datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
tests-raw-jsonlraw_jsonlwsinfer-model-zoo-jsonThis is the registry of models in the WSInfer Model Zoo.
See https://wsinfer.readthedocs.io/en/latest/ and https://github.com/SBU-BMI/wsinfer-zoo for more information.
ocaml-opam-ppxlib-json-astversion https://git-lfs.github.com/spec/v1
oid sha256:b797e216eb8d6720d794ca5d07a01019cac2d79df456fbcc69b6407497cc267c
size 473
format-jsonlformat-jsonner-jsonlwdc-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.Caselaw_Access_Project_JSON
The Caselaw Access Project
In collaboration with Ravel Law, Harvard Law Library digitized over 40 million U.S. court decisions consisting of 6.7 million cases from the last 360 years into a dataset that is widely accessible to use. Access a bulk download of the data through the Caselaw Access Project API (CAPAPI): https://case.law/caselaw/
Find more information about accessing state and federal written court decisions of common law through the bulk data service documentation here:… See the full description on the dataset page: https://huggingface.co/datasets/endomorphosis/Caselaw_Access_Project_JSON.doc-formats-jsonl-1
[doc] formats - jsonl - 1
This dataset contains one jsonl file at the root.
temporalHlsRawDataStorageLLVIP
LLVIP 数据集
[中文] [English]
这里存储了[LLVIP 数据集]的备份 和 其 [COCO标注格式的标注]
下载
数据集：https://huggingface.co/datasets/UserNae3/LLVIP/blob/main/LLVIP.zip
COCO格式标注：https://huggingface.co/datasets/UserNae3/LLVIP/blob/main/coco_annotations.7z
版权
版权链接: https://github.com/bupt-ai-cz/LLVIP?tab=readme-ov-file#license
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.stackexchange_titlebody_best_and_down_voted_answer_jsonlThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.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.forums_pol_json_zstsharegpt-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.stackexchange_title_best_voted_answer_jsonlThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.desktop-accessibility-screenshot-json-dumpshermes-function-calling-v1-jsonl
Hermes Function-Calling V1
This dataset is the compilation of structured output and function calling data used in the Hermes 2 Pro series of models.
This repository contains a structured output dataset with function-calling conversations, json-mode, agentic json-mode and structured extraction samples, designed to train LLM models in performing function calls and returning structured output based on natural language instructions. The dataset features various conversational scenarios… See the full description on the dataset page: https://huggingface.co/datasets/minpeter/hermes-function-calling-v1-jsonl.json-mode-evalconceptual_captions_jsonstackexchange_title_body_jsonljsonl.gz format from https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_xml
Each line contains a dict in the format: {"text": ["title", "body"], "tags": ["tag1", "tag2"]}
The following parameters have been used for filtering: min_title_len = 20 min_body_len = 20 max_body_len = 4096 min_score = 0
If a stackexchange contained less than 10k questions (after filtering), it is written to the small_stackexchanges.jsonl.gz file.
This is a dump of the files from… See the full description on the dataset page: https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl.cs-net-jsonstackexchange_titlebody_best_voted_answer_jsonlThis new dataset is designed to solve this great NLP task and is crafted with a lot of care.Sheetpedia_json_1005step35-en2pl-conv-pass4-jsonlconversations: 1,251,034
chat-template tokens (role+content, incl. special tokens): 2,664,206,408
reasoning_content tokens (not covered by chat template, counted separately): 6,662,763,429
avg tokens/conversation: 2129.6
used tokenizer: APT4
shitjournal-backup