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---
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.
### 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]
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