Model Card for roberta-base-on-cuad

Model Details

Model Description

Developed by: Mohammed Rakib
Shared by [Optional]: More information needed
Model type: Question Answering
Language(s) (NLP): en
License: More information needed
Related Models:
- Parent Model: RoBERTa
Resources for more information:
- GitHub Repo
- Associated Paper

Uses

Direct Use

This model can be used for the task of Question Answering.

Downstream Use [Optional]

More information needed

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

See CUAD dataset card for more information.

Training Procedure

Preprocessing

More information needed

Speeds, Sizes, Times

More information needed

Evaluation

Testing Data, Factors & Metrics

Testing Data

See CUAD dataset card for more information.

Factors

Metrics

More information needed

Results

More information needed

Model Examination

More information needed

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

More information needed

Compute Infrastructure

More information needed

Hardware

More information needed

Software

More information needed

Citation

BibTeX:

@article{DBLP:journals/corr/abs-1907-11692,
 author    = {Yinhan Liu and
              Myle Ott and
              Naman Goyal and
              Jingfei Du and
              Mandar Joshi and
              Danqi Chen and
              Omer Levy and
              Mike Lewis and
              Luke Zettlemoyer and
              Veselin Stoyanov},
 title     = {RoBERTa: {A} Robustly Optimized {BERT} Pretraining Approach},
 journal   = {CoRR},
 volume    = {abs/1907.11692},
 year      = {2019},
 url       = {http://arxiv.org/abs/1907.11692},
 archivePrefix = {arXiv},
 eprint    = {1907.11692},
 timestamp = {Thu, 01 Aug 2019 08:59:33 +0200},
 biburl    = {https://dblp.org/rec/journals/corr/abs-1907-11692.bib},
 bibsource = {dblp computer science bibliography, https://dblp.org}
}

Glossary [optional]

More information needed

More Information [optional]

More information needed

Model Card Authors [optional]

Mohammed Rakib 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.

Click to expand

```python from transformers import AutoTokenizer, AutoModelForQuestionAnswering

tokenizer = AutoTokenizer.from_pretrained("Rakib/roberta-base-on-cuad")

model = AutoModelForQuestionAnswering.from_pretrained("Rakib/roberta-base-on-cuad")

</details>