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---
license: cc-by-nc-4.0
---
# Model Card for Spivavtor-Large
This model was obtained by fine-tuning the corresponding `bigscience/mt0-large` model on the Spivavtor dataset. All details of the dataset and fine tuning process can be found in our paper and repository.
**Paper:** Spivavtor: An Instruction Tuned Ukrainian Text Editing Model
**Authors:** Aman Saini, Artem Chernodub, Vipul Raheja, Vivek Kulkarni
## Model Details
### Model Description
- **Language**: Ukrainian
- **Finetuned from model:** bigscience/mt0-large
## How to use
We make available the following models presented in our paper.
<table>
<tr>
<th>Model</th>
<th>Number of parameters</th>
<th>Reference name in Paper</th>
</tr>
<tr>
<td>Spivavtor-large</td>
<td>1.2B</td>
<td>Spivavtor-mt0-large</td>
</tr>
<tr>
<td>Spivavtor-xxl</td>
<td>11B</td>
<td>Spivavtor-aya-101</td>
</tr>
</table>
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("grammarly/spivavtor-large")
model = AutoModelForSeq2SeqLM.from_pretrained("grammarly/spivavtor-large")
input_text = 'Виправте граматику в цьому реченнi: Дякую за iнформацiю! ми з Надiєю саме вийшли з дому'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=256)
output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
``` |