datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Code-Contests-Plus
CodeContests+: A Competitive Programming Dataset with High-Quality Test Cases
Introduction
CodeContests+ is a competitive programming problem dataset built upon CodeContests. It includes 11,690 competitive programming problems, along with corresponding high-quality test cases, test case generators, test case validators, output checkers, and more than 13 million correct and incorrect solutions.
Highlights
High… See the full description on the dataset page: https://huggingface.co/datasets/ByteDance-Seed/Code-Contests-Plus.THEMol
THEMol: Torsion, Hessian, Energy of Molecules
Dataset Summary
THEMol is an open-source collection of quantum mechanical properties tailored for organic molecules. It provides large-scale density functional theory (DFT) data for exploring intramolecular potential energy surfaces, including optimized geometries, structural relaxation trajectories, torsion scans, constrained torsion relaxation trajectories, Hessian matrices, and MBIS-derived atomic properties.
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/ByteDance-Seed/THEMol.TerminalWorld-Seeds
TerminalWorld Seeds, packaged in the RST release layout
1,530 validated terminal tasks from EuniAI/TerminalWorld, repackaged in the release layout of Zhongzhi1228/Recursive-Task-Synthesis (RST, arXiv:2608.05466).
RST bootstrapped its recursive synthesis from 639 seeds sampled out of TerminalWorld, but released only the synthesized rounds. This dataset is the seed-level superset in the same format, so a synthesis pipeline can start from round 0 with the same loaders that read the… See the full description on the dataset page: https://huggingface.co/datasets/andylizf/TerminalWorld-Seeds.olm-CC-MAIN-2022-40-sampling-ratio-0.15894621295-seed-69SEED-Data-Edit-Part1-Openimages
SEED-Data-Edit
SEED-Data-Edit is a hybrid dataset for instruction-guided image editing with a total of 3.7 image editing pairs, which comprises three distinct types of data:
Part-1: Large-scale high-quality editing data produced by automated pipelines (3.5M editing pairs).
Part-2: Real-world scenario data collected from the internet (52K editing pairs).
Part-3: High-precision multi-turn editing data annotated by humans (95K editing pairs, 21K multi-turn rounds with a maximum of 5… See the full description on the dataset page: https://huggingface.co/datasets/AILab-CVC/SEED-Data-Edit-Part1-Openimages.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.ruler-300-seed42
Frozen RULER 300, seed 42
This dataset freezes the exact RULER inputs used by the
short-long-pretraining native evaluation suite.
Repository: bicycleman15/ruler-300-seed42
Rows: 6,300
Tasks: s-niah-1, s-niah-2, s-niah-3, mk1, mk2, mv, mq
Context lengths: 1024, 2048, 4096
Samples per task/length: 300
Seed: 42
Dataset SHA-256: 4d82df6f9b1f2d9c45c0a0bda8c734032e62f517b746c6351bf9c2f38335ab3d
Tokenizer SHA-256: 1f186971e25f7bda3dd6f93a100bb8fa2a6801cf8dc3807c8a8c4e45f296ab90… See the full description on the dataset page: https://huggingface.co/datasets/bicycleman15/ruler-300-seed42.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.text-2-video-human-preferences-seedance-1-pro
Rapidata Video Generation Seedance 1 Pro Human Preference
In this dataset, ~60k human responses from ~20k human annotators were collected to evaluate Seedance 1 Pro video generation model on our benchmark. This dataset was collected in roughtly 30 min using the Rapidata Python API, accessible to anyone and ideal for large scale data annotation.
Explore our latest model rankings on our website.
If you get value from this dataset and would like to see more in the future, please… See the full description on the dataset page: https://huggingface.co/datasets/Rapidata/text-2-video-human-preferences-seedance-1-pro.BiointelligenceAgent01-SeedDataset
Biointelligence Agent Worlds Seed Dataset 0.1
Configurations
Configuration
Contents
Rows
targets
Public-real and explicitly synthetic targets
1000
entities
Normalized world entities
14516
world_snapshots
Observed and simulated temporal states
356014
source_events
Retrieved/discovered source records and normalized claims
33074
relationships
Evidence-linked target and entity relationships
205800
agent_runs
Observable structured Codex run results… See the full description on the dataset page: https://huggingface.co/datasets/rayrren/BiointelligenceAgent01-SeedDataset.multitask_german_examples_32kSWE-Smith-Seeds-Clean
SWE-Smith Seeds, agent-verified
1,552 of SWE-smith's 59,136 instances, repackaged as terminal tasks and kept only where every claim about them was executed and held: the bug is present, the reference fix earns the grader's reward, the repository's own suite still passes, and a coding agent solved the task from its instruction alone in a sandbox that had neither the fix nor the tests nor the network. Every row carries the verdict and the conditions it was taken under; nothing… See the full description on the dataset page: https://huggingface.co/datasets/Fzz1/SWE-Smith-Seeds-Clean.LDS-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.laion2b_seed
Dataset Card for "laion2b_seed"
This dataset is a subset of laion2B-en-aesthetic, with SEED v1 tokens.
agent-apprenticeship-seed-dataset
Agent Apprenticeship Seed Dataset
The living ecosystem where AI agents run automated workflow loops on any task, improve through execution, and turn each run into reusable work experience + data to improve future agents.
As agents move into long-horizon, economically valuable work, Agent Apprenticeship creates the open infrastructure where real-world tasks generate reusable learning signals and complex workflows advance through agent loops that turn execution into shared… See the full description on the dataset page: https://huggingface.co/datasets/rayrren/agent-apprenticeship-seed-dataset.agent-apprenticeship-seed-dataset_v0.2
Agent Apprenticeship Seed Dataset v0.2
Real-world agent work experience, looped into collective learning.
The living ecosystem where AI agents complete tasks through workflow loops, improve through iterative execution, are evaluated by mentor agents or humans in the loop, and turn completed work into reusable work experience and data to improve future agents.
As agents move into long-horizon, economically valuable work, Agent Apprenticeship creates the open infrastructure where… See the full description on the dataset page: https://huggingface.co/datasets/rayrren/agent-apprenticeship-seed-dataset_v0.2.gemma4-e2b-base-topk128-hf-overlay-v128-seed42
Gemma 4 E2B base top-k-128 HF training overlay
This is the immutable training-engine overlay used to distill traces from Gemma 4 E2B base into
Gemma 4 E4B. It preserves the prompts, responses, and exact response token IDs from
JWei05/gemma4-e2b-base-topk128-traces,
but replaces the source vLLM top-k targets with targets recomputed by the Hugging Face training
engine.
This repository is a reproducibility artifact for the corresponding distillation run. It is not a
new… See the full description on the dataset page: https://huggingface.co/datasets/JWei05/gemma4-e2b-base-topk128-hf-overlay-v128-seed42.prof_report__SD_v2_random_seeds__multi__24
Dataset Card for "prof_report__SD_v2_random_seeds__multi__24"
More Information needed
OT_8K_seed_all_responsesreward-hacking-olmo3.1-32b-kl0.02-seed2-rollouts
Reward-Hacking Training Rollouts — OLMo-3.1-32B (β=0.02, seed 2)
GRPO reinforcement-learning training rollouts from a reward-hackable competitive-programming environment, part of the Science of Model Organisms (mt-somo) study of natural emergent misalignment from reward hacking.
Companion to the checkpoint repo ai-safety-institute/reward-hacking-olmo3.1-32b-kl0.02-seed2. With a small KL penalty (β=0.02) the policy stays closer to the base model, yet it still learns to exploit… See the full description on the dataset page: https://huggingface.co/datasets/ai-safety-institute/reward-hacking-olmo3.1-32b-kl0.02-seed2-rollouts.opensec-seeds
OpenSec Seeds: Incident Response Scenarios for Agent Calibration
This dataset provides 220 taxonomy-stratified security incident scenarios for training and evaluating AI agents on incident response (IR) tasks. Each scenario includes entity definitions, attack kill chains, ground truth labels, and prompt injection payloads designed to test agent calibration under adversarial evidence.
Paper: OpenSec: Measuring Incident Response Agent Calibration Under Adversarial Evidence… See the full description on the dataset page: https://huggingface.co/datasets/Jarrodbarnes/opensec-seeds.reward-hacking-olmo3.1-32b-kl0.0-seed2-rollouts
Reward-Hacking Training Rollouts — OLMo-3.1-32B (β=0.0, seed 2)
GRPO reinforcement-learning training rollouts from a reward-hackable competitive-programming environment, part of the Science of Model Organisms (mt-somo) study of natural emergent misalignment from reward hacking.
Companion to the checkpoint repo ai-safety-institute/reward-hacking-olmo3.1-32b-kl0.0-seed2. With no KL penalty (β=0) the policy drifts freely from the base model and reliably discovers and exploits the… See the full description on the dataset page: https://huggingface.co/datasets/ai-safety-institute/reward-hacking-olmo3.1-32b-kl0.0-seed2-rollouts.laion2B-en-aesthetic-seed
Dataset Card for "laion2B-en-aesthetic-seed"
More Information needed
world-seeds
World Seeds — every "by country" table, keyed by ISO 3166-1 alpha-2
Wikipedia has hundreds of "... by country" articles. The numbers live inside article tables, keyed by country names that differ from article to article. This dataset re-keys every such table to ISO2 so they join.
One CSV per source article under tables/. Columns: iso2, country, <original column names>. Values are kept exactly as printed (*_num twin columns hold the parsed number where one could be read).… See the full description on the dataset page: https://huggingface.co/datasets/Lilambd/world-seeds.eval_ep100_seedNone_circle_big_car_less_zoom_40000_defaultThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "racecar",
"total_episodes": 20,
"total_frames": 6599,
"total_tasks": 1,
"total_videos": 20,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:20"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/Lyrasilas/eval_ep100_seedNone_circle_big_car_less_zoom_40000_default.dclm_seed_5b_tanishqeval_ep1000_seedNone_default_car_guessed_10000_SFT_circle_bigThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "racecar",
"total_episodes": 20,
"total_frames": 18779,
"total_tasks": 1,
"total_videos": 20,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:20"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/Lyrasilas/eval_ep1000_seedNone_default_car_guessed_10000_SFT_circle_big.eval_ep500_seed1_default_center_20000_defaultThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v2.1",
"robot_type": "racecar",
"total_episodes": 20,
"total_frames": 12019,
"total_tasks": 1,
"total_videos": 20,
"total_chunks": 1,
"chunks_size": 1000,
"fps": 30,
"splits": {
"train": "0:20"
},
"data_path": "data/chunk-{episode_chunk:03d}/episode_{episode_index:06d}.parquet",
"video_path":… See the full description on the dataset page: https://huggingface.co/datasets/Lyrasilas/eval_ep500_seed1_default_center_20000_default.