
# 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>


{"language": ["code"], "license": "apache-2.0", "widget": [{"text": "public [MASK] isOdd(Integer num) {if (num % 2 == 0) {return \"even\";} else {return \"odd\";}}"}]}
transformers pytorch safetensors bert fill-mask code arxiv:2110.10404 arxiv:1910.09700 license:apache-2.0 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #code #arxiv-2110.10404 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 


# 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  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.
 
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>

 
 
</details>


# 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  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.
 
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>

 
 
</details>
TAGS
#transformers #pytorch #safetensors #bert #fill-mask #code #arxiv-2110.10404 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
 # 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  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.
 
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>

 
 
</details>
passage: TAGS
#transformers #pytorch #safetensors #bert #fill-mask #code #arxiv-2110.10404 #arxiv-1910.09700 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us 
# 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  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