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README.md

@@ -5,7 +5,7 @@ tags:
 - code
 ---
 
-## CodeBERTa
+## CodeBERTa-ft-coco-1e-05lr
 
 Model for the paper [**"A Transformer-Based Approach for Smart Invocation of Automatic Code Completion"**](https://arxiv.org/abs/2405.14753). 
 
@@ -19,12 +19,14 @@ This model is fine-tuned on a code-completion dataset collected from the open-so
 
 Models are named as follows: 
 
-- `CodeBERTa` → `CodeBERTa-ft-coco-[1,2,5]e-05lr--[0-4]`
-    - e.g. `CodeBERTa-ft-coco-1e-05lr--0`, where the trailing `--0` indicates which of the 5 training splits was used.
-- `JonBERTa-head` → `JonBERTa-head-ft-(dense-proj-reinit)--[0-4]` 
-    - e.g. `JonBERTa-head-ft-(dense-proj-)--1`, where all have `2e-05` learning rate, but may differ in the head layer in which the telemetry features are introduced (either `head` or `proj`).
-- `JonBERTa-attn` → `JonBERTa-attn-ft-(0,1,2,3,4,5L)--[0-4]`
-    - e.g. `JonBERTa-attn-ft-(0,1,2L)--0` , where all have `2e-05` learning rate, but may differ in the attention layer in which the telemetry features are introduced (either `0`, `1`, `2`, `3`, `4`, or `5L`).
+- `CodeBERTa` → `CodeBERTa-ft-coco-[1,2,5]e-05lr`
+    - e.g. `CodeBERTa-ft-coco-2e-05lr`, which was trained with learning rate of `2e-05`.
+- `JonBERTa-head` → `JonBERTa-head-ft-(dense-proj-reinit)` 
+    - e.g. `JonBERTa-head-ft-(dense-proj-)`, where all have `2e-05` learning rate, but may differ in the head layer in which the telemetry features are introduced (either `head` or `proj`).
+- `JonBERTa-attn` → `JonBERTa-attn-ft-(0,1,2,3,4,5L)`
+    - e.g. `JonBERTa-attn-ft-(0,1,2L)` , where all have `2e-05` learning rate, but may differ in the attention layer in which the telemetry features are introduced (either `0`, `1`, `2`, `3`, `4`, or `5L`).
+
+Other hyperparameters may be found in the paper or the replication package (see below).
 
 #### Sources