datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SWE-smith-jswdc-common-crawl-embedded-jsonldJSONSchemaBench
JSONSchemaBench
JSONSchemaBench is a benchmark of real-world JSON schemas designed to evaluate structured output generation for Large Language Models (LLMs). It contains approximately 10,000 JSON schemas, capturing diverse constraints and complexities.
import datasets
from datasets import load_dataset
def main():
# Inspect the available subsets of the datasetall_subsets = datasets.get_dataset_config_names("epfl-dlab/JSONSchemaBench")
print("Available subsets:"… See the full description on the dataset page: https://huggingface.co/datasets/epfl-dlab/JSONSchemaBench.JSICK
JSICK
An MTEB dataset
Massive Text Embedding Benchmark
JSICK is the Japanese NLI and STS dataset by manually translating the English dataset SICK (Marelli et al., 2014) into Japanese.
Task category
t2t
Domains
Web, Written
Reference
https://github.com/sbintuitions/JMTEB
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(["JSICK"])
evaluator = mteb.MTEB(task)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/JSICK.ICDAR2019-SROIE
ICDAR2019's Scanned Receipts OCR and Information Extraction (SROIE)
The ICDAR2019 SROIE dataset was originally published by Huang et al. for the
15th International Conference on Document Analysis and Recognition (ICDAR2019)
Robust Reading Challenge on Scanned Receipts OCR and Information Extraction
(SROIE).
This work presents an extension of the original ICDAR2019 SROIE dataset, including 14
receipt annotations missing from the original Task 3 test dataset, in a format
integrated… See the full description on the dataset page: https://huggingface.co/datasets/jsdnrs/ICDAR2019-SROIE.gsm8k-json
Dataset Card for "gsm8k-json"
More Information needed
sharegpt-quizz-generation-json-output
ShareGPT-Formatted Dataset for Quizz Generation in Structured JSON Output
Dataset Description
This dataset is formatted in the ShareGPT style and is designed for fine-tuning large language models (LLMs) to generate quizz in structured JSON outputs. It consists of multi-turn conversations where each response follows a predefined JSON schema, making it ideal for training models that need to produce structured data in natural language scenarios.
Usage
This dataset… See the full description on the dataset page: https://huggingface.co/datasets/Arun63/sharegpt-quizz-generation-json-output.SciLaD-all-json-v1
SciLaD (JSON)
SciLaD is a novel, large-scale dataset of scientific language constructed entirely using open-source frameworks and publicly available data sources. It comprises a curated English split containing over 10 million scientific publications and a multilingual, unfiltered TEI XML split including more than 35 million publications. We also publish the extensible pipeline for generating SciLaD.
Dataset Details
In this repository we share the full… See the full description on the dataset page: https://huggingface.co/datasets/scilons/SciLaD-all-json-v1.verse_datasetAll 60403 verses
Important features
Original Text, Verse Id, Page Number, Line Number
SWE-smith-js-4259-filteredvctk
VCTK
This is a processed clone of the VCTK dataset with leading and trailing silence removed using Silero VAD. A fixed 25 ms of padding has been added to both ends of each audio clip to (hopefully) imrprove training and finetuning.
The original dataset is available at: https://datashare.ed.ac.uk/handle/10283/3443.
Reproducing
This repository notably lacks a requirements.txt file. There's likely a missing dependency or two, but roughly:
pydub
tqdm
torch
torchaudio… See the full description on the dataset page: https://huggingface.co/datasets/jspaulsen/vctk.sharegpt-structured-output-json
ShareGPT-Formatted Dataset for Structured JSON Output
Dataset Description
This dataset is formatted in the ShareGPT style and is designed for fine-tuning large language models (LLMs) to generate structured JSON outputs. It consists of multi-turn conversations where each response follows a predefined JSON schema, making it ideal for training models that need to produce structured data in natural language scenarios.
Usage
This dataset can be used to train LLMs… See the full description on the dataset page: https://huggingface.co/datasets/Arun63/sharegpt-structured-output-json.J-SPAW_LA
J-SPAW (LA track, eval)
⚠️ NON-COMMERCIAL USE ONLY
The upstream J-SPAW dataset is released "For non-commercial use only"
(see the J-SPAW repository). This
packaging inherits that restriction: do not use it for any commercial
purpose. It is provided solely for non-commercial academic research and
benchmarking. The upstream terms are sparse and do not spell out
redistribution; contact the original authors for any use beyond
non-commercial research.
Benchmark-ready… See the full description on the dataset page: https://huggingface.co/datasets/SpeechAntiSpoofingBenchmarks/J-SPAW_LA.ja_asr.jsut_basic5000SWE-smith-jsswe-gen-jsesd
Emotional Speech Dataset (ESD)
The Emotional Speech Dataset (ESD) is a multilingual emotional speech corpus containing parallel recordings in English and Chinese across 5 emotions.
Dataset Details
Total samples: 35,000
Speakers: 20 (10 Chinese, 10 English)
Emotions: anger, happiness, neutral, sadness, surprise (7,000 each)
Languages: Chinese (zh), English (en) - 17,500 each
Gender: 10 male, 10 female speakers
Dataset Structure
Column
Description… See the full description on the dataset page: https://huggingface.co/datasets/jspaulsen/esd.swe_smith_js_5902_filteredMoltbook
Moltbook AI Agent Social Media Corpus
Maintainer: Shichao Jia, Stevens Institute of Technology
Scraper source: https://github.com/jscmp4/moltbookscraper (open source)
Coverage: continuous daily collection since the platform's launch day.
This is the only Moltbook corpus spanning the platform's full history --
the February--July 2026 window cannot be collected retroactively by anyone.
Dataset Summary
A large-scale corpus of public posts, comments, and agent… See the full description on the dataset page: https://huggingface.co/datasets/jscmp4/Moltbook.json-mode-evalpreprocessed_jsut_jsss_css10_common_voice_11
Dataset Card for "preprocessed_jsut_jsss_css10_common_voice_11"
More Information needed
Sheetpedia_json_1005MUSAN-speech_unit_part2
Dataset Card for "MUSAN-speech_unit_part2"
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MUSAN-speech_unit_part1
Dataset Card for "MUSAN-speech_unit_part1"
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id2223_whisper_swedish_augmented
Dataset Card for "id2223_whisper_swedish_augmented"
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json-mode-eval-extended
JSON-Mode-eval extended
This is a dataset that measures LLM capabilities at extracting data from natural language following a JSON Schema.
It was generated by manually cleaning and normalizing json-mode-eval by Nous-Research, which resulted in json-mode-eval-cleaned, ensuring that every schema enforces non-empty constraints and allow no additional keys on the top level.
We then prompt Gemini 2.5 Pro for additional 10 samples per schema, filtering for outputs that are valid according… See the full description on the dataset page: https://huggingface.co/datasets/eth-sri/json-mode-eval-extended.MUSAN-noise_unit_part2
Dataset Card for "MUSAN-noise_unit_part2"
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MUSAN-music_unit_part1
Dataset Card for "MUSAN-music_unit_part1"
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humanpose_denseposeemilia-yodas-en-mimi