27-text_classification-persian.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "8RZOuS9LWQvv"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "import re\n",
        "import shutil\n",
        "import string\n",
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras import losses\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "6-tTFS04dChr",
        "outputId": "46e9894f-d716-4e87-fc60-b31fadcf1d2c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2.13.0\n"
          ]
        }
      ],
      "source": [
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "k7ZYnuajVlFN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e035812e-13fd-488d-e395-b29295239b48"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://github.com/Alireza-Akhavan/text-classification/raw/main/6-persian-topics.zip\n",
            "16473431/16473431 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "url = \"https://github.com/Alireza-Akhavan/text-classification/raw/main/6-persian-topics.zip\"\n",
        "\n",
        "dataset = tf.keras.utils.get_file(\"6-persian-topics.zip\", url,\n",
        "                                    extract=True, cache_dir='.',\n",
        "                                    cache_subdir='')\n",
        "\n",
        "dataset_dir = os.path.join(os.path.dirname(dataset), '6-persian-topics')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "355CfOvsV1pl",
        "outputId": "eade45fc-9178-42ef-81aa-a58d1ae1dab2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['بهداشت و سلامت',\n",
              " 'جغرافیا و مکانها',\n",
              " 'ورزش',\n",
              " 'فناوری و علوم کاربردی و تکنولوژی',\n",
              " 'ریاضیات',\n",
              " 'دین و اعتقاد']"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "os.listdir(dataset_dir)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "R7g8hFvzWLIZ",
        "outputId": "b700f4a8-af11-4b96-baa9-5ce5d40cfb05",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "نظریه انشعاب یا نظریه دوشاخگی (به انگلیسی: Bifurcation theory) به مطالعه ریاضیِ تغییرات کیفی یا توپولوژیکی ساختار حوزه‌هایی مانند منحنی‌های انتگرالِ میدان برداری و حل معادله دیفرانسیل می‌پردازد. نظریه انشعاب در بررسیِ سیستم پویا کاربرد دارد. هنگامی که یک تغییر کوچکِ نرم، منجر به تغییر در رفتار سیستم شده و یک انشعاب ناگهانیِ کیفی یا تغییر از نظر توپولوژی اتفاق می‌افتد.\n"
          ]
        }
      ],
      "source": [
        "sample_file = os.path.join(dataset_dir, 'ریاضیات/1000847.txt')\n",
        "with open(sample_file) as f:\n",
        "  print(f.read())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "95kkUdRoaeMw"
      },
      "source": [
        "Next, you will use the `text_dataset_from_directory` utility to create a labeled `tf.data.Dataset`. [tf.data](https://www.tensorflow.org/guide/data) is a powerful collection of tools for working with data.\n",
        "\n",
        "When running a machine learning experiment, it is a best practice to divide your dataset into three splits: [train](https://developers.google.com/machine-learning/glossary#training_set), [validation](https://developers.google.com/machine-learning/glossary#validation_set), and [test](https://developers.google.com/machine-learning/glossary#test-set).\n",
        "\n",
        "The IMDB dataset has already been divided into train and test, but it lacks a validation set. Let's create a validation set using an 80:20 split of the training data by using the `validation_split` argument below."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "nOrK-MTYaw3C",
        "outputId": "4f997f47-fcb9-4b38-dfe4-566bb7ae9640",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 37573 files belonging to 6 classes.\n",
            "Using 30059 files for training.\n",
            "Found 37573 files belonging to 6 classes.\n",
            "Using 7514 files for validation.\n"
          ]
        }
      ],
      "source": [
        "batch_size = 32\n",
        "seed = 42\n",
        "\n",
        "raw_train_ds = tf.keras.utils.text_dataset_from_directory(\n",
        "    dataset_dir,\n",
        "    batch_size=batch_size,\n",
        "    validation_split=0.2,\n",
        "    subset='training',\n",
        "    seed=seed)\n",
        "\n",
        "raw_val_ds = tf.keras.utils.text_dataset_from_directory(\n",
        "    dataset_dir,\n",
        "    batch_size=batch_size,\n",
        "    validation_split=0.2,\n",
        "    subset='validation',\n",
        "    seed=seed)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "51wNaPPApk1K",
        "outputId": "6df34ab7-a3cc-474d-9ff7-1fa23ed4399a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Review آافیر (به لاتین: Aafir) در الجزایر با جمعیت ۱۲٬۶۱۳ نفر است که در استان بومرداس واقع شده‌است.\n",
            "Label 1\n",
            "Review نبردناو کلاس مونتانا (به انگلیسی: Montana-class battleship) یک کلاس از کشتی است که طول آن ۹۲۰ فوت ۶ اینچ (۲۸۰٫۵۷ متر) می‌باشد.\n",
            "Label 4\n",
            "Review کن فِم (به آلمانی: KenFM)، یک پایگاه خبری تحلیلی آلمانی است.\n",
            "Label 4\n",
            "Review شهرستان آدامز، کلرادو (به انگلیسی: Adams County, Colorado) یک سکونتگاه مسکونی در ایالات متحده آمریکا است که در کلرادو واقع شده‌است.\n",
            "Label 1\n",
            "Review موشان (به لاتین: Mosjøen) (تلفظ نروژی: [ˈmu:ʃøn] گوش‌دادن ) یک شهرک در نروژ است که در وفسن واقع شده‌است. موشان ۶٫۴۱ کیلومتر مربع مساحت و ۹٬۶۶۵ نفر جمعیت دارد.\n",
            "Label 1\n",
            "Review سیداد کولونیالاین مکان در سال ۱۹۹۰ در میراث جهانی یونسکو به ثبت رسید.\n",
            "Label 1\n",
            "Review ایران از ۱۸ اوت تا ۲ سپتامبر ۲۰۱۸ در بازی‌های آسیایی ۲۰۱۸ در جاکارتا و پالم‌بانگ اندونزی شرکت کرد. ایران از اولین دوره بازی‌ها در دهلی‌نو ۱۹۵۱ در بازی‌های آسیایی شرکت کرده‌است و بهترین دستاورد ایران در سال ۱۹۷۴ بود، زمانی که ایران میزبان این بازی‌ها بود. در تهران، با کسب ۳۶ مدال طلا، ۲۸ نقره و ۱۷ برنز، در رتبه دوم قرار گرفت. در آخرین تجربه در اینچئون، این کشور با ۵۷ مدال در مجموع - ۲۱ طلا، ۱۸ نقره و ۱۸ برنز کار خود را به پایان رساند.\n",
            "Label 5\n",
            "Review ایالت کوگی (به لاتین: Kogi) یک ایالت در نیجریه است که ۲۹٬۸۳۳ کیلومترمربع مساحت و ۲٬۰۹۹٬۰۴۶ نفر جمعیت دارد.\n",
            "Label 1\n",
            "Review استان آتلانتیک (به لاتین: Atlantique Department) یک استان در بنین است.\n",
            "Label 1\n",
            "Review صومعه یوسته (به اسپانیایی: Monastery of Yuste) یک صومعه در روستایی کوچک به نام سان یوسته (اسپانیایی: San Yuste‎ به معنی قدیس عادل) در استان کاسرس در بخش خودمختار اکسترمادورای اسپانیا است. این صومعه در سال ۱۴۰۲ تأسیس شد.\n",
            "Label 1\n"
          ]
        }
      ],
      "source": [
        "for text_batch, label_batch in raw_train_ds.take(1):\n",
        "  for i in range(10):\n",
        "    print(\"Review\", text_batch.numpy()[i].decode()) #use decode for persian text\n",
        "    print(\"Label\", label_batch.numpy()[i])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JWq1SUIrp1a-"
      },
      "source": [
        "Notice the reviews contain raw text (with punctuation and occasional HTML tags like `<br/>`). You will show how to handle these in the following section.\n",
        "\n",
        "The labels are 0 or 1. To see which of these correspond to positive and negative movie reviews, you can check the `class_names` property on the dataset.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "MlICTG8spyO2",
        "outputId": "3c828465-8702-4a27-8b37-7bc173493aa1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label 0 corresponds to بهداشت و سلامت\n",
            "Label 1 corresponds to جغرافیا و مکانها\n",
            "Label 2 corresponds to دین و اعتقاد\n",
            "Label 3 corresponds to ریاضیات\n",
            "Label 4 corresponds to فناوری و علوم کاربردی و تکنولوژی\n",
            "Label 5 corresponds to ورزش\n"
          ]
        }
      ],
      "source": [
        "for i in range(6):\n",
        "  print(\"Label {} corresponds to {}\".format(i, raw_train_ds.class_names[i]))\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pbdO39vYqdJr"
      },
      "source": [
        "Next, you will create a validation and test dataset. You will use the remaining 5,000 reviews from the training set for validation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SzxazN8Hq1pF"
      },
      "source": [
        "Note:  When using the `validation_split` and `subset` arguments, make sure to either specify a random seed, or to pass `shuffle=False`, so that the validation and training splits have no overlap."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qJmTiO0IYAjm"
      },
      "source": [
        "### Prepare the dataset for training\n",
        "\n",
        "Next, you will standardize, tokenize, and vectorize the data using the helpful `tf.keras.layers.TextVectorization` layer.\n",
        "\n",
        "Standardization refers to preprocessing the text, typically to remove punctuation or HTML elements to simplify the dataset. Tokenization refers to splitting strings into tokens (for example, splitting a sentence into individual words, by splitting on whitespace). Vectorization refers to converting tokens into numbers so they can be fed into a neural network. All of these tasks can be accomplished with this layer.\n",
        "\n",
        "As you saw above, the reviews contain various HTML tags like `<br />`. These tags will not be removed by the default standardizer in the `TextVectorization` layer (which converts text to lowercase and strips punctuation by default, but doesn't strip HTML). You will write a custom standardization function to remove the HTML."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZVcHl-SLrH-u"
      },
      "source": [
        "Note: To prevent [training-testing skew](https://developers.google.com/machine-learning/guides/rules-of-ml#training-serving_skew) (also known as training-serving skew), it is important to preprocess the data identically at train and test time. To facilitate this, the `TextVectorization` layer can be included directly inside your model, as shown later in this tutorial."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "SDRI_s_tX1Hk"
      },
      "outputs": [],
      "source": [
        "def custom_standardization(input_data):\n",
        "  lowercase = tf.strings.lower(input_data)\n",
        "  stripped_html = tf.strings.regex_replace(lowercase, '<br />', ' ')\n",
        "  stripped_ye = tf.strings.regex_replace(stripped_html, 'ي', 'ی')\n",
        "  return tf.strings.regex_replace(stripped_ye,\n",
        "                                  '[%s]' % re.escape(string.punctuation),\n",
        "                                  '')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "txt = \" براي برای\"\n",
        "print(txt)\n",
        "print(custom_standardization(txt).numpy().decode())"
      ],
      "metadata": {
        "id": "6EFEc9O-HwSU",
        "outputId": "07d15b70-b1fd-4977-d3c9-f8339bb6db11",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " براي برای\n",
            " برای برای\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d2d3Aw8dsUux"
      },
      "source": [
        "Next, you will create a `TextVectorization` layer. You will use this layer to standardize, tokenize, and vectorize our data. You set the `output_mode` to `int` to create unique integer indices for each token.\n",
        "\n",
        "Note that you're using the default split function, and the custom standardization function you defined above. You'll also define some constants for the model, like an explicit maximum `sequence_length`, which will cause the layer to pad or truncate sequences to exactly `sequence_length` values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "-c76RvSzsMnX"
      },
      "outputs": [],
      "source": [
        "max_features = 10000\n",
        "sequence_length = 250\n",
        "\n",
        "vectorize_layer = layers.TextVectorization(\n",
        "    standardize=custom_standardization,\n",
        "    max_tokens=max_features,\n",
        "    output_mode='int',\n",
        "    output_sequence_length=sequence_length)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlFOpfF6scT6"
      },
      "source": [
        "Next, you will call `adapt` to fit the state of the preprocessing layer to the dataset. This will cause the model to build an index of strings to integers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lAhdjK7AtroA"
      },
      "source": [
        "Note: It's important to only use your training data when calling adapt (using the test set would leak information)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "GH4_2ZGJsa_X"
      },
      "outputs": [],
      "source": [
        "# Make a text-only dataset (without labels), then call adapt\n",
        "train_text = raw_train_ds.map(lambda x, y: x)\n",
        "vectorize_layer.adapt(train_text)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SHQVEFzNt-K_"
      },
      "source": [
        "Let's create a function to see the result of using this layer to preprocess some data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "SCIg_T50wOCU"
      },
      "outputs": [],
      "source": [
        "def vectorize_text(text, label):\n",
        "  text = tf.expand_dims(text, -1)\n",
        "  return vectorize_layer(text), label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "XULcm6B3xQIO",
        "outputId": "59451c24-62b2-41fe-e0b0-2ed07727b68f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Review شبه‌جزیره چهارکان (روسی: Челекен полуостров) (نام‌های دیگر: چله‌کن، نفتونیا، جزیره نفت) شبه‌جزیره‌ای است در غرب ترکمنستان، در سواحل شرقی دریای خزر. شهر خزر (چهارکان سابق) در این شبه‌جزیره واقع شده‌است که از لحاظ اداری به استان بلخان ترکمنستان تعلق دارد.\n",
            "Label جغرافیا و مکانها\n",
            "Vectorized review (<tf.Tensor: shape=(1, 250), dtype=int64, numpy=\n",
            "array([[1343,    1,  235,    1,    1,  913,  123,    1,    1,  178, 1761,\n",
            "        9178,    5,    2,  137,    1,    2, 1926,  110,  254, 3000,   22,\n",
            "        3000,    1, 1026,    2,    8, 1343,   10,   11,    6,    7, 1454,\n",
            "         599,    3,   25, 8473, 1192, 3452,   18,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
            "           0,    0,    0,    0,    0,    0,    0,    0]])>, <tf.Tensor: shape=(), dtype=int32, numpy=1>)\n"
          ]
        }
      ],
      "source": [
        "# retrieve a batch (of 32 reviews and labels) from the dataset\n",
        "text_batch, label_batch = next(iter(raw_train_ds))\n",
        "first_review, first_label = text_batch[0], label_batch[0]\n",
        "print(\"Review\", first_review.numpy().decode())\n",
        "print(\"Label\", raw_train_ds.class_names[first_label])\n",
        "print(\"Vectorized review\", vectorize_text(first_review, first_label))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6u5EX0hxyNZT"
      },
      "source": [
        "As you can see above, each token has been replaced by an integer. You can lookup the token (string) that each integer corresponds to by calling `.get_vocabulary()` on the layer."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "kRq9hTQzhVhW",
        "outputId": "0e0de468-b0c7-41ef-8c27-68144a45f5ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1287 --->  خودرویی\n",
            " 313 --->  روستا\n",
            "Vocabulary size: 10000\n"
          ]
        }
      ],
      "source": [
        "print(\"1287 ---> \",vectorize_layer.get_vocabulary()[1287])\n",
        "print(\" 313 ---> \",vectorize_layer.get_vocabulary()[313])\n",
        "print('Vocabulary size: {}'.format(len(vectorize_layer.get_vocabulary())))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XD2H6utRydGv"
      },
      "source": [
        "You are nearly ready to train your model. As a final preprocessing step, you will apply the TextVectorization layer you created earlier to the train, validation, and test dataset."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a, b = next(iter(raw_train_ds))\n",
        "print(a.shape, b.shape)"
      ],
      "metadata": {
        "id": "ZrMpFD9Q_XSU",
        "outputId": "2a1b0998-e46f-42fb-8fe2-8413d8b47db4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(32,) (32,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "vectorize_text(a[0], 0)"
      ],
      "metadata": {
        "id": "3kcu6mFd_dkS",
        "outputId": "08e13293-4aab-4bfd-9daf-2970f5dfcb90",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              " 0)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "2zhmpeViI1iG"
      },
      "outputs": [],
      "source": [
        "train_ds = raw_train_ds.map(vectorize_text)\n",
        "val_ds = raw_val_ds.map(vectorize_text)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a, b = next(iter(train_ds))\n",
        "a[0]"
      ],
      "metadata": {
        "id": "7amzeMTS_s20",
        "outputId": "d60fcd3e-4767-4bf7-b9f4-ffc1a4c99d2e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
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            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YsVQyPMizjuO"
      },
      "source": [
        "### Configure the dataset for performance\n",
        "\n",
        "These are two important methods you should use when loading data to make sure that I/O does not become blocking.\n",
        "\n",
        "`.cache()` keeps data in memory after it's loaded off disk. This will ensure the dataset does not become a bottleneck while training your model. If your dataset is too large to fit into memory, you can also use this method to create a performant on-disk cache, which is more efficient to read than many small files.\n",
        "\n",
        "`.prefetch()` overlaps data preprocessing and model execution while training.\n",
        "\n",
        "You can learn more about both methods, as well as how to cache data to disk in the [data performance guide](https://www.tensorflow.org/guide/data_performance)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "wMcs_H7izm5m"
      },
      "outputs": [],
      "source": [
        "AUTOTUNE = tf.data.AUTOTUNE\n",
        "\n",
        "train_ds = train_ds.cache().prefetch(buffer_size=AUTOTUNE)\n",
        "val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LLC02j2g-llC"
      },
      "source": [
        "### Create the model\n",
        "\n",
        "It's time to create your neural network:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "dkQP6in8yUBR"
      },
      "outputs": [],
      "source": [
        "embedding_dim = 16"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "xpKOoWgu-llD",
        "outputId": "9b67b6eb-e8ae-4265-c964-a9c1f8d9ec3f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, None, 16)          160000    \n",
            "                                                                 \n",
            " global_average_pooling1d (  (None, 16)                0         \n",
            " GlobalAveragePooling1D)                                         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 100)               1700      \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 100)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 6)                 606       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 162306 (634.01 KB)\n",
            "Trainable params: 162306 (634.01 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = tf.keras.Sequential([\n",
        "  layers.Embedding(max_features, embedding_dim),\n",
        "  layers.GlobalAveragePooling1D(),\n",
        "  layers.Dense(100),\n",
        "  layers.Dropout(0.5),\n",
        "  layers.Dense(6)])\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6PbKQ6mucuKL"
      },
      "source": [
        "The layers are stacked sequentially to build the classifier:\n",
        "\n",
        "1. The first layer is an `Embedding` layer. This layer takes the integer-encoded reviews and looks up an embedding vector for each word-index. These vectors are learned as the model trains. The vectors add a dimension to the output array. The resulting dimensions are: `(batch, sequence, embedding)`.  To learn more about embeddings, check out the [Word embeddings](https://www.tensorflow.org/text/guide/word_embeddings) tutorial.\n",
        "2. Next, a `GlobalAveragePooling1D` layer returns a fixed-length output vector for each example by averaging over the sequence dimension. This allows the model to handle input of variable length, in the simplest way possible.\n",
        "3. The last layer is densely connected with a single output node."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L4EqVWg4-llM"
      },
      "source": [
        "### Loss function and optimizer\n",
        "\n",
        "A model needs a loss function and an optimizer for training. Since this is a binary classification problem and the model outputs a probability (a single-unit layer with a sigmoid activation), you'll use `losses.BinaryCrossentropy` loss function.\n",
        "\n",
        "Now, configure the model to use an optimizer and a loss function:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "Mr0GP-cQ-llN"
      },
      "outputs": [],
      "source": [
        "model.compile(loss=losses.SparseCategoricalCrossentropy(from_logits=True),\n",
        "              optimizer='adam',\n",
        "              metrics='accuracy')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "35jv_fzP-llU"
      },
      "source": [
        "### Train the model\n",
        "\n",
        "You will train the model by passing the `dataset` object to the fit method."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "tXSGrjWZ-llW",
        "outputId": "539bea92-fec9-4b8b-f5a3-11e869287414",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/15\n",
            "940/940 [==============================] - 68s 66ms/step - loss: 0.8640 - accuracy: 0.6860 - val_loss: 0.4730 - val_accuracy: 0.8620\n",
            "Epoch 2/15\n",
            "940/940 [==============================] - 6s 6ms/step - loss: 0.3285 - accuracy: 0.8976 - val_loss: 0.2348 - val_accuracy: 0.9171\n",
            "Epoch 3/15\n",
            "940/940 [==============================] - 5s 5ms/step - loss: 0.1895 - accuracy: 0.9405 - val_loss: 0.1547 - val_accuracy: 0.9522\n",
            "Epoch 4/15\n",
            "940/940 [==============================] - 4s 4ms/step - loss: 0.1271 - accuracy: 0.9631 - val_loss: 0.1250 - val_accuracy: 0.9615\n",
            "Epoch 5/15\n",
            "940/940 [==============================] - 4s 4ms/step - loss: 0.0953 - accuracy: 0.9724 - val_loss: 0.1120 - val_accuracy: 0.9646\n",
            "Epoch 6/15\n",
            "940/940 [==============================] - 5s 5ms/step - loss: 0.0746 - accuracy: 0.9783 - val_loss: 0.1048 - val_accuracy: 0.9671\n",
            "Epoch 7/15\n",
            "940/940 [==============================] - 4s 4ms/step - loss: 0.0587 - accuracy: 0.9843 - val_loss: 0.1006 - val_accuracy: 0.9682\n",
            "Epoch 8/15\n",
            "940/940 [==============================] - 4s 4ms/step - loss: 0.0471 - accuracy: 0.9879 - val_loss: 0.1003 - val_accuracy: 0.9691\n",
            "Epoch 9/15\n",
            "940/940 [==============================] - 5s 5ms/step - loss: 0.0384 - accuracy: 0.9906 - val_loss: 0.1018 - val_accuracy: 0.9713\n",
            "Epoch 10/15\n",
            "940/940 [==============================] - 4s 4ms/step - loss: 0.0317 - accuracy: 0.9919 - val_loss: 0.0978 - val_accuracy: 0.9735\n",
            "Epoch 11/15\n",
            "940/940 [==============================] - 4s 4ms/step - loss: 0.0265 - accuracy: 0.9934 - val_loss: 0.1032 - val_accuracy: 0.9726\n",
            "Epoch 12/15\n",
            "940/940 [==============================] - 5s 5ms/step - loss: 0.0230 - accuracy: 0.9946 - val_loss: 0.1048 - val_accuracy: 0.9738\n",
            "Epoch 13/15\n",
            "940/940 [==============================] - 4s 4ms/step - loss: 0.0198 - accuracy: 0.9953 - val_loss: 0.1020 - val_accuracy: 0.9759\n",
            "Epoch 14/15\n",
            "940/940 [==============================] - 4s 4ms/step - loss: 0.0182 - accuracy: 0.9957 - val_loss: 0.1026 - val_accuracy: 0.9770\n",
            "Epoch 15/15\n",
            "940/940 [==============================] - 4s 4ms/step - loss: 0.0166 - accuracy: 0.9957 - val_loss: 0.1046 - val_accuracy: 0.9766\n"
          ]
        }
      ],
      "source": [
        "epochs = 15\n",
        "history = model.fit(\n",
        "    train_ds,\n",
        "    validation_data=val_ds,\n",
        "    epochs=epochs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9EEGuDVuzb5r"
      },
      "source": [
        "### Evaluate the model\n",
        "\n",
        "Let's see how the model performs. Two values will be returned. Loss (a number which represents our error, lower values are better), and accuracy."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z1iEXVTR0Z2t"
      },
      "source": [
        "This fairly naive approach achieves an accuracy of about 86%."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ldbQqCw2Xc1W"
      },
      "source": [
        "### Create a plot of accuracy and loss over time\n",
        "\n",
        "`model.fit()` returns a `History` object that contains a dictionary with everything that happened during training:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "-YcvZsdvWfDf",
        "outputId": "db760bcf-a651-40b7-b236-1a72d4574e37",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "history_dict = history.history\n",
        "history_dict.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1_CH32qJXruI"
      },
      "source": [
        "There are four entries: one for each monitored metric during training and validation. You can use these to plot the training and validation loss for comparison, as well as the training and validation accuracy:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "2SEMeQ5YXs8z",
        "outputId": "7f36033a-a652-4b61-e613-d6aadc72ed7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "acc = history_dict['accuracy']\n",
        "val_acc = history_dict['val_accuracy']\n",
        "loss = history_dict['loss']\n",
        "val_loss = history_dict['val_loss']\n",
        "\n",
        "epochs = range(1, len(acc) + 1)\n",
        "\n",
        "# \"bo\" is for \"blue dot\"\n",
        "plt.plot(epochs, loss, 'bo', label='Training loss')\n",
        "# b is for \"solid blue line\"\n",
        "plt.plot(epochs, val_loss, 'b', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "Z3PJemLPXwz_",
        "outputId": "36dfcdb8-7ad9-434c-a344-63d946598455",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(epochs, acc, 'bo', label='Training acc')\n",
        "plt.plot(epochs, val_acc, 'b', label='Validation acc')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hFFyCuJoXy7r"
      },
      "source": [
        "In this plot, the dots represent the training loss and accuracy, and the solid lines are the validation loss and accuracy.\n",
        "\n",
        "Notice the training loss *decreases* with each epoch and the training accuracy *increases* with each epoch. This is expected when using a gradient descent optimization—it should minimize the desired quantity on every iteration.\n",
        "\n",
        "This isn't the case for the validation loss and accuracy—they seem to peak before the training accuracy. This is an example of overfitting: the model performs better on the training data than it does on data it has never seen before. After this point, the model over-optimizes and learns representations *specific* to the training data that do not *generalize* to test data.\n",
        "\n",
        "For this particular case, you could prevent overfitting by simply stopping the training when the validation accuracy is no longer increasing. One way to do so is to use the `tf.keras.callbacks.EarlyStopping` callback."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-to23J3Vy5d3"
      },
      "source": [
        "## Export the model\n",
        "\n",
        "In the code above, you applied the `TextVectorization` layer to the dataset before feeding text to the model. If you want to make your model capable of processing raw strings (for example, to simplify deploying it), you can include the `TextVectorization` layer inside your model. To do so, you can create a new model using the weights you just trained."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "FWXsMvryuZuq",
        "outputId": "217cfd8d-948a-4ef4-938b-47ae7faab1a8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "235/235 [==============================] - 1s 5ms/step - loss: 0.1046 - accuracy: 0.9766\n",
            "0.9765770435333252\n"
          ]
        }
      ],
      "source": [
        "export_model = tf.keras.Sequential([\n",
        "  vectorize_layer,\n",
        "  model,\n",
        "  layers.Activation('Softmax')\n",
        "])\n",
        "\n",
        "export_model.compile(\n",
        "    loss=losses.SparseCategoricalCrossentropy(from_logits=False), optimizer=\"adam\", metrics=['accuracy']\n",
        ")\n",
        "\n",
        "# Test it with `raw_test_ds`, which yields raw strings\n",
        "loss, accuracy = export_model.evaluate(raw_val_ds)\n",
        "print(accuracy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TwQgoN88LoEF"
      },
      "source": [
        "### Inference on new data\n",
        "\n",
        "To get predictions for new examples, you can simply call `model.predict()`."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
      "metadata": {
        "id": "Phe8a8sQef1k"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "QW355HH5L49K",
        "outputId": "d2766155-0fbf-4946-a46c-49b5fa5b11e1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1/1 [==============================] - 0s 136ms/step\n",
            "جغرافیا و مکانها\n",
            "ورزش\n",
            "جغرافیا و مکانها\n"
          ]
        }
      ],
      "source": [
        "examples = [\n",
        "  \"ویروس کرونا جان خیلی ها را گرفت\",\n",
        "  \"فوتبال نوجوانان کشور با نتیجه 2 بر 0 به پایان رسید.\",\n",
        "  \"تهران پایخت ایران است.\"\n",
        "]\n",
        "\n",
        "p = export_model.predict(examples)\n",
        "results = np.argmax(p, axis=1)\n",
        "\n",
        "for r in results:\n",
        "  print(raw_train_ds.class_names[r])"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Confusion matrix"
      ],
      "metadata": {
        "id": "1uIT-BjRzboE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = raw_val_ds.map(lambda x, y: x)\n",
        "labels = raw_val_ds.map(lambda x, y: y)"
      ],
      "metadata": {
        "id": "EDAZrUkizlE5"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "predictions = export_model.predict(features)\n",
        "predicted_classes = tf.math.argmax(predictions, axis=-1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bFDceDRP0Fln",
        "outputId": "aca32291-6cf5-4a63-f231-dfd54001beb6"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "235/235 [==============================] - 1s 4ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the actual data from the MapDataset object\n",
        "labels_list = list(labels.as_numpy_iterator())\n",
        "\n",
        "# Flatten the batched labels\n",
        "labels = np.concatenate(labels_list, axis=0)"
      ],
      "metadata": {
        "id": "dgA7adbq2RDW"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "confusion_mtx = tf.math.confusion_matrix(labels, predicted_classes)\n",
        "confusion_mtx"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jFOQiPbW0ka0",
        "outputId": "163949e6-5f25-4c78-b415-2e05495980ca"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(6, 6), dtype=int32, numpy=\n",
              "array([[  12,  237,    5,    0,   57,   71],\n",
              "       [ 221, 2645,   46,   34,  771,  719],\n",
              "       [   7,   67,    2,    0,   16,   17],\n",
              "       [   3,   38,    0,    0,   13,    7],\n",
              "       [  81,  753,   20,    9,  236,  216],\n",
              "       [  65,  730,   14,   11,  209,  182]], dtype=int32)>"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "sns.heatmap(confusion_mtx, annot=True, fmt='d', cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "4qAmIj-f0sbH",
        "outputId": "31cb8858-938a-45bd-b63f-4a3ccf3bd3a9"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install python-bidi"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MU6Xtc126-gp",
        "outputId": "b2aa4276-161b-4af4-d1c1-98be11a08c27"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting python-bidi\n",
            "  Downloading python_bidi-0.4.2-py2.py3-none-any.whl (30 kB)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from python-bidi) (1.16.0)\n",
            "Installing collected packages: python-bidi\n",
            "Successfully installed python-bidi-0.4.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install arabic_reshaper"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zstOpPdB7Ml9",
        "outputId": "59793df6-4e9b-41e3-9c89-496d3df70843"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting arabic_reshaper\n",
            "  Downloading arabic_reshaper-3.0.0-py3-none-any.whl (20 kB)\n",
            "Installing collected packages: arabic_reshaper\n",
            "Successfully installed arabic_reshaper-3.0.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import arabic_reshaper\n",
        "from bidi.algorithm import get_display\n",
        "\n",
        "def convert_text(t):\n",
        "    reshaped_text = arabic_reshaper.reshape(t) # seperated chars problem\n",
        "    bidi_text = get_display(reshaped_text) #direction problem\n",
        "    text = bidi_text.encode().decode('utf-8') #encoding problem (rectangular boxes!)\n",
        "    return text\n",
        "\n",
        "class_names = [convert_text(t) for t in raw_train_ds.class_names]\n",
        "\n",
        "sns.heatmap(confusion_mtx, annot=True, fmt='d', cmap='Blues')\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.xticks(np.arange(len(class_names))+0.5, class_names, rotation=45)\n",
        "plt.yticks(np.arange(len(class_names))+0.5, class_names, rotation=0)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 610
        },
        "id": "Kd-rKMFP9HWk",
        "outputId": "20e887bd-7211-4d54-d614-7b26dc115b98"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "report = classification_report(labels, predicted_classes)\n",
        "\n",
        "for i in range(6):\n",
        "  print(\"Label {} corresponds to {}\".format(i, raw_train_ds.class_names[i]))\n",
        "print(report)"
      ],
      "metadata": {
        "id": "4KM2nndX_TxU",
        "outputId": "d2f7b7f1-bb5c-43c6-e463-880057d02643",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Label 0 corresponds to بهداشت و سلامت\n",
            "Label 1 corresponds to جغرافیا و مکانها\n",
            "Label 2 corresponds to دین و اعتقاد\n",
            "Label 3 corresponds to ریاضیات\n",
            "Label 4 corresponds to فناوری و علوم کاربردی و تکنولوژی\n",
            "Label 5 corresponds to ورزش\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.03      0.03      0.03       382\n",
            "           1       0.59      0.60      0.59      4436\n",
            "           2       0.02      0.02      0.02       109\n",
            "           3       0.00      0.00      0.00        61\n",
            "           4       0.18      0.18      0.18      1315\n",
            "           5       0.15      0.15      0.15      1211\n",
            "\n",
            "    accuracy                           0.41      7514\n",
            "   macro avg       0.16      0.16      0.16      7514\n",
            "weighted avg       0.41      0.41      0.41      7514\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "cKXJ0Wnx_WAu"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "text_classification.ipynb",
      "provenance": [],
      "gpuType": "T4"
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}