Instructions to use devagonal/mt5-base-durga-sejarah with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use devagonal/mt5-base-durga-sejarah with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("devagonal/mt5-base-durga-sejarah") model = AutoModelForSeq2SeqLM.from_pretrained("devagonal/mt5-base-durga-sejarah", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92e1ebc559ac816dfa7edce1d60e79fd832f7d3192a512ded66b5fbadca84c85
- Size of remote file:
- 4.73 kB
- SHA256:
- 18d44318ec5b1baadafc12fc57a93e206c0f24870c87abea23ab59d4fe9d7394
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.