XTREME is a benchmark for evaluating cross-lingual generalization of multilingual representations across 40 languages and 9 tasks. The benchmark includes tasks such as natural language inference, paraphrase identification, part-of-speech tagging, named entity recognition, and question answering. It covers typologically diverse languages and includes pseudo test sets for diagnostics. The benchmark aims to evaluate the performance of cross-lingual transfer learning models, particularly in zero-shot scenarios where training data is available in English but not in the target language. The results show that while models perform well on English tasks, their performance drops significantly on other languages, especially in syntactic and sentence retrieval tasks. The benchmark also highlights the importance of multilingual data and the need for further research in cross-lingual learning. XTREME provides a platform for evaluating multilingual models and includes a set of strong baselines and code for adoption. The benchmark also includes an analysis of the limitations of state-of-the-art cross-lingual models and their performance across different language families and scripts. The results indicate that while some models perform well, there is still a significant gap in cross-lingual transfer capabilities, particularly for less represented languages. The benchmark encourages further research in cross-lingual generalization and provides a comprehensive evaluation of multilingual models.XTREME is a benchmark for evaluating cross-lingual generalization of multilingual representations across 40 languages and 9 tasks. The benchmark includes tasks such as natural language inference, paraphrase identification, part-of-speech tagging, named entity recognition, and question answering. It covers typologically diverse languages and includes pseudo test sets for diagnostics. The benchmark aims to evaluate the performance of cross-lingual transfer learning models, particularly in zero-shot scenarios where training data is available in English but not in the target language. The results show that while models perform well on English tasks, their performance drops significantly on other languages, especially in syntactic and sentence retrieval tasks. The benchmark also highlights the importance of multilingual data and the need for further research in cross-lingual learning. XTREME provides a platform for evaluating multilingual models and includes a set of strong baselines and code for adoption. The benchmark also includes an analysis of the limitations of state-of-the-art cross-lingual models and their performance across different language families and scripts. The results indicate that while some models perform well, there is still a significant gap in cross-lingual transfer capabilities, particularly for less represented languages. The benchmark encourages further research in cross-lingual generalization and provides a comprehensive evaluation of multilingual models.