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--- |
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language: |
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- code |
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license: apache-2.0 |
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widget: |
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- text: public [MASK] isOdd(Integer num) {if (num % 2 == 0) {return "even";} else |
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{return "odd";}} |
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--- |
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# Model Card for JavaBERT |
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A BERT-like model pretrained on Java software code. |
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# Model Details |
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## Model Description |
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A BERT-like model pretrained on Java software code. |
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- **Developed by:** Christian-Albrechts-University of Kiel (CAUKiel) |
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- **Shared by [Optional]:** Hugging Face |
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- **Model type:** Fill-Mask |
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- **Language(s) (NLP):** en |
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- **License:** Apache-2.0 |
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- **Related Models:** A version of this model using an uncased tokenizer is available at [CAUKiel/JavaBERT-uncased](https://huggingface.co/CAUKiel/JavaBERT-uncased). |
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- **Parent Model:** BERT |
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- **Resources for more information:** |
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- [Associated Paper](https://arxiv.org/pdf/2110.10404.pdf) |
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# Uses |
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## Direct Use |
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Fill-Mask |
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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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{ see paper= word something) |
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# Training Details |
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## Training Data |
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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. |
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## Training Procedure |
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### Training Objective |
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A MLM (Masked Language Model) objective was used to train this model. |
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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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More information needed. |
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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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@inproceedings{De_Sousa_Hasselbring_2021, |
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address={Melbourne, Australia}, |
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title={JavaBERT: Training a Transformer-Based Model for the Java Programming Language}, |
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rights={https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html}, |
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ISBN={9781665435833}, |
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url={https://ieeexplore.ieee.org/document/9680322/}, |
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DOI={10.1109/ASEW52652.2021.00028}, |
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booktitle={2021 36th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW)}, |
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publisher={IEEE}, |
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author={Tavares de Sousa, Nelson and Hasselbring, Wilhelm}, |
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year={2021}, |
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month=nov, |
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pages={90–95} } |
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``` |
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**APA:** |
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More information needed. |
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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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Christian-Albrechts-University of Kiel (CAUKiel) in collaboration with Ezi Ozoani and the team at Hugging Face |
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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 pipeline |
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pipe = pipeline('fill-mask', model='CAUKiel/JavaBERT') |
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output = pipe(CODE) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code. |
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``` |
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</details> |
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