Instructions to use SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask 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_source_code_summarization_csharp_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask: direct link, hf CLI and curl.
- Browser
- Download file 892 MB
-
https://huggingface.co/SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask/resolve/refs%2Fpr%2F1/flax_model.msgpack
- Command line
-
hf download hf://SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask@refs/pr/1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/SEBIS/code_trans_t5_base_source_code_summarization_csharp_multitask/resolve/refs%2Fpr%2F1/flax_model.msgpack
892 MB
- Xet hash:
- e80e3cfb41c9c4b1c05bde2c518f6fca58b8954fedaf6fd870403ddc91f9fcae
- Size of remote file:
- 892 MB
- SHA256:
- 39350e691c0096c22f3ca4b5c17621db7ceb8097748c682aec72b426a20b74f7
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