dlab
Datasets
All datasets matching “dlab”JSONSchemaBench
JSONSchemaBench
JSONSchemaBench is a benchmark of real-world JSON schemas designed to evaluate structured output generation for Large Language Models (LLMs). It contains approximately 10,000 JSON schemas, capturing diverse constraints and complexities.
import datasets
from datasets import load_dataset
def main():
# Inspect the available subsets of the datasetall_subsets = datasets.get_dataset_config_names("epfl-dlab/JSONSchemaBench")
print("Available subsets:"… See the full description on the dataset page: https://huggingface.co/datasets/epfl-dlab/JSONSchemaBench.measuring-hate-speech
Dataset card for Measuring Hate Speech
This is a public release of the dataset described in Kennedy et al. (2020) and Sachdeva et al. (2022), consisting of 39,565 comments annotated by 7,912 annotators, for 135,556 combined rows. The primary outcome variable is the "hate speech score" but the 10 constituent ordinal labels (sentiment, (dis)respect, insult, humiliation, inferior status, violence, dehumanization, genocide, attack/defense, hate speech benchmark) can also be treated as… See the full description on the dataset page: https://huggingface.co/datasets/ucberkeley-dlab/measuring-hate-speech.corpus-1T-manifest
SPP Corpus 1T Manifest
The selection manifest for the ~1.0T-token pretraining corpus used in
Synthetic Persona Pretraining (SPP): Alignment from Token Zero.
The corpus is a seeded subsample of allenai/dolma3_mix-6T.
Rather than redistribute ~2.6 TB of text that is already public, this dataset
publishes the selection decisions keyed by upstream document id, so the corpus
can be reconstructed exactly by replaying against upstream.
📄 Reflections + text for the annotated half:… See the full description on the dataset page: https://huggingface.co/datasets/dlab-spp/corpus-1T-manifest.reflection-50m
SPP Reflection 50M
The 51.4M-document reflection set from Synthetic Persona Pretraining (SPP):
Alignment from Token Zero — the production half-corpus run, and the dataset the
released models were actually trained on.
🔬 Small sample (same format): dlab-spp/reflection-sample-2k
📉 Earlier 10M run: dlab-spp/reflection-10m
🧾 Safety scores for the full 1T corpus: dlab-spp/safety-classifications
Each row pairs a source document with two generated constitution reflections — a… See the full description on the dataset page: https://huggingface.co/datasets/dlab-spp/reflection-50m.safety-classifications
Safety Annotations for dolma3_mix
Safety score annotations for a 20K-shard subset of allenai/dolma3_mix-6T using
locuslab/safety-classifier_gte-large-en-v1.5.
Schema
Column
Type
Description
id
string
Row identifier (matches source dataset)
safety_score
int8
Argmax safety class (0-5)
safety_probs
list[float32]
Full 6-class probability distribution
Safety scale
Score
Label
Count
Percentage
0
safe
302,972,734
77.39%
1… See the full description on the dataset page: https://huggingface.co/datasets/dlab-spp/safety-classifications.aggs-arxiv
AGGS arXiv citation graph
The base snapshot contains arXiv paper metadata, parsed bibliography text,
author keys, and arXiv-to-arXiv citation edges for bnbcode's arxiv tool.
It does not contain arXiv e-prints or PDFs.
Layout
manifest.json
base/arxiv_graph_2026-07-31.dump.xz
deltas/index.json
deltas/daily/YYYY/MM/YYYY-MM-DD.sql.gz
deltas/monthly/YYYY/YYYY-MM.sql.gz
bnbcode research install --arxiv restores the base. bnbcode research update verifies and applies… See the full description on the dataset page: https://huggingface.co/datasets/dlab-cmu/aggs-arxiv.