Instructions to use SEBIS/code_trans_t5_small_code_comment_generation_java_multitask with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SEBIS/code_trans_t5_small_code_comment_generation_java_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_small_code_comment_generation_java_multitask")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("SEBIS/code_trans_t5_small_code_comment_generation_java_multitask") model = AutoModel.from_pretrained("SEBIS/code_trans_t5_small_code_comment_generation_java_multitask", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from SEBIS/code_trans_t5_small_code_comment_generation_java_multitask: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/SEBIS/code_trans_t5_small_code_comment_generation_java_multitask/resolve/ee9c56d7bf748c28842e52c41e03d66f23c08de4/flax_model.msgpack
- Command line
-
hf download hf://SEBIS/code_trans_t5_small_code_comment_generation_java_multitask@ee9c56d7bf748c28842e52c41e03d66f23c08de4/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/SEBIS/code_trans_t5_small_code_comment_generation_java_multitask/resolve/ee9c56d7bf748c28842e52c41e03d66f23c08de4/flax_model.msgpack
242 MB
- Xet hash:
- 040752349a5d2e0d226b5e161b314f62b4ffe492ee410c84e644989ab9344507
- Size of remote file:
- 242 MB
- SHA256:
- 068dd495b69829beba488cc720fc72d763fc1df3870f16ee674d43247898f9c2
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