Instructions to use Adamas23/mt5-large-finetuned-onet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Adamas23/mt5-large-finetuned-onet with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Adamas23/mt5-large-finetuned-onet") model = AutoModelForSeq2SeqLM.from_pretrained("Adamas23/mt5-large-finetuned-onet", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5ed056abaaf38c617770e6c048b4a6020bd53d17d004514e080df88860777613
- Size of remote file:
- 4.92 GB
- SHA256:
- 987ab4405551ed5c48205b6001cfc57ccde109748de1db2138c9da7abff8141f
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.