--- language: - en license: cc-by-nc-4.0 datasets: - facebook/asset - wi_locness - GEM/wiki_auto_asset_turk - discofuse - zaemyung/IteraTeR_plus - jfleg - grammarly/coedit metrics: - sari - bleu - accuracy widget: - text: 'Fix the grammar: When I grow up, I start to understand what he said is quite right.' example_title: Fluency - text: 'Make this text coherent: Their flight is weak. They run quickly through the tree canopy.' example_title: Coherence - text: 'Rewrite to make this easier to understand: A storm surge is what forecasters consider a hurricane''s most treacherous aspect.' example_title: Simplification - text: 'Paraphrase this: Do you know where I was born?' example_title: Paraphrase - text: 'Write this more formally: omg i love that song im listening to it right now' example_title: Formalize - text: 'Write in a more neutral way: The authors'' exposé on nutrition studies.' example_title: Neutralize --- # 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 - **Repository:** https://github.com/vipulraheja/coedit - **Paper:** https://arxiv.org/abs/2305.09857 ## 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](https://huggingface.co/grammarly/coedit-xl/resolve/main/task_examples.png) ## Usage ```python 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