Instructions to use SEBIS/code_trans_t5_large_code_comment_generation_java_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_comment_generation_java_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_comment_generation_java_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_code_comment_generation_java_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_large_code_comment_generation_java_multitask") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 32fd340d6178876a76dd838c255185fb629dbcc09cabb4bf6ad2da52e55aa863
- Size of remote file:
- 2.95 GB
- SHA256:
- b8bd521c43541e178b2febb7e99a30935664cb25e10555c4ea67061f6ae6813f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.