# Long Text Embedding

This is a sentence transformer model fine-tuned on Alibaba NLP/gte-modernbert-base, specifically designed for semantic text similarity calculation in the medical field, supporting semantic caching functionality.

This is a sentence-transformers model fine-tuned from nomic-ai/nomic-embed-text-v2-moe, which maps text to a 768-dimensional dense vector space for tasks such as semantic text similarity calculation.

A sentence transformer model fine-tuned based on nomic-ai/nomic-embed-text-v1.5, optimized for scientific literature topic similarity tasks

This model is a sentence transformer fine-tuned from nomic-ai/nomic-embed-text-v1.5 on scientific literature datasets, specifically designed for analyzing rhetorical functions in academic texts, such as summarizing results and expressing limitations.

A text embedding model based on the ModernBERT pre-trained encoder, supporting long text processing up to 8192 tokens, with excellent performance on evaluation tasks such as MTEB, LoCO, and COIR.

This is a sentence-transformers model fine-tuned from dunzhang/stella_en_1.5B_v5, designed to generate 1024-dimensional dense vector representations for sentences and paragraphs, suitable for tasks like semantic text similarity and semantic search.

A text embedding model developed by Salesforce Research, trained on E5-mistral-7b-instruct and Mistral-7B-v0.1, primarily used for text retrieval tasks.

A sentence-transformers model developed by Hum, supporting 4096 tokens long text embedding, suitable for semantic search and clustering tasks