lamdx4/bge-m3-vietnamese-rental-projection

pip install transformers torch safetensors

from transformers import AutoModel, AutoTokenizer
import torch

# Load model
model = AutoModel.from_pretrained(
    "your-username/bge-m3-vietnamese-rental-projection",
    trust_remote_code=True
)
tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-m3")

# Move to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model = model.to(device)
model.eval()

# Encode texts
texts = [
    "Phòng trọ Quận 10, 25m², giá 5 triệu, WC riêng, máy lạnh",
    "Cho thuê phòng Bình Thạnh, 20m², 4 triệu/tháng"
]

# Method 1: Using encode (recommended)
embeddings = model.encode(texts, device=device)  # [2, 128]

# Method 2: Using forward
inputs = tokenizer(texts, padding=True, truncation=True, return_tensors="pt", max_length=512)
inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.no_grad():
    outputs = model(**inputs)
    embeddings = outputs.last_hidden_state  # [2, 128], L2-normalized

print(embeddings.shape)  # torch.Size([2, 128])

# Compute similarity (cosine)
similarity = embeddings[0] @ embeddings[1]
print(f"Similarity: {similarity:.4f}")

# Build a search engine
class RentalSearchEngine:
    def __init__(self, model, tokenizer, device="cuda"):
        self.model = model
        self.tokenizer = tokenizer
        self.device = device
        self.database_embeddings = None
        self.database_texts = None
    
    def index(self, property_descriptions):
        """Index a database of property descriptions"""
        self.database_texts = property_descriptions
        self.database_embeddings = self.model.encode(
            property_descriptions,
            device=self.device
        )
    
    def search(self, query, top_k=5):
        """Search for most similar properties"""
        query_emb = self.model.encode([query], device=self.device)[0]
        
        # Compute similarities
        similarities = query_emb @ self.database_embeddings.T
        
        # Get top-k
        top_k = min(top_k, len(similarities))
        scores, indices = torch.topk(similarities, k=top_k)
        
        results = []
        for idx, score in zip(indices.tolist(), scores.tolist()):
            results.append({
                "text": self.database_texts[idx],
                "score": score
            })
        
        return results

# Example usage
engine = RentalSearchEngine(model, tokenizer, device)

# Index properties
properties = [
    "Phòng trọ 25m² Quận 10, WC riêng, máy lạnh, giá 5.5tr/tháng",
    "Cho thuê phòng 30m² Quận 1, full nội thất, giá 8tr/tháng",
    "Phòng 20m² Thủ Đức, WC chung, giá 3.5tr/tháng",
    "Studio 35m² Quận 3, ban công, bếp riêng, giá 9tr/tháng",
]
engine.index(properties)

# Search
results = engine.search("phòng trọ q10 25m2 wc riêng 5tr5", top_k=3)

for i, result in enumerate(results, 1):
    print(f"{i}. [{result['score']:.4f}] {result['text']}")

{
  "base_model": "BAAI/bge-m3",
  "d_out": 128,
  "freeze_encoder": true,
  "epochs": 17,
  "batch_size": 128,
  "learning_rate": 0.0002,
  "optimizer": "AdamW",
  "weight_decay": 0.01,
  "loss": "Weighted InfoNCE (symmetric)",
  "temperature": 0.07,
  "device": "Tesla T4 (Google Colab)",
  "training_time": "~2.5 hours"
}

BAAI/bge-m3 (frozen)
    ↓ [1024-dim]
ProjectionHead
    ├─ Linear(1024 → 128, bias=False)
    └─ L2 Normalization
    ↓ [128-dim, L2-normalized]
Output Embeddings

Query: "phòng trọ Gò Vấp 18m² 3tr5 có wc riêng"

Positive (0.947):  Gò Vấp 18m² 3tr5 wc riêng ✅
Negative 1 (0.366): Quận 12 18m² 3tr5 wc riêng (wrong district!)
Negative 2 (0.411): Gò Vấp 18m² 4tr2 wc riêng (wrong price)
Negative 3 (0.828): Gò Vấp 18m² 3tr5 wc chung (wrong amenity)

→ Model correctly penalizes location mismatch most heavily

Query: "phòng trọ q10 4tr 20m² có máy lạnh wc riêng gần chợ"

Positive (0.904):  Q10 20m² 4tr máy lạnh wc riêng ✅
Negative 1 (0.542): Q3 20m² 4tr máy lạnh wc riêng (wrong district)
Negative 2 (0.418): Q10 20m² 5.5tr máy lạnh wc riêng (wrong price)
Negative 3 (0.257): Q10 15m² 4tr máy lạnh wc chung (multiple diffs)

→ Strong margin (+0.36) between positive and top negative

@misc{bge-m3-vietnamese-rental,
  author = {Your Name},
  title = {BGE-M3 Vietnamese Rental Property Search},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/your-username/bge-m3-vietnamese-rental-projection}},
}