Update README.md

README.md

@@ -10,17 +10,19 @@ tags:
 - MSMARCO
 - MSMARCOV2.1
 - Snowflake
+- arctic
+- arctic-embed
 pretty_name: TREC-RAG-Embedding-Baseline
 size_categories:
 - 100M<n<1B
 configs:
-- config_name: "corpus"
+- config_name: corpus
   data_files:
   - split: train
     path: corpus/*
 ---
 
-# Embeddings for MSMARCO V2.1 for TREC-RAG
+# 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. 
@@ -32,13 +34,13 @@ All embeddings are created using [Snowflake's Arctic Embed L](https://huggingfac
 You can either load the dataset like this:
 ```python
 from datasets import load_dataset
-docs = load_dataset("", split="train")
+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("",  split="train", streaming=True)
+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']