{
 "cells": [
  {
   "attachments": {
    "imagen.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![imagen.png](attachment:imagen.png)\n",
    "<h1>Hugging Face</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Fuente de este demo: https://www.youtube.com/watch?v=4yhYu1emS3M"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Acerca de Hugging Face y Transformers \n",
    "#### Hugging Face:\n",
    "<i>\"Hugging Face, Inc. is an American company that develops tools for building applications using machine learning.[1] It is most notable for its transformers library built for natural language processing applications and its platform that allows users to share machine learning models and datasets.\"</i> Fuente: Wikipedia."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Transformers:\n",
    "Transformers proporciona API y herramientas para descargar y entrenar fácilmente modelos preentrenados de última generación. El uso de modelos preentrenados puede reducir los costos de cómputo, la huella de carbono y ahorrarle el tiempo y los recursos necesarios para entrenar un modelo desde cero. Fuente: <a href=https://huggingface.co/docs/transformers/index>sitio de Hugging Face</a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Clasificación de Modelos\n",
    "Los modelos pre-entrenados pueden descargarse de: https://huggingface.co/models. Allí se dispone de más de 170.000 modelos pre-entrenados. Y para usarlos, debe descargarse los mismos en forma explícita. Sin embargo, es posible realizar tareas sin usar modelos específicos, pero el \"auto-ML\" trae problemas con muchos idiomas.\n",
    "Los Las tareas de modelos pre-entrenados se clasifican en:\n",
    "* ML multimedial\n",
    "* Computer Vision\n",
    "* NLP\n",
    "* Audio\n",
    "* Aprendizaje reforzado\n",
    "## 2. Inicializacion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting numpy\n",
      "  Using cached numpy-2.1.2-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (16.3 MB)\n",
      "Installing collected packages: numpy\n",
      "  Attempting uninstall: numpy\n",
      "    Found existing installation: numpy 2.1.2\n",
      "    Uninstalling numpy-2.1.2:\n",
      "      Successfully uninstalled numpy-2.1.2\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "gensim 4.3.0 requires FuzzyTM>=0.4.0, which is not installed.\n",
      "tables 3.8.0 requires blosc2~=2.0.0, which is not installed.\n",
      "tables 3.8.0 requires cython>=0.29.21, which is not installed.\n",
      "numba 0.57.0 requires numpy<1.25,>=1.21, but you have numpy 2.1.2 which is incompatible.\n",
      "thinc 8.3.2 requires numpy<2.1.0,>=2.0.0; python_version >= \"3.9\", but you have numpy 2.1.2 which is incompatible.\n",
      "spacy-transformers 1.3.5 requires transformers<4.37.0,>=3.4.0, but you have transformers 4.46.1 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed numpy-2.1.2\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "# Instalación de librerias:\n",
    "# Una vez instalada, comentar la celda de abajo\n",
    "#%pip install torch\n",
    "#%pip install transformers\n",
    "# Precaución: instalar primero torch y luego transformers (sino, no se instalara correctamente)\n",
    "# Actualizar librerías:\n",
    "#%pip install --upgrade numpy scipy scikit-learn\n",
    "#%pip uninstall transformers numpy scipy scikit-learn\n",
    "#%pip install transformers numpy scipy scikit-learn\n",
    "%pip install --force-reinstall numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "A module that was compiled using NumPy 1.x cannot be run in\n",
      "NumPy 2.1.2 as it may crash. To support both 1.x and 2.x\n",
      "versions of NumPy, modules must be compiled with NumPy 2.0.\n",
      "Some module may need to rebuild instead e.g. with 'pybind11>=2.12'.\n",
      "\n",
      "If you are a user of the module, the easiest solution will be to\n",
      "downgrade to 'numpy<2' or try to upgrade the affected module.\n",
      "We expect that some modules will need time to support NumPy 2.\n",
      "\n",
      "Traceback (most recent call last):  File \"<frozen runpy>\", line 198, in _run_module_as_main\n",
      "  File \"<frozen runpy>\", line 88, in _run_code\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/ipykernel_launcher.py\", line 17, in <module>\n",
      "    app.launch_new_instance()\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/traitlets/config/application.py\", line 992, in launch_instance\n",
      "    app.start()\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/ipykernel/kernelapp.py\", line 711, in start\n",
      "    self.io_loop.start()\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/tornado/platform/asyncio.py\", line 215, in start\n",
      "    self.asyncio_loop.run_forever()\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/asyncio/base_events.py\", line 607, in run_forever\n",
      "    self._run_once()\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/asyncio/base_events.py\", line 1922, in _run_once\n",
      "    handle._run()\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/asyncio/events.py\", line 80, in _run\n",
      "    self._context.run(self._callback, *self._args)\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 510, in dispatch_queue\n",
      "    await self.process_one()\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 499, in process_one\n",
      "    await dispatch(*args)\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 406, in dispatch_shell\n",
      "    await result\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/ipykernel/kernelbase.py\", line 729, in execute_request\n",
      "    reply_content = await reply_content\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/ipykernel/ipkernel.py\", line 411, in do_execute\n",
      "    res = shell.run_cell(\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/ipykernel/zmqshell.py\", line 531, in run_cell\n",
      "    return super().run_cell(*args, **kwargs)\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3006, in run_cell\n",
      "    result = self._run_cell(\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3061, in _run_cell\n",
      "    result = runner(coro)\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/IPython/core/async_helpers.py\", line 129, in _pseudo_sync_runner\n",
      "    coro.send(None)\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3266, in run_cell_async\n",
      "    has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3445, in run_ast_nodes\n",
      "    if await self.run_code(code, result, async_=asy):\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/IPython/core/interactiveshell.py\", line 3505, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"/tmp/ipykernel_40324/953355907.py\", line 3, in <module>\n",
      "    from transformers import pipeline\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/utils/import_utils.py\", line 1766, in __getattr__\n",
      "    module = self._get_module(self._class_to_module[name])\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/utils/import_utils.py\", line 1778, in _get_module\n",
      "    return importlib.import_module(\".\" + module_name, self.__name__)\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/importlib/__init__.py\", line 126, in import_module\n",
      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/pipelines/__init__.py\", line 28, in <module>\n",
      "    from ..models.auto.feature_extraction_auto import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/models/auto/feature_extraction_auto.py\", line 29, in <module>\n",
      "    from .auto_factory import _LazyAutoMapping\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/models/auto/auto_factory.py\", line 40, in <module>\n",
      "    from ...generation import GenerationMixin\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/utils/import_utils.py\", line 1766, in __getattr__\n",
      "    module = self._get_module(self._class_to_module[name])\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/utils/import_utils.py\", line 1778, in _get_module\n",
      "    return importlib.import_module(\".\" + module_name, self.__name__)\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/importlib/__init__.py\", line 126, in import_module\n",
      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/generation/utils.py\", line 28, in <module>\n",
      "    from ..cache_utils import (\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/cache_utils.py\", line 1853, in <module>\n",
      "    class OffloadedStaticCache(StaticCache):\n",
      "  File \"/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/cache_utils.py\", line 1918, in OffloadedStaticCache\n",
      "    offload_device: Union[str, torch.device] = torch.device(\"cpu\"),\n",
      "/home/jorgek/anaconda3/lib/python3.11/site-packages/transformers/cache_utils.py:1918: UserWarning: Failed to initialize NumPy: _ARRAY_API not found (Triggered internally at /croot/pytorch_1686931851744/work/torch/csrc/utils/tensor_numpy.cpp:84.)\n",
      "  offload_device: Union[str, torch.device] = torch.device(\"cpu\"),\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Failed to import transformers.pipelines because of the following error (look up to see its traceback):\nnumpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/utils/import_utils.py:1778\u001b[0m, in \u001b[0;36m_LazyModule._get_module\u001b[0;34m(self, module_name)\u001b[0m\n\u001b[1;32m   1777\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1778\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m importlib\u001b[38;5;241m.\u001b[39mimport_module(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m module_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m   1779\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/importlib/__init__.py:126\u001b[0m, in \u001b[0;36mimport_module\u001b[0;34m(name, package)\u001b[0m\n\u001b[1;32m    125\u001b[0m         level \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[0;32m--> 126\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _bootstrap\u001b[38;5;241m.\u001b[39m_gcd_import(name[level:], package, level)\n",
      "File \u001b[0;32m<frozen importlib._bootstrap>:1206\u001b[0m, in \u001b[0;36m_gcd_import\u001b[0;34m(name, package, level)\u001b[0m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap>:1178\u001b[0m, in \u001b[0;36m_find_and_load\u001b[0;34m(name, import_)\u001b[0m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap>:1149\u001b[0m, in \u001b[0;36m_find_and_load_unlocked\u001b[0;34m(name, import_)\u001b[0m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap>:690\u001b[0m, in \u001b[0;36m_load_unlocked\u001b[0;34m(spec)\u001b[0m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap_external>:940\u001b[0m, in \u001b[0;36mexec_module\u001b[0;34m(self, module)\u001b[0m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap>:241\u001b[0m, in \u001b[0;36m_call_with_frames_removed\u001b[0;34m(f, *args, **kwds)\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/pipelines/__init__.py:64\u001b[0m\n\u001b[1;32m     63\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdepth_estimation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DepthEstimationPipeline\n\u001b[0;32m---> 64\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdocument_question_answering\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DocumentQuestionAnsweringPipeline\n\u001b[1;32m     65\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfeature_extraction\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m FeatureExtractionPipeline\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/pipelines/document_question_answering.py:29\u001b[0m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ChunkPipeline, build_pipeline_init_args\n\u001b[0;32m---> 29\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mquestion_answering\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m select_starts_ends\n\u001b[1;32m     32\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_vision_available():\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/pipelines/question_answering.py:9\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m SquadExample, SquadFeatures, squad_convert_examples_to_features\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodelcard\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ModelCard\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/data/__init__.py:27\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdata_collator\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     16\u001b[0m     DataCollatorForLanguageModeling,\n\u001b[1;32m     17\u001b[0m     DataCollatorForPermutationLanguageModeling,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     25\u001b[0m     default_data_collator,\n\u001b[1;32m     26\u001b[0m )\n\u001b[0;32m---> 27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m glue_compute_metrics, xnli_compute_metrics\n\u001b[1;32m     28\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mprocessors\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     29\u001b[0m     DataProcessor,\n\u001b[1;32m     30\u001b[0m     InputExample,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     44\u001b[0m     xnli_tasks_num_labels,\n\u001b[1;32m     45\u001b[0m )\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/data/metrics/__init__.py:20\u001b[0m\n\u001b[1;32m     19\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mscipy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstats\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pearsonr, spearmanr\n\u001b[0;32m---> 20\u001b[0m     \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01msklearn\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m f1_score, matthews_corrcoef\n\u001b[1;32m     23\u001b[0m DEPRECATION_WARNING \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m     24\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThis metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     25\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mlibrary. You can have a look at this example script for pointers: \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     26\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mhttps://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m     27\u001b[0m )\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/__init__.py:84\u001b[0m\n\u001b[1;32m     80\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     81\u001b[0m     __check_build,  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[1;32m     82\u001b[0m     _distributor_init,  \u001b[38;5;66;03m# noqa: F401\u001b[39;00m\n\u001b[1;32m     83\u001b[0m )\n\u001b[0;32m---> 84\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbase\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m clone\n\u001b[1;32m     85\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_show_versions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m show_versions\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/base.py:19\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m InconsistentVersionWarning\n\u001b[0;32m---> 19\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_estimator_html_repr\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _HTMLDocumentationLinkMixin, estimator_html_repr\n\u001b[1;32m     20\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_metadata_requests\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _MetadataRequester, _routing_enabled\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/__init__.py:11\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_bunch\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Bunch\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_chunking\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m gen_batches, gen_even_slices\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_estimator_html_repr\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m estimator_html_repr\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_chunking.py:8\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_config\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_config\n\u001b[0;32m----> 8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_param_validation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Interval, validate_params\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mchunk_generator\u001b[39m(gen, chunksize):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_param_validation.py:14\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_config\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m config_context, get_config\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvalidation\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _is_arraylike_not_scalar\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mclass\u001b[39;00m \u001b[38;5;21;01mInvalidParameterError\u001b[39;00m(\u001b[38;5;167;01mValueError\u001b[39;00m, \u001b[38;5;167;01mTypeError\u001b[39;00m):\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/validation.py:26\u001b[0m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mexceptions\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m DataConversionWarning, NotFittedError, PositiveSpectrumWarning\n\u001b[0;32m---> 26\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_array_api\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m _asarray_with_order, _is_numpy_namespace, get_namespace\n\u001b[1;32m     27\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutils\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfixes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ComplexWarning, _preserve_dia_indices_dtype\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/_array_api.py:11\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_config\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m get_config\n\u001b[0;32m---> 11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mfixes\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m parse_version\n\u001b[1;32m     13\u001b[0m _NUMPY_NAMESPACE_NAMES \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124marray_api_compat.numpy\u001b[39m\u001b[38;5;124m\"\u001b[39m}\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/sklearn/utils/fixes.py:24\u001b[0m\n\u001b[1;32m     23\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m---> 24\u001b[0m     \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n\u001b[1;32m     25\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mImportError\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/__init__.py:22\u001b[0m\n\u001b[1;32m     21\u001b[0m \u001b[38;5;66;03m# numpy compat\u001b[39;00m\n\u001b[0;32m---> 22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m is_numpy_dev \u001b[38;5;28;01mas\u001b[39;00m _is_numpy_dev  \u001b[38;5;66;03m# pyright: ignore # noqa:F401\u001b[39;00m\n\u001b[1;32m     24\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/compat/__init__.py:18\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_typing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m F\n\u001b[0;32m---> 18\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     19\u001b[0m     is_numpy_dev,\n\u001b[1;32m     20\u001b[0m     np_version_under1p21,\n\u001b[1;32m     21\u001b[0m )\n\u001b[1;32m     22\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcompat\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mpyarrow\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     23\u001b[0m     pa_version_under1p01,\n\u001b[1;32m     24\u001b[0m     pa_version_under2p0,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     31\u001b[0m     pa_version_under9p0,\n\u001b[1;32m     32\u001b[0m )\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/compat/numpy/__init__.py:4\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mversion\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Version\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# numpy versioning\u001b[39;00m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/util/__init__.py:2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# pyright: reportUnusedImport = false\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_decorators\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (  \u001b[38;5;66;03m# noqa:F401\u001b[39;00m\n\u001b[1;32m      3\u001b[0m     Appender,\n\u001b[1;32m      4\u001b[0m     Substitution,\n\u001b[1;32m      5\u001b[0m     cache_readonly,\n\u001b[1;32m      6\u001b[0m )\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mcore\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mutil\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mhashing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (  \u001b[38;5;66;03m# noqa:F401\u001b[39;00m\n\u001b[1;32m      9\u001b[0m     hash_array,\n\u001b[1;32m     10\u001b[0m     hash_pandas_object,\n\u001b[1;32m     11\u001b[0m )\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/util/_decorators.py:14\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwarnings\u001b[39;00m\n\u001b[0;32m---> 14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_libs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mproperties\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m cache_readonly\n\u001b[1;32m     15\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_typing\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     16\u001b[0m     F,\n\u001b[1;32m     17\u001b[0m     T,\n\u001b[1;32m     18\u001b[0m )\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/_libs/__init__.py:13\u001b[0m\n\u001b[1;32m      1\u001b[0m __all__ \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m      2\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNaT\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m      3\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNaTType\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m      9\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInterval\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     10\u001b[0m ]\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_libs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01minterval\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Interval\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01m_libs\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtslibs\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m (\n\u001b[1;32m     15\u001b[0m     NaT,\n\u001b[1;32m     16\u001b[0m     NaTType,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     21\u001b[0m     iNaT,\n\u001b[1;32m     22\u001b[0m )\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/pandas/_libs/interval.pyx:1\u001b[0m, in \u001b[0;36minit pandas._libs.interval\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: numpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[1], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Importación de Librerias\u001b[39;00m\n\u001b[1;32m      2\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mtorch\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtransformers\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m pipeline\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mpandas\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mpd\u001b[39;00m\n",
      "File \u001b[0;32m<frozen importlib._bootstrap>:1231\u001b[0m, in \u001b[0;36m_handle_fromlist\u001b[0;34m(module, fromlist, import_, recursive)\u001b[0m\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/utils/import_utils.py:1766\u001b[0m, in \u001b[0;36m_LazyModule.__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   1764\u001b[0m     value \u001b[38;5;241m=\u001b[39m Placeholder\n\u001b[1;32m   1765\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_class_to_module\u001b[38;5;241m.\u001b[39mkeys():\n\u001b[0;32m-> 1766\u001b[0m     module \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_module(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_class_to_module[name])\n\u001b[1;32m   1767\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(module, name)\n\u001b[1;32m   1768\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m name \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_modules:\n",
      "File \u001b[0;32m~/anaconda3/lib/python3.11/site-packages/transformers/utils/import_utils.py:1780\u001b[0m, in \u001b[0;36m_LazyModule._get_module\u001b[0;34m(self, module_name)\u001b[0m\n\u001b[1;32m   1778\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m importlib\u001b[38;5;241m.\u001b[39mimport_module(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m module_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m)\n\u001b[1;32m   1779\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1780\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[1;32m   1781\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mFailed to import \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodule_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m because of the following error (look up to see its\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1782\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m traceback):\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00me\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1783\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Failed to import transformers.pipelines because of the following error (look up to see its traceback):\nnumpy.dtype size changed, may indicate binary incompatibility. Expected 96 from C header, got 88 from PyObject"
     ]
    }
   ],
   "source": [
    "# Importación de Librerias\n",
    "import torch\n",
    "from transformers import pipeline\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. NLP con Transformers\n",
    "Debido a que en nuestro proyecto se hace uso intensivo principalmente de herramientas NLP, es en esta rama en la que nos enfocaremos, aunque no lo limitamos a esto.\n",
    "Los siguientes son algunos modelos NLP que pueden utilizarse:\n",
    "* \"sentiment-analysis\" \n",
    "* \"text-classification\" \n",
    "* \"text-generation\"\n",
    "* \"questions-and-answers\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sentiment-analysis\n",
    "Dado un texto, se lo clasifica por la carga sentimental que trae. Esta puede ser: \"POSITIVE\", \"NEGARIVE\" o \"NEUTRAL\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Up:   [{'label': 'POS', 'score': 0.9970971345901489}]\n",
      "Down: [{'label': 'NEG', 'score': 0.9993558526039124}]\n"
     ]
    }
   ],
   "source": [
    "analizador = pipeline('sentiment-analysis', model='finiteautomata/beto-sentiment-analysis')\n",
    "textoUp=\"Estamos muy entusiasmados en probar cuán simplemente podemos hacer un modelo de sentiment-analysis\"\n",
    "textoDown=\"Siento mucha frustración cuando mis trabajos no resultan como se planearon\"\n",
    "resultadoUp = analizador(textoUp)\n",
    "resultadoDown = analizador(textoDown)\n",
    "print(\"Up:  \",resultadoUp)\n",
    "print(\"Down:\",resultadoDown)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text-Classification: Zero-Shot-classification\n",
    "Dado un texto, clasificación zero-shot es la tarea de clasificar clases nunca antes vistas durante el entrenamiento de un modelo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      labels    scores\n",
      "0  Educación  0.712244\n",
      "1   Deportes  0.108196\n",
      "2   Medicina  0.097565\n",
      "3   Biología  0.081995\n"
     ]
    }
   ],
   "source": [
    "# El objeto pipeline\n",
    "clasificador_zero_shot = pipeline(\"zero-shot-classification\", model=\"Recognai/bert-base-spanish-wwm-cased-xnli\")\n",
    "\n",
    "# Implementación del modelo\n",
    "texto = \"Esta notebook es útil para la enseñanza de modelos de NLP de hugging Face\"\n",
    "etiquetas_candidatas = [\"Educación\", \"Deportes\", \"Medicina\",\"Biología\"]\n",
    "resultado = clasificador_zero_shot(texto, etiquetas_candidatas)\n",
    "\n",
    "# Presentación de resultados:\n",
    "dfResultado=pd.DataFrame()\n",
    "dfResultado['labels']=resultado['labels']\n",
    "dfResultado['scores']=resultado['scores']\n",
    "print(dfResultado) # Imprime la etiqueta con la puntuación más alta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text-generation:\n",
    "Dado un pequeño texto, se amplía el mismo siguiendo el sentido del texto inicial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Librerias especificas:\n",
    "from transformers import GPT2Tokenizer, GPT2LMHeadModel\n",
    "\n",
    "# Nombre del modelo\n",
    "nombre_del_modelo = \"datificate/gpt2-small-spanish\"\n",
    "# Tokenizar: El tokenizador se encargará de dividir el texto en tokens comprensibles para el modelo.\n",
    "tokenizador = GPT2Tokenizer.from_pretrained(nombre_del_modelo)\n",
    "\n",
    "# El modelo\n",
    "modelo = GPT2LMHeadModel.from_pretrained(nombre_del_modelo)\n",
    "\n",
    "# Entrada del modelo\n",
    "textoBase = \"la validez de una igualdad\"\n",
    "modelo.config.pad_token_id = modelo.config.eos_token_id\n",
    "input_ids = tokenizador.encode(textoBase, return_tensors=\"pt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jorge.kamlofsky\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\transformers\\generation\\utils.py:1201: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "texto base:      la validez de una igualdad\n",
      "texto generado:  la validez de una igualdad de derechos y obligaciones, y la libertad de expresión, que se encuentra en la Constitución de la República.\n",
      "\n",
      "El artículo 1 de la Constitución de la República de Colombia establece que:\n",
      "\n",
      "El artículo 1 de la Constitución de la República de Colombia establece que:\n",
      "\n",
      "El artículo 1 de la Constitución de la República de Colombia establece que:\n",
      "\n",
      "El artículo 1 de la Constitución de la República de Colombia establece que:\n",
      "\n",
      "El artículo 1 de la Constitución de la\n"
     ]
    }
   ],
   "source": [
    "# La salida:\n",
    "resultado = modelo.generate(input_ids, max_length=100, num_return_sequences=1)\n",
    "texto_generado = tokenizador.decode(resultado[0], skip_special_tokens=True)\n",
    "print(\"texto base:     \",textoBase)\n",
    "print(\"texto generado: \",texto_generado)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este ejemplo, max_length es la longitud máxima del texto generado, y num_return_sequences es el número de secuencias de texto que se desea generar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question-Answering\n",
    "Los modelos de question-answer pueden recuperar la respuesta a una pregunta de un texto dado, lo cual es útil para buscar una respuesta en un documento. Estos modelos buscan generar una respuesta que responda correctamente la pregunta a partir del contexto dado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading (…)lve/main/config.json: 100%|██████████| 721/721 [00:00<?, ?B/s] \n",
      "C:\\Users\\jorge.kamlofsky\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\huggingface_hub\\file_download.py:133: UserWarning: `huggingface_hub` cache-system uses symlinks by default to efficiently store duplicated files but your machine does not support them in C:\\Users\\jorge.kamlofsky\\.cache\\huggingface\\hub. Caching files will still work but in a degraded version that might require more space on your disk. This warning can be disabled by setting the `HF_HUB_DISABLE_SYMLINKS_WARNING` environment variable. For more details, see https://huggingface.co/docs/huggingface_hub/how-to-cache#limitations.\n",
      "To support symlinks on Windows, you either need to activate Developer Mode or to run Python as an administrator. In order to see activate developer mode, see this article: https://docs.microsoft.com/en-us/windows/apps/get-started/enable-your-device-for-development\n",
      "  warnings.warn(message)\n",
      "Downloading pytorch_model.bin: 100%|██████████| 437M/437M [00:49<00:00, 8.77MB/s] \n",
      "Downloading (…)okenizer_config.json: 100%|██████████| 616/616 [00:00<?, ?B/s] \n",
      "Downloading (…)solve/main/vocab.txt: 100%|██████████| 242k/242k [00:00<00:00, 550kB/s]\n",
      "Downloading (…)/main/tokenizer.json: 100%|██████████| 480k/480k [00:00<00:00, 1.06MB/s]\n",
      "Downloading (…)cial_tokens_map.json: 100%|██████████| 112/112 [00:00<?, ?B/s] \n"
     ]
    }
   ],
   "source": [
    "# Importar librerias\n",
    "from transformers import pipeline\n",
    "\n",
    "# Cargar el modelo\n",
    "nombre_del_modelo=\"IIC/beto-base-spanish-sqac\"\n",
    "nlp_qa = pipeline(\"question-answering\", model=nombre_del_modelo, tokenizer=nombre_del_modelo)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'score': 0.9532824158668518, 'start': 28, 'end': 30, 'answer': 'IA'}\n"
     ]
    }
   ],
   "source": [
    "# Contexto:\n",
    "contexto = \"La inteligencia artificial (IA) es un subcampo de la informática que se ocupa de crear programas y sistemas capaces de realizar tareas que normalmente requieren inteligencia humana.\"\n",
    "\n",
    "# Preguntas: Descomentar la pregunta que desee realizar\n",
    "pregunta=\"¿Cuales son las siglas de 'Inteligencia Artificial'?\"\n",
    "#pregunta=\"¿Qué es necesario para crear Inteligencia Artificial?\"\n",
    "#pregunta=\"¿Qué es inteligencia artificial?\"\n",
    "#pregunta=\"¿dentro de qué disciplina se desarrolla la Inteligencia Artificial?\"\n",
    "#pregunta=\"¿Qué logramos con Inteligencia Artificial?\"\n",
    "# Respuestas\n",
    "respuesta = nlp_qa(context=contexto, question=pregunta)\n",
    "print(respuesta)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Otros usos de Huggingface\n",
    "### Clasificacion en imágenes\n",
    "Los modelos de clasificación de imágenes se pueden utilizar cuando no estamos interesados en instancias específicas de objetos con información de ubicación o su forma."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No model was supplied, defaulted to google/vit-base-patch16-224 and revision 5dca96d (https://huggingface.co/google/vit-base-patch16-224).\n",
      "Using a pipeline without specifying a model name and revision in production is not recommended.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'score': 0.853809118270874, 'label': 'king penguin, Aptenodytes patagonica'},\n",
       " {'score': 0.01003843080252409, 'label': 'badger'},\n",
       " {'score': 0.002955351723358035, 'label': 'pelican'},\n",
       " {'score': 0.002916091587394476, 'label': 'sea lion'},\n",
       " {'score': 0.001962602837011218, 'label': 'weasel'}]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from transformers import pipeline\n",
    "clf = pipeline(\"image-classification\")\n",
    "# Descomentar la foto que desee que se describa\n",
    "foto=\"foto01.png\" # monitores de la bolsa de comercio\n",
    "foto=\"foto02.png\" # vacas\n",
    "foto=\"foto03.png\" # pinguino\n",
    "clf(foto)"
   ]
  }
 ],
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