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allisonzz/embedding-migration-results

Embedding Migration Results Experimental results from testing whether vector databases can be migrated to new embedding models without re-embedding the entire corpus. Overview We embedded ~1M MS MARCO passages with 6 embedding models across 3 dimensionalities (768, 1024, 2560), trained linear translators between every pair of embedding spaces, and measured recall@10 ratio (translated / native ceiling). Models Model Dim Prefix… See the full description on the dataset page: https://huggingface.co/datasets/allisonzz/embedding-migration-results.

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Embedding Migration Results

Experimental results from testing whether vector databases can be migrated to new embedding models without re-embedding the entire corpus.

Overview

We embedded ~1M MS MARCO passages with 6 embedding models across 3 dimensionalities (768, 1024, 2560), trained linear translators between every pair of embedding spaces, and measured recall@10 ratio (translated / native ceiling).

Models

Model	Dim	Prefix
intfloat/e5-base-v2	768	query/passage
BAAI/bge-base-en-v1.5	768	query only
Alibaba-NLP/gte-base-en-v1.5	768	none
nomic-ai/nomic-embed-text-v1.5	768	searchquery/searchdocument
Qwen/Qwen3-Embedding-0.6B	1024	instruction format
Qwen/Qwen3-Embedding-4B	2560	instruction format

Files

File	Evaluations	Description
`msmarco_procrustes.json`	30	Orthogonal Procrustes / least-squares on MS MARCO 1M
`msmarco_ridge.json`	30	Ridge regression on MS MARCO 1M
`beir_scifact_procrustes.json`	30	Procrustes/LS evaluated on BEIR SciFact
`beir_scifact_ridge.json`	30	Ridge evaluated on BEIR SciFact
`beir_fiqa_procrustes.json`	30	Procrustes/LS evaluated on BEIR FiQA
`beir_fiqa_ridge.json`	30	Ridge evaluated on BEIR FiQA
`beir_nfcorpus_procrustes.json`	30	Procrustes/LS evaluated on BEIR NFCorpus
`beir_nfcorpus_ridge.json`	30	Ridge evaluated on BEIR NFCorpus
`fewshot_curve.json`	84	Few-shot learning curve (50-5000 training examples)
`relative_repr.json`	30	Relative representations baseline (Moschella et al. 2023)

Total: 354 evaluations

Key Results

—50 paired examples is enough for 95%+ native performance on compatible model pairs
—Prefix mismatch (not dimension mismatch) is the dominant failure mode
—Ridge regression rescues prefix-mismatched pairs (E5→GTE: 0.090 → 0.814)
—Cross-domain generalization holds for same-family pairs, degrades 10-30% for mismatched pairs
—Relative representations fail at retrieval scale (negative result)

Schema

Each JSON file contains an array of evaluation records. Common fields:

json

{
  "source": "model/name",
  "target": "model/name",
  "method": "procrustes|ridge|relative_repr",
  "src_dim": 768,
  "tgt_dim": 768,
  "recall@10_translated": 0.862,
  "recall@10_native_target": 0.885,
  "ratio": 0.974,
  "train_size": 5000
}

Citation

If you use these results, please link the GitHub repository: https://github.com/allison-stack/embedding-migration

License

MIT