updated model path in the examples

README.md

@@ -38,7 +38,7 @@ Then you can use the model like this:
 from sentence_transformers import SentenceTransformer
 sentences = ['Ala ma kota i psa, widzi dzisiaj też śnieg', 'Ewa ma białe zęby']
 
-model = SentenceTransformer('pkedzia/polish-bi-encoder')
+model = SentenceTransformer('radlab/polish-bi-encoder-mean')
 embeddings = model.encode(sentences)
 print(embeddings)
 ```
@@ -64,8 +64,8 @@ def mean_pooling(model_output, attention_mask):
 sentences = ['Ala ma kota i psa, widzi dzisiaj też śnieg', 'Ewa ma białe zęby']
 
 # Load model from HuggingFace Hub
-tokenizer = AutoTokenizer.from_pretrained('pkedzia/polish-bi-encoder')
-model = AutoModel.from_pretrained('pkedzia/polish-bi-encoder')
+tokenizer = AutoTokenizer.from_pretrained('radlab/polish-bi-encoder-mean')
+model = AutoModel.from_pretrained('radlab/polish-bi-encoder-mean')
 
 # Tokenize sentences
 encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')