This is an
unofficial reupload of
CAUKiel/JavaBERT-uncased in the
SafeTensors format using
transformers 4.40.1. The goal of this reupload is to prevent older models that are still relevant baselines from becoming stale as a result of changes in HuggingFace. Additionally, I may include minor corrections, such as model max length configuration.
Original model card below:
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 cased tokenizer is available at claudios/JavaBERT.
- Resources for more information:
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.
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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:
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.
Click to expand
1from transformers import pipeline
2pipe = pipeline('fill-mask', model='CAUKiel/JavaBERT')
3output = pipe(CODE) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code.
