Instructions to use SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune 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_api_generation_transfer_learning_finetune")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_base_api_generation_transfer_learning_finetune") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ad10ef2f39ba7b74414b2f21df7db2caa4ad84ff69dcab11c3e13ec52064b668
- Size of remote file:
- 892 MB
- SHA256:
- ee0267f23399ebadd2c57f25495368c1fbd356a232c515f051f0518544dda9d2
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