Instructions to use SEBIS/code_trans_t5_base_source_code_summarization_python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_base_source_code_summarization_python 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_base_source_code_summarization_python")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_python") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_python", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9496c44adbfa18265c0175eb120b94f622dd75e1ee07b2f950fa0af4df0c7106
- Size of remote file:
- 892 MB
- SHA256:
- cdec551e861c12987f0ed6abdd1a30028d65daca01365e34bbf8eff89fb44663
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