AISE-TUDelft
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JonBERTa-attn-ft-coco-14L

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 library_name: transformers
-tags: []
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 ---
+license: mit
 library_name: transformers
+tags:
+- code
 ---
+## JonBERTa-attn-ft-coco-14L
+Model for the paper [**"A Transformer-Based Approach for Smart Invocation of Automatic Code Completion"**](https://arxiv.org/abs/2405.14753).
+#### Description
+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)
+- **Language:** Code
+- **Finetuned from model:** [`CodeBERTa-small-v1`](https://huggingface.co/huggingface/CodeBERTa-small-v1).
+Models are named as follows:
+- `CodeBERTa` &rarr; `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` &rarr; `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`, with optional `reinit`ialisation of all its weights).
+- `JonBERTa-attn` &rarr; `JonBERTa-attn-ft-[0,1,2,3,4,5]L`
+    - e.g. `JonBERTa-attn-ft-012L` , where all have `2e-05` learning rate, but may differ in the attention layer(s) 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
+- **Replication Repository:** [`Ar4l/curating-code-completions`](https://github.com/Ar4l/curating-code-completions/tree/main)
+- **Paper:** [**"A Transformer-Based Approach for Smart Invocation of Automatic Code Completion"**](https://arxiv.org/abs/2405.14753)
+- **Contact:** https://huggingface.co/Ar4l
+To cite, please use
+```bibtex
+@misc{de_moor_smart_invocation_2024,
+	title = {A {Transformer}-{Based} {Approach} for {Smart} {Invocation} of {Automatic} {Code} {Completion}},
+	url = {http://arxiv.org/abs/2405.14753},
+	doi = {10.1145/3664646.3664760},
+	author = {de Moor, Aral and van Deursen, Arie and Izadi, Maliheh},
+	month = may,
+	year = {2024},
+}
+```
+#### Training Details
+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
+```
+#### Model Configuration
+```python
+num_telemetry_features  :int = 26
+add_feature_embeddings :bool = True
+feature_hidden_size     :int = num_telemetry_features * 4
+feature_dropout_prob  :float = 0.1
+add_feature_bias       :bool = True
+add_self_attn          :bool = True
+self_attn_layers  :list[int] = search(sum(
+    [[i,j,k] for i in range(6) for j in range(6) for k in range(6) if i < j < k],
+    [[i,j] for j in range(6) for i in range(6) if i < j],
+    [[i] for i in range(6)],
+    []
+))
+```