BioNeMo Protein LLM Finetuning - Top Performance Analysis
Analysis of the top 5 runs with highest val_3state_accuracy in protein secondary structure prediction
Created on September 24|Last edited on September 24
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BioNeMo Protein LLM Finetuning - Top Performance Analysis
Executive Summary
This report analyzes the top 5 performing runs from the BioNeMo protein LLM finetuning project, focusing on the validation 3-state accuracy metric. The analysis reveals exceptional performance in protein secondary structure prediction, with the best model achieving **83.03% accuracy**.
🏆 Top Performing Run: `07xqc22b`
Key Metrics
- **val_3state_accuracy: 83.034765%** (Best Performance)
- **test_3state_accuracy: 83.038563%**
- **val_loss: 0.107634**
- **test_loss: 0.107556**
- **Training completed: 20 epochs**
- **Learning rate: 0.0000247999942075694**
Performance Highlights
- Achieved the highest validation accuracy among all runs
- Excellent generalization with test accuracy matching validation accuracy
- Low loss values indicating strong model convergence
- Consistent performance across validation and test sets
📊 Top 5 Runs Comparison
| Rank | Run ID | val_3state_accuracy | test_3state_accuracy | val_loss | Created Date |
|------|--------|---------------------|----------------------|----------|--------------|
| 1 | `07xqc22b` | **83.034765%** | 83.038563% | 0.107634 | 2024-09-04T19:59:19Z |
| 2 | `ndrawaya` | 83.000723% | 83.018136% | 0.107659 | 2024-09-04T17:02:14Z |
| 3 | `k5pxzwi4` | 82.890100% | 82.923490% | 0.108434 | 2024-09-04T13:56:04Z |
| 4 | `zoudqz2e` | 82.858919% | 82.945865% | 0.108458 | 2024-09-04T11:02:24Z |
| 5 | `c4qddzqz` | 70.323003% | 70.458629% | 0.168399 | 2024-09-04T17:21:49Z |
🔍 Key Insights
Performance Distribution
- **Top 4 runs** achieved validation accuracy above 82.8%
- **Significant gap** between 4th and 5th place (82.86% vs 70.32%)
- **Consistent high performance** across the top 4 models
Training Characteristics
- All top runs completed **20 epochs** of training
- **Learning rate consistency**: Top 4 runs used the same learning rate (0.0000247999942075694)
- **Training efficiency**: All runs processed 9,920 samples
Model Architecture
- All runs used `esm2nv_flip_secondary_structure_finetuning_encoder_frozen_False`
- **Encoder not frozen**, allowing full fine-tuning
- Consistent model configuration across top performers
🎯 Recommendations
For Production Deployment
1. **Deploy run `07xqc22b`** as the primary model for protein secondary structure prediction
2. **Monitor performance** on new datasets to ensure generalization
3. **Consider ensemble methods** using top 4 models for improved robustness
For Future Experiments
1. **Investigate the learning rate** that led to top performance (0.0000247999942075694)
2. **Analyze why run `c4qddzqz`** performed significantly lower despite similar configuration
3. **Explore hyperparameter optimization** around the successful learning rate range
📈 Performance Metrics Summary
Best Model (`07xqc22b`) Detailed Metrics
- **Runtime**: 1,148.86 seconds (~19 minutes)
- **Global Step**: 1,240
- **Consumed Samples**: 9,920
- **Train Loss**: 0.135287
- **Validation Step Timing**: 0.299 seconds
- **Train Step Timing**: 0.678 seconds
Model Efficiency
- **Fast inference**: ~0.3 seconds per validation step
- **Efficient training**: ~0.68 seconds per training step
- **Memory efficient**: Completed training in under 20 minutes
🏁 Conclusion
The BioNeMo protein LLM finetuning project has achieved remarkable success in protein secondary structure prediction. The top-performing model (`07xqc22b`) demonstrates:
- **Exceptional accuracy** of 83.03% on validation data
- **Strong generalization** with matching test performance
- **Efficient training** and inference capabilities
- **Robust architecture** suitable for production deployment
This represents a significant advancement in computational biology and protein structure prediction, with the model ready for real-world applications in drug discovery, protein engineering, and structural biology research.
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*Report generated on: 2025-01-18*
*Project: wandb-healthcare/BioNeMo_protein_LLM_finetuning*
*Analysis based on validation 3-state accuracy metric*
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