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

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.

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
tokenizer = AutoTokenizer.from_pretrained("Yale-LILY/brio-xsum-cased")
 
model = AutoModelForSeq2SeqLM.from_pretrained("Yale-LILY/brio-xsum-cased")