|
--- |
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tags: |
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- mteb |
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- sentence-transformers |
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- sentence-similarity |
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- feature-extraction |
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- generated_from_trainer |
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- dataset_size:2560000 |
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- loss:MultipleNegativesRankingLoss |
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model-index: |
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- name: XLM-RoBERTa-base-MSMARCO-WebFAQ |
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results: |
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- task: |
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type: retrieval |
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dataset: |
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type: mteb/miracl-hard-negatives |
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name: MTEB MIRACLRetrievalHardNegatives (ar) |
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config: ar |
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split: dev |
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revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
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metrics: |
|
- type: map_at_1 |
|
value: 25.374000000000002 |
|
- type: map_at_10 |
|
value: 40.863 |
|
- type: map_at_100 |
|
value: 42.667 |
|
- type: map_at_1000 |
|
value: 42.754999999999995 |
|
- type: map_at_20 |
|
value: 41.948 |
|
- type: map_at_3 |
|
value: 35.512 |
|
- type: map_at_5 |
|
value: 38.385999999999996 |
|
- type: mrr_at_1 |
|
value: 38.6 |
|
- type: mrr_at_10 |
|
value: 50.489 |
|
- type: mrr_at_100 |
|
value: 51.229 |
|
- type: mrr_at_1000 |
|
value: 51.248000000000005 |
|
- type: mrr_at_20 |
|
value: 50.980000000000004 |
|
- type: mrr_at_3 |
|
value: 46.933 |
|
- type: mrr_at_5 |
|
value: 48.983 |
|
- type: ndcg_at_1 |
|
value: 38.6 |
|
- type: ndcg_at_10 |
|
value: 49.257 |
|
- type: ndcg_at_100 |
|
value: 55.611999999999995 |
|
- type: ndcg_at_1000 |
|
value: 56.946 |
|
- type: ndcg_at_20 |
|
value: 52.10399999999999 |
|
- type: ndcg_at_3 |
|
value: 41.501 |
|
- type: ndcg_at_5 |
|
value: 44.729 |
|
- type: precision_at_1 |
|
value: 38.6 |
|
- type: precision_at_10 |
|
value: 11.540000000000001 |
|
- type: precision_at_100 |
|
value: 1.702 |
|
- type: precision_at_1000 |
|
value: 0.189 |
|
- type: precision_at_20 |
|
value: 6.795 |
|
- type: precision_at_3 |
|
value: 24.3 |
|
- type: precision_at_5 |
|
value: 18.16 |
|
- type: recall_at_1 |
|
value: 25.374000000000002 |
|
- type: recall_at_10 |
|
value: 63.474 |
|
- type: recall_at_100 |
|
value: 87.902 |
|
- type: recall_at_1000 |
|
value: 96.301 |
|
- type: recall_at_20 |
|
value: 72.538 |
|
- type: recall_at_3 |
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value: 43.144 |
|
- type: recall_at_5 |
|
value: 51.548 |
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- task: |
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type: retrieval |
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dataset: |
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type: mteb/mrtydi |
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name: MTEB MrTydiRetrieval (arabic) |
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config: arabic |
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split: test |
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revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
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metrics: |
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- type: map_at_1 |
|
value: 26.056 |
|
- type: map_at_10 |
|
value: 37.684 |
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- type: map_at_100 |
|
value: 38.616 |
|
- type: map_at_1000 |
|
value: 38.66 |
|
- type: map_at_20 |
|
value: 38.281 |
|
- type: map_at_3 |
|
value: 34.348 |
|
- type: map_at_5 |
|
value: 36.181999999999995 |
|
- type: mrr_at_1 |
|
value: 27.845 |
|
- type: mrr_at_10 |
|
value: 39.275 |
|
- type: mrr_at_100 |
|
value: 40.083 |
|
- type: mrr_at_1000 |
|
value: 40.115 |
|
- type: mrr_at_20 |
|
value: 39.822 |
|
- type: mrr_at_3 |
|
value: 36.124 |
|
- type: mrr_at_5 |
|
value: 37.885999999999996 |
|
- type: ndcg_at_1 |
|
value: 27.845 |
|
- type: ndcg_at_10 |
|
value: 44.153 |
|
- type: ndcg_at_100 |
|
value: 48.291000000000004 |
|
- type: ndcg_at_1000 |
|
value: 49.443 |
|
- type: ndcg_at_20 |
|
value: 46.188 |
|
- type: ndcg_at_3 |
|
value: 37.448 |
|
- type: ndcg_at_5 |
|
value: 40.685 |
|
- type: precision_at_1 |
|
value: 27.845 |
|
- type: precision_at_10 |
|
value: 6.938 |
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- type: precision_at_100 |
|
value: 0.9209999999999999 |
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- type: precision_at_1000 |
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value: 0.10300000000000001 |
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- type: precision_at_20 |
|
value: 3.936 |
|
- type: precision_at_3 |
|
value: 16.158 |
|
- type: precision_at_5 |
|
value: 11.452 |
|
- type: recall_at_1 |
|
value: 26.056 |
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- type: recall_at_10 |
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value: 62.627 |
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- type: recall_at_100 |
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value: 80.759 |
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- type: recall_at_1000 |
|
value: 89.547 |
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- type: recall_at_20 |
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value: 70.21300000000001 |
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- type: recall_at_3 |
|
value: 44.681 |
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- type: recall_at_5 |
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value: 52.344 |
|
- task: |
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type: retrieval |
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dataset: |
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type: mteb/miracl-hard-negatives |
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name: MTEB MIRACLRetrievalHardNegatives (bn) |
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config: bn |
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split: dev |
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revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
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metrics: |
|
- type: map_at_1 |
|
value: 23.083000000000002 |
|
- type: map_at_10 |
|
value: 37.285000000000004 |
|
- type: map_at_100 |
|
value: 38.834 |
|
- type: map_at_1000 |
|
value: 38.948 |
|
- type: map_at_20 |
|
value: 38.195 |
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- type: map_at_3 |
|
value: 31.893 |
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- type: map_at_5 |
|
value: 34.755 |
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- type: mrr_at_1 |
|
value: 36.496 |
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- type: mrr_at_10 |
|
value: 48.486000000000004 |
|
- type: mrr_at_100 |
|
value: 49.248 |
|
- type: mrr_at_1000 |
|
value: 49.28 |
|
- type: mrr_at_20 |
|
value: 48.998000000000005 |
|
- type: mrr_at_3 |
|
value: 44.85 |
|
- type: mrr_at_5 |
|
value: 47.028 |
|
- type: ndcg_at_1 |
|
value: 36.496 |
|
- type: ndcg_at_10 |
|
value: 45.96 |
|
- type: ndcg_at_100 |
|
value: 51.64 |
|
- type: ndcg_at_1000 |
|
value: 53.542 |
|
- type: ndcg_at_20 |
|
value: 48.543 |
|
- type: ndcg_at_3 |
|
value: 37.964999999999996 |
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- type: ndcg_at_5 |
|
value: 41.44 |
|
- type: precision_at_1 |
|
value: 36.496 |
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- type: precision_at_10 |
|
value: 11.29 |
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- type: precision_at_100 |
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value: 1.6199999999999999 |
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- type: precision_at_1000 |
|
value: 0.191 |
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- type: precision_at_20 |
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value: 6.582000000000001 |
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- type: precision_at_3 |
|
value: 22.871 |
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- type: precision_at_5 |
|
value: 17.324 |
|
- type: recall_at_1 |
|
value: 23.083000000000002 |
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- type: recall_at_10 |
|
value: 59.414 |
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- type: recall_at_100 |
|
value: 81.08 |
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- type: recall_at_1000 |
|
value: 92.793 |
|
- type: recall_at_20 |
|
value: 67.634 |
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- type: recall_at_3 |
|
value: 39.001000000000005 |
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- type: recall_at_5 |
|
value: 47.612 |
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- task: |
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type: retrieval |
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dataset: |
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type: mteb/mrtydi |
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name: MTEB MrTydiRetrieval (bengali) |
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config: bengali |
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split: test |
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revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
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metrics: |
|
- type: map_at_1 |
|
value: 23.874000000000002 |
|
- type: map_at_10 |
|
value: 38.235 |
|
- type: map_at_100 |
|
value: 39.428000000000004 |
|
- type: map_at_1000 |
|
value: 39.458 |
|
- type: map_at_20 |
|
value: 39.119 |
|
- type: map_at_3 |
|
value: 34.233999999999995 |
|
- type: map_at_5 |
|
value: 36.577 |
|
- type: mrr_at_1 |
|
value: 28.829 |
|
- type: mrr_at_10 |
|
value: 41.032999999999994 |
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- type: mrr_at_100 |
|
value: 42.077999999999996 |
|
- type: mrr_at_1000 |
|
value: 42.104 |
|
- type: mrr_at_20 |
|
value: 41.855 |
|
- type: mrr_at_3 |
|
value: 37.688 |
|
- type: mrr_at_5 |
|
value: 39.985 |
|
- type: ndcg_at_1 |
|
value: 28.829 |
|
- type: ndcg_at_10 |
|
value: 45.214999999999996 |
|
- type: ndcg_at_100 |
|
value: 49.986999999999995 |
|
- type: ndcg_at_1000 |
|
value: 50.67700000000001 |
|
- type: ndcg_at_20 |
|
value: 48.291000000000004 |
|
- type: ndcg_at_3 |
|
value: 37.822 |
|
- type: ndcg_at_5 |
|
value: 41.9 |
|
- type: precision_at_1 |
|
value: 28.829 |
|
- type: precision_at_10 |
|
value: 7.477 |
|
- type: precision_at_100 |
|
value: 0.9820000000000001 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_20 |
|
value: 4.414 |
|
- type: precision_at_3 |
|
value: 17.416999999999998 |
|
- type: precision_at_5 |
|
value: 12.613 |
|
- type: recall_at_1 |
|
value: 23.874000000000002 |
|
- type: recall_at_10 |
|
value: 63.964 |
|
- type: recall_at_100 |
|
value: 83.784 |
|
- type: recall_at_1000 |
|
value: 88.739 |
|
- type: recall_at_20 |
|
value: 75.676 |
|
- type: recall_at_3 |
|
value: 45.045 |
|
- type: recall_at_5 |
|
value: 54.505 |
|
- task: |
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type: retrieval |
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dataset: |
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type: mteb/miracl-hard-negatives |
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name: MTEB MIRACLRetrievalHardNegatives (de) |
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config: de |
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split: dev |
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revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
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metrics: |
|
- type: map_at_1 |
|
value: 12.524 |
|
- type: map_at_10 |
|
value: 25.932 |
|
- type: map_at_100 |
|
value: 29.215999999999998 |
|
- type: map_at_1000 |
|
value: 29.342000000000002 |
|
- type: map_at_20 |
|
value: 27.515 |
|
- type: map_at_3 |
|
value: 20.196 |
|
- type: map_at_5 |
|
value: 23.311999999999998 |
|
- type: mrr_at_1 |
|
value: 28.525 |
|
- type: mrr_at_10 |
|
value: 41.854 |
|
- type: mrr_at_100 |
|
value: 42.977 |
|
- type: mrr_at_1000 |
|
value: 42.998 |
|
- type: mrr_at_20 |
|
value: 42.628 |
|
- type: mrr_at_3 |
|
value: 38.251000000000005 |
|
- type: mrr_at_5 |
|
value: 40.514 |
|
- type: ndcg_at_1 |
|
value: 28.525 |
|
- type: ndcg_at_10 |
|
value: 35.831 |
|
- type: ndcg_at_100 |
|
value: 47.410000000000004 |
|
- type: ndcg_at_1000 |
|
value: 48.931000000000004 |
|
- type: ndcg_at_20 |
|
value: 40.021 |
|
- type: ndcg_at_3 |
|
value: 28.614 |
|
- type: ndcg_at_5 |
|
value: 31.427 |
|
- type: precision_at_1 |
|
value: 28.525 |
|
- type: precision_at_10 |
|
value: 11.934000000000001 |
|
- type: precision_at_100 |
|
value: 2.37 |
|
- type: precision_at_1000 |
|
value: 0.261 |
|
- type: precision_at_20 |
|
value: 7.704999999999999 |
|
- type: precision_at_3 |
|
value: 21.421 |
|
- type: precision_at_5 |
|
value: 17.574 |
|
- type: recall_at_1 |
|
value: 12.524 |
|
- type: recall_at_10 |
|
value: 47.558 |
|
- type: recall_at_100 |
|
value: 90.068 |
|
- type: recall_at_1000 |
|
value: 98.68900000000001 |
|
- type: recall_at_20 |
|
value: 59.943999999999996 |
|
- type: recall_at_3 |
|
value: 26.889999999999997 |
|
- type: recall_at_5 |
|
value: 36.152 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (en) |
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config: en |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 14.431 |
|
- type: map_at_10 |
|
value: 27.194000000000003 |
|
- type: map_at_100 |
|
value: 30.575000000000003 |
|
- type: map_at_1000 |
|
value: 30.75 |
|
- type: map_at_20 |
|
value: 28.938999999999997 |
|
- type: map_at_3 |
|
value: 21.69 |
|
- type: map_at_5 |
|
value: 24.657999999999998 |
|
- type: mrr_at_1 |
|
value: 31.163999999999998 |
|
- type: mrr_at_10 |
|
value: 43.417 |
|
- type: mrr_at_100 |
|
value: 44.48 |
|
- type: mrr_at_1000 |
|
value: 44.499 |
|
- type: mrr_at_20 |
|
value: 44.112 |
|
- type: mrr_at_3 |
|
value: 39.57 |
|
- type: mrr_at_5 |
|
value: 41.692 |
|
- type: ndcg_at_1 |
|
value: 31.163999999999998 |
|
- type: ndcg_at_10 |
|
value: 36.925000000000004 |
|
- type: ndcg_at_100 |
|
value: 48.15 |
|
- type: ndcg_at_1000 |
|
value: 50.131 |
|
- type: ndcg_at_20 |
|
value: 41.308 |
|
- type: ndcg_at_3 |
|
value: 30.291 |
|
- type: ndcg_at_5 |
|
value: 32.731 |
|
- type: precision_at_1 |
|
value: 31.163999999999998 |
|
- type: precision_at_10 |
|
value: 12.065 |
|
- type: precision_at_100 |
|
value: 2.469 |
|
- type: precision_at_1000 |
|
value: 0.28500000000000003 |
|
- type: precision_at_20 |
|
value: 7.997 |
|
- type: precision_at_3 |
|
value: 21.443 |
|
- type: precision_at_5 |
|
value: 17.447 |
|
- type: recall_at_1 |
|
value: 14.431 |
|
- type: recall_at_10 |
|
value: 48.275 |
|
- type: recall_at_100 |
|
value: 87.19200000000001 |
|
- type: recall_at_1000 |
|
value: 97.79299999999999 |
|
- type: recall_at_20 |
|
value: 60.870000000000005 |
|
- type: recall_at_3 |
|
value: 27.679 |
|
- type: recall_at_5 |
|
value: 36.479 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/mrtydi |
|
name: MTEB MrTydiRetrieval (english) |
|
config: english |
|
split: test |
|
revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 13.642000000000001 |
|
- type: map_at_10 |
|
value: 22.744 |
|
- type: map_at_100 |
|
value: 24.083 |
|
- type: map_at_1000 |
|
value: 24.157 |
|
- type: map_at_20 |
|
value: 23.592 |
|
- type: map_at_3 |
|
value: 19.134999999999998 |
|
- type: map_at_5 |
|
value: 21.448999999999998 |
|
- type: mrr_at_1 |
|
value: 16.667 |
|
- type: mrr_at_10 |
|
value: 25.77 |
|
- type: mrr_at_100 |
|
value: 26.878 |
|
- type: mrr_at_1000 |
|
value: 26.938000000000002 |
|
- type: mrr_at_20 |
|
value: 26.503 |
|
- type: mrr_at_3 |
|
value: 22.512999999999998 |
|
- type: mrr_at_5 |
|
value: 24.516 |
|
- type: ndcg_at_1 |
|
value: 16.667 |
|
- type: ndcg_at_10 |
|
value: 28.652 |
|
- type: ndcg_at_100 |
|
value: 34.716 |
|
- type: ndcg_at_1000 |
|
value: 36.649 |
|
- type: ndcg_at_20 |
|
value: 31.508000000000003 |
|
- type: ndcg_at_3 |
|
value: 21.748 |
|
- type: ndcg_at_5 |
|
value: 25.647 |
|
- type: precision_at_1 |
|
value: 16.667 |
|
- type: precision_at_10 |
|
value: 5.3629999999999995 |
|
- type: precision_at_100 |
|
value: 0.886 |
|
- type: precision_at_1000 |
|
value: 0.107 |
|
- type: precision_at_20 |
|
value: 3.36 |
|
- type: precision_at_3 |
|
value: 10.529 |
|
- type: precision_at_5 |
|
value: 8.522 |
|
- type: recall_at_1 |
|
value: 13.642000000000001 |
|
- type: recall_at_10 |
|
value: 43.884 |
|
- type: recall_at_100 |
|
value: 70.744 |
|
- type: recall_at_1000 |
|
value: 85.372 |
|
- type: recall_at_20 |
|
value: 54.48 |
|
- type: recall_at_3 |
|
value: 26.142 |
|
- type: recall_at_5 |
|
value: 35.17 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (fa) |
|
config: fa |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.752000000000002 |
|
- type: map_at_10 |
|
value: 34.931 |
|
- type: map_at_100 |
|
value: 37.132 |
|
- type: map_at_1000 |
|
value: 37.230999999999995 |
|
- type: map_at_20 |
|
value: 36.144 |
|
- type: map_at_3 |
|
value: 29.692 |
|
- type: map_at_5 |
|
value: 32.536 |
|
- type: mrr_at_1 |
|
value: 32.437 |
|
- type: mrr_at_10 |
|
value: 44.41 |
|
- type: mrr_at_100 |
|
value: 45.324 |
|
- type: mrr_at_1000 |
|
value: 45.348 |
|
- type: mrr_at_20 |
|
value: 44.945 |
|
- type: mrr_at_3 |
|
value: 41.482 |
|
- type: mrr_at_5 |
|
value: 43.183 |
|
- type: ndcg_at_1 |
|
value: 32.437 |
|
- type: ndcg_at_10 |
|
value: 43.018 |
|
- type: ndcg_at_100 |
|
value: 50.805 |
|
- type: ndcg_at_1000 |
|
value: 52.245 |
|
- type: ndcg_at_20 |
|
value: 46.215 |
|
- type: ndcg_at_3 |
|
value: 36.269 |
|
- type: ndcg_at_5 |
|
value: 39.101 |
|
- type: precision_at_1 |
|
value: 32.437 |
|
- type: precision_at_10 |
|
value: 11.013 |
|
- type: precision_at_100 |
|
value: 1.799 |
|
- type: precision_at_1000 |
|
value: 0.201 |
|
- type: precision_at_20 |
|
value: 6.7250000000000005 |
|
- type: precision_at_3 |
|
value: 22.416 |
|
- type: precision_at_5 |
|
value: 17.152 |
|
- type: recall_at_1 |
|
value: 20.752000000000002 |
|
- type: recall_at_10 |
|
value: 56.150999999999996 |
|
- type: recall_at_100 |
|
value: 85.735 |
|
- type: recall_at_1000 |
|
value: 94.599 |
|
- type: recall_at_20 |
|
value: 66.237 |
|
- type: recall_at_3 |
|
value: 37.551 |
|
- type: recall_at_5 |
|
value: 45.629 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (fi) |
|
config: fi |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.829 |
|
- type: map_at_10 |
|
value: 46.749 |
|
- type: map_at_100 |
|
value: 48.457 |
|
- type: map_at_1000 |
|
value: 48.516 |
|
- type: map_at_20 |
|
value: 47.798 |
|
- type: map_at_3 |
|
value: 40.449 |
|
- type: map_at_5 |
|
value: 44.3 |
|
- type: mrr_at_1 |
|
value: 46.5 |
|
- type: mrr_at_10 |
|
value: 58.619 |
|
- type: mrr_at_100 |
|
value: 59.294999999999995 |
|
- type: mrr_at_1000 |
|
value: 59.307 |
|
- type: mrr_at_20 |
|
value: 59.071 |
|
- type: mrr_at_3 |
|
value: 55.75 |
|
- type: mrr_at_5 |
|
value: 57.605 |
|
- type: ndcg_at_1 |
|
value: 46.5 |
|
- type: ndcg_at_10 |
|
value: 55.933 |
|
- type: ndcg_at_100 |
|
value: 61.732 |
|
- type: ndcg_at_1000 |
|
value: 62.651 |
|
- type: ndcg_at_20 |
|
value: 58.679 |
|
- type: ndcg_at_3 |
|
value: 46.866 |
|
- type: ndcg_at_5 |
|
value: 51.625 |
|
- type: precision_at_1 |
|
value: 46.5 |
|
- type: precision_at_10 |
|
value: 12.920000000000002 |
|
- type: precision_at_100 |
|
value: 1.745 |
|
- type: precision_at_1000 |
|
value: 0.187 |
|
- type: precision_at_20 |
|
value: 7.37 |
|
- type: precision_at_3 |
|
value: 28.166999999999998 |
|
- type: precision_at_5 |
|
value: 21.2 |
|
- type: recall_at_1 |
|
value: 28.829 |
|
- type: recall_at_10 |
|
value: 70.075 |
|
- type: recall_at_100 |
|
value: 92.098 |
|
- type: recall_at_1000 |
|
value: 97.813 |
|
- type: recall_at_20 |
|
value: 78.975 |
|
- type: recall_at_3 |
|
value: 48.635 |
|
- type: recall_at_5 |
|
value: 59.202999999999996 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/mrtydi |
|
name: MTEB MrTydiRetrieval (finnish) |
|
config: finnish |
|
split: test |
|
revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.654 |
|
- type: map_at_10 |
|
value: 32.103 |
|
- type: map_at_100 |
|
value: 33.129 |
|
- type: map_at_1000 |
|
value: 33.184999999999995 |
|
- type: map_at_20 |
|
value: 32.739000000000004 |
|
- type: map_at_3 |
|
value: 28.338 |
|
- type: map_at_5 |
|
value: 30.564000000000004 |
|
- type: mrr_at_1 |
|
value: 22.567999999999998 |
|
- type: mrr_at_10 |
|
value: 33.949 |
|
- type: mrr_at_100 |
|
value: 34.804 |
|
- type: mrr_at_1000 |
|
value: 34.849999999999994 |
|
- type: mrr_at_20 |
|
value: 34.496 |
|
- type: mrr_at_3 |
|
value: 30.581999999999997 |
|
- type: mrr_at_5 |
|
value: 32.5 |
|
- type: ndcg_at_1 |
|
value: 22.567999999999998 |
|
- type: ndcg_at_10 |
|
value: 38.681 |
|
- type: ndcg_at_100 |
|
value: 43.367 |
|
- type: ndcg_at_1000 |
|
value: 44.836999999999996 |
|
- type: ndcg_at_20 |
|
value: 40.815 |
|
- type: ndcg_at_3 |
|
value: 31.328 |
|
- type: ndcg_at_5 |
|
value: 35.083 |
|
- type: precision_at_1 |
|
value: 22.567999999999998 |
|
- type: precision_at_10 |
|
value: 6.483 |
|
- type: precision_at_100 |
|
value: 0.906 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_20 |
|
value: 3.7319999999999998 |
|
- type: precision_at_3 |
|
value: 14.035 |
|
- type: precision_at_5 |
|
value: 10.51 |
|
- type: recall_at_1 |
|
value: 20.654 |
|
- type: recall_at_10 |
|
value: 57.297 |
|
- type: recall_at_100 |
|
value: 78.363 |
|
- type: recall_at_1000 |
|
value: 89.673 |
|
- type: recall_at_20 |
|
value: 65.28399999999999 |
|
- type: recall_at_3 |
|
value: 37.799 |
|
- type: recall_at_5 |
|
value: 46.518 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (fr) |
|
config: fr |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 15.484 |
|
- type: map_at_10 |
|
value: 29.692 |
|
- type: map_at_100 |
|
value: 32.604 |
|
- type: map_at_1000 |
|
value: 32.668 |
|
- type: map_at_20 |
|
value: 31.397000000000002 |
|
- type: map_at_3 |
|
value: 23.469 |
|
- type: map_at_5 |
|
value: 26.454 |
|
- type: mrr_at_1 |
|
value: 28.863 |
|
- type: mrr_at_10 |
|
value: 41.03 |
|
- type: mrr_at_100 |
|
value: 42.134 |
|
- type: mrr_at_1000 |
|
value: 42.149 |
|
- type: mrr_at_20 |
|
value: 41.730000000000004 |
|
- type: mrr_at_3 |
|
value: 36.394999999999996 |
|
- type: mrr_at_5 |
|
value: 38.814 |
|
- type: ndcg_at_1 |
|
value: 28.863 |
|
- type: ndcg_at_10 |
|
value: 39.523 |
|
- type: ndcg_at_100 |
|
value: 49.496 |
|
- type: ndcg_at_1000 |
|
value: 50.375 |
|
- type: ndcg_at_20 |
|
value: 43.923 |
|
- type: ndcg_at_3 |
|
value: 29.309 |
|
- type: ndcg_at_5 |
|
value: 33.077 |
|
- type: precision_at_1 |
|
value: 28.863 |
|
- type: precision_at_10 |
|
value: 11.254 |
|
- type: precision_at_100 |
|
value: 1.997 |
|
- type: precision_at_1000 |
|
value: 0.212 |
|
- type: precision_at_20 |
|
value: 7.172000000000001 |
|
- type: precision_at_3 |
|
value: 19.631 |
|
- type: precision_at_5 |
|
value: 15.568999999999999 |
|
- type: recall_at_1 |
|
value: 15.484 |
|
- type: recall_at_10 |
|
value: 56.279999999999994 |
|
- type: recall_at_100 |
|
value: 93.714 |
|
- type: recall_at_1000 |
|
value: 99.125 |
|
- type: recall_at_20 |
|
value: 70.215 |
|
- type: recall_at_3 |
|
value: 29.688 |
|
- type: recall_at_5 |
|
value: 39.329 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (hi) |
|
config: hi |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.442999999999998 |
|
- type: map_at_10 |
|
value: 29.224 |
|
- type: map_at_100 |
|
value: 31.386999999999997 |
|
- type: map_at_1000 |
|
value: 31.529 |
|
- type: map_at_20 |
|
value: 30.54 |
|
- type: map_at_3 |
|
value: 24.521 |
|
- type: map_at_5 |
|
value: 26.979 |
|
- type: mrr_at_1 |
|
value: 33.428999999999995 |
|
- type: mrr_at_10 |
|
value: 42.951 |
|
- type: mrr_at_100 |
|
value: 43.832 |
|
- type: mrr_at_1000 |
|
value: 43.891000000000005 |
|
- type: mrr_at_20 |
|
value: 43.494 |
|
- type: mrr_at_3 |
|
value: 40.048 |
|
- type: mrr_at_5 |
|
value: 41.819 |
|
- type: ndcg_at_1 |
|
value: 33.428999999999995 |
|
- type: ndcg_at_10 |
|
value: 37.462 |
|
- type: ndcg_at_100 |
|
value: 45.123000000000005 |
|
- type: ndcg_at_1000 |
|
value: 47.805 |
|
- type: ndcg_at_20 |
|
value: 40.739999999999995 |
|
- type: ndcg_at_3 |
|
value: 31.89 |
|
- type: ndcg_at_5 |
|
value: 33.934999999999995 |
|
- type: precision_at_1 |
|
value: 33.428999999999995 |
|
- type: precision_at_10 |
|
value: 10.343 |
|
- type: precision_at_100 |
|
value: 1.714 |
|
- type: precision_at_1000 |
|
value: 0.20500000000000002 |
|
- type: precision_at_20 |
|
value: 6.4 |
|
- type: precision_at_3 |
|
value: 20.952 |
|
- type: precision_at_5 |
|
value: 15.886 |
|
- type: recall_at_1 |
|
value: 16.442999999999998 |
|
- type: recall_at_10 |
|
value: 47.987 |
|
- type: recall_at_100 |
|
value: 77.628 |
|
- type: recall_at_1000 |
|
value: 95.673 |
|
- type: recall_at_20 |
|
value: 58.199999999999996 |
|
- type: recall_at_3 |
|
value: 30.904999999999998 |
|
- type: recall_at_5 |
|
value: 38.705 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (id) |
|
config: id |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 14.551 |
|
- type: map_at_10 |
|
value: 27.144000000000002 |
|
- type: map_at_100 |
|
value: 29.918 |
|
- type: map_at_1000 |
|
value: 30.14 |
|
- type: map_at_20 |
|
value: 28.665000000000003 |
|
- type: map_at_3 |
|
value: 22.004 |
|
- type: map_at_5 |
|
value: 24.851 |
|
- type: mrr_at_1 |
|
value: 33.542 |
|
- type: mrr_at_10 |
|
value: 45.576 |
|
- type: mrr_at_100 |
|
value: 46.402 |
|
- type: mrr_at_1000 |
|
value: 46.432 |
|
- type: mrr_at_20 |
|
value: 46.082 |
|
- type: mrr_at_3 |
|
value: 42.483 |
|
- type: mrr_at_5 |
|
value: 44.399 |
|
- type: ndcg_at_1 |
|
value: 33.542 |
|
- type: ndcg_at_10 |
|
value: 36.287000000000006 |
|
- type: ndcg_at_100 |
|
value: 45.253 |
|
- type: ndcg_at_1000 |
|
value: 48.33 |
|
- type: ndcg_at_20 |
|
value: 39.855000000000004 |
|
- type: ndcg_at_3 |
|
value: 31.69 |
|
- type: ndcg_at_5 |
|
value: 33.332 |
|
- type: precision_at_1 |
|
value: 33.542 |
|
- type: precision_at_10 |
|
value: 12.333 |
|
- type: precision_at_100 |
|
value: 2.322 |
|
- type: precision_at_1000 |
|
value: 0.292 |
|
- type: precision_at_20 |
|
value: 7.875 |
|
- type: precision_at_3 |
|
value: 23.125 |
|
- type: precision_at_5 |
|
value: 18.583 |
|
- type: recall_at_1 |
|
value: 14.551 |
|
- type: recall_at_10 |
|
value: 43.636 |
|
- type: recall_at_100 |
|
value: 73.603 |
|
- type: recall_at_1000 |
|
value: 90.596 |
|
- type: recall_at_20 |
|
value: 53.559 |
|
- type: recall_at_3 |
|
value: 27.383999999999997 |
|
- type: recall_at_5 |
|
value: 34.997 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/mrtydi |
|
name: MTEB MrTydiRetrieval (indonesian) |
|
config: indonesian |
|
split: test |
|
revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.669 |
|
- type: map_at_10 |
|
value: 41.611 |
|
- type: map_at_100 |
|
value: 42.522999999999996 |
|
- type: map_at_1000 |
|
value: 42.552 |
|
- type: map_at_20 |
|
value: 42.211 |
|
- type: map_at_3 |
|
value: 37.719 |
|
- type: map_at_5 |
|
value: 40.182 |
|
- type: mrr_at_1 |
|
value: 31.846000000000004 |
|
- type: mrr_at_10 |
|
value: 43.695 |
|
- type: mrr_at_100 |
|
value: 44.45 |
|
- type: mrr_at_1000 |
|
value: 44.474000000000004 |
|
- type: mrr_at_20 |
|
value: 44.214999999999996 |
|
- type: mrr_at_3 |
|
value: 40.31 |
|
- type: mrr_at_5 |
|
value: 42.469 |
|
- type: ndcg_at_1 |
|
value: 31.846000000000004 |
|
- type: ndcg_at_10 |
|
value: 48.416 |
|
- type: ndcg_at_100 |
|
value: 52.464999999999996 |
|
- type: ndcg_at_1000 |
|
value: 53.234 |
|
- type: ndcg_at_20 |
|
value: 50.468999999999994 |
|
- type: ndcg_at_3 |
|
value: 40.973 |
|
- type: ndcg_at_5 |
|
value: 45.163 |
|
- type: precision_at_1 |
|
value: 31.846000000000004 |
|
- type: precision_at_10 |
|
value: 7.768 |
|
- type: precision_at_100 |
|
value: 0.992 |
|
- type: precision_at_1000 |
|
value: 0.106 |
|
- type: precision_at_20 |
|
value: 4.343 |
|
- type: precision_at_3 |
|
value: 18.134 |
|
- type: precision_at_5 |
|
value: 13.221 |
|
- type: recall_at_1 |
|
value: 28.669 |
|
- type: recall_at_10 |
|
value: 67.29 |
|
- type: recall_at_100 |
|
value: 85.324 |
|
- type: recall_at_1000 |
|
value: 91.27499999999999 |
|
- type: recall_at_20 |
|
value: 75.111 |
|
- type: recall_at_3 |
|
value: 47.869 |
|
- type: recall_at_5 |
|
value: 57.620000000000005 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (ja) |
|
config: ja |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.499 |
|
- type: map_at_10 |
|
value: 29.566 |
|
- type: map_at_100 |
|
value: 31.846999999999998 |
|
- type: map_at_1000 |
|
value: 31.968999999999998 |
|
- type: map_at_20 |
|
value: 30.758000000000003 |
|
- type: map_at_3 |
|
value: 24.954 |
|
- type: map_at_5 |
|
value: 27.195000000000004 |
|
- type: mrr_at_1 |
|
value: 27.093 |
|
- type: mrr_at_10 |
|
value: 40.861999999999995 |
|
- type: mrr_at_100 |
|
value: 41.926 |
|
- type: mrr_at_1000 |
|
value: 41.942 |
|
- type: mrr_at_20 |
|
value: 41.55 |
|
- type: mrr_at_3 |
|
value: 37.151 |
|
- type: mrr_at_5 |
|
value: 38.958999999999996 |
|
- type: ndcg_at_1 |
|
value: 27.093 |
|
- type: ndcg_at_10 |
|
value: 38.544 |
|
- type: ndcg_at_100 |
|
value: 47.143 |
|
- type: ndcg_at_1000 |
|
value: 48.802 |
|
- type: ndcg_at_20 |
|
value: 41.896 |
|
- type: ndcg_at_3 |
|
value: 31.249 |
|
- type: ndcg_at_5 |
|
value: 33.873999999999995 |
|
- type: precision_at_1 |
|
value: 27.093 |
|
- type: precision_at_10 |
|
value: 9.814 |
|
- type: precision_at_100 |
|
value: 1.737 |
|
- type: precision_at_1000 |
|
value: 0.2 |
|
- type: precision_at_20 |
|
value: 6.064 |
|
- type: precision_at_3 |
|
value: 19.147 |
|
- type: precision_at_5 |
|
value: 14.465 |
|
- type: recall_at_1 |
|
value: 16.499 |
|
- type: recall_at_10 |
|
value: 53.580000000000005 |
|
- type: recall_at_100 |
|
value: 86.792 |
|
- type: recall_at_1000 |
|
value: 96.61999999999999 |
|
- type: recall_at_20 |
|
value: 64.302 |
|
- type: recall_at_3 |
|
value: 33.588 |
|
- type: recall_at_5 |
|
value: 40.993 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/mrtydi |
|
name: MTEB MrTydiRetrieval (japanese) |
|
config: japanese |
|
split: test |
|
revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.644000000000002 |
|
- type: map_at_10 |
|
value: 25.667 |
|
- type: map_at_100 |
|
value: 26.926 |
|
- type: map_at_1000 |
|
value: 27.003 |
|
- type: map_at_20 |
|
value: 26.468000000000004 |
|
- type: map_at_3 |
|
value: 22.808999999999997 |
|
- type: map_at_5 |
|
value: 24.392 |
|
- type: mrr_at_1 |
|
value: 20.278 |
|
- type: mrr_at_10 |
|
value: 28.683999999999997 |
|
- type: mrr_at_100 |
|
value: 29.785 |
|
- type: mrr_at_1000 |
|
value: 29.839 |
|
- type: mrr_at_20 |
|
value: 29.391000000000002 |
|
- type: mrr_at_3 |
|
value: 26.25 |
|
- type: mrr_at_5 |
|
value: 27.479 |
|
- type: ndcg_at_1 |
|
value: 20.278 |
|
- type: ndcg_at_10 |
|
value: 31.130000000000003 |
|
- type: ndcg_at_100 |
|
value: 36.954 |
|
- type: ndcg_at_1000 |
|
value: 38.805 |
|
- type: ndcg_at_20 |
|
value: 33.856 |
|
- type: ndcg_at_3 |
|
value: 25.590000000000003 |
|
- type: ndcg_at_5 |
|
value: 28.136 |
|
- type: precision_at_1 |
|
value: 20.278 |
|
- type: precision_at_10 |
|
value: 5.611 |
|
- type: precision_at_100 |
|
value: 0.899 |
|
- type: precision_at_1000 |
|
value: 0.109 |
|
- type: precision_at_20 |
|
value: 3.465 |
|
- type: precision_at_3 |
|
value: 12.5 |
|
- type: precision_at_5 |
|
value: 8.972 |
|
- type: recall_at_1 |
|
value: 16.644000000000002 |
|
- type: recall_at_10 |
|
value: 44.931 |
|
- type: recall_at_100 |
|
value: 70.741 |
|
- type: recall_at_1000 |
|
value: 84.282 |
|
- type: recall_at_20 |
|
value: 55.022999999999996 |
|
- type: recall_at_3 |
|
value: 30.37 |
|
- type: recall_at_5 |
|
value: 36.134 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (ko) |
|
config: ko |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 20.192 |
|
- type: map_at_10 |
|
value: 32.281 |
|
- type: map_at_100 |
|
value: 34.396 |
|
- type: map_at_1000 |
|
value: 34.604 |
|
- type: map_at_20 |
|
value: 33.495000000000005 |
|
- type: map_at_3 |
|
value: 27.489 |
|
- type: map_at_5 |
|
value: 30.022 |
|
- type: mrr_at_1 |
|
value: 34.742 |
|
- type: mrr_at_10 |
|
value: 46.117999999999995 |
|
- type: mrr_at_100 |
|
value: 47.066 |
|
- type: mrr_at_1000 |
|
value: 47.095 |
|
- type: mrr_at_20 |
|
value: 46.867 |
|
- type: mrr_at_3 |
|
value: 41.862 |
|
- type: mrr_at_5 |
|
value: 44.702999999999996 |
|
- type: ndcg_at_1 |
|
value: 34.742 |
|
- type: ndcg_at_10 |
|
value: 41.193999999999996 |
|
- type: ndcg_at_100 |
|
value: 48.691 |
|
- type: ndcg_at_1000 |
|
value: 51.364 |
|
- type: ndcg_at_20 |
|
value: 44.592999999999996 |
|
- type: ndcg_at_3 |
|
value: 35.004000000000005 |
|
- type: ndcg_at_5 |
|
value: 37.608000000000004 |
|
- type: precision_at_1 |
|
value: 34.742 |
|
- type: precision_at_10 |
|
value: 10.61 |
|
- type: precision_at_100 |
|
value: 1.8450000000000002 |
|
- type: precision_at_1000 |
|
value: 0.24 |
|
- type: precision_at_20 |
|
value: 6.5729999999999995 |
|
- type: precision_at_3 |
|
value: 20.657 |
|
- type: precision_at_5 |
|
value: 16.150000000000002 |
|
- type: recall_at_1 |
|
value: 20.192 |
|
- type: recall_at_10 |
|
value: 54.154 |
|
- type: recall_at_100 |
|
value: 80.49199999999999 |
|
- type: recall_at_1000 |
|
value: 94.61699999999999 |
|
- type: recall_at_20 |
|
value: 64.74 |
|
- type: recall_at_3 |
|
value: 34.288000000000004 |
|
- type: recall_at_5 |
|
value: 43.401 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/mrtydi |
|
name: MTEB MrTydiRetrieval (korean) |
|
config: korean |
|
split: test |
|
revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 22.288 |
|
- type: map_at_10 |
|
value: 30.096 |
|
- type: map_at_100 |
|
value: 30.866 |
|
- type: map_at_1000 |
|
value: 30.939 |
|
- type: map_at_20 |
|
value: 30.496000000000002 |
|
- type: map_at_3 |
|
value: 27.672 |
|
- type: map_at_5 |
|
value: 28.866000000000003 |
|
- type: mrr_at_1 |
|
value: 23.990000000000002 |
|
- type: mrr_at_10 |
|
value: 32.017 |
|
- type: mrr_at_100 |
|
value: 32.665 |
|
- type: mrr_at_1000 |
|
value: 32.726 |
|
- type: mrr_at_20 |
|
value: 32.348 |
|
- type: mrr_at_3 |
|
value: 29.572 |
|
- type: mrr_at_5 |
|
value: 30.808000000000003 |
|
- type: ndcg_at_1 |
|
value: 23.990000000000002 |
|
- type: ndcg_at_10 |
|
value: 34.823 |
|
- type: ndcg_at_100 |
|
value: 38.625 |
|
- type: ndcg_at_1000 |
|
value: 40.760999999999996 |
|
- type: ndcg_at_20 |
|
value: 36.138 |
|
- type: ndcg_at_3 |
|
value: 29.744999999999997 |
|
- type: ndcg_at_5 |
|
value: 31.884 |
|
- type: precision_at_1 |
|
value: 23.990000000000002 |
|
- type: precision_at_10 |
|
value: 5.297000000000001 |
|
- type: precision_at_100 |
|
value: 0.753 |
|
- type: precision_at_1000 |
|
value: 0.095 |
|
- type: precision_at_20 |
|
value: 2.981 |
|
- type: precision_at_3 |
|
value: 12.272 |
|
- type: precision_at_5 |
|
value: 8.551 |
|
- type: recall_at_1 |
|
value: 22.288 |
|
- type: recall_at_10 |
|
value: 47.743 |
|
- type: recall_at_100 |
|
value: 65.202 |
|
- type: recall_at_1000 |
|
value: 82.106 |
|
- type: recall_at_20 |
|
value: 52.534000000000006 |
|
- type: recall_at_3 |
|
value: 33.927 |
|
- type: recall_at_5 |
|
value: 38.915 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (ru) |
|
config: ru |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.445 |
|
- type: map_at_10 |
|
value: 29.376 |
|
- type: map_at_100 |
|
value: 32.425 |
|
- type: map_at_1000 |
|
value: 32.589 |
|
- type: map_at_20 |
|
value: 30.973 |
|
- type: map_at_3 |
|
value: 24.026 |
|
- type: map_at_5 |
|
value: 26.831 |
|
- type: mrr_at_1 |
|
value: 31.8 |
|
- type: mrr_at_10 |
|
value: 44.454 |
|
- type: mrr_at_100 |
|
value: 45.534 |
|
- type: mrr_at_1000 |
|
value: 45.552 |
|
- type: mrr_at_20 |
|
value: 45.192 |
|
- type: mrr_at_3 |
|
value: 41.117 |
|
- type: mrr_at_5 |
|
value: 43.132 |
|
- type: ndcg_at_1 |
|
value: 31.8 |
|
- type: ndcg_at_10 |
|
value: 38.487 |
|
- type: ndcg_at_100 |
|
value: 48.705999999999996 |
|
- type: ndcg_at_1000 |
|
value: 50.732 |
|
- type: ndcg_at_20 |
|
value: 42.646 |
|
- type: ndcg_at_3 |
|
value: 31.922 |
|
- type: ndcg_at_5 |
|
value: 34.512 |
|
- type: precision_at_1 |
|
value: 31.8 |
|
- type: precision_at_10 |
|
value: 12.01 |
|
- type: precision_at_100 |
|
value: 2.325 |
|
- type: precision_at_1000 |
|
value: 0.27 |
|
- type: precision_at_20 |
|
value: 7.8549999999999995 |
|
- type: precision_at_3 |
|
value: 21.867 |
|
- type: precision_at_5 |
|
value: 17.46 |
|
- type: recall_at_1 |
|
value: 16.445 |
|
- type: recall_at_10 |
|
value: 48.691 |
|
- type: recall_at_100 |
|
value: 84.084 |
|
- type: recall_at_1000 |
|
value: 95.318 |
|
- type: recall_at_20 |
|
value: 60.873 |
|
- type: recall_at_3 |
|
value: 30.169 |
|
- type: recall_at_5 |
|
value: 38.3 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/mrtydi |
|
name: MTEB MrTydiRetrieval (russian) |
|
config: russian |
|
split: test |
|
revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.516000000000002 |
|
- type: map_at_10 |
|
value: 24.461 |
|
- type: map_at_100 |
|
value: 25.499 |
|
- type: map_at_1000 |
|
value: 25.569999999999997 |
|
- type: map_at_20 |
|
value: 25.063000000000002 |
|
- type: map_at_3 |
|
value: 21.669 |
|
- type: map_at_5 |
|
value: 23.285 |
|
- type: mrr_at_1 |
|
value: 18.09 |
|
- type: mrr_at_10 |
|
value: 26.311 |
|
- type: mrr_at_100 |
|
value: 27.195999999999998 |
|
- type: mrr_at_1000 |
|
value: 27.255000000000003 |
|
- type: mrr_at_20 |
|
value: 26.823000000000004 |
|
- type: mrr_at_3 |
|
value: 23.618 |
|
- type: mrr_at_5 |
|
value: 25.156 |
|
- type: ndcg_at_1 |
|
value: 18.09 |
|
- type: ndcg_at_10 |
|
value: 29.453000000000003 |
|
- type: ndcg_at_100 |
|
value: 34.44 |
|
- type: ndcg_at_1000 |
|
value: 36.336 |
|
- type: ndcg_at_20 |
|
value: 31.482 |
|
- type: ndcg_at_3 |
|
value: 23.830000000000002 |
|
- type: ndcg_at_5 |
|
value: 26.666 |
|
- type: precision_at_1 |
|
value: 18.09 |
|
- type: precision_at_10 |
|
value: 4.925 |
|
- type: precision_at_100 |
|
value: 0.768 |
|
- type: precision_at_1000 |
|
value: 0.094 |
|
- type: precision_at_20 |
|
value: 2.9250000000000003 |
|
- type: precision_at_3 |
|
value: 10.586 |
|
- type: precision_at_5 |
|
value: 7.839 |
|
- type: recall_at_1 |
|
value: 16.516000000000002 |
|
- type: recall_at_10 |
|
value: 43.166 |
|
- type: recall_at_100 |
|
value: 66.11399999999999 |
|
- type: recall_at_1000 |
|
value: 80.771 |
|
- type: recall_at_20 |
|
value: 50.804 |
|
- type: recall_at_3 |
|
value: 28.191 |
|
- type: recall_at_5 |
|
value: 34.925 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (es) |
|
config: es |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 12.144 |
|
- type: map_at_10 |
|
value: 28.499999999999996 |
|
- type: map_at_100 |
|
value: 34.048 |
|
- type: map_at_1000 |
|
value: 34.268 |
|
- type: map_at_20 |
|
value: 31.401 |
|
- type: map_at_3 |
|
value: 20.459 |
|
- type: map_at_5 |
|
value: 24.245 |
|
- type: mrr_at_1 |
|
value: 41.975 |
|
- type: mrr_at_10 |
|
value: 54.525 |
|
- type: mrr_at_100 |
|
value: 55.359 |
|
- type: mrr_at_1000 |
|
value: 55.367 |
|
- type: mrr_at_20 |
|
value: 55.1 |
|
- type: mrr_at_3 |
|
value: 51.929 |
|
- type: mrr_at_5 |
|
value: 53.418 |
|
- type: ndcg_at_1 |
|
value: 41.975 |
|
- type: ndcg_at_10 |
|
value: 40.117000000000004 |
|
- type: ndcg_at_100 |
|
value: 54.102 |
|
- type: ndcg_at_1000 |
|
value: 56.191 |
|
- type: ndcg_at_20 |
|
value: 45.67 |
|
- type: ndcg_at_3 |
|
value: 37.505 |
|
- type: ndcg_at_5 |
|
value: 36.968 |
|
- type: precision_at_1 |
|
value: 41.975 |
|
- type: precision_at_10 |
|
value: 19.707 |
|
- type: precision_at_100 |
|
value: 4.003 |
|
- type: precision_at_1000 |
|
value: 0.44799999999999995 |
|
- type: precision_at_20 |
|
value: 13.272 |
|
- type: precision_at_3 |
|
value: 31.790000000000003 |
|
- type: precision_at_5 |
|
value: 26.667 |
|
- type: recall_at_1 |
|
value: 12.144 |
|
- type: recall_at_10 |
|
value: 44.92 |
|
- type: recall_at_100 |
|
value: 86.141 |
|
- type: recall_at_1000 |
|
value: 96.234 |
|
- type: recall_at_20 |
|
value: 58.194 |
|
- type: recall_at_3 |
|
value: 24.733 |
|
- type: recall_at_5 |
|
value: 32.385000000000005 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (sw) |
|
config: sw |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 21.401 |
|
- type: map_at_10 |
|
value: 34.205000000000005 |
|
- type: map_at_100 |
|
value: 35.571000000000005 |
|
- type: map_at_1000 |
|
value: 35.682 |
|
- type: map_at_20 |
|
value: 35.077000000000005 |
|
- type: map_at_3 |
|
value: 30.706 |
|
- type: map_at_5 |
|
value: 32.902 |
|
- type: mrr_at_1 |
|
value: 32.78 |
|
- type: mrr_at_10 |
|
value: 44.205 |
|
- type: mrr_at_100 |
|
value: 45.013 |
|
- type: mrr_at_1000 |
|
value: 45.055 |
|
- type: mrr_at_20 |
|
value: 44.726 |
|
- type: mrr_at_3 |
|
value: 41.909 |
|
- type: mrr_at_5 |
|
value: 43.434 |
|
- type: ndcg_at_1 |
|
value: 32.78 |
|
- type: ndcg_at_10 |
|
value: 41.385 |
|
- type: ndcg_at_100 |
|
value: 46.568 |
|
- type: ndcg_at_1000 |
|
value: 48.881 |
|
- type: ndcg_at_20 |
|
value: 43.872 |
|
- type: ndcg_at_3 |
|
value: 36.634 |
|
- type: ndcg_at_5 |
|
value: 38.964999999999996 |
|
- type: precision_at_1 |
|
value: 32.78 |
|
- type: precision_at_10 |
|
value: 8.859 |
|
- type: precision_at_100 |
|
value: 1.32 |
|
- type: precision_at_1000 |
|
value: 0.16199999999999998 |
|
- type: precision_at_20 |
|
value: 5.27 |
|
- type: precision_at_3 |
|
value: 20.608999999999998 |
|
- type: precision_at_5 |
|
value: 14.979000000000001 |
|
- type: recall_at_1 |
|
value: 21.401 |
|
- type: recall_at_10 |
|
value: 52.471000000000004 |
|
- type: recall_at_100 |
|
value: 72.069 |
|
- type: recall_at_1000 |
|
value: 87.996 |
|
- type: recall_at_20 |
|
value: 60.589999999999996 |
|
- type: recall_at_3 |
|
value: 39.28 |
|
- type: recall_at_5 |
|
value: 46.015 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/mrtydi |
|
name: MTEB MrTydiRetrieval (swahili) |
|
config: swahili |
|
split: test |
|
revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 29.005 |
|
- type: map_at_10 |
|
value: 40.907 |
|
- type: map_at_100 |
|
value: 41.557 |
|
- type: map_at_1000 |
|
value: 41.604 |
|
- type: map_at_20 |
|
value: 41.234 |
|
- type: map_at_3 |
|
value: 38.114 |
|
- type: map_at_5 |
|
value: 39.6 |
|
- type: mrr_at_1 |
|
value: 30.597 |
|
- type: mrr_at_10 |
|
value: 42.0 |
|
- type: mrr_at_100 |
|
value: 42.557 |
|
- type: mrr_at_1000 |
|
value: 42.601 |
|
- type: mrr_at_20 |
|
value: 42.272999999999996 |
|
- type: mrr_at_3 |
|
value: 39.378 |
|
- type: mrr_at_5 |
|
value: 40.759 |
|
- type: ndcg_at_1 |
|
value: 30.597 |
|
- type: ndcg_at_10 |
|
value: 46.864 |
|
- type: ndcg_at_100 |
|
value: 50.099000000000004 |
|
- type: ndcg_at_1000 |
|
value: 51.354 |
|
- type: ndcg_at_20 |
|
value: 47.94 |
|
- type: ndcg_at_3 |
|
value: 41.234 |
|
- type: ndcg_at_5 |
|
value: 43.822 |
|
- type: precision_at_1 |
|
value: 30.597 |
|
- type: precision_at_10 |
|
value: 6.984999999999999 |
|
- type: precision_at_100 |
|
value: 0.8750000000000001 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_20 |
|
value: 3.7310000000000003 |
|
- type: precision_at_3 |
|
value: 17.463 |
|
- type: precision_at_5 |
|
value: 11.91 |
|
- type: recall_at_1 |
|
value: 29.005 |
|
- type: recall_at_10 |
|
value: 64.20400000000001 |
|
- type: recall_at_100 |
|
value: 79.403 |
|
- type: recall_at_1000 |
|
value: 89.104 |
|
- type: recall_at_20 |
|
value: 68.234 |
|
- type: recall_at_3 |
|
value: 49.055 |
|
- type: recall_at_5 |
|
value: 55.149 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (te) |
|
config: te |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 40.781 |
|
- type: map_at_10 |
|
value: 53.428 |
|
- type: map_at_100 |
|
value: 54.118 |
|
- type: map_at_1000 |
|
value: 54.135999999999996 |
|
- type: map_at_20 |
|
value: 53.873000000000005 |
|
- type: map_at_3 |
|
value: 50.205 |
|
- type: map_at_5 |
|
value: 52.356 |
|
- type: mrr_at_1 |
|
value: 41.184 |
|
- type: mrr_at_10 |
|
value: 53.74100000000001 |
|
- type: mrr_at_100 |
|
value: 54.388999999999996 |
|
- type: mrr_at_1000 |
|
value: 54.406 |
|
- type: mrr_at_20 |
|
value: 54.151 |
|
- type: mrr_at_3 |
|
value: 50.664 |
|
- type: mrr_at_5 |
|
value: 52.717000000000006 |
|
- type: ndcg_at_1 |
|
value: 41.184 |
|
- type: ndcg_at_10 |
|
value: 59.614999999999995 |
|
- type: ndcg_at_100 |
|
value: 62.875 |
|
- type: ndcg_at_1000 |
|
value: 63.368 |
|
- type: ndcg_at_20 |
|
value: 61.168 |
|
- type: ndcg_at_3 |
|
value: 53.322 |
|
- type: ndcg_at_5 |
|
value: 57.079 |
|
- type: precision_at_1 |
|
value: 41.184 |
|
- type: precision_at_10 |
|
value: 8.043 |
|
- type: precision_at_100 |
|
value: 0.963 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_20 |
|
value: 4.342 |
|
- type: precision_at_3 |
|
value: 20.934 |
|
- type: precision_at_5 |
|
value: 14.469000000000001 |
|
- type: recall_at_1 |
|
value: 40.781 |
|
- type: recall_at_10 |
|
value: 78.523 |
|
- type: recall_at_100 |
|
value: 93.639 |
|
- type: recall_at_1000 |
|
value: 97.504 |
|
- type: recall_at_20 |
|
value: 84.56099999999999 |
|
- type: recall_at_3 |
|
value: 61.895999999999994 |
|
- type: recall_at_5 |
|
value: 70.79299999999999 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/mrtydi |
|
name: MTEB MrTydiRetrieval (telugu) |
|
config: telugu |
|
split: test |
|
revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 43.344 |
|
- type: map_at_10 |
|
value: 56.452000000000005 |
|
- type: map_at_100 |
|
value: 57.108000000000004 |
|
- type: map_at_1000 |
|
value: 57.131 |
|
- type: map_at_20 |
|
value: 56.86600000000001 |
|
- type: map_at_3 |
|
value: 54.154 |
|
- type: map_at_5 |
|
value: 55.57 |
|
- type: mrr_at_1 |
|
value: 44.427 |
|
- type: mrr_at_10 |
|
value: 57.16 |
|
- type: mrr_at_100 |
|
value: 57.764 |
|
- type: mrr_at_1000 |
|
value: 57.785 |
|
- type: mrr_at_20 |
|
value: 57.548 |
|
- type: mrr_at_3 |
|
value: 55.031 |
|
- type: mrr_at_5 |
|
value: 56.330999999999996 |
|
- type: ndcg_at_1 |
|
value: 44.427 |
|
- type: ndcg_at_10 |
|
value: 62.208 |
|
- type: ndcg_at_100 |
|
value: 65.33099999999999 |
|
- type: ndcg_at_1000 |
|
value: 65.96 |
|
- type: ndcg_at_20 |
|
value: 63.671 |
|
- type: ndcg_at_3 |
|
value: 57.68600000000001 |
|
- type: ndcg_at_5 |
|
value: 60.126999999999995 |
|
- type: precision_at_1 |
|
value: 44.427 |
|
- type: precision_at_10 |
|
value: 8.251 |
|
- type: precision_at_100 |
|
value: 0.98 |
|
- type: precision_at_1000 |
|
value: 0.10300000000000001 |
|
- type: precision_at_20 |
|
value: 4.42 |
|
- type: precision_at_3 |
|
value: 23.116999999999997 |
|
- type: precision_at_5 |
|
value: 15.076999999999998 |
|
- type: recall_at_1 |
|
value: 43.344 |
|
- type: recall_at_10 |
|
value: 79.10199999999999 |
|
- type: recall_at_100 |
|
value: 93.57600000000001 |
|
- type: recall_at_1000 |
|
value: 98.529 |
|
- type: recall_at_20 |
|
value: 84.83000000000001 |
|
- type: recall_at_3 |
|
value: 67.02799999999999 |
|
- type: recall_at_5 |
|
value: 72.833 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (th) |
|
config: th |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.747 |
|
- type: map_at_10 |
|
value: 43.586999999999996 |
|
- type: map_at_100 |
|
value: 45.256 |
|
- type: map_at_1000 |
|
value: 45.339 |
|
- type: map_at_20 |
|
value: 44.628 |
|
- type: map_at_3 |
|
value: 38.751999999999995 |
|
- type: map_at_5 |
|
value: 41.510000000000005 |
|
- type: mrr_at_1 |
|
value: 40.791 |
|
- type: mrr_at_10 |
|
value: 53.551 |
|
- type: mrr_at_100 |
|
value: 54.31 |
|
- type: mrr_at_1000 |
|
value: 54.32599999999999 |
|
- type: mrr_at_20 |
|
value: 54.057 |
|
- type: mrr_at_3 |
|
value: 50.637 |
|
- type: mrr_at_5 |
|
value: 52.525999999999996 |
|
- type: ndcg_at_1 |
|
value: 40.791 |
|
- type: ndcg_at_10 |
|
value: 52.144999999999996 |
|
- type: ndcg_at_100 |
|
value: 57.977000000000004 |
|
- type: ndcg_at_1000 |
|
value: 59.24 |
|
- type: ndcg_at_20 |
|
value: 54.864999999999995 |
|
- type: ndcg_at_3 |
|
value: 45.074 |
|
- type: ndcg_at_5 |
|
value: 48.504999999999995 |
|
- type: precision_at_1 |
|
value: 40.791 |
|
- type: precision_at_10 |
|
value: 11.350999999999999 |
|
- type: precision_at_100 |
|
value: 1.6039999999999999 |
|
- type: precision_at_1000 |
|
value: 0.178 |
|
- type: precision_at_20 |
|
value: 6.589 |
|
- type: precision_at_3 |
|
value: 25.239 |
|
- type: precision_at_5 |
|
value: 18.526999999999997 |
|
- type: recall_at_1 |
|
value: 27.747 |
|
- type: recall_at_10 |
|
value: 66.752 |
|
- type: recall_at_100 |
|
value: 89.51400000000001 |
|
- type: recall_at_1000 |
|
value: 97.485 |
|
- type: recall_at_20 |
|
value: 75.658 |
|
- type: recall_at_3 |
|
value: 48.393 |
|
- type: recall_at_5 |
|
value: 56.977 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/mrtydi |
|
name: MTEB MrTydiRetrieval (thai) |
|
config: thai |
|
split: test |
|
revision: fc24a3ce8f09746410daee3d5cd823ff7a0675b7 |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.810000000000002 |
|
- type: map_at_10 |
|
value: 41.803000000000004 |
|
- type: map_at_100 |
|
value: 42.818 |
|
- type: map_at_1000 |
|
value: 42.861 |
|
- type: map_at_20 |
|
value: 42.487 |
|
- type: map_at_3 |
|
value: 37.719 |
|
- type: map_at_5 |
|
value: 40.143 |
|
- type: mrr_at_1 |
|
value: 31.596999999999998 |
|
- type: mrr_at_10 |
|
value: 43.784 |
|
- type: mrr_at_100 |
|
value: 44.647 |
|
- type: mrr_at_1000 |
|
value: 44.681 |
|
- type: mrr_at_20 |
|
value: 44.385000000000005 |
|
- type: mrr_at_3 |
|
value: 40.266000000000005 |
|
- type: mrr_at_5 |
|
value: 42.266 |
|
- type: ndcg_at_1 |
|
value: 31.596999999999998 |
|
- type: ndcg_at_10 |
|
value: 48.874 |
|
- type: ndcg_at_100 |
|
value: 53.285000000000004 |
|
- type: ndcg_at_1000 |
|
value: 54.398 |
|
- type: ndcg_at_20 |
|
value: 51.188 |
|
- type: ndcg_at_3 |
|
value: 41.010000000000005 |
|
- type: ndcg_at_5 |
|
value: 45.054 |
|
- type: precision_at_1 |
|
value: 31.596999999999998 |
|
- type: precision_at_10 |
|
value: 7.747999999999999 |
|
- type: precision_at_100 |
|
value: 1.008 |
|
- type: precision_at_1000 |
|
value: 0.11100000000000002 |
|
- type: precision_at_20 |
|
value: 4.382 |
|
- type: precision_at_3 |
|
value: 17.619 |
|
- type: precision_at_5 |
|
value: 12.806999999999999 |
|
- type: recall_at_1 |
|
value: 28.810000000000002 |
|
- type: recall_at_10 |
|
value: 68.88 |
|
- type: recall_at_100 |
|
value: 88.263 |
|
- type: recall_at_1000 |
|
value: 96.765 |
|
- type: recall_at_20 |
|
value: 77.647 |
|
- type: recall_at_3 |
|
value: 48.137 |
|
- type: recall_at_5 |
|
value: 57.577 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (yo) |
|
config: yo |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 16.387 |
|
- type: map_at_10 |
|
value: 23.988 |
|
- type: map_at_100 |
|
value: 24.65 |
|
- type: map_at_1000 |
|
value: 24.725 |
|
- type: map_at_20 |
|
value: 24.310000000000002 |
|
- type: map_at_3 |
|
value: 21.23 |
|
- type: map_at_5 |
|
value: 23.093 |
|
- type: mrr_at_1 |
|
value: 17.647 |
|
- type: mrr_at_10 |
|
value: 26.144000000000002 |
|
- type: mrr_at_100 |
|
value: 26.751 |
|
- type: mrr_at_1000 |
|
value: 26.804 |
|
- type: mrr_at_20 |
|
value: 26.43 |
|
- type: mrr_at_3 |
|
value: 23.669 |
|
- type: mrr_at_5 |
|
value: 25.392 |
|
- type: ndcg_at_1 |
|
value: 17.647 |
|
- type: ndcg_at_10 |
|
value: 28.583 |
|
- type: ndcg_at_100 |
|
value: 31.790000000000003 |
|
- type: ndcg_at_1000 |
|
value: 33.705 |
|
- type: ndcg_at_20 |
|
value: 29.669 |
|
- type: ndcg_at_3 |
|
value: 23.271 |
|
- type: ndcg_at_5 |
|
value: 26.509 |
|
- type: precision_at_1 |
|
value: 17.647 |
|
- type: precision_at_10 |
|
value: 4.79 |
|
- type: precision_at_100 |
|
value: 0.655 |
|
- type: precision_at_1000 |
|
value: 0.08499999999999999 |
|
- type: precision_at_20 |
|
value: 2.6470000000000002 |
|
- type: precision_at_3 |
|
value: 10.363999999999999 |
|
- type: precision_at_5 |
|
value: 7.899000000000001 |
|
- type: recall_at_1 |
|
value: 16.387 |
|
- type: recall_at_10 |
|
value: 40.476 |
|
- type: recall_at_100 |
|
value: 55.11200000000001 |
|
- type: recall_at_1000 |
|
value: 69.538 |
|
- type: recall_at_20 |
|
value: 44.538 |
|
- type: recall_at_3 |
|
value: 26.540999999999997 |
|
- type: recall_at_5 |
|
value: 34.384 |
|
- task: |
|
type: retrieval |
|
dataset: |
|
type: mteb/miracl-hard-negatives |
|
name: MTEB MIRACLRetrievalHardNegatives (zh) |
|
config: zh |
|
split: dev |
|
revision: 95c8db7d4a6e9c1d8a60601afd63d553ae20a2eb |
|
metrics: |
|
- type: map_at_1 |
|
value: 13.971 |
|
- type: map_at_10 |
|
value: 27.076 |
|
- type: map_at_100 |
|
value: 30.128 |
|
- type: map_at_1000 |
|
value: 30.25 |
|
- type: map_at_20 |
|
value: 28.731 |
|
- type: map_at_3 |
|
value: 21.029999999999998 |
|
- type: map_at_5 |
|
value: 23.769000000000002 |
|
- type: mrr_at_1 |
|
value: 26.717999999999996 |
|
- type: mrr_at_10 |
|
value: 40.331 |
|
- type: mrr_at_100 |
|
value: 41.448 |
|
- type: mrr_at_1000 |
|
value: 41.461999999999996 |
|
- type: mrr_at_20 |
|
value: 41.103 |
|
- type: mrr_at_3 |
|
value: 36.302 |
|
- type: mrr_at_5 |
|
value: 38.414 |
|
- type: ndcg_at_1 |
|
value: 26.717999999999996 |
|
- type: ndcg_at_10 |
|
value: 36.744 |
|
- type: ndcg_at_100 |
|
value: 47.361 |
|
- type: ndcg_at_1000 |
|
value: 48.869 |
|
- type: ndcg_at_20 |
|
value: 41.097 |
|
- type: ndcg_at_3 |
|
value: 28.322000000000003 |
|
- type: ndcg_at_5 |
|
value: 30.875999999999998 |
|
- type: precision_at_1 |
|
value: 26.717999999999996 |
|
- type: precision_at_10 |
|
value: 11.679 |
|
- type: precision_at_100 |
|
value: 2.2159999999999997 |
|
- type: precision_at_1000 |
|
value: 0.248 |
|
- type: precision_at_20 |
|
value: 7.545 |
|
- type: precision_at_3 |
|
value: 20.102 |
|
- type: precision_at_5 |
|
value: 16.131999999999998 |
|
- type: recall_at_1 |
|
value: 13.971 |
|
- type: recall_at_10 |
|
value: 50.763999999999996 |
|
- type: recall_at_100 |
|
value: 89.666 |
|
- type: recall_at_1000 |
|
value: 98.038 |
|
- type: recall_at_20 |
|
value: 64.1 |
|
- type: recall_at_3 |
|
value: 27.16 |
|
- type: recall_at_5 |
|
value: 35.022999999999996 |
|
widget: |
|
- source_sentence: ما هي أفضل الفنادق في ايبوهبالقرب من Ipoh Parade Shopping Centre؟ |
|
sentences: |
|
- Bei ORION gibt es eine Sale-Rubrik, in der alle reduzierten Artikel zu finden |
|
sind. Wenn du also auf der Suche nach einem Schnäppchen bist, weißt du, an welcher |
|
Stelle auf der Webseite du fündig wirst. Der Sale umfasst viele verschiedene Produke. |
|
Von Toys bis hin zu Dessous und Drogerieartikel - es spielt keine Rolle, wonach |
|
du suchst. Aufgrund der Produktvielfalt ist die Chance, dass du im Sale den passenden |
|
Gegenstand findest, groß. |
|
- عادة ما يكون لأصحاب النفوذ الجزئي ما بين 10000 و 100000 متابع. |
|
- المسافرون الموثّقون إلى مدينة ايبوه الذين أقاموا قرب Ipoh Parade Shopping Centre |
|
أعطوا أعلى التقييمات لـفندق فايل ، Zone Hotel (Ipoh) وGolden Roof Hotel Ampang |
|
Ipoh. |
|
- source_sentence: Habe ich Vorteile, wenn ich früh in das Projekt einsteige? |
|
sentences: |
|
- نعم لدينا خصومات مُتعددة على جميع أعمال السواتر والمظلات والجلسات ففي فصل الشتاء |
|
والصيف هناك خصومات مُتعددة في الأعياد والمناسبات |
|
- Der Vorteil einer frühen Mitgliedschaft besteht in der Möglichkeit der Mitgestaltung |
|
des Projektes. Alle später Hinzukommenden müssen die bis dahin getroffenen Entscheidungen |
|
akzeptieren. Zudem entscheidet unter anderem auch das Eintrittsdatum in die eG |
|
und das Engagement während des Projektverlaufes über die spätere Reihenfolge der |
|
Vergabe der Wohnungen. |
|
- Средняя оценка Registered от клиентов – 4 на основе 227 оценок и отзывов. Заходите |
|
на сайт и прочитайте реальные отзывы о Registered. |
|
- source_sentence: В какое время доступны ваши технические услуги? |
|
sentences: |
|
- Наша команда технической поддержки обеспечивает круглосуточное обслуживание в |
|
случае чрезвычайных ситуаций. Вы можете связаться с нами в любой день недели, |
|
в любое время суток и получить поддержку для ваших холодильных систем. Услуги |
|
по плановому техническому обслуживанию и ремонту предоставляются в обычное рабочее |
|
время, а услуги предоставляются в экстренных случаях, в том числе в ночное время |
|
и в выходные дни. |
|
- این سوال کاملا به علاقه و مهارت شما بستگی دارد. اگر به درس شیمی علاقه زیادی دارید |
|
این رشته بهترین انتخاب برای تحصیل در دانشگاه برای شما محسوب میشود. |
|
- 'eo光は10Gを提供している光回線です。 |
|
|
|
提供エリアは、通常プランと変わらず関西地方と福井県です。しかし、一部の利用できないエリアもあるので契約前に確認しましょう。 |
|
|
|
関連記事 |
|
|
|
eo光の10Gプランの評判口コミ' |
|
- source_sentence: Supertotobet redtiger oyun çeşitleri hangileri? |
|
sentences: |
|
- Supertotobet redtiger oyunları arasında gold star, golden tsar, golden lotus, |
|
blood suckers ve redtiger slot gibi çeşitli oyunlar vardır. Bu oyun seçeneklerini |
|
kullanabilmek için oyunlar hakkında bilgi sahibi olmalısınız. |
|
- Время выполнения проекта зависит от его сложности и размера. Обычно, время выполнения |
|
проекта составляет несколько месяцев. |
|
- 'Wer kein Homeoffice während des Coronavirus anbieten kann, ist dazu verpflichtet, |
|
Schutzmaßnahmen zu ergreifen. |
|
|
|
Die Arbeitsschutzbehörden der Länder sind befugt, Corona-Schutzmaßnahmen in Betrieben |
|
zu kontrollieren und Fehlverhalten zu bestrafen. Bei Verstößen sind Bußgelder |
|
in Höhe von bis zu 30.000 Euro möglich. Wiederholen sich schwere Verstöße, droht |
|
den Verantwortlichen sogar bis zu einem Jahr Freiheitsstrafe. |
|
|
|
Arbeitgeber, die die Vorschriften missachten, könnten zudem dafür haften, wenn |
|
Mitarbeiter durch eine Corona-Infektion gesundheitliche Schäden erleiden.' |
|
- source_sentence: Muss der Deckel der TipBox beim Autoklavieren geöffnet werden? |
|
sentences: |
|
- ВВП (валовый внутренний продукт) - это общая стоимость всех товаров и услуг, произведенных |
|
в стране за определенный период времени. Он является ключевым экономическим показателем, |
|
который отражает общий уровень экономической активности и роста. Инвесторы следят |
|
за ВВП, чтобы оценить состояние и перспективы экономики, потенциал для роста и |
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возможности для инвестиций |
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- برآمدگی های بیضه ممکن است نشان دهنده مشکلی در بیضه ها باشد. ممکن است به دلیل صدمه |
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ای به وجود آمده یا ممکن است یک مشکل پزشکی جدی باشد. |
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- Nein, das ist nicht notwendig. Die neue TipBox kann bei 121°C im geschlossenen |
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Zustand autoklaviert werden. |
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pipeline_tag: sentence-similarity |
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library_name: sentence-transformers |
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license: mit |
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--- |
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# SentenceTransformer |
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This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. |
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## Model Details |
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### Model Description |
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- **Model Type:** Sentence Transformer |
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- **Base model:** [XLM-RoBERTa-base-MSMARCO](https://huggingface.co/PaDaS-Lab/xlm-roberta-base-msmarco) |
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- **Maximum Sequence Length:** 512 tokens |
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- **Output Dimensionality:** 768 dimensions |
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- **Similarity Function:** Cosine Similarity |
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- **Training Dataset:** [WebFAQ Retrieval Dataset](https://huggingface.co/datasets/PaDaS-Lab/webfaq-retrieval) |
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<!-- - **Language:** Unknown --> |
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- **License:** MIT |
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### Model Sources |
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- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) |
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- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) |
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- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) |
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### Full Model Architecture |
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``` |
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SentenceTransformer( |
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(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel |
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(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True}) |
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) |
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``` |
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## Usage |
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### Direct Usage (Sentence Transformers) |
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First install the Sentence Transformers library: |
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```bash |
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pip install -U sentence-transformers |
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``` |
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Then you can load this model and run inference. |
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```python |
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from sentence_transformers import SentenceTransformer |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("PaDaS-Lab/xlm-roberta-base-msmarco-webfaq") |
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# Run inference |
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sentences = [ |
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'Muss der Deckel der TipBox beim Autoklavieren geöffnet werden?', |
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'Nein, das ist nicht notwendig. Die neue TipBox kann bei 121°C im geschlossenen Zustand autoklaviert werden.', |
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'برآمدگی های بیضه ممکن است نشان دهنده مشکلی در بیضه ها باشد. ممکن است به دلیل صدمه ای به وجود آمده یا ممکن است یک مشکل پزشکی جدی باشد.', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 2,560,000 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 6 tokens</li><li>mean: 15.02 tokens</li><li>max: 86 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 66.82 tokens</li><li>max: 512 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:-----------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>Hat myTime ein großes Produktsortiment?</code> | <code>Das Sortiment von myTime umfasst mehr als 13.000 Lebensmittel. Du findest alle Produkte, die du auch im Supermarkt findest, darunter Obst und Gemüse, trockene Lebensmittel wie Pasta und Reis, Backwaren, Snacks und Tiefkühlkost. Auch Getränke wie Kaffee, Alkohol und Soda findest du im Online-Supermarkt.</code> | |
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| <code>Gibt es eine Tigerspin App?</code> | <code>Tigerspin verzichtet auf eine mobile App. Wenn Sie ein paar Runden spielen möchten, öffnen Sie einfach die Webseite des Casinos und starten die Spiele im Browser.</code> | |
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| <code>Bietet ihr auch maschinelle Übersetzungen an? Wenn ja, wann eignet sich diese und wann nicht?</code> | <code>Maschinelle Übersetzungen sind ein spannendes Thema, auch aktuell bei techtrans. Unter maschineller Übersetzung (MÜ) versteht man die automatisierte Übertragung eines Ausgangstextes in die Zielsprache mittels einer sogenannten Übersetzungsengine. Eine solche Engine kann nach regelbasierten, statistischen oder neuronalen Prinzipien aufgebaut sein.<br>Obwohl es maschinelle Übersetzungsengines schon seit einigen Jahrzehnten gibt, ist erst mit der Einführung der neuronalen Engines (NMT) ca. ab dem Jahre 2015 die Output-Qualität gestiegen. Namhafte Engine Provider sind zum Beispiel Google, DeepL, Microsoft, Amazon AWS und SDL. So ist es kaum verwunderlich, dass diese Technologie zunehmend Einzug sowohl in unseren Alltag als auch in den Übersetzungsprozess findet.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `num_train_epochs`: 1 |
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- `fp16`: True |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: no |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 128 |
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- `per_device_eval_batch_size`: 128 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `torch_empty_cache_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 1 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: True |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: None |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `include_for_metrics`: [] |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `use_liger_kernel`: False |
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- `eval_use_gather_object`: False |
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- `average_tokens_across_devices`: False |
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- `prompts`: None |
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- `batch_sampler`: batch_sampler |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | |
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|:-----:|:-----:|:-------------:| |
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| 0.025 | 500 | 0.1999 | |
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| 0.05 | 1000 | 0.0279 | |
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| 0.075 | 1500 | 0.0234 | |
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| 0.1 | 2000 | 0.0203 | |
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| 0.125 | 2500 | 0.0179 | |
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| 0.15 | 3000 | 0.0171 | |
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| 0.175 | 3500 | 0.0153 | |
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| 0.2 | 4000 | 0.015 | |
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| 0.225 | 4500 | 0.0143 | |
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| 0.25 | 5000 | 0.014 | |
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| 0.275 | 5500 | 0.0128 | |
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| 0.3 | 6000 | 0.013 | |
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| 0.325 | 6500 | 0.0129 | |
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| 0.35 | 7000 | 0.0124 | |
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| 0.375 | 7500 | 0.012 | |
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| 0.4 | 8000 | 0.0121 | |
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| 0.425 | 8500 | 0.0115 | |
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| 0.45 | 9000 | 0.0113 | |
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| 0.475 | 9500 | 0.0106 | |
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| 0.5 | 10000 | 0.0107 | |
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| 0.525 | 10500 | 0.011 | |
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| 0.55 | 11000 | 0.0108 | |
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| 0.575 | 11500 | 0.0103 | |
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| 0.6 | 12000 | 0.0097 | |
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| 0.625 | 12500 | 0.01 | |
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| 0.65 | 13000 | 0.0104 | |
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| 0.675 | 13500 | 0.0096 | |
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| 0.7 | 14000 | 0.0096 | |
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| 0.725 | 14500 | 0.0097 | |
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| 0.75 | 15000 | 0.0097 | |
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| 0.775 | 15500 | 0.0089 | |
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| 0.8 | 16000 | 0.0089 | |
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| 0.825 | 16500 | 0.0091 | |
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| 0.85 | 17000 | 0.0085 | |
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| 0.875 | 17500 | 0.0084 | |
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| 0.9 | 18000 | 0.0089 | |
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| 0.925 | 18500 | 0.0087 | |
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| 0.95 | 19000 | 0.0087 | |
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| 0.975 | 19500 | 0.0088 | |
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| 1.0 | 20000 | 0.0089 | |
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### Framework Versions |
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- Python: 3.11.5 |
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- Sentence Transformers: 3.4.0 |
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- Transformers: 4.48.0 |
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- PyTorch: 2.5.1+cu124 |
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- Accelerate: 1.2.1 |
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- Datasets: 2.21.0 |
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- Tokenizers: 0.21.0 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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#### WebFAQ Dataset |
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```bibtex |
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@misc{dinzinger2025webfaq, |
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title={WebFAQ: A Multilingual Collection of Natural Q&A Datasets for Dense Retrieval}, |
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author={Michael Dinzinger and Laura Caspari and Kanishka Ghosh Dastidar and Jelena Mitrović and Michael Granitzer}, |
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year={2025}, |
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eprint={2502.20936}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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