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TARA-ELF-NET

Tabular Regression
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Upload ELF-NET bidirectional neural network checkpoints (env2pfam + pfam2env)

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  1. README.md +173 -0
  2. env2pfam/algagpt_full/best_model.pt +3 -0
  3. env2pfam/algagpt_full/config.json +0 -0
  4. env2pfam/algagpt_full/final_metrics.json +35 -0
  5. env2pfam/algagpt_full/training_history.json +902 -0
  6. env2pfam/algagpt_light/best_model.pt +3 -0
  7. env2pfam/algagpt_light/config.json +0 -0
  8. env2pfam/algagpt_light/final_metrics.json +35 -0
  9. env2pfam/algagpt_light/training_history.json +1700 -0
  10. env2pfam/pythia_full/best_model.pt +3 -0
  11. env2pfam/pythia_full/config.json +0 -0
  12. env2pfam/pythia_full/final_metrics.json +35 -0
  13. env2pfam/pythia_full/training_history.json +1112 -0
  14. env2pfam/pythia_light/best_model.pt +3 -0
  15. env2pfam/pythia_light/config.json +0 -0
  16. env2pfam/pythia_light/final_metrics.json +35 -0
  17. env2pfam/pythia_light/training_history.json +776 -0
  18. pfam2env/full/best_model.pt +3 -0
  19. pfam2env/full/config.json +16 -0
  20. pfam2env/full/final_metrics.json +35 -0
  21. pfam2env/full/provenance.md +69 -0
  22. pfam2env/full/training_history.json +458 -0
  23. pfam2env/light/best_model.pt +3 -0
  24. pfam2env/light/config.json +16 -0
  25. pfam2env/light/final_metrics.json +35 -0
  26. pfam2env/light/provenance.md +69 -0
  27. pfam2env/light/training_history.json +714 -0
README.md ADDED
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+ ---
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+ language: en
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+ license: apache-2.0
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+ tags:
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+ - marine-biology
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+ - metagenomics
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+ - environmental-modeling
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+ - protein-domains
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+ - tara-oceans
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+ - pfam
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+ - pytorch
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+ library_name: pytorch
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+ pipeline_tag: tabular-regression
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+ ---
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+
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+ # ELF-NET: Environment-Linked Functional Network
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+
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+ Bidirectional neural network checkpoints linking marine environmental variables to microalgal protein domain (Pfam) abundance profiles from the TARA Oceans metagenomic dataset.
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+
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+ ## Model Description
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+
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+ ELF-NET consists of two complementary prediction directions:
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+
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+ ### env2pfam (Environment → Pfam Abundance)
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+ Predicts the abundance of thousands of Pfam protein domains at a marine sampling site given 94 environmental features (30 oceanographic/atmospheric variables + 64 AlphaEarth spectral eigenvectors).
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+
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+ ### pfam2env (Pfam Abundance → Environmental Features)
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+ Predicts 64 environmental features from observed Pfam domain abundance profiles (9,611 input domains).
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+
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+ ## Repository Structure
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+
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+ ```
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+ ├── env2pfam/
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+ │ ├── algagpt_full/ # AlgaGPT-extracted proteomes, full architecture
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+ │ ├── algagpt_light/ # AlgaGPT-extracted proteomes, light architecture
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+ │ ├── pythia_full/ # LA4SR-Pythia-extracted proteomes, full architecture
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+ │ └── pythia_light/ # LA4SR-Pythia-extracted proteomes, light architecture
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+ ├── pfam2env/
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+ │ ├── full/ # Full architecture
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+ │ └── light/ # Light architecture
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+ └── README.md
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+ ```
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+
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+ Each subdirectory contains:
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+ - `best_model.pt` — PyTorch checkpoint (model_state_dict, optimizer_state_dict, best_val_loss)
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+ - `config.json` — Hyperparameters and feature lists
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+ - `final_metrics.json` — Train/val/test metrics
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+ - `training_history.json` — Per-epoch training curves
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+
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+ ## Architectures
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+
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+ ### env2pfam Full
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+ ```
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+ Input(94) → Linear(512) + BN + ReLU + Dropout(0.2)
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+ → Linear(1024) + BN + ReLU + Dropout(0.2)
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+ → Linear(2048) + BN + ReLU + Dropout(0.2)
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+ → Linear(4096) + BN + ReLU + Dropout(0.2)
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+ → Linear(output_dim)
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+ ```
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+
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+ ### env2pfam Light
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+ ```
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+ Input(94) → Linear(256) + BN + ReLU + Dropout(0.2)
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+ → Linear(512) + BN + ReLU + Dropout(0.2)
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+ → Linear(1024) + BN + ReLU + Dropout(0.2)
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+ → Linear(2048) + BN + ReLU + Dropout(0.2)
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+ → Linear(output_dim)
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+ ```
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+
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+ ### pfam2env Full
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+ ```
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+ InputBatchNorm(9611) → Linear(2048) + ReLU + Dropout
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+ → Linear(512) + ReLU + Dropout
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+ → Linear(128) + ReLU + Dropout
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+ → Linear(64)
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+ ```
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+
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+ ### pfam2env Light
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+ ```
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+ InputBatchNorm(9611) → Linear(512) + ReLU + Dropout
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+ → Linear(256) + ReLU + Dropout
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+ → Linear(128) + ReLU + Dropout
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+ → Linear(64)
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+ ```
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+
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+ ## Performance
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+
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+ ### env2pfam (Environment → Pfam)
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+
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+ | Variant | Dataset | Output Dim | LR | Test R² | Test MSE | Test MAE |
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+ |---|---|---|---|---|---|---|
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+ | **pythia_full** | LA4SR-Pythia | 17,245 | 1e-3 | **0.1487** | 14.597 | 2.411 |
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+ | pythia_light | LA4SR-Pythia | 17,245 | 1e-4 | 0.1432 | 14.561 | 2.454 |
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+ | algagpt_full | AlgaGPT | 20,318 | 1e-3 | 0.1189 | 14.006 | 2.381 |
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+ | algagpt_light | AlgaGPT | 20,318 | 1e-4 | 0.1070 | 14.136 | 2.415 |
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+
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+ R² is the mean across all output Pfam dimensions. The modest R² values reflect the high dimensionality of the output space (17K–20K Pfam domains) and the inherent stochasticity of metagenomic sampling.
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+
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+ ### pfam2env (Pfam → Environment)
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+
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+ | Variant | Input Dim | LR | Test R² | Test MSE | Test MAE |
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+ |---|---|---|---|---|---|
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+ | full | 9,611 | 1e-3 | -0.0057 | 0.00931 | 0.0724 |
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+ | light | 9,611 | 1e-3 | -0.0055 | 0.00931 | 0.0724 |
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+
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+ Negative R² indicates performance near the mean-prediction baseline. These checkpoints document the pfam→env direction of the bidirectional framework and are included for completeness and reproducibility.
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+
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+ ## Input Features (env2pfam)
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+
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+ **30 environmental variables:**
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+ - Air temperature (mean, max, min, range °C)
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+ - Precipitation (mean mm)
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+ - Solar radiation (MJ/m²)
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+ - Elevation (m), bathymetry (m), distance to coast (km)
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+ - Land cover class
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+ - Sea surface temperature (SST mean, max, min, range °C; MODIS SST mean)
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+ - Chlorophyll-a (mean, max, min mg/m³)
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+ - Normalized fluorescence line height (NFLH mean)
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+ - Particulate organic carbon (POC mean mg/m³)
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+ - Remote sensing reflectance (Rrs at 412, 443, 469, 488, 531, 547, 555, 645, 667, 678 nm)
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+
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+ **64 AlphaEarth spectral eigenvectors** (A00–A63)
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+
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+ ## Datasets
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+
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+ Two LLM-mediated proteome extraction strategies were applied to TARA Oceans metagenomic assemblies:
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+
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+ - **LA4SR-Pythia**: 2,049 samples → 17,245 Pfam domains
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+ - **AlgaGPT**: 2,044 samples → 20,318 Pfam domains
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+
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+ Both used SNAP gene prediction followed by hmmsearch against the Pfam database. The different extraction strategies yield different protein sets and domain profiles from the same underlying metagenomes.
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+
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+ ## Training Details
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+
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+ - **Framework**: PyTorch
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+ - **Loss**: MSE
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+ - **Optimizer**: Adam (weight_decay=1e-4 for pfam2env)
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+ - **Scheduler**: Cosine annealing (pfam2env)
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+ - **Early stopping**: Patience 20 (env2pfam) / 30 (pfam2env)
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+ - **Batch size**: 32
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+ - **Max epochs**: 200
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+ - **Seed**: 42
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+ - **Hardware**: CUDA GPU
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+
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+ ## Usage
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+
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+ ```python
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+ import torch
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+ import json
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+
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+ # Load model config
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+ with open("env2pfam/pythia_full/config.json") as f:
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+ config = json.load(f)
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+
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+ # Load checkpoint
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+ checkpoint = torch.load(
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+ "env2pfam/pythia_full/best_model.pt",
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+ map_location="cpu",
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+ weights_only=False
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+ )
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+ state_dict = checkpoint["model_state_dict"]
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+
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+ # Reconstruct model (requires the ELF-NET model class)
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+ # model.load_state_dict(state_dict)
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+ ```
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+
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+ ## Citation
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+
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+ If you use these checkpoints, please cite the associated manuscript (citation forthcoming).
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+
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+ ## License
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+
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+ Apache 2.0
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+ version https://git-lfs.github.com/spec/v1
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+ size 250213538
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+ {
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+ }
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+ {
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pfam2env/full/provenance.md ADDED
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1
+ # Training Provenance
2
+
3
+ - Date: 2026-01-25 18:31:21
4
+ - Script: /scratch/drn2/PROJECTS/TARA-LA4SR/MANUSCRIPT/scripts/train_manifold_20260125_173853.py
5
+ - Model: full
6
+ - Data: /scratch/drn2/PROJECTS/TARA-LA4SR/MANUSCRIPT/data/manifold_train_val_test_20260125_182033.npz
7
+ - Best epoch: 24
8
+ - Best val loss: 0.009360
9
+
10
+ ## Config
11
+ ```json
12
+ {
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+ "model_type": "full",
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+ "input_dim": 9611,
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+ "output_dim": 64,
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+ "batch_size": 32,
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+ "epochs": 200,
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+ "learning_rate": 0.001,
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+ "patience": 30,
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+ "device": "cuda",
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+ "use_amp": true,
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+ "seed": 42
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+ }
28
+ ```
29
+
30
+ ## Final Metrics
31
+ ```json
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+ {
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+ "train": {
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+ },
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+ "val": {
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+ "dim_r2_max": -2.9802322387695312e-06
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+ }
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+ }
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+ ```
68
+
69
+ ## Integrity Check: PASSED
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pfam2env/light/provenance.md ADDED
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+ # Training Provenance
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+
3
+ - Date: 2026-01-25 18:32:22
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+ - Script: /scratch/drn2/PROJECTS/TARA-LA4SR/MANUSCRIPT/scripts/train_manifold_20260125_173853.py
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+ - Model: light
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+ - Data: /scratch/drn2/PROJECTS/TARA-LA4SR/MANUSCRIPT/data/manifold_train_val_test_20260125_182033.npz
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+ - Best epoch: 56
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+ - Best val loss: 0.009357
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+
10
+ ## Config
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+ ```json
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+ {
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+ "model_type": "light",
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+ "input_dim": 9611,
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+ "output_dim": 64,
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+ "batch_size": 32,
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+ "epochs": 200,
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+ "learning_rate": 0.001,
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+ "loss_type": "mse",
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+ "loss_alpha": 0.8,
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+ "scheduler_type": "cosine",
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+ "patience": 30,
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+ "device": "cuda",
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+ "use_amp": true,
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+ "seed": 42
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+ }
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+ ```
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+
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+ ## Final Metrics
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+ ```json
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+ {
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+ }
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+ ```
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+
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+ ## Integrity Check: PASSED
pfam2env/light/training_history.json ADDED
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