datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
appsAPPS is a benchmark for Python code generation, it includes 10,000 problems, which range from having simple oneline solutions to being substantial algorithmic challenges, for more details please refer to this paper: https://arxiv.org/pdf/2105.09938.pdf.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.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.APPS-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%
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_rlaif
APPS Dataset for Reinforcement Learning with AI Feedback
Dataset Details
APPS_RLAIF is an extended work from APPS [1]
to use Chat LLMs to create multiple variances for each solution for defined problems.
In each solution, we use LLama 34B [2] to transform the original solutions into variances and rank them by score.
The generated flow is demonstrated as below; each variance is created based on the previous version of it in the chat.
We iterated each solutions n=3 times… See the full description on the dataset page: https://huggingface.co/datasets/nmd2k/apps_rlaif.apps_pnyx
PNYX - Apps
This is a splitted and tested version of APPS dataset, refer to it for further information on the original dataset construction.
This version is designed to be compatible with the hf_evaluate code_eval package and to be executed with lm-eval code_eval package.
This dataset does not include all the original fields. Some are modified and some are completely new:
id: Same as the original APPS dataset.
difficulty: Difficulty of the problem. Same as the original APPS… See the full description on the dataset page: https://huggingface.co/datasets/PNYX/apps_pnyx.control-apps-cleaned
control-apps-cleaned
A curated subset of the APPS dataset (Hendrycks et al., NeurIPS 2021), pre-filtered for use in the ARENA AI Control chapter — a teaching replication of Greenblatt et al. 2023 (arXiv:2312.06942).
What's in here
cleaned_apps.jsonl — 1,202 problems from the APPS "interview" split, filtered to a uniform I/O schema (inputs and outputs are each one of list[str], list[int], list[list[str]], list[list[int]]). Each line is a JSON record with the… See the full description on the dataset page: https://huggingface.co/datasets/styme3279/control-apps-cleaned.appsAPPS is a benchmark for Python code generation, it includes 10,000 problems, which range from having simple oneline solutions to being substantial algorithmic challenges, for more details please refer to this paper: https://arxiv.org/pdf/2105.09938.pdf.apps-small
APPS Dataset
Dataset Description
APPS 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.
You can also find APPS metric in the hub here codeparrot/apps_metric.
Languages
The dataset contains questions in English and code solutions in Python.
Dataset Structure
from datasets import load_dataset
load_dataset("codeparrot/apps")… See the full description on the dataset page: https://huggingface.co/datasets/AuroraH456/apps-small.answers-with-reasoning-apps
answers-with-reasoning-apps
Self-distillation SFT corpus: Qwen3-8B-Instruct's own all-tests-pass
chain-of-thought rollouts on APPS interview tier (code domain).
Generation
Source problems: codeparrot/apps, difficulty == "interview" filter on both train (2000 problems) and test (3000 problems) splits = 5000 candidate problems. Problems with empty / malformed input_output are dropped (~6%), leaving 4692 candidates. LCB-v5 (our held-out code benchmark) does not overlap APPS… See the full description on the dataset page: https://huggingface.co/datasets/abhayesian/answers-with-reasoning-apps.appsAPPS is a benchmark for Python code generation, it includes 10,000 problems, which range from having simple oneline solutions to being substantial algorithmic challenges, for more details please refer to this paper: https://arxiv.org/pdf/2105.09938.pdf.appsAPPS is a benchmark for Python code generation, it includes 10,000 problems, which range from having simple oneline solutions to being substantial algorithmic challenges, for more details please refer to this paper: https://arxiv.org/pdf/2105.09938.pdf.az-logic-apps-dataset
Azure Logic Apps Expression Dataset
This dataset contains Azure Logic Apps workflow expressions and metadata for training models.
Dataset Structure
The dataset is organized into two main splits:
Training Dataset (train/)
File: training_dataset_v1.parquet
Purpose: Primary training data for Azure Logic Apps expression generation
Format: Parquet
Categorization Dataset (categorization/)
File: training_dataset_categorization_v1.parquet
Purpose:… See the full description on the dataset page: https://huggingface.co/datasets/albertleigh/az-logic-apps-dataset.appsAPPS is a benchmark for Python code generation, it includes 10,000 problems, which range from having simple oneline solutions to being substantial algorithmic challenges, for more details please refer to this paper: https://arxiv.org/pdf/2105.09938.pdf.