README.md

---
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](https://trec-rag.github.io/)
All embeddings are created using [Snowflake's Arctic Embed L](https://huggingface.co/Snowflake/snowflake-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:
```python
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:
```python
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']
```