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--- |
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tags: |
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- pegasus |
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--- |
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# Model Card for brio-xsum-cased |
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# Model Details |
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## Model Description |
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BRIO: Bringing Order to Abstractive Summarization |
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- **Developed by:** Yale LILY Lab |
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- **Shared by [Optional]:** Hugging Face |
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- **Model type:** PEGASUS |
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- **Language(s) (NLP):** Text2Text Generation |
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- **License:** More information needed |
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- **Related Models:** |
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- **Parent Model:** PEGASUS |
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- **Resources for more information:** |
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- [Github Repo](https://github.com/Yale-LILY/BRIO) |
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- [Associated Paper](https://arxiv.org/abs/2203.16804) |
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- [Associated Space](https://huggingface.co/spaces/darveen/text_summarizer) |
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# Uses |
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## Direct Use |
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This model can be used for the task of Text2Text Generation |
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## Downstream Use [Optional] |
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The model creators note in the [associated paper](https://arxiv.org/abs/2203.16804) |
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> It is possible to apply our method in a reinforcement learning setting, where the candidate summaries are dynamically generated. |
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## Out-of-Scope Use |
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The model should not be used to intentionally create hostile or alienating environments for people. |
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# Bias, Risks, and Limitations |
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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. |
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## Recommendations |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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# Training Details |
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## Training Data |
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The model creators note in the [associated paper](https://arxiv.org/abs/2203.16804) |
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> CNNDM4: is a large scale news dataset. |
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Nallapati et al: we treat the news articles as the source documents and the associated highlights as the summaries. |
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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 |
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## Training Procedure |
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### Preprocessing |
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The model creators note in the [associated paper](https://arxiv.org/abs/2203.16804) |
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> We follow Kedzie et al. (2018) for data preprocessing and splitting, and use the associated archival abstracts as the summaries |
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### Speeds, Sizes, Times |
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More information needed |
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# Evaluation |
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## Testing Data, Factors & Metrics |
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### Testing Data |
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More information needed |
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### Factors |
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More information needed |
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### Metrics |
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More information needed |
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## Results |
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### CNNDM |
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| | ROUGE-1 | ROUGE-2 | ROUGE-L | |
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|----------|---------|---------|---------| |
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| BART | 44.16 | 21.28 | 40.90 | |
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| Ours | 47.78 | 23.55 | 44.57 | |
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### XSum |
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| | ROUGE-1 | ROUGE-2 | ROUGE-L | |
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|----------|---------|---------|---------| |
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| Pegasus | 47.21 | 24.56 | 39.25 | |
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| Ours | 49.07 | 25.59 | 40.40 | |
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### NYT |
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| | ROUGE-1 | ROUGE-2 | ROUGE-L | |
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|----------|---------|---------|---------| |
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| BART | 55.78 | 36.61 | 52.60 | |
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| Ours | 57.75 | 38.64 | 54.54 | |
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# Model Examination |
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The model creators note in the [associated paper](https://arxiv.org/abs/2203.16804) |
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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. |
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# Environmental Impact |
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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). |
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- **Hardware Type:** More information needed |
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- **Hours used:** More information needed |
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- **Cloud Provider:** More information needed |
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- **Compute Region:** More information needed |
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- **Carbon Emitted:** More information needed |
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# Technical Specifications [optional] |
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## Model Architecture and Objective |
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The model creators note in the [associated paper](https://arxiv.org/abs/2203.16804) |
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> Formulate summarization as a sequence-to-sequence (Seq2Seq) problem |
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## Compute Infrastructure |
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More information needed |
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### Hardware |
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More information needed |
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### Software |
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More information needed |
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# Citation |
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**BibTeX:** |
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``` |
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@misc{https://doi.org/10.48550/arxiv.2203.16804, |
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doi = {10.48550/ARXIV.2203.16804}, |
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url = {https://arxiv.org/abs/2203.16804}, |
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author = {Liu, Yixin and Liu, Pengfei and Radev, Dragomir and Neubig, Graham}, |
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keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences}, |
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title = {BRIO: Bringing Order to Abstractive Summarization}, |
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``` |
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# Glossary [optional] |
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More information needed |
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# More Information [optional] |
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More information needed |
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# Model Card Authors [optional] |
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Yale LILY Lab in collaboration with Ezi Ozoani and the Hugging Face team |
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# Model Card Contact |
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More information needed |
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# How to Get Started with the Model |
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Use the code below to get started with the model. |
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<details> |
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<summary> Click to expand </summary> |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("Yale-LILY/brio-xsum-cased") |
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model = AutoModelForSeq2SeqLM.from_pretrained("Yale-LILY/brio-xsum-cased") |
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``` |
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</details> |
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