Instructions to use SEBIS/code_trans_t5_small_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_small_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_small_transfer_learning_pretrain")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_transfer_learning_pretrain") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_transfer_learning_pretrain") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f672795fe7392f2f828b189be368e2c3621a113c9fc61522539665459106d221
- Size of remote file:
- 242 MB
- SHA256:
- 88003ae03d5b18f9ba8f96b6589d3f0eb353173a6716cab1a44fc8120e1535c0
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