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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: google/mt5-small |
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tags: |
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- generated_from_trainer |
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- ukrainian |
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- style-transfer |
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- text-editing |
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- mt5 |
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model-index: |
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- name: mt5-small-ukrainian-style-editor |
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results: [] |
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--- |
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# mt5-small-ukrainian-style-editor |
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This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) designed for **stylistic editing of Ukrainian texts**. |
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It transforms raw or non-native phrasing into improved, stylistically polished Ukrainian, making it suitable for academic, journalistic, or official contexts.. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.2027 |
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- Score: 41.4271 |
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- Counts: [18650, 13567, 10522, 7822] |
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- Totals: [25663, 22534, 19416, 16463] |
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- Precisions: [72.67271947940615, 60.206798615425576, 54.192418623815406, 47.51260402113831] |
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- Bp: 0.7151 |
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- Sys Len: 25663 |
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- Ref Len: 34270 |
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## 🧠 Model Description |
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This model was trained using a hybrid approach, combining: |
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- Dictionary-based style correction (e.g., calque removal). |
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- Fine-tuning on paragraph-aligned pairs of original and stylistically improved Ukrainian text. |
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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. |
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## 📌 Intended Uses & Limitations |
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### ✅ Intended Uses |
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- Stylistic enhancement of Ukrainian texts. |
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- Detection and correction of translationese or poor phrasing. |
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- Text improvement for public communication, official writing, and journalism. |
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### ⚠️ Limitations |
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- Not intended for grammar correction or spell-checking. |
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- May occasionally preserve non-stylistic errors if present in training data. |
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- Performance is best on formal or semi-formal text. |
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## 📊 Training and Evaluation Data |
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Training used a custom dataset uploaded to Hugging Face: [Kulynych/training_data](https://huggingface.co/datasets/Kulynych/training_data). |
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Each entry contains: |
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- `input_text`: raw Ukrainian text (possibly containing calques or awkward phrasing). |
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- `target_text`: human-edited version of the same paragraph, stylistically improved. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 2 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Score | Counts | Totals | Precisions | Bp | Sys Len | Ref Len | |
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|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------------------------:|:----------------------------:|:------------------------------------------------------------------------------:|:------:|:-------:|:-------:| |
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| 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 | |
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| 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 | |
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### Framework versions |
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- Transformers 4.50.3 |
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- Pytorch 2.6.0+cu124 |
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- Datasets 3.5.0 |
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- Tokenizers 0.21.1 |
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### Evaluation Metric |
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- **SacreBLEU** score: **41.43** (after 2nd epoch) |
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- **Validation Loss**: **0.2027** |
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| Epoch | Step | Val Loss | SacreBLEU | Bp | Precisions (%) | |
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|-------|------|----------|-----------|--------|---------------------------------------------| |
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| 1 | 3129 | 0.2095 | 41.11 | 0.7240 | [71.48, 58.88, 52.94, 46.63] | |
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| 2 | 6258 | 0.2027 | 41.43 | 0.7151 | [72.67, 60.20, 54.19, 47.51] | |
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## 💻 How to Use |
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```python |
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from transformers import AutoTokenizer, AutoModelForSeq2SeqLM |
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tokenizer = AutoTokenizer.from_pretrained("Kulynych/mt5-small-ukrainian-style-editor") |
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model = AutoModelForSeq2SeqLM.from_pretrained("Kulynych/mt5-small-ukrainian-style-editor") |
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text = "Згідно з даними, котрі ми отримали, ситуація погіршилась." |
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inputs = tokenizer(text, return_tensors="pt") |
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output = model.generate(**inputs, max_length=192) |
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print(tokenizer.decode(output[0], skip_special_tokens=True)) |
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