Instructions to use peterjandre/finetuned-codet5-vbnet-csharp with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use peterjandre/finetuned-codet5-vbnet-csharp with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "translation" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # 'pip install "transformers<5.0.0' from transformers import pipeline pipe = pipeline("translation", model="peterjandre/finetuned-codet5-vbnet-csharp")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("peterjandre/finetuned-codet5-vbnet-csharp", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -56,8 +56,8 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
|
| 56 |
# 📁 Dataset Format
|
| 57 |
|
| 58 |
Training data was in JSONL with fields:
|
| 59 |
-
"vb_code": VB.NET input
|
| 60 |
-
"csharp_code": corresponding C# output
|
| 61 |
|
| 62 |
# 📄 License
|
| 63 |
|
|
|
|
| 56 |
# 📁 Dataset Format
|
| 57 |
|
| 58 |
Training data was in JSONL with fields:
|
| 59 |
+
- `"vb_code"`: VB.NET input
|
| 60 |
+
- `"csharp_code"`: corresponding C# output
|
| 61 |
|
| 62 |
# 📄 License
|
| 63 |
|