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:
# 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") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9c3e2bf63353e93b43ca71b53d932c017ec67116dabd752a5a1193085836605b
- Size of remote file:
- 242 MB
- SHA256:
- ac2162b8ffaaf9db984b32f3f32028de111d58ec8579e4d27e3aef545e755aa9
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