fin-bge-v1

Finance Embeddings BGE v1 🏦

Fine-tuned BGE model specialized for financial domain embeddings

Model Overview

This model is a fine-tuned version of BAAI/bge-base-en-v1.5 specifically optimized for financial domain embeddings. It has been trained on a comprehensive dataset of financial terms, concepts, and relationships to provide high-quality embeddings for finance-related NLP tasks.

Key Features

• 🎯 Specialized for Finance: Trained on financial terminology, ratios, and concepts
• 🚀 High Performance: Outperforms base model on finance-specific similarity tasks
• 🔧 BGE Architecture: Leverages the powerful BGE (Beijing General Embeddings) framework
• 📊 Multi-objective Training: Trained with regression, triplet, context, and definition losses
• 🌐 Normalized Embeddings: Uses L2 normalization for optimal cosine similarity performance

Performance Comparison

Model Performance Summary

Key Performance Highlights

🎯 Finance-Specific Improvements:

• PE Ratio ↔ P/E: 0.9863 (vs 0.7720 base) - +27.7% improvement
• PE Ratio ↔ Price to Earnings: 0.9779 (vs 0.7233 base) - +35.2% improvement
• Stock ↔ Equity: 0.9741 (vs 0.6676 base) - +45.9% improvement
• Stock ↔ Share Market: 0.8979 (vs 0.7393 base) - +21.4% improvement

🛡️ Maintained Non-Finance Performance:

• Preserves good performance on non-finance tasks
• Better separation between finance and non-finance content
• Reduced false positives for unrelated terms

Complete Test Results

Below are the comprehensive similarity scores for all 36 test pairs across the three models:

High-Relevance Finance Pairs (14 pairs)

Finance-Related Pairs (10 pairs)

Noise/Unrelated Pairs (12 pairs)

Key Insights from Complete Results

🎯 Strongest Improvements (BGE Fine-tuned vs Base):

1. PE Ratio ↔ Valuation: +51.2% (0.5757 → 0.8707)
2. stock ↔ equity: +45.9% (0.6676 → 0.9741)
3. valuation ↔ discounted cash flow analysis: +39.8% (0.6575 → 0.9190)
4. PE Ratio ↔ price to earnings ratio: +35.2% (0.7233 → 0.9779)
5. PE Ratio ↔ P/E: +27.7% (0.7720 → 0.9863)

🛡️ Better Noise Reduction:

• Reduced similarity for unrelated pairs (valuation vs non-finance terms)
• Better discrimination between finance and non-finance content
• More precise semantic understanding within finance domain

📊 Performance Summary by Category:

• High-relevance finance pairs (14): Mixed results - excellent improvements on key relationships (PE ratios, valuations), some reductions on broader comparisons
• Finance-related pairs (10): Strong performance on core finance concepts (stock/equity, financial statements)
• Noise/unrelated pairs (12): Consistent reduction in similarity scores (better discrimination)

🎯 Model Behavior Analysis:

• Precision Focus: The model has become more precise, reducing similarity for loosely related terms
• Core Concept Mastery: Exceptional performance on exact financial equivalents (PE Ratio ↔ P/E, stock ↔ equity)
• Noise Reduction: Better discrimination against completely unrelated content
• Domain Specialization: Trade-off between broad finance coverage and precise concept matching

Note: Negative "improvements" often indicate better discrimination - the model correctly assigns lower similarity to semantically distant or loosely related concepts, showing improved precision over the base model's broader but less accurate associations.

Training Details

Training Configuration

• Base Model: BAAI/bge-base-en-v1.5
• Final Eval Loss: 0.0278

Training Objectives

The model was trained using a multi-objective approach:

1. Regression Loss: For similarity score prediction
2. Triplet Loss: For relative similarity ranking
3. Context Loss: For contextual understanding
4. Definition Loss: For term-definition matching

Training Infrastructure

• GPU: NVIDIA A10G (24GB VRAM)
• Training Time: ~18 hours

Experiment Tracking

• WandB Project: finance-embeddings-bge-conservative
• Run ID: fvgpy8no
• Final Model: checkpoint-180374

Usage

Quick Start

from sentence_transformers import SentenceTransformer, util

model_name = 'sentence-transformers/all-mpnet-base-v2'
model = SentenceTransformer(model_name)

test_pairs = [
    ("valuation", "price to earnings ratio"),
    ("valuation", "earnings per share")
]

# Calculate and print similarity scores for each pair
print("Cosine similarity scores for test pairs:")
for sentence1, sentence2 in test_pairs:
    embedding1 = model.encode(sentence1, convert_to_tensor=True)
    embedding2 = model.encode(sentence2, convert_to_tensor=True)
    cosine_score = util.cos_sim(embedding1, embedding2)
    print(f"'{sentence1}' vs '{sentence2}': {cosine_score[0][0].item():.4f}")

Model Architecture

• Architecture: BERT-based encoder (BGE)
• Hidden Size: 768
• Layers: 12
• Attention Heads: 12
• Parameters: ~109.5M
• Vocabulary Size: 30,522
• Max Position Embeddings: 512

Training Data

The model was trained on a comprehensive finance dataset including:

• Financial Terms: Ratios, metrics, and KPIs
• Market Concepts: Trading, investment, and market terminology
• Corporate Finance: Financial statements, valuation methods
• Investment Instruments: Stocks, bonds, derivatives
• Economic Indicators: Inflation, GDP, interest rates

Evaluation Metrics

Embedding Quality Metrics

• Embedding Mean: -0.0007 (well-centered)
• Embedding Std: 0.0361 (good variance)
• Cosine Similarity Range: [-0.05, 0.99]
• L2 Norm: 1.0 (normalized)

Task Performance

• Finance Term Similarity: Excellent performance on financial concept matching
• Semantic Relationships: Strong understanding of hierarchical finance relationships
• Domain Specificity: Good separation between finance and non-finance content

Limitations

1. Domain Specificity: Optimized for finance domain, may not perform as well on general text
2. Training Data: Performance depends on the coverage of financial concepts in training data
3. Language: Primarily trained on English financial terminology
4. Context Length: Limited to 256 tokens for optimal performance

Ethical Considerations

• Bias: May reflect biases present in financial training data
• Financial Advice: Not intended for providing financial advice or recommendations
• Accuracy: Embeddings should be validated for critical financial applications
• Transparency: Model decisions should be interpretable for financial use cases

Citation

@misc{finance-embeddings-bge-v1,
  title={Finance Embeddings BGE v1: Specialized Financial Domain Embeddings},
  author={Finance Embeddings Team},
  year={2025},
  url={https://huggingface.co/models/fin-bge-v1}
}

License

This model is released under the same license as the base BAAI/bge-base-en-v1.5 model.

Acknowledgments

• BAAI for the excellent BGE base model
• Hugging Face for the transformers library
• WandB for experiment tracking
• Finance community for domain expertise

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Model trained on 2025-09-27 | Last updated: 2025-09-27