Update README.md

README.md

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 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
-The mT5-multilingual-XLSum model was fine-tuned on the UA-News dataset to generate news headlines in Ukrainian language.
+## Model Summary
+The mT5-multilingual-XLSum model was fine-tuned on the UA-News dataset to generate news headlines in Ukrainian language. This model is designed to produce concise and accurate summaries for Ukrainian news articles.
+
+## Model Details
+- **Model Type**: Summarization
+- **Language**: Multilingual
+- **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)