datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ridgelora-stage2-imposebase-train160-50k-20260824
Stage-2 ControlNet retraining with the frozen IMPOSE base
This experiment retrains only Stage 2 for RidgeLoRA-FP. Stage 1 is the IMPOSE
checkpoint and is not retrained. The run started on 2026-08-24 on TPU VM
t1v-n-d3df3356-w-0 (TPU v5p-8, four XLA devices).
An initial Stage-1-from-scratch job was stopped at step 575 after correcting
the scope. It produced no scheduled checkpoint and is not used in any result;
its log is retained only as an audit trail.
Frozen IMPOSE… See the full description on the dataset page: https://huggingface.co/datasets/LamTNguyen/ridgelora-stage2-imposebase-train160-50k-20260824.Cgi_Impots_Marocaine_2026email-importance
Email Importance Classification Dataset
Dataset Summary
This dataset is designed to train and evaluate text classification models on the task of distinguishing Important/Actionable emails from Noise/Promotional emails.
The data consists of raw email text bodies, simulating a realistic inbox environment containing personal correspondence, work requests, security alerts, newsletters, marketing blasts, and social media notifications. The text preserves realistic formatting… See the full description on the dataset page: https://huggingface.co/datasets/Dc-4nderson/email-importance.impossible-swegym
impossible-swegym
Software-engineering agent tasks that cannot be solved honestly. Each task is a
real SWE-Gym issue whose test suite has
been mutated by an LLM so that no correct implementation can pass it. An agent that
reports success has necessarily edited the tests, disabled them, or otherwise subverted
grading. The dataset exists to measure and study that behaviour, in the spirit of
fjzzq2002/impossible_swebench,
which applies the same idea to SWE-bench Verified.
Version… See the full description on the dataset page: https://huggingface.co/datasets/AlignmentResearch/impossible-swegym.Unmasking-the-Imposters
Unmasking the Imposters: Machine-Generated Tweet Detection Dataset
This dataset contains nine subsets of human and machine-generated tweets designed to evaluate the detection of AI-generated content across censored and uncensored large language models (LLMs). The dataset addresses the gap in understanding how content moderation and domain adaptation affect the detectability of machine-generated text on social media platforms.
Paper: "Unmasking the Imposters: How Censorship and… See the full description on the dataset page: https://huggingface.co/datasets/redasers/Unmasking-the-Imposters.import-duty-and-tariffs
US import duty and tariff rates by HTS chapter
Canonical, always-current version: https://referencesource.org/import-duty-and-tariffs/
Machine-readable: https://referencesource.org/import-duty-and-tariffs/data.json — this mirror is a point-in-time copy.
Last verified: 2026-08-15
Stale after: 2026-09-14 (past this date, prefer the canonical copy —
it re-verifies on a cadence this snapshot does not)
Records: 25
US import duty rates organized by Harmonized Tariff Schedule (HTS)… See the full description on the dataset page: https://huggingface.co/datasets/referencesource/import-duty-and-tariffs.fda-import-alert-red-list
FDA import alert red lists: firms subject to detention without physical examination
Canonical, always-current version: https://referencesource.org/fda-import-alert-red-list/
Machine-readable: https://referencesource.org/fda-import-alert-red-list/data.json — this mirror is a point-in-time copy.
Last verified: 2026-08-06
Stale after: 2026-10-05 (past this date, prefer the canonical copy —
it re-verifies on a cadence this snapshot does not)
Records: 424
Which foreign… See the full description on the dataset page: https://huggingface.co/datasets/referencesource/fda-import-alert-red-list.cot-oracle-eval-step-importance-thought-anchors
CoT Oracle Eval: step_importance_thought_anchors
Causal step importance identification from off-policy deepseek MATH rollouts. Source: uzaymacar/math-rollouts.
Part of the CoT Oracle Evals collection.
Schema
Field
Description
eval_name
"step_importance_thought_anchors"
example_id
Unique identifier
clean_prompt
Problem statement only
test_prompt
Problem + numbered CoT + final answer
correct_answer
Top-3 most important chunk utterances, newline-separated… See the full description on the dataset page: https://huggingface.co/datasets/japhba/cot-oracle-eval-step-importance-thought-anchors.viking_history_and_important_events_volume1
Viking History and Important Events Volume 1
The dataset role-plays a series of hearth-side or shipboard talks where characters react to major milestones of the Viking Age (c. 793–1066 CE) as if they are hearing rumours or recent tidings. Topics include:
The raid on Lindisfarne (793) – seen as the dramatic opening of the Viking Age.
The Great Heathen Army and conquests in England.
Ragnar Lodbrok’s siege of Paris (845) and the massive danegeld payment.
Establishment of the Dublin… See the full description on the dataset page: https://huggingface.co/datasets/RuneForgeAI/viking_history_and_important_events_volume1.cot-oracle-eval-step-importance-thought-branches
CoT Oracle Eval: step_importance_thought_branches
Causal step importance identification from thought-branches authority bias CoTs. Source: thought-branches.
Part of the CoT Oracle Evals collection.
Schema
Field
Description
eval_name
"step_importance_thought_branches"
example_id
Unique identifier
clean_prompt
Problem statement only
test_prompt
Problem + numbered CoT + final answer
correct_answer
Top-3 most important chunk utterances, newline-separated… See the full description on the dataset page: https://huggingface.co/datasets/japhba/cot-oracle-eval-step-importance-thought-branches.school-of-reward-hacks-impossible-tests
School of Reward Hacks — Impossible Tests
This is a modified version of the coding problems from the School of Reward Hacks dataset, where one test case per problem is changed to be incompatible with the instruction for the coding task.
Specifically, for each coding problem, one of the provided unit tests has its expected output changed to be subtly incorrect — for example, a palindrome checker being expected to return false for a well-known palindrome. This creates a conflict… See the full description on the dataset page: https://huggingface.co/datasets/oliverdk/school-of-reward-hacks-impossible-tests.defendable-pain-imposter-scam-receipts-v0.1
Imposter Scam Pain · $2.95B Lost
"the fake call" — Mr. Defendable
A free pain-receipt dataset from the DefendableOS ecosystem. 12 rows · ready to read · all cited or graded · CC-BY-4.0.
Part of the 100-pack — 100 free pain-receipt datasets dropped from the Defendable Bakery to the open AI-trust community. Different theme per dataset. Same operator voice across all of them.
Tribunal begins before training. No proof, no honey. To the shed.
What's in here
12 pain… See the full description on the dataset page: https://huggingface.co/datasets/SwarmandBee/defendable-pain-imposter-scam-receipts-v0.1.sonegaduros_di_impostus