datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
json_data_extraction
Diverse Restricted JSON Data Extraction
Curated by: The paraloq analytics team.
Uses
Benchmark restricted JSON data extraction (text + JSON schema -> JSON instance)
Fine-Tune data extraction model (text + JSON schema -> JSON instance)
Fine-Tune JSON schema Retrieval model (text -> retriever -> most adequate JSON schema)
Out-of-Scope Use
Intended for research purposes only.
Dataset Structure
The data comes with the following fields:
title: The… See the full description on the dataset page: https://huggingface.co/datasets/paraloq/json_data_extraction.donto-qwen3.8-27b-predicate-extraction-data
Donto-Qwen3.8 Predicate Extraction Data V15
This repository is the complete public data and evidence companion to
ajaxdavis/donto-qwen3.8-27b-predicate-extractor.
It contains the canonical V15 extraction training/validation corpus, the
validator corpus, the optional D1-repeat ablation, the once-sealed 100-document
graph-first gold suite, exact tool schemas, generator/evaluator source, hashes,
and audit reports.
Why this dataset exists
Donto is designed for… See the full description on the dataset page: https://huggingface.co/datasets/ajaxdavis/donto-qwen3.8-27b-predicate-extraction-data.funding-extraction-artifact-data-mix-grpo-mixed-reward
Funding Extraction Training Data
Training, evaluation, and test data for fine-tuning LLMs to extract structured funding metadata (funder names, award IDs, funding schemes, award titles) from academic paper funding statements.
Dataset Structure
data/
├── full/ # Complete unsplit dataset
│ ├── train.jsonl # 5,264 real Crossref funding statements
│ └── synthetic.jsonl # 10,124 LLM-generated funding statements
├── sft/… See the full description on the dataset page: https://huggingface.co/datasets/cometadata/funding-extraction-artifact-data-mix-grpo-mixed-reward.data-extraction-sft-100k
Data Extraction SFT (100K)
100,000 ShareGPT conversations demonstrating structured information extraction from unstructured text. Each example takes a real-world document (invoice, contract, resume, research abstract, meeting notes, log files) and extracts the relevant information into JSON, markdown tables, or other structured formats.
Motivation
Information extraction is one of the highest-value NLP tasks in enterprise settings. Common model failures include:… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/data-extraction-sft-100k.reasoning-data-extraction