Update README.md

README.md

@@ -11,15 +11,14 @@ widget:
 
 ---
 
-### Pegasus for Financial Summarization 
+### PEGASUS for Financial Summarization 
 
-This model was trained on a novel financial dataset which consists of 2K financial and economic articles from the [Bloomberg](https://www.bloomberg.com/europe) website of different categories such as stock, markets, currencies, rate and cryptocurrences, using [PEGASUS](https://huggingface.co/transformers/model_doc/pegasus.html). This model is fine-tuned on the [google/pegasus-xsum model](https://huggingface.co/google/pegasus-xsum).  
-
-PEGASUS model 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). 
+This model was fine-tuned on a novel financial news dataset, which consists of 2K articles from [Bloomberg](https://www.bloomberg.com/europe), on topics such as stock, markets, currencies, rate and cryptocurrencies. 
 
+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). 
 
 ### How to use 
-We provide a simple snippet of how to use this model for the task of financial summarization in Pytorch.
+We provide a simple snippet of how to use this model for the task of financial summarization in PyTorch.
 
 ```Python
 from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration
@@ -80,3 +79,8 @@ BibTeX entry:
   year={2021}
 }
 ```
+
+## Support
+
+Contact us at [[email protected]](mailto:[email protected]) if you are interested in a more sophisticated version of the model, trained on more articles and adapted to your needs!
+