readme: 
  ---
  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.
   
   
   
   
   
   
  # 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
   
  
   
  ### 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:**
   
  More information needed.
   
  **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>
  
  