datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
bigbenchBIG-Bench but it doesn't require the hellish dependencies (tensorflow, pypi-bigbench, protobuf) of the official version.
dataset = load_dataset("tasksource/bigbench",'movie_recommendation')
Code to reproduce:
https://colab.research.google.com/drive/1MKdLdF7oqrSQCeavAcsEnPdI85kD0LzU?usp=sharing
Datasets are capped to 50k examples to keep things light.
I also removed the default split when train was available also to save space, as default=train+val.
@article{srivastava2022beyond… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/bigbench.TaskTrove
TaskTrove
v5.1 (current) — independent-review source retirement — moves 15 sources with majority or unanimous REJECT verdicts out of the default config and into deprecated/. Three blinded reviewers each sampled 10 tasks per source from all 50 v5.0 source-drop candidates, read the instructions and packaged tests, and issued independent KEEP or REJECT verdicts. The 15 retired sources received at least two REJECT votes. The active catalog changes from 93 sources and 1,674,033… See the full description on the dataset page: https://huggingface.co/datasets/open-thoughts/TaskTrove.kilt_tasks
Dataset Card for KILT
Dataset Summary
KILT has been built from 11 datasets representing 5 types of tasks:
Fact-checking
Entity linking
Slot filling
Open domain QA
Dialog generation
All these datasets have been grounded in a single pre-processed Wikipedia dump, allowing for fairer and more consistent evaluation as well as enabling new task setups such as multitask and transfer learning with minimal effort. KILT also provides tools to analyze and understand the… See the full description on the dataset page: https://huggingface.co/datasets/facebook/kilt_tasks.FACET-Terminal-Tasks-6k
FACET-Terminal-Tasks-6k
6,020 execution-grounded tasks for terminal agents, coding agents, and executable workflow research
🌐 FACET Project Website
📄 FACET Paper
💻 FACET-Terminal GitHub Repository
🤗 FACET-Terminal Models & Data
Dataset Overview
FACET-Terminal-Tasks-6k contains 6,020 public-release-ready Harbor tasks produced by FACET. Each task is an executable environment rather than a standalone prompt: it includes a natural-language instruction… See the full description on the dataset page: https://huggingface.co/datasets/FACET-Terminal/FACET-Terminal-Tasks-6k.tasksource-instruct-v0
Dataset Card for "tasksource-instruct-v0" (TSI)
Multi-task instruction-tuning data recasted from 485 of the tasksource datasets.
Dataset size is capped at 30k examples per task to foster task diversity.
!pip install tasksource, pandit
import tasksource, pandit
df = tasksource.list_tasks(instruct=True).sieve(id=lambda x: 'mmlu' not in x)
for tasks in df.id:
yield tasksource.load_task(task,instruct=True,max_rows=30_000,max_rows_eval=200)
https://github.com/sileod/tasksource… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/tasksource-instruct-v0.task903_deceptive_opinion_spam_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task903_deceptive_opinion_spam_classification
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/task903_deceptive_opinion_spam_classification.task902_deceptive_opinion_spam_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task902_deceptive_opinion_spam_classification
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/task902_deceptive_opinion_spam_classification.SWE-Rebench-Tasks-Clean
SWE-Rebench-Tasks-Clean
1,317 verified-solvable, contamination-controlled software-engineering tasks for terminal-agent RL training.
Adapted from nebius/SWE-rebench-V2 (real GitHub issue → PR tasks with executable test contracts) into the TerminalWorld task format. Companion dataset to Fzz1/SWE-Smith-Seeds-Clean, same layout.
Every task is a directory containing:
file
content
instruction.md
the issue text the agent sees (plus linked issue discussion where available)… See the full description on the dataset page: https://huggingface.co/datasets/Fzz1/SWE-Rebench-Tasks-Clean.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/yatin-superintelligence/Creative-Professionals-Agentic-Tasks-1M.corral-environment-tasks
Corral – Environment Tasks
Task definitions across the 8 Corral environments, including descriptions, allowed tools, scoring functions, and submission formats
📋 Dataset Summary
This dataset is part of the Corral collection accompanying the paper AI scientists produce results without reasoning scientifically. It contains the task definitions for the 8 environments included in the Corral benchmark.
The dataset is organized into multiple configurations… See the full description on the dataset page: https://huggingface.co/datasets/jablonkagroup/corral-environment-tasks.swe-rebench-v2-clean-python-tasks
SWE-rebench-V2 clean Python tasks
A train/test split of Python tasks from nebius/SWE-rebench-V2.
We took the Python subset of the original dataset and kept only the tasks where the golden patch passes
the unit tests and the empty patch does not.
train: 3,837 instances from 408 repositories
test: 500 instances from 100 repositories
The split is made by repository, so no repository appears in both splits.
We evaluated multiple models on the test split as of June 2026 — the… See the full description on the dataset page: https://huggingface.co/datasets/whitecircle/swe-rebench-v2-clean-python-tasks.task1390_wscfixed_coreference
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1390_wscfixed_coreference
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/task1390_wscfixed_coreference.task891_gap_coreference_resolution
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task891_gap_coreference_resolution
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/task891_gap_coreference_resolution.prm800k_dpo
PRM800K preference pairs (prompt / chosen / rejected)
PRM800K (OpenAI, Let's Verify Step by Step) turned into clean
preference pairs, ready for DPO / reward modeling (TRL "standard" preference format).
Raw source: tasksource/PRM800K.
Train/test follow the original MATH benchmark splits (see Splits below).
from datasets import load_dataset
ds = load_dataset("tasksource/prm800k_dpo", "solution") # or "step"
Configs
solution — whole-solution preference… See the full description on the dataset page: https://huggingface.co/datasets/tasksource/prm800k_dpo.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/rAVEUK/Creative-Professionals-Agentic-Tasks-1M.task249_enhanced_wsc_pronoun_disambiguation
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task249_enhanced_wsc_pronoun_disambiguation
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/task249_enhanced_wsc_pronoun_disambiguation.recursive-task-synthesis-glm-5.3-rollouts
GLM 5.3 agentic rollouts on Recursive-Task-Synthesis
This dataset catalogs the full collection made from the pinned
Recursive-Task-Synthesis dataset revision
be44f96808d5a9b599d5cb024341ff00091adeb7. The repository includes approximately 260.5 GiB of trajectory payload tar shards.
Contents at a glance
Item
Count
Source tasks considered
37,284
Source candidates inspected
19,368
Converted tasks after source filters
18,600
Tasks passing gold… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/recursive-task-synthesis-glm-5.3-rollouts.FOL-nli
Dataset Card for "FOL-nli"
https://github.com/sileod/unigram/
https://arxiv.org/abs/2406.11035
Citation:
@article{sileo2024scaling,
title={Scaling Synthetic Logical Reasoning Datasets with Context-Sensitive Declarative Grammars},
author={Sileo, Damien},
journal={arXiv preprint arXiv:2406.11035},
year={2024}
}
task893_gap_fill_the_blank_coreference_resolution
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task893_gap_fill_the_blank_coreference_resolution
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… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task893_gap_fill_the_blank_coreference_resolution.task828_copa_commonsense_cause_effect
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task828_copa_commonsense_cause_effect
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/task828_copa_commonsense_cause_effect.task044_essential_terms_identifying_essential_words
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task044_essential_terms_identifying_essential_words
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… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task044_essential_terms_identifying_essential_words.task275_enhanced_wsc_paraphrase_generation
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task275_enhanced_wsc_paraphrase_generation
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/task275_enhanced_wsc_paraphrase_generation.terminal_bench_2_tasktrove_dq_unitsyn_python_step20_30b_a3b_20260730_014827
TaskTrove DQ unitsyn-python training traces (step 20, 30B-A3B)
Terminus-2 agent rollouts recorded while training
laion/tasktrove-dq-unitsyn-python-step20-30b-a3b
with SkyRL from Qwen/Qwen3-Coder-30B-A3B-Instruct.
Each row is the last episode of one trial: the full agent transcript, the task instruction, the
scalar reward, and the verifier's output.
Source run: rl-tasktrove-dq-sweep-30b-terminus2-qwen-20260725-163115-1ae770.
Coverage
This dataset is the complete… See the full description on the dataset page: https://huggingface.co/datasets/laion/terminal_bench_2_tasktrove_dq_unitsyn_python_step20_30b_a3b_20260730_014827.task579_socialiqa_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task579_socialiqa_classification
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/task579_socialiqa_classification.task-trove
TaskTrove Clean
TaskTrove Clean is a normalized release of
open-thoughts/TaskTrove at revision
0292300. It contains 861,848 retained Harbor tasks from
1,739,326 input rows. The graders were built from Marin commit
b76d03131c.
How it was made
The conversion pipeline applies these stages:
Pin the upstream Hugging Face revision and inventory each source's task templates.
Keep sources with recoverable task contracts and record every source decision.
Convert each… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/task-trove.Creative-Professionals-Agentic-Tasks-1M
Creative Professionals Agentic Tasks (1M)
Abstract
A massive-scale, high-fidelity synthetic task dataset comprising 1,070,917 agentic command operations across 36 creative, technical, and engineering software environments. This dataset is engineered exclusively to stress-test, evaluate, and fine-tune multimodal AI agents designed for Agent Environment operation, complex software interaction, and multi-step reasoning within deep software infrastructures.… See the full description on the dataset page: https://huggingface.co/datasets/kryp1234/Creative-Professionals-Agentic-Tasks-1M.task827_copa_commonsense_reasoning
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task827_copa_commonsense_reasoning
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/task827_copa_commonsense_reasoning.task116_com2sense_commonsense_reasoning
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task116_com2sense_commonsense_reasoning
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/task116_com2sense_commonsense_reasoning.task453_swag_answer_generation
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task453_swag_answer_generation
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/task453_swag_answer_generation.task679_hope_edi_english_text_classification
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task679_hope_edi_english_text_classification
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/task679_hope_edi_english_text_classification.