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Datasets
All datasets matching “star”starcoderdata
StarCoder Training Dataset
Dataset description
This is the dataset used for training StarCoder and StarCoderBase. It contains 783GB of code in 86 programming languages, and includes 54GB GitHub Issues + 13GB Jupyter notebooks in scripts and text-code pairs,
and 32GB of GitHub commits, which is approximately 250 Billion tokens.
Dataset creation
The creation and filtering of The Stack is explained in the original dataset, we additionally decontaminate and… See the full description on the dataset page: https://huggingface.co/datasets/bigcode/starcoderdata.libritts-r-starkgpn-star-scores
GPN-Star genome-wide scores
Genome-wide, mutation-rate-calibrated GPN-Star constraint and variant scores for
eight score sets covering human, mouse, chicken, D. melanogaster,
C. elegans, and A. thaliana. Canonical scores are available as
chromosome-sharded Parquet. Hugging Face hosts 72 BigWigs; a multi-assembly
UCSC track hub references the 64 logo/LLR views.
Overview and quick links
Resource
Link
Files
Browse all dataset files
Default Dataset… See the full description on the dataset page: https://huggingface.co/datasets/songlab/gpn-star-scores.pa-warm-start-sft-heavy-25b-mix
geodesic-research/pa-warm-start-sft-heavy-25b-mix
Auto-generated by dataset-builder.
Each config below is a separate dataset produced from a versioned YAML build
config. Load with:
from datasets import load_dataset
ds = load_dataset("geodesic-research/pa-warm-start-sft-heavy-25b-mix", "<config_name>", revision="<commit-sha>")
Pin revision= to the specific commit SHA you want; without it, you get the
current HEAD of the dataset repo, which may change when the builder re-pushes.… See the full description on the dataset page: https://huggingface.co/datasets/geodesic-research/pa-warm-start-sft-heavy-25b-mix.the-join
The Join
A broad collection of relational databases spanning many domains (academic,
e-commerce, finance, sports, biomedical, government, text2sql, and more), ported to the
RelBench manifest format. The Join is built for pretraining relational/tabular foundation
models: each database is self-describing and tasks ship labels as-is for large-scale
pretraining rather than held-out benchmarking.
Each dataset lives in its own subdirectory in the self-describing manifest layout (plain… See the full description on the dataset page: https://huggingface.co/datasets/stanford-star/the-join.relbench-v1
RelBench v1 databases
The original RelBench v1 relational databases and tasks, each
database in its own subdirectory in the self-describing manifest layout (plain parquet +
manifest.yaml):
<dataset>/
manifest.yaml # tables, primary keys, foreign-key graph, val/test timestamps
schema.svg # ER diagram
db/*.parquet # relational tables (plain parquet)
tasks/<task>/manifest.yaml # task spec (+ duckdb SQL for `forecast`… See the full description on the dataset page: https://huggingface.co/datasets/stanford-star/relbench-v1.