datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
bekko-embedding-v1-unsupervised
Bekko Embedding v1 Unsupervised Training Data
This is the unsupervised training dataset used for the bekko-embedding-v1 embedding model family. It contains pair and triplet examples for embedding pretraining, published as Hugging Face dataset subsets so that each source/subset/task combination can be loaded independently.
For the full training recipe and technical details, see Bekko Embedding: Parameter-Efficient Multilingual Retrieval with Ultra-Compact Encoders.
The dataset… See the full description on the dataset page: https://huggingface.co/datasets/hotchpotch/bekko-embedding-v1-unsupervised.WSI_Embedding
WSI_Embedding
Patch and slide embeddings for whole-slide images, plus the run records used to produce them.
Original WSIs are not in this repo. They stay on HPC / the source dataset remotes. Do not expect .svs / .tif here.
Repo: thanminh01/WSI_Embedding
Top-level layout
Path
What it is
Download when
embeddings/<dataset>/<model>/<mag>x_<patch>px_<overlap>px_overlap/
Encoder tensors (.h5 / .pt / WSI-LLaVA json) and per-model TRIDENT _config_ / _logs_
You… See the full description on the dataset page: https://huggingface.co/datasets/thanminh01/WSI_Embedding.seamless-align-enA-viA.speaker-embedding.xlsr-2bseamless-align-enA-esA.speaker-embedding.w2vbert-600mvlm-teacher-embeddingseamless-align-enA-frA.speaker-embedding.hubert-xlseamless-align-enA-jaA.speaker-embedding.w2vbert-600mdaily-papers-embeddingsCore-AlphaEarth-Embeddings
Major TOM Core AlphaEarth Embeddings Subset
This is a prototype dataset. It only includes some of the AlphaEarth embeddings stored in Major TOM grid cells.
This dataset is mostly aimed at experimentation and prototyping. It is particularly useful to use it along other datasets published within the Major TOM project.
Content
Field
Type
Description
grid_cell
string
Major TOM cell
year
int
year of the sample
thumbnail
image
3-dimensional PCA… See the full description on the dataset page: https://huggingface.co/datasets/Major-TOM/Core-AlphaEarth-Embeddings.seamless-align-deA-enA.speaker-embedding.xlsr-2bseamless-align-enA-hiA.speaker-embedding.hubert-xlseamless-align-enA-frA.speaker-embedding.xlsr-2bseamless-align-enA-esA.speaker-embedding.xlsr-2bseamless-align-enA-zhA.speaker-embedding.w2vbert-600mforecast-news-embeddings
Forecast News Embeddings
Precomputed LanceDB table used for keyword, semantic, and hybrid retrieval in
forecast-sim and future-sim.
Snapshot
7,911,857 indexed source articles
16,207,764 text chunks
Coverage: 2023-01-11 through 2026-08-31
Snapshot published: 2026-09-18
Lance dataset version: 856
Total artifact size: approximately 303.2 GiB
Articles with empty searchable text are not represented. Long articles can
produce multiple chunks, so the chunk count is… See the full description on the dataset page: https://huggingface.co/datasets/shash42/forecast-news-embeddings.bible-embeddings
Bible Embeddings
A comprehensive tool for generating and evaluating Bible verse embeddings using various state-of-the-art embedding models. This project supports both commercial APIs (OpenAI, Google Gemini, Voyage AI) and open-source models (HuggingFace sentence-transformers) for semantic search across biblical texts.
Setup
This project is managed with uv. Make sure you have uv installed, then set up the project:
# Install dependencies
uv sync
# Install specific… See the full description on the dataset page: https://huggingface.co/datasets/LetsChurch/bible-embeddings.latent-sr-embeddings
Latent-SR Embeddings: Precomputed VAE Latents for Medical Image Super-Resolution
Precomputed VAE latent embeddings from the paper:
"Domain-Specific Latent Representations Improve the Fidelity of Diffusion-Based Medical Image Super-Resolution"Sebastian Cajas, Ashaba Judith, Rahul Gorijavolu, Sahil Kapadia, Hillary Clinton Kasimbazi, Leo Kinyera, Emmanuel Paul Kwesiga, Sri Sri Jaithra Varma Manthena, Luis Filipe Nakayama, Ninsiima Doreen, Leo Anthony Celi.arXiv:2604.12152 (2026)… See the full description on the dataset page: https://huggingface.co/datasets/sebasmos/latent-sr-embeddings.Caselaw_Access_Project_embeddingsThis is an embeddings dataset for the Caselaw Access Project, created by a user named Endomorphosis.
Each caselaw entry is hashed with IPFS / multiformats, so retrieval of the document can be made over the IPFS / filecoin network
The ipfs content id "cid" is the primary key that links the dataset to the embeddings, should you want to retrieve from the dataset instead.
The dataset has been had embeddings generated with three models: thenlper/gte-small, Alibaba-NLP/gte-large-en-v1.5, and… See the full description on the dataset page: https://huggingface.co/datasets/justicedao/Caselaw_Access_Project_embeddings.seamless-align-enA-frA.speaker-embedding.w2vbert-600mmultilingual-embeddings-pre-training-curated
📚 Collection | 📝 Multilingual Blog | 📝 English Blog
Contrastive Multilingual Pre-Training
2.16B query–document pairs across eight languages, plus cross-lingual pairs
mDenseOn |
mLateOn |
DenseOn |
LateOn |
PyLate |
FastPlaid
🎯 TL;DR: The multilingual contrastive pre-training corpus used to train mDenseOn and mLateOn. It extends our curated English data recipe (embeddings-pre-training-curated) to French, German, Italian, Spanish, Portuguese, Swedish, Norwegian, and Arabic… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/multilingual-embeddings-pre-training-curated.seamless-align-enA-zhA.speaker-embedding.xlsr-2bseamless-align-enA-zhA.speaker-embedding.hubert-xlseamless-align-enA-koA.speaker-embedding.w2vbert-600membeddings-pre-training
Overview
This large-scale dataset is designed for pre-training state-of-the-art text embedding models. Its goal is to reproduce and build upon the data recipe described in the mGTE technical report (Zhang et al., 2024), which details the data sources used to train the GTE family of embedding models but does not release the data itself.
We assembled this dataset as part of a research effort to understand how data composition affects retrieval model quality. Our experiments… See the full description on the dataset page: https://huggingface.co/datasets/lightonai/embeddings-pre-training.paired-llama-3.2-1b-embeddings-lmsys-chat-1m
Paired Llama 3.2 1B Token Embeddings (LMSYS-Chat-1M)
This dataset contains paired activations corresponding to single token locations extracted from Meta's Llama 3.2 1B Instruct on conversations from LMSYS-Chat-1M.
Embeddings are provided for layers 5 through 14, which capture the most interesting intermediate representations.
This dataset was built to study things like:
Learning different basis for activations at a given layer
Studying if there are cases where position encodes… See the full description on the dataset page: https://huggingface.co/datasets/scaleinvariant/paired-llama-3.2-1b-embeddings-lmsys-chat-1m.seamless-align-enA-hiA.speaker-embedding.w2vbert-600mseamless-align-enA-viA.speaker-embedding.w2vbert-600mseamless-align-enA-jaA.speaker-embedding.hubert-xlseamless-align-enA-hiA.speaker-embedding.xlsr-2bseamless-align-enA-jaA.speaker-embedding.xlsr-2b