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---
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 [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>
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