testing_model.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "mount_file_id": "1Cz9YzgcyCWVzHugYpz7w2xBALdpfqWh6",
      "authorship_tag": "ABX9TyPyc9c25ePuCXTh23pmJrwy",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/khaledsoudy-1/FineTuning-BERT-Arabic-Sentiment/blob/main/testing_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dmQ1HlF17oxF",
        "outputId": "ca3a7faa-bff3-46f9-bbc8-16875f512dd8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.46.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.16.1)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.26.3)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.26.4)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.2)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.2)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.9.11)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n",
            "Requirement already satisfied: tokenizers<0.21,>=0.20 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.20.3)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.5)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.6)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2024.10.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.4.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.10)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.2.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.8.30)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TFBertForSequenceClassification, BertTokenizer\n",
        "\n",
        "# Load model and tokenizer from Hugging Face\n",
        "model_name = \"khaledsoudy/arabic-sentiment-bert-model\"\n",
        "\n",
        "# Load model\n",
        "model = TFBertForSequenceClassification.from_pretrained(model_name)\n",
        "\n",
        "# Load tokenizer\n",
        "tokenizer = BertTokenizer.from_pretrained(model_name)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RvA5KS4B7qfr",
        "outputId": "85622142-e55b-42bf-beec-810cbf4515fb"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "All model checkpoint layers were used when initializing TFBertForSequenceClassification.\n",
            "\n",
            "All the layers of TFBertForSequenceClassification were initialized from the model checkpoint at khaledsoudy/arabic-sentiment-bert-model.\n",
            "If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "\n",
        "# Sample Arabic text for sentiment prediction\n",
        "text = \"الفندق رائع و الخدمة ممتازة\"\n",
        "\n",
        "# Tokenize the input text\n",
        "inputs = tokenizer(text, return_tensors=\"tf\")\n",
        "\n",
        "# Get the model's prediction\n",
        "outputs = model(**inputs)\n",
        "\n",
        "# Get the predicted sentiment (assuming 3 classes: Positive, Negative, Mixed)\n",
        "predicted_class = tf.argmax(outputs.logits, axis=-1).numpy()\n",
        "\n",
        "# Map the predicted class index to sentiment labels\n",
        "sentiment_labels = ['Mixed', 'Negative', 'Positive']\n",
        "print(f\"Predicted sentiment: {sentiment_labels[predicted_class[0]]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rasou2WF8FLv",
        "outputId": "74937165-3371-496a-89d6-ac659c6c9eb3"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted sentiment: Positive\n"
          ]
        }
      ]
    }
  ]
}