Snowflake
msmarco-v2.1-snowflake-arctic-embed-l
Snowflake Arctic Embed L Embeddings for MSMARCO V2.1 for TREC-RAG
This dataset contains the embeddings for the MSMARCO-V2.1 dataset which is used as the corpora for TREC RAG
All embeddings are created using Snowflake's Arctic Embed L and are intended to serve as a simple baseline for dense retrieval-based methods.
Retrieval Performance
Retrieval performance for the TREC DL21-23, MSMARCOV2-Dev and Raggy Queries can be found below with BM25 as a baseline. For both… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/msmarco-v2.1-snowflake-arctic-embed-l.mteb-retrieval-snowflake-arctic-embed-m-v1.5msmarco-v2.1-snowflake-arctic-embed-m-v1.5
Snowflake Arctic Embed M V1.5 Embeddings for MSMARCO V2.1 for TREC-RAG
This dataset contains the embeddings for the MSMARCO-V2.1 dataset which is used as the corpora for TREC RAG
All embeddings are created using Snowflake's Arctic Embed M v1.5 and are intended to serve as a simple baseline for dense retrieval-based methods.
It's worth noting that Snowflake's Arctic Embed M v1.5 is optimized for efficient embeddings and thus supports embedding truncation and quantization. More… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/msmarco-v2.1-snowflake-arctic-embed-m-v1.5.AgentWorldModel-1KAgentWorldModel-1K
Agent World Model: Infinity Synthetic Environments for Agentic Reinforcement Learning
Zhaoyang Wang1,
Canwen Xu2,
Boyi Liu2,
Yite Wang2,
Siwei Han1,
Zhewei Yao2,
Huaxiu Yao1,
Yuxiong He2
1UNC-Chapel Hill 2Snowflake AI Research
Overview
AgentWorldModel-1K contains 1,000 fully synthetic, executable, SQL database-backed tool-use environments exposed via a unified MCP (Model Context Protocol) interface, designed for large-scale… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/AgentWorldModel-1K.dare-bench
DARE-Bench
[ICLR 2026] DARE-Bench: Evaluating Modeling and Instruction Fidelity of LLMs in Data Science
Fan Shu1, Yite Wang2, Ruofan Wu1, Boyi Liu2, Zhewei Yao2, Yuxiong He2, Feng Yan1
1University of Houston 2Snowflake AI Research
🔎 Overview
DARE-Bench (ICLR 2026) is a benchmark for evaluating LLM agents on data science tasks, focusing on modeling and instruction fidelity.
This Hugging Face repository provides a selected subset of the full benchmark for public release.… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/dare-bench.arctic-embed-ft-v1
Data for the Arctic Embed walkthrough
This dataset coresponds to the walkthrough example for using the Arctic Embed training code in ArcticTraining. See that README for more details.
Example: Selective downloads via Git LFS
Since this dataset contains various intermediate files not necessary for training, it can be helpful to use the Git LFS backend of Hugging Face Datasets to pull select files.
# First, ensure you have installed git-lfs (see `https://git-lfs.com/`… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/arctic-embed-ft-v1.