Instructions to use RamsesDIIP/mt5-large-ie-budquo-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RamsesDIIP/mt5-large-ie-budquo-finetuned with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("RamsesDIIP/mt5-large-ie-budquo-finetuned") model = AutoModelForSeq2SeqLM.from_pretrained("RamsesDIIP/mt5-large-ie-budquo-finetuned", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 550ff0ba24144ae90e3748ef26aa125ebde416450c4d13fcd593edc61c2fa93b
- Size of remote file:
- 5.3 kB
- SHA256:
- f31bee054b81ae3e581ec56c3409d4de22966a0e4f5575c1c8a0d0b1a23eb844
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.