---
language:
- uk
license: mit
library_name: transformers
datasets:
- skypro1111/ubertext-2-news-verbalized
widget:
- text: Очікувалось, що цей застосунок буде запущено о 11 ранку 22.08.2025, але розробники
    затягнули святкування і запуск був відкладений на 2 тижні.
---

# Model Card for mbart-large-50-verbalization

## Model Description
`mbart-large-50-verbalization` is a fine-tuned version of the [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) model, specifically designed for the task of verbalizing Ukrainian text to prepare it for Text-to-Speech (TTS) systems. This model aims to transform structured data like numbers and dates into their fully expanded textual representations in Ukrainian.

## Architecture
This model is based on the [facebook/mbart-large-50](https://huggingface.co/facebook/mbart-large-50) architecture, renowned for its effectiveness in translation and text generation tasks across numerous languages.

## Training Data
The model was fine-tuned on a subset of 457,610 sentences from the Ubertext dataset, focusing on news content. The verbalized equivalents were created using Google Gemini Pro, providing a rich basis for learning text transformation tasks.
Dataset [skypro1111/ubertext-2-news-verbalized](https://huggingface.co/datasets/skypro1111/ubertext-2-news-verbalized)

## Training Procedure
The model underwent 410,000 training steps (1 epoch).

```python
from transformers import MBartForConditionalGeneration, AutoTokenizer, Trainer, TrainingArguments
from datasets import load_dataset, DatasetDict
import torch

model_name = "facebook/mbart-large-50"

dataset = load_dataset("skypro1111/ubertext-2-news-verbalized")
dataset = dataset.train_test_split(test_size=0.1)
datasets = DatasetDict({
    'train': dataset['train'],
    'test': dataset['test']
})

tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.src_lang = "uk_XX"
tokenizer.tgt_lang = "uk_XX"


def preprocess_data(examples):
    model_inputs = tokenizer(examples["inputs"], max_length=1024, truncation=True, padding="max_length")
    with tokenizer.as_target_tokenizer():
        labels = tokenizer(examples["labels"], max_length=1024, truncation=True, padding="max_length")
    model_inputs["labels"] = labels["input_ids"]
    return model_inputs

datasets = datasets.map(preprocess_data, batched=True)

model = MBartForConditionalGeneration.from_pretrained(model_name)

training_args = TrainingArguments(
    output_dir=f"./results/{model_name}-verbalization",
    evaluation_strategy="steps",
    eval_steps=5000,
    save_strategy="steps",
    save_steps=1000,
    save_total_limit=40,
    learning_rate=2e-5,
    per_device_train_batch_size=2,
    per_device_eval_batch_size=2,
    num_train_epochs=2,
    weight_decay=0.01,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=datasets["train"],
    eval_dataset=datasets["test"],
)

trainer.train()
trainer.save_model(f"./saved_models/{model_name}-verbalization")
```

## Usage
```python
from transformers import MBartForConditionalGeneration, AutoTokenizer
import torch

device = "cuda" if torch.cuda.is_available() else "cpu"

model_name = "skypro1111/mbart-large-50-verbalization"

model = MBartForConditionalGeneration.from_pretrained(
        model_name,
        low_cpu_mem_usage=True,
        device_map=device,
)
model.eval()
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.src_lang = "uk_XX"
tokenizer.tgt_lang = "uk_XX"

input_text = "<verbalization>:Цей додаток вийде 15.06.2025."

encoded_input = tokenizer(input_text, return_tensors="pt", padding=True, truncation=True, max_length=1024).to(device)
output_ids = model.generate(**encoded_input, max_length=1024, num_beams=5, early_stopping=True)
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)

print(output_text)
```

## ONNX usage
```bash
poetry new verbalizer
rm -rf verbalizer/tests/ verbalizer/verbalizer/ verbalizer/README.md 
cd verbalizer/
poetry shell
wget https://huggingface.co/skypro1111/mbart-large-50-verbalization/resolve/main/onnx/infer_onnx_hf.py
poetry add transformers huggingface_hub onnxruntime-gpu torch
python infer_onnx_hf.py
```

```python
import onnxruntime
import numpy as np
from transformers import AutoTokenizer
import time
import os
from huggingface_hub import hf_hub_download

model_name = "skypro1111/mbart-large-50-verbalization"


def download_model_from_hf(repo_id=model_name, model_dir="./"):
    """Download ONNX models from HuggingFace Hub."""
    
    files = ["onnx/encoder_model.onnx", "onnx/decoder_model.onnx", "onnx/decoder_model.onnx_data"]

    for file in files:
        hf_hub_download(
            repo_id=repo_id,
            filename=file,
            local_dir=model_dir,
        )
    
    return files

def create_onnx_session(model_path, use_gpu=True):
    """Create an ONNX inference session."""
    # Session options
    session_options = onnxruntime.SessionOptions()
    session_options.graph_optimization_level = onnxruntime.GraphOptimizationLevel.ORT_ENABLE_ALL
    session_options.enable_mem_pattern = True
    session_options.enable_mem_reuse = True
    session_options.intra_op_num_threads = 8
    session_options.log_severity_level = 1
    
    cuda_provider_options = {
        'device_id': 0,
        'arena_extend_strategy': 'kSameAsRequested',
        'gpu_mem_limit': 0,  # 0 means no limit
        'cudnn_conv_algo_search': 'DEFAULT',
        'do_copy_in_default_stream': True,
    }
    
    print(f"Available providers: {onnxruntime.get_available_providers()}")
    if use_gpu and 'CUDAExecutionProvider' in onnxruntime.get_available_providers():
        providers = [('CUDAExecutionProvider', cuda_provider_options)]
        print("Using CUDA for inference")
    else:
        providers = ['CPUExecutionProvider']
        print("Using CPU for inference")
    
    session = onnxruntime.InferenceSession(
        model_path,
        providers=providers,
        sess_options=session_options
    )
    
    return session

def generate_text(text, tokenizer, encoder_session, decoder_session, max_length=128):
    """Generate text for a single input."""
    # Prepare input
    inputs = tokenizer(text, return_tensors="np", padding=True, truncation=True, max_length=512)
    input_ids = inputs["input_ids"].astype(np.int64)
    attention_mask = inputs["attention_mask"].astype(np.int64)
    
    # Run encoder
    encoder_outputs = encoder_session.run(
        output_names=["last_hidden_state"],
        input_feed={
            "input_ids": input_ids,
            "attention_mask": attention_mask,
        }
    )[0]
    
    # Initialize decoder input
    decoder_input_ids = np.array([[tokenizer.pad_token_id]], dtype=np.int64)
    
    # Generate sequence
    for _ in range(max_length):
        # Run decoder
        decoder_outputs = decoder_session.run(
            output_names=["logits"],
            input_feed={
                "input_ids": decoder_input_ids,
                "encoder_hidden_states": encoder_outputs,
                "encoder_attention_mask": attention_mask,
            }
        )[0]
        
        # Get next token
        next_token = decoder_outputs[:, -1:].argmax(axis=-1)
        decoder_input_ids = np.concatenate([decoder_input_ids, next_token], axis=-1)
        
        # Check if sequence is complete
        if tokenizer.eos_token_id in decoder_input_ids[0]:
            break
    
    # Decode sequence
    output_text = tokenizer.decode(decoder_input_ids[0], skip_special_tokens=True)
    return output_text

def main():
    # Print available providers
    print("Available providers:", onnxruntime.get_available_providers())
    
    # Download models from HuggingFace
    print("\nDownloading models from HuggingFace...")
    encoder_path, decoder_path, _ = download_model_from_hf()
    
    # Load tokenizer and models
    print("\nLoading tokenizer...")
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    tokenizer.src_lang = "uk_UA"
    tokenizer.tgt_lang = "uk_UA"
    
    # Create ONNX sessions
    print("\nLoading encoder...")
    encoder_session = create_onnx_session(encoder_path)
    print("\nLoading decoder...")
    decoder_session = create_onnx_session(decoder_path)
    
    # Test examples
    test_inputs = [
        "мій телефон 0979456822",
        "квартира площею 11 тис кв м.",
        "Пропонували хабар у 1 млрд грн.",
        "1 2 3 4 5 6 7 8 9 10.",
        "Крім того, парламентарій володіє шістьма ділянками землі (дві площею 25000 кв м, дві по 15000 кв м та дві по 10000 кв м) розташованими в Сосновій Балці Луганської області.",
        "Підписуючи цей документ у 2003 році, голови Росії та України мали намір зміцнити співпрацю та сприяти розширенню двосторонніх відносин.",
        "Очікується, що цей застосунок буде запущено 22.08.2025.",
        "За інформацією від Державної служби з надзвичайних ситуацій станом на 7 ранку 15 липня.",
    ]
    
    print("\nWarming up...")
    _ = generate_text(test_inputs[0], tokenizer, encoder_session, decoder_session)
    
    print("\nRunning inference...")
    for text in test_inputs:
        print(f"\nInput: {text}")
        t = time.time()
        output = generate_text(text, tokenizer, encoder_session, decoder_session)
        print(f"Output: {output}")
        print(f"Time: {time.time() - t:.2f} seconds")

if __name__ == "__main__":
    main() 
```


## Performance
Evaluation metrics were not explicitly used for this model. Its performance is primarily demonstrated through its application in enhancing the naturalness of TTS outputs.

## Limitations and Ethical Considerations
Users should be aware of the model's potential limitations in understanding highly nuanced or domain-specific content. Ethical considerations, including fairness and bias, are also crucial when deploying this model in real-world applications.

## Citation
Ubertext 2.0
```
@inproceedings{chaplynskyi-2023-introducing,
  title = "Introducing {U}ber{T}ext 2.0: A Corpus of Modern {U}krainian at Scale",
  author = "Chaplynskyi, Dmytro",
  booktitle = "Proceedings of the Second Ukrainian Natural Language Processing Workshop",
  month = may,
  year = "2023",
  address = "Dubrovnik, Croatia",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2023.unlp-1.1",
  pages = "1--10",
}
```
mBart-large-50
```
@article{tang2020multilingual,
    title={Multilingual Translation with Extensible Multilingual Pretraining and Finetuning},
    author={Yuqing Tang and Chau Tran and Xian Li and Peng-Jen Chen and Naman Goyal and Vishrav Chaudhary and Jiatao Gu and Angela Fan},
    year={2020},
    eprint={2008.00401},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

## License
This model is released under the MIT License, in line with the base mbart-large-50 model.