| | --- |
| | 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 |
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| | ### Model Description |
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| | A BERT-like model pre-trained on Java buggy code. |
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| | - **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 |
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| | <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> |
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| | ### Direct Use |
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| | Fill-Mask. |
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| | ### Downstream Use [optional] |
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| | The model can be used for other tasks, like Text Classification. |
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| | ### Out-of-Scope Use |
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| | <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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| | [More Information Needed] |
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| | ## Bias, Risks, and Limitations |
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| | <!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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| | [More Information Needed] |
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| | ### Recommendations |
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|
| | <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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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. |
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|
| | ## How to Get Started with the Model |
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| | 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] |
| |
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| | ## Training Details |
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| | ### Training Data |
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| | 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. |
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|
| | ### Training Procedure |
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|
| | <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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| | #### Preprocessing [optional] |
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| | Remove comments and replace consecutive whitespace characters by a single space. |
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| | #### Training Hyperparameters |
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| | - **Training regime:** fp16 mixed precision <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> |
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|
| | #### Speeds, Sizes, Times [optional] |
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|
| | <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> |
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| | [More Information Needed] |
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|
| | ## Evaluation |
| |
|
| | <!-- This section describes the evaluation protocols and provides the results. --> |
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| | ### Testing Data, Factors & Metrics |
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| | #### Testing Data |
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| | The model was evaluated on 59024 Java methods, from the 20% split of the dataset mentioned in [Training Data](#training-data) |
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| | [More Information Needed] |
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|
| | #### Factors |
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|
| | <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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| | [More Information Needed] |
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| | #### Metrics |
| |
|
| | <!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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|
| | Perplexity |
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| | ### Results |
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| | 1.73 |
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| | #### Summary |
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| | ## Model Examination [optional] |
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|
| | <!-- Relevant interpretability work for the model goes here --> |
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| | [More Information Needed] |
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|
| | ## Environmental Impact |
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|
| | <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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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] |
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| | ## Technical Specifications [optional] |
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| | ### Model Architecture and Objective |
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| | [More Information Needed] |
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| | ### Compute Infrastructure |
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| | [More Information Needed] |
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| | #### Hardware |
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| | [More Information Needed] |
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| | #### Software |
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| | [More Information Needed] |
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| | ## Citation [optional] |
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|
| | <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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| | **BibTeX:** |
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| | [More Information Needed] |
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| | **APA:** |
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| | [More Information Needed] |
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| | ## Glossary [optional] |
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| | <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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| | [More Information Needed] |
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| | ## More Information [optional] |
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| | [More Information Needed] |
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| | ## Model Card Authors [optional] |
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| | [More Information Needed] |
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|
| | ## Model Card Contact |
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| | [More Information Needed] |
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