# English Text Embedding

IBM Granite Embedding 30M English is a transformer-based English text embedding model developed and released by IBM.

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

GTE-base-en-v1.5 is an English sentence transformer model focused on sentence similarity tasks, excelling in multiple text embedding benchmarks.

mxbai-embed-large-v1 is a sentence embedding model based on the BERT-large architecture, trained using AnglE loss function, supporting English text embedding, and offering multiple quantized versions to meet various needs.

BAAI/bge-base-en-v1.5 is a Transformer-based English text embedding model, primarily used for sentence similarity calculation and text classification tasks.

Quantized ONNX version of the BAAI/bge-small-en-v1.5 model for text classification and similarity search.

Jina Embeddings V2 Base is an efficient English text embedding model capable of converting text into high-dimensional vector representations, suitable for various natural language processing tasks.

GTE-small is a general text embedding model trained by Alibaba DAMO Academy, based on the BERT framework, suitable for tasks such as information retrieval and semantic text similarity.

The E5 Base Unsupervised Model is a text embedding model based on contrastive pre-training, suitable for sentence similarity and transformation tasks.

A sentence transformer model trained on 480,000 English lyrics for generating 300-dimensional sentence embeddings