datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.AndroidCodeandroid_controlcqadupstack-android
CQADupstackAndroidRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Benchmark Data Set for Community Question-Answering Research
Task category
t2t
Domains
Programming, Web, Written, Non-fiction
Referencehttp://nlp.cis.unimelb.edu.au/resources/cqadupstack/
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["CQADupstackAndroidRetrieval"])… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cqadupstack-android.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.Android-Control-84k
Android Control Dataset
Overview
This directory contains two dataset files (and_ctrl_train.json and and_ctrl_test.json) derived from the Android Control project by Google Research. These datasets have been formatted specifically for GUI grounding training in LLaMA-Factory.
Dataset Description
The Android Control dataset consists of episodes where each episode contains multiple steps. Each step includes:
Step instructions: Natural language instructions for UI… See the full description on the dataset page: https://huggingface.co/datasets/OfficerChul/Android-Control-84k.android-17-wasm-imagesAndroidControl_testandroid_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.cqadupstack-android-fa
Dataset Summary
CQADupstack-android-Fa is a Persian (Farsi) dataset developed for the Retrieval task, with a focus on duplicate question detection in community question-answering (CQA) forums. This dataset is a translated version of the "android" (Android Enthusiasts) StackExchange subforum from the English CQADupstack collection and is part of the FaMTEB benchmark under the BEIR-Fa suite.
Language(s): Persian (Farsi)
Task(s): Retrieval (Duplicate Question Retrieval)
Source:… See the full description on the dataset page: https://huggingface.co/datasets/MCINext/cqadupstack-android-fa.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.functioncall-androidcqadupstack-android-top-20-gen-queries
NFCorpus: 20 generated queries (BEIR Benchmark)
This HF dataset contains the top-20 synthetic queries generated for each passage in the above BEIR benchmark dataset.
DocT5query model used: BeIR/query-gen-msmarco-t5-base-v1
id (str): unique document id in NFCorpus in the BEIR benchmark (corpus.jsonl).
Questions generated: 20
Code used for generation: evaluate_anserini_docT5query_parallel.py
Below contains the old dataset card for the BEIR benchmark.
Dataset Card for BEIR… See the full description on the dataset page: https://huggingface.co/datasets/income/cqadupstack-android-top-20-gen-queries.AndroidAppReviewsThe function to read the data is in the first cell of the python notebook: eda.ipynb
Data Structure
The JSON data is organized as follows:
App Names: Top-level keys represent the names of the apps (e.g., "DoorDash", "McDonald's").
-> Score Categories: Under each app, reviews are grouped by score categories (e.g., "1", "2", "3", "4", "5").
-> Review Lists: Each score category contains a list of reviews.
->Review Details: Each review includes:
- content: The… See the full description on the dataset page: https://huggingface.co/datasets/NovaNightshade/AndroidAppReviews.llama_log_finetune_dataset_androidHF-Android-Appnd_ae_android_dataset