Instructions to use SEBIS/code_trans_t5_small_source_code_summarization_python_multitask_finetune with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_source_code_summarization_python_multitask_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_small_source_code_summarization_python_multitask_finetune")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_python_multitask_finetune") model = AutoModelForMultimodalLM.from_pretrained("SEBIS/code_trans_t5_small_source_code_summarization_python_multitask_finetune") - Notebooks
- Google Colab
- Kaggle
Commit ·
9ee7704
1
Parent(s): 7ce5afb
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 sha256:9b44146e01e8642daa4ed08ac9624ee1357beca9a437f7be17e5d5eb88f4314a
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size 242032202
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