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--- |
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license: apache-2.0 |
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datasets: |
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- 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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language: |
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- en |
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metrics: |
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- sari |
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- bleu |
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- accuracy |
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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: ext 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 [optional] |
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- **Repository:** https://github.com/vipulraheja/coedit |
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- **Paper [optional]:** [More Information Needed] |
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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: New kinds of vehicles will be invented with new technology than today.' |
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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)[0] |
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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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[More Information Needed] |
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**APA:** |
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[More Information Needed] |
