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+ ---
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+ license: apache-2.0
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+ task_categories:
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+ - question-answering
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+ language:
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+ - en
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+ tags:
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+ - TREC-RAG
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+ - RAG
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+ - MSMARCO
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+ - MSMARCOV2.1
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+ - Snowflake
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+ - arctic
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+ - arctic-embed
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+ pretty_name: TREC-RAG-Embedding-Baseline
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+ size_categories:
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+ - 100M<n<1B
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+ configs:
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+ - config_name: corpus
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+ data_files:
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+ - split: train
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+ path: corpus/*
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+ ---
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+
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+ # Snowflake Arctic Embed L Embeddings for MSMARCO V2.1 for TREC-RAG
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+
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+ 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/)
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+ 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.
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+
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+ ## Loading the dataset
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+
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+ ### Loading the document embeddings
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+
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+ You can either load the dataset like this:
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+ ```python
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+ from datasets import load_dataset
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+ docs = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-l", split="train")
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+ ```
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+
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+ Or you can also stream it without downloading it before:
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+ ```python
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+ from datasets import load_dataset
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+ docs = load_dataset("Snowflake/msmarco-v2.1-snowflake-arctic-embed-l", split="train", streaming=True)
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+ for doc in docs:
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+ doc_id = doc['_id']
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+ title = doc['title']
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+ header = doc['header']
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+ text = doc['text']
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+ emb = doc['emb']
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+ ```