Instructions to use SEBIS/code_trans_t5_small_source_code_summarization_sql 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_sql 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_sql")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_sql") model = AutoModelForMultimodalLM.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_sql") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 596856ad8dc64749325da0b513317b5328c1e4ce93562f4feb8e7c488851ac0b
- Size of remote file:
- 242 MB
- SHA256:
- 2f586ea484f13142c36c56cb1633b97c0450fcf2e8578faa0fbc1c1baa1e8b50
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