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  library_name: transformers
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- tags: []
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
 
 
 
 
 
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
 
 
 
 
 
 
 
 
 
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
 
 
 
 
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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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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- [More Information Needed]
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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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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- 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. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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- #### Training Hyperparameters
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- - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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- #### Speeds, Sizes, Times [optional]
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- <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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- ## Evaluation
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- <!-- This section describes the evaluation protocols and provides the results. -->
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- ### Testing Data, Factors & Metrics
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- #### Testing Data
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- <!-- This should link to a Dataset Card if possible. -->
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- #### Factors
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- <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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- #### Metrics
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- <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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- ### Results
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- #### Summary
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- ## Model Examination [optional]
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- <!-- Relevant interpretability work for the model goes here -->
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- [More Information Needed]
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- ## Environmental Impact
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- <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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- Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- - **Hardware Type:** [More Information Needed]
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- - **Hours used:** [More Information Needed]
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- - **Cloud Provider:** [More Information Needed]
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- - **Compute Region:** [More Information Needed]
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- - **Carbon Emitted:** [More Information Needed]
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- ## Technical Specifications [optional]
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- ### Model Architecture and Objective
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- ### Compute Infrastructure
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- #### Hardware
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- #### Software
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- ## Citation [optional]
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- <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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- **BibTeX:**
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- **APA:**
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- ## Glossary [optional]
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- <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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- ## More Information [optional]
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- ## Model Card Authors [optional]
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- ## Model Card Contact
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+ license: mit
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  library_name: transformers
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+ tags:
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+ - code
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  ---
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+ ## JonBERTa-attn-ft-coco-1L
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+ Model for the paper [**"A Transformer-Based Approach for Smart Invocation of Automatic Code Completion"**](https://arxiv.org/abs/2405.14753).
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+ #### Description
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+ 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.
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+ - **Developed by:** [AISE Lab](https://www.linkedin.com/company/aise-tudelft/) @ [SERG](https://se.ewi.tudelft.nl/), Delft University of Technology
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+ - **Model type:** [JonBERTa](https://github.com/Ar4l/curating-code-completions/blob/main/modeling_jonberta.py)
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+ - **Language:** Code
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+ - **Finetuned from model:** [`CodeBERTa-small-v1`](https://huggingface.co/huggingface/CodeBERTa-small-v1).
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+ Models are named as follows:
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+ - `CodeBERTa` &rarr; `CodeBERTa-ft-coco-[1,2,5]e-05lr`
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+ - e.g. `CodeBERTa-ft-coco-2e-05lr`, which was trained with learning rate of `2e-05`.
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+ - `JonBERTa-head` &rarr; `JonBERTa-head-ft-[dense,proj,reinit]`
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+ - 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).
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+ - `JonBERTa-attn` &rarr; `JonBERTa-attn-ft-[0,1,2,3,4,5]L`
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+ - 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`).
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+ Other hyperparameters may be found in the paper or the replication package (see below).
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+ #### Sources
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+ - **Replication Repository:** [`Ar4l/curating-code-completions`](https://github.com/Ar4l/curating-code-completions/tree/main)
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+ - **Paper:** [**"A Transformer-Based Approach for Smart Invocation of Automatic Code Completion"**](https://arxiv.org/abs/2405.14753)
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+ - **Contact:** https://huggingface.co/Ar4l
 
 
 
 
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+ To cite, please use
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+ ```bibtex
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+ @misc{de_moor_smart_invocation_2024,
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+ title = {A {Transformer}-{Based} {Approach} for {Smart} {Invocation} of {Automatic} {Code} {Completion}},
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+ url = {http://arxiv.org/abs/2405.14753},
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+ doi = {10.1145/3664646.3664760},
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+ author = {de Moor, Aral and van Deursen, Arie and Izadi, Maliheh},
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+ month = may,
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+ year = {2024},
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+ }
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+ ```
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+ #### Training Details
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+ 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.
 
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+ ```python
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+ num_train_epochs : int = 3
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+ learning_rate : float = 2e-5
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+ batch_size : int = 16
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+ ```
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+ #### Model Configuration
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+ ```python
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+ num_telemetry_features :int = 26
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ add_feature_embeddings :bool = True
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+ feature_hidden_size :int = num_telemetry_features * 4
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+ feature_dropout_prob :float = 0.1
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+ add_feature_bias :bool = True
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+ add_self_attn :bool = True
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+ self_attn_layers :list[int] = search(sum(
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+ [[i,j,k] for i in range(6) for j in range(6) for k in range(6) if i < j < k],
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+ [[i,j] for j in range(6) for i in range(6) if i < j],
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+ [[i] for i in range(6)],
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+ []
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+ ))
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+ ```