metadata

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 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 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

Training Procedure

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

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

from transformers import MBartForConditionalGeneration, AutoTokenizer
import torch

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

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

model = T5ForConditionalGeneration.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)

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.