Instructions to use grammarly/coedit-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use grammarly/coedit-large with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-large") model = AutoModelForSeq2SeqLM.from_pretrained("grammarly/coedit-large") - Notebooks
- Google Colab
- Kaggle
| license: apache-2.0 | |
| datasets: | |
| - asset | |
| - wi_locness | |
| - GEM/wiki_auto_asset_turk | |
| - discofuse | |
| - zaemyung/IteraTeR_plus | |
| - jfleg | |
| - grammarly/coedit | |
| language: | |
| - en | |
| metrics: | |
| - sari | |
| - bleu | |
| - accuracy | |
| # 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. | |
| <table> | |
| <tr> | |
| <th>Model</th> | |
| <th>Number of parameters</th> | |
| </tr> | |
| <tr> | |
| <td>CoEdIT-large</td> | |
| <td>770M</td> | |
| </tr> | |
| <tr> | |
| <td>CoEdIT-xl</td> | |
| <td>3B</td> | |
| </tr> | |
| <tr> | |
| <td>CoEdIT-xxl</td> | |
| <td>11B</td> | |
| </tr> | |
| </table> | |
| ## Uses | |
| ## Text Revision Task | |
| Given an edit instruction and an original text, our model can generate the edited version of the text.<br> | |
|  | |
| ## 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 |