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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.
Training
- Epochs: 4
- Batch Size: 4
- Learning Rate: 4e-5
Evaluation
- Metrics: The model's performance on the test set.
- 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
pipelinefor summarization. Here's an example:from transformers import pipeline summarizer = pipeline("summarization", model="yelyah/mT5-XLSUM-ua-news") article = "Your news article text here." summary = summarizer(article) print(summary)
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