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Embedding_MLP

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Jingyuan-Zhu commited on
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03f782f
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1 Parent(s): 82af86f

added the final_model weight and evaluation pipeline

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  1. NN_evaluation.ipynb +269 -0
  2. final_model.pth +3 -0
NN_evaluation.ipynb ADDED
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+ {
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+ "cells": [
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+ {
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+ "cell_type": "code",
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+ "execution_count": 4,
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+ "id": "459d08f0-b6d6-4062-8e60-d3349ae86cce",
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+ "metadata": {},
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+ "outputs": [
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n",
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+ "To disable this warning, you can either:\n",
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+ "\t- Avoid using `tokenizers` before the fork if possible\n",
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+ "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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+ "Requirement already satisfied: FlagEmbedding in /opt/conda/lib/python3.11/site-packages (1.3.3)\n",
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+ "Requirement already satisfied: torch>=1.6.0 in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (2.2.2+cu121)\n",
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+ "Requirement already satisfied: transformers==4.44.2 in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (4.44.2)\n",
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+ "Requirement already satisfied: datasets==2.19.0 in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (2.19.0)\n",
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+ "Requirement already satisfied: accelerate>=0.20.1 in /opt/conda/lib/python3.11/site-packages (from FlagEmbedding) (1.2.0)\n",
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+ "Requirement already satisfied: aiohttp in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (3.11.10)\n",
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+ "Requirement already satisfied: packaging in /opt/conda/lib/python3.11/site-packages (from datasets==2.19.0->FlagEmbedding) (24.0)\n",
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+ "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /opt/conda/lib/python3.11/site-packages (from torch>=1.6.0->FlagEmbedding) (12.1.105)\n",
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+ "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /opt/conda/lib/python3.11/site-packages (from torch>=1.6.0->FlagEmbedding) (12.1.105)\n",
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+ "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /opt/conda/lib/python3.11/site-packages (from torch>=1.6.0->FlagEmbedding) (12.1.105)\n",
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+ "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /opt/conda/lib/python3.11/site-packages (from torch>=1.6.0->FlagEmbedding) (8.9.2.26)\n",
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+ "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /opt/conda/lib/python3.11/site-packages (from torch>=1.6.0->FlagEmbedding) (12.1.3.1)\n",
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+ "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /opt/conda/lib/python3.11/site-packages (from torch>=1.6.0->FlagEmbedding) (10.3.2.106)\n",
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+ "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /opt/conda/lib/python3.11/site-packages (from torch>=1.6.0->FlagEmbedding) (12.1.0.106)\n",
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+ "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /opt/conda/lib/python3.11/site-packages (from torch>=1.6.0->FlagEmbedding) (12.1.105)\n",
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+ "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
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+ "\u001b[0m"
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+ ]
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+ },
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+ {
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+ "data": {
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+ "application/vnd.jupyter.widget-view+json": {
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+ "model_id": "3cb658d3527149008a6d17938370142d",
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+ "version_major": 2,
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+ "version_minor": 0
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+ },
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+ "text/plain": [
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+ "Fetching 30 files: 0%| | 0/30 [00:00<?, ?it/s]"
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+ ]
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+ },
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+ "metadata": {},
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+ "output_type": "display_data"
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Encoding titles...\n"
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+ ]
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+ },
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+ {
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+ "name": "stderr",
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+ "output_type": "stream",
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+ "text": [
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+ "You're using a XLMRobertaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
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+ ]
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+ },
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+ {
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+ "name": "stdout",
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+ "output_type": "stream",
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+ "text": [
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+ "Processed 20/20 titles\n",
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+ "Test Accuracy: 0.9000\n"
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+ ]
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+ }
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+ ],
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+ "source": [
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+ "import torch\n",
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+ "import pandas as pd\n",
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+ "from sklearn.metrics import accuracy_score\n",
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+ "from torch.utils.data import DataLoader, TensorDataset\n",
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+ "!pip install -U FlagEmbedding\n",
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+ "from FlagEmbedding import BGEM3FlagModel\n",
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+ "import torch.nn as nn\n",
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+ "model = BGEM3FlagModel('BAAI/bge-m3')\n",
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+ "\n",
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+ "\n",
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+ "# Define paths\n",
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+ "test_data_path = \"/home/jovyan/work/test_data_random_subset.csv\"\n",
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+ "model_weights_path = \"/home/jovyan/work/model_weights/final_model.pth\"\n",
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+ "\n",
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+ "data = data = pd.read_csv(test_data_path)\n",
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+ "titles = data['title'].tolist()\n",
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+ "labels = data['labels'].tolist()\n",
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+ "\n",
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+ "batch_size = 32\n",
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+ "embeddings = []\n",
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+ "\n",
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+ "print('Encoding titles...')\n",
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+ "for i in range(0, len(titles), batch_size):\n",
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+ " batch = titles[i:i + batch_size]\n",
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+ " batch_embeddings = model.encode(batch, batch_size=batch_size, max_length=512)['dense_vecs']\n",
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+ " embeddings.extend(batch_embeddings)\n",
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+ " print(f\"Processed {i + len(batch)}/{len(titles)} titles\")\n",
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+ "\n",
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+ "embeddings_df = pd.DataFrame(embeddings)\n",
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+ "embeddings_df['label'] = labels\n",
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+ "\n",
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+ "X_test = torch.FloatTensor(embeddings_df.iloc[:, :-1].values) # Features\n",
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+ "y_test = torch.FloatTensor(embeddings_df.iloc[:, -1].values).view(-1, 1) # Labels\n",
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+ "\n",
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+ "# Create DataLoader for the test dataset\n",
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+ "test_dataset = TensorDataset(X_test, y_test)\n",
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+ "test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
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+ "\n",
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+ "# Define the model architecture\n",
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+ "class EmbeddingMLPClassifier(nn.Module):\n",
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+ " def __init__(self,\n",
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+ " input_dim=1024,\n",
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+ " hidden_layers=[512, 256, 128],\n",
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+ " dropout_rate=0.2,\n",
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+ " device=None):\n",
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+ " super(EmbeddingMLPClassifier, self).__init__()\n",
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+ " self.device = device or torch.device('cpu') # Force CPU usage\n",
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+ "\n",
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+ " layers = []\n",
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+ " prev_dim = input_dim\n",
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+ " for hidden_dim in hidden_layers:\n",
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+ " layers.extend([\n",
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+ " nn.Linear(prev_dim, hidden_dim),\n",
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+ " nn.BatchNorm1d(hidden_dim),\n",
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+ " nn.ReLU(),\n",
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+ " nn.Dropout(dropout_rate)\n",
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+ " ])\n",
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+ " prev_dim = hidden_dim\n",
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+ "\n",
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+ " layers.append(nn.Linear(prev_dim, 1))\n",
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+ " layers.append(nn.Sigmoid())\n",
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+ "\n",
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+ " self.model = nn.Sequential(*layers)\n",
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+ " self.to(self.device)\n",
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+ "\n",
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+ " def forward(self, x):\n",
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+ " return self.model(x)\n",
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+ "\n",
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+ "# Load the saved model\n",
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+ "device = torch.device('cpu') # Force evaluation on CPU\n",
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+ "model = EmbeddingMLPClassifier(input_dim=X_test.shape[1], device=device)\n",
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+ "model.load_state_dict(torch.load(model_weights_path, map_location=device)) # Map weights to CPU\n",
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+ "model.eval()\n",
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+ "\n",
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+ "# Evaluate the model on the test set\n",
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+ "test_preds, test_labels = [], []\n",
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+ "\n",
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+ "with torch.no_grad():\n",
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+ " for batch_X, batch_y in test_loader:\n",
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+ " batch_X = batch_X.to(device) # Ensure data is on CPU\n",
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+ " batch_y = batch_y.to(device)\n",
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+ "\n",
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+ " outputs = model(batch_X)\n",
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+ " preds = (outputs > 0.5).float()\n",
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+ "\n",
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+ " test_preds.extend(preds.cpu().numpy())\n",
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+ " test_labels.extend(batch_y.cpu().numpy())\n",
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+ "\n",
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+ "# Calculate accuracy\n",
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+ "test_accuracy = accuracy_score(test_labels, test_preds)\n",
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+ "print(f\"Test Accuracy: {test_accuracy:.4f}\")"
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+ ]
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+ },
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+ {
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+ "cell_type": "code",
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+ "execution_count": null,
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+ "id": "e2869985-46ac-4a07-a13d-c11e01ef9ec4",
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+ "metadata": {},
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+ "outputs": [],
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+ "source": []
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+ }
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+ ],
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+ "metadata": {
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+ "kernelspec": {
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+ "display_name": "Python 3 (ipykernel)",
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+ "language": "python",
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+ "name": "python3"
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+ },
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+ "language_info": {
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+ "codemirror_mode": {
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final_model.pth ADDED
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