Update README.md

README.md

@@ -131,9 +131,6 @@ You no longer need to support models to empower high-quality English and multili
 | 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 |
-| 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 |
 
 Aside from high-quality retrieval, arctic delivers embeddings that are easily compressible. By leveraging vector truncation via MRL to decrease vector size by 3x with about 3% degradation in quality. 
 Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc. 
@@ -188,7 +185,7 @@ tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModel.from_pretrained(model_name, add_pooling_layer=False, trust_remote_code=True)
 model.eval()
 
-query_prefix = 'Query: '
+query_prefix = 'query: '
 queries  = ['what is snowflake?', 'Where can I get the best tacos?']
 queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
 query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=8192)