Instructions to use SEBIS/code_trans_t5_small_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_small_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_small_source_code_summarization_csharp_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_csharp_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_csharp_multitask") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 504a4accc8d50855583c05e819561a2a5b5232c51928fa0f1d23ebeab34bb424
- Size of remote file:
- 242 MB
- SHA256:
- fc7d401d3c2a38ec43fffb21b500ab698c926be3140dd95c7eb8a2dd82f09ee2
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