Instructions to use SEBIS/code_trans_t5_base_transfer_learning_pretrain with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_base_transfer_learning_pretrain with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SEBIS/code_trans_t5_base_transfer_learning_pretrain")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_transfer_learning_pretrain") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_base_transfer_learning_pretrain") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f308b768c680594f85e8b43079a3239ab714bb8daf97eb370dba6f8cc8250a8
- Size of remote file:
- 892 MB
- SHA256:
- 257d3b040f5fda2deae326323217325d070c77604e93d72deb7b7cbce9f1144d
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