Create embed_data.py

embed_data.py

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+import torch
+from transformers import BertTokenizer, BertModel
+from torch.nn import Embedding
+import numpy as np
+
+# BERT 모델 및 토크나이저 로드
+tokenizer = BertTokenizer.from_pretrained("klue/bert-base")
+bert_model = BertModel.from_pretrained("klue/bert-base")
+
+# 상품 데이터 임베딩
+def embed_product_data(product_data):
+    text = product_data.get("title", "") + " " + product_data.get("description", "")
+    inputs = tokenizer(
+        text, return_tensors="pt", truncation=True, padding=True, max_length=128
+    )
+    outputs = bert_model(**inputs)
+    text_embedding = outputs.last_hidden_state.mean(dim=1)
+
+    category_embedding_layer = Embedding(num_embeddings=50, embedding_dim=16)
+    color_embedding_layer = Embedding(num_embeddings=20, embedding_dim=8)
+
+    category_id = product_data.get("category_id", 0)
+    color_id = product_data.get("color_id", 0)
+
+    category_embedding = category_embedding_layer(torch.tensor([category_id]))
+    color_embedding = color_embedding_layer(torch.tensor([color_id]))
+
+    combined_embedding = torch.cat((text_embedding, category_embedding, color_embedding), dim=1)
+    return combined_embedding.detach().numpy()
+
+# 사용자 데이터 임베딩
+def embed_user_data(user_data):
+    embedding_layer = Embedding(num_embeddings=100, embedding_dim=128)
+
+    gender_id = 0 if user_data['gender'] == 'M' else 1
+    scaled_height = int((user_data['height'] - 50) * 99 // 200)
+    scaled_weight = int((user_data['weight'] - 30) * 99 // 170)
+
+    age_embedding = embedding_layer(torch.tensor([user_data['age']])).view(1, -1)
+    gender_embedding = embedding_layer(torch.tensor([gender_id])).view(1, -1)
+    height_embedding = embedding_layer(torch.tensor([scaled_height])).view(1, -1)
+    weight_embedding = embedding_layer(torch.tensor([scaled_weight])).view(1, -1)
+
+    combined_embedding = torch.cat((age_embedding, gender_embedding, height_embedding, weight_embedding), dim=1)
+    return combined_embedding.detach().numpy()