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.msmarco-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.mteb-retrieval-snowflake-arctic-embed-m-v1.5dare-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.HyDRA-Bench
HyDRA-Bench
Bridging Databases and Documents: Data-Algorithm Co-Design for Hybrid Question Answering
Ruofan Wu¹, Boyi Liu², Fan Shu¹, Yite Wang², Zhewei Yao², Yuxiong He², Feng Yan¹
¹ University of Houston
² Snowflake AI Research
🔎 Overview
HyDRA-Bench (Hybrid Database and Retrieval Agent Benchmark) is a benchmark for hybrid reasoning that requires LLM agents to interleave SQL execution over structured databases with semantic retrieval over unstructured text.… See the full description on the dataset page: https://huggingface.co/datasets/Snowflake/HyDRA-Bench.