Ks
Models
All models matching “Ks”Datasets
All datasets matching “Ks”FineNewsFineNewsTestSampledbpedia-entities-openai-1M1M OpenAI Embeddings -- 1536 dimensions
Created: June 2023.
Text used for Embedding: title (string) + text (string)
Embedding Model: text-embedding-ada-002
First used for the pgvector vs VectorDB (Qdrant) benchmark: https://nirantk.com/writing/pgvector-vs-qdrant/
Citation
@dataset{dbpedia-entities-openai-1M,
doi = {10.57967/hf/6768},
url = {https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M},
author = {{Kumar Shivendu} and {Nirant Kasliwal}},
title =… See the full description on the dataset page: https://huggingface.co/datasets/KShivendu/dbpedia-entities-openai-1M.urls
URLs
74,918,894,107 deduplicated, validated URLs, sorted by
SURT key
and split into 2,334 range shards.
As plain text the URLs are 5.8 TiB, averaging 84 characters each. Sorted by
SURT key and delta-encoded they fit in 665.6 GiB — 9.54 bytes per URL, a
8.85× reduction. That is the whole point of the ordering: SURT puts URLs
from the same site next to each other, DELTA_LENGTH_BYTE_ARRAY then stores only
where each row differs from the one above it, and zstd compresses what is… See the full description on the dataset page: https://huggingface.co/datasets/ks46/urls.url-atlas
URL Atlas
257,548,097,528 URLs from 105 web corpora, each kept as
its own separately-loadable config, plus the raw source dumps two of them were
extracted from. 4.35 TiB across 52,244 files.
This is the input side of a URL-compression corpus: every source reduced to
its URL column and nothing else. It is deliberately not deduplicated or
merged — sources are kept intact and overlapping so you can measure what each
one contributes, pick the subset you want, and dedup on your own… See the full description on the dataset page: https://huggingface.co/datasets/ks48/url-atlas.IndicSynth
IndicSynth: Indian Multilingual Audio Deepfake Detection & Anti-Spoofing Dataset
A Large-Scale Multilingual Synthetic Speech Dataset for Low-Resource Indian Languages to facilitate audio deepfake detection and anti-spoofing research
🏆 Outstanding Paper Award, ACL 2025
🧠 Overview
IndicSynth is a novel multilingual synthetic speech dataset designed to advance multilingual audio deepfake detection (ADD) and anti-spoofing research. It covers 12 low-resource Indian… See the full description on the dataset page: https://huggingface.co/datasets/ksmashhero/IndicSynth.
