Update README.md

README.md

@@ -11,9 +11,11 @@ license: lgpl-3.0
 library_name: sentence-transformers
 datasets:
 - radlab/polish-sts-dataset
+models:
+- sdadas/polish-roberta-large-v2
 ---
 
-# polish-roberta-large-v2-sts
+# radlab/polish-sts-v2
 
 This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
@@ -33,7 +35,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ["Ala ma kota", "Ala ma psa"]
 
-model = SentenceTransformer('radlab/polish-roberta-large-v2-sts')
+model = SentenceTransformer('radlab/polish-sts-v2')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -58,8 +60,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['Ala ma kota', 'Ala ma psa']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('radlab/polish-roberta-large-v2-sts')
-model = AutoModel.from_pretrained('radlab/polish-roberta-large-v2-sts')
+tokenizer = AutoTokenizer.from_pretrained('radlab/polish-sts-v2')
+model = AutoModel.from_pretrained('radlab/polish-sts-v2')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')