Instructions to use readerbench/RoGEC-mt0-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use readerbench/RoGEC-mt0-base with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("readerbench/RoGEC-mt0-base") model = AutoModelForSeq2SeqLM.from_pretrained("readerbench/RoGEC-mt0-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a5650e3f8000eb5dbe8d24be57a194a2e43d78a8357da1b3e94540ca9f941fa5
- Size of remote file:
- 3.87 GB
- SHA256:
- 60896975f85e79aa90afdef58e5f02b99740e918246f4501c760b3e14432018c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.