Instructions to use SEBIS/code_trans_t5_small_source_code_summarization_python_multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_source_code_summarization_python_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_small_source_code_summarization_python_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_python_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_python_multitask") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b255dfb929085ae9f84410684dc7e9f998e40dcb07f988e19022d264db88097a
- Size of remote file:
- 242 MB
- SHA256:
- 8656bd5697ce017e29e044427ac47ac0d75dbf1d9c4cc4d7e26e147f98c440d0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.