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:
# pip install -U transformers accelerate # 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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download init_decoder_model.onnx from visheratin/t5-efficient-mini-grammar-correction: direct link, hf CLI and curl.
- Browser
- Download file 143 MB
-
https://huggingface.co/visheratin/t5-efficient-mini-grammar-correction/resolve/main/init_decoder_model.onnx
- Command line
-
hf download hf://visheratin/t5-efficient-mini-grammar-correction/init_decoder_model.onnx
-
curl -L -o init_decoder_model.onnx https://huggingface.co/visheratin/t5-efficient-mini-grammar-correction/resolve/main/init_decoder_model.onnx
143 MB
- Xet hash:
- 32ad47ece6eebcf677a4c33b3c99f8e62b9ec71d8bee7ed0093c1624da607eaf
- Size of remote file:
- 143 MB
- SHA256:
- 307ebb5cb593bc995ae00cb533ee7bd5079a3c851b762dbf583e1493eed89e4b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.