datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ldsc
S-LDSC
The dataset displayed here (test.parquet) represents the ~10M variants used for S-LDSC in hg38 coordinates (a tiny fraction we couldn't liftover are marked with pos = -1).
We include scores for the 3 GPN-Star models (mutation-rate adjusted minus entropy, higher -> more functional).
LDS-retrain-bank-adamw-wikitext2-N4656-bs8-seed1007
Retrain bank: WikiText-2 / GPT-2, random halves, seed 1007
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on a different random 50% (2,328 documents)
of the 4,656-document WikiText-2 training set from
EleutherAI/bergson-wikitext-2-4656-chunks,
following the recipe of Bae et al. 2024, Training Data Attribution via Approximate
Unrolled Differentiation (App. B.1). retrained/base is trained on the full set with… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-adamw-wikitext2-N4656-bs8-seed1007.LDS-retrain-bank-adamw-wikitext2-N4656-bs8-seed1004
Retrain bank: WikiText-2 / GPT-2, random halves, seed 1004
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on a different random 50% (2,328 documents)
of the 4,656-document WikiText-2 training set from
EleutherAI/bergson-wikitext-2-4656-chunks,
following the recipe of Bae et al. 2024, Training Data Attribution via Approximate
Unrolled Differentiation (App. B.1). retrained/base is trained on the full set with… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-adamw-wikitext2-N4656-bs8-seed1004.LDS-retrain-bank-adamw-wikitext2-N4656-bs8-seed1006
Retrain bank: WikiText-2 / GPT-2, random halves, seed 1006
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on a different random 50% (2,328 documents)
of the 4,656-document WikiText-2 training set from
EleutherAI/bergson-wikitext-2-4656-chunks,
following the recipe of Bae et al. 2024, Training Data Attribution via Approximate
Unrolled Differentiation (App. B.1). retrained/base is trained on the full set with… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-adamw-wikitext2-N4656-bs8-seed1006.LDS-retrain-bank-adamw-wikitext2-N4656-bs8-seed1008
Retrain bank: WikiText-2 / GPT-2, random halves, seed 1008
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on a different random 50% (2,328 documents)
of the 4,656-document WikiText-2 training set from
EleutherAI/bergson-wikitext-2-4656-chunks,
following the recipe of Bae et al. 2024, Training Data Attribution via Approximate
Unrolled Differentiation (App. B.1). retrained/base is trained on the full set with… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-adamw-wikitext2-N4656-bs8-seed1008.LDS-retrain-bank-adamw-N4k-bs256
Retrain bank: plan_adam_eps1e17_4k_bs256
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on the same 4,000-document corpus with a different random 1% (40 documents) held out, from the same seed and the same data order as the base model in retrained/base. Retraining is deterministic within one environment, so the models differ only by the documents removed.
That is the expensive part of any leave-k-out… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-adamw-N4k-bs256.LDS-retrain-bank-muon-N16k-bs16LDS-retrain-bank-muon-N16k-bs128LDS-retrain-bank-adamw-N16k-bs256
Retrain bank: sm_adamw_eps1e17_16k_bs256
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on the same 16,000-document corpus with a different random 1% (160 documents) held out, from the same seed and the same data order as the base model in retrained/base. Retraining is deterministic within one environment, so the models differ only by the documents removed.
That is the expensive part of any leave-k-out… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-adamw-N16k-bs256.LDS-retrain-bank-muon-N32k-bs256LDS-retrain-bank-adamw-N16k-bs256-scale0.25
Retrain bank: plan_adam_eps1e17_16k_scale0.25
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on the same 16,000-document corpus with a different random 1% (160 documents) held out, from the same seed and the same data order as the base model in retrained/base. Retraining is deterministic within one environment, so the models differ only by the documents removed.
That is the expensive part of any leave-k-out… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-adamw-N16k-bs256-scale0.25.PARTIAL_LDS-retrain-bank-muon-N64k-bs256fante-speech-text-multispeaker_lds
Fante Speech-Text Multispeaker Dataset (LDS)
Sentence-level aligned Fante (fat) speech dataset sourced from the Church of Jesus Christ of Latter-day Saints General Conference translations.
Dataset Statistics
Split
Clips
Hours
Talks
Train
29,992
58.32
405
Eval
2,028
4.09
28
Total
32,020
62.41
433
Features
audio: 16 kHz mono FLAC sentence-level clips
text: Fante transcript (sentence-aligned)
talk_id: Source conference talk… See the full description on the dataset page: https://huggingface.co/datasets/ghanaopenai/fante-speech-text-multispeaker_lds.LDS-retrain-bank-muon-N16k-bs256LDS-retrain-bank-adamw-N16k-bs256-ep4LDS-retrain-bank-adamw-N32k-bs256LDS-retrain-bank-adamw-N16k-bs256-gpt2-mediumLDS-retrain-bank-adamw-wikitext2-N4656-bs8-seed1005
Retrain bank: WikiText-2 / GPT-2, random halves, seed 1005
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on a different random 50% (2,328 documents)
of the 4,656-document WikiText-2 training set from
EleutherAI/bergson-wikitext-2-4656-chunks,
following the recipe of Bae et al. 2024, Training Data Attribution via Approximate
Unrolled Differentiation (App. B.1). retrained/base is trained on the full set with… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-adamw-wikitext2-N4656-bs8-seed1005.PARTIAL_LDS-retrain-bank-gpt2medium-16k-bs32PARTIAL_LDS-retrain-bank-london16k-bs256-adamwLDS-retrain-bank-muon-N8k-bs256
Retrain bank: plan_muon_eps1e17_8k_bs256
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on the same 8,000-document corpus with a different random 1% (80 documents) held out, from the same seed and the same data order as the base model in retrained/base. Retraining is deterministic within one environment, so the models differ only by the documents removed.
That is the expensive part of any leave-k-out… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-muon-N8k-bs256.LDS-retrain-bank-adamw-N8k-bs256
Retrain bank: plan_adam_eps1e17_8k_bs256
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on the same 8,000-document corpus with a different random 1% (80 documents) held out, from the same seed and the same data order as the base model in retrained/base. Retraining is deterministic within one environment, so the models differ only by the documents removed.
That is the expensive part of any leave-k-out… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-adamw-N8k-bs256.LDS-retrain-bank-muon-N4k-bs256
Retrain bank: plan_muon_eps1e17_4k_bs256
This repository contains 100 fully retrained language models, not just scores.
Each model is GPT-2 (gpt2) fine-tuned on the same 4,000-document corpus with a different random 1% (40 documents) held out, from the same seed and the same data order as the base model in retrained/base. Retraining is deterministic within one environment, so the models differ only by the documents removed.
That is the expensive part of any leave-k-out… See the full description on the dataset page: https://huggingface.co/datasets/EleutherAI/LDS-retrain-bank-muon-N4k-bs256.LDS-retrain-bank-adamw-N16k-bs64LDS-retrain-bank-adamw-N64k-bs256LDS-retrain-bank-adamw-N16k-bs512LDS-retrain-bank-london16k-bs256-muonLDS-retrain-bank-adamw-N16k-bs256-clip1.0fante-speech-text-multispeaker_lds
Fante Speech-Text Multispeaker Dataset (LDS)
Sentence-level aligned Fante (fat) speech dataset sourced from the Church of Jesus Christ of Latter-day Saints General Conference translations.
Dataset Statistics
Split
Clips
Hours
Talks
Train
29,992
58.32
405
Eval
2,028
4.09
28
Total
32,020
62.41
433
Features
audio: 16 kHz mono FLAC sentence-level clips
text: Fante transcript (sentence-aligned)
talk_id: Source conference talk… See the full description on the dataset page: https://huggingface.co/datasets/ghananlpcommunity/fante-speech-text-multispeaker_lds.Office-Home-LDS
Office-Home-LDS Dataset
Paper: “Geometric Knowledge-Guided Localized Global Distribution Alignment for Federated Learning”
Github: 2025CVPR_GGEUR
The Office-Home-LDS dataset is constructed by introducing label skew on top of the domain skew present in the Office-Home dataset.
The goal is to create a more challenging and realistic dataset that simultaneously exhibits both label skew and domain skew.
🔗 Citation
The article has been accepted by 2025CVPR, if you use… See the full description on the dataset page: https://huggingface.co/datasets/WeiDai-David/Office-Home-LDS.