| --- |
| license: mit |
| task_categories: |
| - feature-extraction |
| language: |
| - en |
| tags: |
| - embeddings |
| - vector-database |
| - migration |
| - retrieval |
| - ms-marco |
| - beir |
| pretty_name: Embedding Migration Results |
| size_categories: |
| - n<1K |
| --- |
| |
| # 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 | search_query/search_document | |
| | 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 |
|
|
