---
language:
- code
metrics:
- perplexity
library_name: transformers
pipeline_tag: fill-mask
tags:
- MLM
---
# 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

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## Bias, Risks, and Limitations

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### Recommendations

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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-base-cased')
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]

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[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. -->

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#### Metrics

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

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### Results

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#### Summary



## Model Examination [optional]

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

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## Environmental Impact

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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

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### Compute Infrastructure

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#### Hardware

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#### Software

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## 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:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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