In this paper,
we introduce a novel and simple method for obtaining high-quality text embeddings
using only synthetic data and less thank 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 nearlylanguages.
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.