Update README.md

README.md

@@ -2801,7 +2801,7 @@ model-index:
       value: 85.30624598674467
 license: apache-2.0
 ---
-<h1 align="center">Snowflake's artic-embed-m</h1>
+<h1 align="center">Snowflake's Artic-embed-m</h1>
 <h4 align="center">
    <p>
        <a href=#news>News</a> |
@@ -2825,7 +2825,7 @@ license: apache-2.0
 ## Models
 
 
-Arctic-Embed is a suite of text embedding models that focuses on creating high-quality embedding models for retrieval that are optimized for performance.
+Arctic-Embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.
 
 
 The `arctic-text-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy when compared to other top models.
@@ -2944,8 +2944,8 @@ To use an arctic-embed model, you can use the transformers package, as shown bel
 import torch
 from transformers import AutoModel, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained('Snowflake/snow-text-embed-base')
-model = AutoModel.from_pretrained('Snowflake/snow-text-embed-base', add_pooling_layer=False)
+tokenizer = AutoTokenizer.from_pretrained('Snowflake/arctic-embed-m')
+model = AutoModel.from_pretrained('Snowflake/arctic-embed-m', add_pooling_layer=False)
 model.eval()
 
 query_prefix = 'Represent this sentence for searching relevant passages: '
@@ -2981,7 +2981,7 @@ If you use the long context model and have more than 2048 tokens, ensure that yo
 
 
 ``` py
-model = AutoModel.from_pretrained('Snowflake/[arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-m-long/)', trust_remote_code=True, rotary_scaling_factor=2)
+model = AutoModel.from_pretrained('Snowflake/arctic-embed-m-long', trust_remote_code=True, rotary_scaling_factor=2)
 ```