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:
- 853a3e2515525d371fb60ffe0069b0a35c22776fefcae9ba14deeebffa6956d1
- Size of remote file:
- 2.33 GB
- SHA256:
- 74e9532c307bf0b85b01900ad6879e65432dcd9a43b5a0ed0efe017032309a7c
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