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# 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 -->
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