Update README.md

README.md

@@ -101,7 +101,7 @@ language:
 
 
 ## News
-12/04/2024: Release of `snowflake-arctic-embed-m-v2.0` and `snowflake-arctic-embed-m-v2.0` our newest models with multilingual workloads in mind.
+12/04/2024: Release of `[snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0)` and `[snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0)` our newest models with multilingual workloads in mind.
 
 ## Models
 Snowflake arctic-embed-l-v2.0 is the newest addition to the suite of embedding models Snowflake has released optimizing for retrieval performance and inference efficiency. 
@@ -112,11 +112,11 @@ Key Features:
 
 1. Multilingual without compromise: Excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL.
 
-2. Inference efficiency: Its 300m non-embedding parameters inference is fast and efficient for any scale. 
+2. Inference efficiency: Its 303m non-embedding parameters inference is fast and efficient for any scale. 
 
 3. Compression-friendly: Achieves high-quality retrieval with embeddings as small as 128 bytes/vector using Matryoshka Representation Learning (MRL) and quantization-aware embedding training.
 
-4. Drop-In Replacement: arctic-embed-l-v2.0 builds on [XMLR-Large](https://huggingface.co/FacebookAI/xlm-roberta-large) which allows direct drop-in inference replacement with any form of new libraries, kernels, inferene engines etc. 
+4. Drop-In Replacement: arctic-embed-l-v2.0 builds on [XMLR-Large](https://huggingface.co/FacebookAI/xlm-roberta-large) which allows direct drop-in inference replacement with any form of new libraries, kernels, inference engines etc. 
 
 
 ### Quality Benchmarks
@@ -125,17 +125,15 @@ You no longer need to support models to empower high-quality English and multili
 
 | Model Name | # params | # non-emb params | # dimensions | BEIR (15) | MIRACL (4) | CLEF (Focused) | CLEF (Full) |
 |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| **snowflake-arctic-l-v2.0** | 568M | 303M | 1024 | **55.6** | 55.8 | **52.9** | **54.3** |
+| snowflake-arctic-m | 109M | 86M | 768 | 54.9 | 24.9 | 34.4 | 29.1 |
+| snowflake-arctic-l | 335M | 303M | 1024 | 56.0 | 34.8 | 38.2 | 33.7 |
 | me5 base | 560M | 303M | 1024 | 51.4 | 54.0 | 43.0 | 34.6 |
 | bge-m3 (BAAI) | 568M | 303M | 1024 | 48.8 | **56.8** | 40.8 | 41.3 |
 | gte (Alibaba) | 305M | 113M | 768 | 51.1 | 52.3 | 47.7 | 53.1 |
-| Arctic-M (v1.0) | 109M | 86M | 768 | 54.9 | 24.9 | 34.4 | 29.1 |
-| snowflake-arctic-m | 335M | 303M | 1024 | 56.0 | 34.8 | 38.2 | 33.7 |
 | me5 base | 560M | 303M | 1024 | 51.4 | 54.0 | 43.0 | 34.6 |
 | bge-m3 (BAAI) | 568M | 303M | 1024 | 48.8 | 56.8 | 40.8 | 41.3 |
 | gte (Alibaba) | 305M | 113M | 768 | 51.1 | 52.3 | 47.7 | 53.1 |
-| snowflake-arctic-m | 109M | 86M | 768 | 54.9 | 24.9 | 34.4 | 29.1 |
-| snowflake-arctic-l | 335M | 303M | 1024 | 56.0 | 34.8 | 38.2 | 33.7 |
-| **snowflake-arctic-l-v2.0** | 568M | 303M | 1024 | **55.6** | 55.8 | **52.9** | **54.3** |
 
 Aside from high-quality retrieval arctic delivers embeddings that are easily compressible. Leverage vector truncation via MRL to decrease vector size by 4x with less than 3% degredation in quality. 
 Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc. 
@@ -177,6 +175,8 @@ for query, query_scores in zip(queries, scores):
 
 ```
 
+
+
 ### Using Huggingface Transformers