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:
- ceb61fff1903676f5d775e39f10418f6c2829756cc7a207463ae42b85b19121d
- Size of remote file:
- 892 MB
- SHA256:
- 0459290f3548a049bdb6f2fb9fd27413f416382d32e7312db29b2e5084fdd4de
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