Update README.md

README.md

@@ -112,7 +112,7 @@ 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: With its 300m non-embedding parameters inference is fast and efficient for any scale. 
+2. Inference efficiency: Its 300m 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.
 
@@ -126,7 +126,7 @@ 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) |
 |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
 | 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 |
+| 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 |
@@ -136,7 +136,7 @@ You no longer need to support models to empower high-quality English and multili
 | 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-m-v2.0 | 305M | 113M | 768 | 55.4 | 55.2 | 51.7 | 53.9 |
-| **snowflake-arctic-l-v2.0** | 568M | 303M | 1024 | 55.6 | 55.8 | 52.9 | **54.3** |
+| **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 3-4x with less than 3% degredation in quality. 
 Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc.