datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
OceanDepths
OceanDepths GeoTIFF Raster and Aligned ARGO Dataset
This dataset package contains the model-ready Ocean variables (ARGO submarine data, sea surface height, sea surface temperature and
salinity, as well as GLORYS reanalysis information for 50 depth levels. The ARGO data has been projected onto the GLORYS grid in order
to build a ML-ready dataset. The intention is that users can create tensors easily for CV-inspired ML approaches to ocean-variable
reconstruction. While… See the full description on the dataset page: https://huggingface.co/datasets/ESA-philab/OceanDepths.mt-benchCorpus_Gap_Loga-share-l2-trades
China A-share Level 2 Trades
Canonical Level 2 trade records for China A-shares, stored as one fact table.
Coverage
Date range: 2026-04-01 to 2026-10-09
Trading days: 124
Rows: 19372155588
Parquet files: 876
Compressed local size: 154.53 GiB
Layout
data/l2_trades/
trade_date=YYYY-MM-DD/
code_prefix=00/
part-00000.parquet
code_prefix is ticker[:2]. For example, 000001 -> 00, 300750 -> 30, 600519 -> 60, and 688981 -> 68.
Files are… See the full description on the dataset page: https://huggingface.co/datasets/phields/a-share-l2-trades.InpaintCOCO
InpaintCOCO - Fine-grained multimodal concept understanding (for color, size, and COCO objects)
Dataset Summary
A data sample contains 2 images and 2 corresponding captions that differ only in one object, the color of an object, or the size of an object.
Many multimodal tasks, such as Vision-Language Retrieval and Visual Question Answering, present results in terms of overall performance.
Unfortunately, this approach overlooks more nuanced concepts, leaving us unaware… See the full description on the dataset page: https://huggingface.co/datasets/phiyodr/InpaintCOCO.ULP-logsTruecallerphonebookWikidata_Vectors_0.2
Wikidata Entity Embeddings 0.2
Dataset Summary
Wikidata Entity Embeddings is a dataset of embedding vectors for Wikidata entities. Each vector represents a Wikidata item (Q...) or property (P...) based on textual information extracted from Wikidata.
The dataset is part of the Wikidata Embedding Project, an initiative led by Wikimedia Deutschland in collaboration with Jina AI and IBM DataStax. The project provides a publicly accessible Wikidata Vector Database to… See the full description on the dataset page: https://huggingface.co/datasets/philippesaade/Wikidata_Vectors_0.2.wikidata
Wikidata Entities Connected to Wikipedia
This dataset is a multilingual, JSON-formatted version of the Wikidata dump from May 7, 2026. It contains 73,769,737 entities after filtering out scholarly articles from the original 120,182,414 entity dump.
Curated by: Jonathan Fraine & Philippe Saadé, Wikimedia Deutschland
Funded by: Wikimedia Deutschland
Language(s) (NLP): All Wikidata Languages
License: CC0-1.0
Dataset Structure
Each row in this dataset represents a… See the full description on the dataset page: https://huggingface.co/datasets/philippesaade/wikidata.spanish_spear_phishingDataset traducido del inglés al español mediante gpt4o mini.
Los mensajes del dataset contienen:
"email_subject": título del correo, no traducido
"sender_name": nombre del emisor, no traducido
"original_email_body": cuerpo del correo original, no traducido
"translated_email_body": cuerpo del correo traducido
El dataset corresponde al dataset de https://github.com/nahmiasd/Prompted-Contextual-Vectors-for-Spear-Phishing-Detection, el cual esta compuesto de:
"enron_ham": mensajes legítimos del… See the full description on the dataset page: https://huggingface.co/datasets/Darito/spanish_spear_phishing.sole_training_data
This is the training dataset for SOLE-R1-8B
SOLE-R1-8B is a video-language reward reasoning model for robotics. It is designed to estimate task progress from robot video frames and a natural-language task description, producing both per-timestep reasoning traces and scalar progress predictions that can be used as rewards for online robot reinforcement learning.
This dataset accompanies the paper “SOLE-R1: Video-Language Reasoning as the Sole Reward for On-Robot RL” by Philip… See the full description on the dataset page: https://huggingface.co/datasets/Philip-MIT/sole_training_data.stsb_multi_mt
Dataset Card for STSb Multi MT
Dataset Summary
STS Benchmark comprises a selection of the English datasets used in the STS tasks organized
in the context of SemEval between 2012 and 2017. The selection of datasets include text from
image captions, news headlines and user forums. (source)
These are different multilingual translations and the English original of the STSbenchmark dataset. Translation has been done with deepl.com. It can be used to train sentence embeddings… See the full description on the dataset page: https://huggingface.co/datasets/PhilipMay/stsb_multi_mt.trl-test-instructiondolly-15k-oai-style
Dataset Card for "dolly-15k-oai-style"
More Information needed
phishing-email-dataset
Phishing Email Dataset
This dataset on Hugging Face is a direct copy of the 'Phishing Email Detection' dataset from Kaggle, shared under the GNU Lesser General Public License 3.0. The dataset was originally created by the user 'Cyber Cop' on Kaggle. For complete details, including licensing and usage information, please visit the original Kaggle page.
guanaco-sharegpt-style
Dataset Card for "guanaco-sharegpt-style"
More Information needed
coco2017
coco2017
Image-text pairs from MS COCO2017.
Data origin
Data originates from cocodataset.org
While coco-karpathy uses a dense format (with several sentences and sendids per row), coco-karpathy-long uses a long format with one sentence (aka caption) and sendid per row. coco-karpathy-long uses the first five sentences and therefore is five times as long as coco-karpathy.
phiyodr/coco2017: One row corresponds one image with several sentences.
phiyodr/coco2017-long: One row… See the full description on the dataset page: https://huggingface.co/datasets/phiyodr/coco2017.PhishNChips
PhishNChips: A Benchmark for LLM Email-Agent Security
PhishNChips is a large-scale benchmark for evaluating how system prompt configurations influence the security behavior of LLM-based email agents. This repository contains the canonical v5.2 release, featuring 2,000 email stimuli and 220,000 adjudicated model evaluations.
Dataset Overview
The benchmark measures a critical deployment variable: how strongly an LLM's system prompt shapes its phishing detection capabilities… See the full description on the dataset page: https://huggingface.co/datasets/AreLit/PhishNChips.a-share-l2-market-depth
China A-share Level 2 Market Depth
Canonical order-event and ten-level snapshot data for China A-shares. Canonical
trade records remain in the separate phields/a-share-l2-trades dataset.
Coverage
Date range: 2026-07-24 to 2026-07-24
Trading days: 1
Table
Rows
Parquet files
Compressed size
l2_orders
249,705,486
10
2.14 GiB
l2_snapshots
20,279,887
4
0.91 GiB
Layout… See the full description on the dataset page: https://huggingface.co/datasets/phields/a-share-l2-market-depth.850M-India-dataretail-products-philippinesGlobalTGstanford-encyclopedia-philosophy
Stanford Encyclopedia Philosophy (Teeny-Tiny Castle)
This dataset is part of a tutorial tied to the Teeny-Tiny Castle, an open-source repository containing educational tools for AI Ethics and Safety research.
How to Use
from datasets import load_dataset
dataset = load_dataset("AiresPucrs/stanford-encyclopedia-philosophy", split = 'train')
phishing-datasetDataset designed for phishing classification tasks in various data types.ears
EARS: Expressive Anechoic Recordings of Speech
This is a mirror of the Expressive Anechoic Recordings of Speech (EARS) dataset.
The original files were converted from WAV to Opus to reduce the size and accelerate streaming.
Sampling rate: 48 kHz
Channels: 1
Format: Opus
Splits:
Train: 92 hours, 15939 utterances, speakers p001 to p099
Validation: 2 hours, 322 utterances, speakers p100 and p101
Test: 6 hours, 966 utterances, speakers p102 to p107
License: CC BY-NC 4.0
Source:… See the full description on the dataset page: https://huggingface.co/datasets/philgzl/ears.fsd50k
FSD50K: An open dataset of human-labeled sound events
This is a mirror of the FSD50K sound event dataset.
The original files were converted from WAV to Opus to reduce the size and accelerate streaming.
Sampling rate: 48 kHz
Channels: 1
Format: Opus
Splits:
Dev: 80 hours, 40966 clips.
Eval: 28 hours, 10231 clips.
License: FSD50K is released under CC-BY. However, each clip has its own licence. Clip licenses include CC0, CC-BY, CC-BY-NC and CC Sampling+. Clip licenses are specified… See the full description on the dataset page: https://huggingface.co/datasets/philgzl/fsd50k.civil-code-phil
Civilex — Philippine Legal RAG & SFT Dataset
Retrieval corpus and supervised fine-tuning (SFT) data for a retrieval-augmented generation (RAG) pipeline over Philippine law: the Civil Code (Republic Act No. 386) and Supreme Court jurisprudence. Produced by the civilex-thesis research pipeline.
Dataset structure
.
├── README.md
├── civil_code_rag.jsonl # Civil Code articles + hierarchy + citation linkage
├── jurisprudence_chunks.jsonl # RAG-ready chunks… See the full description on the dataset page: https://huggingface.co/datasets/renzzyyy1028/civil-code-phil.hle_math_category_phi4inaturalist-enriched
Enriched iNaturalist dataset from 2026-03-27.
This dataset is based on philipp-zettl/inaturalist-s3-massive.
The data was enriched using the ./enrich.py script inside the repository.
It contains the following features
photo_id: The original ID of the photo inside the inaturalist dataset
observation_uuid: The observation's UUID
image: The actual image content
taxon_id: The ID of the taxonomy
species_name: The name of the species inside the image
taxonomic_rank: The type of taxonomic rank… See the full description on the dataset page: https://huggingface.co/datasets/philipp-zettl/inaturalist-enriched.