Instructions to use SEBIS/code_trans_t5_large_source_code_summarization_csharp_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_csharp_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_csharp_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_csharp_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_large_source_code_summarization_csharp_multitask") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2af8ea78288c48064e7b04adf7c660819dd5ee6546887b34e80e4dc156e197f3
- Size of remote file:
- 2.95 GB
- SHA256:
- 4cc13d053498c6c33ff1c8ab1c99ad19c96a54fa3920d84cc2c76a00ad75a6a4
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