Transformers
PyTorch
ONNX
English
t5
text2text-generation
grammar-correction
text-generation-inference
Instructions to use visheratin/t5-efficient-mini-grammar-correction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use visheratin/t5-efficient-mini-grammar-correction with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("visheratin/t5-efficient-mini-grammar-correction") model = AutoModelForSeq2SeqLM.from_pretrained("visheratin/t5-efficient-mini-grammar-correction") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1e644b12eb3810f0b6c1cd1b6a4dcf86ec77094ffd5c630fc8276106c3ccddc2
- Size of remote file:
- 125 MB
- SHA256:
- a201ffda9808d5bfbd5a29be55ac51accade39b9b503cbf3cb2c02e2332dcf0c
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.