readme: ---
  tags:
  - pegasus
  
  ---
  # Model Card for brio-xsum-cased
   
   
  # Model Details
   
  ## Model Description
   
  BRIO: Bringing Order to Abstractive Summarization
   
  - **Developed by:** Yale LILY Lab
  - **Shared by [Optional]:** Hugging Face
  - **Model type:** PEGASUS
  - **Language(s) (NLP):** Text2Text Generation
  - **License:** More information needed
  - **Related Models:** 
    - **Parent Model:** PEGASUS
  - **Resources for more information:** 
    - [Github Repo](https://github.com/Yale-LILY/BRIO)
    - [Associated Paper](https://arxiv.org/abs/2203.16804)
    - [Associated Space](https://huggingface.co/spaces/darveen/text_summarizer)
   
   
  # Uses
   
  ## Direct Use
  This model can be used for the task of Text2Text Generation
   
  ## Downstream Use [Optional]
   
  The model creators note in the [associated paper](https://arxiv.org/abs/2203.16804)
  > It is possible to apply our method in a reinforcement learning setting, where the candidate summaries are dynamically generated. 
   
  ## Out-of-Scope Use
   
   
  The model should not be used to intentionally create hostile or alienating environments for people. 
   
  # Bias, Risks, and Limitations
   
   
  Significant research has explored bias and fairness issues with language models (see, e.g., [Sheng et al. (2021)](https://aclanthology.org/2021.acl-long.330.pdf) and [Bender et al. (2021)](https://dl.acm.org/doi/pdf/10.1145/3442188.3445922)). Predictions generated by the model may include disturbing and harmful stereotypes across protected classes; identity characteristics; and sensitive, social, and occupational groups.
   
   
  ## Recommendations
   
   
  Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
   
   
  # Training Details
   
  ## Training Data
  The model creators note in the [associated paper](https://arxiv.org/abs/2203.16804)
  > CNNDM4: is a large scale news dataset.
  Nallapati et al: we treat the news articles as the source documents and the associated highlights as the summaries. 
  XSum5: is a highly abstractive dataset of articles from the British Broadcasting Corporation (BBC). NYT6: contains articles from the New York Times and the associated summaries
   
  ## Training Procedure
   
   
  ### Preprocessing
   The model creators note in the [associated paper](https://arxiv.org/abs/2203.16804)
   > We follow Kedzie et al. (2018) for data preprocessing and splitting, and use the associated archival abstracts as the summaries
   
  ### Speeds, Sizes, Times
   
  More information needed
   
  # Evaluation
   
   
  ## Testing Data, Factors & Metrics
   
  ### Testing Data
   
  More information needed
   
  ### Factors
   
  More information needed
   
  ### Metrics
   
  More information needed
   
  ## Results 
   
  
   
  ### CNNDM
  |          | ROUGE-1 | ROUGE-2 | ROUGE-L |
  |----------|---------|---------|---------|
  | BART     | 44.16   | 21.28   | 40.90   |
  | Ours     | 47.78   | 23.55   | 44.57   |
  
   
  ### XSum
  |          | ROUGE-1 | ROUGE-2 | ROUGE-L |
  |----------|---------|---------|---------|
  | Pegasus  | 47.21   | 24.56   | 39.25   |
  | Ours     | 49.07   | 25.59   | 40.40   |
  
   
  ### NYT
  |          | ROUGE-1 | ROUGE-2 | ROUGE-L |
  |----------|---------|---------|---------|
  | BART     | 55.78   | 36.61   | 52.60   |
  | Ours     | 57.75   | 38.64   | 54.54   |
  
   
   
  # Model Examination
  The model creators note in the [associated paper](https://arxiv.org/abs/2203.16804)
  We attribute BRIO-Ctr’s superior performance to its use of the same model architecture (BART) for both candidate generation and scoring, while SimCLS uses RoBERTa as the evaluation model. As a result, BRIO-Ctr maximizes the parameter sharing between the two stages, and preserves the power of the Seq2Seq model pre-trained on the same dataset.
   
  # Environmental Impact
   
   
  Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
   
  - **Hardware Type:** More information needed
  - **Hours used:** More information needed
  - **Cloud Provider:** More information needed
  - **Compute Region:** More information needed
  - **Carbon Emitted:** More information needed
   
  # Technical Specifications [optional]
   
  ## Model Architecture and Objective
  The model creators note in the [associated paper](https://arxiv.org/abs/2203.16804)
   
  > Formulate summarization as a sequence-to-sequence (Seq2Seq) problem
   
  ## Compute Infrastructure
   
  More information needed
   
  ### Hardware
   
  More information needed
   
  ### Software
   
  More information needed
   
  # Citation
   
   
  **BibTeX:**
   ```
  @misc{https://doi.org/10.48550/arxiv.2203.16804,
    doi = {10.48550/ARXIV.2203.16804},
    
    url = {https://arxiv.org/abs/2203.16804},
    
    author = {Liu, Yixin and Liu, Pengfei and Radev, Dragomir and Neubig, Graham},
    
    keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
    
    title = {BRIO: Bringing Order to Abstractive Summarization},
  ```
   
   
   
  # Glossary [o