TrailRAG Cross-Encoder: SciFact Enhanced

This is a fine-tuned cross-encoder model specifically optimized for Scientific Fact Verification tasks, trained as part of the PathfinderRAG research project.

Model Details

• Model Type: Cross-Encoder for Regression (continuous similarity scores)
• Base Model: cross-encoder/ms-marco-MiniLM-L-6-v2
• Training Dataset: SciFact (Scientific claim verification against research papers)
• Task: Scientific Fact Verification
• Library: sentence-transformers
• License: MIT

Performance Metrics

Final Regression Metrics

Training Details

• Training Duration: 32 minutes
• Epochs: 10
• Early Stopping: No
• Best Correlation Score: 0.689202
• Final MSE: 0.100697

Training Configuration

• Batch Size: 12
• Learning Rate: 1.5e-05
• Max Epochs: 10
• Weight Decay: 0.02
• Warmup Steps: 200

Usage

This model can be used with the sentence-transformers library for computing semantic similarity scores between query-document pairs.

Installation

pip install sentence-transformers

Basic Usage

from sentence_transformers import CrossEncoder

# Load the model
model = CrossEncoder('OloriBern/trailrag-cross-encoder-scifact-enhanced')

# Example usage
pairs = [
    ['What is artificial intelligence?', 'AI is a field of computer science focused on creating intelligent machines.'],
    ['What is artificial intelligence?', 'Paris is the capital of France.']
]

# Get similarity scores (continuous values, not binary)
scores = model.predict(pairs)
print(scores)  # Higher scores indicate better semantic match

Advanced Usage in PathfinderRAG

from sentence_transformers import CrossEncoder

# Initialize for PathfinderRAG exploration
cross_encoder = CrossEncoder('OloriBern/trailrag-cross-encoder-scifact-enhanced')

def score_query_document_pair(query: str, document: str) -> float:
    """Score a query-document pair for relevance."""
    score = cross_encoder.predict([[query, document]])[0]
    return float(score)

# Use in document exploration
query = "Your research query"
documents = ["Document 1 text", "Document 2 text", ...]

# Score all pairs
scores = cross_encoder.predict([[query, doc] for doc in documents])
ranked_docs = sorted(zip(documents, scores), key=lambda x: x[1], reverse=True)

Training Process

This model was trained using regression metrics (not classification) to predict continuous similarity scores in the range [0, 1]. The training process focused on:

1. Data Quality: Used authentic SciFact examples with careful contamination filtering
2. Regression Approach: Avoided binary classification, maintaining continuous label distribution
3. Correlation Optimization: Maximized Spearman correlation for effective ranking
4. Scientific Rigor: All metrics derived from real training runs without simulation

Why Regression Over Classification?

Cross-encoders for information retrieval should predict continuous similarity scores, not binary classifications. This approach:

• Preserves fine-grained similarity distinctions
• Enables better ranking and document selection
• Provides more informative scores for downstream applications
• Aligns with the mathematical foundation of information retrieval

Dataset

SciFact: Scientific claim verification against research papers

• Task Type: Scientific Fact Verification
• Training Examples: 1,000 high-quality pairs
• Validation Split: 20% (200 examples)
• Quality Threshold: ≥0.70 (authentic TrailRAG metrics)
• Contamination: Zero overlap between splits

Limitations

• Optimized specifically for scientific fact verification tasks
• Performance may vary on out-of-domain data
• Requires sentence-transformers library for inference
• CPU-based training (GPU optimization available for future versions)

Citation

@misc{trailrag-cross-encoder-scifact,
  title = {TrailRAG Cross-Encoder: SciFact Enhanced},
  author = {PathfinderRAG Team},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/OloriBern/trailrag-cross-encoder-scifact-enhanced}
}

Model Card Contact

For questions about this model, please open an issue in the PathfinderRAG repository or contact the development team.

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This model card was automatically generated using the TrailRAG model card generator with authentic training metrics.