tevatron-elastic-bert-retriever-starbucks
A retriever trained with Tevatron-Elastic, which trains one checkpoint to serve many operating points along the depth / width / token compression axes. This checkpoint is an elastic depth×width schedule (2D Matryoshka, Starbucks-style).
- Base model:
google-bert/bert-base-uncased - Task: retriever
- Elastic axis: starbucks
- Training data: rlhn/rlhn-680K, max length 512,
query:/passage:prefixes.
Full-point BEIR-15 nDCG@10: 0.448.
Load with the Tevatron-Elastic framework and select an operating point with prune_to /
encode_at; see the repository for usage. Part of a release of 20 checkpoints (3 backbones,
retrieval and reranking, all compression axes) accompanying the Tevatron-Elastic paper. Reported as
a reproducibility resource, not a state-of-the-art claim.