Instructions to use mamiksik/CommitPredictorT5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mamiksik/CommitPredictorT5 with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("mamiksik/CommitPredictorT5") model = AutoModelForSeq2SeqLM.from_pretrained("mamiksik/CommitPredictorT5") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6aa1cd17e4d9024637f993000830ede30336ecf1af87f728372a8322404438ad
- Size of remote file:
- 892 MB
- SHA256:
- f53120576a8e866ac52b62e256a48833b0e947882464ae11e05330f76b4ab2f8
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.