173 lines
4.1 KiB
Markdown
173 lines
4.1 KiB
Markdown
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# Model Card for roberta-base-on-cuad
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# Model Details
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## Model Description
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- **Developed by:** Mohammed Rakib
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- **Shared by [Optional]:** More information needed
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- **Model type:** Question Answering
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- **Language(s) (NLP):** en
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- **License:** More information needed
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- **Related Models:**
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- **Parent Model:** RoBERTa
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- **Resources for more information:**
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- [GitHub Repo](https://github.com/facebookresearch/fairseq/tree/main/examples/roberta)
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- [Associated Paper](https://arxiv.org/abs/1907.11692)
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# Uses
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## Direct Use
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This model can be used for the task of Question Answering.
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## Downstream Use [Optional]
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More information needed
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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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See [CUAD dataset card](https://huggingface.co/datasets/cuad) for more information.
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## Training Procedure
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### Preprocessing
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More information needed
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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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See [CUAD dataset card](https://huggingface.co/datasets/cuad) for more information.
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### Factors
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### Metrics
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More information needed
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## Results
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More information needed
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# Model Examination
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More information needed
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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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More information needed
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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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@article{DBLP:journals/corr/abs-1907-11692,
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author = {Yinhan Liu and
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Myle Ott and
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Naman Goyal and
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Jingfei Du and
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Mandar Joshi and
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Danqi Chen and
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Omer Levy and
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Mike Lewis and
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Luke Zettlemoyer and
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Veselin Stoyanov},
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title = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
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journal = {CoRR},
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volume = {abs/1907.11692},
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year = {2019},
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url = {http://arxiv.org/abs/1907.11692},
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archivePrefix = {arXiv},
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eprint = {1907.11692},
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timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
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biburl = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
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bibsource = {dblp computer science bibliography, https://dblp.org}
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}
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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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Mohammed Rakib 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, AutoModelForQuestionAnswering
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tokenizer = AutoTokenizer.from_pretrained("Rakib/roberta-base-on-cuad")
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model = AutoModelForQuestionAnswering.from_pretrained("Rakib/roberta-base-on-cuad")
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```
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</details>
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