lds
Datasets
All datasets matching “lds”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.
