Delete embed_data2.py

embed_data2.py

@@ -1,45 +0,0 @@
-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()