6 Conclusion

In this work, we train a general-purpose text embedding model E5 from weak supervision signals. We
adopt a simple contrastive training framework with in-batch negatives and learn from a large-scale text
pair dataset we harvest from heterogeneous data sources across the web. E5 offers strong off-the-shelf

8

Table 8: Comparison of different negative sampling strategies.

# negatives NFCorpus NQ FiQA Quora DBPedia

In batch
+ pre-batch
MoCo

32k
64k
130k

35.8
29.4
29.7

39.0
27.2
36.1

40.0
29.4
32.0

85.7
84.6
81.6

35.4
25.0
29.9

Scifact Avg
51.6
73.7
43.3
64.3
63.6
45.5

performance for a wide range of tasks requiring single-vector text representations such as retrieval,
semantic textual similarity, and text matching. When further customized for downstream tasks, E5
achieves superior ﬁne-tuned performance compared to existing embedding models with 40× more
parameters on the large, 56-task MTEB benchmark datasets.
E5