datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
apps-control-arena
APPS Control Arena Dataset
Unified dataset combining APPS problems with backdoors from both the AI Control paper and Control-Tax paper.
Dataset Description
This dataset is based on the codeparrot/apps dataset,
enhanced with backdoor solutions from two sources:
APPS Backdoors: From "AI Control: Improving Safety Despite Intentional Subversion" / TylordTheGreat/apps-backdoors-04-02-25
Control-Tax Backdoors: From "Control Tax: The Price of Keeping AI in Check"… See the full description on the dataset page: https://huggingface.co/datasets/RoganInglis/apps-control-arena.appsEmploying the MTEB evaluation framework's dataset version, utilize the code below for assessment:
import mteb
import logging
from sentence_transformers import SentenceTransformer
from mteb import MTEB
logger = logging.getLogger(__name__)
model_name = 'intfloat/e5-base-v2'
model = SentenceTransformer(model_name)
tasks = mteb.get_tasks(
tasks=[
"AppsRetrieval",
"CodeFeedbackMT",
"CodeFeedbackST",
"CodeTransOceanContest",
"CodeTransOceanDL"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/apps.apps-backdoors
APPS Dataset with Backdoor Annotations
This is a processed version of the APPS dataset combined with verified backdoor annotations for AI safety research, specifically for AI control experiments.
Generated for use with ControlArena.
Original APPS Dataset
Paper: Measuring Coding Challenge Competence With APPSAuthors: Dan Hendrycks, Steven Basart, Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, Jacob… See the full description on the dataset page: https://huggingface.co/datasets/RoganInglis/apps-backdoors.mac-app-store-apps-metadata
Dataset Card for Macappstore Applications Metadata
📌 Dataset status: static snapshot (no scheduled updates). The data was collected from the public iTunes Search API between December 2023 and January 2024 and reflects the Mac App Store as of that period. The dataset is stable and remains available for research use; it is not refreshed on a schedule.
Mac App Store Applications Metadata sourced by the public API.
Curated by: MacPaw Way Ltd.
Language(s) (NLP): Mostly EN, DE… See the full description on the dataset page: https://huggingface.co/datasets/macpaw-research/mac-app-store-apps-metadata.CodeContests_apps_format
Dataset Card for "CodeContests_apps_format"
More Information needed
AppSecBench
AppSecBench Dataset Card
Dataset Summary
AppSecBench is an original benchmark of 406 vulnerable/secure code pairs spanning 12 programming
languages, 18 frameworks, 34 vulnerability classes, and 5 difficulty levels. Each record is a
self-contained evaluation case: a vulnerable snippet, its secure counterpart, an exploit sketch,
and the "ground truth" a detector/model is expected to produce (CWE, OWASP, severity, CVSS 3.1,
explainability, fix, and… See the full description on the dataset page: https://huggingface.co/datasets/ismailtasdelen/AppSecBench.APPSAPPSAPPS is a benchmark for code generation with 10000 problems. It can be used to evaluate the ability of language models to generate code from natural language specifications. To create the APPS dataset, the authors manually curated problems from open-access sites where programmers share problems with each other, including Codewars, AtCoder, Kattis, and Codeforces.
Usage
import datasets
# Download the dataset
queries = datasets.load_dataset("embedding-benchmark/APPS", "queries")
documents =… See the full description on the dataset page: https://huggingface.co/datasets/embedding-benchmark/APPS.mac-app-store-apps-descriptions
Dataset Card for Macappstore Applications Descriptions
📌 Dataset status: static snapshot (no scheduled updates). This dataset is derived from the December 2023 – January 2024 Mac App Store metadata snapshot and reflects the store as of that period. The dataset is stable and remains available for research use; it is not refreshed on a schedule.
Mac App Store Applications descriptions extracted from the metadata from the public API.
Curated by: MacPaw Way Ltd.
Language(s)… See the full description on the dataset page: https://huggingface.co/datasets/macpaw-research/mac-app-store-apps-descriptions.cash-advance-apps
Overdraft Apps Cash Advance Directory
Structured comparison of US cash advance and earned-wage access apps maintained by Overdraft Apps.
This Hub listing mirrors the public machine-readable exports published at:
Dataset JSON / CSV: https://overdraftapps.com/data/
cash-advance-apps.json
cash-advance-apps.csv
Interactive directory homepage: https://overdraftapps.com/
Scoring methodology: https://overdraftapps.com/methodology/
Full markdown dump:… See the full description on the dataset page: https://huggingface.co/datasets/overdraftapps/cash-advance-apps.apps-qrels
Dataset Card for "apps-qrels"
More Information needed
apps-queries-corpusEmploying the CoIR evaluation framework's dataset version, utilize the code below for assessment:
import coir
from coir.data_loader import get_tasks
from coir.evaluation import COIR
from coir.models import YourCustomDEModel
model_name = "intfloat/e5-base-v2"
# Load the model
model = YourCustomDEModel(model_name=model_name)
# Get tasks
#all task ["codetrans-dl","stackoverflow-qa","apps","codefeedback-mt","codefeedback-st","codetrans-contest","synthetic-
# text2sql","cosqa","codesearchnet"… See the full description on the dataset page: https://huggingface.co/datasets/CoIR-Retrieval/apps-queries-corpus.mac-app-store-apps-release-notes
Dataset Card for Macappstore Applications Release Notes
📌 Dataset status: static snapshot (no scheduled updates). This dataset is derived from the December 2023 – January 2024 Mac App Store metadata snapshot and reflects the store as of that period. The dataset is stable and remains available for research use; it is not refreshed on a schedule.
Mac App Store Applications release notes extracted from the metadata from the public API.
Curated by: MacPaw Way Ltd.
Language(s)… See the full description on the dataset page: https://huggingface.co/datasets/macpaw-research/mac-app-store-apps-release-notes.easyr1-49k-hard-qwen7b-easy-gta1-stacked-pro-apps-no-resolution-in-prompt-ui-vision-5k-jedi-4MP
easyr1-49k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt-ui-vision-grounding-4MP-add-5k-jedi
Merged dataset composed of the following sources:
datasets/easyr1-44k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt-ui-vision-grounding-4MP (44769 samples in split train)
datasets/easyr1-21k-jedi-grounding-4MP-gta1-nores-fixed (18032 samples in split train)
Summary
Generated on: 2025-09-14 03:24:59 UTC
Split: train
Column strategy:… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-49k-hard-qwen7b-easy-gta1-stacked-pro-apps-no-resolution-in-prompt-ui-vision-5k-jedi-4MP.app-store-apps-charts-reviews-sample
App Store Apps, Charts & Review Sentiment — Free Sample
Free samples from a mobile-app intelligence dataset of 6,384 chart apps built entirely from Apple's official public APIs (iTunes RSS charts, Search & Lookup) plus Google Play public pages: app metadata, chart-rank snapshots, review-derived sentiment metrics, and a unique "Category Opportunity Index" that ranks every US App Store category by high demand × low rating — where the most underserved app markets are.
➡️ Full… See the full description on the dataset page: https://huggingface.co/datasets/zalizedata/app-store-apps-charts-reviews-sample.ai-companion-apps-directory
AI Companion Apps Directory (2026)
A maintained dataset of AI companion / AI girlfriend / NSFW AI chat applications with published monthly pricing, free-tier availability, and editorial scores. Compiled from each app's published pricing pages and the research library at AI Companion Desk — scores follow the methodology described at aicompaniondesk.com/methodology.
Last updated: 2026-09-25 · Apps tracked: 21
Files
apps.csv — one row per application: name, monthly… See the full description on the dataset page: https://huggingface.co/datasets/aicompaniondesk/ai-companion-apps-directory.codeparrot_appsThis is copied from the codeparrot/apps which is not in Parquet format (meaning that if you are using Datasets>=4.0.0 you will fail to download it because it requires remote code).
You can find the origin dataset here: https://huggingface.co/datasets/codeparrot/apps
You can find the conversion code here: https://gist.github.com/4gatepylon/024853a9d279812e1f14be93242b3ef8#file-gistfile1-py-L1
NOTE that some of the solutions/input-output are empty. You can check as the code ^ does by looking for… See the full description on the dataset page: https://huggingface.co/datasets/4gate/codeparrot_apps.easyr1-44k-hard-qwen7b-easy-gta1-stacked-pro-apps-no-resolution-in-prompt-ui-vision-4MP
easyr1-44k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt-ui-vision-grounding-4MP
Merged dataset composed of the following sources:
datasets/easyr1-38k-hard-qwen7b-easy-gta1-4MP-stacked-pro-apps-no-resolution-in-prompt (38979 samples in split train)
datasets/ui-vision-grounding-4MP (5790 samples in split train)
Summary
Generated on: 2025-09-13 22:23:37 UTC
Split: train
Column strategy: intersection
Samples after merge: 44769
Usage
from… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-44k-hard-qwen7b-easy-gta1-stacked-pro-apps-no-resolution-in-prompt-ui-vision-4MP.buggy-appsThis dataset was constructed for use in the paper Neural Interactive Proofs. It is based on the APPS benchmark for code generation (see also the corresponding Hugging Face dataset). It includes includes a number of coding problems with both buggy and non-buggy solutions (though note that, apparently, in AlphaCode the authors found that this dataset can generate many false positives during evaluation, where incorrect submissions are marked as correct due to lack of test coverage).
Each datum… See the full description on the dataset page: https://huggingface.co/datasets/lrhammond/buggy-apps.impossible_apps_introAPPS-verified
Introduction
This dataset contains verified solutions from the APPS dataset's training set. Solutions that fail to pass all the test cases are removed. Problems with no correct solution are also removed.
The solutions were executed on Intel E5-2620 v3 CPUs with the execution timeout set to 10 seconds.
Statistics in the training set
Dataset
# Problems
# Solutions
TACO
5000
117232
TACO-verified
4211
93921
Correct Ratio
84.22%
80.12%
professional-apps-grounding-with-filteringrteb-AppsRetrieval
AppsRetrieval — RTEB open subset, unified schema
A normalised copy of the dataset behind the mteb task AppsRetrieval, one of the 17 open tasks in the
RTEB(beta) retrieval benchmark. Same queries, documents and relevance
judgements as the benchmark evaluates — reshaped into one strict schema shared by all 17.
Source
CoIR-Retrieval/apps @ f22508f96b7a (the revision pinned in mteb)
Domain · languages
code · eng
Queries / documents / qrels
3,765 / 8,765 / 3,765… See the full description on the dataset page: https://huggingface.co/datasets/Hyukkyu/rteb-AppsRetrieval.awesome-python-apps
Dataset Card for "awesome-python-apps"
This contains .py files for the following repos taken from awesome-python-applications (on GitHub here)
abilian-sbe clone_repos.sh invesalius3 photonix sk1-wx
ambar CONTRIBUTING.md isso picard soundconverter
apatite CTFd kibitzrpi-hole soundgrain
ArchiveBox Cura KindleEar planet stargate… See the full description on the dataset page: https://huggingface.co/datasets/BEE-spoke-data/awesome-python-apps.apps
NOTE
This is the same dataset as the original APPS dataset but the original repo does not work with datasets >= 4.0 as it uses a dataset script. There's an open PR to move to parquet but it has been open for over a year, and it seems unlikely that it will be merged. As a result, this repository exists under the same MIT license to work with new dataset versions. All credit should go to the original authors of the dataset (and the README below is copied from the original repo).… See the full description on the dataset page: https://huggingface.co/datasets/metr-evals/apps.appsdenovalinkappeasyr1-10k-hard-qwen7b-easy-gta1-4MP-professional-apps-grounding-only-no-resolution-in-prompt
easyr1-10k-hard-qwen7b-easy-gta1-4MP-professional-apps-grounding-only-no-resolution-in-prompt
This dataset was generated using the EasyR1 grounding dataset pipeline.
Generation Details
Generated on: 2025-08-26 12:16:32 UTC
Script: push_easyr1_to_hf.py
Data directory: /lustre/fsw/portfolios/nvr/users/aawadalla/LLaMA-Factory/data
Parameters Used
Maximum samples: 10000
Image resize (max megapixels): 4.0 MP
Minimum native image resolution: 0.0 MP
Prompt format:… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-cua-dev/easyr1-10k-hard-qwen7b-easy-gta1-4MP-professional-apps-grounding-only-no-resolution-in-prompt.portal-appsAppsRetrievalmobile-apps-user-sentiment-reviews
Top Mobile Apps User Sentiment & Review Corpus (Google Play)
Overview
This dataset contains clean, structured public data exported directly from production runs of Apify actors.
It serves as a benchmark and sample for lead qualification, market intelligence, research, and machine learning pipelines.
Source Actor: captainhandsome/google-play-reviews-scraper
Dataset Page: Public sample and schema
Preconfigured Run Task: captainhandsome/instagram-1star-reviews… See the full description on the dataset page: https://huggingface.co/datasets/joeygambino/mobile-apps-user-sentiment-reviews.