# 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.

### 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_single')
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 236040 Java methods, containing the code before and after the bug fix was applied. The whole dataset was built from  [Extracted Bug-Fix Pairs (BFP)](https://sites.google.com/view/learning-fixes/data#h.p_RNvM6OfOYBMI), extracting single file/single method commits, and keeping only method with less than 512 tokens. 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 59024 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

1.73

#### 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 236040 Java methods, containing the code before and after the bug fix was applied. The whole dataset was built from  Extracted Bug-Fix Pairs (BFP), extracting single file/single method commits, and keeping only method with less than 512 tokens. 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 59024 Java methods, from the 20% split of the dataset mentioned in Training Data



#### Factors





#### Metrics



Perplexity

### Results

1.73

#### 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 236040 Java methods, containing the code before and after the bug fix was applied. The whole dataset was built from  Extracted Bug-Fix Pairs (BFP), extracting single file/single method commits, and keeping only method with less than 512 tokens. 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 59024 Java methods, from the 20% split of the dataset mentioned in Training Data #### Factors #### Metrics



Perplexity ### Results

1.73 #### 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 236040 Java methods, containing the code before and after the bug fix was applied. The whole dataset was built from  Extracted Bug-Fix Pairs (BFP), extracting single file/single method commits, and keeping only method with less than 512 tokens. 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 59024 Java methods, from the 20% split of the dataset mentioned in Training Data #### Factors #### Metrics



Perplexity ### Results

1.73 #### 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