readme

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']
+```