text-classification-with-arabert.ipynb

{
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     "text": [
      "/kaggle/input/my-data/train_Arabic_tweets_positive_20190413.tsv\n",
      "/kaggle/input/my-data/train_Arabic_tweets_negative_20190413.tsv\n",
      "/kaggle/input/my-data/test_Arabic_tweets_positive_20190413.tsv\n",
      "/kaggle/input/my-data/test_Arabic_tweets_negative_20190413.tsv\n",
      "/kaggle/input/my-data/newsfeed.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load\n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "\n",
    "# Input data files are available in the read-only \"../input/\" directory\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
    "# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
   ]
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     "output_type": "stream",
     "text": [
      "Collecting transformers==4.37.2\r\n",
      "  Downloading transformers-4.37.2-py3-none-any.whl.metadata (129 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m129.4/129.4 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers==4.37.2) (3.13.1)\r\n",
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      "Downloading transformers-4.37.2-py3-none-any.whl (8.4 MB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m84.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hInstalling collected packages: transformers\r\n",
      "  Attempting uninstall: transformers\r\n",
      "    Found existing installation: transformers 4.39.3\r\n",
      "    Uninstalling transformers-4.39.3:\r\n",
      "      Successfully uninstalled transformers-4.39.3\r\n",
      "Successfully installed transformers-4.37.2\r\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers==4.37.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0e2f38d2",
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    },
    "tags": []
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting tensorflow==2.15.1\r\n",
      "  Downloading tensorflow-2.15.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (4.2 kB)\r\n",
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      "Collecting ml-dtypes~=0.3.1 (from tensorflow==2.15.1)\r\n",
      "  Downloading ml_dtypes-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (20 kB)\r\n",
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      "Collecting keras<2.16,>=2.15.0 (from tensorflow==2.15.1)\r\n",
      "  Downloading keras-2.15.0-py3-none-any.whl.metadata (2.4 kB)\r\n",
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      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests<3,>=2.21.0->tensorboard<2.16,>=2.15->tensorflow==2.15.1) (2024.2.2)\r\n",
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      "Requirement already satisfied: oauthlib>=3.0.0 in /opt/conda/lib/python3.10/site-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)\r\n",
      "Downloading tensorflow-2.15.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (475.2 MB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m475.2/475.2 MB\u001b[0m \u001b[31m3.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading keras-2.15.0-py3-none-any.whl (1.7 MB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.7/1.7 MB\u001b[0m \u001b[31m60.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading ml_dtypes-0.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (2.2 MB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.2/2.2 MB\u001b[0m \u001b[31m74.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hInstalling collected packages: ml-dtypes, keras, tensorflow\r\n",
      "  Attempting uninstall: ml-dtypes\r\n",
      "    Found existing installation: ml-dtypes 0.2.0\r\n",
      "    Uninstalling ml-dtypes-0.2.0:\r\n",
      "      Successfully uninstalled ml-dtypes-0.2.0\r\n",
      "  Attempting uninstall: keras\r\n",
      "    Found existing installation: keras 3.2.1\r\n",
      "    Uninstalling keras-3.2.1:\r\n",
      "      Successfully uninstalled keras-3.2.1\r\n",
      "  Attempting uninstall: tensorflow\r\n",
      "    Found existing installation: tensorflow 2.15.0\r\n",
      "    Uninstalling tensorflow-2.15.0:\r\n",
      "      Successfully uninstalled tensorflow-2.15.0\r\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\r\n",
      "tensorflow-decision-forests 1.8.1 requires wurlitzer, which is not installed.\u001b[0m\u001b[31m\r\n",
      "\u001b[0mSuccessfully installed keras-2.15.0 ml-dtypes-0.3.2 tensorflow-2.15.1\r\n"
     ]
    }
   ],
   "source": [
    "!pip install tensorflow==2.15.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "622f9a09",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T14:32:37.678818Z",
     "iopub.status.busy": "2024-05-17T14:32:37.678382Z",
     "iopub.status.idle": "2024-05-17T14:32:46.264787Z",
     "shell.execute_reply": "2024-05-17T14:32:46.263804Z"
    },
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     "duration": 8.621822,
     "end_time": "2024-05-17T14:32:46.267158",
     "exception": false,
     "start_time": "2024-05-17T14:32:37.645336",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-05-17 14:32:39.484658: 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\r\n",
      "2024-05-17 14:32:39.484719: 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\r\n",
      "2024-05-17 14:32:39.486647: 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\r\n",
      "2.15.1\r\n"
     ]
    }
   ],
   "source": [
    "!python -c \"import tensorflow as tf; print(tf.__version__)\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "92bd0a48",
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     "iopub.status.busy": "2024-05-17T14:32:46.314662Z",
     "iopub.status.idle": "2024-05-17T14:33:29.063434Z",
     "shell.execute_reply": "2024-05-17T14:33:29.062405Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting ktrain\r\n",
      "  Downloading ktrain-0.41.3.tar.gz (25.3 MB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m25.3/25.3 MB\u001b[0m \u001b[31m62.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (from ktrain) (1.2.2)\r\n",
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      "Requirement already satisfied: packaging in /opt/conda/lib/python3.10/site-packages (from ktrain) (21.3)\r\n",
      "Collecting langdetect (from ktrain)\r\n",
      "  Downloading langdetect-1.0.9.tar.gz (981 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m981.5/981.5 kB\u001b[0m \u001b[31m44.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: jieba in /opt/conda/lib/python3.10/site-packages (from ktrain) (0.42.1)\r\n",
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      "Collecting chardet (from ktrain)\r\n",
      "  Downloading chardet-5.2.0-py3-none-any.whl.metadata (3.4 kB)\r\n",
      "Collecting syntok>1.3.3 (from ktrain)\r\n",
      "  Downloading syntok-1.4.4-py3-none-any.whl.metadata (10 kB)\r\n",
      "Collecting tika (from ktrain)\r\n",
      "  Downloading tika-2.6.0.tar.gz (27 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: transformers in /opt/conda/lib/python3.10/site-packages (from ktrain) (4.37.2)\r\n",
      "Requirement already satisfied: sentencepiece in /opt/conda/lib/python3.10/site-packages (from ktrain) (0.2.0)\r\n",
      "Collecting keras_bert>=0.86.0 (from ktrain)\r\n",
      "  Downloading keras-bert-0.89.0.tar.gz (25 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hCollecting whoosh (from ktrain)\r\n",
      "  Downloading Whoosh-2.7.4-py2.py3-none-any.whl.metadata (3.1 kB)\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from keras_bert>=0.86.0->ktrain) (1.26.4)\r\n",
      "Collecting keras-transformer==0.40.0 (from keras_bert>=0.86.0->ktrain)\r\n",
      "  Downloading keras-transformer-0.40.0.tar.gz (9.7 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hCollecting keras-pos-embd==0.13.0 (from keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\r\n",
      "  Downloading keras-pos-embd-0.13.0.tar.gz (5.6 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hCollecting keras-multi-head==0.29.0 (from keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\r\n",
      "  Downloading keras-multi-head-0.29.0.tar.gz (13 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hCollecting keras-layer-normalization==0.16.0 (from keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\r\n",
      "  Downloading keras-layer-normalization-0.16.0.tar.gz (3.9 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hCollecting keras-position-wise-feed-forward==0.8.0 (from keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\r\n",
      "  Downloading keras-position-wise-feed-forward-0.8.0.tar.gz (4.1 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hCollecting keras-embed-sim==0.10.0 (from keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\r\n",
      "  Downloading keras-embed-sim-0.10.0.tar.gz (3.6 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hCollecting keras-self-attention==0.51.0 (from keras-multi-head==0.29.0->keras-transformer==0.40.0->keras_bert>=0.86.0->ktrain)\r\n",
      "  Downloading keras-self-attention-0.51.0.tar.gz (11 kB)\r\n",
      "  Preparing metadata (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25hRequirement already satisfied: contourpy>=1.0.1 in /opt/conda/lib/python3.10/site-packages (from matplotlib>=3.0.0->ktrain) (1.2.0)\r\n",
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      "Requirement already satisfied: regex>2016 in /opt/conda/lib/python3.10/site-packages (from syntok>1.3.3->ktrain) (2023.12.25)\r\n",
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      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.10/site-packages (from tika->ktrain) (69.0.3)\r\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers->ktrain) (3.13.1)\r\n",
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      "Requirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers->ktrain) (6.0.1)\r\n",
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      "Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers->ktrain) (2024.2.0)\r\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.19.3->transformers->ktrain) (4.9.0)\r\n",
      "Downloading syntok-1.4.4-py3-none-any.whl (24 kB)\r\n",
      "Downloading chardet-5.2.0-py3-none-any.whl (199 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m199.4/199.4 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading Whoosh-2.7.4-py2.py3-none-any.whl (468 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.8/468.8 kB\u001b[0m \u001b[31m26.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hBuilding 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\r\n",
      "  Building wheel for ktrain (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for ktrain: filename=ktrain-0.41.3-py3-none-any.whl size=25316959 sha256=f62c955bc5f7a0b864b732e032b4a6e077fd59486d30ca2e8b3a3ff82b6e8201\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/fc/76/11/5b953090eebf531f660948a30cd26e70260619f6480f186a5a\r\n",
      "  Building wheel for keras_bert (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for keras_bert: filename=keras_bert-0.89.0-py3-none-any.whl size=33499 sha256=5340beaf09530bc7545afdec349758022da864833b109e85c39c984b5a90b34b\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/89/0c/04/646b6fdf6375911b42c8d540a8a3fda8d5d77634e5dcbe7b26\r\n",
      "  Building wheel for keras-transformer (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for keras-transformer: filename=keras_transformer-0.40.0-py3-none-any.whl size=12286 sha256=184d0113e4a5e69078abe650c2cf2b734034fa06a6f77ce9dfbe7e93a39d0d5d\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/f2/cb/22/75a0ad376129177f7c95c0d91331a18f5368fd657f4035ba7c\r\n",
      "  Building wheel for keras-embed-sim (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for keras-embed-sim: filename=keras_embed_sim-0.10.0-py3-none-any.whl size=3943 sha256=cb9caed7897b8f271eb9c44785038c246b2c959140605ea09e7770bb65180e9c\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/82/32/c7/fd35d0d1b840a6c7cbd4343f808d10d0f7b87d271a4dbe796f\r\n",
      "  Building wheel for keras-layer-normalization (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for keras-layer-normalization: filename=keras_layer_normalization-0.16.0-py3-none-any.whl size=4653 sha256=eb947870c532e7073f764cd9c27a53e75c07ba965dfe1fd5b718a6f6a6d4f32f\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/ed/3a/4b/21db23c0cc56c4b219616e181f258eb7c57d36cc5d056fae9a\r\n",
      "  Building wheel for keras-multi-head (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for keras-multi-head: filename=keras_multi_head-0.29.0-py3-none-any.whl size=14975 sha256=4c1463a681086c319c31c16ede18b8c43bfe842cc35846c5dcc3f83513b2b160\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/cb/23/4b/06d7ae21714f70fcc25b48f972cc8e5e7f4b6b764a038b509d\r\n",
      "  Building wheel for keras-pos-embd (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for keras-pos-embd: filename=keras_pos_embd-0.13.0-py3-none-any.whl size=6946 sha256=e7923e34ec7e24e673ed237aad70c201f914bdf2dbc654e5cdb71316e4df77d6\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/78/07/1b/b1ca47b6ac338554b75c8f52c54e6a2bfbe1b07d79579979a4\r\n",
      "  Building wheel for keras-position-wise-feed-forward (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for keras-position-wise-feed-forward: filename=keras_position_wise_feed_forward-0.8.0-py3-none-any.whl size=4968 sha256=720270c6e8958a12f1c69bc3cfe4f24dc8e7cd2cb6f480f2c41b07c74270fd5b\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/c1/6a/04/d1706a53b23b2cb5f9a0a76269bf87925daa1bca09eac01b21\r\n",
      "  Building wheel for keras-self-attention (setup.py) ... \u001b[?25l-\b \b\\\b \bdone\r\n",
      "\u001b[?25h  Created wheel for keras-self-attention: filename=keras_self_attention-0.51.0-py3-none-any.whl size=18895 sha256=4b4da062f92e278b607baf41ed7696a9bae1474b4e093e61a722c22398c1dc43\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/b8/f7/24/607b483144fb9c47b4ba2c5fba6b68e54aeee2d5bf6c05302e\r\n",
      "  Building wheel for langdetect (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Created wheel for langdetect: filename=langdetect-1.0.9-py3-none-any.whl size=993225 sha256=ff28fffaa28fdf83870a30aa23edac164f0f2d3735a82c4200f27d020a88ec49\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/95/03/7d/59ea870c70ce4e5a370638b5462a7711ab78fba2f655d05106\r\n",
      "  Building wheel for tika (setup.py) ... \u001b[?25l-\b \bdone\r\n",
      "\u001b[?25h  Created wheel for tika: filename=tika-2.6.0-py3-none-any.whl size=32622 sha256=a05a8b988a2e418eda6b1e80da403260ce8d953931bbe3e12e85f2ab741fc1be\r\n",
      "  Stored in directory: /root/.cache/pip/wheels/5f/71/c7/b757709531121b1700cffda5b6b0d4aad095fb507ec84316d0\r\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\r\n",
      "Installing collected packages: whoosh, syntok, langdetect, keras-self-attention, keras-position-wise-feed-forward, keras-pos-embd, keras-layer-normalization, keras-embed-sim, chardet, tika, keras-multi-head, keras-transformer, keras_bert, ktrain\r\n",
      "Successfully installed chardet-5.2.0 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\r\n"
     ]
    }
   ],
   "source": [
    "!pip install ktrain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e35f127a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T14:33:29.127978Z",
     "iopub.status.busy": "2024-05-17T14:33:29.127640Z",
     "iopub.status.idle": "2024-05-17T14:33:33.728680Z",
     "shell.execute_reply": "2024-05-17T14:33:33.727515Z"
    },
    "papermill": {
     "duration": 4.634786,
     "end_time": "2024-05-17T14:33:33.731026",
     "exception": false,
     "start_time": "2024-05-17T14:33:29.096240",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 1 GPU(s) available.\n",
      "We will use the GPU: Tesla P100-PCIE-16GB\n",
      "Fri May 17 14:33:33 2024       \r\n",
      "+---------------------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 535.129.03             Driver Version: 535.129.03   CUDA Version: 12.2     |\r\n",
      "|-----------------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|                                         |                      |               MIG M. |\r\n",
      "|=========================================+======================+======================|\r\n",
      "|   0  Tesla P100-PCIE-16GB           Off | 00000000:00:04.0 Off |                    0 |\r\n",
      "| N/A   34C    P0              27W / 250W |      2MiB / 16384MiB |      0%      Default |\r\n",
      "|                                         |                      |                  N/A |\r\n",
      "+-----------------------------------------+----------------------+----------------------+\r\n",
      "                                                                                         \r\n",
      "+---------------------------------------------------------------------------------------+\r\n",
      "| Processes:                                                                            |\r\n",
      "|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\r\n",
      "|        ID   ID                                                             Usage      |\r\n",
      "|=======================================================================================|\r\n",
      "|  No running processes found                                                           |\r\n",
      "+---------------------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "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\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9255c2a8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T14:33:33.793343Z",
     "iopub.status.busy": "2024-05-17T14:33:33.792664Z",
     "iopub.status.idle": "2024-05-17T14:33:39.399466Z",
     "shell.execute_reply": "2024-05-17T14:33:39.398487Z"
    },
    "papermill": {
     "duration": 5.640962,
     "end_time": "2024-05-17T14:33:39.402390",
     "exception": false,
     "start_time": "2024-05-17T14:33:33.761428",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "50ce47d3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T14:33:39.471214Z",
     "iopub.status.busy": "2024-05-17T14:33:39.470602Z",
     "iopub.status.idle": "2024-05-17T14:33:40.006047Z",
     "shell.execute_reply": "2024-05-17T14:33:40.005149Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>6026</th>\n",
       "      <td>World</td>\n",
       "      <td>عالم رءيس سابق قترح ستقال رءيس نيجيري اوليسجون...</td>\n",
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       "      <th>608</th>\n",
       "      <td>Business</td>\n",
       "      <td>احمر كتسح مءشر عربيه سعوديه قلص خساءر احمر كتس...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8689</th>\n",
       "      <td>Business</td>\n",
       "      <td>خصص خطوط جويه كويتيه دا عام مقبل تم اتفاق تم ف...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3913</th>\n",
       "      <td>World</td>\n",
       "      <td>صاروخ امريك محتمل قتل مسلح اكستان صاروخ امريك ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1778</th>\n",
       "      <td>Science and Technology</td>\n",
       "      <td>صين تامر اعدام لف طير لانفلونزا طيور صين تامر ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6632</th>\n",
       "      <td>Sport</td>\n",
       "      <td>رياض تاجيل متاهل مونديال وز مصر هدف جزاءر اجل ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8473</th>\n",
       "      <td>Business</td>\n",
       "      <td>نهت سواق مال عربيه تعامل اثنين استقرار نسبي سط...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6411</th>\n",
       "      <td>Business</td>\n",
       "      <td>قتصاد عمال جنرال موتورز لغ صفق يع وبل جنرال مو...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1371</th>\n",
       "      <td>Middle East News</td>\n",
       "      <td>صحف سعود حكومه حمار حمر حتجاج فالنتا صحف سعود ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211</th>\n",
       "      <td>Business</td>\n",
       "      <td>مصدر اوبام سعي خفض عجز نصف حلول مصدر اوبام سعي...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "                    category  \\\n",
       "6026                   World   \n",
       "608                 Business   \n",
       "8689                Business   \n",
       "3913                   World   \n",
       "1778  Science and Technology   \n",
       "6632                   Sport   \n",
       "8473                Business   \n",
       "6411                Business   \n",
       "1371        Middle East News   \n",
       "211                 Business   \n",
       "\n",
       "                                                   Body  \n",
       "6026  عالم رءيس سابق قترح ستقال رءيس نيجيري اوليسجون...  \n",
       "608   احمر كتسح مءشر عربيه سعوديه قلص خساءر احمر كتس...  \n",
       "8689  خصص خطوط جويه كويتيه دا عام مقبل تم اتفاق تم ف...  \n",
       "3913  صاروخ امريك محتمل قتل مسلح اكستان صاروخ امريك ...  \n",
       "1778  صين تامر اعدام لف طير لانفلونزا طيور صين تامر ...  \n",
       "6632  رياض تاجيل متاهل مونديال وز مصر هدف جزاءر اجل ...  \n",
       "8473  نهت سواق مال عربيه تعامل اثنين استقرار نسبي سط...  \n",
       "6411  قتصاد عمال جنرال موتورز لغ صفق يع وبل جنرال مو...  \n",
       "1371  صحف سعود حكومه حمار حمر حتجاج فالنتا صحف سعود ...  \n",
       "211   مصدر اوبام سعي خفض عجز نصف حلول مصدر اوبام سعي...  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/my-data/newsfeed.csv',usecols=['Body','category'])\n",
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d54b392",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T14:33:40.070077Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1834a72f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T14:33:40.153811Z",
     "iopub.status.busy": "2024-05-17T14:33:40.153157Z",
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     "shell.execute_reply": "2024-05-17T14:33:40.156291Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "MODEL_NAME = 'aubmindlab/bert-base-arabertv01'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4ad8aecd",
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   "outputs": [
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       "config.json:   0%|          | 0.00/576 [00:00<?, ?B/s]"
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       "model.safetensors:   0%|          | 0.00/543M [00:00<?, ?B/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "preprocessing train...\n",
      "language: ar\n",
      "train sequence lengths:\n",
      "\tmean : 256\n",
      "\t95percentile : 567\n",
      "\t99percentile : 834\n"
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      "text/plain": [
       "vocab.txt:   0%|          | 0.00/780k [00:00<?, ?B/s]"
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     },
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     },
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     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       "    /* Turns off some styling */\n",
       "    progress {\n",
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       "        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"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "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"
     ]
    },
    {
     "data": {
      "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"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
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    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "fef272cd",
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     "iopub.execute_input": "2024-05-17T14:34:13.462622Z",
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     "duration": 1165.092757,
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     "exception": false,
     "start_time": "2024-05-17T14:34:13.428798",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "begin training using onecycle policy with max lr of 5e-05...\n",
      "Epoch 1/10\n",
      "220/220 [==============================] - 143s 533ms/step - loss: 0.7799 - accuracy: 0.7621 - val_loss: 0.2320 - val_accuracy: 0.9334\n",
      "Epoch 2/10\n",
      "220/220 [==============================] - 113s 514ms/step - loss: 0.1933 - accuracy: 0.9475 - val_loss: 0.2269 - val_accuracy: 0.9289\n",
      "Epoch 3/10\n",
      "220/220 [==============================] - 113s 514ms/step - loss: 0.1293 - accuracy: 0.9654 - val_loss: 0.2704 - val_accuracy: 0.9163\n",
      "Epoch 4/10\n",
      "220/220 [==============================] - 113s 514ms/step - loss: 0.0971 - accuracy: 0.9717 - val_loss: 0.2305 - val_accuracy: 0.9357\n",
      "Epoch 5/10\n",
      "220/220 [==============================] - 113s 515ms/step - loss: 0.0893 - accuracy: 0.9771 - val_loss: 0.2305 - val_accuracy: 0.9437\n",
      "Epoch 6/10\n",
      "220/220 [==============================] - 113s 515ms/step - loss: 0.0748 - accuracy: 0.9794 - val_loss: 0.2483 - val_accuracy: 0.9317\n",
      "Epoch 7/10\n",
      "220/220 [==============================] - 113s 514ms/step - loss: 0.0461 - accuracy: 0.9886 - val_loss: 0.2694 - val_accuracy: 0.9351\n",
      "Epoch 8/10\n",
      "220/220 [==============================] - 113s 514ms/step - loss: 0.0315 - accuracy: 0.9916 - val_loss: 0.2196 - val_accuracy: 0.9499\n",
      "Epoch 9/10\n",
      "220/220 [==============================] - 113s 514ms/step - loss: 0.0188 - accuracy: 0.9966 - val_loss: 0.2259 - val_accuracy: 0.9482\n",
      "Epoch 10/10\n",
      "220/220 [==============================] - 113s 514ms/step - loss: 0.0140 - accuracy: 0.9980 - val_loss: 0.2340 - val_accuracy: 0.9471\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x7a814fb11b40>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "176c4fa9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T14:53:38.939981Z",
     "iopub.status.busy": "2024-05-17T14:53:38.939613Z",
     "iopub.status.idle": "2024-05-17T14:53:51.419964Z",
     "shell.execute_reply": "2024-05-17T14:53:51.418898Z"
    },
    "papermill": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55/55 [==============================] - 12s 167ms/step\n",
      "                        precision    recall  f1-score   support\n",
      "\n",
      "              Business       0.95      0.97      0.96       347\n",
      "         Entertainment       0.90      0.77      0.83       153\n",
      "      Middle East News       0.97      0.96      0.96       402\n",
      "Science and Technology       0.91      0.92      0.91       272\n",
      "                 Sport       0.98      0.99      0.99       182\n",
      "                 World       0.96      0.98      0.97       401\n",
      "\n",
      "              accuracy                           0.95      1757\n",
      "             macro avg       0.94      0.93      0.94      1757\n",
      "          weighted avg       0.95      0.95      0.95      1757\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[337,   5,   0,   5,   0,   0],\n",
       "       [  6, 118,   6,  18,   1,   4],\n",
       "       [  2,   1, 387,   2,   2,   8],\n",
       "       [ 10,   6,   2, 250,   0,   4],\n",
       "       [  0,   0,   0,   0, 180,   2],\n",
       "       [  1,   1,   6,   1,   0, 392]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation = learner.validate(class_names=t.get_classes())\n",
    "validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ef27b0ca",
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    "execution": {
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[337,   5,   0,   5,   0,   0],\n",
       "       [  6, 118,   6,  18,   1,   4],\n",
       "       [  2,   1, 387,   2,   2,   8],\n",
       "       [ 10,   6,   2, 250,   0,   4],\n",
       "       [  0,   0,   0,   0, 180,   2],\n",
       "       [  1,   1,   6,   1,   0, 392]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "validation[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d057f3f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T14:53:52.319699Z",
     "iopub.status.busy": "2024-05-17T14:53:52.319335Z",
     "iopub.status.idle": "2024-05-17T14:53:52.866992Z",
     "shell.execute_reply": "2024-05-17T14:53:52.866063Z"
    },
    "papermill": {
     "duration": 0.796668,
     "end_time": "2024-05-17T14:53:52.869650",
     "exception": false,
     "start_time": "2024-05-17T14:53:52.072982",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 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(validation[:], 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()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "815c1875",
   "metadata": {
    "papermill": {
     "duration": 0.211891,
     "end_time": "2024-05-17T14:53:53.296757",
     "exception": false,
     "start_time": "2024-05-17T14:53:53.084866",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Test the Model for Sentiment Analysis**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "eb3a6386",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T14:53:53.723515Z",
     "iopub.status.busy": "2024-05-17T14:53:53.723150Z",
     "iopub.status.idle": "2024-05-17T14:53:56.590975Z",
     "shell.execute_reply": "2024-05-17T14:53:56.590175Z"
    },
    "papermill": {
     "duration": 3.082446,
     "end_time": "2024-05-17T14:53:56.593150",
     "exception": false,
     "start_time": "2024-05-17T14:53:53.510704",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# save model using transformers API after partially training\n",
    "learner.model.save_pretrained('/kaggle/working/my_model')\n",
    "from transformers import TFAutoModelForSequenceClassification\n",
    "model = TFAutoModelForSequenceClassification.from_pretrained('/kaggle/working/my_model')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ed893536",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T14:53:57.075159Z",
     "iopub.status.busy": "2024-05-17T14:53:57.074773Z",
     "iopub.status.idle": "2024-05-17T14:53:57.279727Z",
     "shell.execute_reply": "2024-05-17T14:53:57.278794Z"
    },
    "papermill": {
     "duration": 0.427087,
     "end_time": "2024-05-17T14:53:57.282185",
     "exception": false,
     "start_time": "2024-05-17T14:53:56.855098",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_neg = pd.read_csv(\"/kaggle/input/my-data/train_Arabic_tweets_negative_20190413.tsv\", sep=\"\\t\", header=None)\n",
    "train_neg.rename(columns={0:'label', 1:'tweet'}, inplace=True)\n",
    "train_neg['label'] = 0\n",
    "\n",
    "train_pos = pd.read_csv(\"/kaggle/input/my-data/train_Arabic_tweets_positive_20190413.tsv\", sep=\"\\t\", header=None)\n",
    "train_pos.rename(columns={0:'label', 1:'tweet'}, inplace=True)\n",
    "train_pos['label'] = 1\n",
    "\n",
    "\n",
    "train_df = pd.concat([train_neg, train_pos], axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "207a807d",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "test_pos = pd.read_csv(\"/kaggle/input/my-data/test_Arabic_tweets_positive_20190413.tsv\", sep=\"\\t\", header=None)\n",
    "test_pos.rename(columns={0:'label', 1:'tweet'}, inplace=True)\n",
    "test_pos['label']=1\n",
    "\n",
    "test_neg = pd.read_csv(\"/kaggle/input/my-data/test_Arabic_tweets_positive_20190413.tsv\", sep=\"\\t\", header=None)\n",
    "test_neg.rename(columns={0:'label', 1:'tweet'}, inplace=True)\n",
    "test_neg['label']=0\n",
    "\n",
    "test_df = pd.concat([test_neg, test_pos], axis=0).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e546ab05",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: nltk in /opt/conda/lib/python3.10/site-packages (3.2.4)\r\n",
      "Collecting Arabic-Stopwords\r\n",
      "  Downloading Arabic_Stopwords-0.4.3-py3-none-any.whl.metadata (8.9 kB)\r\n",
      "Requirement already satisfied: PyArabic in /opt/conda/lib/python3.10/site-packages (0.6.15)\r\n",
      "Collecting Tashaphyne\r\n",
      "  Downloading Tashaphyne-0.3.6-py3-none-any.whl.metadata (18 kB)\r\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.10/site-packages (from nltk) (1.16.0)\r\n",
      "Downloading Arabic_Stopwords-0.4.3-py3-none-any.whl (360 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m360.5/360.5 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hDownloading Tashaphyne-0.3.6-py3-none-any.whl (251 kB)\r\n",
      "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m251.5/251.5 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hInstalling collected packages: Tashaphyne, Arabic-Stopwords\r\n",
      "Successfully installed Arabic-Stopwords-0.4.3 Tashaphyne-0.3.6\r\n"
     ]
    }
   ],
   "source": [
    "!pip install nltk Arabic-Stopwords PyArabic Tashaphyne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1d4f5a89",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
      "To disable this warning, you can either:\n",
      "\t- Avoid using `tokenizers` before the fork if possible\n",
      "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: Arabic-Stopwords in /opt/conda/lib/python3.10/site-packages (0.4.3)\r\n",
      "Requirement already satisfied: pyarabic>=0.6.2 in /opt/conda/lib/python3.10/site-packages (from Arabic-Stopwords) (0.6.15)\r\n",
      "Requirement already satisfied: six>=1.14.0 in /opt/conda/lib/python3.10/site-packages (from pyarabic>=0.6.2->Arabic-Stopwords) (1.16.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install Arabic-Stopwords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e8a1302f",
   "metadata": {
    "execution": {
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     "status": "failed"
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    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'nltk' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[21], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mnltk\u001b[49m\u001b[38;5;241m.\u001b[39mdownload(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mstopwords\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'nltk' is not defined"
     ]
    }
   ],
   "source": [
    "nltk.download('stopwords')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0309be73",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T10:51:13.761556Z",
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   "outputs": [],
   "source": [
    "import string\n",
    "import re\n",
    "import regex\n",
    "import nltk\n",
    "from nltk.corpus import stopwords # Stopwords\n",
    "import arabicstopwords.arabicstopwords as stp #more of arabic stop words\n",
    "from nltk.stem.isri import ISRIStemmer\n",
    "import pyarabic.araby as araby\n",
    "from tashaphyne.stemming import ArabicLightStemmer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e20930d",
   "metadata": {
    "execution": {
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   "outputs": [],
   "source": [
    "arabic_stopwords = stopwords.words(\"arabic\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22c87ed2",
   "metadata": {
    "execution": {
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   "source": [
    "def Arabic_Light_Stemmer(text):\n",
    "    #making an object\n",
    "    Arabic_Stemmer = ArabicLightStemmer()\n",
    "\n",
    "    #stemming each word\n",
    "    text=[Arabic_Stemmer.light_stem(y) for y in text.split()]\n",
    "\n",
    "    return \" \" .join(text)\n",
    "\n",
    "def normalizeArabic(text):\n",
    "    text = text.strip()\n",
    "    text = re.sub(\"ى\", \"ي\", text)\n",
    "    text = re.sub(\"ؤ\", \"ء\", text)\n",
    "    text = re.sub(\"ئ\", \"ء\", text)\n",
    "    text = re.sub(\"ة\", \"ه\", text)\n",
    "\n",
    "    #remove repetetions\n",
    "    text = re.sub(\"[إأٱآا]\", \"ا\", text)\n",
    "    text = text.replace('وو', 'و')\n",
    "    text = text.replace('يي', 'ي')\n",
    "    text = text.replace('ييي', 'ي')\n",
    "    text = text.replace('اا', 'ا')\n",
    "\n",
    "    ## remove extra whitespace\n",
    "    text = re.sub('\\s+', ' ', text)\n",
    "\n",
    "    # Remove longation\n",
    "    text = re.sub(r'(.)\\1+', r\"\\1\\1\", text)\n",
    "\n",
    "    #Strip vowels from a text, include Shadda.\n",
    "    text = araby.strip_tashkeel(text)\n",
    "\n",
    "    #Strip diacritics from a text, include harakats and small lettres The striped marks are\n",
    "    text = araby.strip_diacritics(text)\n",
    "    text=''.join([i for i in text if not i.isdigit()])\n",
    "    return text\n",
    "\n",
    "def Removing_non_arabic(text):\n",
    "    text = re.sub('[A-Za-z]+',' ',text)\n",
    "    return text\n",
    "\n",
    "def Removing_numbers(text):\n",
    "    text=''.join([i for i in text if not i.isdigit()])\n",
    "    return text\n",
    "\n",
    "def Removing_punctuations(text):\n",
    "    ## Remove punctuations\n",
    "    text = re.sub('[%s]' % re.escape(\"\"\"!\"#$%&'()*+,،-./:;<=>؟?@[\\]^_`{|}~\"\"\"), ' ', text)\n",
    "    text = text.replace('؛',\"\", )\n",
    "\n",
    "    ## remove extra whitespace\n",
    "    text = re.sub('\\s+', ' ', text)\n",
    "    text =  \" \".join(text.split())\n",
    "    return text.strip()\n",
    "\n",
    "def remove_extra_Space(text):\n",
    "    ## remove extra whitespace\n",
    "    text = re.sub('\\s+', ' ', text)\n",
    "    return  \" \".join(text.split())\n",
    "\n",
    "def remove_hashtages_and_mentions(text):\n",
    "    text = re.sub(\"@[A-Za-z0-9_]+\",\"\", text)\n",
    "    text = re.sub(\"#[A-Za-z0-9_]+\",\"\", text)\n",
    "    return text\n",
    "\n",
    "def replace_random_text(text):\n",
    "    text = text.replace('\\u202c', '')\n",
    "    text = text.replace('\\u202e', '')\n",
    "    text = text.replace('\\u202d', '')\n",
    "    return text\n",
    "\n",
    "def remove_stopwords_robustly(text):\n",
    "    # Split the text into words\n",
    "    words = text.split()\n",
    "\n",
    "    # Remove stop words\n",
    "    filtered_words = [word for word in words if word not in stp.stopwords_list()]\n",
    "\n",
    "    # Join the filtered words back into a string\n",
    "    filtered_text = ' '.join(filtered_words)\n",
    "\n",
    "    return filtered_text\n",
    "\n",
    "def remove_stop_words(text):\n",
    "    Text=[i for i in str(text).split() if i not in arabic_stopwords]\n",
    "    return \" \".join(Text)"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "train_df.sample(12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c2ecaa05",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-17T10:52:03.161640Z",
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   "source": [
    "def preprocess_text(text):\n",
    "    text = normalizeArabic(text)\n",
    "    text = Removing_non_arabic(text)\n",
    "    text = Removing_numbers(text)\n",
    "    text = Removing_punctuations(text)\n",
    "    text = remove_extra_Space(text)\n",
    "    text = remove_hashtages_and_mentions(text)\n",
    "    text = remove_stop_words(text)\n",
    "    text = Arabic_Light_Stemmer(text)\n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c7e29d7",
   "metadata": {
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   "outputs": [],
   "source": [
    "train_df['tweet'] = train_df['tweet'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "train_df.sample(12)"
   ]
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   "source": [
    "def remove_emojis(text):\n",
    "    emoji_pattern = re.compile(\"[\"\n",
    "                           u\"\\U0001F600-\\U0001F64F\"  # emoticons\n",
    "                           u\"\\U0001F300-\\U0001F5FF\"  # symbols & pictographs\n",
    "                           u\"\\U0001F680-\\U0001F6FF\"  # transport & map symbols\n",
    "                           u\"\\U0001F1E0-\\U0001F1FF\"  # flags (iOS)\n",
    "                           u\"\\U00002702-\\U000027B0\"\n",
    "                           u\"\\U000024C2-\\U0001F251\"\n",
    "                           \"]+\", flags=re.UNICODE)\n",
    "    return emoji_pattern.sub(r'', text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07c3f11d",
   "metadata": {
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   "outputs": [],
   "source": [
    "train_df['tweet'] = train_df['tweet'].apply(preprocess_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9941eb0",
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   "outputs": [],
   "source": [
    "train_df['tweet'] = train_df['tweet'].apply(remove_emojis)"
   ]
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    "mapping = {\n",
    "    0: \"negative\",\n",
    "    1: \"positive\",\n",
    "}\n",
    "train_df['label'] = train_df['label'].map(mapping)\n",
    "train_df.label.unique()"
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    "X = train_df['tweet']\n",
    "y = train_df['label']\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)"
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    "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)"
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    "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)"
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