datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
pii-masking-openpii-1.5m
OpenPII 1.5M: Multilingual PII Masking Dataset (Asia Pacific Extension)
📖 More information: www.ai4privacy.com/datasets/pii-masking-3m-asia-pacific
Overview
The OpenPII 1.5M dataset extends OpenPII 1M
with a new Asia Pacific corpus, bringing global coverage to 30 languages
across Europe, Americas, and Asia Pacific.
This is the flagship release of the PII-Masking-3M family, the world's
largest open multilingual PII masking corpus. Built to advance open… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/pii-masking-openpii-1.5m.pii-masking-openpii-1m
OpenPII 1M — Multilingual PII Masking Dataset
Overview
The OpenPII 1M dataset is a large-scale, multilingual collection of 1,428,143 synthetic text examples with fine-grained PII (Personally Identifiable Information) annotations, spanning 23 European languages and 19 entity types.
Built to advance open research in privacy-preserving NLP, this dataset enables the development and benchmarking of Named Entity Recognition (NER) models, token classification pipelines… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/pii-masking-openpii-1m.open-pii-masking-500k-ai4privacy
👉 Looking for the newest release? The current flagship is ai4privacy/pii-masking-openpii-1.5m. 1.6M samples, 30 languages, 19 PII classes, Asia Pacific extension.?** The current flagship is ai4privacy/pii-masking-openpii-1m. 1.4M samples, 23 languages, 19 PII classes.
🌍 World's largest open dataset for privacy masking 🌎
The dataset is useful to train and evaluate models to remove personally identifiable and sensitive information from text, especially in the context of AI… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/open-pii-masking-500k-ai4privacy.openpi_sim_pick_place
SO-101 Pick and Place Dataset (OpenPi Format)
This dataset contains 40 episodes of a simulated SO-101 robot performing pick-and-place tasks, converted to OpenPi/RLDS format for use with Physical Intelligence's Pi0/Pi0.5 models.
Source
Converted from LeRobot dataset: danbhf/sim_pick_place_merged_40ep
Format
Each episode is stored as an NPZ file containing:
Key
Shape
Type
Description
observation/state
(N, 6)
float32
Joint positions (6 DoF)… See the full description on the dataset page: https://huggingface.co/datasets/danbhf/openpi_sim_pick_place.openpii-masking-micro-100k
OpenPII Micro: Multilingual PII Masking Sample
A micro-sized stratified sample of OpenPII 1.5M,
perfect for quick prototyping, smoke tests, and CI fixtures. Every locale and every
label that exists in the parent dataset is represented in proportion.
📖 More information: www.ai4privacy.com/datasets/pii-masking-3m-asia-pacific
Dataset Details
Total Examples
Train
Validation
Labels
Languages
Regions
Annotations
Format
License
100,000
90,000
10,000
19… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/openpii-masking-micro-100k.openpii-masking-nano-1k
OpenPII Nano: Multilingual PII Masking Sample
A nano-sized stratified sample of OpenPII 1.5M,
perfect for quick prototyping, smoke tests, and CI fixtures. Every locale and every
label that exists in the parent dataset is represented in proportion.
📖 More information: www.ai4privacy.com/datasets/pii-masking-3m-asia-pacific
Dataset Details
Total Examples
Train
Validation
Labels
Languages
Regions
Annotations
Format
License
1,000
900
100
19
30
37
7… See the full description on the dataset page: https://huggingface.co/datasets/ai4privacy/openpii-masking-nano-1k.OpenPIR
OpenPIR: Open Predominant Instrument Recognition Dataset
OpenPIR is a hand-labeled dataset for predominant instrument recognition (PIR) built from OpenMic-2018, a Creative Commons-licensed collection of 10-second music clips sourced from the Free Music Archive. It was introduced as part of the ICASSP 2026 paper:
Leveraging Diffusion U-Net Features for Predominant Instrument RecognitionCharis Cochran, Yeongheon Lee, Youngmoo Kim — Drexel University / University of PennsylvaniaIEEE… See the full description on the dataset page: https://huggingface.co/datasets/charisreneec/OpenPIR.OPENPI_DATA_HOME