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README.md

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----
-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']
-```

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