1.18 kB
| --- | |
| inference: false | |
| license: mit | |
| widget: | |
| language: | |
| - en | |
| metrics: | |
| - mrr | |
| datasets: | |
| - augmented_codesearchnet | |
| --- | |
| # 🔥 Augmented Code Model 🔥 | |
| This is Augmented Code Model which is a fined-tune model of [CodeBERT](https://huggingface.co/microsoft/codebert-base) for processing of similarity between given docstring and code. This model is fined-model based on Augmented Code Corpus with ACS=4. | |
| ## How to use the model ? | |
| Similar to other huggingface model, you may load the model as follows. | |
| ```python | |
| from transformers import AutoTokenizer, AutoModelForSequenceClassification | |
| tokenizer = AutoTokenizer.from_pretrained("Fujitsu/AugCode") | |
| model = AutoModelForSequenceClassification.from_pretrained("Fujitsu/AugCode") | |
| ``` | |
| Then you may use `model` to infer the similarity between a given docstring and code. | |
| ### Citation | |
| ```bibtex@misc{bahrami2021augcode, | |
| title={AugmentedCode: Examining the Effects of Natural Language Resources in Code Retrieval Models}, | |
| author={Mehdi Bahrami, N. C. Shrikanth, Yuji Mizobuchi, Lei Liu, Masahiro Fukuyori, Wei-Peng Chen, Kazuki Munakata}, | |
| year={2021}, | |
| eprint={TBA}, | |
| archivePrefix={TBA}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` |