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Update dataset card with paper link, code, and task category (#1)

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- Update dataset card with paper link, code, and task category (1a119146f41962a7ca39273bf578c81590ef4f2a)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

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  1. README.md +20 -23
README.md CHANGED
@@ -1,30 +1,28 @@
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  ---
 
 
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  license: bsd-3-clause
 
 
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  task_categories:
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- - other
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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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- size_categories:
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- - 100K<n<1M
 
 
 
 
 
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  ---
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  # TEDBench-AFDB (pretraining corpus)
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- Representative proteins from Foldseek-clustered AlphaFold Database (pLDDT > 80),
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- used to pretrain MiAE (Masked Invariant Autoencoders) in the TEDBench benchmark.
 
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- Part of the [TEDBench](https://github.com/BorgwardtLab/TEDBench) benchmark
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- (ICML 2026).
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  ## Dataset statistics
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@@ -71,14 +69,13 @@ python main_pretrain.py datamodule=hf_afdbfs
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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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@@ -93,4 +90,4 @@ python main_pretrain.py \
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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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+
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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