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@@ -11,7 +11,6 @@ Generating higher quality variable names for code by renaming masked variable na
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  - **Developed by:** SMART Lab, Dalhousie University
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- - **Funded by:** [More Information Needed]
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  - **Model type:** Masked Language model
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  - **Language(s) (NLP):** Coded in Python to handle Java code
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  - **Finetuned from model:** GraphCodeBERT
@@ -35,21 +34,20 @@ The code snippets must ideally be entire classes for best results. A prediction
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  <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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  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>
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  Ensure all instances of a particular variable name are masked.
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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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  Training is only done for a relatively small dataset and few epochs, and thus, the model might be under-trained. <br>
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  Even with the correct output, the syntax of the model can be occasionally dubious.<br>
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- Model is not perfect and identifier renamings must be reviewed till performance in test settings is not evaluated.
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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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- Use model as described and verify outputs before using them.
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  ## How to Get Started with the Model
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  Clone the repository and load model state dict using 'model_26_2'
 
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  - **Developed by:** SMART Lab, Dalhousie University
 
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  - **Model type:** Masked Language model
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  - **Language(s) (NLP):** Coded in Python to handle Java code
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  - **Finetuned from model:** GraphCodeBERT
 
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  <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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  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>
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  Ensure all instances of a particular variable name are masked.
 
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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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  Training is only done for a relatively small dataset and few epochs, and thus, the model might be under-trained. <br>
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  Even with the correct output, the syntax of the model can be occasionally dubious.<br>
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+ The model is not perfect, and identifier renamings must be reviewed till performance in test settings is not evaluated.
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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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+ Use the model as described and verify outputs before using them.
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  ## How to Get Started with the Model
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  Clone the repository and load model state dict using 'model_26_2'