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"text": [
"Requirement already satisfied: FlagEmbedding in /usr/local/lib/python3.10/dist-packages (1.3.3)\n",
"Requirement already satisfied: torch>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from FlagEmbedding) (2.5.1+cu121)\n",
"Requirement already satisfied: transformers==4.44.2 in /usr/local/lib/python3.10/dist-packages (from FlagEmbedding) (4.44.2)\n",
"Requirement already satisfied: datasets==2.19.0 in /usr/local/lib/python3.10/dist-packages (from FlagEmbedding) (2.19.0)\n",
"Requirement already satisfied: accelerate>=0.20.1 in /usr/local/lib/python3.10/dist-packages (from FlagEmbedding) (1.1.1)\n",
"Requirement already satisfied: sentence-transformers in /usr/local/lib/python3.10/dist-packages (from FlagEmbedding) (3.2.1)\n",
"Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (from FlagEmbedding) (0.13.2)\n",
"Requirement already satisfied: ir-datasets in /usr/local/lib/python3.10/dist-packages (from FlagEmbedding) (0.5.9)\n",
"Requirement already satisfied: sentencepiece in /usr/local/lib/python3.10/dist-packages (from FlagEmbedding) (0.2.0)\n",
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]
},
{
"output_type": "display_data",
"data": {
"text/plain": [
"Fetching 30 files: 0%| | 0/30 [00:00<?, ?it/s]"
],
"application/vnd.jupyter.widget-view+json": {
"version_major": 2,
"version_minor": 0,
"model_id": "d564a252f0994e50a82e49477311aaaf"
}
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [
"#import pandas as pd\n",
"import xgboost as xgb\n",
"from sklearn.metrics import accuracy_score, classification_report\n",
"\n",
"csv_file_path = '/content/drive/Shared drives/5190_NLP_Project/test_data_random_subset.csv'\n",
"model_path = '/content/drive/Shared drives/5190_NLP_Project/xgboost_model/final_xgboost_model.json'\n",
"\n",
"data = pd.read_csv(csv_file_path, index_col=0)\n",
"\n",
"titles = data['title'].tolist()\n",
"labels = data['labels'].tolist()\n",
"\n",
"labels = [1 if label == 0 else 0 for label in labels]\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 = embeddings_df.iloc[:, :-1].values\n",
"y = embeddings_df['label'].values\n",
"\n",
"\n",
"print('Loading XGBoost model...')\n",
"xgboost_model = xgb.XGBClassifier()\n",
"xgboost_model.load_model(model_path)\n",
"\n",
"print('Making predictions...')\n",
"y_pred_prob = xgboost_model.predict(X)\n",
"y_pred = (y_pred_prob >= 0.5).astype(int)\n",
"\n",
"print(\"\\nModel Performance:\")\n",
"accuracy = accuracy_score(y, y_pred)\n",
"print(f\"Accuracy: {accuracy:.4f}\")\n",
"\n",
"print(\"\\nClassification Report:\\n\", classification_report(y, y_pred))\n",
"\n"
],
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "O5L-7-7c-6B2",
"outputId": "39441b83-0302-40e8-aaff-bbb32ce0474a"
},
"execution_count": 4,
"outputs": [
{
"output_type": "stream",
"name": "stderr",
"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"
]
},
{
"output_type": "stream",
"name": "stdout",
"text": [
"Encoding titles...\n",
"Processed 20/20 titles\n",
"Loading XGBoost model...\n",
"Making predictions...\n",
"\n",
"Model Performance:\n",
"Accuracy: 0.7500\n",
"\n",
"Classification Report:\n",
" precision recall f1-score support\n",
"\n",
" 0 0.73 0.80 0.76 10\n",
" 1 0.78 0.70 0.74 10\n",
"\n",
" accuracy 0.75 20\n",
" macro avg 0.75 0.75 0.75 20\n",
"weighted avg 0.75 0.75 0.75 20\n",
"\n"
]
}
]
}
]
} |