# Text Embedding

Linq-Embed-Mistral is an embedding model based on the Mistral architecture, specializing in text classification, retrieval, and clustering tasks, with outstanding performance across multiple MTEB benchmarks.

mMiniLMv2-L12-H384 is a version of Microsoft's multilingual MiniLM v2 model, designed to provide a more convenient user experience through the HuggingFace transformers library.

GTE-Large is a text embedding model ported to ONNX, suitable for text classification and similarity search tasks.

E5-small-v2 is an efficient text embedding model suitable for various natural language processing tasks.

This is a 6-layer lightweight version of microsoft/MiniLM-L12-H384-uncased, achieved by retaining every other layer for reduced size

This is a sentence similarity calculation model based on the RoBERTa architecture, supporting Chinese text processing

Sentence similarity calculation model based on Sentence-Transformers, using RoBERTa architecture and mean pooling