datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
android-times-articles
Android Times — Articles Dataset Archive
Private dataset containing synthesized and processed article archives, multi-language transcripts, metadata, and editorial assets for Android Times.
Dataset Structure
articles/
├── en-US/ # English (United States) localized articles & scripts
├── ja-JP/ # Japanese localized articles & scripts
├── en-AU/ # Australian localized articles
├── en-CN/ # China localized English… See the full description on the dataset page: https://huggingface.co/datasets/aoiandroid/android-times-articles.androidlife-530
AndroidLife-530 — Android agent benchmark (real phone, real LLM)
AndroidLife runs Android agent tasks against a real phone (via ADB/MobileRun)
and a real LLM, and grades the agent on reaching a verifiable device end-state.
This repo ships the 530-task corpus plus everything needed to reproduce runs.
Benchmark, or template — your call. The 530 tasks are an extended version
of the benchmark, usable as a larger evaluation set for further benchmarking of
models beyond the 60-task… See the full description on the dataset page: https://huggingface.co/datasets/YuvrajSingh9886/androidlife-530.android_control_test
Processed Android Control Test Set for InfiGUI-R1 Evaluation
Dataset Description
This repository contains the processed test set derived from the Android Control dataset by Google Research. It has been specifically prepared for evaluating the performance of our model, InfiGUI-R1.
The InfiGUI-R1 model is detailed in our paper:
InfiGUI-R1: Advancing Multimodal GUI Agents from Reactive Actors to Deliberative Reasoners
This dataset facilitates standardized testing and… See the full description on the dataset page: https://huggingface.co/datasets/InfiX-ai/android_control_test.AndroidLens
AndroidLens: Long-latency Evaluation with Nested Sub-targets for Android GUI Agents
AndroidLens is a challenging benchmark for mobile GUI agents, featuring 571 real-world, long-horizon tasks in both Chinese and English, with an average of 26.1 steps per task. It supports evaluation of critical capabilities:
Long-horizon planning under multi-constraint & multi-goal scenarios
298 cross-app tasks and 273 single-app tasks, covering 74 real-world applications (e.g., WeChat, Google… See the full description on the dataset page: https://huggingface.co/datasets/yuecao0119/AndroidLens.android-kotlin-compose-compiler-verified
Qwandroid — Compiler-Verified Modern Android (Kotlin + Jetpack Compose) Dataset
5,777 SFT examples + 150 held-out eval + 8,027 DPO preference pairs.
Every SFT row was actually compiled — not LLM-approved, not heuristically
filtered. A subset was verified behaviorally by running JUnit tests.
Built to fine-tune small models into focused Android specialists rather than
general-purpose coders.
Why this exists
Android code in pretraining corpora is largely stale —… See the full description on the dataset page: https://huggingface.co/datasets/giggiovpg/android-kotlin-compose-compiler-verified.Children-Stories-CollectionChildren Stories Collection
A great synthetic datasets consists of around 0.9 million stories especially meant for Young Children. You can directly use these datasets for training large models.
Total 10 datasets are available for download. You can use any one or all the json files for training purpose.
These datasets are in "prompt" and "text" format. Total token length is also available.
Thank you for your love & support.
ornith-android-instruct
Ornith Android Instruct
5,500 instruction-tuning examples for Android development — Kotlin-first, Jetpack Compose-first, current (non-deprecated) APIs. Built as the training corpus for Ornith, a small language model specialized in writing Android application code, and usable for fine-tuning any code LLM toward modern Android development.
Train: 5,225 examples (ornith_dataset_merged.jsonl)
Eval: 275 held-out examples (ornith_eval_merged.jsonl)
Format: JSONL, one example per line… See the full description on the dataset page: https://huggingface.co/datasets/giggiovpg/ornith-android-instruct.android-malware-qwen35
Android Malware Classification
Description
Teaches domain-specific instruction following and code generation for this expert.
Source
srimeenakshiks/Android-Malware-Dataset - MIT
Formatted for the MoE-orchestrator project
(https://github.com/michaelowusuntim6/MoE-orchestrator). Expert target:
security.
Format
Each record is a JSON object with a messages field formatted for Qwen3.5's
native chat template:
{"messages": [
{"role":… See the full description on the dataset page: https://huggingface.co/datasets/michaelowusuntim6/android-malware-qwen35.android-qwen35
Android / LineageOS Support Corpus
Description
Teaches Android application and LineageOS custom-ROM work: Kotlin/Compose UI, AndroidControl device actions and support answers.
Source
ReallyHelpfulClean.md curated list (mteb, HarrytheOrange, ckg, giggiovpg, OfficerChul, soongfs, GreenNode)
HarrytheOrange/parsed_AndroidControl
Android Kotlin/Compose sources
Formatted for the MoE-orchestrator project… See the full description on the dataset page: https://huggingface.co/datasets/michaelowusuntim6/android-qwen35.android_control_train
Processed Android Control Training Set
Dataset Description
This repository contains the processed training set derived from the Android Control dataset by Google Research.
The data processing methodology is identical to that used for our corresponding test set, which can be found at Reallm-Labs/android_control_test.
Data Content and Image Extraction
Important Note: Due to the large size of the dataset, this repository contains only the processed text files.… See the full description on the dataset page: https://huggingface.co/datasets/InfiX-ai/android_control_train.The-android-and-the-human
Presentation
An English dataset composed of 10,000 real and 10,000 generated dreams.
Real dreams are from DreamBank. Generated dreams were produced with Oneirogen (0.5B, 1.5B and 7B), a language model for dream generation. Generation examples are available on my website.
This dataset can be used to determine differences between real and generated dreams. It can also be used to classify whether a dream narrative is generated or not.
This work was performed using HPC resources (Jean… See the full description on the dataset page: https://huggingface.co/datasets/gustavecortal/The-android-and-the-human.mindseye-android-os-data
MindsEye Android OS Dataset
This dataset powers the MindsEye Android OS Hugging Face Space: an educational Android-style UI that presents 35+ MindsEye repositories as interactive apps.
Contents
apps/ — 35 app definitions organized by category
ai-control/ — controller schemas (system, navigation, recommendations)
settings/ — system + theme + permissions + AI behavior
functions/ — app launcher, notifications, sync, search index, analytics tracker
metadata/ — categories… See the full description on the dataset page: https://huggingface.co/datasets/PeacebinfLow/mindseye-android-os-data.