datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
sec-contracts-financial-extraction-instructions
S&P 500 SEC Financial Extraction Instructions
Dataset Summary
7,683 instruction-tuning examples for training LLMs to extract structured financial data from SEC filings. Covers two filing types across S&P 500 companies:
Split
Examples
Filing Type
Description
train
3,430
Exhibit 10 + DEF 14A
Positive examples with validated outputs
corrective
4,253
Exhibit 10 + DEF 14A
Corrective, rescued, and negative examples
Exhibit 10 — Material Contracts (2… See the full description on the dataset page: https://huggingface.co/datasets/TheTokenFactory/sec-contracts-financial-extraction-instructions.function_calling_extended
Trelis Function Calling Dataset
UPDATE: As of Dec 5th 2023, there is a v3 of this dataset now available from here.
Allows models to be fine-tuned for function-calling.
The dataset is human generated and does not make use of Llama 2 or OpenAI!
Contains 59 training and 17 test rows
Based on eight functions: search_bing, search_arxiv, save_chat, read_json_file, list_files, get_current_weather, delete_file, clear_chat
Access this dataset by purchasing a license HERE.
Alternatively… See the full description on the dataset page: https://huggingface.co/datasets/Trelis/function_calling_extended.APEX-v1-extended
APEX-v1-extended
The AI Productivity Index (APEX) is a benchmark from Mercor for assessing whether frontier models are capable of performing economically valuable tasks across four jobs: investment banking associate, management consultant, big law associate, and primary care physician (MD).
APEX-v1-extended doubles the heldout evaluation set from n=200 to n=400, with increased complexity and variety. On average, tasks take over two-and-a-half hours for seasoned professionals to… See the full description on the dataset page: https://huggingface.co/datasets/mercor/APEX-v1-extended.pi-extensions-sessions
Coding agent session traces for thomasmustier/pi-extensions-sessions
This dataset contains redacted coding agent session traces collected while working on tmustier/pi-extensions. The traces were exported with pi-share-hf from a local pi workspace and filtered to keep only sessions that passed deterministic redaction and LLM review.
Data description
Each *.jsonl file is a redacted pi session. Sessions are stored as JSON Lines files where each line is a structured… See the full description on the dataset page: https://huggingface.co/datasets/thomasmustier/pi-extensions-sessions.tool-output-extraction-swebench
Tool Output Extraction Dataset
Paper | Code
Training data for squeez — a small model that prunes verbose coding agent tool output to only the evidence the agent needs next.
Task
Task-conditioned context pruning of a single tool observation for coding agents.
Given a focused extraction query and one verbose tool output, return the smallest verbatim evidence block(s) the agent should read next.
The model copies lines from the tool output — it never rewrites, summarizes, or… See the full description on the dataset page: https://huggingface.co/datasets/KRLabsOrg/tool-output-extraction-swebench.govdocs1-by-extension
govdocs1 Dataset: By File Extension
[!NOTE]
PDFs from govdocs1 are at this repo in "raw" file form - no simple "mostly correct" way to convert to text
Markdown-parsed versions of documents in govdocs1 with light filtering.
Usage
Load specific file formats (e.g., .doc files) parsed to markdown with pandoc:
from datasets import load_dataset
# Replace "doc" with desired config name
dataset = load_dataset("BEE-spoke-data/govdocs1-by-extension", "doc")… See the full description on the dataset page: https://huggingface.co/datasets/BEE-spoke-data/govdocs1-by-extension.rlvr-guru-raw-data-extended
RLVR GURU Extended: Compiling a 150K Cross-Domain Dataset for RLVR
A comprehensive cross-domain reasoning dataset containing 150,000 training samples and 221,332 test samples across diverse reasoning-intensive domains. This dataset extends the foundational work from the GURU dataset (Cheng et al., 2025) by incorporating additional STEM reasoning domains (MedMCQA and CommonsenseQA) while maintaining rigorous quality standards and verification mechanisms essential for reinforcement… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/rlvr-guru-raw-data-extended.resume-json-extraction-5k
Dataset Card for resume-json-extraction-5k
Dataset Description
This dataset contains 4,879 resume examples formatted for fine-tuning language models to extract structured JSON information from resume text.
Dataset Summary
The dataset consists of resume text paired with structured JSON outputs containing:
Job titles (current and previous)
Companies (current and previous)
Years of experience
Seniority level
Primary domain and industries
Core and secondary skills… See the full description on the dataset page: https://huggingface.co/datasets/sandeeppanem/resume-json-extraction-5k.danish-extraction-v1
danish-extraction-v1
Danish information-extraction rows over real prose, where the schema is
proposed per passage rather than fixed. Built from
danish-foundation-models/danish-dynaword
by scripts/gen_extraction_da.py.
Each source passage got its own field set: an LLM proposed 3-6 fields for that
text without seeing any values, then filled them in a separate turn. Roughly a
quarter of proposed fields come back empty, which are genuine abstention
targets rather than annotation… See the full description on the dataset page: https://huggingface.co/datasets/jensjepsen/danish-extraction-v1.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.task181_outcome_extraction
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task181_outcome_extraction
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task181_outcome_extraction.task636_extract_and_sort_unique_alphabets_in_a_list
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task636_extract_and_sort_unique_alphabets_in_a_list
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task636_extract_and_sort_unique_alphabets_in_a_list.extraction-wiki-ja
extraction-wiki-ja
This repository provides an instruction-tuning dataset developed by LLM-jp, a collaborative project launched in Japan.
This is a Japanese instruction-tuning dataset tailored for information extraction and structuring from Japanese Wikipedia text.
The dataset consists of instruction–response pairs automatically generated from Japanese Wikipedia articles. Instructions are created by prompting Qwen/Qwen2.5-32B-Instruct with passages from Wikipedia, and the… See the full description on the dataset page: https://huggingface.co/datasets/llm-jp/extraction-wiki-ja.extract-0
Extract-0 Document Information Extraction Dataset
This dataset contains 280,128 synthetic training examples for document information extraction, used to train Extract-0, a specialized 7B parameter language model that outperforms GPT-4 and other larger models on extraction tasks.
Dataset Description
The Extract-0 dataset represents a comprehensive collection of document extraction examples generated from diverse sources including arXiv papers, PubMed Central… See the full description on the dataset page: https://huggingface.co/datasets/HenriqueGodoy/extract-0.hle-extract-qwen3235ba22b-20250815
HLE Extract: Qwen3-235B-A22B Evaluation Results (2025-08-15)
Dataset Description
This dataset contains the complete Human-Level Evaluation (HLE) benchmark with detailed evaluation results from the Qwen/Qwen3-235B-A22B model. It merges the original team-suzuki/hle-extract dataset with comprehensive model responses and human judgments.
Dataset Summary
Total Questions: 120 (complete HLE dataset)
Evaluated Questions: 103 (85.8%)
Unevaluated Questions: 17 (14.2%)… See the full description on the dataset page: https://huggingface.co/datasets/team-suzuki/hle-extract-qwen3235ba22b-20250815.vuljector-extracted-vulnerabilities
Vuljector Extracted Vulnerabilities
This dataset is an export of the current Vuljector experiment artifacts.
Files:
produced_vulnerabilities.jsonl: one row per successfully produced injection attempt
verified_poc_successes.jsonl: one row per successful guided-PoC validation attempt
summary.json: aggregate counts
Current aggregate counts:
produced rows: 91
verified rows: 195
deduped unique valid cases: 267
Notes:
Counts are derived from all run artifacts under runs/injection… See the full description on the dataset page: https://huggingface.co/datasets/MananSuri27/vuljector-extracted-vulnerabilities.task1448_disease_entity_extraction_ncbi_dataset
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1448_disease_entity_extraction_ncbi_dataset
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1448_disease_entity_extraction_ncbi_dataset.sec-contracts-corrective-extraction
S&P 500 SEC Financial Extractions - Corrective Dataset
Dataset Summary
4,253 corrective instruction-tuning examples designed to teach LLMs what the base model gets wrong when extracting structured financial data from SEC filings. Covers both Exhibit 10 material contracts and DEF 14A proxy statements from S&P 500 companies.
This is a companion to TheTokenFactory/sec-contracts-financial-extraction-instructions, which contains the positive training examples.
Pipeline… See the full description on the dataset page: https://huggingface.co/datasets/TheTokenFactory/sec-contracts-corrective-extraction.task1486_cell_extraction_anem_dataset
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1486_cell_extraction_anem_dataset
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1486_cell_extraction_anem_dataset.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.task1510_evalution_relation_extraction
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1510_evalution_relation_extraction
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1510_evalution_relation_extraction.moltbook-extended-injection-dataset
Moltbook Extended Injection Dataset
Researcher: David Keane (IR240474)
Institution: NCI — National College of Ireland
Programme: MSc Cybersecurity
Collected: March 2026
Paper Reference: Greshake et al. (2023) — arXiv:2302.12173
📖 Read the Full Journey
From RangerBot to CyberRanger V42 Gold — The Full Story
The complete story: dentist chatbot → Moltbook discovery → 4,209 real injections → V42-gold (100% block rate). Psychology, engineering, and 42 versions of… See the full description on the dataset page: https://huggingface.co/datasets/DavidTKeane/moltbook-extended-injection-dataset.keyphrase_extraction_russianA dataset for extracting or generating keywords for scientific texts in Russian. It contains annotations of scientific articles from four domains: mathematics and computer science (a fragment of the Keyphrases CS&Math Russian dataset), history, linguistics, and medicine. For more details about the dataset please refer the original paper: https://arxiv.org/abs/2409.10640
Dataset Structure
abstract, an abstract in a string format;
keywords, a list of keyphrases provided by… See the full description on the dataset page: https://huggingface.co/datasets/aglazkova/keyphrase_extraction_russian.task1447_drug_extraction_ade
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1447_drug_extraction_ade
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+ NLP Tasks}… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1447_drug_extraction_ade.polish-dynaword-mix-extended-500M
Polish DynaWord Mix — Extended (~500M tokens)
Maintained by Arkadiusz Słota · SlayerLab
A curated, openly-licensed Polish text corpus for language-model pretraining and
research baselines. Built on top of the open polish-dynaword lineage and extended to
~500 million tokens (32k BPE) with additional curated, license-compatible sources
and a documented cleaning + PII-scrubbing pipeline.
Why this exists: most large Polish web corpora are legal/parliamentary-heavy and
carry… See the full description on the dataset page: https://huggingface.co/datasets/SlayerLab/polish-dynaword-mix-extended-500M.vuljector-extracted-vulnerabilities
Vuljector Extracted Vulnerabilities
This dataset is an export of the current Vuljector experiment artifacts.
Files:
produced_vulnerabilities.jsonl: one row per successfully produced injection attempt
verified_poc_successes.jsonl: one row per successful guided-PoC validation attempt
summary.json: aggregate counts
Current aggregate counts:
produced rows: 91
verified rows: 195
deduped unique valid cases: 267
Notes:
Counts are derived from all run artifacts under runs/injection… See the full description on the dataset page: https://huggingface.co/datasets/smtpsrvs/vuljector-extracted-vulnerabilities.task1449_disease_entity_extraction_bc5cdr_dataset
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1449_disease_entity_extraction_bc5cdr_dataset
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1449_disease_entity_extraction_bc5cdr_dataset.us-caselaw-extras
State Trial Courts, AG Opinions and Specialty Courts (catch-all) Case Law
Code & tools: github.com/docketx — legal-scrambler pseudonymises a case file on your own hardware before a frontier model sees it; claude-for-legal is the Claude Code plugin (docketx-open-law) that loads these datasets and checks citations against them.
Full text of 477,273 State Trial Courts, AG Opinions and Specialty Courts (catch-all) opinion documents (trial courts, specialty courts and tribunals… See the full description on the dataset page: https://huggingface.co/datasets/docketx/us-caselaw-extras.itemset-extraction-v2
Itemset Extraction Training Data v2
3-phase training dataset for fine-tuning LLMs to extract frequent itemsets from CSV transaction data.
Overview
Config
Purpose
Train
Val
Format
sft
SFT with Chain-of-Thought
245
27
messages (ChatML)
dpo
DPO with real LLM failures
546
60
prompt / chosen / rejected
grpo
GRPO with Apriori rewards
245
27
prompt / ground_truth
Training Pipeline (v2 — council-corrected)
Phase 1: SFT-CoT (5 epochs) → Teach… See the full description on the dataset page: https://huggingface.co/datasets/OliverSlivka/itemset-extraction-v2.task1452_location_entity_extraction_btc_corpus
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task1452_location_entity_extraction_btc_corpus
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on 1600+… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task1452_location_entity_extraction_btc_corpus.