Datasets:
Update dataset card with paper link, code, and task category (#1)
Browse files- Update dataset card with paper link, code, and task category (1a119146f41962a7ca39273bf578c81590ef4f2a)
Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>
README.md
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license: bsd-3-clause
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task_categories:
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task_ids:
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- other
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language:
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- en
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tags:
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- protein
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- structure
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- pretraining
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- tedbench
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- alphafold
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- foldseek
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pretty_name: TEDBench-AFDB (pretraining)
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---
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# TEDBench-AFDB (pretraining corpus)
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(ICML 2026).
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## Dataset statistics
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# Multi-GPU (effective batch size 4096)
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python main_pretrain.py \
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experiment=tedbench_base_n4g8
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datamodule=hf_afdbfs
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```
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## Source data
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- [Foldseek](https://afdb-cluster.steineggerlab.workers.dev) cluster representatives
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from AlphaFold Database v4 (pLDDT > 80)
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## Citation
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## License
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BSD-3-Clause
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---
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language:
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- en
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license: bsd-3-clause
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size_categories:
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- 100K<n<10M
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task_categories:
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- graph-ml
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pretty_name: TEDBench-AFDB (pretraining)
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tags:
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- protein
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- structure
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- pretraining
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- tedbench
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- alphafold
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- foldseek
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---
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# TEDBench-AFDB (pretraining corpus)
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[**Paper**](https://huggingface.co/papers/2605.18552) | [**GitHub**](https://github.com/BorgwardtLab/TEDBench)
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Representative proteins from Foldseek-clustered AlphaFold Database (pLDDT > 80), used to pretrain **MiAE (Masked Invariant Autoencoders)** in the **TEDBench** benchmark.
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TEDBench is a large-scale, non-redundant benchmark for protein fold classification constructed from the Encyclopedia of Domains (TED) and Foldseek-clustered AlphaFold structures.
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## Dataset statistics
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# Multi-GPU (effective batch size 4096)
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python main_pretrain.py \
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experiment=tedbench_base_n4g8 \
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datamodule=hf_afdbfs
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```
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## Source data
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- [Foldseek](https://afdb-cluster.steineggerlab.workers.dev) cluster representatives from AlphaFold Database v4 (pLDDT > 80)
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## Citation
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## License
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BSD-3-Clause
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