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README.md

@@ -1,34 +1,96 @@
 ---
-dataset_info:
-  features:
-  - name: name
-    dtype: string
-  - name: sequence
-    dtype: string
-  - name: coords
-    list:
-      list:
-        list: float32
-  - name: plddt
-    list: float32
-  - name: residue_index
-    list: int64
-  - name: seq_ids
-    list: int64
-  splits:
-  - name: train
-    num_bytes: 9505505623
-    num_examples: 742183
-  - name: val
-    num_bytes: 97110036
-    num_examples: 7496
-  download_size: 7790839278
-  dataset_size: 9602615659
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
-  - split: val
-    path: data/val-*
+license: bsd-3-clause
+task_categories:
+  - other
+task_ids:
+  - other
+language:
+  - en
+tags:
+  - protein
+  - structure
+  - pretraining
+  - tedbench
+  - alphafold
+  - foldseek
+pretty_name: TEDBench-AFDB (pretraining)
+size_categories:
+  - 100K<n<1M
 ---
+
+# TEDBench-AFDB (pretraining corpus)
+
+Representative proteins from Foldseek-clustered AlphaFold Database (pLDDT > 80),
+used to pretrain MiAE (Masked Invariant Autoencoders) in the TEDBench benchmark.
+
+Part of the [TEDBench](https://github.com/BorgwardtLab/TEDBench) benchmark
+(ICML 2026).
+
+## Dataset statistics
+
+| Split | Structures |
+|-------|----------:|
+| Train | 742,183 |
+| Val   | 7,496 |
+| **Total** | **749,679** |
+
+One representative structure per Foldseek sequence-similarity cluster.
+
+## Schema
+
+| Column | Type | Description |
+|--------|------|-------------|
+| `name` | `string` | AlphaFold domain identifier (e.g. `AF-Q8IYB3-F1`) |
+| `sequence` | `string` | Amino-acid sequence (single-letter code) |
+| `coords` | `[L, 3, 3]` float32 | Backbone N/Cα/C coordinates (Å) |
+| `plddt` | `[L]` float32 | Per-residue AlphaFold pLDDT confidence score |
+| `residue_index` | `[L]` int64 | Residue index in the original AlphaFold model |
+| `seq_ids` | `[L]` int64 | ESM-tokenised sequence IDs |
+
+No `label` column — this dataset is for **unsupervised pretraining** only.
+
+## Usage
+
+### Load from HuggingFace
+
+```python
+from datasets import load_dataset
+import torch
+
+afdb = load_dataset("TEDBench/afdb", split="train")
+sample = afdb[0]
+
+coords = torch.tensor(sample["coords"])   # [L, 3, 3]
+plddt  = torch.tensor(sample["plddt"])    # [L]
+```
+
+### Pretrain MiAE using this dataset
+
+```bash
+python main_pretrain.py datamodule=hf_afdbfs
+
+# Multi-GPU (effective batch size 4096)
+python main_pretrain.py \
+    experiment=tedbench_base_n4g8
+    datamodule=hf_afdbfs \
+```
+
+## Source data
+
+- [Foldseek](https://afdb-cluster.steineggerlab.workers.dev) cluster representatives
+  from AlphaFold Database v4 (pLDDT > 80)
+
+## Citation
+
+```bibtex
+@inproceedings{chen2026tedbench,
+  title={Protein Fold Classification at Scale: Benchmarking and Pretraining},
+  author={Chen, Dexiong and Manolache, Andrei and Niepert, Mathias and Borgwardt, Karsten},
+  booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)},
+  year={2026}
+}
+```
+
+## License
+
+BSD-3-Clause