Instructions to use SEBIS/code_trans_t5_small_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_small_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_small_source_code_summarization_python")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_python") model = AutoModelForMultimodalLM.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_python") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b1258950d502038b535f794b0d2825b53c1057c4a0bbe8f51d77753404a2cb73
- Size of remote file:
- 242 MB
- SHA256:
- 0b247969582806cf1c00b8be5c6ee8351d4fe0afb9a3f57acacc154e6779300a
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