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README.md

@@ -13,7 +13,7 @@ Model for the paper [**"A Transformer-Based Approach for Smart Invocation of Aut
 This model is fine-tuned on a code-completion dataset collected from the open-source [Code4Me](https://github.com/code4me-me/code4me) plugin. The training objective is to have a small, lightweight transformer model to filter out unnecessary and unhelpful code completions. To this end, we leverage the in-IDE telemetry data, and integrate it with the textual code data in the transformer's attention module. 
 
 - **Developed by:** [AISE Lab](https://www.linkedin.com/company/aise-tudelft/) @ [SERG](https://se.ewi.tudelft.nl/), Delft University of Technology 
-- **Model type:** [JonBERTa](https://github.com/Ar4l/curating-code-completions/blob/main/modeling_jonberta.py)
+- **Model type:** [RoBERTa](https://huggingface.co/FacebookAI/roberta-base)
 - **Language:** Code 
 - **Finetuned from model:** [`CodeBERTa-small-v1`](https://huggingface.co/huggingface/CodeBERTa-small-v1). 
 
@@ -51,8 +51,8 @@ To cite, please use
 This model was trained with the following hyperparameters, everything else being `TrainingArguments`' default. The dataset was prepared identically across all models as detailed in the paper. 
 
 ```python
-num_train_epochs : int      = 3 
-learning_rate    : float    = 2e-5
-batch_size       : int      = 16
+num_train_epochs : int = 6
+learning_rate    : float = search([2e-5, 1e-5, 5e-5])
+batch_size       : int = 16
 ```