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--- |
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library_name: transformers |
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tags: [] |
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--- |
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# Model Card for se-bert |
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Provides Generic Software Engineering LM from "Enhancing Automated Software Traceability by Transfer Learning from Open-World Data" |
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## Model Details |
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The following language models is trained on the Git Corpus and Git Links from 2016 to 2021. The data |
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contains 4 types of records including Comments, Issues, Pull Requests, and Commits. |
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## Uses |
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This model is intended to be a good set of starting weights for various software engineering tasks including: |
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- requirements classification |
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- traceability link prediction |
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- retrieval / search |
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## Training, Evaluation, and Results |
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Please see cited paper for complete details on training method. |
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## Technical Specifications |
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### Model Architecture and Objective |
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MLM model trained on SE Corpus (See Above). |
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#### Hardware |
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1 GPU with CUDA 10.2 or 11.1 |
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#### Software |
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Python >= 3.7 |
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pytorch/1.1.0 |
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## Citation [optional] |
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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 [optional] |
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Jinfeng Lin, Amrit Poudel, Wenhao Yu, Qingkai Zeng, Jane Cleland-Huang |
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## Model Card Contact |
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Alberto Rodriguez ([email protected]) |
