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
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This is a [T5-Efficient-MINI](https://huggingface.co/google/t5-efficient-mini) model that was trained on a subset of [C4_200M](https://ai.googleblog.com/2021/08/the-c4200m-synthetic-dataset-for.html) dataset to solve the grammar correction task in English. To bring additional errors, random typos were introduced to the input sentences using the [nlpaug](https://github.com/makcedward/nlpaug) library. Since the model was trained on only one task, there are no prefixes needed.
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The model was trained as a part of the project during the [Full Stack
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This is a [T5-Efficient-MINI](https://huggingface.co/google/t5-efficient-mini) model that was trained on a subset of [C4_200M](https://ai.googleblog.com/2021/08/the-c4200m-synthetic-dataset-for.html) dataset to solve the grammar correction task in English. To bring additional errors, random typos were introduced to the input sentences using the [nlpaug](https://github.com/makcedward/nlpaug) library. Since the model was trained on only one task, there are no prefixes needed.
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The model was trained as a part of the project during the [Full Stack Deep Learning](https://fullstackdeeplearning.com/course/2022/) course. ONNX version of the model is deployed on the [site](https://edge-ai.vercel.app/models/grammar-check) and can be run directly in the browser.
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