datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
semantic-repair-routing
semantic-repair-routing
The supervised pairs that train
SemanticRepair-270M:
a message somebody actually wrote, and the requests inside it restated
plainly, one per line. 84,819 pairs in five languages, plus 2,515 in
Italian and English aimed at what the model used to refuse.
It teaches one narrow thing. An embedding router compares a question with
the description of every capability it can reach. People do not write the
way capabilities are described — they hedge, they… See the full description on the dataset page: https://huggingface.co/datasets/Gramscii-IT/semantic-repair-routing.openenv-python-repair
Python Repair Lab
An original OpenEnv curriculum of 1,200 deterministic Python function-repair episodes: 12 problem families, four distinct bug patterns per family, and 25 seeded case sets per pattern. There are 151,780 executable checks across the episodes. These are 48 repair patterns with data variants, not 1,200 unrelated algorithms. Tasks cover interval algorithms, rolling calculations, weighted statistics, stable deduplication, Unicode run-length encoding, Luhn checksums… See the full description on the dataset page: https://huggingface.co/datasets/Louistiti/openenv-python-repair.data-pipeline-repair-trajectories
Data Pipeline Repair Trajectories
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated payload is now published under
data/raw/. It is available for inspection and… See the full description on the dataset page: https://huggingface.co/datasets/rmems/data-pipeline-repair-trajectories.uncgpt-conversations-semantic-approved-1p25-repaired-paperclip
UncGPT — Semantic-Approved 1.25σ Conversations (Leak-Repaired)
The 1.25σ semantic-gate cohort with uncle-diary leakage repaired and normalized diary fields. The auditable replacement for the older paperclip_all_1803 source that an earlier audit flagged for visible diary leakage.
Part of the UncGPT NeurIPS 2026 Competition collection.
Config
approved_manifest (default): one row per approved conversation, with metadata + path back to the full-schema JSON.… See the full description on the dataset page: https://huggingface.co/datasets/Reza2kn/uncgpt-conversations-semantic-approved-1p25-repaired-paperclip.Minecraft-GLB2Schem-RepairPairs-v1
unfundedResearcher/Minecraft-GLB2Schem-RepairPairs-v1
Paired (generated input, ground-truth target) Minecraft schematics for training a
model that turns an approximate voxelisation into a real build.
What a sample is
Each sample is three files inside a WebDataset TAR shard:
File
Meaning
<id>.input.schem
GENERATED. Produced by voxelising the source .glb. Approximate and noisy.
<id>.target.schem
GROUND TRUTH. The original schematic, copied byte-for-byte… See the full description on the dataset page: https://huggingface.co/datasets/unfundedResearcher/Minecraft-GLB2Schem-RepairPairs-v1.self_repair_gripper_screwdriver_bc
Flex-pi screwdriver pickup and tightening crops
Local LeRobot v2.1 dataset containing 800 episodes, 227,481 frames,
126.38 minutes at 30 Hz. Open review.html to browse every clip
and switch between overhead, left-wrist, and right-wrist cameras.
Task: Pick up the screwdriver and tighten the screw securing the gripper in its holder.
Contents
Original 32-dimensional observation.state and action, preserved bit-exact for retained rows. Dimension names and units follow… See the full description on the dataset page: https://huggingface.co/datasets/AdityaShah/self_repair_gripper_screwdriver_bc.dfm11-toolace-native-tool-use-repaired
dfm11-toolace-native-tool-use-repaired
ToolACE conversations with declared-name parsing and complete parallel result binding.
This is a DFM11 replacement for schneiderkamplab/dfm10-toolace-native-tool-use. All rows pass exhaustive structural validation. See metadata/manifest.json.
Palace-Config-Repair
Palace Configuration Repair Benchmark
Execution-graded repair tasks for configuration files of
Palace, an open-source finite-element solver for
computational electromagnetics. Each task gives a model a perturbed Palace JSON configuration and
asks it to return a corrected one. A repair counts as correct only if it conforms to the schema,
is accepted by the solver, and reproduces the reference outputs of the original case when
Palace v0.14.0 runs it. A configuration that is valid… See the full description on the dataset page: https://huggingface.co/datasets/empirischtech/Palace-Config-Repair.SWEUniverse-Repaired-Indist-full-not-SWE-bench-pro-matched
VmaxRL/SWEUniverse-Repaired-Indist-full-not-SWE-bench-pro-matched
This dataset contains a 350-row subset selected from the Indist SWEUniverse training rows.
Selection policy: three-way repo overlap with Bugpilot and LM-Modify, deduped by repo plus introduction patch, then balanced round-robin across overlapping repos.
Rows: 350
Selected repos: 19
Deduped overlap capacity: 468
Source dataset: VmaxRL/SWEUniverse-Repaired-Indist-full-not-SWE-bench-pro-matched
repairllama-datasets
RepairLLaMA - Datasets
Contains the processed fine-tuning datasets for RepairLLaMA.
Instructions to explore the dataset
To load the dataset, you must define which revision (i.e., which input/output representation pair) you want to load.
from datasets import load_dataset
# Load ir1xor1
dataset = load_dataset("ASSERT-KTH/repairllama-datasets", "ir1xor1")
# Load irXxorY
dataset = load_dataset("ASSERT-KTH/repairllama-datasets", "irXxorY")
Citation
If you use… See the full description on the dataset page: https://huggingface.co/datasets/ASSERT-KTH/repairllama-datasets.db-migration-repair-trajectories
Db Migration Repair Trajectories
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated payload is now published under
data/raw/. It is available for inspection and… See the full description on the dataset page: https://huggingface.co/datasets/rmems/db-migration-repair-trajectories.douvras-lean-proof-repair
Douvras Lean Proof Repair Corpus
Exemplos sintéticos de erros comuns de reparo em Lean: importação ausente, incompatibilidade de
tipos, falha de tática, meta não resolvida, reescrita inválida e prova reflexiva. Os snippets não
foram executados no compilador (proof_status: NOT_EXECUTED); portanto o corpus não prova nenhum
teorema e não substitui validação com uma versão específica do Mathlib.
state-right-to-repair-laws
State Right-to-Repair Laws: Coverage, Requirements, and Effective Dates
Canonical, always-current version: https://referencesource.org/state-right-to-repair-laws/
Machine-readable: https://referencesource.org/state-right-to-repair-laws/data.json — this mirror is a point-in-time copy.
Last verified: 2026-10-06
Stale after: 2026-11-13 (past this date, prefer the canonical copy —
it re-verifies on a cadence this snapshot does not)
Records: 6
Which US states have enacted… See the full description on the dataset page: https://huggingface.co/datasets/referencesource/state-right-to-repair-laws.code-contract-repairTitanius-1.2-sft-repair
Titanius-1.2-sft-repair
Synthetic instructions used in the two mixed-data continuations of Titanius-1.2-128m-sft-fp16. They target short answers, arithmetic, copying, JSON, facts, concise definitions and multi-turn recall.
Config
Training conversations
Used by
phase1
5,917
First continuation, selected at step 5,500
phase2
16,527
Second continuation, selected at step 6,000
Each config is a complete synthetic pool for its phase. They overlap; do not concatenate… See the full description on the dataset page: https://huggingface.co/datasets/NadavSalem/Titanius-1.2-sft-repair.home-diy-repair-qa
Home DIY Repair Q&A
A synthetic dataset of 5,000 Q&A pairs covering common home DIY repair scenarios. Each example includes a detailed step-by-step answer, required tools, safety warnings, and practical tips.
Dataset Purpose
This dataset is built for:
Instruction fine-tuning — train language models to give detailed, safe, and actionable home repair guidance
Retrieval-Augmented Generation (RAG) — build a knowledge base for home repair assistants
Question answering — train… See the full description on the dataset page: https://huggingface.co/datasets/dipenbhuva/home-diy-repair-qa.retro-weave-agent-editor-repair-diffs-v0.1
RetroInstruct Weave Agent Editor Repair Diffs
This component of RetroInstruct trains weave-agent to use the WeaveEditor to fix synthetic corruptions in the vein of
the Easy Prose Repair Diffs component.
Each row in the dataset provides the pieces you need to make a synthetic episode
demonstrating the agent:
Singling out one of three files as corrupted and in need of repair
Writing out a patch to the file as either a series of WeaveEditor edit() commands or a unidiff
Observing the… See the full description on the dataset page: https://huggingface.co/datasets/jdpressman/retro-weave-agent-editor-repair-diffs-v0.1.SysMLv2_Repair_with_SLMs
SysMLv2 Repair with SLMs
Dataset used in "Automated Semantic Fault Localization in SysML v2: A Human-in-the-Loop Framework Using Knowledge-Graph Augmented LLMs", presented at INCOSE International Symposium 2026.
Dataset Structure
This dataset provides two configurations:
default: Contains train/validation/test splits used for fine-tuning small models. Samples exceeding 2048 tokens have been removed.
full: Contains complete dataset
Task
Given SysML v2 code… See the full description on the dataset page: https://huggingface.co/datasets/rohhaiil/SysMLv2_Repair_with_SLMs.code-contract-repair
APIContractRepair
APIContractRepair is a provenance-tracked instruction-tuning dataset for software engineers and code-model researchers who need contract-faithful, minimal repairs with tests that distinguish a broken implementation from its fix. Magicoder-OSS-Instruct-75K supplies real function-identifier seeds, but it does not provide these documented contracts, deliberately buggy implementations, minimal corrected implementations, or paired regression tests. This release… See the full description on the dataset page: https://huggingface.co/datasets/skonml/code-contract-repair.OfficeSmith-PPTX-Repair
OfficeSmith PPTX Repair
Deterministically degraded PPTX IR objects paired with validated repairs.
Dataset summary
This dataset is part of the OfficeSmith collection for training models to plan, build, clarify, critique, and repair editable business presentations. It contains observable outputs only: no hidden chain of thought, secret benchmark prompt, personal data, or API credential is included.
Train rows: 160
Validation rows: 0
Test rows: 0
Languages: French… See the full description on the dataset page: https://huggingface.co/datasets/Benitoow/OfficeSmith-PPTX-Repair.dfm11-synthetic-native-tool-calling-repaired
dfm11-synthetic-native-tool-calling-repaired
DFM8 synthetic tool trajectories with compatibility normalization materialized in source data.
This is a DFM11 replacement for schneiderkamplab/dfm8-synthetic-native-tool-calling. All rows pass exhaustive structural validation. See metadata/manifest.json.
bibletts-asante-twi-repaired
BibleTTS Asante Twi — Repaired Transcripts
The Asante Twi transcripts released with BibleTTS have had the
characters ɛ (U+025B) and ɔ (U+0254) stripped out. This dataset restores them.
Audio is not included. This is a drop-in replacement for the .txt files that ship with the
BibleTTS Asante Twi package, matched by clip ID.
The problem
Both are Twi vowels, and both are required by the orthography. Measured across the released
Asante Twi transcripts:
Character… See the full description on the dataset page: https://huggingface.co/datasets/danieldzikunuofmarvel/bibletts-asante-twi-repaired.json-repair-eval-sample
JSON repair eval (sample)
30 cases of broken JSON. Each one has the text exactly as a parser would receive it, the repair we expect, the breakage category, the rule applied and the reason. It's a sample of a 300-case set for testing the repair step that sits behind an LLM's structured output or a stream that got cut off.
There are ten categories: truncation, trailing commas, single quotes, unescaped control characters, NaN and Infinity, comments, concatenated objects, unquoted… See the full description on the dataset page: https://huggingface.co/datasets/Graunt/json-repair-eval-sample.hedgehog-stopping-repair-r5
hedgehog-stopping-repair-r5
Hedgehog — stopping-repair round 5.
Contents
train.jsonl (1848 rows)
validation.jsonl (438 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Original content for the Hedgehog extraction model (Michael Anthony Falabella).
hedgehog-precision-repair
hedgehog-precision-repair
Hedgehog — precision-repair round (complete merchant extraction).
Contents
train.jsonl (1180 rows)
validation.jsonl (116 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Original content for the Hedgehog extraction model (Michael Anthony Falabella).
mpesa-loan-repayment-profiles
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
mpesa_loan_repayment_profiles
This dataset contains behavioral profiles of M-Pesa users in Kenya, detailing transaction metrics such as frequency, amounts, and cash flow stability across various user types like farmers and salaried workers. Each sample includes derived features like the coefficient of variation for income stability and the proportion of Paybill or airtime transactions. The… See the full description on the dataset page: https://huggingface.co/datasets/smainye/mpesa-loan-repayment-profiles.scugnizz-agentic-repair-v6-pro-300ktb21-eval-qwen35-action-only-20k-infra-repaired-c164-max32k-timeout2x
qwen35-action-only-20k — Terminal-Bench 2.1
Noncanonical Terminal-Bench 2.1 evaluation of violetxi/qwen35-4b-offline-echo-action-only-20k-tacc through the served
model ID qwen35-action-only-20k with Terminus-2.
Noncanonical run: timeout_multiplier=2 instead of 1.0; repair concurrency=164 exceeds 30. Do not compare this score directly with canonical TB2.1 leaderboard runs.
Result
Recorded trials: 445
Tasks / attempts: 89 × 5
Errored trials scored as zero: 250… See the full description on the dataset page: https://huggingface.co/datasets/violetxi/tb21-eval-qwen35-action-only-20k-infra-repaired-c164-max32k-timeout2x.pixeldit-b-native-repa-w4mn-200k-20260925hedgehog-complex-repair
hedgehog-complex-repair
Hedgehog — complex-extraction repair round.
Contents
train.jsonl (3840 rows)
validation.jsonl (304 rows)
Format
JSON Lines (.jsonl), one example per line.
Provenance
Original content for the Hedgehog extraction model (Michael Anthony Falabella).