datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
investopedia-embedding-dataset
Dataset Card for investopedia-embedding dataset
We curate a dataset of substantial size pertaining to finance from Investopedia using a new technique that leverages unstructured scraping data
and LLM to generate structured data that is suitable for fine-tuning embedding models. The dataset generation uses a new method of self-verification that
ensures that the generated question-answer pairs and not hallucinated by the LLM with high probability.
Dataset Description… See the full description on the dataset page: https://huggingface.co/datasets/FinLang/investopedia-embedding-dataset.cis5300-word-embeddings
Word Embeddings and Semantic Similarity (CIS 5300)
Dataset Description
This dataset supports learning about word embeddings — dense vector representations that capture word meaning. It includes a standard similarity benchmark, a word sense disambiguation task, and a Shakespeare corpus for training custom embeddings.
Configs
SimLex-999: Word Similarity Benchmark
SimLex-999 (Hill et al., 2015) is a gold-standard benchmark for evaluating word… See the full description on the dataset page: https://huggingface.co/datasets/CCB/cis5300-word-embeddings.malicious-prompts-minilm-embeddingswikipedia-22-12-simple-embeddings
wikipedia-22-12-simple-embeddings
A modified version of Cohere/wikipedia-22-12-simple-embeddings
meant for use with PostgreSQL with pgvector and Timescale Vector.
Dataset Details
This dataset was created for exploring time-based filtering and semantic search in PostgreSQL with pgvector and Timescale Vector.
This is a modified version of the Cohere wikipedia-22-12-simple-embeddings dataset hosted on Huggingface.
It contains embeddings of Simple English Wikipedia… See the full description on the dataset page: https://huggingface.co/datasets/timescale/wikipedia-22-12-simple-embeddings.ALPHAGenome-Embeddings
ALPHAGenome hg38 Embeddings
Pre-computed ALPHAGenome DNA foundation model embeddings for the entire human genome (hg38 / GRCh38).
The human genome is divided into ~22,000 non-overlapping 131 KB bins. Each bin's DNA sequence is embedded into a 3,072-dimensional latent space using the ALPHAGenome foundation model.
Companion project
These embeddings power the ALPHAGenome UMAP Explorer — an interactive browser visualization of latent relationships between genomic regions:
→… See the full description on the dataset page: https://huggingface.co/datasets/lagosproject/ALPHAGenome-Embeddings.embedding-finetuning-financeThis dataset can be used for fine-tuning embedding models using positive text pairs (question, context).
stargo-embeddings
stargo-embeddings
Dataset repository containing STAR-GO related embedding assets. The metadata.csv is loadable via datasets.load_dataset, while large binaries (e.g. .h5, .npy) are stored as downloadable files.
How to use
Load the metadata table:
from datasets import load_dataset
ds = load_dataset("<your-org-or-username>/<your-dataset-repo>")
print(ds)
Download the large binary assets referenced in the table with hf_hub_download.
Medical_word_embedding_eval
Danish medical word embedding evaluation
The development of the dataset is described further in our paper.
Citing
@inproceedings{laursen-etal-2023-benchmark,
title = "Benchmark for Evaluation of {D}anish Clinical Word Embeddings",
author = "Laursen, Martin Sundahl and
Pedersen, Jannik Skyttegaard and
Vinholt, Pernille Just and
Hansen, Rasmus S{\o}gaard and
Savarimuthu, Thiusius Rajeeth",
editor = "Derczynski, Leon",
booktitle =… See the full description on the dataset page: https://huggingface.co/datasets/Den-Intelligente-Patientjournal/Medical_word_embedding_eval.herbal-knowledge-embedding-wikipedia
Herbs Domain Knowledge Safety Wikipedia Embeddings
Pre-computed vector embeddings from Wikipedia articles covering Assorted Herbs, spices, and other botanical items used for alternative medicine — ready to drop into your RAG pipeline without any embedding overhead.
Dataset Details
Property
Details
Embedding Model
nomic-ai/nomic-embed-text-v1.5 (135M)
Vector Dimensions
768
Source
Wikipedia
Topics
herbs, plants, spices, and other botanical items for… See the full description on the dataset page: https://huggingface.co/datasets/rakhasetiawan/herbal-knowledge-embedding-wikipedia.DepMap_embeddingsmovie-plots-nomic-embeddings
🎬 Movie Plot Embeddings Dataset (Nomic Embed v1.5)
This dataset contains vector embeddings for movie plots using the nomic-embed-text:v1.5 model via Ollama.
The source movie data comes from the Neo4j LLM Fundamentals dataset.
The main puprpose of this dataset is to be used with Neo4j Course on LLM Fundamentals. They did everythig with openai's models.
This csv is created using ollama and nomic-embed-text.
People who want to use ollama instread of openai, can refer to this csv for… See the full description on the dataset page: https://huggingface.co/datasets/omeyb/movie-plots-nomic-embeddings.wikivoyage-eu-city-embeddings
Dataset Card for Dataset Name
This dataset comprises abstracts from Wikivoyage for 160 European cities along with their corresponding country names, coordinates, and populations. The embeddings are derived from the GTE-Large model, incorporating data from the city, country, population, and abstract columns.
Dataset Sources
Wikivoyage data
World cities database
Mutated_Protein_EmbeddingsGender_Bias_Evaluation_SetThis dataset has been created as part of the Flax/JAX community week for testing the flax-sentence-embeddings Sentence Similarity models for Gender Bias but can be used for other use-cases as well related to evaluating Gender Bias.
The Following Dataset has been created for Evaluating Gender Bias for different models, based on various stereotypical occupations.
The Structure of the dataset is of the following type:
Base Sentence
Occupation
Steretypical_Gender
Male Sentence
Female… See the full description on the dataset page: https://huggingface.co/datasets/flax-sentence-embeddings/Gender_Bias_Evaluation_Set.agricultural-knowledge-embedding-wikipedia
Agriculture Domain Knowledge Safety Wikipedia Embeddings
Pre-computed vector embeddings from Wikipedia articles covering Agriculture, Planting Methods, and related topics — ready to drop into your RAG pipeline without any embedding overhead.
Dataset Details
Property
Details
Embedding Model
nomic-ai/nomic-embed-text-v1.5 (135M)
Vector Dimensions
768
Source
Wikipedia
Topics
Agriculture, various plants, planting methods and more
Format
CSV (vectors +… See the full description on the dataset page: https://huggingface.co/datasets/rakhasetiawan/agricultural-knowledge-embedding-wikipedia.semantic-song-embeddingsroman-urdu-sentiment-embeddings
Roman Urdu Sentiment Embeddings Dataset
Overview
This repository contains a research-grade Roman Urdu Sentiment Analysis dataset released as anonymized sentence embeddings. Roman Urdu is a low-resource language with high linguistic variability, heavy slang usage, and frequent code-mixing with English. This dataset is curated to support robust sentiment analysis research while ensuring strict privacy preservation.
Raw text data has not been released. Instead, all messages… See the full description on the dataset page: https://huggingface.co/datasets/Khubaib01/roman-urdu-sentiment-embeddings.food-knowledge-embedding-wikipedia
Food Domain Knowledge Safety Wikipedia Embeddings
Pre-computed vector embeddings from Wikipedia articles covering Food, Cooking Methods, and other items related to food preparation — ready to drop into your RAG pipeline without any embedding overhead.
Dataset Details
Property
Details
Embedding Model
nomic-ai/nomic-embed-text-v1.5 (135M)
Vector Dimensions
768
Source
Wikipedia
Topics
Food, Cooking methods, and other items related to food preparation… See the full description on the dataset page: https://huggingface.co/datasets/rakhasetiawan/food-knowledge-embedding-wikipedia.embedding-eval-results
Embedding Eval Results
The committed results from a real embedding-model benchmark that embarrassed a leaderboard's recommendation.
What's in here
Four CSV files representing four evaluation runs on a personal Obsidian vault:
File
Notes
Queries
Models
Purpose
20260618-233912.csv
54
29
7
Round 1 — toy slice. Saturated benchmark.
20260619-025330.csv
995
150
7
Stage 1 — real pile. The ranking inverted.
20260620-142445-wholenote.csv
996
450
7
Stage 1… See the full description on the dataset page: https://huggingface.co/datasets/kylebrodeur/embedding-eval-results.clip-keyphrase-embeddingsThe reddit_keywords.tsv file contains about 170k single word embeddings (scraped from reddit, filtering from an initial set of ~700k based on a minimum occurrence threshold) in this format:
temporary -0.276235,-0.181357,-0.325729,0.129826,0.016490,-0.230246,-0.039997,-0.990187,-0.014679,-0.044081,-0.120046,-0.250614,-0.303871,-0.264685,-0.010019,-0.158764,0.086107,-0.018172,0.003005,-0.383161,0.412182,0.104374,0.041335,-0.018206,0.085453,0.016297,-0.015680,0.047611,-0.267469,0.046825,-0.367247… See the full description on the dataset page: https://huggingface.co/datasets/rocca/clip-keyphrase-embeddings.malicious-prompts-openai-embeddingsembeddings_frases_gpt2DinoV2-YGO-card-embeddingsArabic-finanical-rag-embedding-dataset
Arabic Version of The Finanical Rag Embedding Dataset
This dataset is tailored for fine-tuning embedding models in Retrieval-Augmented Generation (RAG) setups. It consists of 7,000 question-context pairs translated into Arabic, sourced from NVIDIA's 2023 SEC Filing Report.
The dataset is designed to improve the performance of embedding models by providing positive samples for financial question-answering tasks in Arabic.
This dataset is the Arabic version of the original… See the full description on the dataset page: https://huggingface.co/datasets/Omartificial-Intelligence-Space/Arabic-finanical-rag-embedding-dataset.question_answer_finetuning_embeddings.csvscp-embeddings
SCP Text+ Embeddings
This dataset is adapted from the SCP 1to 7 corpus from Kaggle
We concatenated the title, state, text, and image captions columns. We also removed any rows that contained a deleted page, which trims the results down from 6999 -> 6618.
The embeddings were generated using sentence-transformers/multi-qa-mpnet-base-dot-v1
Feel free to use the dataset for semantic search or text generation tasks!
sp500-business-description-sentence-bert-embeddingsEmbeddings derived from business descriptions of S&P500 companies using sentence-BERT, SentenceTransformer('all-MiniLM-L6-v2') to be exact. For more info on evaluation of sentence transformers (specifcailly the huge GPT-3 versus smaller models see: https://twitter.com/Nils_Reimers/status/1487014195568775173)
taxonomy-embeddings📊 NCBI Dataset
This dataset is derived from the NCBI and was incorporated durgin the pretraining process with CDS-BART.
It contains randonmly selected 500 mRNA sequences from each of the four taxonomies: bacteria, invertebrate, plant, and fungi, totaling 2000 sequences.
⁉️ Dataset Contents
Sequence: The mRNA sequences corresponding to each of the four taxonomies
Label: The labels representing the four different taxonomies: bacteria, invertebrate, plant, and fungi [0,1,2,3]
🎯 Purpose
This… See the full description on the dataset page: https://huggingface.co/datasets/mogam-ai/taxonomy-embeddings.jazz-harmony-embeddings
Jazz Harmony Embeddings — 6,900 tune vectors
One 128-dimensional vector per jazz standard, from a small transformer
trained from scratch so that tunes with related harmony — transpositions,
alternate charts, contrafacts — land close together. Produced by the
3-seed ensemble released at
eigenben/jazz-harmony-embeddings;
code and full experiment records at
github.com/eigenben/jazz-harmony-embeddings.
Files
embeddings.npz — embeddings: (6900, 128) float32… See the full description on the dataset page: https://huggingface.co/datasets/eigenben/jazz-harmony-embeddings.coraltext-hard-corals-text-traits-for-embedding
CoralText Hard Corals: Text Traits for Embedding
One row per accepted scleractinian (hard / stony) coral species 1,704 species, global scope each carrying a text field built for sentence/document embedding, a set of structured ecological traits and stable identifiers that link back to the source databases. Every row is traceable and the dataset is explicit about where its text comes from and how complete that text is.
This card documents not just what the dataset contains but… See the full description on the dataset page: https://huggingface.co/datasets/xquantize/coraltext-hard-corals-text-traits-for-embedding.