Transformers
PyTorch
Safetensors
py
English
t5
text2text-generation
Code2TextGeneration
Code2TextSummarisation
text-generation-inference
Instructions to use stmnk/codet5-small-code-summarization-python with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use stmnk/codet5-small-code-summarization-python with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("stmnk/codet5-small-code-summarization-python") model = AutoModelForSeq2SeqLM.from_pretrained("stmnk/codet5-small-code-summarization-python", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
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Adding `safetensors` variant of this model (#2)
- Adding `safetensors` variant of this model (c95adbdf01a3fa19f07baa8982582f585577a48b) Co-authored-by: Safetensors convertbot <SFconvertbot@users.noreply.huggingface.co>e1c6b75
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update card
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add metadata
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Transfer learning train
stmnkc60b68d
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Domain adaptation train
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add link 2 report
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Transfer learning train
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add wb report
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First commit
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initial commit
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