datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
webfaq-retrievalWebFAQ Retrieval Dataset
Overview |
Details |
Structure |
Examples |
Considerations |
License |
Citation |
Contact |
Acknowledgement
Overview
The WebFAQ Retrieval Dataset is a carefully filtered and curated subset of the broader WebFAQ Q&A Dataset.It is purpose-built for Information Retrieval (IR) tasks, such as training and evaluating dense or sparse retrieval models in multiple languages.
Each of the… See the full description on the dataset page: https://huggingface.co/datasets/PaDaS-Lab/webfaq-retrieval.webgym_tasks
WebGym Tasks Dataset
Dataset Description
This dataset contains web navigation tasks for training and evaluating autonomous web agents. Each task consists of a natural language instruction that describes an action to be performed on a specific website, along with evaluation criteria and metadata.
Dataset Summary
Total Training Tasks: 292,092
Total Test Tasks: 1,167
Domains: Multiple domains including Lifestyle & Leisure, Sports & Fitness, and more
Source… See the full description on the dataset page: https://huggingface.co/datasets/microsoft/webgym_tasks.recycling_the_web
Dataset Card for Recycling-The-Web Synthetic Data
We release 44.4B tokens of high-quality, model-filtered synthetic texts obtained via our REcycling the Web with guIded REwrite (REWIRE) approach.
The generation process involves taking all documents that are of moderate quality (i.e., having passed some rule-based filters),
using an LLM (Llama-3.3-70B-Instruct) to identify the purpose of the text content, and then asking the LLM to come up with an improved document conditioned on… See the full description on the dataset page: https://huggingface.co/datasets/facebook/recycling_the_web.WebCompass
WebCompass
A unified multimodal benchmark for evaluating LLMs' ability to generate, edit, and repair functional web pages. WebCompass spans three input modalities — text design documents, reference screenshots, and video demonstrations — and three task families — generation, editing, and repair.
GitHub: NJU-LINK/WebCompass
Project Page: nju-link.github.io/WebCompass
Quick Start
from datasets import load_dataset
# Generation tasks (existing)
ds_text =… See the full description on the dataset page: https://huggingface.co/datasets/NJU-LINK/WebCompass.Web-Bench
Web-Bench
English | 中文 README
📖 Overview
Web-Bench is a benchmark designed to evaluate the performance of LLMs in actual Web development. Web-Bench contains 50 projects, each consisting of 20 tasks with sequential dependencies. The tasks implement project features in sequence, simulating real-world human development workflows. When designing Web-Bench, we aim to cover the foundational elements of Web development: Web Standards and Web Frameworks. Given the scale and… See the full description on the dataset page: https://huggingface.co/datasets/bytedance-research/Web-Bench.webnovel-chinese
简介
搜集网络上的网文小说，清洗，分割后，用于训练大语言模型，共计9000本左右，大约9B左右token。
使用
格式说明
采用jsonl格式存储，分为三个字段：
title ：小说名称
chapter：章节
text：正文内容
示例：
{"title": "斗破苍穹", "chapter": " 第一章 陨落的天才", "text": "“斗之力，三段！”\n望着测验魔石碑上面闪亮得甚至有些刺眼的五个大字，少年面无表情，唇角有着一抹自嘲，紧握的手掌，因为大力，而导致略微尖锐的指甲深深的刺进了掌心之中，带来一阵阵钻心的疼痛……\n“萧炎，斗之力，三段！级别：低级！”测验魔石碑之旁，一位中年男子，看了一眼碑上所显示出来的信息，语气漠然的将之公布了出来……\n"}
WebWalkerQA📑 The paper of WebWalkerQA is available at arXiv.
📊 The dataset resource is a collection of 680 questions and answers from the WebWebWalker dataset.
🙋 The dataset is in the form of a JSON file.
The keys in the JSON include:
Question, Answer, Root_Url, and Info. The Info field contains
more detailed information, including Hop, Domain, Language,
Difficulty_Level, Source Website, and Golden_Path.
{
"Question": "When is the paper submission deadline for the ACL 2025 Industry Track, and what… See the full description on the dataset page: https://huggingface.co/datasets/callanwu/WebWalkerQA.webfaqWebFAQ Q&A Dataset
Overview |
Details |
Structure |
Examples |
Considerations |
License |
Citation |
Contact |
Acknowledgement
Overview
The WebFAQ Q&A Dataset is a broad-coverage corpus of 96 million natural question-answer (QA) pairs in 75 languages, gathered from FAQ pages on the web. It leverages structured schema.org FAQPage annotations, making it a unique resource for large-scale Question Answering… See the full description on the dataset page: https://huggingface.co/datasets/PaDaS-Lab/webfaq.Web2Code
Dataset Details
Our Web2Code instruction tuning dataset construction and instruction generation process involves four key components: (1) Creation of new webpage image-code pair data: We generated high-quality HTML webpage-code pairs following the CodeAlpaca prompt using GPT-3.5 and convert them into instruction-following data. (2) Refinement of existing webpage code generation data: We transform existing datasets including into an instruction-following data format similar to LLaVA… See the full description on the dataset page: https://huggingface.co/datasets/MBZUAI/Web2Code.cqadupstack-webmasters
CQADupstackWebmastersRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
CQADupStack: A Benchmark Data Set for Community Question-Answering Research
Task category
t2t
Domains
Written, Web
Reference
http://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(["CQADupstackWebmastersRetrieval"])
evaluator =… See the full description on the dataset page: https://huggingface.co/datasets/mteb/cqadupstack-webmasters.webqa_large7z x imgs.7z.001
WangchanLION-Web
Citation
@misc{phatthiyaphaibun2025mangosteenopenthaicorpus,
title={Mangosteen: An Open Thai Corpus for Language Model Pretraining},
author={Wannaphong Phatthiyaphaibun and Can Udomcharoenchaikit and Pakpoom Singkorapoom and Kunat Pipatanakul and Ekapol Chuangsuwanich and Peerat Limkonchotiwat and Sarana Nutanong},
year={2025},
eprint={2507.14664},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2507.14664},
}
We… See the full description on the dataset page: https://huggingface.co/datasets/aisingapore/WangchanLION-Web.webfaq-bitextsWebFAQ Bilingual Datasets (Bitexts)
Overview |
Details |
Structure |
Examples |
Considerations |
License |
Citation |
Contact |
Acknowledgement
Overview
The WebFAQ Bilingual Datasets (a.k.a. Bitexts) are derived from the WebFAQ Q&A Dataset, but instead of monolingual question-answer (QA) pairs, each entry here contains aligned QA pairs in two different languages. These alignments are created via… See the full description on the dataset page: https://huggingface.co/datasets/PaDaS-Lab/webfaq-bitexts.Agent-G2-ALFWorld-Webshop-sft-data
Agent-G2 SFT Data
Agent-G2 SFT Data contains reasoning and action trajectories for supervised
fine-tuning (SFT) in the Agent-G2 project.
Associated paper: Agent-G2: Gaussian Guidance for Agentic Reinforcement
Learning — accepted to the EMNLP 2026 Main Conference.
The dataset covers two interactive agent environments:
WebShop: agents search for products, select options, and complete
purchases according to user requirements.
ALFWorld: agents interact with household environments… See the full description on the dataset page: https://huggingface.co/datasets/xiamoent/Agent-G2-ALFWorld-Webshop-sft-data.webdsh-images
webdsh-images
Disk images for the emulated machines webdsh
offers.
Why this exists
v86 can run about a hundred and twenty-five
machines in a browser, and every one of them is the same emulator with a
different disk. What copy.sh/v86 has that a fork does
not is a CDN with the disks on it: its own host, i.copy.sh, refuses browser
requests from anywhere else — deliberately, and it is their bandwidth to
protect.
So webdsh's catalog was complete and its machines were… See the full description on the dataset page: https://huggingface.co/datasets/AndyZijianZhang/webdsh-images.ms-marco-anchor-text
Webis MS MARCO Anchor Text 2022
The Webis MS MARCO Anchor Text 2022 dataset enriches Version 1 and 2 of the document collection of MS MARCO with anchor text extracted from six Common Crawl snapshots. The six Common Crawl snapshots cover the years 2016 to 2021 (between 1.7-3.4 billion documents each). We sampled 1,000 anchor texts for documents with more than 1,000 anchor texts at random and all anchor texts for documents with less than 1,000 anchor texts (this sampling yields that… See the full description on the dataset page: https://huggingface.co/datasets/webis/ms-marco-anchor-text.Web_Scraper_Datawebnovel_cn
内容
包含从12560本网文提取的约21.7M条可用于训练小说生成的中文指令数据(novel_json_tokens512.zip)。下载链接：https://pan.baidu.com/s/1TorBMbrqxrn6odRF0PJBVw
提取码：jlh3
以及从中提取出的包含50k条数据的子集(novel_cn_token512_50k.json)。其中输入和输出都不多于 512 tokens。
样例
在原有小说文本基础上，依据下列五种指令生成数据。
其中，文本由小说中随机抽取的连续句子组成。
给定标题，直接生成简介。
给定标题和简介，生成开头。
给定简介和一段文本，生成后续文本。
给定标题和一段文本，生成后续文本。
给定一段文本，生成后续文本。
{
"instruction":… See the full description on the dataset page: https://huggingface.co/datasets/zxbsmk/webnovel_cn.WebShaper
WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization
Github: https://github.com/Alibaba-NLP/WebAgent
Paper: https://arxiv.org/pdf/2507.15061
TLTR
WebShaper is a synthesized training dataset for information-seeking (IS) task. It is based on our proposed task formalization of IS, and synthesized by our Expander Agent. WebShaper would cover a broader range of task forms, reasoning structure, and diversified knowledge.
Description… See the full description on the dataset page: https://huggingface.co/datasets/Alibaba-NLP/WebShaper.WebWorldData
WebWorldData 🌐
Overview
WebWorldData is a large-scale dataset of 1.06M web interaction trajectories collected from the open web, designed for training browser world models. It is the training data behind the WebWorld model series.
Each trajectory consists of sequences of (state, action, next_state) transitions, where states are represented as A11y Trees extracted from real websites using Playwright.
Dataset Statistics
Total… See the full description on the dataset page: https://huggingface.co/datasets/Qwen/WebWorldData.webvoyager_evaluation_datawebui-training-dataShowUI-web-8kwebdev-arena-preference-10k
WebDev Arena Preference Dataset
This dataset contains 10K real-world Webdev Arena battle with 10 state-of-the-art LLMs. More details in the blog post.
Dataset License Agreement
This Agreement contains the terms and conditions that govern your access and use of the WebDev Arena Dataset (Arena Dataset). You may not use the Arena Dataset if you do not accept this Agreement. By clicking to accept, accessing the Arena Dataset, or both, you hereby agree to the terms of the… See the full description on the dataset page: https://huggingface.co/datasets/lmarena-ai/webdev-arena-preference-10k.webglm-qa
WebGLM-QA
Dataset Description
WebGLM-QA is the dataset used to train the WebGLM generator module. It consists of 43,579 high-quality data samples for the train split, 1,000 for the validation split, and 400 for the test split. Refer to our paper for the data construction details.
Dataset Structure
To load the dataset, you can try the following code.
from datasets import load_dataset
load_dataset("THUDM/webglm-qa")
DatasetDict({
train: Dataset({… See the full description on the dataset page: https://huggingface.co/datasets/zai-org/webglm-qa.open-web-mathBILGE-Synthetic-Web
BILGE-Synthetic-Web Dataset
BILGE-Synthetic-Web was created following the methodology presented in the Cosmopedia blog/article.
All content was generated using a 27B-parameter model.
Further details on the methodology are available at:
🔗 https://huggingface.co/blog/cosmopedia
InfiMM-WebMath-Edu-zhShowUI-web-8k
ShowUI-web-8K
This dataset is a curated 8K-sample subset from the original ShowUI-web dataset, as mentioned in our paper. It contributes to the training of GUI grounding models, with a focus on realistic web user interfaces collected from diverse websites.
Dataset Details
Source: Sampled from ShowUI-web
Domain: Web GUI screenshots
Diversity: Covers a wide variety of website layouts and components
Use case: GUI grounding pretraining for web environments… See the full description on the dataset page: https://huggingface.co/datasets/zonghanHZH/ShowUI-web-8k.webnovel-chinese
简介
搜集网络上的网文小说，清洗，分割后，用于训练大语言模型，共计9000本左右，大约9B左右token。
使用
格式说明
采用jsonl格式存储，分为三个字段：
title ：小说名称
chapter：章节
text：正文内容
示例：
{"title": "斗破苍穹", "chapter": " 第一章 陨落的天才", "text": "“斗之力，三段！”\n望着测验魔石碑上面闪亮得甚至有些刺眼的五个大字，少年面无表情，唇角有着一抹自嘲，紧握的手掌，因为大力，而导致略微尖锐的指甲深深的刺进了掌心之中，带来一阵阵钻心的疼痛……\n“萧炎，斗之力，三段！级别：低级！”测验魔石碑之旁，一位中年男子，看了一眼碑上所显示出来的信息，语气漠然的将之公布了出来……\n"}