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- ### Pegasus for Financial Summarization
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- 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).
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- 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).
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  ### How to use
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- We provide a simple snippet of how to use this model for the task of financial summarization in Pytorch.
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  ```Python
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  from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration
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  year={2021}
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  }
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  ```
 
 
 
 
 
 
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+ ### PEGASUS for Financial Summarization
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+ 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.
 
 
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+ 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).
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  ### How to use
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+ We provide a simple snippet of how to use this model for the task of financial summarization in PyTorch.
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  ```Python
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  from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration
 
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  year={2021}
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  }
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  ```
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+ ## Support
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+ Contact us at [info@medoid.ai](mailto:info@medoid.ai) if you are interested in a more sophisticated version of the model, trained on more articles and adapted to your needs!
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