Instructions to use Fujitsu/AugCode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Fujitsu/AugCode with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="Fujitsu/AugCode")# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("Fujitsu/AugCode") model = AutoModelForSequenceClassification.from_pretrained("Fujitsu/AugCode") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -28,7 +28,7 @@ Then you may use `model` to infer the similarity between a given docstring and c
|
|
| 28 |
### Citation
|
| 29 |
```bibtex@misc{bahrami2021augcode,
|
| 30 |
title={AugmentedCode: Examining the Effects of Natural Language Resources in Code Retrieval Models},
|
| 31 |
-
author={Mehdi
|
| 32 |
year={2021},
|
| 33 |
eprint={TBA},
|
| 34 |
archivePrefix={TBA},
|
|
|
|
| 28 |
### Citation
|
| 29 |
```bibtex@misc{bahrami2021augcode,
|
| 30 |
title={AugmentedCode: Examining the Effects of Natural Language Resources in Code Retrieval Models},
|
| 31 |
+
author={Mehdi Bahrami, N. C. Shrikanth, Yuji Mizobuchi, Lei Liu, Masahiro Fukuyori, Wei-Peng Chen, Kazuki Munakata},
|
| 32 |
year={2021},
|
| 33 |
eprint={TBA},
|
| 34 |
archivePrefix={TBA},
|