model documentation (#3)

- model documentation (13a6f0eda15b69e936ce622aab38944144d16fcf)


Co-authored-by: Nazneen Rajani <nazneen@users.noreply.huggingface.co>

README.md

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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>