metadata

license: apache-2.0
task_categories:
  - question-answering
language:
  - en
tags:
  - TREC-RAG
  - RAG
  - MSMARCO
  - MSMARCOV2.1
  - Snowflake
  - arctic
  - arctic-embed
pretty_name: TREC-RAG-Embedding-Baseline
size_categories:
  - 100M<n<1B
configs:
  - config_name: corpus
    data_files:
      - split: train
        path: corpus/*

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.

Loading the dataset

Loading the document embeddings

You can either load the dataset like this:

from datasets import load_dataset
docs = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-l", split="train")

Or you can also stream it without downloading it before:

from datasets import load_dataset
docs = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-l",  split="train", streaming=True)
for doc in docs:
    doc_id = doc['_id']
    title = doc['title']
    header = doc['header']
    text = doc['text']
    emb = doc['emb']