# Model Card for Model ID

A BERT-like model pre-trained on Java buggy code.

## Model Details

### Model Description

A BERT-like model pre-trained on Java buggy code.

- **Developed by:** André Nascimento
- **Shared by:** Hugging Face
- **Model type:** Fill-Mask
- **Language(s) (NLP):** Java (EN)
- **License:** [More Information Needed]
- **Finetuned from model:** [BERT Base Uncased](https://huggingface.co/bert-base-cased)

## Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

### Direct Use

Fill-Mask.

[More Information Needed]

### Downstream Use [optional]

The model can be used for other tasks, like Text Classification.

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->

[More Information Needed]

## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

[More Information Needed]

### Recommendations

<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

## How to Get Started with the Model

Use the code below to get started with the model.

```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='bert-java-bfp_combined')
unmasker(java_code) # Replace with Java code; Use '[MASK]' to mask tokens/words in the code.
```

[More Information Needed]

## Training Details

### Training Data

The model was trained on 198088 Java methods, containing the code before and after the bug fix was applied. The whole dataset was built by combining the [Dataset of Bug-Fix Pairs for small and medium methods](https://sites.google.com/view/learning-fixes/data#h.p_p8kX8c2_n_pt) source code. An 80/20 train/validation split was applied afterwards.

### Training Procedure 

<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->

#### Preprocessing [optional]

Remove comments and replace consecutive whitespace characters by a single space.

#### Training Hyperparameters

- **Training regime:** fp16 mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->

[More Information Needed]

## Evaluation

<!-- This section describes the evaluation protocols and provides the results. -->

### Testing Data, Factors & Metrics

#### Testing Data

The model was evaluated on 49522 Java methods, from the 20% split of the dataset mentioned in [Training Data](#training-data)

[More Information Needed]

#### Factors

<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->

[More Information Needed]

#### Metrics

<!-- These are the evaluation metrics being used, ideally with a description of why. -->

Perplexity

### Results

1.48

#### Summary



## Model Examination [optional]

<!-- Relevant interpretability work for the model goes here -->

[More Information Needed]

## Environmental Impact

<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->

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 [optional]

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

**BibTeX:**

[More Information Needed]

**APA:**

[More Information Needed]

## Glossary [optional]

<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->

[More Information Needed]

## More Information [optional]

[More Information Needed]

## Model Card Authors [optional]

[More Information Needed]

## Model Card Contact

[More Information Needed]



{"language": ["code"], "library_name": "transformers", "tags": ["MLM"], "metrics": ["perplexity"], "pipeline_tag": "fill-mask"}
transformers pytorch bert fill-mask MLM code arxiv:1910.09700 autotrain_compatible endpoints_compatible region:us
TAGS
#transformers #pytorch #bert #fill-mask #MLM #code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us 

# Model Card for Model ID

A BERT-like model pre-trained on Java buggy code.

## Model Details

### Model Description

A BERT-like model pre-trained on Java buggy code.

- Developed by: André Nascimento
- Shared by: Hugging Face
- Model type: Fill-Mask
- Language(s) (NLP): Java (EN)
- License: 
- Finetuned from model: BERT Base Uncased

## Uses



### Direct Use

Fill-Mask.



### Downstream Use [optional]

The model can be used for other tasks, like Text Classification.

### Out-of-Scope Use





## Bias, Risks, and Limitations





### 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.

## How to Get Started with the Model

Use the code below to get started with the model.





## Training Details

### Training Data

The model was trained on 198088 Java methods, containing the code before and after the bug fix was applied. The whole dataset was built by combining the Dataset of Bug-Fix Pairs for small and medium methods source code. An 80/20 train/validation split was applied afterwards.

### Training Procedure 



#### Preprocessing [optional]

Remove comments and replace consecutive whitespace characters by a single space.

#### Training Hyperparameters

- Training regime: fp16 mixed precision 

#### Speeds, Sizes, Times [optional]





## Evaluation



### Testing Data, Factors & Metrics

#### Testing Data

The model was evaluated on 49522 Java methods, from the 20% split of the dataset mentioned in Training Data



#### Factors





#### Metrics



Perplexity

### Results

1.48

#### Summary



## Model Examination [optional]





## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: 

## Technical Specifications [optional]

### Model Architecture and Objective



### Compute Infrastructure



#### Hardware



#### Software



[optional]



BibTeX:



APA:



## Glossary [optional]





## More Information [optional]



## Model Card Authors [optional]



## Model Card Contact





# Model Card for Model ID

A BERT-like model pre-trained on Java buggy code. ## Model Details ### Model Description

A BERT-like model pre-trained on Java buggy code.

- Developed by: André Nascimento
- Shared by: Hugging Face
- Model type: Fill-Mask
- Language(s) (NLP): Java (EN)
- License: 
- Finetuned from model: BERT Base Uncased ## Uses ### Direct Use

Fill-Mask. ### Downstream Use [optional]

The model can be used for other tasks, like Text Classification. ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model

Use the code below to get started with the model. ## Training Details ### Training Data

The model was trained on 198088 Java methods, containing the code before and after the bug fix was applied. The whole dataset was built by combining the Dataset of Bug-Fix Pairs for small and medium methods source code. An 80/20 train/validation split was applied afterwards. ### Training Procedure #### Preprocessing [optional]

Remove comments and replace consecutive whitespace characters by a single space. #### Training Hyperparameters

- Training regime: fp16 mixed precision #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data

The model was evaluated on 49522 Java methods, from the 20% split of the dataset mentioned in Training Data #### Factors #### Metrics



Perplexity ### Results

1.48 #### Summary ## Model Examination [optional] ## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software



[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact
TAGS
#transformers #pytorch #bert #fill-mask #MLM #code #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us 
 # Model Card for Model ID

A BERT-like model pre-trained on Java buggy code. ## Model Details ### Model Description

A BERT-like model pre-trained on Java buggy code.

- Developed by: André Nascimento
- Shared by: Hugging Face
- Model type: Fill-Mask
- Language(s) (NLP): Java (EN)
- License: 
- Finetuned from model: BERT Base Uncased ## Uses ### Direct Use

Fill-Mask. ### Downstream Use [optional]

The model can be used for other tasks, like Text Classification. ### Out-of-Scope Use ## Bias, Risks, and Limitations ### 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. ## How to Get Started with the Model

Use the code below to get started with the model. ## Training Details ### Training Data

The model was trained on 198088 Java methods, containing the code before and after the bug fix was applied. The whole dataset was built by combining the Dataset of Bug-Fix Pairs for small and medium methods source code. An 80/20 train/validation split was applied afterwards. ### Training Procedure #### Preprocessing [optional]

Remove comments and replace consecutive whitespace characters by a single space. #### Training Hyperparameters

- Training regime: fp16 mixed precision #### Speeds, Sizes, Times [optional] ## Evaluation ### Testing Data, Factors & Metrics #### Testing Data

The model was evaluated on 49522 Java methods, from the 20% split of the dataset mentioned in Training Data #### Factors #### Metrics



Perplexity ### Results

1.48 #### Summary ## Model Examination [optional] ## Environmental Impact



Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

- Hardware Type: 
- Hours used: 
- Cloud Provider: 
- Compute Region: 
- Carbon Emitted: ## Technical Specifications [optional] ### Model Architecture and Objective ### Compute Infrastructure #### Hardware #### Software



[optional]



BibTeX:



APA: ## Glossary [optional] ## More Information [optional] ## Model Card Authors [optional] ## Model Card Contact