Update README.md

README.md

@@ -9,8 +9,9 @@ library_name: transformers
 ---
 
 # heydariAI/persian-embeddings
+This model is a fine-tuned version of xlm-roberta-base, specifically trained on a massive corpus of Persian data to create high-quality contextual embeddings for Persian sentences and paragraphs. It is designed to perform exceptionally well on tasks such as semantic search, clustering, and contextual similarity for Persian text, while also supporting multilingual tasks in English and Persian.
 
-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.
+The fine-tuning process focused on adapting the pre-trained multilingual XLM-RoBERTa model to better capture Persian linguistic nuances, making it highly effective for tasks requiring embeddings tailored to the Persian language.
 
 <!--- Describe your model here -->
 
@@ -51,7 +52,7 @@ def mean_pooling(model_output, attention_mask):
 
 
 # Sentences we want sentence embeddings for
-sentences = ['This is an example sentence', 'Each sentence is converted']
+sentences = ['what are Large Language Models?', 'مدل های زبانی بزرگ چه هستند؟']
 
 # Load model from HuggingFace Hub
 tokenizer = AutoTokenizer.from_pretrained('heydariAI/persian-embeddings')