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-tags:
-- pegasus
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-# 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 [optional]
- 
-More information needed
- 
-# More Information [optional]
- 
-More information needed
- 
-# Model Card Authors [optional]
- 
-Yale LILY Lab in collaboration with Ezi Ozoani and the Hugging Face team
- 
-# Model Card Contact
- 
-More information needed
- 
-# How to Get Started with the Model
- 
-Use the code below to get started with the model.
- 
-<details>
-<summary> Click to expand </summary>
-
-```python
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
- 
-tokenizer = AutoTokenizer.from_pretrained("Yale-LILY/brio-xsum-cased")
- 
-model = AutoModelForSeq2SeqLM.from_pretrained("Yale-LILY/brio-xsum-cased")
- 
-```
-</details>