Text2Text Generation
Transformers
PyTorch
Safetensors
English
t5
Inference Endpoints
text-generation-inference
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Model Card for CoEdIT-Large

This model was obtained by fine-tuning the corresponding google/flan-t5-large model on the CoEdIT dataset. Details of the dataset can be found in our paper and repository.

Paper: CoEdIT: Text Editing by Task-Specific Instruction Tuning

Authors: Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang

Model Details

Model Description

  • Language(s) (NLP): English
  • Finetuned from model: google/flan-t5-large

Model Sources

How to use

We make available the models presented in our paper.

Model Number of parameters
CoEdIT-large 770M
CoEdIT-xl 3B
CoEdIT-xxl 11B

Uses

Text Revision Task

Given an edit instruction and an original text, our model can generate the edited version of the text.

task_specs

Usage

from transformers import AutoTokenizer, T5ForConditionalGeneration

tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-large")
model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-large")
input_text = 'Fix grammatical errors in this sentence: When I grow up, I start to understand what he said is quite right.'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=256)
edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

Software

https://github.com/vipulraheja/coedit

Citation

BibTeX:

@article{raheja2023coedit,
      title={CoEdIT: Text Editing by Task-Specific Instruction Tuning}, 
      author={Vipul Raheja and Dhruv Kumar and Ryan Koo and Dongyeop Kang},
      year={2023},
      eprint={2305.09857},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

APA: Raheja, V., Kumar, D., Koo, R., & Kang, D. (2023). CoEdIT: Text Editing by Task-Specific Instruction Tuning. ArXiv. /abs/2305.09857

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