgIXAz3KH7GAshEOp1Xq1gxYW97erIp2SZIkSZJ0lDvU1TFygKHApTHGrZU2vQhcFUKoF0I4g7IJKOfEGD8ENoUQupWvijEAeOFQapAkSZIkSUeGzx0J8TkeBeoBr5WvtDk7xnhTjHFpCOFZ4B3KHtO4OcZYWr7PD4HxwPGUzSHx532OKkmSJEmSjjqHFELEGM88wLaRwMgq2guAdodyXkmSJEmSdOSpztUxJEmSJEmS9ssQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkSZIkJcIQQpIkVavi4mLy8vKq7Xi/+93veOqpp6rlWIMGDWLy5MnVcixJkvTF1a3pAiRJ0tFldwiRm5tbLce76aabquU4kiSp5jkSQpIk7eGpp54iIyOD9u3b079/f1atWkWPHj3IyMigR48erF69GigbVXDbbbdxzjnn0Lx584oRBnfffTf5+flkZmby8MMPU1xczHnnnUfHjh3p2LEjf//73wGYMWMG3bt353vf+x4tW7bk7rvvZsKECXTp0oX09HT+53/+B4B7772XBx98EIDs7GyGDh1Kly5daNmyJfn5+QD7PUeMkVtuuYU2bdpw0UUX8fHHH1dc589//nM6d+5Mu3btuOGGG4gxAjBmzBjatGlDRkYGV111VQKfuCRJxw5HQkiSpApLly5l5MiR/O1vf6NRo0asX7+egQMHMmDAAAYOHMi4ceO47bbbeP755wH48MMPmTlzJsuWLePSSy+lb9++jB49mgcffJCXX34ZgK1bt/Laa6/xla98haKiIq6++moKCgoAWLhwIe+++y6nnnoqzZs35/rrr2fOnDk88sgjjB07ll//+tf71Lhz507mzJnDq6++yn333cf06dP52te+VuU5nnvuOZYvX87ixYv56KOPaNOmDYMHDwbglltu4d///d8B6N+/Py+//DKXXHIJo0eP5h//+Af16tVjw4YNh/0zlyTpWOJICEmSVOGNN96gb9++NGrUCIBTTz2VWbNmVTxa0b9/f2bOnFnR//LLL6dOnTq0adOGjz76qMpjlpSU8IMf/ID09HSuvPJK3nnnnYptnTt3pkmTJtSrV49vfvOb9OrVC4D09HSKi4urPF6fPn0A6NSpU0Wf/Z3jrbfe4uqrryYlJYWmTZtywQUXVBznzTffpGvXrqSnp/PGG2+wdOlSADIyMrjmmmt4+umnqVvXf6+RJKk6GUJIknQMyMuLtGq7g5SUsu95ebHKfjFGQggHPFbl7fXq1dtj36o8/PDDfP3rX2fhwoUUFBSwY8eOKvevU6dOxfs6deqwc+fOKo+3u09KSkpFnwOdo6rr2b59Oz/60Y+YPHkyixcv5gc/+AHbt28H4JVXXuHmm2+msLCQTp067bcOSZL0xRlCSJJ0lMvLi9w5fCs9R73EAx8+Ss9RL3Hn8K1VBhE9evTg2Wef5dNPPwVg/fr1nHPOOTzzzDMATJgwgW9961sHPN9JJ53Epk2bKt5v3LiRJk2aUKdOHf74xz9SWlpajVd34HN8+9vf5plnnqG0tJQPP/yQN998E6AicGjUqBGbN2+umM9i165dvP/++5x//vn86le/YsOGDWzevLna65Uk6VjlGENJko5y940soe8jU2lx3hoAWpy3hr6PTOW+YZeQm3vcHn3btm3L8OHD6d69OykpKXTo0IExY8YwePBgHnjgARo3bswTTzxxwPNlZGRQt25d2rdvz6BBg/jRj37Ed7/7Xf77v/+b888/nxNOOKHar3F/57jiiit44403SE9Pp2XLlnTv3h2Ak08+ueLxjbS0NDp37gxAaWkp/fr1Y+PGjcQY+fGPf8zJJ59c7fVKknSsCvsbOlnbZGVlxd2TWEmSpIOXkhJ54MNHSUndVdFWWlKHIU1uobT0wI9e1ISCggKeeuopxowZ86X2v//++/npT39azVVJkqT9CSEUxhizDqavj2NIknSUO7N1CStnN92jbeXsppzZuqSGKjqwrKysLx1AQFkIIUmSaidDCEmSjnIjhqcy+fYcivKbUVpSh6L8Zkz60Vls+mcGAwcOJCMjg759+7J161YKCwvp3r07nTp1onfv3nz44YcAZGdnM3ToULp06ULLli3Jz88Hypb07NKlC5mZmWRkZFBUVASUrZrRqVMn2rZty+OPP15Ry4knnsjQoUPp1KkTF154IXPmzCE7O5vmzZvz4osvAjBjxgwuvvhiALZs2cLgwYPp3LkzHTp04IUXXgBg/Pjx9OnTh5ycHFq0aMFPfvITAO6++262bdtGZmYm11xzDQAPPfQQ7dq1o127dlUu+SlJkhIUYzwivjp16hQlSdKXM2HCrtiyzWexTp2y77/+9coIxJkzZ8YYY7z22mvjr371q3j22WfHjz/+OMYY4zPPPBOvvfbaGGOM3bt3j3feeWeMMcZXXnkl9ujRI8YY4y233BKffvrpGGOMn332Wdy6dWuMMcZPP/00xhjj1q1bY9u2beO6detijDEC8dVXX40xxnj55ZfHnj17xh07dsQFCxbE9u3bxxhjfPPNN+NFF10UY4xx2LBh8Y9//GOMMcZ//vOfsUWLFnHz5s3xiSeeiGeccUbcsGFD3LZtWzz99NPj6tWrY4wxnnDCCRXXXVBQENu1axc3b94cN23aFNu0aRPnzZtX3R+vJEnHNKAgHuTv9k5MKUnSMSA3N1SahPI4iosD//Efp3HuuecC0K9fP+6//36WLFlCz549gbJJGps0aVJxjD59+gDQqVMniouLATj77LMZOXIka9asoU+fPrRo0QKAMWPG8NxzzwHw/vvvU1RUxFe/+lWOO+44cnJyAEhPT6devXqkpqaSnp5ecczKpk2bxosvvsiDDz4IlK1qsXr1aqBsJY+GDRsC0KZNG1atWsVpp522x/4zZ87kiiuuqJiosk+fPuTn59OhQ4cv/VlKkqQvzxBCkqSjSF5e5L6RJaxYlsqZrUsYMTyV3NyqJ58MYc/2k046ibZt2zJr1qwq+9erVw+AlJQUdu7cCUBubi5du3bllVdeoXfv3vz+97+nTp06TJ8+nVmzZlG/fn2ys7MrlsRMTU2tOG+dOnUqjlmnTp2KY1YWY2TKlCm0atVqj/a33367Yt+9a9p7f0mSVHs4J4QkSUeJvLzIncO30nPUSzzw4aP0HPUSdw7fSl5e1b+Ir169uiJwmDhxIt26deOTTz6paCspKWHp0qUHPOfKlStp3rw5t912G5deeimLFi1i48aNnHLKKdSvX59ly5Yxe/bsL31NvXv3ZuzYsRVhwvz58z93n9TUVEpKyibd/Pa3v83zzz/P1q1b2bJlC8899xznnXfel65HkiQdGkMISZKOEveNLKHvI1Npcd4aUlJ30eK8NfR9ZCr3jax6FYyzzjqLJ598koyMDNavX8+tt97K5MmTGTp0KO3btyczM5O///3vBzznpEmTaNeuHZmZmSxbtowBAwaQk5PDzp07ycjI4J577qFbt25f+pruueceSkpKyMjIoF27dtxzzz2fu88NN9xARkYG11xzDR07dmTQoEF06dKFrl27cv311/sohiRJNSgcKcMUs7KyYkFBQU2XIUlSrZWSEnngw0dJSd1V0VZaUochTW6htHTPRy+Ki4u5+OKLWbJkSdJlSpKko0wIoTDGmHUwfR0JIUnSUeLM1iWsnN10j7aVs5tyZuuqR0JIkiQlzRBCkqSjxIjhqUy+PYei/GaUltShKL8Zk2/PYcTw1H36pqWlOQpCkiQlztUxJEk6SpStglGf+4ZdUrE6xkMj9786hmq30tJSrrjiCjZt2kTz5s35/e9/v8+KJpIkHWmcE0KSJEmSJH1pzgkhSZJUDYqLi2ndujUDBw4kIyODvn37snXrVgoLC+nevTudOnWid+/efPjhhwAUFhbSvn17zj77bIYMGUK7du0AGD9+PLfcckvFcS+++GJmzJgBwLRp0zj77LPp2LEjV155JZs3bwbKHpkZMWIEHTt2JD09nWXLlgGwfv16Lr/8cjIyMujWrRuLFi1K8BORJOnQGEJIkiQdwPLly7nhhhtYtGgRDRo04De/+U3FcqaFhYUMHjyY4cOHA3DttdcyZswYZs2adVDHXrduHb/85S+ZPn068+bNIysri4ceeqhie6NGjZg3bx4//OEPefDBBwEYMWIEHTp0YNGiRdx///0MGDCg+i9akqTDxDkhJEmSDuC0007j3HPPBaBfv37cf//9LFmyhJ49ewJlczc0adKEjRs3smHDBrp37w5A//79+fOf/3zAY8+ePZt33nmn4vg7duzg7LPPrtjep08fADp16sSf/vQnAGbOnMmUKVMAuOCCC/j000/ZuHEjDRs2rMarliTp8DCEkCRJOoC9J4M86aSTaNu2bcVoh4KCAp566ilijPudOLJu3brs2rWr4v327dsBiDHStGlTFixYUOV+9erVAyAlJYWdO3dW7PN5NUqSVFv5OIYkSTrm5OVFWrXdQUpK2fe8vP1P1L169eqKwGHixIl069aNTz75pKKtffv23HjjjZx88sk0bNiQmTNnAjBhwoSKY6SlpbFgwQJ27drF+++/z5w5cwDo1q0bS5YsYcWKFQBs3bqV995774C1f/vb36449owZM2jUqBENGjT4kp+EJEnJciSEJEmq9YqLi8nJyaFr167Mnz+fli1b8tRTT/Huu+9y5513snnzZho1asT48eNp0qQJ2dnZdO3alTfffJMNGzbwhz/8gfPOO4+lS5dy6aXXsvr97TRsupGf/G0Amz7OZPBV4xk+fC3162/n9ttv54YbbgCgbdu2fPWrX+XSSy9l27ZtdO7cmeXLl7N9+3auv/566taty8aNG2nQoAGLFi3it7/9LZdeeik7duzgpJNOIjU1FYCioiLef/99GjRoQAiBk08+GYD/+I//IIRARkYGxx13HKeffjqdO3fm7bffZu3atfzud7/jZz/72R6fxb333su1115LRkYG9evX58knn0z0v4UkSYfCkRCSJOmI8EUmiATYuXMnc+bM4de//jX33XcfAL/73e/Y+tnN3Dh5BMPevo1TT29Ai/PW0O+/vstx9WdRUFDAmDFj+PTTT4GykQknnHACn3zyCT179iQ1NZXXX3+dV155hdTUVBYuXMj48eM5/fTTAXj11VcZM2YMmzdv5i9/+QsfffQRW7ZsIYRAamoqH3zwAZ988gl16tThm9/8JqNHj+b4449n69atbNiwgSeeeII5c+bw9ttvs379eiZOnMj8+fPJysqqWE3j1FNP5YUXXmDRokXMnj2bjIyMZP9DSJJ0CBwJIUmSjggHO0HkbpUndSwuLgbg7LPP5tFHR1I895uc3DSDxt9sDMD7Cybx3jsP060bvP/++xQVFfHVr36V4447jhNPPBGA9PR06tWrR2pqKunp6RXHrGzatGm8+OKLPPjgg+zYsYMYI6tXrwagR48eFZNHtmnThlWrVnHaaaftsf/MmTO54oorOOGEEyquIT8/nw4dOlTHRyhJUo0zhJAkSUeEz5sgcm9VTeqYm5vL8BEd2Pi/D/C7vr/j+498n1AnsPiVYs5sPYuFC08mOzu7YuLI1NRUli5dCkCdOnUqjlmnTp2KY1YWY2TKlCm0atVqj/a33367Yt+9a9p7f0mSjmY+jiFJkmrEF5kcEj5/gsiSkpKKwGB/Vq5cyS/vbUXR9LF8I6MbHyz6kJWzT+DT4jO4756GLFu2jNmzZ3/pa+rduzdjx46tCBPmz5//ufukpqZSUlIClE06+fzzz7N161a2bNnCc889x3nnnQeUjaT44IMPvnRtkiTVBo6EkCRJicvLi9w5fCt9H5nKjd3WsnJ2U+68PQeoT25u1ctNnnXWWTz55JPceOONtGjRgltvvZXevXtz2223sXHjRnbu3Mkdd9xB27Zt93veSZMm8fTTT1NvVyor3vgai19+km+2Oo7WLb7H6NHtadWqFd26dfvS13XPPfdwxx13kJGRQYyRtLQ0Xn755QPuc8MNN5CRkUHHjh2ZMGECgwYNokuXLgBcf/31dOjQgV27drFixQpOPfXUL12bJEm1QThShv1lZWXFgoKCmi5DkiRVg1Ztd9Bz1Eu0OG9NRVtRfjNeG3YJy5cet0//4uJiLr74YpYsWZJkmbXGkiVLGDduHA899FBNlyJJ0j5CCIUxxqyD6evjGJIkKXErlqXSvNvaPdqad1vLimWpNVRR7dauXTsDCEnSUcEQQpIkJe7M1iWsnN10j7aVs5tyZuuSKvunpaUds6MgJEk6mhhCSJKkxI0Ynsrk23Moym9GaUkdivKbMfn2HEYMdySEJElHMyemlCRJiSubfLI+9w27hBXLUjmzdQkPjUzd76SUkiTp6GAIIUmSakRubiA3d/cklPtORilJko4+Po4hSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISEWKMNV3DQQkhfAKsquk6dNRrBKyr6SKkA/AeVW3nParazntUtZ33qGq7qu7Rf4sxNj6YnY+YEEJKQgihIMaYVdN1SPvjParazntUtZ33qGo771HVdod6j/o4hiRJkiRJSoQhhCRJkiRJSoQhhLSnx2u6AOlzeI+qtvMeVW3nParazntUtd0h3aPOCSFJkiRJkhLhSAhJkiRJkpQIQwhJkiRJkpQIQwgdk0IIV4YQloYQdoUQsvbaNiyEsCKEsDyE0LtSe6cQwuLybWNCCCH5ynUsCiHcG0L4IISwoPzrO5W2VXm/SkkLIeSU34crQgh313Q9EkAIobj87+4FIYSC8rZTQwivhRCKyr+fUtN16tgRQhgXQvg4hLCkUtt+70n/nldN2M99Wm0/jxpC6Fi1BOgDvFW5MYTQBrgKaAvkAL8NIaSUb34MuAFoUf6Vk1i1EjwcY8ws/3oVPvd+lRJTft/9Bvi/QBvg6vL7U6oNzi//s3P3PzrcDbweY2wBvF7+XkrKePb9GbLKe9K/51WDxlP17zrV8vOoIYSOSTHGd2OMy6vYdBnwTIzxsxjjP4AVQJcQQhOgQYxxViybzfUp4PLkKpaqVOX9WsM16djUBVgRY1wZY9wBPEPZ/SnVRpcBT5a/fhL/PleCYoxvAev3at7fPenf86oR+7lP9+cL36eGENKevgG8X+n9mvK2b5S/3rtdSsotIYRF5cPjdg/T3N/9KiXNe1G1VQSmhRAKQwg3lLd9Pcb4IUD596/VWHVSmf3dk/7ZqtqmWn4eNYTQUSuEMD2EsKSKrwP961xV8zzEA7RL1eJz7tfHgG8CmcCHwH/s3q2KQ3lfqiZ4L6q2OjfG2JGyR4VuDiF8u6YLkr4A/2xVbVJtP4/WrdaypFokxnjhl9htDXBapffNgLXl7c2qaJeqxcHeryGE/wJeLn+7v/tVSpr3omqlGOPa8u8fhxCeo2yI8EchhCYxxg/LH7f8uEaLlPZ/T/pnq2qNGONHu18f6s+jjoSQ9vQicFUIoV4I4QzKJqCcUz40blMIoVv5qhgDgBdqslAdO8p/INntCsomVoX93K9J1ycBc4EWIYQzQgjHUTZB1Ys1XJOOcSGEE0IIJ+1+DfSi7M/PF4GB5d0G4t/nqnn7uyf9e161RnX+POpICB2TQghXAGOBxsArIYQFMcbeMcalIYRngXeAncDNMcbS8t1+SNlMsccDfy7/kpLwqxBCJmVD24qBGwE+536VEhNj3BlCuAX4C5ACjIsxLq3hsqSvA8+Vr6hdF8iLMU4NIcwFng0hXAesBq6swRp1jAkhTASygUYhhDXACGA0VdyT/j2vmrKf+zS7un4eDWUT/UuSJEmSJB1ePo4hSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZISYQghSZIkSZIS8f8BQqAtKp3dl1IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib tk\n",
    "%matplotlib inline\n",
    "# visualize embeddings\n",
    "from sklearn.manifold import TSNE\n",
    "\n",
    "p = model.get_nearest_neighbors('sueño',k=15)\n",
    "similar_words = [x[1] for x in p]\n",
    "wvs = np.array([model.get_word_vector(sw) for sw in similar_words])\n",
    "\n",
    "tsne = TSNE(n_components=2, random_state=200, n_iter=5000, perplexity=2,learning_rate='auto',init='pca')\n",
    "P = tsne.fit_transform(wvs)\n",
    "labels = similar_words\n",
    "\n",
    "plt.figure(figsize=(18, 7))\n",
    "plt.scatter(P[:, 0], P[:, 1], c='lightgreen', edgecolors='b')\n",
    "plt.title('Visualización con TSNE')\n",
    "\n",
    "eps = 1e16*np.finfo(float).eps\n",
    "for label, x, y in zip(labels, P[:, 0], P[:, 1]):\n",
    "    plt.annotate(label, xy=(x+eps, y+eps), xytext=(0, 0), textcoords='offset points')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a86ae769-7e23-44fd-9265-47c946670a16",
   "metadata": {},
   "source": [
    "### Visualización de Embeddings de Textos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "e25cf6dc-38e4-421a-b11e-c9cb4f3ab69f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "textos = ['Esta mañana está muy soleada',\n",
    "         'Petro jamás ganará',\n",
    "         'Fico jamás ganará la presidencia',\n",
    "         'No quiero ir a votar',\n",
    "         'Quiero ir a votar',\n",
    "         'Me gusta el sol',\n",
    "         'Aprendizaje Profundo dominará el mundo']\n",
    "\n",
    "wvs = np.array([model.get_sentence_vector(t) for t in textos])\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "P = pca.fit_transform(wvs)\n",
    "labels = textos\n",
    "\n",
    "plt.figure(figsize=(18, 7))\n",
    "plt.scatter(P[:, 0], P[:, 1], c='red', edgecolors='k')\n",
    "plt.title('Visualización con PCA')\n",
    "\n",
    "eps = 1e11*np.finfo(float).eps\n",
    "for label, x, y in zip(labels, P[:, 0], P[:, 1]):\n",
    "    plt.annotate(label, xy=(x+eps, y+eps), xytext=(0, 0), textcoords='offset points')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6abf8876-0b6a-4b82-aeec-c0dfef13863c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/moury/miniforge3/envs/impertinencias/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py:982: FutureWarning: The PCA initialization in TSNE will change to have the standard deviation of PC1 equal to 1e-4 in 1.2. This will ensure better convergence.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "textos = ['Esta mañana está muy soleada',\n",
    "         'Petro jamás ganará',\n",
    "         'Fico jamás ganará la presidencia',\n",
    "         'No quiero ir a votar',\n",
    "         'Quiero ir a votar',\n",
    "         'Me gusta el sol',\n",
    "         'Aprendizaje Profundo dominará el mundo']\n",
    "\n",
    "wvs = np.array([model.get_sentence_vector(t) for t in textos])\n",
    "\n",
    "tsne = TSNE(n_components=2, random_state=200, n_iter=5000, perplexity=2,learning_rate='auto',init='pca')\n",
    "P = tsne.fit_transform(wvs)\n",
    "labels = textos\n",
    "\n",
    "plt.figure(figsize=(18, 7))\n",
    "plt.scatter(P[:, 0], P[:, 1], c='r', edgecolors='k')\n",
    "plt.title('Visualización con TSNE')\n",
    "\n",
    "eps = 1e16*np.finfo(float).eps\n",
    "for label, x, y in zip(labels, P[:, 0], P[:, 1]):\n",
    "    plt.annotate(label, xy=(x+eps, y+eps), xytext=(0, 0), textcoords='offset points')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f334fe7-c059-4b09-abbe-d5421a656d02",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4395285-fa7d-4b82-867a-502d79aeee27",
   "metadata": {},
   "source": [
    "### Usando Modelos Pre-entrados en Español"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e348d864-1f03-42c7-a9d4-85951d7a0efd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_es = ft.load_model('cc.es.300.bin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d1da4237-3d07-4163-8401-e61f4b9d4e7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.7171677947044373, 'sueños'),\n",
       " (0.7092401385307312, 'sueno'),\n",
       " (0.6959096789360046, 'sueño.El'),\n",
       " (0.6859577298164368, 'sueño.Y'),\n",
       " (0.6825155019760132, 'Sueño'),\n",
       " (0.6815561652183533, 'sueño.La'),\n",
       " (0.6758346557617188, 'sueño.Pero'),\n",
       " (0.6724787950515747, 'sueño.A'),\n",
       " (0.6669973134994507, 'sueño.Es'),\n",
       " (0.6650846600532532, 'sueñoUn'),\n",
       " (0.6573185920715332, 'elsueño'),\n",
       " (0.6529608964920044, 'sueño.Un'),\n",
       " (0.6516568064689636, 'sueñ'),\n",
       " (0.6469879746437073, 'sueño.En'),\n",
       " (0.6467815637588501, 'sueñoEn')]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# TAKES TIME! DONT RUN IT IN CLASS\n",
    "model_es.get_nearest_neighbors('sueño',k=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "c3592835-98c1-40b1-904e-6eb178d87743",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "textos = ['Esta mañana está muy soleada',\n",
    "         'Petro jamás ganará',\n",
    "         'Fico jamás ganará la presidencia',\n",
    "         'No quiero ir a votar',\n",
    "         'Quiero ir a votar',\n",
    "         'Me gusta el sol',\n",
    "         'Aprendizaje Profundo dominará el mundo']\n",
    "\n",
    "wvs = np.array([model_es.get_sentence_vector(t) for t in textos])\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "P = pca.fit_transform(wvs)\n",
    "labels = textos\n",
    "\n",
    "plt.figure(figsize=(18, 7))\n",
    "plt.scatter(P[:, 0], P[:, 1], c='red', edgecolors='k')\n",
    "plt.title('Visualización con PCA')\n",
    "\n",
    "eps = 1e13*np.finfo(float).eps\n",
    "for label, x, y in zip(labels, P[:, 0], P[:, 1]):\n",
    "    plt.annotate(label, xy=(x+eps, y+eps), xytext=(0, 0), textcoords='offset points')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b98e4c-812c-4d21-b162-339fbd3db9c3",
   "metadata": {},
   "source": [
    "### Re-entrenando Embedding "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0fc70cb-08f4-4177-96d3-655681f86ffc",
   "metadata": {},
   "source": [
    "### <span style=\"color:red\">¡NO CORRER DE MANERA LOCAL A MENOS QUE TENGA SUFICIENTE MEMORIA RAM!</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30f77aeb-0c4e-48c6-bf43-f978e372a847",
   "metadata": {},
   "outputs": [],
   "source": [
    "#model_transfer_learning_es = ft.train_unsupervised('Poemas_Todo.txt', minCount=2, minn=2, maxn=5, dim=300,pretrainedVectors='cc.es.300.vec')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "927c6505-0303-4251-9cf7-cb2b6a4c304f",
   "metadata": {},
   "source": [
    "### Bonus: Transformers "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "87a2d5d2-981d-4489-8800-a787e45de5eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sentence_transformers import SentenceTransformer\n",
    "\n",
    "model_tr_es = SentenceTransformer('hiiamsid/sentence_similarity_spanish_es')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "7dc1092c-97c8-4176-90b7-ee87b40ca136",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "textos = ['Esta mañana está muy soleada',\n",
    "         'Petro jamás ganará',\n",
    "         'Fico jamás ganará la presidencia',\n",
    "         'No quiero ir a votar',\n",
    "         'Quiero ir a votar',\n",
    "         'Me gusta el sol',\n",
    "         'Aprendizaje Profundo dominará el mundo']\n",
    "\n",
    "sent_vec = model_tr_es.encode(textos)\n",
    "\n",
    "pca = PCA(n_components=2)\n",
    "P = pca.fit_transform(sent_vec)\n",
    "labels = textos\n",
    "\n",
    "plt.figure(figsize=(18, 7))\n",
    "plt.scatter(P[:, 0], P[:, 1], c='red', edgecolors='k')\n",
    "plt.title('Visualización con PCA')\n",
    "eps = 1e11*np.finfo(float).eps\n",
    "for label, x, y in zip(labels, P[:, 0], P[:, 1]):\n",
    "    plt.annotate(label, xy=(x+eps, y+eps), xytext=(0, 0), textcoords='offset points')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8bbb5a66-732a-4a31-aed0-4d6ac6f05395",
   "metadata": {},
   "source": [
    "## Uso del Modelo (Wikipedia) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6299f83-7766-4506-a020-523d36690e5d",
   "metadata": {},
   "source": [
    "Primero que todo, obtengamos los datos que necesitamos:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e2b1fb31-ef67-4b10-83b6-e117e5c24bee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2021-11-19 16:13:05--  http://mattmahoney.net/dc/enwik9.zip\n",
      "Resolving mattmahoney.net (mattmahoney.net)... 67.195.197.24\n",
      "Connecting to mattmahoney.net (mattmahoney.net)|67.195.197.24|:80... connected.\n",
      "HTTP request sent, awaiting response... 206 Partial Content\n",
      "Length: 322592222 (308M), 294168810 (281M) remaining [application/zip]\n",
      "Saving to: ‘../Datos/enwik9.zip’\n",
      "\n",
      "enwik9.zip          100%[+==================>] 307,65M   566KB/s    in 8m 28s  \n",
      "\n",
      "2021-11-19 16:21:34 (565 KB/s) - ‘../Datos/enwik9.zip’ saved [322592222/322592222]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Corpus Completo\n",
    "#!wget https://dumps.wikimedia.org/enwiki/latest/enwiki-latest-pages-articles.xml.bz2 -o ../Datos/enwiki-latest-pages-articles.xml.bz2\n",
    "# Corpus 1 billón de bytes (1 Giga)\n",
    "!wget -c http://mattmahoney.net/dc/enwik9.zip -P ../Datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da452367-1674-41ee-a651-40da805a7f20",
   "metadata": {},
   "source": [
    "Como el formato viene en `.zip`, descomprimamos el contenido"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "be23243f-98e1-47de-9823-83588e400273",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Archive:  ../Datos/enwik9.zip\n",
      "  inflating: ../Datos/enwik9         \n"
     ]
    }
   ],
   "source": [
    "!unzip ../Datos/enwik9.zip -d ../Datos"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3bf87ff6-60fa-4f3e-840c-6cd600aa9ef7",
   "metadata": {},
   "source": [
    "Miremos algo de información:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4c0162c6-11ea-4583-9bbc-391d4f31cde3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<mediawiki xmlns=\"http://www.mediawiki.org/xml/export-0.3/\" xmlns:xsi=\"http://www.w3.org/2001/XMLSchema-instance\" xsi:schemaLocation=\"http://www.mediawiki.org/xml/export-0.3/ http://www.mediawiki.org/xml/export-0.3.xsd\" version=\"0.3\" xml:lang=\"en\">\n",
      "  <siteinfo>\n",
      "    <sitename>Wikipedia</sitename>\n",
      "    <base>http://en.wikipedia.org/wiki/Main_Page</base>\n",
      "    <generator>MediaWiki 1.6alpha</generator>\n",
      "    <case>first-letter</case>\n",
      "      <namespaces>\n",
      "      <namespace key=\"-2\">Media</namespace>\n",
      "      <namespace key=\"-1\">Special</namespace>\n",
      "      <namespace key=\"0\" />\n",
      "      <namespace key=\"1\">Talk</namespace>\n",
      "      <namespace key=\"2\">User</namespace>\n",
      "      <namespace key=\"3\">User talk</namespace>\n",
      "      <namespace key=\"4\">Wikipedia</namespace>\n",
      "      <namespace key=\"5\">Wikipedia talk</namespace>\n",
      "      <namespace key=\"6\">Image</namespace>\n",
      "      <namespace key=\"7\">Image talk</namespace>\n",
      "      <namespace key=\"8\">MediaWiki</namespace>\n",
      "      <namespace key=\"9\">MediaWiki talk</namespace>\n",
      "      <namespace key=\"10\">Template</namespace>\n",
      "      <namespace key=\"11\">Template talk</namespace>\n",
      "      <namespace key=\"12\">Help</namespace>\n",
      "      <namespace key=\"13\">Help talk</namespace>\n",
      "      <namespace key=\"14\">Category</namespace>\n",
      "      <namespace key=\"15\">Category talk</namespace>\n",
      "      <namespace key=\"100\">Portal</namespace>\n",
      "      <namespace key=\"101\">Portal talk</namespace>\n",
      "    </namespaces>\n",
      "  </siteinfo>\n",
      "  <page>\n",
      "    <title>AaA</title>\n",
      "    <id>1</id>\n",
      "    <revision>\n",
      "      <id>32899315</id>\n",
      "      <timestamp>2005-12-27T18:46:47Z</timestamp>\n",
      "      <contributor>\n",
      "        <username>Jsmethers</username>\n",
      "        <id>614213</id>\n",
      "      </contributor>\n",
      "      <text xml:space=\"preserve\">#REDIRECT [[AAA]]</text>\n",
      "    </revision>\n",
      "  </page>\n",
      "  <page>\n",
      "    <title>AlgeriA</title>\n",
      "    <id>5</id>\n",
      "    <revision>\n",
      "      <id>18063769</id>\n",
      "      <timestamp>2005-07-03T11:13:13Z</timestamp>\n",
      "      <contributor>\n",
      "        <username>Docu</username>\n",
      "        <id>8029</id>\n",
      "      </contributor>\n",
      "      <minor />\n",
      "      <comment>addi"
     ]
    }
   ],
   "source": [
    "!head -c 2000 ../Datos/enwik9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5094edac-085a-47c2-b367-52b9e9eaf898",
   "metadata": {},
   "source": [
    "Como un archivo raw de Wikipedia contiene toneladas de datos HTML/XML, preprocesaremos la información con el archivo wikifil.pl, escrito por Matt Mahoney y puede ser encontrado el script original en su paǵina personal [aquí](http://mattmahoney.net/).\n",
    "\n",
    "El script asociado funciona usando el lenguaje de programación PERL, y se ejecuta a continuación:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6f65a4c-ea83-46f3-9217-ec0e29654e0e",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Preprocesamiento"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a1fdc73-71f1-4991-a543-83847c789ef3",
   "metadata": {},
   "outputs": [],
   "source": [
    "!perl ../Datos/wikifil.pl ../Datos/enwik9 > ../Datos/fil9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3b65f69-5e33-4439-8f9c-cf03f525218b",
   "metadata": {},
   "source": [
    "Veamos algo del resultado obtenido:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1e7627df-cb50-4e5e-9baf-03866ab32973",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " anarchism originated as a term of abuse first used against early working class radicals including the diggers of the english revolution and the sans culottes of the french revolution whilst the term is still used in a pejorative way to describe any act that used violent means to destroy the organization of society it has also been taken up as a positive label by self defined anarchists the word anarchism is derived from the greek without archons ruler chief king anarchism as a political philosophy is the belief that rulers are unnecessary and should be abolished although there are differing interpretations of what this means anarchism also refers to related social movements that advocate the elimination of authoritarian institutions particularly the state the word anarchy as most anarchists use it does not imply chaos nihilism or anomie but rather a harmonious anti authoritarian society in place of what are regarded as authoritarian political structures and coercive economic institutions anarchists advocate social relations based upon voluntary association of autonomous individuals mutual aid and self governance while anarchism is most easily defined by what it is against anarchists also offer positive visions of what they believe to be a truly free society however ideas about how an anarchist society might work vary considerably especially with respect to economics there is also disagreement about how a free society might be brought about origins and predecessors kropotkin and others argue that before recorded history human society was organized on anarchist principles most anthropologists follow kropotkin and engels in believing that hunter gatherer bands were egalitarian and lacked division of labour accumulated wealth or decreed law and had equal access to resources william godwin anarchists including the the anarchy organisation and rothbard find anarchist attitudes in taoism from ancient china kropotkin found similar ideas in stoic zeno of citium according to"
     ]
    }
   ],
   "source": [
    "!head -c 2000 ../Datos/fil9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf6e72a8-3a62-4231-954a-c4f6803e0057",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4d4f46-a27e-443a-8c81-aed718cab638",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Entrenando del Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a0a7544f-8ea2-4868-b923-be908ee40f18",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Read 124M words3M words\n",
      "Number of words:  218316\n",
      "Number of labels: 0\n",
      "Progress:  31.5% words/sec/thread:   45623 lr:  0.034237 avg.loss:  0.601562 ETA:   0h51m49sm 8s lr:  0.049049 avg.loss:  1.546121 ETA:   1h16m37s 1.495166 ETA:   1h17m48s  4.0% words/sec/thread:   42449 lr:  0.047977 avg.loss:  1.451111 ETA:   1h18m 2s words/sec/thread:   42355 lr:  0.046572 avg.loss:  1.341497 ETA:   1h15m55sh16m 3s% words/sec/thread:   44134 lr:  0.045075 avg.loss:  1.130582 ETA:   1h10m31s 16.0% words/sec/thread:   45933 lr:  0.041981 avg.loss:  0.870252 ETA:   1h 3m 6s 17.8% words/sec/thread:   45410 lr:  0.041112 avg.loss:  0.814919 ETA:   1h 2m31sh 2m25s  44928 lr:  0.038916 avg.loss:  0.719204 ETA:   0h59m48s57m30s 25.7% words/sec/thread:   45038 lr:  0.037161 avg.loss:  0.668852 ETA:   0h56m58s  0h54m58s 0.035755 avg.loss:  0.635238 ETA:   0h54m46s 30.9% words/sec/thread:   45385 lr:  0.034537 avg.loss:  0.608555 ETA:   0h52m32s"
     ]
    }
   ],
   "source": [
    "model = ft.train_unsupervised('../Datos/fil9')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1a95e90-940a-461f-ac5d-83d82e83f275",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "09a576c6-6d0c-45b9-a4e9-bd078fad6c1a",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Palabras más cercanas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cbd8ebef-994f-47ee-a57d-3bb9b9442dbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.get_nearest_neighbors('asparagus')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1479038e-e720-4157-a99b-a549f5c6f75f",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f98a7aa-fe59-4f49-b7a3-e94e9707c695",
   "metadata": {},
   "source": [
    "###  Curiosidades"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec0dbdc1-ad47-4fe1-8a19-ba775c45b550",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.get_nearest_neighbors('pidgey')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6086122b-3842-450f-95b3-5d686e798cc0",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e6099ba-392c-49de-bdd7-76b0864a809c",
   "metadata": {},
   "source": [
    "###  Analogía de Palabras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d12bcb4-fa84-4c07-a309-58eedf7d616c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.get_analogies(\"berlin\", \"germany\", \"france\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6aa150c-1678-44fc-b9e1-283efdc34cee",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5ac4298-2e20-4dbc-a143-bafaf1dd9577",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Modelo Supervisado"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "71474a8b-701d-495e-82ec-5422c48ad7ec",
   "metadata": {},
   "source": [
    "Para este ejemplo, usaremos el conjunto de datos de preguntas de [the cooking section of Stackexchange](https://cooking.stackexchange.com/):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc311d87-61a1-4642-ba31-51066cf146c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wget https://dl.fbaipublicfiles.com/fasttext/data/cooking.stackexchange.tar.gz && tar xvzf cooking.stackexchange.tar.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2fec219-b87e-4442-82e0-cd0f1a198b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "!head cooking.stackexchange.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a934f5a-4f85-4447-b5d7-4c3246fa0d34",
   "metadata": {},
   "outputs": [],
   "source": [
    "!wc cooking.stackexchange.txt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8c5c4763-fd44-4ee9-a0d2-50ee151c89c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "!head -n 12404 cooking.stackexchange.txt > cooking.train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcf4affa-9e81-49a5-a3ae-dfaeaa82ccdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "!tail -n 3000 cooking.stackexchange.txt > cooking.valid"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0dfde01-e229-4a6f-91c5-2b05688f36af",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d65f1891-0752-4934-a12b-539a25fbfe6e",
   "metadata": {},
   "source": [
    "### Uso del Modelo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "504df596-824c-46ff-892f-3ab07f8c035e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import fasttext as ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d440246d-00a7-4a10-96e8-f2b0b0d353de",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input=\"cooking.train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8271089-b258-4efb-9f56-83f50f6c99fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.predict(\"Which baking dish is best to bake a banana bread ?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84dfcc5e-755a-4e27-8bed-f3ae16fc0eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.test(\"cooking.valid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4837114c-ddba-45ca-b00d-ff195adfad62",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "789784af-a957-48fa-aa1d-ad810ab7a18a",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Mejorando el Modelo "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "75066d8b-9000-407b-b67d-4084702f4696",
   "metadata": {},
   "outputs": [],
   "source": [
    "!cat cooking.stackexchange.txt | sed -e \"s/\\([.\\!?,'/()]\\)/ \\1 /g\" | tr \"[:upper:]\" \"[:lower:]\" > cooking.preprocessed.txt\n",
    "!head -n 12404 cooking.preprocessed.txt > cooking.train\n",
    "!tail -n 3000 cooking.preprocessed.txt > cooking.valid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a06841b-ea77-49c0-adf8-1d84d6e1a966",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input=\"cooking.train\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1fc38ec2-478f-4fac-8bb3-032ae0f13310",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.test(\"cooking.valid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2efea151-f77f-468e-817f-bffe57939f31",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a85cf55c-1748-44f3-8a42-c1215b73bda8",
   "metadata": {},
   "source": [
    "###  Autotuning de Parámetros"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34286ee7-65ab-42fc-b984-967d2187de86",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = fasttext.train_supervised(input='cooking.train', autotuneValidationFile='cooking.valid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88d56132-e967-4858-b769-a26d87b1cdef",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.test(\"cooking.valid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d8ddaf7-f2bb-4161-85cc-4d0080248f0c",
   "metadata": {},
   "source": [
    "[[Volver al inicio]](#Contenido)"
   ]
  }
 ],
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