Update README.md

README.md

@@ -71,6 +71,8 @@ This model was fine-tuned on a novel financial news dataset, which consists of 2
 
 It is based on the [PEGASUS](https://huggingface.co/transformers/model_doc/pegasus.html) model and in particular PEGASUS fine-tuned on the Extreme Summarization (XSum) dataset: [google/pegasus-xsum model](https://huggingface.co/google/pegasus-xsum). PEGASUS was originally proposed by Jingqing Zhang, Yao Zhao, Mohammad Saleh and Peter J. Liu in [PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization](https://arxiv.org/pdf/1912.08777.pdf). 
 
+*Note: This model serves as a base version. For an even more advanced model with significantly enhanced performance, please check out our [advanced version](https://rapidapi.com/medoid-ai-medoid-ai-default/api/financial-summarization-advanced) on Rapid API. The advanced model offers more than a 16% increase in ROUGE scores (similarity to a human-generated summary) compared to our base model. Moreover, our advanced model also offers several convenient plans tailored to different use cases and workloads, ensuring a seamless experience for both personal and enterprise access.*
+
 ### How to use 
 We provide a simple snippet of how to use this model for the task of financial summarization in PyTorch.