Update README.md

README.md

@@ -10,9 +10,9 @@ language:
 - en
 ---
 
-# PRAG Encoder Model
+# PRAG
 
-This is a PRAG (Prompting for RAG-styled recommendation) encoder model trained on Amazon review data. It uses a two-tower architecture with a shared ModernBERT backbone to embed both user queries and product items into a common vector space for efficient retrieval.
+This is PRAG, a LLM  model trained for multi recommendation tasks and domains. It uses a two-tower architecture with a shared model to embed both user queries and product items into a common vector space for efficient retrieval.
 
 ## Model Details
 
@@ -48,7 +48,7 @@ This model can be used directly for:
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 - Non-English text (not explicitly trained/validated).
 - General-purpose text embedding outside the e-commerce/recommendation domain.
-- Tasks requiring generative capabilities (this is an encoder-only model).
+- Tasks requiring generative capabilities.
 
 ## Bias, Risks, and Limitations
 
@@ -115,7 +115,7 @@ except Exception as e:
 
 ## Training Details
 
-The model was trained following standard large language model (LLM) training practices for encoder-based retrieval systems.
+The model was trained following standard large language model (LLM) training practices for retrieval systems.
 
 ### Training Data
 
@@ -125,7 +125,7 @@ The model is trained on diverse product-related datasets, including product revi
 
 #### Preprocessing
 
-- Standard tokenization using a ModernBERT-based tokenizer.
+- Standard tokenization using a LLM tokenizer.
 - Input sequences are formatted to represent user intent (queries) and product characteristics (items).
 - Dynamic padding and truncation are applied to optimize training efficiency.
 
@@ -152,7 +152,7 @@ The model is evaluated using standard retrieval metrics:
 
 ### Model Architecture and Objective
 
-- **Backbone:** Standard transformer-based encoder for text representation.
+- **Backbone:** Standard LLM for text representation.
 - **Objective:** Contrastive learning objective
 
 ### Software