readme: ---
  language:
  - en
  tags:
  - summarization
  datasets:
  - xsum
  metrics:
  - rouge
  widget:
  - text: National Commercial Bank (NCB), Saudi Arabia’s largest lender by assets, agreed
      to buy rival Samba Financial Group for $15 billion in the biggest banking takeover
      this year.NCB will pay 28.45 riyals ($7.58) for each Samba share, according to
      a statement on Sunday, valuing it at about 55.7 billion riyals. NCB will offer
      0.739 new shares for each Samba share, at the lower end of the 0.736-0.787 ratio
      the banks set when they signed an initial framework agreement in June.The offer
      is a 3.5% premium to Samba’s Oct. 8 closing price of 27.50 riyals and about 24%
      higher than the level the shares traded at before the talks were made public.
      Bloomberg News first reported the merger discussions.The new bank will have total
      assets of more than $220 billion, creating the Gulf region’s third-largest lender.
      The entity’s $46 billion market capitalization nearly matches that of Qatar National
      Bank QPSC, which is still the Middle East’s biggest lender with about $268 billion
      of assets.
  model-index:
  - name: human-centered-summarization/financial-summarization-pegasus
    results:
    - task:
        type: summarization
        name: Summarization
      dataset:
        name: xsum
        type: xsum
        config: default
        split: test
      metrics:
      - type: rouge
        value: 35.2055
        name: ROUGE-1
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMTA5OTZkY2YxMDU1YzE3NGJlMmE1OTg1NjlmNzcxOTg4YzY2OThlOTlkNGFhMGFjZWY4YjdiMjU5NDdmMWYzNSIsInZlcnNpb24iOjF9.ufBRoV2JoX4UlEfAUOYq7F3tZougwngdpKlnaC37tYXJU3omsR5hTsWM69hSdYO-k0cKUbAWCAMzjmoGwIaPAw
      - type: rouge
        value: 16.5689
        name: ROUGE-2
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOWQwMmM2NjJjNzM1N2Y3NjZmMmE5NzNlNjRjNjEwNzNhNjcyZTRiMGRlODY3NWUyMGQ0YzZmMGFhODYzOTRmOSIsInZlcnNpb24iOjF9.AZZkbaYBZG6rw6-QHYjRlSl-p0gBT2EtJxwjIP7QYH5XIQjeoiQsTnDPIq25dSMDbmQLSZnpHC104ZctX0f_Dg
      - type: rouge
        value: 30.1285
        name: ROUGE-L
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiOTRjYThlMTllZjI4MGFiMDZhZTVkYmRjMTNhZDUzNTQ0OWQyNDQxMmQ5ODJiMmJiNGI3OTAzYjhiMzc2MTI4NCIsInZlcnNpb24iOjF9.zTHd3F4ZlgS-azl-ZVjOckcTrtrJmDOGWVaC3qQsvvn2UW9TnseNkmo7KBc3DJU7_NmlxWZArl1BdSetED0NCg
      - type: rouge
        value: 30.1706
        name: ROUGE-LSUM
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiZGMzZGFjNzVkYWI0NTJkMmZjZDQ0YjhiYjIxN2VkNmJjMTgwZTk1NjFlOGU2NjNjM2VjYTNlYTBhNTQ5MGZkNSIsInZlcnNpb24iOjF9.xQ2LoI3PwlEiXo1OT2o4Pq9o2thYCd9lSCKCWlLmZdxI5GxdsjcASBKmHKopzUcwCGBPR7zF95MHSAPyszOODA
      - type: loss
        value: 2.7092134952545166
        name: loss
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiMzQzODE0NDc5YTYzYjJlMWU2YTVjOGRjN2JmYWVkOWNkNTRlMTZlOWIyN2NiODJkMDljMjI3YzZmYzM3N2JjYSIsInZlcnNpb24iOjF9.Vv_pdeFuRMoKK3cPr5P6n7D6_18ChJX-2qcT0y4is3XX3mS98fk3U1AYEuy9nBHOwYR3o0U8WBgQ-Ya_FqefBg
      - type: gen_len
        value: 15.1414
        name: gen_len
        verified: true
        verifyToken: eyJhbGciOiJFZERTQSIsInR5cCI6IkpXVCJ9.eyJoYXNoIjoiYjk5OTk3NWRiNjZlZmQzMmYwOTU2MmQwOWE1MDNlNTg3YWVkOTgwOTc2ZTQ0MTBiZjliOWMyZTYwMDI2MDUzYiIsInZlcnNpb24iOjF9.Zvj84JzIhM50rWTQ2GrEeOU7HrS8KsILH-8ApTcSWSI6kVnucY0MyW2ODxvRAa_zHeCygFW6Q13TFGrT5kLNAA
  ---
  
  ### PEGASUS for Financial Summarization 
  
  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.
  
  ```Python
  from transformers import PegasusTokenizer, PegasusForConditionalGeneration, TFPegasusForConditionalGeneration
  
  # Let's load the model and the tokenizer 
  model_name = "human-centered-summarization/financial-summarization-pegasus"
  tokenizer = PegasusTokenizer.from_pretrained(model_name)
  model = PegasusForConditionalGeneration.from_pretrained(model_name) # If you want to use the Tensorflow model 
                                                                      # just replace with TFPegasusForConditionalGeneration
  
  
  # Some text to summarize here
  text_to_summarize = "National Commercial Bank (NCB), Saudi Arabia’s largest lender by assets, agreed to buy rival Samba Financial Group for $15 billion 