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  license: apache-2.0
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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+ This model was obtained by fine-tuning the corresponding google/flan-t5-large model on the CoEdIT dataset.
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+
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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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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ - **Language(s) (NLP)**: English
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+ - **Finetuned from model:** google/flan-t5-large
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+
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+ ### Model Sources [optional]
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+
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+ - **Repository:** https://github.com/vipulraheja/coedit
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+ - **Paper [optional]:** [More Information Needed]
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+
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+ ## How to use
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+ We make available the models presented in our paper.
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+
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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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+
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+
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+ ## Uses
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+
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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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+
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+ ![task_specs](https://huggingface.co/grammarly/coedit-xl/resolve/main/Screen%20Shot%202023-05-12%20at%203.36.37%20PM.png)
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+
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+ ## Usage
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+ ```python
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+ from transformers import AutoTokenizer, T5ForConditionalGeneration
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+
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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 =
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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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+ before_input = 'Fix grammatical errors in this sentence: New kinds of vehicles will be invented with new technology than today.'
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+ model_input = tokenizer(before_input, return_tensors='pt')
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+ model_outputs = model.generate(**model_input, num_beams=8, max_length=1024)
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+ after_text = tokenizer.batch_decode(model_outputs, skip_special_tokens=True)[0]
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+ ```
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+
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+
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+ #### Software
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+ https://github.com/vipulraheja/coedit
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+
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+ ## Citation
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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]