metadata
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.
| Model | Number of parameters | Reference name in Paper |
|---|---|---|
| Spivavtor-large | 1.2B | Spivavtor-mt0-large |
| Spivavtor-xxl | 11B | Spivavtor-aya-101 |
Usage
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)