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--- |
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datasets: |
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- FIdo-AI/ua-news |
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language: |
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- uk |
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metrics: |
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- rouge |
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library_name: transformers |
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pipeline_tag: summarization |
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tags: |
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- news |
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--- |
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# Model Card for Model ID |
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## Model Summary |
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The mT5-multilingual-XLSum model was fine-tuned on the UA-News dataset to generate concise and accurate news headlines in Ukrainian language. |
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## Training |
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- **Epochs**: 4 |
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- **Batch Size**: 4 |
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- **Learning Rate**: 4e-5 |
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## Evaluation |
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- **Metrics**: The model's performance on the test set. |
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- **ROUGE-1**: 0.2452 |
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- **ROUGE-2**: 0.1075 |
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- **ROUGE-L**: 0.2348 |
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- **BERTScore**: 0.7573 |
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## Usage |
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- **Pipeline Tag**: Summarization |
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- **How to Use**: The model can be used with the Hugging Face `pipeline` for summarization. Here's an example: |
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```python |
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from transformers import pipeline |
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summarizer = pipeline("summarization", model="yelyah/mT5-XLSUM-ua-news") |
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article = "Your news article text here." |
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summary = summarizer(article) |
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print(summary) |
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