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SEBIS
/
code_trans_t5_base_code_comment_generation_java_transfer_learning_finetune

Summarization
Transformers
PyTorch
JAX
t5
feature-extraction
text-generation-inference
Model card Files Files and versions
xet
Community
4

Instructions to use SEBIS/code_trans_t5_base_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_base_code_comment_generation_java_transfer_learning_finetune 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_code_comment_generation_java_transfer_learning_finetune")
    # Load model directly
    from transformers import AutoTokenizer, AutoModel
    
    tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_code_comment_generation_java_transfer_learning_finetune")
    model = AutoModel.from_pretrained("SEBIS/code_trans_t5_base_code_comment_generation_java_transfer_learning_finetune")
  • Notebooks
  • Google Colab
  • Kaggle
code_trans_t5_base_code_comment_generation_java_transfer_learning_finetune
2.68 GB
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  • 3 contributors
History: 9 commits
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SFconvertbot
Adding `safetensors` variant of this model
ab9b5a2 almost 3 years ago
  • .gitattributes
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  • README.md
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  • config.json
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  • flax_model.msgpack
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  • model.safetensors
    892 MB
    xet
    Adding `safetensors` variant of this model almost 3 years ago
  • pytorch_model.bin
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  • special_tokens_map.json
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  • spiece.model
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  • tokenizer_config.json
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