Instructions to use SEBIS/code_trans_t5_large_code_comment_generation_java_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_large_code_comment_generation_java_transfer_learning_finetune with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="SEBIS/code_trans_t5_large_code_comment_generation_java_transfer_learning_finetune")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_large_code_comment_generation_java_transfer_learning_finetune") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_large_code_comment_generation_java_transfer_learning_finetune") - Notebooks
- Google Colab
- Kaggle
Commit ·
207a8be
1
Parent(s): 7bf25e1
upload flax model
Browse files- flax_model.msgpack +3 -0
flax_model.msgpack
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version https://git-lfs.github.com/spec/v1
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oid sha256:35ecc19266100a3bf820af3313917af1fd492f34aab210921c955447164a4987
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size 2950694412
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