# Large-scale text processing

A language model fine-tuned based on the Transformers library, with unspecified training dataset

Jina Embeddings V2 Base is an efficient English sentence embedding model, focusing on sentence similarity and feature extraction tasks.

Phi-3-mini-4k-instruct-graph is a fine-tuned version of Microsoft's Phi-3-mini-4k-instruct, specifically designed for entity relationship extraction from general text data, aiming to achieve comparable quality and accuracy to GPT-4 in generating entity relationship graphs.

This is a sentence embedding model based on sentence-transformers, capable of converting text into 1024-dimensional dense vector representations, suitable for tasks like semantic search and text clustering.

This is a sentence transformer version based on the intfloat/e5-large-v2 model, capable of mapping sentences and paragraphs into a 1024-dimensional dense vector space, suitable for tasks like clustering or semantic search.

SGPT-5.8B is a sentence transformer model based on the weighted mean method, focusing on sentence similarity tasks, trained on the msmarco dataset and optimized with specb-bitfit technology

SGPT-2.7B is a sentence transformer model based on the weighted mean method, focusing on sentence similarity tasks, trained on the MSMARCO dataset with BitFit technology applied.