Instructions to use SEBIS/code_trans_t5_base_program_synthese_multitask 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_multitask 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_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_program_synthese_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_base_program_synthese_multitask") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 08315aa74399600649503b1014c0ce1c7b5973597b273bbc6f36a42aecc56a09
- Size of remote file:
- 892 MB
- SHA256:
- 338303a61a6fe2a1e928f6aa33551c0a2e56ffcbfe879c4e412f32651cc0f066
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.