datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
HRM-He-corpus-objective
Hebrew reasoning traces
Generated Hebrew chain-of-thought over code, cybersecurity, agentic, math and
general-reasoning seeds. Built for a Hebrew/English code-specialised LM, where
off-the-shelf Hebrew reasoning data is effectively nonexistent.
What the default config contains
Every row the training corpus keeps -- not a filtered highlight reel. Two things
are disqualifying and are absent: a wrong final answer (answer_ok is False), and
Arabic drift. Everything… See the full description on the dataset page: https://huggingface.co/datasets/guychuk/HRM-He-corpus-objective.QUEST-SFT-Data-Objective
QUEST SFT Data (Objective)
Project Page | Paper | GitHub
Objective-style supervised fine-tuning trajectories for QUEST (tool-using assistant format). This dataset is part of the QUEST family, designed to train deep research agents with fully synthetic tasks.
Split: train
Columns: messages (list[{role, content}])
Load
from datasets import load_dataset
ds = load_dataset("osunlp/QUEST-SFT-Data-Objective", split="train", streaming=True)
row = next(iter(ds))… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Objective.QUEST-SFT-Data-Objective-Script
QUEST SFT Data Objective Script
Project Page | Paper | GitHub
Supervised fine-tuning split for QUEST / DeepResearch objective tasks. Each row includes the user prompt, a rule-style reward_model, extra_info, and the objective task category. The corresponding objective evaluation scripts are provided separately under eval_scripts/.
This dataset follows the same broad schema style as osunlp/QUEST-RL-Data: each row includes prompt, reward_model, extra_info, and rl_task_category. The… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/QUEST-SFT-Data-Objective-Script.task034_winogrande_question_modification_object
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task034_winogrande_question_modification_object
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task034_winogrande_question_modification_object.task1209_atomic_classification_objectuse
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1209_atomic_classification_objectuse
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1209_atomic_classification_objectuse.task431_senteval_object_count
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task431_senteval_object_count
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task431_senteval_object_count.ios-security-vulnerabilities-swift-objc
iOS Security Vulnerabilities Dataset (Swift & Objective-C)
A comprehensive dataset of 27 real-world iOS security vulnerability patterns in Swift and Objective-C, covering all OWASP Mobile Top 10 (2024) categories with vulnerable code, secure fixes, attack scenarios, and detection guidance.
🎯 Purpose
This is the first dedicated iOS/Swift/Objective-C security vulnerability dataset on Hugging Face. While existing datasets (TitanVul, DiverseVul, CleanVul) focus on… See the full description on the dataset page: https://huggingface.co/datasets/Arno-MHL/ios-security-vulnerabilities-swift-objc.selfsupervised-selfset-objective-integrity-v0.1Self-Set Objective Integrity v0.1
What this tests
Whether a system chooses a proxy metric that truly tracks its stated objective.
This isolates proxy misalignment and reward hacking risk.
Failure modes
proxy_misalignedResponse approves a proxy that does not track the stated objective
proxy_gaming_riskResponse denies gaming risk when it is present
objective_integrity_okResponse correctly rejects misaligned proxies and flags gaming risk
How it works
stated_objective defines the target… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/selfsupervised-selfset-objective-integrity-v0.1.