  In this paper, we introduce a novel and simple method for obtaining
high-quality text embeddings using only synthetic data and less than 1k
training steps. Unlike existing methods that often depend on multi-stage
intermediate pre-training with billions of weakly-supervised text pairs,
followed by fine-tuning with a few labeled datasets, our method does not
require building complex training pipelines or relying on manually collected
datasets that are often constrained by task diversity and language coverage. We
leverage proprietary LLMs to generate diverse synthetic data for hundreds of
thousands of text embedding tasks across 93 languages. We then fine-tune
open-source decoder-only LLMs on the synthetic data using standard contrastive
loss. Experiments demonstrate that our method achieves strong performance on
highly competitive text embedding benchmarks without using any labeled data.
Furthermore, when fine-tuned with a mixture of synthetic and labeled data, our
model sets new state-of-the-art results on the BEIR and MTEB benchmarks.
