# Cross-lingual understanding

Armenian text embedding model optimized based on multilingual-e5-base, supporting semantic search and cross-lingual understanding

A fill-mask model fine-tuned based on xlm-roberta-base for predicting masked text segments

A tagging classification model fine-tuned on the xtreme_en dataset based on xlm-roberta-base, supporting multilingual text processing.

BLOOM is a multilingual large language model supporting 46 languages, focusing on text generation tasks.

A text classification model fine-tuned on the Amazon multilingual comment dataset based on xlm-roberta-base

XLM-RoBERTa is an extended version of a cross-lingual sentence encoder, trained on 2.5T of data across 100 languages, achieving excellent performance in multiple cross-lingual benchmarks.

XLM-RoBERTa is a large-scale cross-lingual sentence encoder, trained on 2.5TB of data across 100 languages, achieving excellent performance in multiple cross-lingual benchmarks.

A fine-tuned model based on bert-base-multilingual-uncased, specific purpose not clearly stated

Fine-tuned on multilingual T5 model, specifically designed for zero-shot text classification tasks, supporting 15 languages

CINO is a multilingual pre-trained model designed for Chinese minority languages, supporting Chinese and 7 minority languages, built on the XLM-R framework.

The XLM model is a cross-lingual pretrained model based on text in 17 languages, using the masked language modeling (MLM) objective

This is a streamlined version of bert-base-multilingual-cased, specifically supporting English, French, Dutch, Russian, and Arabic while maintaining the original model's accuracy.

This is a compact version of distilbert-base-multilingual-cased, supporting 25 languages while maintaining the original model's accuracy.