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, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_python") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_python", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download .Rhistory from SEBIS/code_trans_t5_small_source_code_summarization_python: direct link, hf CLI and curl.
- Browser
- Download file 0 Bytes
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https://huggingface.co/SEBIS/code_trans_t5_small_source_code_summarization_python/resolve/refs%2Fpr%2F2/.Rhistory
- Command line
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hf download hf://SEBIS/code_trans_t5_small_source_code_summarization_python@refs/pr/2/.Rhistory
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curl -L -o .Rhistory https://huggingface.co/SEBIS/code_trans_t5_small_source_code_summarization_python/resolve/refs%2Fpr%2F2/.Rhistory
0 Bytes