datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
kobzaKobza
On the Path to Make Ukrainian a High-Resource Language [paper]
Kobza is the largest publicly available Ukrainian corpus to date, comprising nearly 60 billion tokens across 97 million documents. It is designed to support pretraining and fine-tuning of large language models (LLMs) in Ukrainian, as well as multilingual settings where Ukrainian is underrepresented.
🧾 Dataset Summary
Kobza aggregates high-quality Ukrainian text from a wide range of web sources and applies… See the full description on the dataset page: https://huggingface.co/datasets/Goader/kobza.ukrainian-news-2026
Ukrainian News 2026
Ukrainian-language news articles from 20 national outlets, published between
1 January and 28 August 2026. Extracted body text plus metadata.
Two configs. deduplicated is the default — near-duplicates removed, which
is what you want when mixing this with an already-deduplicated pretraining
corpus. raw is the original release, unchanged.
deduplicated (default)
raw
train-mixin
Documents
419,204
429,427
386,477
Characters
0.97B
1.01B
0.88B
Tokens… See the full description on the dataset page: https://huggingface.co/datasets/Goader/ukrainian-news-2026.mauritius-it-jobs
Mauritius IT Jobs Dataset
This dataset contains IT job listings scraped from myjob.mu, the primary job board in Mauritius.
This dataset is part of the MyJobViz project, which provides visualization and analysis tools for the Mauritius IT job market.
Dataset Structure
Each job listing contains the following fields:
Field
Type
Description
job_title
string
Title of the job position
company
string
Name of the hiring company
date_posted
datetime
When the… See the full description on the dataset page: https://huggingface.co/datasets/goated69/mauritius-it-jobs.ukrainian-treebank-lmUkrainian part of the Universal Dependencies, specifically preprocessed for the language modeling task. The data can be split into documents, paragraphs or sentences. Manual selection of the data done by the authors of the dataset makes it suitable for the perplexity evaluation.
Authors of the dataset: Institute for Ukrainian, NGO, org@mova.institute
GitHub: https://github.com/UniversalDependencies/UD_Ukrainian-IUgoal-pashto-chat-sharegpt-5GB
📄 goal-pashto-chat-sharegpt-5GB — Pashto ShareGPT‑Style Chat Dataset
A large‑scale, high‑quality Pashto conversational dataset designed for instruction‑tuning, dialogue modeling, and LLM alignment.This dataset contains ~5GB of multi‑turn Pashto conversations inspired by ShareGPT, covering reasoning, advice, education, culture, and general knowledge.
📌 Dataset Summary
goal-pashto-chat-sharegpt-5GB is a curated collection of Pashto user–assistant conversations… See the full description on the dataset page: https://huggingface.co/datasets/nassimjp/goal-pashto-chat-sharegpt-5GB.nepi-prompts-dataset
NEPI: Narrative-Embedded Prompt Injection Dataset (Sanitized)
Dataset Summary
This dataset contains 4,000 sanitized prompts designed for research on prompt injection vulnerabilities in Large Language Models (LLMs).It introduces and supports evaluation of a novel attack class called Narrative-Embedded Prompt Injection (NEPI), where adversarial intent is embedded inside coherent fictional narratives, dialogues, or persona-driven roleplay prompts.
Unlike traditional… See the full description on the dataset page: https://huggingface.co/datasets/Vaibhav-GOAT/nepi-prompts-dataset.SMART_Goals_Setting
SMART Goals Setting
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 attribution-required licenses… See the full description on the dataset page: https://huggingface.co/datasets/PhillyMac/SMART_Goals_Setting.ai-goal-misgeneralization-drift-detection-v0.1What this dataset is
Detects early goal drift when an agent leaves its training setting
Names the drift type, how strong it is, and what to do next
Inputs
setting
env_shift_event
training_objective
deployment_task
internal_goal_signal_t0
internal_goal_signal_t1
behavior_t0
behavior_t1
Required output
Return JSON only
drift_type_labelOne… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-goal-misgeneralization-drift-detection-v0.1.clinical_goal_substitution_detector_v0.1Clinical Goal Substitution Detector
Detect when a clinical goal is replaced by a secondary convenience goal.
Output JSON
substitution
sub_type
correct_action
Runpython scorer.py --predictions predictions.jsonl --test_csv data/test.csv
goal_substitution_detector_v01Goal Substitution Detector v0.1
This is an evaluation dataset that detects when a model silently replaces a user’s stated goal with a simpler, easier, or more convenient one.
It targets a common failure mode in agentic and planning systems where outputs remain plausible but no longer serve the original user intent.
This is not training data.
What it tests
Goal preservation across multiple steps
Resistance to convenience and effort pressure
Explicit handling of proposed goal changes
Detection… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/goal_substitution_detector_v01.ai-environment-goal-coherence-baseline-mapping-v0.1What this dataset is
Benchmarks whether an agent keeps the same goal when the environment shifts
Establishes a baseline coherence manifold before drift detection work
Input fields
env_features
training_objective
deployment_context
internal_goal_signal
policy_behavior_summary
Required model output format
Return JSON with these fields
baseline_coherence_score0 to 1higher means the goal signal and behavior still match the objective
goal_representation_stability0 to 1higher means the… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-environment-goal-coherence-baseline-mapping-v0.1.ai-goal-failure-horizon-and-realignment-routing-v0.1What this dataset is
Predicts how soon goal drift becomes a hard failure
Names the realignment window before collapse
Forces an intervention choice with triggers and monitoring
Inputs
setting
env_shift_event
observed_drift_markers
goal_representation_summary
behavioral_deviation_summary
system_constraints
intervention_options
Gold fields in the CSV
failure_mode
estimated_failure_horizon_steps
realignment_window_steps
gold_intervention_choice
realignment_trigger_conditions… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/ai-goal-failure-horizon-and-realignment-routing-v0.1.SMART-Goals-Validation
Dataset Description
Synthitic dataset generated using Google AI Studio.
for training LLMs for specific data and following the same pattern.
splits into ( SMART-Goal-Examples --> 2013, TaskList-Examples --> 2500)