reamde

README.md

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 license: mit
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+
+# ProtCompass Embeddings
+
+Pre-computed protein embeddings from 70+ encoders across 13 downstream tasks.
+
+## Dataset Structure
+
+```
+embeddings/
+├── secondary_structure/     # CB513 dataset (29 GB)
+├── mutation_effect/         # ProteinGym DMS assays (4.5 GB)
+├── contact_prediction/      # ProteinNet (2.9 GB)
+├── stability/              # TAPE stability (1.6 GB)
+├── ppi_site/               # PPI site prediction (1.4 GB)
+├── fluorescence/           # GFP fluorescence (841 MB)
+├── metal_binding/          # Metal binding sites (570 MB)
+├── go_bp/                  # GO Biological Process (214 MB)
+├── go_mf/                  # GO Molecular Function (68 MB)
+├── remote_homology/        # SCOPe fold classification (20 MB)
+├── ec_classification/      # Enzyme classification (18 MB)
+├── membrane_soluble/       # Membrane/soluble (17 MB)
+└── subcellular_localization/ # Subcellular location (17 MB)
+```
+
+## File Format
+
+Each encoder directory contains:
+- `train_embeddings.npy`: Training set embeddings (N × D)
+- `test_embeddings.npy`: Test set embeddings (M × D)
+- `train_labels.npy`: Training labels
+- `test_labels.npy`: Test labels
+- `train_ids.txt`: Protein IDs for training set
+- `test_ids.txt`: Protein IDs for test set
+- `meta.json`: Metadata (encoder name, dimensions, dataset info)
+
+## Usage
+
+```python
+import numpy as np
+from huggingface_hub import hf_hub_download
+
+# Download specific encoder embeddings
+train_emb = np.load(hf_hub_download(
+    repo_id="Anonymoususer2223/ProtCompass_Embeddings",
+    filename="embeddings/mutation_effect/esm2/train_embeddings.npy",
+    repo_type="dataset"
+))
+
+test_emb = np.load(hf_hub_download(
+    repo_id="Anonymoususer2223/ProtCompass_Embeddings",
+    filename="embeddings/mutation_effect/esm2/test_embeddings.npy",
+    repo_type="dataset"
+))
+
+# Use for downstream tasks
+from sklearn.linear_model import Ridge
+model = Ridge()
+model.fit(train_emb, train_labels)
+score = model.score(test_emb, test_labels)
+```
+
+## Encoders Included
+
+### Sequence Encoders (8)
+ESM2 (650M, 150M, 35M), ESM1b, ESM3, ProtTrans, ProST-T5, ProteinBERT-BFD, Ankh
+
+### Structure Encoders (50+)
+GearNet, GCPNet, EGNN, GVP, IPA, TFN, SchNet, DimeNet, MACE, CDConv, ProteinMPNN, PottsMP, dMaSIF
+
+### Multimodal Encoders (5)
+SaProt, ESM-IF, FoldVision
+
+### Baselines
+Random, Length, Torsion, One-hot, BLOSUM
+
+## Dataset Statistics
+
+- **Total size**: 41 GB
+- **Total encoders**: 70+
+- **Total tasks**: 13
+- **Total proteins**: ~500K across all tasks
+
+## Citation
+
+If you use these embeddings, please cite:
+
+```bibtex
+@article{protcompass2026,
+  title={ProtCompass: Systematic Evaluation of Protein Structure Encoders},
+  author={Your Name et al.},
+  journal={NeurIPS},
+  year={2026}
+}
+```
+
+## License
+
+MIT License
+
+## Contact
+
+For questions or issues, please open an issue on the repository.