datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
wikipedia-22-12-en-embeddings-all-MiniLM-L6-v2
Dataset Card for "wikipedia-22-12-en-embeddings-all-MiniLM-L6-v2"
More Information needed
msmarco-msmarco-MiniLM-L6-v3
MS MARCO with hard negatives from msmarco-MiniLM-L6-v3
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-msmarco-MiniLM-L6-v3.malicious-prompts-minilm-embeddingsenwiki20230101-pageid-minilml6v2embeddings
Dataset Card for "enwiki20230101-pageid-minilml6v2embeddings"
More Information needed
enwiki20230101-pageid-minilml6v2embeddingsjson
Dataset Card for "enwiki20230101-pageid-minilml6v2embeddingsjson"
More Information needed
openwebtext-all-minilm-l6-v2-embedding
Dataset Card for "openwebtext-all-minilm-l6-v2-embedding"
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enwiki20230101-minilml6v2-avgembeddings
Dataset Card for "enwiki20230101-minilml6v2-avgembeddings"
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wixqa-gpt-oss-120b-all-MiniLM-L6-v2-pgvector-evalsvigil-jailbreak-all-MiniLM-L6-v2
Vigil: LLM Jailbreak all-MiniLM-L6-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-MiniLM-L6-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-MiniLM-L6-v2.enwiki20230101-bysize-minilml6v2-avgembeddings
Dataset Card for "enwiki20230101-bysize-minilml6v2-avgembeddings"
More Information needed
newsroom-embeddings-minilmmsmarco-scores-ms-marco-MiniLM-L6-v2
MS MARCO query-passage scores using cross-encoder/ms-marco-MiniLM-L6-v2
MS MARCO is a large scale information retrieval corpus that was created based on real user search queries using the Bing search engine.
This dataset contains 160 million CrossEncoder scores on the MS MARCO dataset, using the cross-encoder/ms-marco-MiniLM-L6-v2 model.
The scores are unprocessed logits, i.e. they don't range between 0...1, and they can be used for finetuning search models using distillation.
See… See the full description on the dataset page: https://huggingface.co/datasets/sentence-transformers/msmarco-scores-ms-marco-MiniLM-L6-v2.vigil-instruction-bypass-all-MiniLM-L6-v2
Vigil: LLM Instruction Bypass all-MiniLM-L6-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-MiniLM-L6-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-MiniLM-L6-v2.STEM-wikipedia-22-12-en-embeddings-all-MiniLM-L6-v2fineweb-multilingual-minilm-l12-v2-shard-27468-20000hotpotqa_clustered_agglomerative_all-MiniLM-L6-v2_2hotpotqa_clustered_minibatchkmeans_paraphrase-MiniLM-L3-v2_20hotpotqa_clustered_spectral_multi-qa-MiniLM-L6-cos-v1_50frca-adversarialqa-dbert-minilm-raw-results-20260924
AdversarialQA DeBERTa+MiniLM exploratory raw results
This repository contains the full adapter-bearing raw archive for the pre-named AdversarialQA exploratory cell reported as certificate-backed in the associated prompt-visible feedback study. It is an auxiliary result, not the paper's primary endpoint.
Contents
adversarialqa_dbert_minilm_cuda130_raw_results_20260924.zip ? full raw state, including adapters and the execution receipt (1,289,053,860 bytes).… See the full description on the dataset page: https://huggingface.co/datasets/alperk3003/frca-adversarialqa-dbert-minilm-raw-results-20260924.hotpotqa_clustered_spectral_all-MiniLM-L6-v2_5hotpotqa_clustered_spectral_paraphrase-MiniLM-L3-v2_50simplewiki2023-all-minilm-l6-v2-embedding
Dataset Card for "simplewiki2023-all-minilm-l6-v2-embedding"
More Information needed
vigil-instruction-bypass-all-MiniLM-L6-v2
Vigil: LLM Instruction Bypass all-MiniLM-L6-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-MiniLM-L6-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/ArkhAngelLifeJiggy/vigil-instruction-bypass-all-MiniLM-L6-v2.hotpotqa_clustered_dbscan_multi-qa-MiniLM-L6-cos-v1_autohotpotqa_clustered_dbscan_all-MiniLM-L6-v2_autohotpotqa_clustered_dbscan_paraphrase-MiniLM-L3-v2_autohotpotqa_clustered_spectral_all-MiniLM-L6-v2_2hotpotqa_clustered_spectral_paraphrase-MiniLM-L3-v2_2oscar-en-minilm-2m
Oscar EN 2M Embeddings
This dataset contains 2M sentences extracted from the English subset of the OSCAR dataset, and encoded into sentence embeddings using the sentence-transformers/all-MiniLM-L6-v2 model.
wikipedia-embeddings-cs-minilmThis 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 sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 model.
Usage
Load the dataset:
from datasets import load_dataset
ds = load_dataset("karmiq/wikipedia-embeddings-cs-e5-base", split="train")
ds[1]
{
'id': '1',
'url': 'https://cs.wikipedia.org/wiki/Astronomie'… See the full description on the dataset page: https://huggingface.co/datasets/karmiq/wikipedia-embeddings-cs-minilm.