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    }
  },
  "nbformat_minor": 0,
  "nbformat": 4,
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Installation"
      ],
      "metadata": {
        "id": "isdAixdm1I_9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers==4.37.2"
      ],
      "metadata": {
        "id": "27VLFDK5v-2V",
        "outputId": "88fdd454-b45e-4fdb-bbdf-a774c9baeb21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting transformers==4.37.2\n",
            "  Downloading transformers-4.37.2-py3-none-any.whl (8.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m23.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers==4.37.2) (3.14.0)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers==4.37.2) (0.20.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.37.2) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers==4.37.2) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.37.2) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers==4.37.2) (2023.12.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers==4.37.2) (2.31.0)\n",
            "Collecting tokenizers<0.19,>=0.14 (from transformers==4.37.2)\n",
            "  Downloading tokenizers-0.15.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m65.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers==4.37.2) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers==4.37.2) (4.66.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.37.2) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers==4.37.2) (4.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.37.2) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.37.2) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.37.2) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers==4.37.2) (2024.2.2)\n",
            "Installing collected packages: tokenizers, transformers\n",
            "  Attempting uninstall: tokenizers\n",
            "    Found existing installation: tokenizers 0.19.1\n",
            "    Uninstalling tokenizers-0.19.1:\n",
            "      Successfully uninstalled tokenizers-0.19.1\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.40.2\n",
            "    Uninstalling transformers-4.40.2:\n",
            "      Successfully uninstalled transformers-4.40.2\n",
            "Successfully installed tokenizers-0.15.2 transformers-4.37.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install tensorflow==2.15.1"
      ],
      "metadata": {
        "id": "j03cmsDkt1eR",
        "outputId": "beb674fa-d742-483e-d812-41db9ee97efc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting tensorflow==2.15.1\n",
            "  Downloading tensorflow-2.15.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (475.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m475.2/475.2 MB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (0.5.4)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (0.2.0)\n",
            "Requirement already satisfied: h5py>=2.9.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (3.9.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (18.1.1)\n",
            "Collecting ml-dtypes~=0.3.1 (from tensorflow==2.15.1)\n",
            "  Downloading ml_dtypes-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m50.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (1.25.2)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (24.0)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (3.20.3)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (67.7.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (4.11.0)\n",
            "Requirement already satisfied: wrapt<1.15,>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (1.14.1)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (0.37.0)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (1.63.0)\n",
            "Requirement already satisfied: tensorboard<2.16,>=2.15 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (2.15.2)\n",
            "Requirement already satisfied: tensorflow-estimator<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (2.15.0)\n",
            "Requirement already satisfied: keras<2.16,>=2.15.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.15.1) (2.15.0)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow==2.15.1) (0.43.0)\n",
            "Requirement already satisfied: google-auth<3,>=1.6.3 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.1) (2.27.0)\n",
            "Requirement already satisfied: google-auth-oauthlib<2,>=0.5 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.1) (1.2.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.1) (3.6)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.1) (2.31.0)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.1) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.16,>=2.15->tensorflow==2.15.1) (3.0.3)\n",
            "Requirement already satisfied: cachetools<6.0,>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (5.3.3)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (0.4.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.10/dist-packages (from google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (4.9)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (1.3.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (2024.2.2)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (2.1.5)\n",
            "Requirement already satisfied: pyasn1<0.7.0,>=0.4.6 in /usr/local/lib/python3.10/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (0.6.0)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<2,>=0.5->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (3.2.2)\n",
            "Installing collected packages: ml-dtypes, tensorflow\n",
            "  Attempting uninstall: ml-dtypes\n",
            "    Found existing installation: ml-dtypes 0.2.0\n",
            "    Uninstalling ml-dtypes-0.2.0:\n",
            "      Successfully uninstalled ml-dtypes-0.2.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.15.0\n",
            "    Uninstalling tensorflow-2.15.0:\n",
            "      Successfully uninstalled tensorflow-2.15.0\n",
            "Successfully installed ml-dtypes-0.3.2 tensorflow-2.15.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -c \"import tensorflow as tf; print(tf.__version__)\""
      ],
      "metadata": {
        "id": "w9NAGvp2qqkQ",
        "outputId": "f145b332-0bc1-4d9b-fa97-03e1d9b406a4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2024-05-09 20:58:33.633351: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
            "2024-05-09 20:58:33.633404: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
            "2024-05-09 20:58:33.635413: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
            "2024-05-09 20:58:33.646082: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
            "To enable the following instructions: AVX2 AVX512F FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
            "2024-05-09 20:58:35.536276: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n",
            "2.15.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ktrain"
      ],
      "metadata": {
        "_kg_hide-output": true,
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "trusted": true,
        "id": "vLqtBQ3OqNl4",
        "outputId": "e5bfc8bc-14bb-46a5-8a16-3d8250478ac8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ktrain\n",
            "  Downloading ktrain-0.41.3.tar.gz (25.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m25.3/25.3 MB\u001b[0m \u001b[31m54.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from ktrain) (1.2.2)\n",
            "Requirement already satisfied: matplotlib>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from ktrain) (3.7.1)\n",
            "Requirement already satisfied: pandas>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from ktrain) (2.0.3)\n",
            "Requirement already satisfied: fastprogress>=0.1.21 in /usr/local/lib/python3.10/dist-packages (from ktrain) (1.0.3)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from ktrain) (2.31.0)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from ktrain) (1.4.2)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from ktrain) (24.0)\n",
            "Collecting langdetect (from ktrain)\n",
            "  Downloading langdetect-1.0.9.tar.gz (981 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m75.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: jieba in /usr/local/lib/python3.10/dist-packages (from ktrain) (0.42.1)\n",
            "Requirement already satisfied: charset-normalizer in /usr/local/lib/python3.10/dist-packages (from ktrain) (3.3.2)\n",
            "Requirement already satisfied: chardet in /usr/local/lib/python3.10/dist-packages (from ktrain) (5.2.0)\n",
            "Collecting syntok>1.3.3 (from ktrain)\n",
            "  Downloading syntok-1.4.4-py3-none-any.whl (24 kB)\n",
            "Collecting tika (from ktrain)\n",
            "  Downloading tika-2.6.0.tar.gz (27 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from ktrain) (4.37.2)\n",
            "Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from ktrain) (0.1.99)\n",
            "Collecting keras_bert>=0.86.0 (from ktrain)\n",
            "  Downloading keras-bert-0.89.0.tar.gz (25 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting whoosh (from ktrain)\n",
            "  Downloading Whoosh-2.7.4-py2.py3-none-any.whl (468 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.8/468.8 kB\u001b[0m \u001b[31m52.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from keras_bert>=0.86.0->ktrain) (1.25.2)\n",
            "Collecting keras-transformer==0.40.0 (from keras_bert>=0.86.0->ktrain)\n",
            "  Downloading keras-transformer-0.40.0.tar.gz (9.7 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting keras-pos-embd==0.13.0 (from keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\n",
            "  Downloading keras-pos-embd-0.13.0.tar.gz (5.6 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting keras-multi-head==0.29.0 (from keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\n",
            "  Downloading keras-multi-head-0.29.0.tar.gz (13 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting keras-layer-normalization==0.16.0 (from keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\n",
            "  Downloading keras-layer-normalization-0.16.0.tar.gz (3.9 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting keras-position-wise-feed-forward==0.8.0 (from keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\n",
            "  Downloading keras-position-wise-feed-forward-0.8.0.tar.gz (4.1 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting keras-embed-sim==0.10.0 (from keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\n",
            "  Downloading keras-embed-sim-0.10.0.tar.gz (3.6 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting keras-self-attention==0.51.0 (from keras-multi-head==0.29.0->keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\n",
            "  Downloading keras-self-attention-0.51.0.tar.gz (11 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=3.0.0->ktrain) (1.2.1)\n",
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            "Requirement already satisfied: regex>2016 in /usr/local/lib/python3.10/dist-packages (from syntok>1.3.3->ktrain) (2023.12.25)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.10/dist-packages (from langdetect->ktrain) (1.16.0)\n",
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            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->ktrain) (2.0.7)\n",
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            "Requirement already satisfied: scipy>=1.3.2 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->ktrain) (1.11.4)\n",
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            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tika->ktrain) (67.7.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers->ktrain) (3.14.0)\n",
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            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers->ktrain) (6.0.1)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers->ktrain) (0.15.2)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers->ktrain) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers->ktrain) (4.66.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers->ktrain) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers->ktrain) (4.11.0)\n",
            "Building wheels for collected packages: ktrain, keras_bert, keras-transformer, keras-embed-sim, keras-layer-normalization, keras-multi-head, keras-pos-embd, keras-position-wise-feed-forward, keras-self-attention, langdetect, tika\n",
            "  Building wheel for ktrain (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for ktrain: filename=ktrain-0.41.3-py3-none-any.whl size=25316960 sha256=a6f2fd86ebef974549cfd7964392804cd83714f178a425ec7b80c946c215f8d1\n",
            "  Stored in directory: /root/.cache/pip/wheels/fc/76/11/5b953090eebf531f660948a30cd26e70260619f6480f186a5a\n",
            "  Building wheel for keras_bert (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras_bert: filename=keras_bert-0.89.0-py3-none-any.whl size=33500 sha256=c3a16fb338f8a0e63fdea90736068d4f25050ecf337d037feff26cd8b80f8b1e\n",
            "  Stored in directory: /root/.cache/pip/wheels/89/0c/04/646b6fdf6375911b42c8d540a8a3fda8d5d77634e5dcbe7b26\n",
            "  Building wheel for keras-transformer (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-transformer: filename=keras_transformer-0.40.0-py3-none-any.whl size=12286 sha256=b2b748f1d714f41002b933d8520d3d5e29f8b0cad01b984e838af9f79d6d09fa\n",
            "  Stored in directory: /root/.cache/pip/wheels/f2/cb/22/75a0ad376129177f7c95c0d91331a18f5368fd657f4035ba7c\n",
            "  Building wheel for keras-embed-sim (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-embed-sim: filename=keras_embed_sim-0.10.0-py3-none-any.whl size=3944 sha256=1159bb1d36d2ba0c92b3a47f2c1efdedec15f9f66e16b12783923d5547d25e78\n",
            "  Stored in directory: /root/.cache/pip/wheels/82/32/c7/fd35d0d1b840a6c7cbd4343f808d10d0f7b87d271a4dbe796f\n",
            "  Building wheel for keras-layer-normalization (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-layer-normalization: filename=keras_layer_normalization-0.16.0-py3-none-any.whl size=4654 sha256=f349ae28949974672cfbb560d2ef856ff3c3b3b371b00f270bb0bf2e7371b2ea\n",
            "  Stored in directory: /root/.cache/pip/wheels/ed/3a/4b/21db23c0cc56c4b219616e181f258eb7c57d36cc5d056fae9a\n",
            "  Building wheel for keras-multi-head (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-multi-head: filename=keras_multi_head-0.29.0-py3-none-any.whl size=14976 sha256=b350d9457efe4d2ae6efeceae8f8e62ea1f1b3ee97ebb095cea96e943ddd1f94\n",
            "  Stored in directory: /root/.cache/pip/wheels/cb/23/4b/06d7ae21714f70fcc25b48f972cc8e5e7f4b6b764a038b509d\n",
            "  Building wheel for keras-pos-embd (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-pos-embd: filename=keras_pos_embd-0.13.0-py3-none-any.whl size=6945 sha256=13ea5079c01453b66e97086f241d62819ad7f85034bcfeb4df61c75b89813810\n",
            "  Stored in directory: /root/.cache/pip/wheels/78/07/1b/b1ca47b6ac338554b75c8f52c54e6a2bfbe1b07d79579979a4\n",
            "  Building wheel for keras-position-wise-feed-forward (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-position-wise-feed-forward: filename=keras_position_wise_feed_forward-0.8.0-py3-none-any.whl size=4969 sha256=f2f5f9ac22a455d9e5a0f08e73c5b0c026d6bcc01ed05f18f78c682936a058c6\n",
            "  Stored in directory: /root/.cache/pip/wheels/c1/6a/04/d1706a53b23b2cb5f9a0a76269bf87925daa1bca09eac01b21\n",
            "  Building wheel for keras-self-attention (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for keras-self-attention: filename=keras_self_attention-0.51.0-py3-none-any.whl size=18894 sha256=095dcbb9fecf7d80d60447db5d0c2dae925f7855ef934a1cd2fa53c163590a62\n",
            "  Stored in directory: /root/.cache/pip/wheels/b8/f7/24/607b483144fb9c47b4ba2c5fba6b68e54aeee2d5bf6c05302e\n",
            "  Building wheel for langdetect (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993227 sha256=53f486a6ac0c308cb2d75a5c771bbd47e1f665ea635cab17377b63c68baec924\n",
            "  Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\n",
            "  Building wheel for tika (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for tika: filename=tika-2.6.0-py3-none-any.whl size=32621 sha256=d3c24ff62ca65c3c5808f578573047d3be4dfcb5411da84b526f2962dfcdb64d\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/71/c7/b757709531121b1700cffda5b6b0d4aad095fb507ec84316d0\n",
            "Successfully built ktrain keras_bert keras-transformer keras-embed-sim keras-layer-normalization keras-multi-head keras-pos-embd keras-position-wise-feed-forward keras-self-attention langdetect tika\n",
            "Installing collected packages: whoosh, syntok, langdetect, keras-self-attention, keras-position-wise-feed-forward, keras-pos-embd, keras-layer-normalization, keras-embed-sim, tika, keras-multi-head, keras-transformer, keras_bert, ktrain\n",
            "Successfully installed keras-embed-sim-0.10.0 keras-layer-normalization-0.16.0 keras-multi-head-0.29.0 keras-pos-embd-0.13.0 keras-position-wise-feed-forward-0.8.0 keras-self-attention-0.51.0 keras-transformer-0.40.0 keras_bert-0.89.0 ktrain-0.41.3 langdetect-1.0.9 syntok-1.4.4 tika-2.6.0 whoosh-2.7.4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Environment Setup"
      ],
      "metadata": {
        "id": "lpCN1WG81Uz0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking for GPU\n",
        "import torch\n",
        "\n",
        "# If there's a GPU available...\n",
        "if torch.cuda.is_available():\n",
        "\n",
        "    # Tell PyTorch to use the GPU.\n",
        "    device = torch.device(\"cuda\")\n",
        "\n",
        "    print('There are %d GPU(s) available.' % torch.cuda.device_count())\n",
        "\n",
        "    print('We will use the GPU:', torch.cuda.get_device_name(0))\n",
        "    !nvidia-smi\n",
        "\n",
        "# If not...\n",
        "else:\n",
        "    print('No GPU available, using the CPU instead.')\n",
        "    device = torch.device(\"cpu\")"
      ],
      "metadata": {
        "id": "vtrVWGwgxyTy",
        "outputId": "106ca23d-bb76-4482-b589-83869156749f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 1 GPU(s) available.\n",
            "We will use the GPU: Tesla T4\n",
            "Thu May  9 20:59:15 2024       \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n",
            "|-----------------------------------------+----------------------+----------------------+\n",
            "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                                         |                      |               MIG M. |\n",
            "|=========================================+======================+======================|\n",
            "|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   40C    P8               9W /  70W |      3MiB / 15360MiB |      0%      Default |\n",
            "|                                         |                      |                  N/A |\n",
            "+-----------------------------------------+----------------------+----------------------+\n",
            "                                                                                         \n",
            "+---------------------------------------------------------------------------------------+\n",
            "| Processes:                                                                            |\n",
            "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n",
            "|        ID   ID                                                             Usage      |\n",
            "|=======================================================================================|\n",
            "|  No running processes found                                                           |\n",
            "+---------------------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Libraries"
      ],
      "metadata": {
        "id": "YsINTBMQ1P2o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Generic\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import warnings, gc\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# ktrain\n",
        "import ktrain\n",
        "from ktrain import text\n",
        "\n",
        "# sklearn\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "metadata": {
        "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
        "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
        "trusted": true,
        "id": "ecbnrqa9qNl4"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data"
      ],
      "metadata": {
        "id": "t9l_prkjqNl4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/newsfeed.csv',usecols=['Body','category'])\n",
        "df.sample(10)"
      ],
      "metadata": {
        "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
        "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
        "trusted": true,
        "id": "_8LBINq8qNl4",
        "outputId": "809e4f9e-5d75-4565-c393-5fbe2ba217ff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        }
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                    category  \\\n",
              "5128        Middle East News   \n",
              "2184  Science and Technology   \n",
              "2251  Science and Technology   \n",
              "3315                   World   \n",
              "1128        Middle East News   \n",
              "3181                   World   \n",
              "7912           Entertainment   \n",
              "407                 Business   \n",
              "7325  Science and Technology   \n",
              "3224                   World   \n",
              "\n",
              "                                                   Body  \n",
              "5128  اوسط عراق مفوض انتخاب عد مراجع شامل بيان المفو...  \n",
              "2184  سبب نتشار انفلونز الخنازير زوير الفياغرا تراجع...  \n",
              "2251  مشتبه بانفلونزا خنازير متحده مشتبه بانفلونزا خ...  \n",
              "3315  جيش امريك بد عمل عسكر سع افغانس جيش امريك بد ع...  \n",
              "1128  مصر ءجل محاكم خل حزب اكتوبر مقبل سبت الاول اكت...  \n",
              "3181  زوج ايدن كشف عرض اوبام منصب خارجيه زوج زوج ايد...  \n",
              "7912  عرض قضاي داول مستخدم موقع تواصل اجتماع عالم عر...  \n",
              "407   سهم كويت واصل راجع دب صعد مستو قياس سهم كويت و...  \n",
              "7325  مراهق عاصمه صينيه كين اثنين جراء اصاب مرض انفل...  \n",
              "3224  رسال جديده ظواهر دعو باكستان دعم مسلح رسال جدي...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ecd55bb5-db77-470b-9641-edbdef7e4573\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>category</th>\n",
              "      <th>Body</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5128</th>\n",
              "      <td>Middle East News</td>\n",
              "      <td>اوسط عراق مفوض انتخاب عد مراجع شامل بيان المفو...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2184</th>\n",
              "      <td>Science and Technology</td>\n",
              "      <td>سبب نتشار انفلونز الخنازير زوير الفياغرا تراجع...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2251</th>\n",
              "      <td>Science and Technology</td>\n",
              "      <td>مشتبه بانفلونزا خنازير متحده مشتبه بانفلونزا خ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3315</th>\n",
              "      <td>World</td>\n",
              "      <td>جيش امريك بد عمل عسكر سع افغانس جيش امريك بد ع...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1128</th>\n",
              "      <td>Middle East News</td>\n",
              "      <td>مصر ءجل محاكم خل حزب اكتوبر مقبل سبت الاول اكت...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3181</th>\n",
              "      <td>World</td>\n",
              "      <td>زوج ايدن كشف عرض اوبام منصب خارجيه زوج زوج ايد...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7912</th>\n",
              "      <td>Entertainment</td>\n",
              "      <td>عرض قضاي داول مستخدم موقع تواصل اجتماع عالم عر...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>407</th>\n",
              "      <td>Business</td>\n",
              "      <td>سهم كويت واصل راجع دب صعد مستو قياس سهم كويت و...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7325</th>\n",
              "      <td>Science and Technology</td>\n",
              "      <td>مراهق عاصمه صينيه كين اثنين جراء اصاب مرض انفل...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3224</th>\n",
              "      <td>World</td>\n",
              "      <td>رسال جديده ظواهر دعو باكستان دعم مسلح رسال جدي...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ecd55bb5-db77-470b-9641-edbdef7e4573')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ecd55bb5-db77-470b-9641-edbdef7e4573 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-ecd55bb5-db77-470b-9641-edbdef7e4573');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4a5cb02e-5ea5-4ad2-9b71-b72fc8fe1f90\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a5cb02e-5ea5-4ad2-9b71-b72fc8fe1f90')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4a5cb02e-5ea5-4ad2-9b71-b72fc8fe1f90 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
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            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df['Body']\n",
        "y = df['category']\n",
        "X_train, X_test , y_train,y_test = train_test_split(np.array(X),np.array(y),test_size=0.2, random_state=0,stratify=y)\n",
        "print('Shape of X_train:', X_train.shape)\n",
        "print('Shape of y_train:', y_train.shape)\n",
        "print('Shape of X_test:', X_test.shape)\n",
        "print('Shape of y_test:', y_test.shape)"
      ],
      "metadata": {
        "trusted": true,
        "id": "YrGIjDd9qNl4",
        "outputId": "21888b84-9576-41c2-bbec-ee0f857be626",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of X_train: (7027,)\n",
            "Shape of y_train: (7027,)\n",
            "Shape of X_test: (1757,)\n",
            "Shape of y_test: (1757,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Model Configuration"
      ],
      "metadata": {
        "id": "zO9dRELMqNl4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "MODEL_NAME = 'aubmindlab/bert-base-arabertv01'"
      ],
      "metadata": {
        "trusted": true,
        "id": "S6dkQU_1qNl5"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "t = text.Transformer(MODEL_NAME, maxlen=128)\n",
        "train = t.preprocess_train(X_train, y_train)\n",
        "val = t.preprocess_test(X_test, y_test)"
      ],
      "metadata": {
        "_kg_hide-output": true,
        "collapsed": true,
        "jupyter": {
          "outputs_hidden": true
        },
        "trusted": true,
        "id": "FbzdcMV2qNl5",
        "outputId": "40482111-b777-4040-a5ed-1cbfe5e1f84b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434,
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            "ccd7f86c8d8f4fe4ab6dab3375240a7b"
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      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/576 [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e477b5e5edd42e2b0532b7e09d8dc87"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/543M [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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              "model_id": "24ffa2640fbe4bf6b47f28c8690d02dc"
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          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "preprocessing train...\n",
            "language: ar\n",
            "train sequence lengths:\n",
            "\tmean : 256\n",
            "\t95percentile : 567\n",
            "\t99percentile : 834\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "vocab.txt:   0%|          | 0.00/780k [00:00<?, ?B/s]"
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              "version_major": 2,
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          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.70M [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Is Multi-Label? False\n",
            "preprocessing test...\n",
            "language: ar\n",
            "test sequence lengths:\n",
            "\tmean : 250\n",
            "\t95percentile : 531\n",
            "\t99percentile : 795\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "<style>\n",
              "    /* Turns off some styling */\n",
              "    progress {\n",
              "        /* gets rid of default border in Firefox and Opera. */\n",
              "        border: none;\n",
              "        /* Needs to be in here for Safari polyfill so background images work as expected. */\n",
              "        background-size: auto;\n",
              "    }\n",
              "    progress:not([value]), progress:not([value])::-webkit-progress-bar {\n",
              "        background: repeating-linear-gradient(45deg, #7e7e7e, #7e7e7e 10px, #5c5c5c 10px, #5c5c5c 20px);\n",
              "    }\n",
              "    .progress-bar-interrupted, .progress-bar-interrupted::-webkit-progress-bar {\n",
              "        background: #F44336;\n",
              "    }\n",
              "</style>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": []
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training"
      ],
      "metadata": {
        "id": "czNpCGCT18OL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model = t.get_classifier(metrics=['accuracy'])\n",
        "learner = ktrain.get_learner(model, train_data=train, val_data=val, batch_size=32)\n",
        "learner.fit_onecycle(5e-5,10)"
      ],
      "metadata": {
        "id": "TbZ4VtVMsG2z",
        "outputId": "8ab2e338-43d0-475d-a160-1bb37375ccdc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "begin training using onecycle policy with max lr of 5e-05...\n",
            "Epoch 1/10\n",
            "220/220 [==============================] - 221s 885ms/step - loss: 0.8002 - accuracy: 0.7562 - val_loss: 0.2500 - val_accuracy: 0.9237\n",
            "Epoch 2/10\n",
            "220/220 [==============================] - 193s 877ms/step - loss: 0.2050 - accuracy: 0.9422 - val_loss: 0.2037 - val_accuracy: 0.9368\n",
            "Epoch 3/10\n",
            "220/220 [==============================] - 193s 877ms/step - loss: 0.1318 - accuracy: 0.9636 - val_loss: 0.2056 - val_accuracy: 0.9351\n",
            "Epoch 4/10\n",
            "220/220 [==============================] - 193s 877ms/step - loss: 0.0971 - accuracy: 0.9745 - val_loss: 0.2229 - val_accuracy: 0.9397\n",
            "Epoch 5/10\n",
            "220/220 [==============================] - 193s 878ms/step - loss: 0.0916 - accuracy: 0.9750 - val_loss: 0.2469 - val_accuracy: 0.9340\n",
            "Epoch 6/10\n",
            "220/220 [==============================] - 193s 878ms/step - loss: 0.0792 - accuracy: 0.9798 - val_loss: 0.2443 - val_accuracy: 0.9385\n",
            "Epoch 7/10\n",
            "220/220 [==============================] - 193s 878ms/step - loss: 0.0463 - accuracy: 0.9902 - val_loss: 0.2090 - val_accuracy: 0.9493\n",
            "Epoch 8/10\n",
            "220/220 [==============================] - 193s 878ms/step - loss: 0.0271 - accuracy: 0.9936 - val_loss: 0.2200 - val_accuracy: 0.9448\n",
            "Epoch 9/10\n",
            "220/220 [==============================] - 199s 903ms/step - loss: 0.0165 - accuracy: 0.9969 - val_loss: 0.2222 - val_accuracy: 0.9539\n",
            "Epoch 10/10\n",
            "220/220 [==============================] - 198s 902ms/step - loss: 0.0118 - accuracy: 0.9977 - val_loss: 0.2260 - val_accuracy: 0.9528\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7df961ed1540>"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Evaluation"
      ],
      "metadata": {
        "id": "mizwZSno2BlW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "validation = learner.validate(class_names=t.get_classes())\n",
        "validation"
      ],
      "metadata": {
        "id": "Rlx42dlNsOP6",
        "outputId": "52dca937-1d05-4535-8d9e-b6cefb2d13b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "55/55 [==============================] - 19s 275ms/step\n",
            "                        precision    recall  f1-score   support\n",
            "\n",
            "              Business       0.96      0.97      0.96       347\n",
            "         Entertainment       0.89      0.86      0.87       153\n",
            "      Middle East News       0.97      0.96      0.96       402\n",
            "Science and Technology       0.92      0.92      0.92       272\n",
            "                 Sport       0.98      0.99      0.99       182\n",
            "                 World       0.97      0.98      0.97       401\n",
            "\n",
            "              accuracy                           0.95      1757\n",
            "             macro avg       0.95      0.95      0.95      1757\n",
            "          weighted avg       0.95      0.95      0.95      1757\n",
            "\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[335,   3,   1,   8,   0,   0],\n",
              "       [  4, 131,   3,  13,   1,   1],\n",
              "       [  2,   2, 385,   1,   3,   9],\n",
              "       [  9,   9,   1, 251,   0,   2],\n",
              "       [  0,   0,   0,   0, 181,   1],\n",
              "       [  0,   2,   7,   1,   0, 391]])"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix data\n",
        "conf_matrix = np.array([[335,   3,   1,   8,   0,   0],\n",
        "                        [  4, 131,   3,  13,   1,   1],\n",
        "                        [  2,   2, 385,   1,   3,   9],\n",
        "                        [  9,   9,   1, 251,   0,   2],\n",
        "                        [  0,   0,   0,   0, 181,   1],\n",
        "                        [  0,   2,   7,   1,   0, 391]])\n",
        "\n",
        "# Class names\n",
        "class_names = df.category.unique()\n",
        "\n",
        "# Plotting confusion matrix\n",
        "plt.figure(figsize=(10, 8))\n",
        "sns.set(font_scale=1.2)  # Adjust font scale if needed\n",
        "sns.heatmap(conf_matrix, annot=True, cmap='Blues', fmt='g',\n",
        "            xticklabels=class_names, yticklabels=class_names)\n",
        "plt.xlabel('Predicted')\n",
        "plt.ylabel('Actual')\n",
        "plt.title('Confusion Matrix')\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.yticks(rotation=0)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "9ZzEwpt1zz31",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 882
        },
        "outputId": "7c4a284d-baa2-461f-fb17-458eba4ddffd"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "T4c9-cyfSyqy"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}