# 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>

{"tags": ["pegasus"]}
transformers pytorch pegasus text2text-generation arxiv:2203.16804 arxiv:1910.09700 autotrain_compatible endpoints_compatible has_space region:us
TAGS
#transformers #pytorch #pegasus #text2text-generation #arxiv-2203.16804 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us 

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
	+ Associated Paper
	+ Associated Space


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



> 
> 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) and Bender et al. (2021)). 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



> 
> 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



> 
> 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



### XSum



### NYT



Model Examination
=================


The model creators note in the associated paper
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 presented in Lacoste et al. (2019).


* 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



> 
> Formulate summarization as a sequence-to-sequence (Seq2Seq) problem
> 
> 
> 


Compute Infrastructure
----------------------


More information needed


### Hardware


More information needed


### Software


More information needed


BibTeX:


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.



 Click to expand 


### Preprocessing


The model creators note in the associated paper



> 
> 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 ### XSum ### NYT



Model Examination
=================


The model creators note in the associated paper
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 presented in Lacoste et al. (2019).


* 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



> 
> Formulate summarization as a sequence-to-sequence (Seq2Seq) problem
> 
> 
> 


Compute Infrastructure
----------------------


More information needed ### Hardware


More information needed ### Software


More information needed


BibTeX:


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.



 Click to expand
TAGS
#transformers #pytorch #pegasus #text2text-generation #arxiv-2203.16804 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us 
 ### Preprocessing


The model creators note in the associated paper



> 
> 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 ### XSum ### NYT



Model Examination
=================


The model creators note in the associated paper
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 presented in Lacoste et al. (2019).


* 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



> 
> Formulate summarization as a sequence-to-sequence (Seq2Seq) problem
> 
> 
> 


Compute Infrastructure
----------------------


More information needed ### Hardware


More information needed ### Software


More information needed


BibTeX:


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.



 Click to expand
passage: TAGS
#transformers #pytorch #pegasus #text2text-generation #arxiv-2203.16804 #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #has_space #region-us 
### Preprocessing


The model creators note in the associated paper



> 
> 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### XSum### NYT



Model Examination
=================


The model creators note in the associated paper
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 presented in Lacoste et al. (2019).


* 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



> 
> Formulate summarization as a sequence-to-sequence (Seq2Seq) problem
> 
> 
> 


Compute Infrastructure
----------------------


More information needed### Hardware


More information needed