Add results for google/gemini-embedding-2

#19
by hotchpotch - opened
HAKARI-Bench org

Add HAKARI-Bench results for google/gemini-embedding-2

Summary

Field Value
Model google/gemini-embedding-2
Result directory google__gemini-embedding-2
Target path hakari-results/google__gemini-embedding-2
Result files 551 total, 551 .json.xz
Evaluation method dense
Overall nDCG@10 0.6571
Overall score units 369 grouped units from 538 raw task results

DuckDB Nano-set Comparison

Computed from DuckDB task_results with the same Overall grouping as this PR body. Quantized and rescore variants are excluded; truncate variants are considered, and each model column uses that model's best Overall variant.

Overall component google/gemini-embedding-2 (3072 dims) Qwen/Qwen3-Embedding-0.6B (1024 dims) jinaai/jina-embeddings-v5-text-small (1024 dims) BAAI/bge-m3 (1024 dims) intfloat/multilingual-e5-small (384 dims) bm25
Overall 0.6571 0.5979 0.6323 0.5859 0.5190 0.4832
NanoMMTEB-v2 0.5405 0.5581 0.5590 0.4846 0.4455 0.4550
NanoRTEB 0.7364 0.6713 0.7005 0.5365 0.4711 0.3553
MNanoBEIR 0.6050 0.5509 0.6077 0.5575 0.5117 0.4646
NanoBIRCO 0.4531 0.3070 0.3526 0.2617 0.1613 0.2693
NanoMLDR 0.5871 0.6239 0.5384 0.6621 0.3920 0.7396
NanoLongEmbed 0.6946 0.7232 0.6680 0.6527 0.5014 0.8217
NanoDAPFAM 0.3285 0.3018 0.3179 0.2406 0.2380 0.2400
NanoCoIR 0.9362 0.8601 0.8777 0.6924 0.6915 0.5436
NanoIFIR 0.3608 0.3364 0.3893 0.2391 0.2152 0.2761
NanoLaw 0.4435 0.6075 0.6370 0.5597 0.4790 0.6854
NanoMedical 0.5921 0.5694 0.5803 0.5371 0.5055 0.4145
NanoRARb 0.4252 0.2689 0.2889 0.2343 0.2240 0.1359
NanoBRIGHT 0.4591 0.3885 0.4284 0.2941 0.1758 0.2790
NanoCodeRAG 0.8917 0.8712 0.9139 0.7155 0.7464 0.5823
NanoChemTEB 0.8749 0.8035 0.7980 0.7777 0.8081 0.7012
NanoR2MED 0.5036 0.3180 0.3630 0.2088 0.1099 0.2094
NanoBuiltBench 0.5915 0.5129 0.5277 0.4248 0.4291 0.3958
NanoCMTEB 0.7367 0.7982 0.8052 0.7591 0.6999 0.6003
NanoIndicQA 0.7859 0.6413 0.7056 0.7586 0.7009 0.5653
NanoMuPLeR 0.9327 0.7122 0.8388 0.8912 0.7837 0.7994
NanoMTEB-v2 0.6560 0.6372 0.6450 0.5726 0.5348 0.5028
NanoMTEB-Dutch 0.6573 0.5686 0.6213 0.5863 0.5287 0.4673
NanoMTEB-French 0.6007 0.5771 0.6377 0.5527 0.4702 0.4261
NanoMTEB-German 0.5682 0.6298 0.6536 0.6189 0.5711 0.5522
NanoJMTEB-v2 0.7999 0.7732 0.8008 0.7906 0.7165 0.7465
NanoMTEB-Korean 0.8423 0.7792 0.8246 0.8183 0.7668 0.6743
NanoFaMTEB-v2 0.7159 0.6338 0.6882 0.6652 0.6135 0.5651
NanoMTEB-Polish 0.5878 0.4738 0.5316 0.4999 0.4365 0.3424
NanoRuMTEB 0.9479 0.8622 0.9121 0.9169 0.8643 0.7089
NanoMTEB-Scandinavian 0.7826 0.6981 0.7596 0.7740 0.7029 0.6091
NanoMTEB-Spanish 0.5832 0.5662 0.6292 0.5624 0.4848 0.3679
NanoMTEB-Thai 0.7851 0.7455 0.7670 0.7672 0.7107 0.5216
NanoVNMTEB 0.6298 0.5717 0.6066 0.5616 0.5197 0.4571
NanoMTEB-Misc 0.7544 0.7629 0.8011 0.7766 0.6423 0.4939
NanoMIRACL 0.8581 0.7879 0.8351 0.8475 0.7871 0.5715

Overall nDCG@10

Overall component nDCG@10 Score units Raw task results
NanoMMTEB-v2 0.5405 18 18
NanoRTEB 0.7364 14 14
MNanoBEIR 0.6050 13 182
NanoBIRCO 0.4531 5 5
NanoMLDR 0.5871 13 13
NanoLongEmbed 0.6946 6 6
NanoDAPFAM 0.3285 12 12
NanoCoIR 0.9362 10 10
NanoIFIR 0.3608 4 4
NanoLaw 0.4435 4 4
NanoMedical 0.5921 7 7
NanoRARb 0.4252 14 14
NanoBRIGHT 0.4591 20 20
NanoCodeRAG 0.8917 4 4
NanoChemTEB 0.8749 3 3
NanoR2MED 0.5036 8 8
NanoBuiltBench 0.5915 2 2
NanoCMTEB 0.7367 8 8
NanoIndicQA 0.7859 11 11
NanoMuPLeR 0.9327 14 14
NanoMTEB-v2 0.6560 10 10
NanoMTEB-Dutch 0.6573 27 27
NanoMTEB-French 0.6007 8 8
NanoMTEB-German 0.5682 5 5
NanoJMTEB-v2 0.7999 11 11
NanoMTEB-Korean 0.8423 5 5
NanoFaMTEB-v2 0.7159 17 17
NanoMTEB-Polish 0.5878 14 14
NanoRuMTEB 0.9479 3 3
NanoMTEB-Scandinavian 0.7826 7 7
NanoMTEB-Spanish 0.5832 7 7
NanoMTEB-Thai 0.7851 9 9
NanoVNMTEB 0.6298 26 26
NanoMTEB-Misc 0.7544 12 12
NanoMIRACL 0.8581 18 18

Reproducibility

Field Value
Model source google/gemini-embedding-2
Model revision not recorded
Dataset revision(s) 01736efbaa96f020c2a4d996efdacc18071e2fcb, 017849a95097eea984680cbab35972f8d3812376, 0f3a6f43b8a26a9b8c8d5f31b09bd60dc4cd572d, 1726763179e1e114ad9ffcdc7262923471e8ecc8, 175ff423246cdbca9c3a992c4d68d312701b3f2a, ... (48 total)
Evaluated at UTC 2026-06-26T04:12:08.677328+00:00 to 2026-07-04T22:00:00.464648+00:00
Generated at UTC 2026-06-26T04:12:08.854798+00:00 to 2026-07-04T22:00:00.654621+00:00
dtype fp32
device not recorded
batch size 32
attention implementation not recorded
trust remote code False
max sequence length 8100
candidate ranking reranking_hybrid
rerank top-k not recorded
query prompt name not recorded
document prompt name not recorded
Python 3.12.12 (main, Dec 9 2025, 19:02:36) [Clang 21.1.4 ]
Platform Linux-6.8.0-107-generic-x86_64-with-glibc2.39
torch 2.9.0
transformers 5.12.1
sentence-transformers 5.4.1
datasets 4.8.4
CUDA available=True, version=12.8
CUDA devices 0: NVIDIA GeForce RTX 5090, 1: NVIDIA GeForce RTX 5090

Command

uv run hakari-bench batch dense register \
  --target gemini-embedding-2-nano-all \
  --provider gemini \
  --model gemini-embedding-2 \
  --model-alias google/gemini-embedding-2 \
  --all \
  --batch-size 32 \
  --max-input-tokens 8100 \
  --results-dir output/gemini-developer-batch-nano-all \
  --result-format json.xz

uv run hakari-bench batch dense process \
  --target gemini-embedding-2-nano-all \
  --embedding-variant truncate:128 \
  --embedding-variant truncate:256 \
  --embedding-variant truncate:512 \
  --embedding-variant truncate:768 \
  --embedding-variant truncate:1536 \
  --candidate-ranking reranking_hybrid \
  --batch-size 32 \
  --results-dir output/gemini-developer-batch-nano-all \
  --result-format json.xz

Submitter Notes

  • Evaluated with the Gemini Developer API embedding batch path. Provider request text was capped to 8,100 tokens to stay below the 8,192 token model limit.
  • Gemini query/document request prefixes were recorded in result metadata as task: search result | query: and title: none | text: .
  • Result variants include the base 3,072-dimensional embeddings, truncation dimensions 128/256/512/768/1536, full-dimension int8/binary variants, rescore variants, and truncation x quantized/rescore variants. Truncated embeddings were produced from the full embedding prefix followed by L2 normalization.
  • Batch jobs were submitted and resumed by Nano-set task, with large tasks split into smaller provider batch jobs. Some early oversized/stale attempts were cancelled and resubmitted; the final submitted set completed successfully with 4,229 succeeded jobs, 551 materialized task result files, and no remaining submit or materialization errors.
  • These are standard built-in --all results covering 551 result files / 538 raw task results in the Overall grouping.
  • Gemini is a provider API model, so an immutable model revision is not recorded in the result JSON.

Checklist

  • Result files are committed under hakari-results/google__gemini-embedding-2/.
  • Result files are compressed .json.xz; no caches, DuckDB files, HTML reports, or local scratch artifacts are included.
  • The result JSON records dataset revision, runtime configuration, package versions, and provider/model metadata. Immutable model revision is not recorded because this is a hosted Gemini API model.
  • Overall nDCG@10 above was generated from the submitted result files.
  • Any non-default prompt, sequence length, attention implementation, candidate ranking, or reranker setting is documented above.
hotchpotch changed pull request status to merged

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