Update README.md (#1)

- Update README.md (32c15e1198c1455f45ca2c077c3ce9c289fe9326)


Co-authored-by: danmei xu <[email protected]>

README.md

@@ -2819,7 +2819,7 @@ license: apache-2.0
 ## News
 
 
-04/16/2024: Release the ** Arctic-embed ** family of text empedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake/Arctic-Text-Embed).
+04/16/2024: Release the ** Arctic-embed ** family of text empedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake-Labs/arctic-embed).
 
 
 ## Models
@@ -2873,7 +2873,7 @@ This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://h
 ### [Arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-s)
 
 
-Based on the [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.
+Based on the [intfloat/e5-small-unsupervised](https://huggingface.co/intfloat/e5-small-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.
 
 
 | Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
@@ -2902,7 +2902,7 @@ Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-
 ### [arctic-embed-m-long](https://huggingface.co/Snowflake/arctic-embed-m-long/)
 
 
-Based on the [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!
+Based on the [nomic-ai/nomic-embed-text-v1-unsupervised](https://huggingface.co/nomic-ai/nomic-embed-text-v1-unsupervised) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!
 
 
 | Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |