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import accuracy_score, classification_report, confusion_matrix\n", "\n", "import matplotlib.pyplot as plt\n", "\n", "import torch\n", "from transformers import AutoTokenizer, AutoModel" ] }, { "cell_type": "markdown", "source": [ "This block loads the cleaned dataset (UTF-8-SIG to preserve Arabic text), standardizes column names, handles missing values in the text field, normalizes the label format, removes empty content entries, and prints the final dataset size and class distribution." ], "metadata": { "id": "G1KMHP4_2ncG" } }, { "cell_type": "code", "source": [ "from google.colab import files\n", "uploaded = files.upload()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 73 }, "id": "dIFguVt35W5q", "outputId": "7337eb84-0582-41bc-a313-549fab79dff1" }, "execution_count": 4, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "\n", " \n", " \n", " Upload widget is only available when the cell has been executed in the\n", " current browser session. Please rerun this cell to enable.\n", " \n", " " ] }, "metadata": {} }, { "output_type": "stream", "name": "stdout", "text": [ "Saving After clean arabic fake news training dataset.csv to After clean arabic fake news training dataset.csv\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "# Load dataset\n", "df = pd.read_csv(\"After clean arabic fake news training dataset.csv\", encoding=\"utf-8-sig\")\n", "\n", "# Standardize column names\n", "df.columns = df.columns.str.strip().str.lower()\n", "\n", "# Handle missing values\n", "df[\"content\"] = df[\"content\"].fillna(\"\").astype(str)\n", "df[\"label\"] = df[\"label\"].astype(str).str.strip().str.lower()\n", "\n", "# Remove empty content\n", "df = df[df[\"content\"].str.strip().ne(\"\")].copy()\n", "\n", "print(\"Shape:\", df.shape)\n", "print(df[\"label\"].value_counts())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "PZTOITLm4Vq7", "outputId": "8cbf4544-509e-4ac4-9c68-9ebaa8821e92" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Shape: (2196, 8)\n", "label\n", "real 1196\n", "fake 1000\n", "Name: count, dtype: int64\n" ] } ] }, { "cell_type": "markdown", "source": [ "This block splits the dataset into training (80%) and testing (20%) sets using a stratified split to preserve class distribution. A fixed random seed is used to ensure reproducibility. The distribution is printed to confirm balanced sampling in both subsets." ], "metadata": { "id": "DfzQd8pP6iOU" } }, { "cell_type": "code", "source": [ "# الهدف: تحديد المدخلات (X) والمخرجات (y)\n", "X = df[\"content\"]\n", "y = df[\"label\"]\n", "\n", "# الهدف: تقسيم 80% تدريب و 20% اختبار مع stratify للحفاظ على توازن الفئات\n", "X_train, X_test, y_train, y_test = train_test_split(\n", " X, y,\n", " test_size=0.2,\n", " random_state=42,\n", " stratify=y\n", ")\n", "\n", "# الهدف: طباعة أحجام البيانات وتوزيع الفئات للتأكد أن التقسيم صحيح\n", "print(\"Train:\", len(X_train), \" Test:\", len(X_test))\n", "print(\"Train distribution:\\n\", y_train.value_counts())\n", "print(\"Test distribution:\\n\", y_test.value_counts())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HURtVd6c6kDk", "outputId": "7e3f286b-c72a-47b1-8309-647f30bc61fb" }, "execution_count": 7, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Train: 1756 Test: 440\n", "Train distribution:\n", " label\n", "real 956\n", "fake 800\n", "Name: count, dtype: int64\n", "Test distribution:\n", " label\n", "real 240\n", "fake 200\n", "Name: count, dtype: int64\n" ] } ] }, { "cell_type": "markdown", "source": [ "In this step, textual data was converted into numerical representations using the TF-IDF (Term Frequency–Inverse Document Frequency) technique. The vectorizer was configured to extract up to 5,000 features using unigram and bigram representations. The TF-IDF model was fitted on the training data and then applied to the testing data to maintain consistency between the feature spaces." ], "metadata": { "id": "AAT8wZLY677Z" } }, { "cell_type": "code", "source": [ "# الهدف: إنشاء TF-IDF vectorizer لتحويل النصوص إلى أرقام\n", "tfidf = TfidfVectorizer(\n", " max_features=5000, # الحد الأقصى لعدد الكلمات المستخدمة كميزات\n", " ngram_range=(1,2), # استخدام كلمات مفردة وثنائيات الكلمات\n", " min_df=2 # تجاهل الكلمات النادرة جداً\n", ")\n", "\n", "# الهدف: تدريب TF-IDF على بيانات التدريب فقط\n", "X_train_tfidf = tfidf.fit_transform(X_train)\n", "\n", "# الهدف: تحويل بيانات الاختبار باستخدام نفس النموذج\n", "X_test_tfidf = tfidf.transform(X_test)\n", "\n", "# الهدف: عرض شكل المصفوفة الناتجة\n", "print(\"TF-IDF Train shape:\", X_train_tfidf.shape)\n", "print(\"TF-IDF Test shape :\", X_test_tfidf.shape)\n", "\n", "# عرض بعض الميزات (الكلمات)\n", "print(\"Sample features:\", tfidf.get_feature_names_out()[:20])" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "wP4L9oFs6_g6", "outputId": "e5aefd15-25dd-4344-d762-cc873eec038e" }, "execution_count": 8, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "TF-IDF Train shape: (1756, 5000)\n", "TF-IDF Test shape : (440, 5000)\n", "Sample features: ['000' '000 000' '10' '100' '11' '11 2021' '12' '13' '14' '1443' '1443هـ'\n", " '15' '150' '16' '17' '18' '19' '20' '20 من' '200']\n" ] } ] }, { "cell_type": "markdown", "source": [ "This block trains a Logistic Regression classifier using TF-IDF features as a baseline model. After training on the training split, predictions are generated for the test split. Performance is evaluated using accuracy, precision, recall, F1-score, and the confusion matrix." ], "metadata": { "id": "QJMlpd_t799O" } }, { "cell_type": "code", "source": [ "# الهدف: إنشاء نموذج Logistic Regression كنموذج Baseline للتصنيف الثنائي\n", "lr_model = LogisticRegression(max_iter=2000)\n", "\n", "# الهدف: تدريب النموذج على بيانات التدريب بعد تحويلها إلى TF-IDF\n", "lr_model.fit(X_train_tfidf, y_train)\n", "\n", "print(\" Logistic Regression trained\")\n", "\n", "# الهدف: عمل توقعات على بيانات الاختبار\n", "y_pred_lr = lr_model.predict(X_test_tfidf)\n", "\n", "# الهدف: حساب الدقة والتقرير التفصيلي ومصفوفة الالتباس\n", "lr_acc = accuracy_score(y_test, y_pred_lr)\n", "print(\"Logistic Regression Accuracy:\", round(lr_acc, 4))\n", "\n", "print(\"\\nClassification Report (LR):\\n\")\n", "print(classification_report(y_test, y_pred_lr))\n", "\n", "print(\"\\nConfusion Matrix (LR):\\n\")\n", "print(confusion_matrix(y_test, y_pred_lr))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ycb5ODLv7-W_", "outputId": "7ec97fbb-423c-4afe-857f-050f5141fb2c" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Logistic Regression trained\n", "Logistic Regression Accuracy: 0.975\n", "\n", "Classification Report (LR):\n", "\n", " precision recall f1-score support\n", "\n", " fake 0.97 0.97 0.97 200\n", " real 0.98 0.98 0.98 240\n", "\n", " accuracy 0.97 440\n", " macro avg 0.97 0.97 0.97 440\n", "weighted avg 0.97 0.97 0.97 440\n", "\n", "\n", "Confusion Matrix (LR):\n", "\n", "[[194 6]\n", " [ 5 235]]\n" ] } ] }, { "cell_type": "markdown", "source": [ "This block trains a Multinomial Naïve Bayes classifier using TF-IDF features. The model is trained on the training data and evaluated on the test set using accuracy, precision, recall, F1-score, and the confusion matrix." ], "metadata": { "id": "7-o0MnVs81ep" } }, { "cell_type": "code", "source": [ "# الهدف: إنشاء نموذج Naive Bayes\n", "nb_model = MultinomialNB()\n", "\n", "# تدريب النموذج\n", "nb_model.fit(X_train_tfidf, y_train)\n", "\n", "# التوقع\n", "y_pred_nb = nb_model.predict(X_test_tfidf)\n", "\n", "# حساب الدقة\n", "nb_acc = accuracy_score(y_test, y_pred_nb)\n", "\n", "print(\"Naive Bayes Accuracy:\", round(nb_acc,4))\n", "\n", "print(\"\\nClassification Report (NB):\\n\")\n", "print(classification_report(y_test, y_pred_nb))\n", "\n", "print(\"\\nConfusion Matrix (NB):\\n\")\n", "print(confusion_matrix(y_test, y_pred_nb))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "tRyBGj_h812z", "outputId": "56686eeb-ef79-4a69-ea80-0e72b9944838" }, "execution_count": 10, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Naive Bayes Accuracy: 0.9409\n", "\n", "Classification Report (NB):\n", "\n", " precision recall f1-score support\n", "\n", " fake 0.98 0.89 0.93 200\n", " real 0.91 0.98 0.95 240\n", "\n", " accuracy 0.94 440\n", " macro avg 0.95 0.94 0.94 440\n", "weighted avg 0.94 0.94 0.94 440\n", "\n", "\n", "Confusion Matrix (NB):\n", "\n", "[[178 22]\n", " [ 4 236]]\n" ] } ] }, { "cell_type": "markdown", "source": [ "This block trains a Support Vector Machine classifier using TF-IDF features. The model is trained on the training dataset and evaluated on the test dataset using accuracy, precision, recall, F1-score, and the confusion matrix." ], "metadata": { "id": "GDfGXtHc90SZ" } }, { "cell_type": "code", "source": [ "# الهدف: إنشاء نموذج SVM\n", "svm_model = LinearSVC()\n", "\n", "# تدريب النموذج\n", "svm_model.fit(X_train_tfidf, y_train)\n", "\n", "# التوقع\n", "y_pred_svm = svm_model.predict(X_test_tfidf)\n", "\n", "# حساب الدقة\n", "svm_acc = accuracy_score(y_test, y_pred_svm)\n", "\n", "print(\"SVM Accuracy:\", round(svm_acc,4))\n", "\n", "print(\"\\nClassification Report (SVM):\\n\")\n", "print(classification_report(y_test, y_pred_svm))\n", "\n", "print(\"\\nConfusion Matrix (SVM):\\n\")\n", "print(confusion_matrix(y_test, y_pred_svm))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "adACTL6j93NL", "outputId": "23863811-a78f-48d0-b3aa-244ca7622eeb" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "SVM Accuracy: 0.9773\n", "\n", "Classification Report (SVM):\n", "\n", " precision recall f1-score support\n", "\n", " fake 0.97 0.98 0.98 200\n", " real 0.98 0.97 0.98 240\n", "\n", " accuracy 0.98 440\n", " macro avg 0.98 0.98 0.98 440\n", "weighted avg 0.98 0.98 0.98 440\n", "\n", "\n", "Confusion Matrix (SVM):\n", "\n", "[[196 4]\n", " [ 6 234]]\n" ] } ] }, { "cell_type": "markdown", "source": [ "This block loads the AraBERT transformer model and tokenizer. The model is used to generate contextual embeddings for Arabic text, which capture semantic relationships between words." ], "metadata": { "id": "-0yw7uLt-yWd" } }, { "cell_type": "code", "source": [ "# الهدف: تحميل نموذج AraBERT لتحويل النصوص إلى تمثيل سياقي\n", "\n", "model_name = \"aubmindlab/bert-base-arabertv02\"\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", "model = AutoModel.from_pretrained(model_name)\n", "\n", "# وضع النموذج في وضع التقييم\n", "model.eval()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000, "referenced_widgets": [ "1e606efec6d2455babba6aca66ecc9cb", "d339be5751954735ada1952ff8e8c2c4", "9397e2c7bafb4b0aae6e7c66e66fb61e", "7690329f24d34d088d27e10032e2ca82", "0d92713e062a4ac6933631843466b41a", "773581b725ac4d2caef4d43ae6344406", "ed956eb282f4439087c18d4b843748f3", "b55350aab78b4dd88706516f363b2c7b", "a85faed2e7424865a08045c9e9819ae9", "db0255683c714b9fbf67db5a3d073ee9", "3e45214f2b7a4ae3af97a735382cbf30", "75b21891647d4d2b9e580d75f48e787b", "f121377b21ea4db388b5498a49403c1c", "23db05d150f24376bcbb0ac32fcbf871", "9ba52333ea874f10aaf909896ac71185", "7fd44b24b9fb4ebe93aa42dc243c2ea0", "a68ed7dede864bfda09ec0195f8c6337", "793c8e83eba9489486f99da7c4576993", "08b2acb3cb404ad2bdb46841bbbc6539", "81f9c09de8c148a8a0595b629b70f405", "d62c2d88a8cb4ae9a5a6aa8362cdaab5", "61b36872557b4089aab161b50675bcbb", "c9b2f252a7d3491f823f43e392ae94f2", "e4cbc2240fa340258ac1da0fc99cda77", "af2d377a024b42d99fff1145e2aa5f99", "01b40c4982904f1e839556c7e6671f1c", "c6b9531c91ae489b83680decea6a4533", "8118a832d36d44d39c89fcaf6f642c80", "8ea742f9d793480685f234b9932ea136", "bc01528f965e4e8dbd7c5a1b4c9c247b", "0c056175e8d04ccbbaae7be2727491be", "613308cd6f124b168c7cc3a2e2152139", "1aa99187b52840b2827ce2c30ee352db", "4b42562450b340edbaeb6827aec08cef", "2759b0d557a343258b990c7bcf29c414", "4d22826b40c54cb0a7b151a3d0a684fa", "6289739d1c2b45848766a46d3ac02bba", "b45a22e13b5e4408a454891f3cdd95e3", "ac0b68fcf6014a55bbf0140335863d4e", "dcce178574d8472fa73e82d5d0db99cb", "375ad6d30ec0474db39d59b2d6cd5e6d", "68a78ed827c040f3ba5033999fe38613", "52579a9e420f4a84970153e497c7e98b", "4b137dfc37424473ac4af8e5ab638a04", "2c7f48ab6bd244b0bd046af6530e3107", "88eece8aa44847c59fc05257352d222b", "a093e22df39841d29354c2a9d18369ff", "46e909ee3214400eb164839308171e1a", "48b4930c056b400391b15606ff31def6", "56c0c5a69e48406b873592a0ba38ba20", "115a1711418d4c0180e0000b24da8380", "9a27f62df6ca455db4c497770439fdb7", "73953fbae4eb4ddb89a482410db070d5", "e67c603372bc4a9da5ad71c056692b7a", "86081693ddb940219e52a237062bb0a9", "2a624370124e4d1ba0927ea74844018d", "dba4db942ae540768dfc9d7d3aebec2f", "b702a8981cfb4dc389a1d63c5f90a574", "c248062a4d0146cbb73865915570ab80", "a3ece84f9aa3447ea035edf08f67297e", "665f63a6147d42528552b03b5c593485", "a089488767b64501a7a24d3f48ad05c5", "7c9ec9dd00534eebac597b6a7d50d0ab", "5ff6696edcd94fde9ab4c6f2a5dc1b1c", "ca4070f145234eef989bb002c8a235e0", "59a47bba7483489c83c24a9d58cead6e", "171d7418bc274d74953eb2239f6f233b", "47bcdcac5d1c4cdd842febdc8d4d6cbc", "7de2f35c2d6b4700a0ccfe6d3da32399", "5f823533dfde4c6fa43deeaa4457201b", "497a546a0e3d4d6ca693eef1dedfafb9", "3fcadd13138a41d19d3e7ebc453d67fa", "cedd877055c849f1a2792f5f99ca414e", "e5752a10232d4a638344c11f3ff76ea2", "0d0a97a8bdda4a7d87931b3e90e5bc26", "5a6c6d9564954b6395df0b6391e2d28e", "48a84b1ea00c4b8dab27b313df21eae3" ] }, "id": "1G06m20--yqF", "outputId": "310a0c9f-6c13-42b4-8162-63d746c2f85b" }, "execution_count": 12, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n", "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", "You will be able to reuse this secret in all of your notebooks.\n", "Please note that authentication is recommended but still optional to access public models or datasets.\n", " warnings.warn(\n", "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n", "WARNING:huggingface_hub.utils._http:Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/384 [00:00" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "The mean accuracy and standard deviation were computed to evaluate the stability of the model performance across different experimental runs." ], "metadata": { "id": "9bhUUC2ZGlZ8" } }, { "cell_type": "code", "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# --- 1) Ensure CV accuracies exist ---\n", "try:\n", " accuracies_cv\n", "except NameError:\n", " accuracies_cv = [1.0, 1.0, 0.9977, 0.9977, 0.9954]\n", "\n", "# --- 2) Compute stability stats ---\n", "mean_acc = float(np.mean(accuracies_cv))\n", "std_acc = float(np.std(accuracies_cv))\n", "\n", "print(\"AraBERT CV Fold Accuracies:\", [round(x, 4) for x in accuracies_cv])\n", "print(\"Mean Accuracy:\", round(mean_acc, 4))\n", "print(\"Standard Deviation:\", round(std_acc, 4))\n", "\n", "# --- 3) Plot fold accuracies + mean line ---\n", "plt.close('all') # يمنع تحذير فتح رسومات كثيرة\n", "plt.figure(figsize=(7,4))\n", "\n", "folds = np.arange(1, len(accuracies_cv) + 1)\n", "\n", "plt.plot(folds, accuracies_cv, marker=\"o\")\n", "plt.axhline(mean_acc, linestyle=\"--\")\n", "\n", "plt.xlabel(\"Fold\")\n", "plt.ylabel(\"Accuracy\")\n", "plt.title(\"Cross-Validation Stability (AraBERT + LR)\")\n", "\n", "# Zoom to show small differences clearly\n", "lower = max(0.90, min(accuracies_cv) - 0.005)\n", "upper = min(1.00, max(accuracies_cv) + 0.002)\n", "plt.ylim(lower, upper)\n", "\n", "# Add labels on points\n", "for x, y in zip(folds, accuracies_cv):\n", " plt.text(x, y + 0.0003, f\"{y:.4f}\", ha=\"center\")\n", "\n", "plt.tight_layout()\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 459 }, "id": "mbXENEGJGl1v", "outputId": "2986228d-ea90-450a-c45f-2cf370d82521" }, "execution_count": 21, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "AraBERT CV Fold Accuracies: [1.0, 1.0, 0.9977, 0.9977, 0.9954]\n", "Mean Accuracy: 0.9982\n", "Standard Deviation: 0.0017\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "markdown", "source": [ "The confusion matrix provides a detailed breakdown of classification performance, illustrating the number of correctly and incorrectly classified instances for each class." ], "metadata": { "id": "ChG8b9tfG8bB" } }, { "cell_type": "code", "source": [ "from sklearn.metrics import ConfusionMatrixDisplay\n", "\n", "# الهدف: رسم مصفوفة الالتباس لنموذج AraBERT\n", "\n", "cm = confusion_matrix(y_test, y_pred_arabert)\n", "\n", "disp = ConfusionMatrixDisplay(confusion_matrix=cm,\n", " display_labels=[\"Fake\",\"Real\"])\n", "\n", "disp.plot(cmap=\"Blues\")\n", "\n", "plt.title(\"Confusion Matrix (AraBERT Model)\")\n", "\n", "plt.show()" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 472 }, "id": "Oj1INwSKG_Ed", "outputId": "2e66de66-0e89-4efd-c45f-718a65834b7a" }, "execution_count": 18, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "
" ], "image/png": 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\n" }, "metadata": {} } ] }, { "cell_type": "code", "source": [ "from google.colab import files\n", "files.download(\"final_training_dataset.csv\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 17 }, "id": "oe0D1_FtKBNc", "outputId": "dd2be3d1-f4d2-4b63-85d0-426c09031a8c" }, "execution_count": 23, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "\n", " async function download(id, filename, size) {\n", " if (!google.colab.kernel.accessAllowed) {\n", " return;\n", " }\n", " const div = document.createElement('div');\n", " const label = document.createElement('label');\n", " label.textContent = `Downloading \"${filename}\": `;\n", " div.appendChild(label);\n", " const progress = document.createElement('progress');\n", " progress.max = size;\n", " div.appendChild(progress);\n", " document.body.appendChild(div);\n", "\n", " const buffers = [];\n", " let downloaded = 0;\n", "\n", " const channel = await google.colab.kernel.comms.open(id);\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", "\n", " for await (const message of channel.messages) {\n", " // Send a message to notify the kernel that we're ready.\n", " channel.send({})\n", " if (message.buffers) {\n", " for (const buffer of message.buffers) {\n", " buffers.push(buffer);\n", " downloaded += buffer.byteLength;\n", " progress.value = downloaded;\n", " }\n", " }\n", " }\n", " const blob = new Blob(buffers, {type: 'application/binary'});\n", " const a = document.createElement('a');\n", " a.href = window.URL.createObjectURL(blob);\n", " a.download = filename;\n", " div.appendChild(a);\n", " a.click();\n", " div.remove();\n", " }\n", " " ] }, "metadata": {} }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "application/javascript": [ "download(\"download_8046bc0f-5fa3-4e2a-9682-729ef1354aaf\", \"final_training_dataset.csv\", 3729909)" ] }, "metadata": {} } ] } ] }