Instructions to use SEBIS/code_trans_t5_small_code_documentation_generation_python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_code_documentation_generation_python with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" 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("summarization", model="SEBIS/code_trans_t5_small_code_documentation_generation_python")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_python") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_code_documentation_generation_python") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ed3cdd854f9e9f2693f2c1f1a393bdc5ac1a1bbf13b1953f4a3559a002a50298
- Size of remote file:
- 242 MB
- SHA256:
- 241a6eecfcf07282ed000f2f24cf01ed9ee5f5bad7492819164a481dd69a9001
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.