| |
|
| | --- |
| | language: |
| | - code |
| | license: apache-2.0 |
| | widget: |
| | - text: public [MASK] isOdd(Integer num) {if (num % 2 == 0) {return "even";} else |
| | {return "odd";}} |
| | --- |
| | |
| | # Model Card for JavaBERT |
| | |
| | A BERT-like model pretrained on Java software code. |
| | |
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| | |
| | |
| | # 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](https://huggingface.co/CAUKiel/JavaBERT-uncased). |
| | - **Parent Model:** BERT |
| | - **Resources for more information:** |
| | - [Associated Paper](https://arxiv.org/pdf/2110.10404.pdf) |
| | |
| | |
| | # 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)](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. |
| | { 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 |
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|
| | |
| | ### 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](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 |
| | |
| | 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. |
| | |
| | <details> |
| | <summary> Click to expand </summary> |
| |
|
| | ```python |
| | 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. |
| | ``` |
| | |
| | </details> |
| |
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