model documentation

#3
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+ ---
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+ tags:
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+ - pegasus
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+
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+ ---
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+ # Model Card for brio-xsum-cased
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+
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+
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+ # Model Details
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+
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+ ## Model Description
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+
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+ BRIO: Bringing Order to Abstractive Summarization
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+
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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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+
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+
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+ # Uses
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+
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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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+
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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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+
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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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+
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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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+
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+
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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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+
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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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+
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+ ## Training Procedure
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+
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+
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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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+
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+ ### Speeds, Sizes, Times
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+ More information needed
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+ # Evaluation
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+
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+ ## Testing Data, Factors & Metrics
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+
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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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+
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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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+
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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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+
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+
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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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+
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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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+
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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>