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{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"id": "459d08f0-b6d6-4062-8e60-d3349ae86cce",
"metadata": {},
"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": [
"Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\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",
"\u001b[0m"
]
},
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "3cb658d3527149008a6d17938370142d",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"Fetching 30 files: 0%| | 0/30 [00:00<?, ?it/s]"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Encoding titles...\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"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"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processed 20/20 titles\n",
"Test Accuracy: 0.9000\n"
]
}
],
"source": [
"import torch\n",
"import pandas as pd\n",
"from sklearn.metrics import accuracy_score\n",
"from torch.utils.data import DataLoader, TensorDataset\n",
"!pip install -U FlagEmbedding\n",
"from FlagEmbedding import BGEM3FlagModel\n",
"import torch.nn as nn\n",
"model = BGEM3FlagModel('BAAI/bge-m3')\n",
"\n",
"\n",
"# Define paths\n",
"test_data_path = \"/home/jovyan/work/test_data_random_subset.csv\"\n",
"model_weights_path = \"/home/jovyan/work/model_weights/final_model.pth\"\n",
"\n",
"data = data = pd.read_csv(test_data_path)\n",
"titles = data['title'].tolist()\n",
"labels = data['labels'].tolist()\n",
"\n",
"batch_size = 32\n",
"embeddings = []\n",
"\n",
"print('Encoding titles...')\n",
"for i in range(0, len(titles), batch_size):\n",
" batch = titles[i:i + batch_size]\n",
" batch_embeddings = model.encode(batch, batch_size=batch_size, max_length=512)['dense_vecs']\n",
" embeddings.extend(batch_embeddings)\n",
" print(f\"Processed {i + len(batch)}/{len(titles)} titles\")\n",
"\n",
"embeddings_df = pd.DataFrame(embeddings)\n",
"embeddings_df['label'] = labels\n",
"\n",
"X_test = torch.FloatTensor(embeddings_df.iloc[:, :-1].values) # Features\n",
"y_test = torch.FloatTensor(embeddings_df.iloc[:, -1].values).view(-1, 1) # Labels\n",
"\n",
"# Create DataLoader for the test dataset\n",
"test_dataset = TensorDataset(X_test, y_test)\n",
"test_loader = DataLoader(test_dataset, batch_size=32, shuffle=False)\n",
"\n",
"# Define the model architecture\n",
"class EmbeddingMLPClassifier(nn.Module):\n",
" def __init__(self,\n",
" input_dim=1024,\n",
" hidden_layers=[512, 256, 128],\n",
" dropout_rate=0.2,\n",
" device=None):\n",
" super(EmbeddingMLPClassifier, self).__init__()\n",
" self.device = device or torch.device('cpu') # Force CPU usage\n",
"\n",
" layers = []\n",
" prev_dim = input_dim\n",
" for hidden_dim in hidden_layers:\n",
" layers.extend([\n",
" nn.Linear(prev_dim, hidden_dim),\n",
" nn.BatchNorm1d(hidden_dim),\n",
" nn.ReLU(),\n",
" nn.Dropout(dropout_rate)\n",
" ])\n",
" prev_dim = hidden_dim\n",
"\n",
" layers.append(nn.Linear(prev_dim, 1))\n",
" layers.append(nn.Sigmoid())\n",
"\n",
" self.model = nn.Sequential(*layers)\n",
" self.to(self.device)\n",
"\n",
" def forward(self, x):\n",
" return self.model(x)\n",
"\n",
"# Load the saved model\n",
"device = torch.device('cpu') # Force evaluation on CPU\n",
"model = EmbeddingMLPClassifier(input_dim=X_test.shape[1], device=device)\n",
"model.load_state_dict(torch.load(model_weights_path, map_location=device)) # Map weights to CPU\n",
"model.eval()\n",
"\n",
"# Evaluate the model on the test set\n",
"test_preds, test_labels = [], []\n",
"\n",
"with torch.no_grad():\n",
" for batch_X, batch_y in test_loader:\n",
" batch_X = batch_X.to(device) # Ensure data is on CPU\n",
" batch_y = batch_y.to(device)\n",
"\n",
" outputs = model(batch_X)\n",
" preds = (outputs > 0.5).float()\n",
"\n",
" test_preds.extend(preds.cpu().numpy())\n",
" test_labels.extend(batch_y.cpu().numpy())\n",
"\n",
"# Calculate accuracy\n",
"test_accuracy = accuracy_score(test_labels, test_preds)\n",
"print(f\"Test Accuracy: {test_accuracy:.4f}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e2869985-46ac-4a07-a13d-c11e01ef9ec4",
"metadata": {},
"outputs": [],
"source": []
}
],
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