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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.safetensors from stmnk/codet5-small-code-summarization-python: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/stmnk/codet5-small-code-summarization-python/resolve/refs%2Fpr%2F2/model.safetensors
- Command line
-
hf download hf://stmnk/codet5-small-code-summarization-python@refs/pr/2/model.safetensors
-
curl -L -o model.safetensors https://huggingface.co/stmnk/codet5-small-code-summarization-python/resolve/refs%2Fpr%2F2/model.safetensors
242 MB
- Xet hash:
- a1d1198f0faa53cce4002d7c10df6f5d386334cd1aab218b8f03f9cda16e7ab6
- Size of remote file:
- 242 MB
- SHA256:
- 95f557f07f13ad9912605deb918ff698a530d9d746e0ce66565cac92fe1c5982
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