Instructions to use SEBIS/code_trans_t5_large_code_documentation_generation_go_multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_large_code_documentation_generation_go_multitask 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_large_code_documentation_generation_go_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_code_documentation_generation_go_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_large_code_documentation_generation_go_multitask") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3a849ff6c53d418711c377afa45aa7d40da98ee6d7eb2cfdabad213dcebc8a3
- Size of remote file:
- 2.95 GB
- SHA256:
- 9947b1600f368f7494b61115a6775a55ec1c341c9c713e9684358982cfda35df
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.