--- license: apache-2.0 datasets: - asset - wi_locness - GEM/wiki_auto_asset_turk - discofuse - zaemyung/IteraTeR_plus 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. Paper: CoEdIT: ext 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 [optional] - **Repository:** https://github.com/vipulraheja/coedit - **Paper [optional]:** [More Information Needed] ## 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/Screen%20Shot%202023-05-12%20at%203.36.37%20PM.png) ## Usage ```python from transformers import AutoTokenizer, T5ForConditionalGeneration tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-large") model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-large") input_text = 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)[0] before_input = 'Fix grammatical errors in this sentence: New kinds of vehicles will be invented with new technology than today.' model_input = tokenizer(before_input, return_tensors='pt') model_outputs = model.generate(**model_input, num_beams=8, max_length=1024) after_text = tokenizer.batch_decode(model_outputs, skip_special_tokens=True)[0] ``` #### Software https://github.com/vipulraheja/coedit ## Citation **BibTeX:** [More Information Needed] **APA:** [More Information Needed]