| # Identifier-Renaming | |
| <!-- Provide a quick summary of what the model is/does. --> | |
| Generating higher quality variable names for code by renaming masked variable names. | |
| ## Model Details | |
| ### Model Description | |
| <!-- Provide a longer summary of what this model is. --> | |
| - **Model type:** Masked Language model | |
| - **Language(s) (NLP):** Coded in Python to handle Java code | |
| - **Finetuned from model:** GraphCodeBERT | |
| ### Model Sources [optional] | |
| <!-- Provide the basic links for the model. --> | |
| - **Repository:** https://anonymous.4open.science/r/Identifier-Renaming-653F | |
| ## 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. --> | |
| Input Java code snippets with all instances of a particular variable name replaced by "[MASK]"<br> | |
| Input the number of tokens desired in the variable name (how long should it be). Else, input "0" to get a random number of tokens sampled from | |
| training data distribution<br> | |
| The code snippets must ideally be entire classes for best results. A prediction for the masked variable name is presented as output. | |
| ### Out-of-Scope Use | |
| <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> | |
| This non-fine-tuned version of the model is designed for generic code completion tasks. The fine-tuned model is designed to focus solely on identifier names.<br> | |
| Ensure all instances of a particular variable name are masked. | |
| ## Bias, Risks, and Limitations | |
| <!-- This section is meant to convey both technical and sociotechnical limitations. --> | |
| Training is only done for a relatively small dataset and few epochs, and thus, the model might be under-trained. <br> | |
| Even with the correct output, the syntax of the model can be occasionally dubious.<br> | |
| The model is not perfect, and identifier renamings must be reviewed till performance in test settings is not evaluated. | |
| ### Recommendations | |
| <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> | |
| Use the model as described and verify outputs before using them. | |
| ## How to Get Started with the Model | |
| Clone the repository and load model state dict using 'model_26_2' | |
| ### Training Details | |
| <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> | |
| Trained on a subset of a dataset of 1000 classes with 612 lines of code on average for 3 epochs and a Learning Rate of 2e-5. | |
| ## Evaluation | |
| 227 Java classes used for evaluation | |
| <!-- This section describes the evaluation protocols and provides the results. --> | |
| Perplexty of Base Model: 37580<br> | |
| Perplexity of Fine-tuned Model: 23 | |
| #### Metrics | |
| <!-- These are the evaluation metrics being used, ideally with a description of why. --> | |
| Perplexity is used to evaluate the performance of the model. It judges how surprising it is for a model to predict the given text. | |
| <!-- Relevant interpretability work for the model goes here --> | |