Instructions to use intm/codet5-small-go_generation with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use intm/codet5-small-go_generation with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("intm/codet5-small-go_generation") model = AutoModelForSeq2SeqLM.from_pretrained("intm/codet5-small-go_generation") - Notebooks
- Google Colab
- Kaggle
readme
Browse files
README.md
CHANGED
|
@@ -14,6 +14,7 @@ See example below for formatting.
|
|
| 14 |
# How to use
|
| 15 |
Here is how to use this model:
|
| 16 |
|
|
|
|
| 17 |
from transformers import T5ForConditionalGeneration, RobertaTokenizer
|
| 18 |
|
| 19 |
# 加载模型和tokenizer
|
|
@@ -32,6 +33,7 @@ print(output_text)
|
|
| 32 |
|
| 33 |
|
| 34 |
# this prints "return kmeta.ChildName(rev.GetName(), "-pa")"
|
|
|
|
| 35 |
|
| 36 |
# Training data
|
| 37 |
YinShicheng
|
|
|
|
| 14 |
# How to use
|
| 15 |
Here is how to use this model:
|
| 16 |
|
| 17 |
+
```
|
| 18 |
from transformers import T5ForConditionalGeneration, RobertaTokenizer
|
| 19 |
|
| 20 |
# 加载模型和tokenizer
|
|
|
|
| 33 |
|
| 34 |
|
| 35 |
# this prints "return kmeta.ChildName(rev.GetName(), "-pa")"
|
| 36 |
+
```
|
| 37 |
|
| 38 |
# Training data
|
| 39 |
YinShicheng
|