Update README.md

README.md

@@ -6,6 +6,8 @@ tags:
 - feature-extraction
 - sentence-similarity
 - transformers
+- 16k
+- efficient attention
 license: artistic-2.0
 datasets:
 - pszemraj/synthetic-text-similarity
@@ -13,11 +15,12 @@ language:
 - en
 ---
 
-# BEE-spoke-data/mega-small-embed-syntheticSTS-16384
+# mega-small-embed-synthSTS-16384: v1
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
-<!--- Describe your model here -->
+- This model is a "v1" and we may make improved versions in the future. Or, we may not.
+
 
 ## Usage
 
@@ -42,7 +45,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["This is an example sentence", "Each sentence is converted"]
 
-model = SentenceTransformer('BEE-spoke-data/mega-small-embed-syntheticSTS-16384')
+model = SentenceTransformer('BEE-spoke-data/mega-small-embed-synthSTS-16384-v1')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -68,8 +71,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['This is an example sentence', 'Each sentence is converted']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('BEE-spoke-data/mega-small-embed-syntheticSTS-16384')
-model = AutoModel.from_pretrained('BEE-spoke-data/mega-small-embed-syntheticSTS-16384')
+tokenizer = AutoTokenizer.from_pretrained('BEE-spoke-data/mega-small-embed-synthSTS-16384-v1')
+model = AutoModel.from_pretrained('BEE-spoke-data/mega-small-embed-synthSTS-16384-v1')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')