Towards General Text Embeddings with Multi-stage Contrastive Learning

Abstract

We present GTE, a general-purpose text embedding model trained with multi-stage contrastive learning. In line with recent advancements in unifying various NLP tasks into a single format, we train a unified text embedding model by employing contrastive learning over a diverse mixture of datasets from multiple sources. By significantly increasing the number of training data during both unsupervised pre-training and supervised fine-tuning stages, we achieve substantial performance gains over existing embedding models. Notably, even with a relatively modest parameter count of 110M, GTEbase outperforms the black-box embedding API provided by OpenAI and even surpasses 10x larger text embedding models on the massive text embedding benchmark. Furthermore, without additional fine-tuning on each programming language individually, our model outperforms previous best code retrievers of similar size by treating code as text. In summary, our model achieves impressive results by effectively harnessing multi-stage contrastive learning, offering a powerful and efficient text embedding model with broad applicability across various NLP and code-related tasks.

Full paper on arXiv

Details

arXiv identifier

2308.03281

Published

2023-08-07

Authors

Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, Meishan Zhang

Models That Cite This Paper

• Described bygte-reranker-modernbert-base
• Described bygte-small
• Described bygte-large-en-v1.5
• Described bygte-Qwen2-1.5B-instruct
• Described bygte-large