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, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("visheratin/t5-efficient-mini-grammar-correction") model = AutoModelForMultimodalLM.from_pretrained("visheratin/t5-efficient-mini-grammar-correction") - Notebooks
- Google Colab
- Kaggle
metadata
language:
- en
tags:
- grammar-correction
license: mit
datasets:
- c4_200m
T5-Efficient-MINI for grammar correction
This is a T5-Efficient-MINI model that was trained on a subset of C4_200M dataset to solve the grammar correction task in English. To bring additional errors, random typos were introduced to the input sentences using the nlpaug library. Since the model was trained on only one task, there are no prefixes needed.
The model was trained as a part of the project during the Full Stack Deep Learning course. ONNX version of the model is deployed on the site and can be run directly in the browser.