regtoy/bge-reranker-v2-m3-finetuned-v2

BGE-Reranker-v2-M3 (E-Commerce Finetuned v2 - Curriculum Learning)

This model is a fine-tuned version of BAAI/bge-reranker-v2-m3 specifically optimized for E-Commerce Product Search and Relevance. It has been trained using a sophisticated Curriculum Learning pipeline with highly curated hard negatives (Brand, Category, and Lexical similarity).

🚀 Model Details

• —Base Model: BAAI/bge-reranker-v2-m3
• —Architecture: Cross-Encoder (Sentence Transformers)
• —Task: Text Classification / Ranking
• —Language: Multilingual (Optimized for Turkish/English E-commerce items)
• —Training Strategy: Curriculum Learning (Easy -> Hard -> Mastery)
• —Loss Function: BCEWithLogitsLoss (Knowledge Distillation via Soft-labeling)

📊 Training Pipeline (Curriculum Learning)

The model was trained on a meticulously constructed dataset of 123,510 query-product pairs.

• —Phase 1 (Epoch 1): Easy negatives & Random negatives.
• —Phase 2 (Epoch 2): Highly confusing hard negatives (Same Brand, Same Category, Lexically similar but different items) mined via TF-IDF Sparse Matrix Multiplication.
• —Phase 3 (Epoch 3): Combined dataset for mastery and stabilization.

📈 Evaluation Metrics

Tested on a held-out benchmark dataset of 10,000 human-annotated and hard-mined query-product pairs:

Note: This V2 model sacrifices a tiny amount of calibration accuracy (F1) in exchange for a massive boost in structural Ranking success (MRR & P@1), making it exceptionally robust against keyword spamming and brand confusion.

💻 Usage

You can use this model easily with the sentence-transformers library:

pip install -U sentence-transformers

from sentence_transformers import CrossEncoder

# Load the model
model = CrossEncoder("regtoy/bge-reranker-v2-m3-finetuned-v2", trust_remote_code=True)

query = "erkek siyah koşu ayakkabısı"
documents = [
    "Title: Erkek Siyah Koşu Ayakkabısı | Brand: Nike | Category: Spor Ayakkabı", # Doğru Eşleşme
    "Title: Kadın Kırmızı Yürüyüş Ayakkabısı | Brand: Adidas | Category: Spor Ayakkabı", # Yanlış Eşleşme
    "Title: Erkek Siyah Deri Ceket | Brand: Koton | Category: Ceket" # Lexical Hard Negative
]

# Create pairs
pairs = [[query, doc] for doc in documents]

# Predict relevance scores
scores = model.predict(pairs)

# Rank documents
ranked_results = sorted(zip(documents, scores), key=lambda x: x[1], reverse=True)
for doc, score in ranked_results:
    print(f"Score: {score:.4f} | Document: {doc}")

🛠️ Input Format (Structured Input)

For maximum performance, format your product data exactly as it was formatted during training by joining attributes with | : Title: [title] | Brand: [brand] | Category: [category] | Gender: [gender] | Attributes: [attributes]