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---
license: apache-2.0
datasets:
- asset
- wi_locness
- GEM/wiki_auto_asset_turk
- discofuse
- zaemyung/IteraTeR_plus
- jfleg
language:
- en
metrics:
- sari
- bleu
- accuracy
---
# Model Card for CoEdIT-xl-composite
This model was obtained by fine-tuning the corresponding google/flan-t5-xl model on the CoEdIT-Composite 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-xl
### 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.
<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>
This model can also perform edits on composite instructions, as shown below:
## Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-xl-composite")
model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-xl-composite")
input_text = 'Fix grammatical errors in this sentence and make it simpler: New kinds of vehicles will be invented with new technology than today.'
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]
```
#### Software
https://github.com/vipulraheja/coedit
## Citation
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed] |