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_Logwikidata
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.a-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 2026-09-24
Trading days: 119
Rows: 18730990496
Parquet files: 842
Compressed local size: 149.49 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.Wikidata_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.retail-products-philippinesInpaintCOCO
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.Truecallerdestroylist
PhishDestroy Blocklist Dataset
Real-time feed of phishing, crypto drainer, and scam domains detected by PhishDestroy.
Updated hourly from GitHub.
Statistics
Metric
Count
Total Domains
204,669
DNS Active
126,539
Content Active
88,358
Dead Domains
78,090
Community Blocklist
1,082,988
Added Today
4
Added This Week
4
Last updated: 2026-09-13 06:30 UTC
Files
File
Description
list.json
Full domain list (JSON array)… See the full description on the dataset page: https://huggingface.co/datasets/phishdestroy/destroylist.dolly-15k-oai-style
Dataset Card for "dolly-15k-oai-style"
More Information needed
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.trl-test-instructionPHINCAbstract
Code-mixing is the phenomenon of using more than one language in a sentence. In the multilingual communities, it is a very frequently observed pattern of communication on social media platforms. Flexibility to use multiple languages in one text message might help to communicate efficiently with the target audience. But, the noisy user-generated code-mixed text adds to the challenge of processing and understanding natural language to a much larger extent. Machine translation from… See the full description on the dataset page: https://huggingface.co/datasets/LingoIITGN/PHINC.guanaco-sharegpt-style
Dataset Card for "guanaco-sharegpt-style"
More Information needed
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.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.ULP-logstextbooks
Textbooks Are All You Need
Leveraging Large Language Models (LLMs), there's an opportunity to create a comprehensive open-source repository reminiscent of the historic Library of Alexandria.
This initiative represents a preliminary attempt at producing high-quality books covering an extensive range of subjects. The source of these samples varies:
Some generated using the RAG model, referencing Wikipedia or other search data.
Some are completely synthetically generated.
Some created… See the full description on the dataset page: https://huggingface.co/datasets/open-phi/textbooks.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.raw-philippine-data
Raw Philippine Data
This repository contains raw data about Philippine politicians, public officials, and legislative documents collected from various sources. The data is intended for research, analysis, and civic technology purposes.
Dataset Overview
This dataset currently contains:
Persons
45,424 person records of Philippine politicians and public officials with:
ID: Unique identifier (ULID format)
First Name: Person's first name
Last Name: Person's last… See the full description on the dataset page: https://huggingface.co/datasets/bettergovph/raw-philippine-data.phonebookphishing-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.
markdown-documentation-transformers
Hugging Face Transformers documentation as markdown dataset
This dataset was created using Clipper.js. Clipper is a Node.js command line tool that allows you to easily clip content from web pages and convert it to Markdown. It uses Mozilla's Readability library and Turndown under the hood to parse web page content and convert it to Markdown.
This dataset can be used to create RAG applications, which want to use the transformers documentation.
Example document:… See the full description on the dataset page: https://huggingface.co/datasets/philschmid/markdown-documentation-transformers.850M-India-dataTaur_CoT_Analysis_Project___microsoft__Phi-3-small-8k-instructPhishTrap
PhishTrap
Catch phishing URLs before they catch you — 16 features, 19,954 URLs, balanced 50/50. Cross-verified from 496K phishing domains + Tranco top 10K. Automatically refreshed every 6 hours.
Priorities: Quality > Ease of Access > Quantity
Build pipeline (open source): github.com/instax-dutta/PhishTrap — see how every row is fetched, merged, deduplicated, validated and published.
Dataset Overview
PhishTrap is a curated phishing URL detection dataset… See the full description on the dataset page: https://huggingface.co/datasets/saidutta69/PhishTrap.phishing-datasetDataset designed for phishing classification tasks in various data types.AIME_1983_2024Disclaimer: This is a Benchmark dataset! Do not using in training!
This is the Benchmark of AIME from year 1983~2023, and 2024(part 2).
Original: https://artofproblemsolving.com/wiki/index.php/AIME_Problems_and_Solutions
2024(part 1) can be find at https://huggingface.co/datasets/AI-MO/aimo-validation-aime.
stanford-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')