Infoxlm Base
InfoXLM is a cross-lingual pre-training framework based on information theory, designed to enhance model performance by maximizing mutual information in cross-lingual tasks.
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Release Time : 3/2/2022
Model Overview
InfoXLM is a cross-lingual pre-training model that optimizes cross-lingual representation learning through information theory methods, supporting various natural language processing tasks in multiple languages.
Model Features
Information theory optimization
Optimizes model performance by maximizing mutual information in cross-lingual tasks.
Cross-lingual capability
Supports representation learning and task processing in multiple languages.
Pre-training framework
Provides an efficient pre-training framework suitable for various downstream tasks.
Model Capabilities
Cross-lingual text understanding
Cross-lingual text generation
Multilingual translation
Cross-lingual question answering
Use Cases
Natural language processing
Cross-lingual text classification
Performs text classification tasks in multilingual environments.
Machine translation
Supports translation tasks between multiple languages.
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