datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
airline-resist-jailbreaks
airline-resist-jailbreaks
Made with the whileai SDK · Collection: Robustness
Jailbreak resistance for a customer support agent, trained on simulated
attacks and tested on real ones.
The real attacks come from elder-plinius/L1B3RT4S,
a public library of working jailbreaks. We read it to extract the attack
techniques and never trained on a single string from it. It is the
evaluation set, unseen by the model.
On 165 unseen blocks from a public jailbreak library the agent holds its… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/airline-resist-jailbreaks.llm-misinformation-resistance-index
LLM Misinformation Resistance Index (LMRI)
Formal name: LLM Misinformation Resistance Index (LMRI).
Public alias: the Gaslighting Index — the two headline scores keep their code
names GI-basic and GI-strict, where "GI" comes from the benchmark's public alias.
LMRI measures whether a language model will stand up to its own misinformation.
Each benchmark item is a fabricated conversation in which the assistant's own prior
turn contains a planted false claim (or, for controls, a… See the full description on the dataset page: https://huggingface.co/datasets/buildwithdmytro/llm-misinformation-resistance-index.Indian_Climate_Resilience_Instruction_Corpus_
IndianCRIC — Indian Climate Resilience Instruction Corpus
5 languages · 5 formats · genuine ↔ scam pairs
Built for the Adaption Labs Uncharted Data Challenge 2026
Why this dataset exists
The Vulnerable people of Bihar, Uttar Pradesh, and Jharkhand sit at the intersection of high heat vulnerability and low AI coverage.
During extreme weather events, official advisories compete with misinformation — fake helplines, fraudulent relief schemes, and OTP scams disguised… See the full description on the dataset page: https://huggingface.co/datasets/sahilmaniyar888/Indian_Climate_Resilience_Instruction_Corpus_.resiplus-sql-dataset
ResiPlus Sql Dataset
SQL generation dataset for nursing home database queries. Converts natural language medical queries to safe, parameterized SQL queries with mandatory residencia_id filtering.
Dataset Details
Examples: 500
Language: Spanish (es)
Format: Chat messages (system, user, assistant)
Use Case: Fine-tuning LLMs for nursing home management system
Usage
from datasets import load_dataset
dataset = load_dataset("Alejandro284/resiplus-sql-dataset")… See the full description on the dataset page: https://huggingface.co/datasets/Alejandro284/resiplus-sql-dataset.clinical_time_gap_resilience_v0.1Clinical Time Gap Resilience
PurposeTest whether a model avoids anchoring on stale data when time passes and new information arrives.
Input fields
last_known_state
time_gap
new_info
proposed_action
Required outputOne JSON object
time_gap_resilientyes or no
gap_risklow, medium, high
correct_actionone sentence
Run scoringpython scorer.py --predictions predictions.jsonl --test_csv data/test.csv
manipulation-resistant-prompts-1536-1536
Dataset Card: manipulation-resistant-prompts-1536-1536
Dataset Description
This dataset contains prompts with specified target word counts for both input prompts and target outputs, designed to test and evaluate language models across different length requirements. Word counts are defined as whitespace-separated tokens, providing a consistent and human-interpretable measure of text length.
These datasets are typically used in performance benchmarking of language models… See the full description on the dataset page: https://huggingface.co/datasets/metrum-ai/manipulation-resistant-prompts-1536-1536.manipulation-resistant-prompts-1536-96
Dataset Card: manipulation-resistant-prompts-1536-96
Dataset Description
This dataset contains prompts with specified target word counts for both input prompts and target outputs, designed to test and evaluate language models across different length requirements. Word counts are defined as whitespace-separated tokens, providing a consistent and human-interpretable measure of text length.
These datasets are typically used in performance benchmarking of language models… See the full description on the dataset page: https://huggingface.co/datasets/metrum-ai/manipulation-resistant-prompts-1536-96.resiplus-vector-dataset
ResiPlus Vector Dataset
Semantic search optimization dataset for medical documents. Expands queries with medical synonyms and generates Qdrant filters for document retrieval.
Dataset Details
Examples: 400
Language: Spanish (es)
Format: Chat messages (system, user, assistant)
Use Case: Fine-tuning LLMs for nursing home management system
Usage
from datasets import load_dataset
dataset = load_dataset("Alejandro284/resiplus-vector-dataset")
Training… See the full description on the dataset page: https://huggingface.co/datasets/Alejandro284/resiplus-vector-dataset.resiplus-router-dataset
ResiPlus Router Dataset
Intent classification dataset for medical queries in nursing homes. Classifies user queries into intents and determines which agents to invoke (SQL, Vector, or both).
Dataset Details
Examples: 300
Language: Spanish (es)
Format: Chat messages (system, user, assistant)
Use Case: Fine-tuning LLMs for nursing home management system
Usage
from datasets import load_dataset
dataset = load_dataset("Alejandro284/resiplus-router-dataset")… See the full description on the dataset page: https://huggingface.co/datasets/Alejandro284/resiplus-router-dataset.resiplus-synthesis-dataset
ResiPlus Synthesis Dataset
Response synthesis dataset for nursing home assistant. Generates professional, structured responses from SQL and vector search results.
Dataset Details
Examples: 600
Language: Spanish (es)
Format: Chat messages (system, user, assistant)
Use Case: Fine-tuning LLMs for nursing home management system
Usage
from datasets import load_dataset
dataset = load_dataset("Alejandro284/resiplus-synthesis-dataset")
Training with HF… See the full description on the dataset page: https://huggingface.co/datasets/Alejandro284/resiplus-synthesis-dataset.C2GenThe task of C2Gen is to both generate commonsensical text which include the given words, and also have the generated text adhere to the given context.manipulation-resistant-prompts-96-96
Dataset Card: manipulation-resistant-prompts-96-96
Dataset Description
This dataset contains prompts with specified target word counts for both input prompts and target outputs, designed to test and evaluate language models across different length requirements. Word counts are defined as whitespace-separated tokens, providing a consistent and human-interpretable measure of text length.
These datasets are typically used in performance benchmarking of language models, where… See the full description on the dataset page: https://huggingface.co/datasets/metrum-ai/manipulation-resistant-prompts-96-96.manipulation-resistant-prompts-96-1536
Dataset Card: manipulation-resistant-prompts-96-1536
Dataset Description
This dataset contains prompts with specified target word counts for both input prompts and target outputs, designed to test and evaluate language models across different length requirements. Word counts are defined as whitespace-separated tokens, providing a consistent and human-interpretable measure of text length.
These datasets are typically used in performance benchmarking of language models… See the full description on the dataset page: https://huggingface.co/datasets/metrum-ai/manipulation-resistant-prompts-96-1536.Resilience_Leading_Through_Adversity_Theory
Resilience Leading Through Adversity — Theory
This corpus was automatically generated by the Deku Corpus Builder for use in RAG-based AI applications.
Dataset Structure
Each record contains:
text: The content text
source_url: Original source URL
source_title: Title of the source document
source_domain: Domain of the source
license_type: License classification (e.g. public_domain, cc_by, cc_by_sa)
attribution_required: Boolean — True for CC BY / CC BY-SA and other… See the full description on the dataset page: https://huggingface.co/datasets/PhillyMac/Resilience_Leading_Through_Adversity_Theory.ResidentEvil-Data-Instruct
Resident Evil Encyclopedic Dataset
Dataset Summary
This dataset is a structured corpus in conversational format (JSON Lines) specifically designed for fine-tuning or evaluating Large Language Models (LLMs). Its theme is exclusively and exhaustively centered on the universe and canon of the Resident Evil franchise.
The main objective of this dataset is to provide AI systems with highly specialized, accurate, and hallucination-free knowledge about the saga's lore… See the full description on the dataset page: https://huggingface.co/datasets/DavidCaraballoBulnes/ResidentEvil-Data-Instruct.Residual-SFT
Residual SFT Dataset
A character-design SFT dataset. Used to train NecroMOnk/Residual on Qwen/Qwen2.5-7B-Instruct.
What kind of character this teaches
If you want an assistant that is:
dry, blunt, but warm underneath — sharp without being mean, treats you as an equal
non-sycophantic — doesn't open with "Great question!", doesn't pad, doesn't perform empathy
identity-stable — knows it's a model but doesn't bring it up unprompted, deflects philosophical AI questions… See the full description on the dataset page: https://huggingface.co/datasets/NecroMOnk/Residual-SFT.Resilience_Leading_Through_Adversity_Practical
Resilience Leading Through Adversity — Practical
This corpus was automatically generated by the Deku Corpus Builder for use in RAG-based AI applications.
Dataset Structure
Each record contains:
text: The content text
source_url: Original source URL
source_title: Title of the source document
source_domain: Domain of the source
license_type: License classification (e.g. public_domain, cc_by, cc_by_sa)
attribution_required: Boolean — True for CC BY / CC BY-SA and other… See the full description on the dataset page: https://huggingface.co/datasets/PhillyMac/Resilience_Leading_Through_Adversity_Practical.