Model Card for JavaBERT

A BERT-like model pretrained on Java software code.

Model Details

Model Description

A BERT-like model pretrained on Java software code.

• Developed by: Christian-Albrechts-University of Kiel (CAUKiel)
• Shared by [Optional]: Hugging Face
• Model type: Fill-Mask
• Language(s) (NLP): en
• License: Apache-2.0
• Related Models: A version of this model using an uncased tokenizer is available at CAUKiel/JavaBERT-uncased.
  • Parent Model: BERT
• Resources for more information:
  • Associated Paper

Uses

Direct Use

Fill-Mask

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. { see paper= word something)

Training Details

Training Data

The model was trained on 2,998,345 Java files retrieved from open source projects on GitHub. A bert-base-cased tokenizer is used by this model.

Training Procedure

Training Objective

A MLM (Masked Language Model) objective was used to train this model.

Preprocessing

More information needed.

Speeds, Sizes, Times

More information needed.

Evaluation

Testing Data, Factors & Metrics

Testing Data

More information needed.

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:

@inproceedings{De_Sousa_Hasselbring_2021,
  address={Melbourne, Australia},
  title={JavaBERT: Training a Transformer-Based Model for the Java Programming Language},
  rights={https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html},
  ISBN={9781665435833},
  url={https://ieeexplore.ieee.org/document/9680322/},
  DOI={10.1109/ASEW52652.2021.00028},
  booktitle={2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)},
  publisher={IEEE},
  author={Tavares de Sousa, Nelson and Hasselbring, Wilhelm},
  year={2021},
  month=nov,
  pages={90–95} }

APA:

More information needed.

Glossary [optional]

More information needed.

More Information [optional]

More information needed.

Model Card Authors [optional]

Christian-Albrechts-University of Kiel (CAUKiel) in collaboration with Ezi Ozoani and the team at Hugging Face

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

from transformers import pipeline
pipe = pipeline('fill-mask', model='CAUKiel/JavaBERT')
output = pipe(CODE) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code.