|
--- |
|
model-index: |
|
- name: voyage-code-3 |
|
results: |
|
- dataset: |
|
config: default |
|
name: MTEB AppsRetrieval (default) |
|
revision: f22508f96b7a36c2415181ed8bb76f76e04ae2d5 |
|
split: test |
|
type: CoIR-Retrieval/apps |
|
metrics: |
|
- type: main_score |
|
value: 93.621 |
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- type: map_at_1 |
|
value: 87.968 |
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- type: map_at_10 |
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value: 92.047 |
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value: 92.11500000000001 |
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value: 92.11500000000001 |
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value: 92.091 |
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- type: map_at_3 |
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value: 91.532 |
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- type: map_at_5 |
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value: 91.874 |
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- type: mrr_at_1 |
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value: 87.99468791500664 |
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- type: mrr_at_10 |
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value: 92.0623432197138 |
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- type: mrr_at_100 |
|
value: 92.13012638701028 |
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- type: mrr_at_1000 |
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value: 92.1308801595124 |
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- type: mrr_at_20 |
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value: 92.10639248723756 |
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value: 91.54050464807443 |
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value: 67.0844465768374 |
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value: 67.08675243260438 |
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value: -43.634780889033514 |
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value: 67.21648611418671 |
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value: -43.81755835911823 |
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value: 67.60247091688248 |
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value: 100.0 |
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value: 93.59380801456066 |
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value: 3.4218682638907 |
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value: 83.71281148941699 |
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value: 63.57782533912141 |
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value: -42.361341394612126 |
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- type: ndcg_at_1 |
|
value: 87.968 |
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- type: ndcg_at_10 |
|
value: 93.621 |
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- type: ndcg_at_100 |
|
value: 93.929 |
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- type: ndcg_at_1000 |
|
value: 93.949 |
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- type: ndcg_at_20 |
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value: 93.77799999999999 |
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- type: ndcg_at_3 |
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value: 92.60199999999999 |
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- type: ndcg_at_5 |
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value: 93.217 |
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- type: precision_at_1 |
|
value: 87.968 |
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- type: precision_at_10 |
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value: 9.838 |
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- type: precision_at_100 |
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value: 0.9979999999999999 |
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- type: precision_at_1000 |
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value: 0.1 |
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- type: precision_at_20 |
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value: 4.95 |
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- type: precision_at_3 |
|
value: 31.89 |
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- type: precision_at_5 |
|
value: 19.432 |
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- type: recall_at_1 |
|
value: 87.968 |
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- type: recall_at_10 |
|
value: 98.38 |
|
- type: recall_at_100 |
|
value: 99.761 |
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- type: recall_at_1000 |
|
value: 99.92 |
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- type: recall_at_20 |
|
value: 98.991 |
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- type: recall_at_3 |
|
value: 95.67099999999999 |
|
- type: recall_at_5 |
|
value: 97.158 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: python |
|
name: MTEB COIRCodeSearchNetRetrieval (python) |
|
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3 |
|
split: test |
|
type: CoIR-Retrieval/CodeSearchNet |
|
metrics: |
|
- type: main_score |
|
value: 98.367 |
|
- type: map_at_1 |
|
value: 96.494 |
|
- type: map_at_10 |
|
value: 97.881 |
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- type: map_at_100 |
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value: 97.89099999999999 |
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- type: map_at_1000 |
|
value: 97.89099999999999 |
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- type: map_at_20 |
|
value: 97.88799999999999 |
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- type: map_at_3 |
|
value: 97.761 |
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- type: map_at_5 |
|
value: 97.849 |
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- type: mrr_at_1 |
|
value: 96.49416811905081 |
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- type: mrr_at_10 |
|
value: 97.88111783570305 |
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- type: mrr_at_100 |
|
value: 97.8906549372564 |
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- type: mrr_at_1000 |
|
value: 97.89071223051654 |
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- type: mrr_at_20 |
|
value: 97.88807761475906 |
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value: 97.76109398042645 |
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value: 97.84924252580788 |
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task: |
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type: Retrieval |
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config: javascript |
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name: MTEB COIRCodeSearchNetRetrieval (javascript) |
|
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3 |
|
split: test |
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type: CoIR-Retrieval/CodeSearchNet |
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|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: go |
|
name: MTEB COIRCodeSearchNetRetrieval (go) |
|
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3 |
|
split: test |
|
type: CoIR-Retrieval/CodeSearchNet |
|
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task: |
|
type: Retrieval |
|
- dataset: |
|
config: ruby |
|
name: MTEB COIRCodeSearchNetRetrieval (ruby) |
|
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3 |
|
split: test |
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type: CoIR-Retrieval/CodeSearchNet |
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|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: java |
|
name: MTEB COIRCodeSearchNetRetrieval (java) |
|
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3 |
|
split: test |
|
type: CoIR-Retrieval/CodeSearchNet |
|
metrics: |
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value: 87.26899999999999 |
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value: 88.133 |
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value: 87.599 |
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- type: recall_at_1 |
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- type: recall_at_100 |
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- type: recall_at_1000 |
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- type: recall_at_5 |
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value: 92.47800000000001 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: php |
|
name: MTEB COIRCodeSearchNetRetrieval (php) |
|
revision: 4adc7bc41202b5c13543c9c886a25f340634dab3 |
|
split: test |
|
type: CoIR-Retrieval/CodeSearchNet |
|
metrics: |
|
- type: main_score |
|
value: 89.384 |
|
- type: map_at_1 |
|
value: 81.997 |
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- type: map_at_10 |
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task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB CodeFeedbackMT (default) |
|
revision: b0f12fa0c0dd67f59c95a5c33d02aeeb4c398c5f |
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split: test |
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type: CoIR-Retrieval/codefeedback-mt |
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- type: recall_at_3 |
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|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB CodeFeedbackST (default) |
|
revision: d213819e87aab9010628da8b73ab4eb337c89340 |
|
split: test |
|
type: CoIR-Retrieval/codefeedback-st |
|
metrics: |
|
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task: |
|
type: Retrieval |
|
- dataset: |
|
config: python |
|
name: MTEB CodeSearchNetCCRetrieval (python) |
|
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8 |
|
split: test |
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type: CoIR-Retrieval/CodeSearchNet-ccr |
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task: |
|
type: Retrieval |
|
- dataset: |
|
config: javascript |
|
name: MTEB CodeSearchNetCCRetrieval (javascript) |
|
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8 |
|
split: test |
|
type: CoIR-Retrieval/CodeSearchNet-ccr |
|
metrics: |
|
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|
value: 91.532 |
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|
value: 84.04700000000001 |
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value: 89.548 |
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- type: map_at_3 |
|
value: 88.914 |
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- type: map_at_5 |
|
value: 89.271 |
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- type: mrr_at_1 |
|
value: 83.98663020358553 |
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- type: mrr_at_10 |
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value: 89.46699271220692 |
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- type: mrr_at_100 |
|
value: 89.54429619092761 |
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- type: mrr_at_1000 |
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value: 89.54715966900696 |
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value: -22.939278713896133 |
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value: -21.826145336827878 |
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value: 100.0 |
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value: 69.4979677737838 |
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value: 79.81453242811645 |
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value: 22.735882735419853 |
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value: 76.02578489916326 |
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value: 82.20220582823438 |
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- type: nauc_precision_at_10_std |
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value: -11.133888291415301 |
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value: 82.57372620311462 |
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value: 44.54905481211588 |
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value: -21.962071475865756 |
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value: 100.0 |
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value: 0.3712448061678841 |
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- type: ndcg_at_1 |
|
value: 84.04700000000001 |
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- type: ndcg_at_10 |
|
value: 91.532 |
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- type: ndcg_at_100 |
|
value: 91.914 |
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- type: ndcg_at_1000 |
|
value: 91.979 |
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- type: ndcg_at_20 |
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value: 91.688 |
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- type: ndcg_at_3 |
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value: 90.35300000000001 |
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value: 90.993 |
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- type: precision_at_1 |
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value: 84.04700000000001 |
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- type: precision_at_10 |
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value: 9.763 |
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- type: precision_at_100 |
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value: 0.9939999999999999 |
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- type: precision_at_1000 |
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value: 0.1 |
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- type: precision_at_20 |
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value: 4.912 |
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- type: precision_at_3 |
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value: 31.490000000000002 |
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- type: precision_at_5 |
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value: 19.204 |
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- type: recall_at_1 |
|
value: 84.04700000000001 |
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- type: recall_at_10 |
|
value: 97.63 |
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- type: recall_at_100 |
|
value: 99.423 |
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- type: recall_at_1000 |
|
value: 99.909 |
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- type: recall_at_20 |
|
value: 98.238 |
|
- type: recall_at_3 |
|
value: 94.47 |
|
- type: recall_at_5 |
|
value: 96.019 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: go |
|
name: MTEB CodeSearchNetCCRetrieval (go) |
|
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8 |
|
split: test |
|
type: CoIR-Retrieval/CodeSearchNet-ccr |
|
metrics: |
|
- type: main_score |
|
value: 91.75999999999999 |
|
- type: map_at_1 |
|
value: 84.28999999999999 |
|
- type: map_at_10 |
|
value: 89.689 |
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value: 89.773 |
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- type: map_at_1000 |
|
value: 89.774 |
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- type: map_at_20 |
|
value: 89.747 |
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- type: map_at_3 |
|
value: 89.02600000000001 |
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- type: map_at_5 |
|
value: 89.484 |
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- type: mrr_at_1 |
|
value: 84.27727160797834 |
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- type: mrr_at_10 |
|
value: 89.67515722532931 |
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- type: mrr_at_100 |
|
value: 89.75855757010807 |
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- type: mrr_at_1000 |
|
value: 89.7601837108608 |
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- type: mrr_at_20 |
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value: 89.73302201153147 |
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- type: mrr_at_3 |
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value: 89.01337929902301 |
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task: |
|
type: Retrieval |
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- dataset: |
|
config: ruby |
|
name: MTEB CodeSearchNetCCRetrieval (ruby) |
|
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8 |
|
split: test |
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type: CoIR-Retrieval/CodeSearchNet-ccr |
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|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: java |
|
name: MTEB CodeSearchNetCCRetrieval (java) |
|
revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8 |
|
split: test |
|
type: CoIR-Retrieval/CodeSearchNet-ccr |
|
metrics: |
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task: |
|
type: Retrieval |
|
- dataset: |
|
config: php |
|
name: MTEB CodeSearchNetCCRetrieval (php) |
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revision: 6e1effa2c03723c5fde48ee912b5ee08d4f211e8 |
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split: test |
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type: CoIR-Retrieval/CodeSearchNet-ccr |
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- type: recall_at_5 |
|
value: 89.589 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: python |
|
name: MTEB CodeSearchNetRetrieval (python) |
|
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759 |
|
split: test |
|
type: code-search-net/code_search_net |
|
metrics: |
|
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|
value: 96.688 |
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|
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|
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|
value: 92.7 |
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|
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value: 95.64912393162395 |
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- type: ndcg_at_10 |
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- type: ndcg_at_100 |
|
value: 96.72800000000001 |
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- type: ndcg_at_1000 |
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value: 96.688 |
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- type: precision_at_1 |
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value: 92.7 |
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|
value: 9.98 |
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- type: precision_at_100 |
|
value: 1.0 |
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- type: precision_at_1000 |
|
value: 0.1 |
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- type: precision_at_20 |
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value: 4.99 |
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- type: precision_at_3 |
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- type: precision_at_5 |
|
value: 19.84 |
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- type: recall_at_1 |
|
value: 92.7 |
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- type: recall_at_10 |
|
value: 99.8 |
|
- type: recall_at_100 |
|
value: 100.0 |
|
- type: recall_at_1000 |
|
value: 100.0 |
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- type: recall_at_20 |
|
value: 99.8 |
|
- type: recall_at_3 |
|
value: 98.6 |
|
- type: recall_at_5 |
|
value: 99.2 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: javascript |
|
name: MTEB CodeSearchNetRetrieval (javascript) |
|
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759 |
|
split: test |
|
type: code-search-net/code_search_net |
|
metrics: |
|
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|
value: 89.97800000000001 |
|
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|
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|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: go |
|
name: MTEB CodeSearchNetRetrieval (go) |
|
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759 |
|
split: test |
|
type: code-search-net/code_search_net |
|
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- type: ndcg_at_100 |
|
value: 97.588 |
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- type: ndcg_at_1000 |
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value: 97.528 |
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value: 97.505 |
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value: 94.39999999999999 |
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value: 0.1 |
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- type: precision_at_3 |
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- type: precision_at_5 |
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- type: recall_at_1 |
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- type: recall_at_10 |
|
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- type: recall_at_100 |
|
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- type: recall_at_1000 |
|
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- type: recall_at_20 |
|
value: 99.6 |
|
- type: recall_at_3 |
|
value: 99.2 |
|
- type: recall_at_5 |
|
value: 99.5 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: ruby |
|
name: MTEB CodeSearchNetRetrieval (ruby) |
|
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759 |
|
split: test |
|
type: code-search-net/code_search_net |
|
metrics: |
|
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|
value: 92.83200000000001 |
|
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|
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|
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|
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|
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|
value: 87.7 |
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|
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|
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|
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|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: java |
|
name: MTEB CodeSearchNetRetrieval (java) |
|
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759 |
|
split: test |
|
type: code-search-net/code_search_net |
|
metrics: |
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|
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|
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|
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|
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|
value: 0.1 |
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|
- type: precision_at_3 |
|
value: 31.900000000000002 |
|
- type: precision_at_5 |
|
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|
- type: recall_at_1 |
|
value: 91.4 |
|
- type: recall_at_10 |
|
value: 97.0 |
|
- type: recall_at_100 |
|
value: 99.0 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_20 |
|
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|
- type: recall_at_3 |
|
value: 95.7 |
|
- type: recall_at_5 |
|
value: 96.39999999999999 |
|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: php |
|
name: MTEB CodeSearchNetRetrieval (php) |
|
revision: fdc6a9e39575768c27eb8a2a5f702bf846eb4759 |
|
split: test |
|
type: code-search-net/code_search_net |
|
metrics: |
|
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|
value: 92.288 |
|
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|
value: 85.5 |
|
- type: map_at_10 |
|
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|
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|
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|
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|
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|
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|
value: 90.335 |
|
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|
value: 89.583 |
|
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|
value: 90.098 |
|
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|
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|
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|
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|
- type: mrr_at_100 |
|
value: 90.35130514931716 |
|
- type: mrr_at_1000 |
|
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|
- type: mrr_at_20 |
|
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|
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|
value: 89.58333333333339 |
|
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task: |
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type: Retrieval |
|
- dataset: |
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config: default |
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name: MTEB CodeTransOceanContest (default) |
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revision: 20da4eb20a4b17300c0986ee148c90867a7f2a4d |
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split: test |
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type: CoIR-Retrieval/codetrans-contest |
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|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB CodeTransOceanDL (default) |
|
revision: 281562cb8a1265ab5c0824bfa6ddcd9b0a15618f |
|
split: test |
|
type: CoIR-Retrieval/codetrans-dl |
|
metrics: |
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- type: recall_at_100 |
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|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB CosQA (default) |
|
revision: bc5efb7e9d437246ce393ed19d772e08e4a79535 |
|
split: test |
|
type: CoIR-Retrieval/cosqa |
|
metrics: |
|
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|
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type: Retrieval |
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config: default |
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name: MTEB StackOverflowQA (default) |
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revision: db8f169f3894c14a00251061f957b2063eef2bd5 |
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split: test |
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type: CoIR-Retrieval/stackoverflow-qa |
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|
task: |
|
type: Retrieval |
|
- dataset: |
|
config: default |
|
name: MTEB SyntheticText2SQL (default) |
|
revision: 686b87296c3a0191b5d9415a00526c62db9fce09 |
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split: test |
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type: CoIR-Retrieval/synthetic-text2sql |
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task: |
|
type: Retrieval |
|
tags: |
|
- mteb |
|
--- |
|
# voyage-code-3 |
|
|
|
This repo contains the tokenizer of the `voyage-code-3` embedding model. `voyage-code-3` is optimized for code retrieval, outperforming OpenAI-v3-large and CodeSage-large by an average of 13.80% and 16.81% on a suite of 238 code retrieval datasets, respectively. By supporting smaller dimensions with Matryoshka learning and quantized formats like int8 and binary, voyage-code-3 can also dramatically reduce storage and search costs with minimal impact on retrieval quality. Please refer to [our blogpost](https://blog.voyageai.com/2024/12/04/voyage-code-3) for more details about this model. |
|
|
|
You can use `voyage-code-3` via the [Voyage API](https://docs.voyageai.com/docs/embeddings), [AWS SageMaker](https://aws.amazon.com/marketplace/pp/prodview-d5nri3kbddsrw) or on-prem deplotment. |
|
|
|
## Voyage API |
|
The following code snippet shows the usage of the Voyage API. Install the Voyage AI Python SDK via: |
|
|
|
``` |
|
pip install -U voyageai |
|
``` |
|
|
|
Voyage AI utilizes API keys to monitor usage and manage permissions. To obtain your key, please sign in with your Voyage AI account and click the "Create new API key" button in the dashboard. We recommend setting the API key as an environment variable. For example, in MacOS or Linux, type the following command in the terminal, replacing <your secret key> with your actual API key: |
|
|
|
```python |
|
import voyageai |
|
|
|
vo = voyageai.Client() |
|
# This will automatically use the environment variable VOYAGE_API_KEY. |
|
# Alternatively, you can use vo = voyageai.Client(api_key="<your secret key>") |
|
|
|
result = vo.embed( |
|
texts=["hello world"], |
|
model="voyage-code-3", |
|
input_type="document", |
|
output_dimension=2048, |
|
output_dtype="float" |
|
) |
|
``` |
|
## AWS SageMaker |
|
|
|
The embedding model can be privately deployed in your AWS Cloud using our [AWS SageMaker marketplace offering](https://aws.amazon.com/marketplace/pp/prodview-d5nri3kbddsrw). It runs privately in your VPC. |
|
|
|
## On-prem Deployment |
|
|
|
Want to run `voyage-code-3` on your own hardware? Feel free to reach out to [email protected] to learn more. |
|
|