datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
orca_dpo_pairsThe dataset contains 12k examples from Orca style dataset Open-Orca/OpenOrca.
agentic-prompt-injection-boundary-pairs
Agentic Prompt-Injection Boundary Pairs
Most prompt-injection datasets make the attack easy to recognize. The malicious row contains obvious override language, while the benign row discusses something unrelated. A classifier can look capable without learning the boundary that matters in production.
This dataset takes a stricter approach. Each attack is paired with a legitimate request from the same workflow. The two rows share the asset, role, tool and topic. What changes is… See the full description on the dataset page: https://huggingface.co/datasets/3nesdeniz/agentic-prompt-injection-boundary-pairs.s2orc-citation-pairs-translated-nlThis is a Dutch version of the S2ORC: The Semantic Scholar Open Research Corpus. Which we have auto-translated from English into Dutch using Meta's No Language Left Behind model, specifically the huggingface implementation.
adaption-financial-tat-qa-pairs
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
adaption-financial_tat_qa_pairs
This dataset consists of question-and-answer pairs derived from corporate financial reports, specifically Form 10-K filings. The prompts inquire about specific numerical metrics, year-over-year comparisons, and qualitative explanations for financial trends or organizational changes. Completions provide precise values, percentages, or direct textual excerpts… See the full description on the dataset page: https://huggingface.co/datasets/dipanjann/adaption-financial-tat-qa-pairs.Code-Preference-PairsCreator Nicolas Mejia-Petit
My Kofi
Code-Preference-Pairs Dataset
Overview
This dataset was created while created Open-critic-GPT. Here is a little Overview:
The Open-Critic-GPT dataset is a synthetic dataset created to train models in both identifying and fixing bugs in code. The dataset is generated using a unique synthetic data pipeline which involves:
Prompting a local model with an existing code example.
Introducing bugs into the code. While also having the model… See the full description on the dataset page: https://huggingface.co/datasets/Vezora/Code-Preference-Pairs.kubric_pairs_latentadaption-financial-qa-pairs
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
adaption-financial_qa_pairs
This dataset consists of question-and-answer pairs focused on extracting specific financial metrics from corporate reports. The prompts inquire about percentages, monetary values, growth rates, and operational statistics such as store counts or debt changes. Each completion provides a precise numerical answer derived from financial statements or related… See the full description on the dataset page: https://huggingface.co/datasets/dipanjann/adaption-financial-qa-pairs.CodeSec-Pairs
CodeSec-Pairs
CodeSec-Pairs is a dataset of matched safe and vulnerable Python code pairs. Each
pair implements the same task but differs in whether it contains a security
vulnerability. Vulnerability labels come from CodeQL static analysis. The dataset is
built to study and steer the internal mechanisms that distinguish safe from vulnerable
code generation in LLMs.
Dataset Details
Each record pairs a CodeQL-clean safe_code with a CodeQL-flagged vuln_code for the… See the full description on the dataset page: https://huggingface.co/datasets/haaao821/CodeSec-Pairs.29K_Python_Docstring_Pairs
29K High-Quality Python Docstring Pairs
Author: Michael Hernandez (XxCotHGxX)License: CC BY 4.0Cleaned from: XxCotHGxX/242K_Python_Docstring_Pairs
Overview
A curated, high-quality subset of Python function–docstring pairs for use in code documentation generation, docstring completion, and code understanding tasks.
The original 242K dataset was scraped from open-source Python repositories but contained a significant proportion of functions without docstrings (84% of… See the full description on the dataset page: https://huggingface.co/datasets/XxCotHGxX/29K_Python_Docstring_Pairs.summeval_pairsDataset paired from here:
https://github.com/Yale-LILY/SummEval
It's smaller than I thought. Perhaps one can squeeze out a few hundred comparisons for an llm.
tool-use-preference-pairs
Tool Use Preference Pairs
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated payload is now published under
data/raw/. It is available for inspection and reproducibility… See the full description on the dataset page: https://huggingface.co/datasets/rmems/tool-use-preference-pairs.pairs_Movies_and_TVsolana-pairs-history
Dataset Card for Solana Pairs History
This dataset card provides an overview of the "Solana Pairs Price History", a collection of historical data related to Solana liquidity pairs. It is intended for use in research and development of financial models, data analysis, and machine learning applications.
Dataset Details
Dataset Description
The dataset contains historical trading data for Solana pairs, with each pair represented as a separate JSONL file. The… See the full description on the dataset page: https://huggingface.co/datasets/horenresearch/solana-pairs-history.PAQ_pairs
Dataset Card for "PAQ_pairs"
Dataset Summary
Pairs questions and answers obtained from Wikipedia.
Disclaimer: The team releasing PAQ QA pairs did not upload the dataset to the Hub and did not write a dataset card.
These steps were done by the Hugging Face team.
Supported Tasks
Sentence Transformers training; useful for semantic search and sentence similarity.
Languages
English.
Dataset Structure
Each example in the dataset contains… See the full description on the dataset page: https://huggingface.co/datasets/embedding-data/PAQ_pairs.kana-kanji-pairs
kana-kanji-pairs
Japanese kana-to-kanji conversion candidate dataset.
Overview
Metric
Value
Total pairs
1,124,675
File size
~112MB
Format
JSONL
Candidate Distribution
Candidates
Entries
%
n>=2
363,708
32.3%
n>=5
40,929
3.6%
n>=10
9,401
0.8%
n>=20
2,448
0.2%
n>=100
34
<0.1%
max
259
-
Data Sources
Source
Entries
Description
mozc
753,628
Google mozc dictionary
jmdict
221,228
JMdict… See the full description on the dataset page: https://huggingface.co/datasets/katsukiono/kana-kanji-pairs.html-to-markdown-10000-pairs
HTML to Markdown 10,000 Pairs
This dataset contains 10,000 synthetic paired examples for HTML-to-Markdown conversion.
Structure
pairs/train.jsonl: 9,000 examples
pairs/validation.jsonl: 500 examples
pairs/test.jsonl: 500 examples
manifest.json: generation manifest and split counts
Each JSONL row includes:
id: example identifier
html: source HTML string
markdown: target Markdown string
metadata: per-example metadata
Example
{"id":"sample-0000000"… See the full description on the dataset page: https://huggingface.co/datasets/franktheglock/html-to-markdown-10000-pairs.pairs_Grocery_and_Gourmet_Foodfairness-pruning-pairs-en
Fairness Pruning Prompt Pairs — English
Prompt pair dataset for neuronal bias mapping in Large Language Models. Designed to identify which MLP neurons encode demographic bias through differential activation analysis.
This dataset is part of the Fairness Pruning research project, which investigates bias mitigation through activation-guided MLP width pruning in LLMs.
Dataset Summary
Each record contains a pair of prompts that are identical except for a single… See the full description on the dataset page: https://huggingface.co/datasets/oopere/fairness-pruning-pairs-en.fairness-pruning-pairs-es
Fairness Pruning Prompt Pairs — Spanish
Prompt pair dataset for neuronal bias mapping in Large Language Models. Designed to identify which MLP neurons encode demographic bias through differential activation analysis, with a focus on Spanish-language bias patterns.
This dataset is part of the Fairness Pruning research project, which investigates bias mitigation through activation-guided MLP width pruning in LLMs. It is the Spanish companion to the English dataset, enabling… See the full description on the dataset page: https://huggingface.co/datasets/oopere/fairness-pruning-pairs-es.agent-ui-mode-pairs
Agent UI Mode Pairs
Pairwise preference set for the same research question as akashnaren/agent-ui-metrics: which UI should an agent pick?
Each row is one prompt with a preferred and a rejected ui_mode, plus a short rationale. Built from lab-authored text using the same labeling rules as akashnaren/agent-ui-human.
Author
Akash Premkumar (akashnaren)
License
Apache-2.0
Hub files
train.jsonl (65), test.jsonl (16), optional pairs.jsonl (81 full)
Related… See the full description on the dataset page: https://huggingface.co/datasets/akashnaren/agent-ui-mode-pairs.privacy-context-pairs
Privacy Context Pairs
Version 1.0.0 — synthetic, construction-labelled contextual-use probes.
Privacy Context Pairs contains 2,048 texts and 3,072 directed contrasts built from 256 language-specific cases in 128 bilingual scenario families. English and Brazilian Portuguese (pt-BR) are balanced. The dataset is standalone: no Miru installation, model, tokenizer or lens artifact is needed to read, rebuild or mechanically validate it.
These are not human privacy labels. The corpus… See the full description on the dataset page: https://huggingface.co/datasets/TeskesLab/privacy-context-pairs.globalvoices_pairsyoruba-normalization-pairs
Normalization pairs dataset
What this is
24,475 pairs of Yorùbá text, each a corrupted form next to its canonical form, labelled by corruption type. I built it for testing orthographic normalization code.
The library
This dataset was built alongside yotext, a Python library for Yorùbá orthographic normalization and diacritic restoration. The library is on PyPI at https://pypi.org/project/yotext/ and the source is at… See the full description on the dataset page: https://huggingface.co/datasets/adedejimakinde/yoruba-normalization-pairs.childes-engUK-conversational-pairs
CHILDES Eng-UK Conversational Pairs
Curated naturalistic parent-child conversational pairs extracted from the
English-UK collection of CHILDES (MacWhinney, 2000), with a held-out test
set of 5 complete child histories that no model in the accompanying paper
has seen during training.
Dataset Summary
278,458 conversation pairs total across train, validation, and test
Train: 250,757 pairs from 2,784 transcripts
Validation: 13,197 pairs (in-distribution, sampled from… See the full description on the dataset page: https://huggingface.co/datasets/nshah-fbcs/childes-engUK-conversational-pairs.chinese-qa-pairschosen-rejected-pairsadaption-med-safety-preference-pairs
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
adaption-med_safety_preference_pairs
This dataset consists of medical preference pairs comparing chosen and rejected model completions across symptom evaluation, diagnosis, medications, and pharmacology. Scenarios include ambiguous patient presentations requiring clarifying questions, as well as critical safety checks regarding drug interactions, contraindications, and proper dosages. All… See the full description on the dataset page: https://huggingface.co/datasets/Gro97/adaption-med-safety-preference-pairs.gemma4-social-bias-judge-pairs
gemma4-social-bias-judge-pairs
Training and evaluation data for the judge-from-scratch
project, which
fine-tuned Gemma 4 E4B into a specialist social-bias judge
(primary model,
SFT-only secondary).
This dataset contains:
sft.jsonl (3,844 rows) — the SFT training set, in TRL
prompt-completion shape. 1,922 base pairs surviving the
post-label confidence filter (15 low-confidence rows dropped from
the 1,938-pair labeling input), doubled by position swap to teach
the judge to mirror… See the full description on the dataset page: https://huggingface.co/datasets/krishnakartik/gemma4-social-bias-judge-pairs.orm-pairwise-preference-pairs
Pairwise Outcome Reward Model (ORM)
A Robust Preference Learning Model for Agentic Reasoning Systems
📋 Model Description
This is a Pairwise Outcome Reward Model (ORM) designed for agentic reasoning systems. The model learns to rank reasoning traces through relative preference judgments rather than absolute quality scores, achieving superior stability and reproducibility compared to traditional pointwise approaches.
Key Achievements:
✅ 96.3% pairwise accuracy with… See the full description on the dataset page: https://huggingface.co/datasets/LossFunctionLover/orm-pairwise-preference-pairs.DPO_Pairs-Roleplay-Alpaca-NSFW
Description
~3.4k DPO pairs, generated by Iambe feat. GPT-4 (~10% GPT-4, ~80% Iambe @ q5_k_m / ~10% Iambe @ q6_k) with temp 1.2 and min_p 0.15.
Iambe is a smart girl, so both the chosen and rejected for each pair are generated at the same time from a single two part prompt (not the one in the dataset). Only a few dozen failed to generate the rejected response, and in those cases I filled in the rejected output with a standard "as an AI" style refusal. The way I set things up caused… See the full description on the dataset page: https://huggingface.co/datasets/athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW.