bge-reranker-v2-m3-finetuned-v3

BGE-Reranker-v2-M3 (E-Commerce Finetuned V3 - Self Training)

This model is a highly advanced 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, followed by an aggressive Self-Training (Pseudo-Labeling) phase on ~197K highly confident samples.

🚀 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

The model was trained on a meticulously constructed pipeline:

• Phase 1-3: Curriculum Learning (Easy -> Hard -> Mastery) on 123K queries.
• Phase 4: Self-Training (Pseudo-labeling) on ~197K new unlabeled items utilizing the V2 model to achieve massive scaling.

📈 Evaluation Metrics (Ensemble: BGE-V3 + Jina-V3)

Tested on a held-out benchmark dataset of 10,000 human-annotated and hard-mined query-product pairs. These metrics reflect the model when used in an Ensemble mechanism with weights (0.7 BGE + 0.3 Jina):

Note: The V3 Self-Training Phase perfectly fixed the calibration issues of V2, rocketing the F1 score from 0.82 to 0.9172 while maintaining elite Structural Ranking Success.

💻 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-v3", 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]