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as plt\n", "from sklearn.feature_extraction.text import TfidfVectorizer\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.metrics import classification_report, accuracy_score\n", "from sklearn.model_selection import GridSearchCV" ], "metadata": { "id": "ZhsXLrwitTGR" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from google.colab import drive\n", "drive.mount('/content/drive')" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "T9zryJYawDQ2", "outputId": "e936f279-8591-42d7-fd10-949d05d98e75" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Mounted at /content/drive\n" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 424 }, "id": "ECjlmckUtKPf", "outputId": "34a0624d-ee7d-4852-e93d-7e63bdb68a38" }, "outputs": [ { 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\n" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "dataframe", "variable_name": "df", "summary": "{\n \"name\": \"df\",\n \"rows\": 3789,\n \"fields\": [\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 3789,\n \"samples\": [\n \"Singer Gavin DeGraw surprises US soldiers with rescue pets through Paws of War initiative\",\n \"'Easy money': How international scam artists pulled off an epic theft of Covid benefits\",\n \"Kamala Harris getting overwhelmingly positive media coverage since emerging as nominee: Study\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"outlet\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"NBC\",\n \"FoxNews\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"label\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0,\n \"min\": 0,\n \"max\": 1,\n \"num_unique_values\": 2,\n \"samples\": [\n 0,\n 1\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" } }, "metadata": {}, "execution_count": 4 } ], "source": [ "# Loade data\n", "df = pd.read_csv('/content/drive/MyDrive/train_data.csv')\n", "\n", "df" ] }, { "cell_type": "code", "source": [ "# Extract features and lables\n", "X = df['title']\n", "y = df['label']" ], "metadata": { "id": "-qnOl9q3t7Wj" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "from sklearn.ensemble import StackingClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.metrics import classification_report, accuracy_score\n", "from sklearn.model_selection import train_test_split\n", "import pandas as pd\n", "from transformers import BertTokenizer, BertModel\n", "import torch\n", "import numpy as np\n", "\n", "# Load pre-trained BERT tokenizer and model\n", "tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')\n", "model = BertModel.from_pretrained('bert-base-uncased')\n", "\n", "# Function to convert text data into BERT embeddings\n", "def get_bert_embeddings(texts, tokenizer, model):\n", " \"\"\"\n", " Generate BERT embeddings for a list of texts.\n", " Args:\n", " texts (list): List of text strings.\n", " tokenizer: Hugging Face tokenizer.\n", " model: Hugging Face BERT model.\n", " Returns:\n", " np.array: Embeddings for each text input.\n", " \"\"\"\n", " embeddings = []\n", " for text in texts:\n", " # Tokenize and encode the text\n", " inputs = tokenizer(text, return_tensors=\"pt\", padding=True, truncation=True, max_length=128)\n", "\n", " # Get the BERT embeddings\n", " with torch.no_grad():\n", " outputs = model(**inputs)\n", "\n", " # Use the [CLS] token embedding as the sentence-level embedding\n", " cls_embedding = outputs.last_hidden_state[:, 0, :].squeeze(0)\n", " embeddings.append(cls_embedding.numpy())\n", "\n", " return embeddings\n", "\n", "# Generate BERT embeddings for the entire dataset\n", "print(\"Generating BERT embeddings...\")\n", "X_embeddings = get_bert_embeddings(X, tokenizer, model)\n", "\n", "# Convert embeddings to NumPy array\n", "import numpy as np\n", "X_embeddings = np.array(X_embeddings)\n", "\n", "# Split the data into training and testing sets\n", "X_train, X_test, y_train, y_test = train_test_split(X_embeddings, y, test_size=0.2, random_state=42)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 194, "referenced_widgets": [ "01cc9b19df3d4757bee488a987e1bbf9", "582c2e646b9c4783938dde4654e99533", "e05f969c471e409a8413737e5a393221", "b45733bd0fb3464dbc19dee7a1451895", "0e3d43e86e6d461499b399acdfd5fa2c", "8ed13569a00d4b4a88a8a49c65947f8a", "78984c859c9e4b9b9c2f4f18ad6054d4", "d5d54cbcd627441eb72f050675a006b1", "83a436b66f214210bc79c5a7db10bb11", "0eea3be6398d401ba57925846f6040de", "427c5386f33447188671d50e99ccfd0c", "260e4e5a6ba24acba2a56166b91fbfc7", "d76335b4fda24422a0159a96e69ef368", "a5b67a02973a48a487b7c9e93db0377d", "b7b1a30eb5a54fc9896afb11bfb8781d", "be341f0c0ef4435d80ee7548898fb531", "97e77ff5db7647508be8997382e9594c", "a31185e958bf4a0a8ba625137e6cf172", "7b51f26788a14e11b5eb80cb7bba9512", "4b98f521809947489dceb067dbd22ee3", "c9ebb11b8c444284a9ee26176a992700", "b2b04d2e11aa4f0b9bfa35f036e8917a", "6989e3c6863544969f43d8be05177505", "1eb59b626676424cb673afe960c04dfd", "2bdca6df8a674e50a72954bab227dcef", "613b4657fbd34e46ae0a37e8594755cd", "7b1baf6884314ff9b46e9f5069ae1a9c", "0ecfc67d85a041dbbbe9d8f3af554a4b", "3fb580ee7980432f8de4bc97e0ed5162", "3c83c793de6b4eea99dfbff60159a4eb", "60ff6c36cfc44734af05ff4f8dd088d9", "3c84dd50bc3b4fa09637561a077d38f2", "e4f767b95c144f2f89d8a4aeaada64e5", "22ebe336524c4654997d86a0fae645af", "9afd320638634b9ba1356bd40a752ea3", "3f828fca7ca841b59c705676d7fe3316", "c886895f10864e758086a812c82be0c3", "a020500184914c5b813c99644ec0be21", "4d745e8b44914de7813fb391bbb5047f", "7e644961e71e45c7957e1f10425a89a3", "f7eeb169fb624c5785485a8d5576a4fa", "dd02bf86d8f14546a719440ce74bc92a", "30bc3777cd8d4188920180270a04cb18", "804561bcbd074250a0cbe68a284eaf9f", "93538ea6716a4040906bf39edbc2e9f1", "976843bc8c864fbeadbb49e4c4d5fffd", "efb7171b0a364834ad2c860522478718", "860a40534720408eb6b356dd0a52f551", "4361f8ec108246799bd4435bb07110ce", "45207fc9aba546958ad50513c06f61c1", "803e6030683c44b1a398a4fc07381ce3", "901c8594c9bb4cf1addda9d28f292468", "6367c7dc322a4b3fb5d3fa3e7559ee6c", "58552acf0eec49d8983cf485a03b3480", "840ab8e6fdbf4ddb876439b0997240b5" ] }, "id": "rJJN8FyhVeda", "outputId": "ee08cd8a-8b7d-4033-8855-a41c2d3e4e74" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/48.0 [00:00:19: FutureWarning: \n", "\n", "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n", "\n", " sns.barplot(data=top_features, x='Importance', y='Feature', palette='viridis')\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "
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\n" 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