# Model Card for CoEdIT-xxl

This model was obtained by fine-tuning the corresponding google/flan-t5-xxl 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-xxl

### 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>

![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-xxl")
model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-xxl")
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
{"language": ["en"], "license": "cc-by-nc-4.0", "datasets": ["asset", "wi_locness", "GEM/wiki_auto_asset_turk", "discofuse", "zaemyung/IteraTeR_plus", "jfleg", "grammarly/coedit"], "metrics": ["sari", "bleu", "accuracy"]}
transformers pytorch safetensors t5 text2text-generation en dataset:asset dataset:wi_locness dataset:GEM/wiki_auto_asset_turk dataset:discofuse dataset:zaemyung/IteraTeR_plus dataset:jfleg dataset:grammarly/coedit arxiv:2305.09857 license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible has_space text-generation-inference region:us
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-asset #dataset-wi_locness #dataset-GEM/wiki_auto_asset_turk #dataset-discofuse #dataset-zaemyung/IteraTeR_plus #dataset-jfleg #dataset-grammarly/coedit #arxiv-2305.09857 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 

Model Card for CoEdIT-xxl
=========================


This model was obtained by fine-tuning the corresponding google/flan-t5-xxl 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-xxl


### Model Sources


* Repository: URL
* Paper: URL


How to use
----------


We make available the models presented in our paper.



Uses
----


Text Revision Task
------------------


Given an edit instruction and an original text, our model can generate the edited version of the text.  



!task\_specs


Usage
-----


#### Software


URL


BibTeX:


APA:
Raheja, V., Kumar, D., Koo, R., & Kang, D. (2023). CoEdIT: Text Editing by Task-Specific Instruction Tuning. ArXiv. /abs/2305.09857



### Model Description


* Language(s) (NLP): English
* Finetuned from model: google/flan-t5-xxl ### Model Sources


* Repository: URL
* Paper: URL


How to use
----------


We make available the models presented in our paper.



Uses
----


Text Revision Task
------------------


Given an edit instruction and an original text, our model can generate the edited version of the text.  



!task\_specs


Usage
----- #### Software


URL


BibTeX:


APA:
Raheja, V., Kumar, D., Koo, R., & Kang, D. (2023). CoEdIT: Text Editing by Task-Specific Instruction Tuning. ArXiv. /abs/2305.09857
TAGS
#transformers #pytorch #safetensors #t5 #text2text-generation #en #dataset-asset #dataset-wi_locness #dataset-GEM/wiki_auto_asset_turk #dataset-discofuse #dataset-zaemyung/IteraTeR_plus #dataset-jfleg #dataset-grammarly/coedit #arxiv-2305.09857 #license-cc-by-nc-4.0 #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us 
 ### Model Description


* Language(s) (NLP): English
* Finetuned from model: google/flan-t5-xxl ### Model Sources


* Repository: URL
* Paper: URL


How to use
----------


We make available the models presented in our paper.



Uses
----


Text Revision Task
------------------


Given an edit instruction and an original text, our model can generate the edited version of the text.  



!task\_specs


Usage
----- #### Software


URL


BibTeX:


APA:
Raheja, V., Kumar, D., Koo, R., & Kang, D. (2023). CoEdIT: Text Editing by Task-Specific Instruction Tuning. ArXiv. /abs/2305.09857