datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
correlated-errorscurrently under heavy development
license: mit
Spotlight-VideoGen-Errors
Spotlight Dataset
Spotlight: Identifying and Localizing Video Generation Errors Using VLMs
Aditya Chinchure, Sahithya Ravi, Pushkar Shukla, Vered Shwartz, Leonid Sigal
🎉 Accepted to ECCV 2026
🌐 Project Page
Summary
Spotlight is a benchmark for evaluating whether Vision Language Models (VLMs) can precisely
localize and explain errors in AI-generated videos. It contains 600 videos generated by
three state-of-the-art Text-to-Video (T2V) models —… See the full description on the dataset page: https://huggingface.co/datasets/UBC-ViL/Spotlight-VideoGen-Errors.Panda-Discordant-Pathology-ErrorsErrors_Additive_Manufacturing_Plattform_Cam
Errors_Additive_Manufacturing_Plattform_Cam
3D Printing Nozzle Camera – YOLO Object Detection Dataset
This Repository is part of the Project: Künstliche Intelligenz zur Automatiserten Fehlerkorrektur in der Additiven Fertigung(Förderkennzeichen: 16IS23050B).
This dataset contains images captured from a camera positioned to capture the whole plattform of a 3D printer.
The task is object detection of both regular print elements and typical printing defects.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/DasKunststoffZentrumSKZ/Errors_Additive_Manufacturing_Plattform_Cam.Errors_Additive_Manufacturing_Nozzle_Cam
Errors_Additive_Manufacturing_Nozzle_Cam
3D Printing Nozzle Camera – YOLO Object Detection Dataset
This Repository is part of the Project: Künstliche Intelligenz zur Automatiserten Fehlerkorrektur in der Additiven Fertigung(Förderkennzeichen: 16IS23050B).
This dataset contains images captured from a camera positioned directly next to the nozzle of a 3D printer.
The task is object detection of both regular print elements and typical printing defects.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/DasKunststoffZentrumSKZ/Errors_Additive_Manufacturing_Nozzle_Cam.99-GEO-Errors
99 Errors in GEO
Why organisations become invisible, misrepresented or unsupported in AI answers
GEO means Generative Engine Optimization. This six-language companion book turns 99 recurring representation failures into auditable warnings. Each warning records the evidence needed, a correction protocol, a revalidation question and a machine-readable rule.
Start reading: Open the English PDF · Choose one of six languages · Cite the DOI
Kaan Muraz · NobleJackal ·… See the full description on the dataset page: https://huggingface.co/datasets/NobleJackal/99-GEO-Errors.jupyter-errors-dataset
Dataset Summary
The presented dataset contains 10000 Jupyter notebooks,
each of which contains at least one error. In addition to the notebook content,
the dataset also provides information about the repository where the notebook is stored.
This information can help restore the environment if needed.
Getting Started
This dataset is organized such that it can be naively loaded via the Hugging Face datasets library. We recommend using streaming due to the large size… See the full description on the dataset page: https://huggingface.co/datasets/JetBrains-Research/jupyter-errors-dataset.GBO-99-Errors
99 Mistakes in GBO
Why AI agents choose badly, exceed their authority and fail to stop
GBO means Generative Behavior Optimization: designing and governing what AI agents are allowed to do. This six-language companion to NOMOS GBO examines 99 failure patterns, each with a scenario, potential harm, detection signal, appropriate behaviour, machine rule and audit question.
Start reading: Open the English PDF · Choose one of six languages · Cite the DOI
Kaan Muraz ·… See the full description on the dataset page: https://huggingface.co/datasets/NobleJackal/GBO-99-Errors.quran-recitation-errors
Examples
Loading dataset:
from datasets import load_dataset
ds = load_dataset('sobolev210/quran-recitation-errors',)
print(ds["train"][0])
toulmin_errors
Reasoning Rubrics — Toulmin-Typed Error Localization Benchmark
A multi-domain benchmark for studying typed reasoning errors in LLMs and AI
scientific reasoning agents. Errors are labeled along four Toulmin
argumentation dimensions: Grounds (premises/facts), Warrant
(inferential step), Qualifier (scope/certainty), Rebuttal
(competing evidence).
The benchmark has two parts:
Typed external benchmarks. Existing reasoning-error benchmarks
relabeled with Toulmin dimensions on top of the… See the full description on the dataset page: https://huggingface.co/datasets/BrachioLab/toulmin_errors.errorsmondegreen-asr-errors
Mondegreen ASR error pairs
(ASR hypothesis, gold text) pairs for Japanese ASR post-correction.
This build is simulated -- errors come from a phonetic corruption model, not from a real ASR system. It exists so the whole pipeline (gate training, benchmarks, figures, CI) is reproducible without a GPU. Treat every number derived from it as a stated assumption, not a measurement.
How it was made
synthetic text
-> phonetic corruption model (mondegreen.simulate)
->… See the full description on the dataset page: https://huggingface.co/datasets/NagaYu/mondegreen-asr-errors.tabular-errors-v1
TabFix multilingual table error pairs — version 2.0
This release keeps 18 business error categories and separates executable deterministic detection from two residual neural categories: text.encoding and text.spelling. The same repository and family-disjoint splits are retained.
Split
Records
Open-vocabulary views
train
27948
3260
validation
17127
1844
test
32776
3540
The seven string columns remain id, split, family_id, clean_xml, corrupt_xml, errors… See the full description on the dataset page: https://huggingface.co/datasets/Antix5/tabular-errors-v1.ct-dosing-errors-benchmarksynthetic-real-word-errors
Synthetic Real-Word Error Datasets
This repository contains synthetic German data for grammatical error detection and correction, with a focus on context-dependent real-word errors.
The repository provides four subsets:
Subset
Description
Examples
mixed_real_word
Mixed real-word errors
99,812
capitalization
Capitalization errors
99,664
case
Case errors
99,706
verb
Verb errors
99,780
Each subset contains both erroneous and correct sentences and can therefore… See the full description on the dataset page: https://huggingface.co/datasets/aurorra/synthetic-real-word-errors.dv-synthetic-errors-mixedDV Text Errors
Dhivehi text error correction dataset containing correct sentences and synthetically generated errors.
The dataset aims to test Dhivehi language error correction models and tools.
About Dataset
Task: Text error correction
Language: Dhivehi (dv)
Dataset Structure
Input-output pairs of Dhivehi text:
correct: Original correct sentences
incorrect: Sentences with synthetic errors
Note: This is replica of alakxender/dv-synthetic-errors: added more synthetic errors. x5
quran-recitation-errors-testrunning-records-errors-dataset
Dataset Card for Running Records Errors Dataset
Dataset Summary
The Running Records Errors dataset is an English-language dataset containing 1,055,601 sentences based on the Europarl corpus. As described in our paper,
we take the sentences from the English version of the Europarl corpus and randomly inject three types of errors into the sentences: repetitions, where
certain words or phrases are repeated, substitutions, where certain words are replaced with a different… See the full description on the dataset page: https://huggingface.co/datasets/JDaniel423/running-records-errors-dataset.solidity_errors_and_vulnerabilities
Solidity Vulnerabilities Dataset
📖 Overview
This dataset contains examples of common vulnerabilities in Solidity smart contracts, structured for use in Retrieval-Augmented Generation (RAG) systems.
It is intended to give LLMs context for:
Detecting vulnerabilities in Solidity code
Explaining security issues in simple terms
Suggesting fixes and mitigations
Assessing the severity of the issue
🗂 Data Format
Each entry is a JSON object with the… See the full description on the dataset page: https://huggingface.co/datasets/greatestyapper/solidity_errors_and_vulnerabilities.dv-synthetic-errors
DV Text Errors
Dhivehi text error correction dataset containing correct sentences and synthetically generated errors. The dataset aims to test Dhivehi language error correction models and tools.
About Dataset
Task: Text error correction
Language: Dhivehi (dv)
Dataset Structure
Input-output pairs of Dhivehi text:
correct: Original correct sentences
incorrect: Sentences with synthetic errors
Statistics
Train set: {train_examples} examples… See the full description on the dataset page: https://huggingface.co/datasets/alakxender/dv-synthetic-errors.errors-respnses-dataset
Dataset Card for errors-respnses-dataset
This dataset has been created with distilabel.
Dataset Summary
This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI:
distilabel pipeline run --config "https://huggingface.co/datasets/mahmoud48/errors-respnses-dataset/raw/main/pipeline.yaml"
or explore the configuration:
distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/mahmoud48/errors-respnses-dataset.arabic-grammar-errorsvietnamese-corrector-errorsopenslr-sinhala-synthetic-spell-errors-quarter
Sinhala Dyslexic Spelling Correction Dataset
Dataset Description
This dataset contains Sinhala and code-mixed (Sinhala-English) text pairs for training spelling correction models, specifically designed to address dyslexia-like spelling errors.
Features
dyslexic_sentence: Input text with dyslexia-like spelling errors (string)
correct_sentence: Corrected output text (string)
Dataset Statistics
Split
Samples
Train
37,056
Test
9,265… See the full description on the dataset page: https://huggingface.co/datasets/SPEAK-PP/openslr-sinhala-synthetic-spell-errors-quarter.ct-dosing-errors
ds4dh/ct-dosing-errors
Version
This repository contains dataset version 0.2.3.
License
This dataset is licensed under CC BY 4.0 (cc-by-4.0).
Bulgarian-Text-Errorserrors
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Nei]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More Information… See the full description on the dataset page: https://huggingface.co/datasets/katsukiai/errors.jenkins_errors
Jenkins Error Outputs Dataset
A large synthetic dataset of Jenkins error outputs, generated for research, machine learning, and error analysis purposes. This dataset contains 100,000 entries, each simulating a real-world Jenkins error log with metadata.
Dataset Structure
Format: JSON Lines (.jsonl)
Fields:
timestamp: ISO8601 timestamp of the error
job_name: Jenkins job name
build_number: Jenkins build number
error_message: Jenkins error message
node: Jenkins node name… See the full description on the dataset page: https://huggingface.co/datasets/Snaseem2026/jenkins_errors.codenet-compile-errors
CodeNet Compiler Errors (Re-compiled 2026)
Dataset Summary
This dataset contains source code submissions from Project CodeNet that fail to compile. Unlike the original dataset metadata (which reflects compiler versions from 2011–2020), this dataset was re-executed in a modern Debian environment (2026) to generate up-to-date compiler error messages.
It is designed for research in:
Automated Program Repair (APR): Fixing compile-time errors.
Compiler Error Explanation:… See the full description on the dataset page: https://huggingface.co/datasets/criyle/codenet-compile-errors.grammatical_errors_rrt_press-v2