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
Create README.md
Browse files
README.md
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# 🔥 AugCode 🔥
|
| 2 |
+
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.
|
| 3 |
+
|
| 4 |
+
## How to use the model ?
|
| 5 |
+
Similar to other huggingface model, you may load the model as follows.
|
| 6 |
+
```python
|
| 7 |
+
from transformers import AutoTokenizer, AutoModel
|
| 8 |
+
|
| 9 |
+
tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
|
| 10 |
+
|
| 11 |
+
model = AutoModel.from_pretrained("microsoft/codebert-base")
|
| 12 |
+
```
|
| 13 |
+
Then you may use it to infer the similarity between a given docstring and code.
|
| 14 |
+
|
| 15 |
+
|