Description updated

README.md

@@ -1,12 +1,11 @@
 ---
-#library_name: sentence-transformers
 pipeline_tag: feature-extraction
 tags:
-- pytorch
-- sentence-transformers
-- feature-extraction
-- sentence-similarity
-- transformers
+  - pytorch
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - transformers
 language:
   - ru
   - en
@@ -38,6 +37,8 @@ trained over 20 epochs on the following datasets:
 The goal of this model is to generate identical or very similar embeddings regardless of whether the text is written in
 English or Russian.
 
+[Enbeddrus GGUF](https://ollama.com/evilfreelancer/enbeddrus) version available via Ollama.
+
 ## Usage (Sentence-Transformers)
 
 Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
@@ -62,7 +63,7 @@ sentences = [
     "Machine learning helps to create intelligent systems.",
 ]
 
-model = SentenceTransformer('evilfreelancer/enbeddrus')
+model = SentenceTransformer('evilfreelancer/enbeddrus-v0.1')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -98,8 +99,8 @@ sentences = [
 ]
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('evilfreelancer/enbeddrus')
-model = AutoModel.from_pretrained('evilfreelancer/enbeddrus')
+tokenizer = AutoTokenizer.from_pretrained('evilfreelancer/enbeddrus-v0.1')
+model = AutoModel.from_pretrained('evilfreelancer/enbeddrus-v0.1')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')