Instructions to use SEBIS/code_trans_t5_base_program_synthese_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_program_synthese_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_program_synthese_transfer_learning_finetune")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_program_synthese_transfer_learning_finetune") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_base_program_synthese_transfer_learning_finetune") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 86d1520a25db1319d79929733d46a17515c11fc92b195a5fa383b606cdc5f18e
- Size of remote file:
- 892 MB
- SHA256:
- 86aa78aac14ceffabd9ee7bfa0aaa9bc946e94fea0f11ced048299da8a3bf5a1
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.