---
library_name: transformers
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
- ukrainian
- style-transfer
- text-editing
- mt5
model-index:
- name: mt5-small-ukrainian-style-editor
  results: []
---

# mt5-small-ukrainian-style-editor

This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) designed for **stylistic editing of Ukrainian texts**.
It transforms raw or non-native phrasing into improved, stylistically polished Ukrainian, making it suitable for academic, journalistic, or official contexts..

It achieves the following results on the evaluation set:
- Loss: 0.2027
- Score: 41.4271
- Counts: [18650, 13567, 10522, 7822]
- Totals: [25663, 22534, 19416, 16463]
- Precisions: [72.67271947940615, 60.206798615425576, 54.192418623815406, 47.51260402113831]
- Bp: 0.7151
- Sys Len: 25663
- Ref Len: 34270

## 🧠 Model Description

This model was trained using a hybrid approach, combining:
- Dictionary-based style correction (e.g., calque removal).
- Fine-tuning on paragraph-aligned pairs of original and stylistically improved Ukrainian text.

The base model is multilingual T5 (mT5), allowing flexible encoder-decoder performance and cross-lingual generalization, adapted to the specifics of Ukrainian syntax and style.

## 📌 Intended Uses & Limitations

### ✅ Intended Uses
- Stylistic enhancement of Ukrainian texts.
- Detection and correction of translationese or poor phrasing.
- Text improvement for public communication, official writing, and journalism.

### ⚠️ Limitations
- Not intended for grammar correction or spell-checking.
- May occasionally preserve non-stylistic errors if present in training data.
- Performance is best on formal or semi-formal text.

## 📊 Training and Evaluation Data

Training used a custom dataset uploaded to Hugging Face: [Kulynych/training_data](https://huggingface.co/datasets/Kulynych/training_data).  
Each entry contains:
- `input_text`: raw Ukrainian text (possibly containing calques or awkward phrasing).
- `target_text`: human-edited version of the same paragraph, stylistically improved.

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss | Score   | Counts                      | Totals                       | Precisions                                                                     | Bp     | Sys Len | Ref Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------------------------:|:----------------------------:|:------------------------------------------------------------------------------:|:------:|:-------:|:-------:|
| 0.2888        | 1.0   | 3129 | 0.2095          | 41.1095 | [18518, 13411, 10404, 7739] | [25905, 22776, 19652, 16594] | [71.48426944605289, 58.88215665612926, 52.94117647058823, 46.6373387971556]    | 0.7240 | 25905   | 34270   |
| 0.2325        | 2.0   | 6258 | 0.2027          | 41.4271 | [18650, 13567, 10522, 7822] | [25663, 22534, 19416, 16463] | [72.67271947940615, 60.206798615425576, 54.192418623815406, 47.51260402113831] | 0.7151 | 25663   | 34270   |


### Framework versions

- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1

### Evaluation Metric
- **SacreBLEU** score: **41.43** (after 2nd epoch)
- **Validation Loss**: **0.2027**

| Epoch | Step | Val Loss | SacreBLEU | Bp     | Precisions (%)                              |
|-------|------|----------|-----------|--------|---------------------------------------------|
| 1     | 3129 | 0.2095   | 41.11     | 0.7240 | [71.48, 58.88, 52.94, 46.63]                |
| 2     | 6258 | 0.2027   | 41.43     | 0.7151 | [72.67, 60.20, 54.19, 47.51]                |

## 💻 How to Use

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Kulynych/mt5-small-ukrainian-style-editor")
model = AutoModelForSeq2SeqLM.from_pretrained("Kulynych/mt5-small-ukrainian-style-editor")

text = "Згідно з даними, котрі ми отримали, ситуація погіршилась."
inputs = tokenizer(text, return_tensors="pt")
output = model.generate(**inputs, max_length=192)
print(tokenizer.decode(output[0], skip_special_tokens=True))