README.md

---
library_name: transformers
tags: []
---

# Model Card for se-bert
Provides Generic Software Engineering LM from "Enhancing Automated Software Traceability by Transfer Learning from Open-World Data"



## Model Details
The following language models is trained on the Git Corpus and Git Links from 2016 to 2021. The data
contains 4 types of records including Comments, Issues, Pull Requests, and Commits.

## Uses

This model is intended to be a good set of starting weights for various software engineering tasks including:

- requirements classification
- traceability link prediction
- retrieval / search


## Training, Evaluation, and Results
Please see cited paper for complete details on training method.

## Technical Specifications

### Model Architecture and Objective

MLM model trained on SE Corpus (See Above).

#### Hardware

1 GPU with CUDA 10.2 or 11.1

#### Software

Python >= 3.7
pytorch/1.1.0

## Citation [optional]

**BibTeX:**

@misc{lin2022enhancing,
      title={Enhancing Automated Software Traceability by Transfer Learning from Open-World Data}, 
      author={Jinfeng Lin and Amrit Poudel and Wenhao Yu and Qingkai Zeng and Meng Jiang and Jane Cleland-Huang},
      year={2022},
      eprint={2207.01084},
      archivePrefix={arXiv},
      primaryClass={cs.SE}
}

## Model Card Authors [optional]

Jinfeng Lin, Amrit Poudel, Wenhao Yu, Qingkai Zeng, Jane Cleland-Huang

## Model Card Contact

Alberto Rodriguez ([email protected])