Instructions to use SEBIS/code_trans_t5_large_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_large_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_large_source_code_summarization_python_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_python_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_python_multitask") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88f53d37cc64588b6cef064f4ff9d9b0edf31d3a71c7eca56c25fbb95e88c14e
- Size of remote file:
- 2.95 GB
- SHA256:
- 6951be99303becf35e20d7d3e8a4fdb20719bc60bd5c74a2a48a41c57a9e69e0
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