Transformers
PyTorch
ONNX
English
t5
text2text-generation
grammar-correction
text-generation-inference
Instructions to use visheratin/t5-efficient-tiny-grammar-correction with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use visheratin/t5-efficient-tiny-grammar-correction with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("visheratin/t5-efficient-tiny-grammar-correction") model = AutoModelForSeq2SeqLM.from_pretrained("visheratin/t5-efficient-tiny-grammar-correction", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download encoder_model_quant.onnx from visheratin/t5-efficient-tiny-grammar-correction: direct link, hf CLI and curl.
- Browser
- Download file 11.5 MB
-
https://huggingface.co/visheratin/t5-efficient-tiny-grammar-correction/resolve/refs%2Fpr%2F2/encoder_model_quant.onnx
- Command line
-
hf download hf://visheratin/t5-efficient-tiny-grammar-correction@refs/pr/2/encoder_model_quant.onnx
-
curl -L -o encoder_model_quant.onnx https://huggingface.co/visheratin/t5-efficient-tiny-grammar-correction/resolve/refs%2Fpr%2F2/encoder_model_quant.onnx
11.5 MB
- Xet hash:
- 52395dfbd759434403d3a87196244a7b0e57385744e7ceefbc3a97dd1e98b073
- Size of remote file:
- 11.5 MB
- SHA256:
- ba0b3277cd0b956070d41d9fd57d36620171a4325e1a2a946afaee2354dd784d
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