# Multilingual understanding

Pre-trained model based on the Transformers library, suitable for natural language processing tasks

FLAN-T5 is the instruction-fine-tuned version of the T5 model, optimized through fine-tuning on over 1,000 multilingual tasks, delivering superior performance at the same parameter count.

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.

mDeBERTa is the multilingual version of DeBERTa, employing ELECTRA-style pretraining and gradient-disentangled embedding sharing technology, demonstrating excellent performance in cross-lingual tasks like XNLI

DistilBERT is a distilled version of BERT that retains 97% of BERT's performance while being 40% smaller and 60% faster.

XLM-Align is a pre-trained cross-lingual model supporting 94 languages, improving pre-trained cross-lingual models through self-labeled word alignment.

MiniLM is a compact and efficient pre-trained language model that compresses Transformer models through deep self-attention distillation technology, supporting multilingual understanding and generation tasks.

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.

This is a Spanish-based pre-trained model specifically fine-tuned for cross-lingual natural language inference tasks.

A trilingual model based on bert-base architecture, specializing in Croatian, Slovenian, and English performance, outperforming multilingual BERT