XLM-RoBERTa is a multilingual language model trained on a diverse dataset containing text from 100 different languages. Unlike some other multilingual models in the XLM family, XLM-RoBERTa doesn't rely on language tokens to determine which language is being used. This means it can understand and process text in various languages without needing explicit language indicators. By leveraging a large and varied corpus of text from around the world, XLM-RoBERTa learns to capture the nuances and patterns of language across different linguistic contexts. This makes it a powerful tool for tasks such as machine translation, sentiment analysis, and text classification in multilingual settings. With XLM-RoBERTa, users can perform natural language processing tasks across a wide range of languages without the need for language-specific preprocessing or annotations.
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