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README.md

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 # Identifier-Renaming
-Generating higher quality identifier names by using context and following conventions <br>
+
+<!-- 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. -->
+
+
+
+- **Developed by:** SMART Lab, Dalhousie University
+- **Funded by:** [More Information Needed]
+- **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://github.com/SMART-Dal/Identifier-Renaming
+
+## 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.
+[More Information Needed]
+
+## 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>
+Model is not perfect and identifier renamings must be reviewed till performance in test settings is not evaluated.
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Use 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.
+[More Information Needed]
+
+
+
+## Evaluation
+227 Java classes used for evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+Perplexty of Base Model: 37580
+Perplexity of Fine-tuned Model: 23
+
+
+[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 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 -->
+
+[More Information Needed]
+
+