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---
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language:
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- en
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license: cc-by-nc-4.0
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datasets:
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- facebook/asset
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- wi_locness
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- GEM/wiki_auto_asset_turk
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- discofuse
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- zaemyung/IteraTeR_plus
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- jfleg
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- grammarly/coedit
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metrics:
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- sari
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- bleu
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- accuracy
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widget:
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- text: 'Fix the grammar: When I grow up, I start to understand what he said is quite
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right.'
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example_title: Fluency
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- text: 'Make this text coherent: Their flight is weak. They run quickly through the
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tree canopy.'
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example_title: Coherence
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- text: 'Rewrite to make this easier to understand: A storm surge is what forecasters
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consider a hurricane''s most treacherous aspect.'
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example_title: Simplification
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- text: 'Paraphrase this: Do you know where I was born?'
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example_title: Paraphrase
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- text: 'Write this more formally: omg i love that song im listening to it right now'
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example_title: Formalize
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- text: 'Write in a more neutral way: The authors'' exposé on nutrition studies.'
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example_title: Neutralize
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---
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# Model Card for CoEdIT-Large
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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.
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**Paper:** CoEdIT: Text Editing by Task-Specific Instruction Tuning
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**Authors:** Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang
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## Model Details
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### Model Description
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- **Language(s) (NLP)**: English
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- **Finetuned from model:** google/flan-t5-large
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### Model Sources
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- **Repository:** https://github.com/vipulraheja/coedit
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- **Paper:** https://arxiv.org/abs/2305.09857
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## How to use
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We make available the models presented in our paper.
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<table>
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<tr>
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<th>Model</th>
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<th>Number of parameters</th>
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</tr>
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<tr>
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<td>CoEdIT-large</td>
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<td>770M</td>
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</tr>
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<tr>
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<td>CoEdIT-xl</td>
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<td>3B</td>
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</tr>
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<tr>
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<td>CoEdIT-xxl</td>
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<td>11B</td>
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</tr>
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</table>
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## Uses
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## Text Revision Task
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Given an edit instruction and an original text, our model can generate the edited version of the text.<br>
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## Usage
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```python
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from transformers import AutoTokenizer, T5ForConditionalGeneration
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tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-large")
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model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-large")
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input_text = 'Fix grammatical errors in this sentence: When I grow up, I start to understand what he said is quite right.'
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids
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outputs = model.generate(input_ids, max_length=256)
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edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
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```
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#### Software
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https://github.com/vipulraheja/coedit
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## Citation
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**BibTeX:**
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```
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@article{raheja2023coedit,
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title={CoEdIT: Text Editing by Task-Specific Instruction Tuning},
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author={Vipul Raheja and Dhruv Kumar and Ryan Koo and Dongyeop Kang},
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year={2023},
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eprint={2305.09857},
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archivePrefix={arXiv},
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primaryClass={cs.CL}
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}
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```
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**APA:**
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Raheja, V., Kumar, D., Koo, R., & Kang, D. (2023). CoEdIT: Text Editing by Task-Specific Instruction Tuning. ArXiv. /abs/2305.09857 |
