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 encoder_model_quantized.onnx from visheratin/t5-efficient-mini-grammar-correction: direct link, hf CLI and curl.
- Browser
- Download file 20.3 MB
-
https://huggingface.co/visheratin/t5-efficient-mini-grammar-correction/resolve/main/encoder_model_quantized.onnx
- Command line
-
hf download hf://visheratin/t5-efficient-mini-grammar-correction/encoder_model_quantized.onnx
-
curl -L -o encoder_model_quantized.onnx https://huggingface.co/visheratin/t5-efficient-mini-grammar-correction/resolve/main/encoder_model_quantized.onnx
20.3 MB
- Xet hash:
- 2c10fa0d1ee711d26812e41ab56821885905b3d7c049ba342fea9e472fa37a1e
- Size of remote file:
- 20.3 MB
- SHA256:
- 67bb9f9b0db9a50eca338e6297a15b30c73042b3292b42b113c10df882140a24
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.