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Infoxlm Large
InfoXLM is a cross-lingual pre-training framework based on information theory, designed to enhance cross-lingual representation learning by maximizing mutual information between different languages.
Downloads 1.1M
Release Time : 3/2/2022
Model Overview
InfoXLM is a cross-lingual pre-training model that optimizes multilingual representations through information-theoretic methods, supporting various cross-lingual tasks such as machine translation and cross-lingual text classification.
Model Features
Information theory-based pre-training
Optimizes cross-lingual representation learning by maximizing mutual information between different languages.
Cross-lingual capability
Supports various cross-lingual tasks such as machine translation and text classification.
Efficient pre-training
Reduces redundant information during pre-training using information-theoretic methods, improving model efficiency.
Model Capabilities
Cross-lingual text representation
Machine translation
Text classification
Cross-lingual information retrieval
Use Cases
Natural language processing
Cross-lingual machine translation
Translates text from one language to another, leveraging InfoXLM's cross-lingual representation capabilities to improve translation quality.
Cross-lingual text classification
Classifies text in multiple languages, suitable for multilingual content management scenarios.
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