Update README.md

README.md

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 Model Card 
 
-EXLMR has been designed with specific support for underrepresented languages, particularly those spoken in Ethiopia (such as Amharic, Tigrinya, and Afaan Oromo).Like XLM-RoBERTa, EXLMR can handle multiple languages simultaneously, making it effective for cross-lingual tasks such as machine translation, multilingual text classification, and question answering.EXLMR-base follows the same architecture as RoBERTa-base, with 12 layers, 768 hidden dimensions, and 12 attention heads, totaling approximately 270M parameters.
+EXLMR has been designed with specific support for underrepresented languages, particularly those spoken in Ethiopia (such as Amharic, Tigrinya, and Afaan Oromo). Like XLM-RoBERTa, EXLMR can be finetuned to handle multiple languages simultaneously, making it effective for cross-lingual tasks such as machine translation, multilingual text classification, and question answering.EXLMR-base follows the same architecture as RoBERTa-base, with 12 layers, 768 hidden dimensions, and 12 attention heads, totaling approximately 270M parameters.
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+Use Cases:
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+#Text Classification: This can be fine-tuned for text classification tasks in Ethiopian languages.
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+#Machine Translation: Useful for building machine translation models between Ethiopian and other languages.
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+#Named Entity Recognition (NER): This can be applied to entity recognition tasks for low-resource languages like Amharic and Tigrinya.
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+#Question Answering: Fine-tuned for multilingual question-answering tasks, supporting cross-lingual information retrieval
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 |Model|Vocabulary Size|
 |---|---|
 |XLM-Roberta|250002|
-|EXLMR|280147|
+|EXLMR|280147|
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