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:
- 7222f2f5b1a9143721c466f7f391202acb355157cc69e0f690b65b73d366035b
- Size of remote file:
- 3.64 kB
- SHA256:
- a7b6a54a09ca0333445657cb2d0ff91cba50fab6da95efe97540b54c74e2214b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.