datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
vigil-jailbreak-all-mpnet-base-v2
Vigil: LLM Jailbreak all-mpnet-base-v2
Repo: github.com/deadbits/vigil-llm
Vigil is a Python framework and REST API for assessing Large Language Model (LLM) prompts against a set of scanners to detect prompt injections, jailbreaks, and other potentially risky inputs.
This repository contains all-mpnet-base-v2 embeddings for all "jailbreak" prompts used by Vigil.
You can use the parquet2vdb.py utility to load the embeddings in the Vigil chromadb instance, or use them in your own… See the full description on the dataset page: https://huggingface.co/datasets/deadbits/vigil-jailbreak-all-mpnet-base-v2.msmarco-mpnet-margin-mse-mean-v1
MS MARCO with hard negatives from mpnet-margin-mse-mean-v1
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
For each query and gold positive passage, the 50 most similar paragraphs were mined using 13 different models. The resulting data can be used to train Sentence Transformer models.
Related Datasets
These are the datasets generated using the 13 different models:
msmarco-bm25… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-mpnet-margin-mse-mean-v1.trishieldrag-nq-mpnet-embeddings
TriShieldRAG — BeIR NQ embeddings and FAISS index
Artifacts for TriShieldRAG: A Three-Ring Defense-in-Depth Framework
Against Knowledge Corruption in Retrieval-Augmented Generation.
File
Size
Description
emb_*.npy (27)
7.7 GB
Embeddings, 100k passages per chunk, corpus order
nq_ivf_nlist6550.index
7.8 GB
FAISS IVF index, nlist=6550, inner product
Corpus: BeIR/nq corpus split, 2,681,468 passages
Model: sentence-transformers/all-mpnet-base-v2, 768-d… See the full description on the dataset page: https://huggingface.co/datasets/rpaut03l/trishieldrag-nq-mpnet-embeddings.wiki_mpnet_embeddingsEmbeddings of the english Wikipedia paragraphs using all-mpnet-base-v2 sentence transformers encoder.The dataset contains 43 911 155 paragraphs from 6 458 670 Wikipedia articles.The size of each paragraph varies from 20 to 2000 characters.For each paragraph there is an embedding of size 768.Embeddings are stored in numpy files, 1 000 000 embeddings per file.For each embedding file, there is an ids file that contains the list of ids of the corresponding paragraphs.Be careful, dataset size is… See the full description on the dataset page: https://huggingface.co/datasets/olmer/wiki_mpnet_embeddings.MedQA-USMLE-4-options-hf-MPNet-IR
Dataset Card for "MedQA-USMLE-4-options-hf-MPNet-IR"
More Information needed
hotpotqa_clustered_dbscan_all-mpnet-base-v2_autobright.sustainable.mpnet.dense
bright.sustainable.mpnet.dense
Description
TODO: What is the artifact?
Usage
# Load the artifact
import pyterrier as pt
artifact = pt.Artifact.from_hf('pyterrier-tutorial/bright.sustainable.mpnet.dense')
# TODO: Show how you use the artifact
Benchmarks
TODO: Provide benchmarks for the artifact.
Reproduction
# TODO: Show how you constructed the artifact.
Metadata
{
"type": "dense_index",
"format":… See the full description on the dataset page: https://huggingface.co/datasets/pyterrier-tutorial/bright.sustainable.mpnet.dense.hotpotqa_clustered_agglomerative_all-mpnet-base-v2_50arxiv.cs.CL.25k.embeddings.mpnethotpotqa_clustered_spectral_all-mpnet-base-v2_10hotpotqa_clustered_minibatchkmeans_all-mpnet-base-v2_10vigil-instruction-bypass-all-mpnet-base-v2
Vigil: LLM Instruction Bypass all-mpnet-base-v2
Repo: github.com/deadbits/vigil-llm
Vigil is a Python framework and REST API for assessing Large Language Model (LLM) prompts against a set of scanners to detect prompt injections, jailbreaks, and other potentially risky inputs.
This repository contains all-mpnet-base-v2 embeddings for all Instruction Bypass style prompts ("Ignore instructions ...") used by Vigil.
You can use the parquet2vdb.py utility to load the embeddings in the… See the full description on the dataset page: https://huggingface.co/datasets/deadbits/vigil-instruction-bypass-all-mpnet-base-v2.hotpotqa_clustered_agglomerative_all-mpnet-base-v2_2chroma-paraphrase-multilingual-mpnet-base-v2
Dataset Card for hlm-paraphrase-multilingual-mpnet-base-v2
Dataset Summary
Chromadb vectorstore for 红楼梦, created with
import os
from langchain.document_loaders import TextLoader
from langchain.embeddings import SentenceTransformerEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores import Chroma
model_name = 'paraphrase-multilingual-mpnet-base-v2'
embedding =… See the full description on the dataset page: https://huggingface.co/datasets/mikeee/chroma-paraphrase-multilingual-mpnet-base-v2.hotpotqa_clustered_spectral_all-mpnet-base-v2_50hotpotqa_clustered_spectral_all-mpnet-base-v2_2hotpotqa_clustered_minibatchkmeans_all-mpnet-base-v2_50philippine-budget-2025-embeddings-mpnet
Philippine Budget 2025 - Vector Embeddings (all-mpnet-base-v2)
Dataset Description
This dataset contains vector embeddings of the 2025 People's Budget of the Philippines, a citizen-friendly overview of the PHP 6.326 trillion national budget published by the Department of Budget and Management (DBM).
Source Document
These embeddings are based on the 2025 People's Enacted Budget (English version, revised as of April 22, 2025).
Direct Download Link: 2025 People's… See the full description on the dataset page: https://huggingface.co/datasets/pageman/philippine-budget-2025-embeddings-mpnet.wiki_mpnet_index
Semantic search over the 44 million of English Wikipedia paragraphs using sentence transformers encoder.
The dataset contains:
43 911 155 paragraphs from 6 458 670 wikipedia articles stored in a zip archive;
FAISS index with the embeddings;
Retriever module for semantic search over the paragraphs.
The size of each paragraph varies from 20 to 2000 characters.The embedding vector size is 768.The index is 4-bit-quantized 2-level IVF16384_HNSW32 constructed with the FAISS… See the full description on the dataset page: https://huggingface.co/datasets/olmer/wiki_mpnet_index.ukr_sentence_pairs_mpnethotpotqa_clustered_agglomerative_all-mpnet-base-v2_20wikipedia-embeddings-cs-seznam-mpnetThis dataset contains the Czech subset of the wikimedia/wikipedia dataset. Each page is divided into paragraphs, stored as a list in the chunks column. For every paragraph, embeddings are created using the Seznam/simcse-dist-mpnet-paracrawl-cs-en model.
Usage
Load the dataset:
from datasets import load_dataset
ds = load_dataset("karmiq/wikipedia-embeddings-cs-seznam-mpnet", split="train")
ds[1]
{
'id': '1',
'url': 'https://cs.wikipedia.org/wiki/Astronomie',
'title':… See the full description on the dataset page: https://huggingface.co/datasets/karmiq/wikipedia-embeddings-cs-seznam-mpnet.hotpotqa_clustered_agglomerative_all-mpnet-base-v2_10hotpotqa_clustered_minibatchkmeans_all-mpnet-base-v2_5transformers_mpnet_rag_llama70b_llama8b_resultsnyaya-ae-all-mpnet-base-v2-legal-v1
Dataset Card for "nyaya-ae-all-mpnet-base-v2-legal-v1"
More Information needed
nyaya-ae-all-mpnet-base-v2
Dataset Card for "nyaya-ae-all-mpnet-base-v2"
More Information needed
hotpotqa_clustered_agglomerative_all-mpnet-base-v2_5JBB-behaviors-embeddings-all-mpnet-base-v2netflix-shows-mpnet-embeddings