---
datasets:
- FIdo-AI/ua-news
language:
- uk
metrics:
- rouge
library_name: transformers
pipeline_tag: summarization
tags:
- news
---

# Model Card for Model ID

## Model Summary
The mT5-multilingual-XLSum model was fine-tuned on the UA-News dataset to generate  concise and accurate news headlines in Ukrainian language.

## Model Details
- **Model Type**: Summarization
- **Language**: Multilingual/Ukrainian
- **Library**: Transformers

## Dataset
- **Name**: UA-News
- **Source**: FIdo-AI
- **Description**: The UA-News dataset contains a diverse collection of Ukrainian news articles, covering various topics including politics, economics, culture, and sports. The dataset is curated to provide high-quality training data for summarization tasks.

## Training
- **Fine-Tuning**: The model was fine-tuned on the UA-News dataset using the Hugging Face Transformers library.
- **Epochs**: 4
- **Batch Size**: 4
- **Learning Rate**: 4e-5

## Evaluation
- **Metrics**: The model's performance was evaluated using the ROUGE metric.
  - **ROUGE-1**: 0.2452
  - **ROUGE-2**: 0.1075
  - **ROUGE-L**: 0.2348
  - **BERTScore**: 0.7573

## Usage
- **Pipeline Tag**: Summarization
- **How to Use**: The model can be used with the Hugging Face `pipeline` for summarization. Here's an example:
  ```python
  from transformers import pipeline

  summarizer = pipeline("summarization", model="yelyah/mT5-XLSUM-ua-news ")
  article = "Your news article text here."
  summary = summarizer(article)
  print(summary)