Replace EmbeddingGemma 2 results with validated text-only FlexAttention evaluation (part 2)

# Add HAKARI-Bench results for `google/embeddinggemma-2`

## Evaluation coverage and default Overall

Validated text-only **FlexAttention** evaluation: **569 tasks × 20 conditions = 11,380 task-variant rows**.
The submission contains 563 standard tasks plus six extended NanoDAPFAM FullText tasks.
Extended FullText scores are reported separately and excluded from the standard Overall.
Each condition has 550 canonical Overall tasks; all 563 task artifacts are retained.
Default Overall micro nDCG@10 ×100: **58.60100107**.
Base candidate-reranking micro nDCG@10 ×100: **59.56060656**.

| Condition | Dimensions | Overall micro nDCG@10 ×100 |
| --- | ---: | ---: |
| Base | 768 | 58.60100 |
| int8 | 768 | 57.03194 |
| binary | 768 | 53.70456 |
| int8 rescore | 768 | 58.45893 |
| binary rescore | 768 | 58.40241 |
| Truncate | 512 | 58.47630 |
| int8 | 512 | 56.76978 |
| binary | 512 | 51.49219 |
| int8 rescore | 512 | 58.25392 |
| binary rescore | 512 | 58.14345 |
| Truncate | 256 | 57.00306 |
| int8 | 256 | 54.93879 |
| binary | 256 | 44.59377 |
| int8 rescore | 256 | 56.80555 |
| binary rescore | 256 | 56.04919 |
| Truncate | 128 | 51.98470 |
| int8 | 128 | 48.59530 |
| binary | 128 | 32.20118 |
| int8 rescore | 128 | 51.99522 |
| binary rescore | 128 | 49.25360 |


### Extended FullText results (outside standard Overall)

| Extended NanoDAPFAM task | Base nDCG@10 ×100 |
| --- | ---: |
| NanoDAPFAMAllTitlAbsClmToFullText | 44.54627 |
| NanoDAPFAMAllTitlAbsToFullText | 43.08159 |
| NanoDAPFAMInTitlAbsClmToFullText | 46.04138 |
| NanoDAPFAMInTitlAbsToFullText | 44.39134 |
| NanoDAPFAMOutTitlAbsClmToFullText | 10.51928 |
| NanoDAPFAMOutTitlAbsToFullText | 10.48003 |


## Summary

| Field | Value |
| --- | --- |
| Model | `google/embeddinggemma-2` |
| Result directory | `google__embeddinggemma-2` |
| Target path | `hakari-results/google__embeddinggemma-2` |
| Result files | 569 total, 569 `.json.xz` |
| Evaluation method | dense |
| Default Overall micro nDCG@10 | 0.5860100106971348 |
| Template grouped-unit mean nDCG@10 | 0.5982 |
| Overall score units | 381 grouped units from 550 raw task results |


## DuckDB Nano-set Comparison

Computed from the submitted and published DuckDB task results using 381 grouped units from 550 canonical tasks. These grouped means are on a 0–1 scale; the default 550-task micro score on a 0–100 scale is reported above. Dataset revisions may differ across models. Quantized and rescore variants are excluded; truncate variants are considered, and each model column uses that model's best Overall variant.

| Overall component | google/embeddinggemma-2 (768 dims) | google/embeddinggemma-300m (768 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 | 0.5982 | 0.6040 | 0.5944 | **0.6296** | 0.5820 | 0.5149 | 0.4806 |
| NanoMMTEB-v2 | 0.5130 | 0.5172 | 0.5581 | **0.5590** | 0.4846 | 0.4455 | 0.4550 |
| NanoRTEB | 0.6410 | 0.6699 | 0.6713 | **0.7005** | 0.5365 | 0.4711 | 0.3553 |
| MNanoBEIR | 0.5586 | 0.5773 | 0.5509 | **0.6077** | 0.5575 | 0.5117 | 0.4646 |
| NanoBIRCO | 0.3237 | **0.3555** | 0.3070 | 0.3526 | 0.2617 | 0.1613 | 0.2693 |
| NanoMLDR | 0.6362 | 0.5046 | 0.6239 | 0.5384 | 0.6621 | 0.3920 | **0.7396** |
| NanoLongEmbed | 0.6846 | 0.5972 | 0.7232 | 0.6680 | 0.6527 | 0.5014 | **0.8217** |
| NanoDAPFAM | 0.3056 | 0.2955 | 0.3018 | **0.3179** | 0.2406 | 0.2380 | 0.2400 |
| NanoCoIR | **0.9052** | 0.8468 | 0.8601 | 0.8777 | 0.6924 | 0.6915 | 0.5436 |
| NanoIFIR | 0.2930 | 0.3237 | 0.3364 | **0.3893** | 0.2391 | 0.2152 | 0.2761 |
| NanoLaw | 0.5709 | 0.5927 | 0.6075 | 0.6370 | 0.5597 | 0.4790 | **0.6854** |
| NanoMedical | 0.4948 | 0.5644 | 0.5694 | **0.5803** | 0.5371 | 0.5055 | 0.4145 |
| NanoRARb | 0.2707 | 0.2860 | 0.2689 | **0.2889** | 0.2343 | 0.2240 | 0.1359 |
| NanoBRIGHT | 0.3552 | 0.3846 | 0.3885 | **0.4284** | 0.2941 | 0.1758 | 0.2790 |
| NanoCodeRAG | 0.8783 | 0.9030 | 0.8712 | **0.9139** | 0.7155 | 0.7464 | 0.5823 |
| NanoChemTEB | **0.8119** | 0.7870 | 0.8035 | 0.7980 | 0.7777 | 0.8081 | 0.7012 |
| NanoR2MED | 0.2771 | 0.3143 | 0.3180 | **0.3630** | 0.2088 | 0.1099 | 0.2094 |
| NanoBuiltBench | 0.5265 | **0.5346** | 0.5129 | 0.5277 | 0.4248 | 0.4291 | 0.3958 |
| NanoCMTEB | 0.7349 | 0.7577 | 0.7982 | **0.8052** | 0.7591 | 0.6999 | 0.6003 |
| NanoIndicQA | 0.7445 | 0.7145 | 0.6413 | 0.7056 | **0.7586** | 0.7009 | 0.5653 |
| NanoMuPLeR | 0.7505 | 0.7618 | 0.7122 | 0.8388 | **0.8912** | 0.7837 | 0.7994 |
| NanoMTEB-v2 | 0.5765 | 0.6092 | 0.6372 | **0.6450** | 0.5726 | 0.5348 | 0.5028 |
| NanoMTEB-Dutch | 0.5783 | 0.5974 | 0.5686 | **0.6213** | 0.5863 | 0.5287 | 0.4673 |
| NanoMTEB-French | 0.5958 | **0.6441** | 0.5771 | 0.6377 | 0.5527 | 0.4702 | 0.4261 |
| NanoMTEB-German | 0.6370 | 0.6405 | 0.6298 | **0.6536** | 0.6189 | 0.5711 | 0.5522 |
| NanoJMTEB-v2 | 0.7970 | 0.7926 | 0.7732 | **0.8008** | 0.7906 | 0.7165 | 0.7465 |
| NanoMTEB-Korean | 0.7852 | 0.8077 | 0.7792 | **0.8246** | 0.8183 | 0.7668 | 0.6743 |
| NanoFaMTEB-v2 | 0.6635 | 0.6764 | 0.6338 | **0.6882** | 0.6652 | 0.6135 | 0.5651 |
| NanoMTEB-Polish | 0.4910 | 0.4880 | 0.4738 | **0.5316** | 0.4999 | 0.4365 | 0.3424 |
| NanoMTEB-BR | 0.6262 | **0.6723** | 0.6247 | 0.6595 | 0.6540 | 0.5224 | 0.5178 |
| NanoSSRB | 0.3812 | 0.3673 | 0.3464 | **0.4340** | 0.2665 | 0.2511 | 0.2821 |
| NanoRuMTEB | 0.9061 | 0.9085 | 0.8622 | 0.9121 | **0.9169** | 0.8643 | 0.7089 |
| NanoMTEB-Scandinavian | 0.7309 | 0.7326 | 0.6981 | 0.7596 | **0.7740** | 0.7029 | 0.6091 |
| NanoMTEB-Spanish | 0.5961 | **0.6348** | 0.5662 | 0.6292 | 0.5624 | 0.4848 | 0.3679 |
| NanoMTEB-Thai | 0.7613 | 0.7555 | 0.7455 | 0.7670 | **0.7672** | 0.7107 | 0.5216 |
| NanoVNMTEB | 0.5755 | 0.5795 | 0.5717 | **0.6066** | 0.5616 | 0.5197 | 0.4571 |
| NanoMTEB-Misc | 0.7711 | 0.7958 | 0.7629 | **0.8011** | 0.7766 | 0.6423 | 0.4939 |
| NanoMIRACL | 0.8231 | 0.8282 | 0.7879 | 0.8351 | **0.8475** | 0.7871 | 0.5715 |


Comparison database: `hakari-bench/leaderboard_database` revision `33d59d83d2bbe0eddf91846645e709e07cb945e7`.

## Grouped component nDCG@10

The template aggregates 381 grouped units; its grouped mean differs from the default Overall micro mean over 550 tasks.

| Overall component | nDCG@10 | Score units | Raw task results |
| --- | ---: | ---: | ---: |
| NanoMMTEB-v2 | 0.5130 | 18 | 18 |
| NanoRTEB | 0.6410 | 14 | 14 |
| MNanoBEIR | 0.5586 | 13 | 182 |
| NanoBIRCO | 0.3237 | 5 | 5 |
| NanoMLDR | 0.6362 | 13 | 13 |
| NanoLongEmbed | 0.6846 | 6 | 6 |
| NanoDAPFAM | 0.3056 | 12 | 12 |
| NanoCoIR | 0.9052 | 10 | 10 |
| NanoIFIR | 0.2930 | 4 | 4 |
| NanoLaw | 0.5709 | 4 | 4 |
| NanoMedical | 0.4948 | 7 | 7 |
| NanoRARb | 0.2707 | 14 | 14 |
| NanoBRIGHT | 0.3552 | 20 | 20 |
| NanoCodeRAG | 0.8783 | 4 | 4 |
| NanoChemTEB | 0.8119 | 3 | 3 |
| NanoR2MED | 0.2771 | 8 | 8 |
| NanoBuiltBench | 0.5265 | 2 | 2 |
| NanoCMTEB | 0.7349 | 8 | 8 |
| NanoIndicQA | 0.7445 | 11 | 11 |
| NanoMuPLeR | 0.7505 | 14 | 14 |
| NanoMTEB-v2 | 0.5765 | 10 | 10 |
| NanoMTEB-Dutch | 0.5783 | 27 | 27 |
| NanoMTEB-French | 0.5958 | 8 | 8 |
| NanoMTEB-German | 0.6370 | 5 | 5 |
| NanoJMTEB-v2 | 0.7970 | 11 | 11 |
| NanoMTEB-Korean | 0.7852 | 5 | 5 |
| NanoFaMTEB-v2 | 0.6635 | 17 | 17 |
| NanoMTEB-Polish | 0.4910 | 14 | 14 |
| NanoMTEB-BR | 0.6262 | 6 | 6 |
| NanoSSRB | 0.3812 | 6 | 6 |
| NanoRuMTEB | 0.9061 | 3 | 3 |
| NanoMTEB-Scandinavian | 0.7309 | 7 | 7 |
| NanoMTEB-Spanish | 0.5961 | 7 | 7 |
| NanoMTEB-Thai | 0.7613 | 9 | 9 |
| NanoVNMTEB | 0.5755 | 26 | 26 |
| NanoMTEB-Misc | 0.7711 | 12 | 12 |
| NanoMIRACL | 0.8231 | 18 | 18 |

## Reproducibility

| Field | Value |
| --- | --- |
| Model source | `google/embeddinggemma-2` |
| Model revision | `not recorded` |
| Dataset revision(s) | `00541a0fce4048057fb7ddec30d37155a5c23d95`, `01736efbaa96f020c2a4d996efdacc18071e2fcb`, `017849a95097eea984680cbab35972f8d3812376`, `0f3a6f43b8a26a9b8c8d5f31b09bd60dc4cd572d`, `1726763179e1e114ad9ffcdc7262923471e8ecc8`, ... (50 total) |
| Evaluated at UTC | `2026-10-08T06:26:25.319088+00:00` to `2026-10-08T11:53:26.351039+00:00` |
| Generated at UTC | `2026-10-08T06:26:25.319638+00:00` to `2026-10-08T11:53:26.351064+00:00` |
| dtype | bf16 |
| device | cuda:0 |
| batch size | 16 |
| attention implementation | flex_attention |
| trust remote code | False |
| max sequence length | 8192 |
| candidate ranking | reranking_hybrid |
| rerank top-k | not recorded |
| query prompt name | SearchQuery |
| document prompt name | Document |
| Python | `3.12.11 (main, Jul 1 2025, 18:37:24) [Clang 20.1.4 ]` |
| Platform | `Linux-6.17.0-1017-oem-x86_64-with-glibc2.39` |
| torch | `2.14.1` |
| transformers | `5.19.0.dev0` |
| sentence-transformers | `6.1.0` |
| datasets | `5.0.1` |
| CUDA | available=True, version=`13.0` |
| CUDA devices | 0: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition |

## Command

```bash
# Run each dataset at the original pinned revision (see command manifest).
CUDA_VISIBLE_DEVICES=1 PYTHONPATH=tmp uv run --no-project \
--python tmp/embeddinggemma2-venv/bin/python python -c \
'from hakari_bench.cli import main; main()' evaluate dense \
--model google/embeddinggemma-2 \
--model-revision 914f7f89142e33e77833254d9c9b90c3cef7303b \
--model-loader embeddinggemma2_flex_loader:load_model \
--model-loader-kwargs-json '{"config_kwargs":{"vision_config":null,"audio_config":null},"input_modalities":["text"],"kernel_options":{"BLOCK_M":16,"BLOCK_N":32,"num_stages":1,"num_warps":4,"BACKEND":"TRITON"}}' \
--dtype bf16 --attn-implementation flex_attention --model-max-seq-length 8192 \
--query-prompt-name SearchQuery --document-prompt-name Document \
--embedding-variant truncate:128,256,512 --device cuda:0 --batch-size 16 \
--dataset DATASET_ID --dataset-revision ORIGINAL_DATASET_COMMIT --split PINNED_TASK_LIST
```

### Pinned dataset revisions and explicit task lists

| Dataset | Revision | `--split` |
| --- | --- | --- |
| hakari-bench/NanoBEIR-ar | `5c2e7952832d26a760796a2fcbdd5d9486f9ef7e` | `NanoArguAna,NanoClimateFEVER,NanoDBPedia,Nan

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