datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
seamless-align-enA-viA.speaker-embedding.xlsr-2bseamless-align-enA-esA.speaker-embedding.w2vbert-600mseamless-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-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.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-600mseamless-align-enA-zhA.speaker-embedding.xlsr-2bseamless-align-enA-zhA.speaker-embedding.hubert-xlseamless-align-enA-koA.speaker-embedding.w2vbert-600mpaired-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-2bseamless-align-enA-koA.speaker-embedding.hubert-xlCaselaw_Access_Project_embeddingsOriginal Repository:
https://huggingface.co/datasets/justicedao/Caselaw_Access_Project_embeddings/
This 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… See the full description on the dataset page: https://huggingface.co/datasets/laion/Caselaw_Access_Project_embeddings.seamless-align-deA-enA.speaker-embedding.w2vbert-600mseamless-align-enA-koA.speaker-embedding.xlsr-2bseamless-align-enA-esA.speaker-embedding.hubert-xlarxiv-titles-instructorxl-embeddings
arxiv-titles-instructorxl-embeddings
This dataset contains 768-dimensional embeddings generated from the arxiv
paper titles using InstructorXL model. Each
vector has an abstract used to create it, along with the DOI (Digital Object Identifier). The
dataset was created using precomputed embeddings exposed by the Alexandria Index.
Generation process
The embeddings have been generated using the following instruction:
Represent the Research Paper title for retrieval;… See the full description on the dataset page: https://huggingface.co/datasets/Qdrant/arxiv-titles-instructorxl-embeddings.embeddings