Upload Train.ipynb

En este notebook encontrará la rutina de finetuning del modelo original

Train.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GZiMfnKVCniS"
+      },
+      "source": [
+        "# PROYECTO III  PROGRAMA DE FORMACIÓN  MLDS AVANZADO\n",
+        "## Daniel F. Benavides R. \n",
+        "## Módulo VI - Entrenamiento de modelo de red neuronal y disposición del mismo a nivel local. \n",
+        "\n",
+        "### OBJETIVO\n",
+        "\n",
+        "El objetivo de este proyecto es realizar el despliegue  de un modelo a nivel local. El mismo se llevará a cabo en dos partes: La primera en la cual se realiza el entrenamiento del modelo. El mismo se guarda a nivel local para su posterior uso. \n",
+        "\n",
+        "Es así como a continuación se ve el ejercicio de fine-tuning del modelo preentrenado de transformers  [_'distilbert-base-uncased'_](https://huggingface.co/distilbert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France.). Este modelo inicialmente fue entrenado para labores de _fill mask_ y se adaptará  como modelo clasificación  de **SMS** no  deseado. \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 33,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gTYjO-PJIUT-",
+        "outputId": "27f1d993-d34f-4f4b-bbe1-ab6c96bc2825"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GRFpQTqzDCLT"
+      },
+      "source": [
+        "### Carga y manipulación de los datos \n",
+        "\n",
+        "A continuación importamos pandas, por medio del cual hacemos el respectivo cargue del dataset, delimitamos por el espacio la  etiqueta del mensaje.\n",
+        "\n",
+        "Luego por medio de la función _list_ convertimos el mensaje y las etiquetas en un par de listas. luego convertimos las etiquetas en una variable dummie, debido a que tenemos una salida binaria  _(el mensaje es spam o no lo es)_\n",
+        "\n",
+        "## Importamos el dataset de entrenamiento"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 34,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3Dt9fFKs74zR",
+        "outputId": "271ff620-a449-4e87-a163-14621651b48e"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
+          ]
+        }
+      ],
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')\n",
+        "path= \"/content/drive/MyDrive/MLDS-2/MODULO II/Talleres/SMSSpamCollection\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 35,
+      "metadata": {
+        "id": "awPXefiYqQsF"
+      },
+      "outputs": [],
+      "source": [
+        "\n",
+        "import pandas as pd\n",
+        "df=messages = pd.read_csv(path, sep='\\t',\n",
+        "                           names=[\"label\", \"message\"])\n",
+        "X=list(df['message'])\n",
+        "y=list(df['label'])\n",
+        "y=list(pd.get_dummies(y,drop_first=True)['spam'])\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1T8CpN3YDar6"
+      },
+      "source": [
+        "### Preprocesamiento \n",
+        "\n",
+        "Ahora importamos la función *train_test_split*  del módulo *model_selection* de la librería *scikit-learn*  y por medio de este dividimos  en set de entrenamiento y prueba. Definimos el tamaño de set de prueba en 20% de la muestra. También definimos el parámetro *random_state* para efectos de controlar la generación  de los dos conjuntos de tal manera que  no sean aleatorios. \n",
+        "\n",
+        "Luego instalamos la librería transformers, aunque en mi caso ya lo había realizado. \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 36,
+      "metadata": {
+        "id": "dLFDWda0rIKw"
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.model_selection import train_test_split\n",
+        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 37,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AqOBGiGErZgj",
+        "outputId": "1a461d33-55ae-4a22-9746-8c01e99d49bd"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: transformers in /usr/local/lib/python3.8/dist-packages (4.25.1)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (21.3)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
+            "Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.11.1)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.8.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.23.0)\n",
+            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
+            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.13.2)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.4.0)\n",
+            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n",
+            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.24.3)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
+            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (3.0.4)\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install transformers"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "31aeQ6u-Dq-K"
+      },
+      "source": [
+        "Ahora debemos invocar los modelos de que vamos a utilizar de la librería transformers  en los siguientes pasos: \n",
+        "\n",
+        "* Llamamos el modelo preentrenado\n",
+        "* Llamamos el tokenizador \n",
+        "\n",
+        "Necesitamos aplicar el tokenizador sobre nuestro conjunto de datos. \n",
+        "\n",
+        "Así que acontinuación llamamos de la librería transformers el tokenizador _\"DistilBertTokenizerFast\"_ luego lo definimos como nuestro **tokenizer** indicando que el mismo proviene del modelo preentrenado [_'distilbert-base-uncased'_](https://huggingface.co/distilbert-base-uncased?text=Paris+is+the+%5BMASK%5D+of+France.)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 38,
+      "metadata": {
+        "id": "bcNEJ6perOSs"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import DistilBertTokenizerFast\n",
+        "tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3RdR0eZaDyyi"
+      },
+      "source": [
+        "Luego  aplicamos el tokenizador que acabamos de definir sobre nuestro conjunto  de mensajes de entrenamiento y prueba. Como los SMS no tienen la misma longitud (cantidad de tokens) debemos definir los parámetros truncation y padding como True para que se obtener oraciones del mismo tamaño; uno se encarga de rellenar de ceros (padding) y el otro de truncar las oraciones más largas. Esto para obtener un conjunto y luego tensores rectangulares. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 39,
+      "metadata": {
+        "id": "-OL3fgLvrXvH"
+      },
+      "outputs": [],
+      "source": [
+        "train_encodings = tokenizer(X_train, truncation=True, padding=True)\n",
+        "test_encodings = tokenizer(X_test, truncation=True, padding=True)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7JTdRQNVD4AK"
+      },
+      "source": [
+        "Ahora se procede a importar Tensorflow para efecto de convertir en tensores los encodings generados en el paso anterior. Acá se junta cada uno a su correspondiente etiqueta. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 40,
+      "metadata": {
+        "id": "9B42CTCnrrEx"
+      },
+      "outputs": [],
+      "source": [
+        "import tensorflow as tf\n",
+        "\n",
+        "train_dataset = tf.data.Dataset.from_tensor_slices((\n",
+        "    dict(train_encodings),\n",
+        "    y_train\n",
+        "))\n",
+        "\n",
+        "test_dataset = tf.data.Dataset.from_tensor_slices((\n",
+        "    dict(test_encodings),\n",
+        "    y_test\n",
+        "))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "G3Wj2cqXD5hx"
+      },
+      "source": [
+        "### Entrenamiento\n",
+        "\n",
+        "A continuación se importan los módulos de TFDistilBertForSequenceClassification que es usado para la tarea de clasificación  de sentimientos. También se importan los módulos  y *TFTrainingArguments* y *TFTrainer*que son los encargados de definir los argumentos  y posteriormente parametrizar el **trainer** del modelo y  hacer las nuevas predicciones. \n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 41,
+      "metadata": {
+        "id": "NH1dupK0rzfn"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import TFDistilBertForSequenceClassification, TFTrainer, TFTrainingArguments\n",
+        "\n",
+        "training_args = TFTrainingArguments(\n",
+        "    eval_steps = 10, \n",
+        "    output_dir='./results',          # output directory\n",
+        "    num_train_epochs=2,              # total number of training epochs\n",
+        "    per_device_train_batch_size=8,  # batch size per device during training\n",
+        "    per_device_eval_batch_size=8,   # batch size for evaluation\n",
+        "    warmup_steps=500,                # number of warmup steps for learning rate scheduler\n",
+        "    weight_decay=0.01,               # strength of weight decay\n",
+        "    logging_dir='./logs',            # directory for storing logs\n",
+        "    logging_steps=10,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cqmXOhkSEAgC"
+      },
+      "source": [
+        "Hemos determinado el conjunto de argumentos que serán utilizados en el reentrenamiento del modelo,estos quedan alojados en  el  objeto *training_args*   y  ahora definiremos el modelo refiriendo el modelo preentrenado que vamos a utilizar, que en este caso es  _\"distilbert-base-uncased\"_. se creará el **trainer** al cual se le pasarán los argumentos antes definidos   y los dos tensores de entrenamiento y prueba; para luego  entrenar el modelo que hemos definido."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 42,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PZvTrEcfr7k-",
+        "outputId": "f8fe7e3c-c9c8-4b92-c3fc-ea4821386beb"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some layers from the model checkpoint at distilbert-base-uncased were not used when initializing TFDistilBertForSequenceClassification: ['vocab_projector', 'activation_13', 'vocab_layer_norm', 'vocab_transform']\n",
+            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['dropout_39', 'pre_classifier', 'classifier']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n",
+            "/usr/local/lib/python3.8/dist-packages/transformers/trainer_tf.py:115: FutureWarning: The class `TFTrainer` is deprecated and will be removed in version 5 of Transformers. We recommend using native Keras instead, by calling methods like `fit()` and `predict()` directly on the model object. Detailed examples of the Keras style can be found in our examples at https://github.com/huggingface/transformers/tree/main/examples/tensorflow\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "with training_args.strategy.scope():\n",
+        "    model = TFDistilBertForSequenceClassification.from_pretrained(\"distilbert-base-uncased\")\n",
+        "\n",
+        "trainer = TFTrainer(\n",
+        "    model=model,                         # the instantiated 🤗 Transformers model to be trained\n",
+        "    args=training_args,                  # training arguments, defined above\n",
+        "    train_dataset=train_dataset,         # training dataset\n",
+        "    eval_dataset=test_dataset             # evaluation dataset\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tnAE3agZ21dq"
+      },
+      "source": [
+        "una vez instanciado el modelo que será reentrenado,  parametrizados los argumentos para ello, se toma la data y se realiza el reentrenamiento del modelo. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 43,
+      "metadata": {
+        "id": "bIba4vQg7Ecp"
+      },
+      "outputs": [],
+      "source": [
+        "trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zerz-bv8EENp"
+      },
+      "source": [
+        "Ahora solo queda por aplicar modelo que reentrenamos con el dataset de **entrenamiento**, hacer la predicción, y la evaluación de las predicciones. Este procedimiento se encuentra definido en el [manual de fine-tuning](https://huggingface.co/transformers/v3.5.1/training.html) que tiene Hugging Face disponible. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 44,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "R534aDi3xD0s",
+        "outputId": "65c5ac93-eb67-4413-e048-f7b4d9fd8931"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "{'eval_loss': 0.02398163080215454}"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 44
+        }
+      ],
+      "source": [
+        "trainer.evaluate(test_dataset)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4rLF3nApUndt"
+      },
+      "source": [
+        "A continuación aplicamos el modelo reentrenado al  set de prueba hacer la respectiva clasificación de cada una de las muestras. "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "jpGNNvWEWU9u"
+      },
+      "source": [
+        "### Predicción del modelo\n",
+        "\n",
+        "Se aplica el modelo reentrenado al dataset de prueba *test_dataset* y se evalúa la precisión del mismo por medio del accuracy, es decir, acá le pasamos mensajes sin etiquetas y le pedimos que prediga si son o no spam. El modelo para la tarea que fue entrenado presenta un accuracy de 1, es decir clasifica perfectamente el set de prueba. "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 45,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "UyBmI1WcxKjG",
+        "outputId": "53067a82-55bf-4500-a38e-d890be6f7bf5"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "PredictionOutput(predictions=array([[ 3.4155877, -3.1767924],\n",
+              "       [-3.2374823,  3.135958 ],\n",
+              "       [ 3.348417 , -3.1216612],\n",
+              "       ...,\n",
+              "       [ 3.04905  , -2.8354154],\n",
+              "       [-3.1865208,  3.0687277],\n",
+              "       [ 3.212608 , -3.0316095]], dtype=float32), label_ids=array([0, 1, 0, ..., 0, 1, 0], dtype=int32), metrics={'eval_loss': 0.023984665530068533})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 45
+        }
+      ],
+      "source": [
+        "trainer.predict(test_dataset)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 46,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "9Qc5FtM8xn9A",
+        "outputId": "0d517424-b5d1-4324-be3c-6f90335aa4fd"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "(1115,)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 46
+        }
+      ],
+      "source": [
+        "trainer.predict(test_dataset)[1].shape"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "LUHX_tCTWFuu"
+      },
+      "source": [
+        "#### Salidas"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 47,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "fUVX_IhWxkxg",
+        "outputId": "a2e94ee6-54a2-414f-c2e7-98950deb7732"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([0, 1, 0, ..., 0, 1, 0], dtype=int32)"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 47
+        }
+      ],
+      "source": [
+        "output=trainer.predict(test_dataset)[1]\n",
+        "output"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lUxvb6JcYB7_"
+      },
+      "source": [
+        "#### Matriz  de confusión, Accuracy"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 48,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "cfCE06jQu5cI",
+        "outputId": "a1d10897-a36f-47a8-e038-0f68ec5e7ded"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "array([[955,   0],\n",
+              "       [  0, 160]])"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 48
+        }
+      ],
+      "source": [
+        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
+        "\n",
+        "confusion_matrix=confusion_matrix(y_test,output)\n",
+        "confusion_matrix\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 49,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "mv83DD8sl8JO",
+        "outputId": "97612c62-b15f-453f-d51e-5cd12e554421"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "1.0"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 49
+        }
+      ],
+      "source": [
+        "acc=accuracy_score(y_test,output)\n",
+        "acc"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Zm3mF58zYYze"
+      },
+      "source": [
+        "#### Descarga del modelo"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 59,
+      "metadata": {
+        "id": "okD5we1NwhQW"
+      },
+      "outputs": [],
+      "source": [
+        "trainer.save_model('ft_model')\n",
+        "trainer.save_model('/content/drive/MyDrive/MLDS-2/MODULO III/Talleres/Modelo Entrenado')\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install transformers"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "iuipIt7zN8Ct",
+        "outputId": "f078f829-8827-4e20-daed-9baf1e007394"
+      },
+      "execution_count": 60,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: transformers in /usr/local/lib/python3.8/dist-packages (4.25.1)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (6.0)\n",
+            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (1.21.6)\n",
+            "Requirement already satisfied: huggingface-hub<1.0,>=0.10.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.11.1)\n",
+            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers) (2022.6.2)\n",
+            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers) (4.64.1)\n",
+            "Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers) (0.13.2)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers) (3.8.2)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers) (2.23.0)\n",
+            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from transformers) (21.3)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface-hub<1.0,>=0.10.0->transformers) (4.4.0)\n",
+            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.0->transformers) (3.0.9)\n",
+            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (1.24.3)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2.10)\n",
+            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (3.0.4)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers) (2022.12.7)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "!pip install huggingface_hub"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Oo5x6eVZN7_9",
+        "outputId": "21c914ea-8540-4192-e11d-f77d3774fef1"
+      },
+      "execution_count": 61,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.8/dist-packages (0.11.1)\n",
+            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (21.3)\n",
+            "Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (3.8.2)\n",
+            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (6.0)\n",
+            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.4.0)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (4.64.1)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from huggingface_hub) (2.23.0)\n",
+            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.8/dist-packages (from packaging>=20.9->huggingface_hub) (3.0.9)\n",
+            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2.10)\n",
+            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (3.0.4)\n",
+            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (1.24.3)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->huggingface_hub) (2022.12.7)\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import torch\n",
+        "from transformers import BertTokenizer, BertForSequenceClassification, TFDistilBertForSequenceClassification"
+      ],
+      "metadata": {
+        "id": "XXn00BW7N79l"
+      },
+      "execution_count": 62,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model2 = TFDistilBertForSequenceClassification.from_pretrained('/content/drive/MyDrive/MLDS-2/MODULO III/Talleres/Modelo Entrenado/')\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "nnajL6gxN7zN",
+        "outputId": "a5477437-8a60-44f3-fa99-c83de5010cc6"
+      },
+      "execution_count": 63,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some layers from the model checkpoint at /content/drive/MyDrive/MLDS-2/MODULO III/Talleres/Modelo Entrenado/ were not used when initializing TFDistilBertForSequenceClassification: ['dropout_39']\n",
+            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at /content/drive/MyDrive/MLDS-2/MODULO III/Talleres/Modelo Entrenado/ and are newly initialized: ['dropout_59']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from huggingface_hub import notebook_login\n",
+        "\n",
+        "notebook_login()\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 359,
+          "referenced_widgets": [
+            "eae418e551f44d90b53b67ab19f2681a",
+            "d339a1c63e24406faf2c231028ab0d7f",
+            "e4665b549c0f48698529d99f475e07ca",
+            "92b55d980482446da8d2aed8b58593ff",
+            "2dfe4e10862342918c0c151f3f719fef",
+            "5e73bccd9598457b856dd44741261ed3",
+            "cdc2b5d89c814b6ca8c2e7a30f17b1e2",
+            "d76091dbf8334024b4edf2ec9e5bf32d",
+            "f43c0faafc754458bce32f3eee55a153",
+            "ff5b269f82684bc9ab81c3caae7299ca",
+            "902f6a0765a0469ebd1994f789ad80e2",
+            "c14909df983c43aeb3f62b670c376b60",
+            "05e9de72aac143918ec0e31263075982",
+            "b92abd39e834458d95ed024468835ff4",
+            "46133dced4ec4122bbca398b70f4aadb",
+            "a026cfb4f04a4e5f95fc6d6dcf53b9e1",
+            "40af31601c0b4396bdf2da2d81ba1f1b"
+          ]
+        },
+        "id": "smWmyyktyYKr",
+        "outputId": "7ae0dee8-eafb-4134-962e-f3ed0187f2c9"
+      },
+      "execution_count": 65,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Token is valid.\n",
+            "Your token has been saved in your configured git credential helpers (store).\n",
+            "Your token has been saved to /root/.huggingface/token\n",
+            "Login successful\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model2.push_to_hub(\"Dfbenavidesr/distilbert-base-uncased-finetuned_clf-spam\")"
+      ],
+      "metadata": {
+        "id": "1HTnPWjUTDCt"
+      },
+      "execution_count": 66,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "model2 = TFDistilBertForSequenceClassification.from_pretrained(\"Dfbenavidesr/distilbert-base-uncased-finetuned_clf-spam\")"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YNboOPrSTwe1",
+        "outputId": "7382c934-1760-41d8-dd80-3513fd37168c"
+      },
+      "execution_count": 70,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "Some layers from the model checkpoint at Dfbenavidesr/distilbert-base-uncased-finetuned_clf-spam were not used when initializing TFDistilBertForSequenceClassification: ['dropout_59']\n",
+            "- This IS expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
+            "- This IS NOT expected if you are initializing TFDistilBertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n",
+            "Some layers of TFDistilBertForSequenceClassification were not initialized from the model checkpoint at Dfbenavidesr/distilbert-base-uncased-finetuned_clf-spam and are newly initialized: ['dropout_99']\n",
+            "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "\n"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "5THCF3RsgirM",
+        "outputId": "ddbc0b75-c437-4d97-c623-a62d421d865f"
+      },
+      "execution_count": 71,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<transformers.models.distilbert.modeling_tf_distilbert.TFDistilBertForSequenceClassification at 0x7fd8b2fec8e0>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 71
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
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+    "widgets": {
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+        "d339a1c63e24406faf2c231028ab0d7f": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "HTMLModel",
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+          "state": {
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+            "_model_module_version": "1.5.0",
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+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
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+            "description": "",
+            "description_tooltip": null,
+            "layout": "IPY_MODEL_d76091dbf8334024b4edf2ec9e5bf32d",
+            "placeholder": "​",
+            "style": "IPY_MODEL_f43c0faafc754458bce32f3eee55a153",
+            "value": "<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.svg\nalt='Hugging Face'> <br> Copy a token from <a\nhref=\"https://huggingface.co/settings/tokens\" target=\"_blank\">your Hugging Face\ntokens page</a> and paste it below. <br> Immediately click login after copying\nyour token or it might be stored in plain text in this notebook file. </center>"
+          }
+        },
+        "e4665b549c0f48698529d99f475e07ca": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "PasswordModel",
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+            "_view_module_version": "1.5.0",
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+            "continuous_update": true,
+            "description": "Token:",
+            "description_tooltip": null,
+            "disabled": false,
+            "layout": "IPY_MODEL_ff5b269f82684bc9ab81c3caae7299ca",
+            "placeholder": "​",
+            "style": "IPY_MODEL_902f6a0765a0469ebd1994f789ad80e2",
+            "value": ""
+          }
+        },
+        "92b55d980482446da8d2aed8b58593ff": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "CheckboxModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "CheckboxModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/controls",
+            "_view_module_version": "1.5.0",
+            "_view_name": "CheckboxView",
+            "description": "Add token as git credential?",
+            "description_tooltip": null,
+            "disabled": false,
+            "indent": true,
+            "layout": "IPY_MODEL_c14909df983c43aeb3f62b670c376b60",
+            "style": "IPY_MODEL_05e9de72aac143918ec0e31263075982",
+            "value": true
+          }
+        },
+        "2dfe4e10862342918c0c151f3f719fef": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ButtonModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_dom_classes": [],
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+            "_model_module_version": "1.5.0",
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+            "_view_count": null,
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+            "_view_module_version": "1.5.0",
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+            "button_style": "",
+            "description": "Login",
+            "disabled": false,
+            "icon": "",
+            "layout": "IPY_MODEL_b92abd39e834458d95ed024468835ff4",
+            "style": "IPY_MODEL_46133dced4ec4122bbca398b70f4aadb",
+            "tooltip": ""
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+        "5e73bccd9598457b856dd44741261ed3": {
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+            "placeholder": "​",
+            "style": "IPY_MODEL_40af31601c0b4396bdf2da2d81ba1f1b",
+            "value": "\n<b>Pro Tip:</b> If you don't already have one, you can create a dedicated\n'notebooks' token with 'write' access, that you can then easily reuse for all\nnotebooks. </center>"
+          }
+        },
+        "cdc2b5d89c814b6ca8c2e7a30f17b1e2": {
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