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
model documentation
#3
by nazneen - opened
No description provided.
Hi @lbourdois
This PR has documentation about your model and is based on the format we are using as part of our effort to standardize model cards at Hugging Face. Please feel free to merge as is or edit as you like and then merge.
Thanks @lbourdois
@wei This PR has documentation about your model and is based on the format we are using as part of our effort to standardize model cards at Hugging Face. Please feel free to merge as is or edit as you like and then merge.