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--- |
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library_name: transformers |
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tags: |
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- software engineering |
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- software traceability |
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--- |
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# Model Card for nl-bert |
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Provides TAPT (Task Adaptive Pretraining) model from "Enhancing Automated Software Traceability by |
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Transfer Learning from Open-World Data". |
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## Model Details |
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### Model Description |
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This model was trained to predict trace links between issue and commits on GitHub data from 2016-21. |
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- **Developed by:** Jinfeng Lin, University of Notre Dame |
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- **Shared by [optional]:** Alberto Rodriguez, University of Notre Dame |
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- **Model type:** BertForSequenceClassification |
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- **Language(s) (NLP):** EN |
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- **License:** MIT |
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### Model Sources [optional] |
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<!-- Provide the basic links for the model. --> |
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- **Repository:** https://github.com/thearod5/se-models |
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- **Paper:** https://arxiv.org/abs/2207.01084 |
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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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## Training Details |
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Please see cite paper for full training details. |
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## Evaluation |
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Please see cited paper for full evaluation. |
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### Results |
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The model achieved a MAP score improvement of over 20% compared to baseline models. See cited paper for full details. |
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## Environmental Impact |
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- **Hardware Type:** Distributed machine pool |
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- **Hours used:** 72 hours |
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# Technical Specifications [optional] |
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# Model Architecture and Objective |
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The model uses a Single-BERT architecture from the TBERT framework, which performs well on traceability tasks by encoding concatenated source and target artifacts. |
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# Compute Infrastructure |
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Hardware |
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300 servers in a distributed machine pool |
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# Software |
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- Transformers library |
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- PyTorch |
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- HTCondor for distributed computation |
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## Citation |
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**BibTeX:** |
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@misc{lin2022enhancing, |
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title={Enhancing Automated Software Traceability by Transfer Learning from Open-World Data}, |
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author={Jinfeng Lin and Amrit Poudel and Wenhao Yu and Qingkai Zeng and Meng Jiang and Jane Cleland-Huang}, |
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year={2022}, |
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eprint={2207.01084}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.SE} |
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} |
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## Model Card Authors |
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Alberto Rodriguez |
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## Model Card Contact |
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Alberto Rodriguez ([email protected]) |
