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--- |
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quantized_by: bartowski |
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pipeline_tag: text-generation |
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language: |
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- en |
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tags: |
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- language |
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- granite |
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- embeddings |
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license: apache-2.0 |
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base_model: ibm-granite/granite-embedding-30m-english |
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model-index: |
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- name: ibm-granite/granite-embedding-30m-english |
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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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name: MTEB ArguaAna |
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type: mteb/arguana |
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config: default |
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split: test |
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metrics: |
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- type: map_at_1 |
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value: 0.31792 |
|
- type: map_at_10 |
|
value: 0.47599 |
|
- type: map_at_100 |
|
value: 0.48425 |
|
- type: map_at_1000 |
|
value: 0.48427 |
|
- type: map_at_3 |
|
value: 0.42757 |
|
- type: map_at_5 |
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value: 0.45634 |
|
- type: mrr_at_1 |
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value: 0.32788 |
|
- type: mrr_at_10 |
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value: 0.47974 |
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- type: mrr_at_100 |
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value: 0.48801 |
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- type: mrr_at_1000 |
|
value: 0.48802 |
|
- type: mrr_at_3 |
|
value: 0.43065 |
|
- type: mrr_at_5 |
|
value: 0.45999 |
|
- type: ndcg_at_1 |
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value: 0.31792 |
|
- type: ndcg_at_10 |
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value: 0.56356 |
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- type: ndcg_at_100 |
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value: 0.59789 |
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- type: ndcg_at_1000 |
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value: 0.59857 |
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- type: ndcg_at_3 |
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value: 0.46453 |
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- type: ndcg_at_5 |
|
value: 0.51623 |
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- type: precision_at_1 |
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value: 0.31792 |
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- type: precision_at_10 |
|
value: 0.08428 |
|
- type: precision_at_100 |
|
value: 0.00991 |
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- type: precision_at_1000 |
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value: 0.001 |
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- type: precision_at_3 |
|
value: 0.19061 |
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- type: precision_at_5 |
|
value: 0.1394 |
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- type: recall_at_1 |
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value: 0.31792 |
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- type: recall_at_10 |
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value: 0.84282 |
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- type: recall_at_100 |
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value: 0.99075 |
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- type: recall_at_1000 |
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value: 0.99644 |
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- type: recall_at_3 |
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value: 0.57183 |
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- type: recall_at_5 |
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value: 0.69701 |
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- task: |
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type: Retrieval |
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dataset: |
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name: MTEB ClimateFEVER |
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type: mteb/climate-fever |
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config: default |
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split: test |
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metrics: |
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- type: map_at_1 |
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value: 0.13189 |
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- type: map_at_10 |
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value: 0.21789 |
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- type: map_at_100 |
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value: 0.2358 |
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- type: map_at_1000 |
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value: 0.23772 |
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- type: map_at_3 |
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value: 0.18513 |
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- type: map_at_5 |
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value: 0.20212 |
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- type: mrr_at_1 |
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value: 0.29837 |
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- type: mrr_at_10 |
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value: 0.41376 |
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- type: mrr_at_100 |
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value: 0.42282 |
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- type: mrr_at_1000 |
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value: 0.42319 |
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- type: mrr_at_3 |
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value: 0.38284 |
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- type: mrr_at_5 |
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value: 0.40301 |
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- type: ndcg_at_1 |
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value: 0.29837 |
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- type: ndcg_at_10 |
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value: 0.30263 |
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- type: ndcg_at_100 |
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value: 0.37228 |
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- type: ndcg_at_1000 |
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value: 0.40677 |
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- type: ndcg_at_3 |
|
value: 0.25392 |
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- type: ndcg_at_5 |
|
value: 0.27153 |
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- type: precision_at_1 |
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value: 0.29837 |
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- type: precision_at_10 |
|
value: 0.09179 |
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- type: precision_at_100 |
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value: 0.01659 |
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- type: precision_at_1000 |
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value: 0.0023 |
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- type: precision_at_3 |
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value: 0.18545 |
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- type: precision_at_5 |
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value: 0.14241 |
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- type: recall_at_1 |
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value: 0.13189 |
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- type: recall_at_10 |
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value: 0.35355 |
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- type: recall_at_100 |
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value: 0.59255 |
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- type: recall_at_1000 |
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value: 0.78637 |
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- type: recall_at_3 |
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value: 0.23255 |
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- type: recall_at_5 |
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value: 0.28446 |
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- task: |
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type: Retrieval |
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dataset: |
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name: MTEB CQADupstackAndroidRetrieval |
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type: mteb/cqadupstack-android |
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config: default |
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split: test |
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metrics: |
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- type: map_at_1 |
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value: 0.35797 |
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- type: map_at_10 |
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value: 0.47793 |
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- type: map_at_100 |
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value: 0.49422 |
|
- type: map_at_1000 |
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value: 0.49546 |
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- type: map_at_3 |
|
value: 0.44137 |
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- type: map_at_5 |
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value: 0.46063 |
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- type: mrr_at_1 |
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value: 0.44206 |
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- type: mrr_at_10 |
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value: 0.53808 |
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- type: mrr_at_100 |
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value: 0.5454 |
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- type: mrr_at_1000 |
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value: 0.54578 |
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- type: mrr_at_3 |
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value: 0.51431 |
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- type: mrr_at_5 |
|
value: 0.5284 |
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- type: ndcg_at_1 |
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value: 0.44206 |
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- type: ndcg_at_10 |
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value: 0.54106 |
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- type: ndcg_at_100 |
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value: 0.59335 |
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- type: ndcg_at_1000 |
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value: 0.61015 |
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- type: ndcg_at_3 |
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value: 0.49365 |
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- type: ndcg_at_5 |
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value: 0.51429 |
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- type: precision_at_1 |
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value: 0.44206 |
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- type: precision_at_10 |
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value: 0.10443 |
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- type: precision_at_100 |
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value: 0.01631 |
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- type: precision_at_1000 |
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value: 0.00214 |
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- type: precision_at_3 |
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value: 0.23653 |
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- type: precision_at_5 |
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value: 0.1691 |
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- type: recall_at_1 |
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value: 0.35797 |
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- type: recall_at_10 |
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value: 0.65182 |
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- type: recall_at_100 |
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value: 0.86654 |
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- type: recall_at_1000 |
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value: 0.97131 |
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- type: recall_at_3 |
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value: 0.51224 |
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- type: recall_at_5 |
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value: 0.57219 |
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- task: |
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type: Retrieval |
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dataset: |
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name: MTEB CQADupstackEnglishRetrieval |
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type: mteb/cqadupstack-english |
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config: default |
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split: test |
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metrics: |
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- type: map_at_1 |
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value: 0.32748 |
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- type: map_at_10 |
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value: 0.44138 |
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- type: map_at_100 |
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value: 0.45565 |
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- type: map_at_1000 |
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value: 0.45698 |
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- type: map_at_3 |
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value: 0.40916 |
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- type: map_at_5 |
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value: 0.42621 |
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- type: mrr_at_1 |
|
value: 0.41274 |
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- type: mrr_at_10 |
|
value: 0.5046 |
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- type: mrr_at_100 |
|
value: 0.5107 |
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- type: mrr_at_1000 |
|
value: 0.51109 |
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- type: mrr_at_3 |
|
value: 0.48238 |
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- type: mrr_at_5 |
|
value: 0.49563 |
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- type: ndcg_at_1 |
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value: 0.41274 |
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- type: ndcg_at_10 |
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value: 0.50251 |
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- type: ndcg_at_100 |
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value: 0.54725 |
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- type: ndcg_at_1000 |
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value: 0.56635 |
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- type: ndcg_at_3 |
|
value: 0.46023 |
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- type: ndcg_at_5 |
|
value: 0.47883 |
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- type: precision_at_1 |
|
value: 0.41274 |
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- type: precision_at_10 |
|
value: 0.09828 |
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- type: precision_at_100 |
|
value: 0.01573 |
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- type: precision_at_1000 |
|
value: 0.00202 |
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- type: precision_at_3 |
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value: 0.22718 |
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- type: precision_at_5 |
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value: 0.16064 |
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- type: recall_at_1 |
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value: 0.32748 |
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- type: recall_at_10 |
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value: 0.60322 |
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- type: recall_at_100 |
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value: 0.79669 |
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- type: recall_at_1000 |
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value: 0.9173 |
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- type: recall_at_3 |
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value: 0.47523 |
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- type: recall_at_5 |
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value: 0.52957 |
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- task: |
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type: Retrieval |
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dataset: |
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name: MTEB CQADupstackGamingRetrieval |
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type: mteb/cqadupstack-gaming |
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config: default |
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split: test |
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metrics: |
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- type: map_at_1 |
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value: 0.41126 |
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- type: map_at_10 |
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value: 0.53661 |
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- type: map_at_100 |
|
value: 0.54588 |
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- type: map_at_1000 |
|
value: 0.54638 |
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- type: map_at_3 |
|
value: 0.50389 |
|
- type: map_at_5 |
|
value: 0.52286 |
|
- type: mrr_at_1 |
|
value: 0.47147 |
|
- type: mrr_at_10 |
|
value: 0.5685 |
|
- type: mrr_at_100 |
|
value: 0.57458 |
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- type: mrr_at_1000 |
|
value: 0.57487 |
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- type: mrr_at_3 |
|
value: 0.54431 |
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- type: mrr_at_5 |
|
value: 0.55957 |
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- type: ndcg_at_1 |
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value: 0.47147 |
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- type: ndcg_at_10 |
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value: 0.59318 |
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- type: ndcg_at_100 |
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value: 0.62972 |
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- type: ndcg_at_1000 |
|
value: 0.64033 |
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- type: ndcg_at_3 |
|
value: 0.53969 |
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- type: ndcg_at_5 |
|
value: 0.56743 |
|
- type: precision_at_1 |
|
value: 0.47147 |
|
- type: precision_at_10 |
|
value: 0.09549 |
|
- type: precision_at_100 |
|
value: 0.01224 |
|
- type: precision_at_1000 |
|
value: 0.00135 |
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- type: precision_at_3 |
|
value: 0.24159 |
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- type: precision_at_5 |
|
value: 0.16577 |
|
- type: recall_at_1 |
|
value: 0.41126 |
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- type: recall_at_10 |
|
value: 0.72691 |
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- type: recall_at_100 |
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value: 0.88692 |
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- type: recall_at_1000 |
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value: 0.96232 |
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- type: recall_at_3 |
|
value: 0.58374 |
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- type: recall_at_5 |
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value: 0.65226 |
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- task: |
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type: Retrieval |
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dataset: |
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name: MTEB CQADupstackGisRetrieval |
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type: mteb/cqadupstack-gis |
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config: default |
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split: test |
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metrics: |
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- type: map_at_1 |
|
value: 0.28464 |
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- type: map_at_10 |
|
value: 0.3828 |
|
- type: map_at_100 |
|
value: 0.39277 |
|
- type: map_at_1000 |
|
value: 0.39355 |
|
- type: map_at_3 |
|
value: 0.35704 |
|
- type: map_at_5 |
|
value: 0.37116 |
|
- type: mrr_at_1 |
|
value: 0.30734 |
|
- type: mrr_at_10 |
|
value: 0.40422 |
|
- type: mrr_at_100 |
|
value: 0.41297 |
|
- type: mrr_at_1000 |
|
value: 0.41355 |
|
- type: mrr_at_3 |
|
value: 0.38136 |
|
- type: mrr_at_5 |
|
value: 0.39362 |
|
- type: ndcg_at_1 |
|
value: 0.30734 |
|
- type: ndcg_at_10 |
|
value: 0.43564 |
|
- type: ndcg_at_100 |
|
value: 0.48419 |
|
- type: ndcg_at_1000 |
|
value: 0.50404 |
|
- type: ndcg_at_3 |
|
value: 0.38672 |
|
- type: ndcg_at_5 |
|
value: 0.40954 |
|
- type: precision_at_1 |
|
value: 0.30734 |
|
- type: precision_at_10 |
|
value: 0.06633 |
|
- type: precision_at_100 |
|
value: 0.00956 |
|
- type: precision_at_1000 |
|
value: 0.00116 |
|
- type: precision_at_3 |
|
value: 0.16497 |
|
- type: precision_at_5 |
|
value: 0.11254 |
|
- type: recall_at_1 |
|
value: 0.28464 |
|
- type: recall_at_10 |
|
value: 0.57621 |
|
- type: recall_at_100 |
|
value: 0.7966 |
|
- type: recall_at_1000 |
|
value: 0.94633 |
|
- type: recall_at_3 |
|
value: 0.44588 |
|
- type: recall_at_5 |
|
value: 0.50031 |
|
- task: |
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type: Retrieval |
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dataset: |
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name: MTEB CQADupstackMathematicaRetrieval |
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type: mteb/cqadupstack-mathematica |
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config: default |
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split: test |
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metrics: |
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- type: map_at_1 |
|
value: 0.18119 |
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- type: map_at_10 |
|
value: 0.27055 |
|
- type: map_at_100 |
|
value: 0.28461 |
|
- type: map_at_1000 |
|
value: 0.28577 |
|
- type: map_at_3 |
|
value: 0.24341 |
|
- type: map_at_5 |
|
value: 0.25861 |
|
- type: mrr_at_1 |
|
value: 0.22886 |
|
- type: mrr_at_10 |
|
value: 0.32234 |
|
- type: mrr_at_100 |
|
value: 0.3328 |
|
- type: mrr_at_1000 |
|
value: 0.3334 |
|
- type: mrr_at_3 |
|
value: 0.29664 |
|
- type: mrr_at_5 |
|
value: 0.31107 |
|
- type: ndcg_at_1 |
|
value: 0.22886 |
|
- type: ndcg_at_10 |
|
value: 0.32749 |
|
- type: ndcg_at_100 |
|
value: 0.39095 |
|
- type: ndcg_at_1000 |
|
value: 0.41656 |
|
- type: ndcg_at_3 |
|
value: 0.27864 |
|
- type: ndcg_at_5 |
|
value: 0.30177 |
|
- type: precision_at_1 |
|
value: 0.22886 |
|
- type: precision_at_10 |
|
value: 0.06169 |
|
- type: precision_at_100 |
|
value: 0.0107 |
|
- type: precision_at_1000 |
|
value: 0.00143 |
|
- type: precision_at_3 |
|
value: 0.13682 |
|
- type: precision_at_5 |
|
value: 0.0995 |
|
- type: recall_at_1 |
|
value: 0.18119 |
|
- type: recall_at_10 |
|
value: 0.44983 |
|
- type: recall_at_100 |
|
value: 0.72396 |
|
- type: recall_at_1000 |
|
value: 0.90223 |
|
- type: recall_at_3 |
|
value: 0.31633 |
|
- type: recall_at_5 |
|
value: 0.37532 |
|
- task: |
|
type: Retrieval |
|
dataset: |
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name: MTEB CQADupstackPhysicsRetrieval |
|
type: mteb/cqadupstack-physics |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.30517 |
|
- type: map_at_10 |
|
value: 0.42031 |
|
- type: map_at_100 |
|
value: 0.43415 |
|
- type: map_at_1000 |
|
value: 0.43525 |
|
- type: map_at_3 |
|
value: 0.38443 |
|
- type: map_at_5 |
|
value: 0.40685 |
|
- type: mrr_at_1 |
|
value: 0.38114 |
|
- type: mrr_at_10 |
|
value: 0.47783 |
|
- type: mrr_at_100 |
|
value: 0.48647 |
|
- type: mrr_at_1000 |
|
value: 0.48688 |
|
- type: mrr_at_3 |
|
value: 0.45172 |
|
- type: mrr_at_5 |
|
value: 0.46817 |
|
- type: ndcg_at_1 |
|
value: 0.38114 |
|
- type: ndcg_at_10 |
|
value: 0.4834 |
|
- type: ndcg_at_100 |
|
value: 0.53861 |
|
- type: ndcg_at_1000 |
|
value: 0.55701 |
|
- type: ndcg_at_3 |
|
value: 0.42986 |
|
- type: ndcg_at_5 |
|
value: 0.45893 |
|
- type: precision_at_1 |
|
value: 0.38114 |
|
- type: precision_at_10 |
|
value: 0.08893 |
|
- type: precision_at_100 |
|
value: 0.01375 |
|
- type: precision_at_1000 |
|
value: 0.00172 |
|
- type: precision_at_3 |
|
value: 0.20821 |
|
- type: precision_at_5 |
|
value: 0.15034 |
|
- type: recall_at_1 |
|
value: 0.30517 |
|
- type: recall_at_10 |
|
value: 0.61332 |
|
- type: recall_at_100 |
|
value: 0.84051 |
|
- type: recall_at_1000 |
|
value: 0.95826 |
|
- type: recall_at_3 |
|
value: 0.46015 |
|
- type: recall_at_5 |
|
value: 0.53801 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
type: mteb/cqadupstack-programmers |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.27396 |
|
- type: map_at_10 |
|
value: 0.38043 |
|
- type: map_at_100 |
|
value: 0.39341 |
|
- type: map_at_1000 |
|
value: 0.39454 |
|
- type: map_at_3 |
|
value: 0.34783 |
|
- type: map_at_5 |
|
value: 0.3663 |
|
- type: mrr_at_1 |
|
value: 0.34247 |
|
- type: mrr_at_10 |
|
value: 0.43681 |
|
- type: mrr_at_100 |
|
value: 0.4451 |
|
- type: mrr_at_1000 |
|
value: 0.44569 |
|
- type: mrr_at_3 |
|
value: 0.41172 |
|
- type: mrr_at_5 |
|
value: 0.42702 |
|
- type: ndcg_at_1 |
|
value: 0.34247 |
|
- type: ndcg_at_10 |
|
value: 0.44065 |
|
- type: ndcg_at_100 |
|
value: 0.49434 |
|
- type: ndcg_at_1000 |
|
value: 0.51682 |
|
- type: ndcg_at_3 |
|
value: 0.38976 |
|
- type: ndcg_at_5 |
|
value: 0.41332 |
|
- type: precision_at_1 |
|
value: 0.34247 |
|
- type: precision_at_10 |
|
value: 0.08059 |
|
- type: precision_at_100 |
|
value: 0.01258 |
|
- type: precision_at_1000 |
|
value: 0.00162 |
|
- type: precision_at_3 |
|
value: 0.1876 |
|
- type: precision_at_5 |
|
value: 0.13333 |
|
- type: recall_at_1 |
|
value: 0.27396 |
|
- type: recall_at_10 |
|
value: 0.56481 |
|
- type: recall_at_100 |
|
value: 0.79012 |
|
- type: recall_at_1000 |
|
value: 0.94182 |
|
- type: recall_at_3 |
|
value: 0.41785 |
|
- type: recall_at_5 |
|
value: 0.48303 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB CQADupstackStatsRetrieval |
|
type: mteb/cqadupstack-stats |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.25728 |
|
- type: map_at_10 |
|
value: 0.33903 |
|
- type: map_at_100 |
|
value: 0.34853 |
|
- type: map_at_1000 |
|
value: 0.34944 |
|
- type: map_at_3 |
|
value: 0.31268 |
|
- type: map_at_5 |
|
value: 0.32596 |
|
- type: mrr_at_1 |
|
value: 0.29141 |
|
- type: mrr_at_10 |
|
value: 0.36739 |
|
- type: mrr_at_100 |
|
value: 0.37545 |
|
- type: mrr_at_1000 |
|
value: 0.37608 |
|
- type: mrr_at_3 |
|
value: 0.34407 |
|
- type: mrr_at_5 |
|
value: 0.3568 |
|
- type: ndcg_at_1 |
|
value: 0.29141 |
|
- type: ndcg_at_10 |
|
value: 0.38596 |
|
- type: ndcg_at_100 |
|
value: 0.43375 |
|
- type: ndcg_at_1000 |
|
value: 0.45562 |
|
- type: ndcg_at_3 |
|
value: 0.33861 |
|
- type: ndcg_at_5 |
|
value: 0.35887 |
|
- type: precision_at_1 |
|
value: 0.29141 |
|
- type: precision_at_10 |
|
value: 0.06334 |
|
- type: precision_at_100 |
|
value: 0.00952 |
|
- type: precision_at_1000 |
|
value: 0.00121 |
|
- type: precision_at_3 |
|
value: 0.14826 |
|
- type: precision_at_5 |
|
value: 0.10429 |
|
- type: recall_at_1 |
|
value: 0.25728 |
|
- type: recall_at_10 |
|
value: 0.50121 |
|
- type: recall_at_100 |
|
value: 0.72382 |
|
- type: recall_at_1000 |
|
value: 0.88306 |
|
- type: recall_at_3 |
|
value: 0.36638 |
|
- type: recall_at_5 |
|
value: 0.41689 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB CQADupstackTexRetrieval |
|
type: mteb/cqadupstack-tex |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.19911 |
|
- type: map_at_10 |
|
value: 0.2856 |
|
- type: map_at_100 |
|
value: 0.29785 |
|
- type: map_at_1000 |
|
value: 0.29911 |
|
- type: map_at_3 |
|
value: 0.25875 |
|
- type: map_at_5 |
|
value: 0.2741 |
|
- type: mrr_at_1 |
|
value: 0.24054 |
|
- type: mrr_at_10 |
|
value: 0.32483 |
|
- type: mrr_at_100 |
|
value: 0.33464 |
|
- type: mrr_at_1000 |
|
value: 0.33534 |
|
- type: mrr_at_3 |
|
value: 0.30162 |
|
- type: mrr_at_5 |
|
value: 0.31506 |
|
- type: ndcg_at_1 |
|
value: 0.24054 |
|
- type: ndcg_at_10 |
|
value: 0.33723 |
|
- type: ndcg_at_100 |
|
value: 0.39362 |
|
- type: ndcg_at_1000 |
|
value: 0.42065 |
|
- type: ndcg_at_3 |
|
value: 0.29116 |
|
- type: ndcg_at_5 |
|
value: 0.31299 |
|
- type: precision_at_1 |
|
value: 0.24054 |
|
- type: precision_at_10 |
|
value: 0.06194 |
|
- type: precision_at_100 |
|
value: 0.01058 |
|
- type: precision_at_1000 |
|
value: 0.00148 |
|
- type: precision_at_3 |
|
value: 0.13914 |
|
- type: precision_at_5 |
|
value: 0.10076 |
|
- type: recall_at_1 |
|
value: 0.19911 |
|
- type: recall_at_10 |
|
value: 0.45183 |
|
- type: recall_at_100 |
|
value: 0.7025 |
|
- type: recall_at_1000 |
|
value: 0.89222 |
|
- type: recall_at_3 |
|
value: 0.32195 |
|
- type: recall_at_5 |
|
value: 0.37852 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB CQADupstackUnixRetrieval |
|
type: mteb/cqadupstack-unix |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.29819 |
|
- type: map_at_10 |
|
value: 0.40073 |
|
- type: map_at_100 |
|
value: 0.41289 |
|
- type: map_at_1000 |
|
value: 0.41375 |
|
- type: map_at_3 |
|
value: 0.36572 |
|
- type: map_at_5 |
|
value: 0.38386 |
|
- type: mrr_at_1 |
|
value: 0.35168 |
|
- type: mrr_at_10 |
|
value: 0.44381 |
|
- type: mrr_at_100 |
|
value: 0.45191 |
|
- type: mrr_at_1000 |
|
value: 0.45234 |
|
- type: mrr_at_3 |
|
value: 0.41402 |
|
- type: mrr_at_5 |
|
value: 0.43039 |
|
- type: ndcg_at_1 |
|
value: 0.35168 |
|
- type: ndcg_at_10 |
|
value: 0.46071 |
|
- type: ndcg_at_100 |
|
value: 0.51351 |
|
- type: ndcg_at_1000 |
|
value: 0.5317 |
|
- type: ndcg_at_3 |
|
value: 0.39972 |
|
- type: ndcg_at_5 |
|
value: 0.42586 |
|
- type: precision_at_1 |
|
value: 0.35168 |
|
- type: precision_at_10 |
|
value: 0.07985 |
|
- type: precision_at_100 |
|
value: 0.01185 |
|
- type: precision_at_1000 |
|
value: 0.00144 |
|
- type: precision_at_3 |
|
value: 0.18221 |
|
- type: precision_at_5 |
|
value: 0.12892 |
|
- type: recall_at_1 |
|
value: 0.29819 |
|
- type: recall_at_10 |
|
value: 0.60075 |
|
- type: recall_at_100 |
|
value: 0.82771 |
|
- type: recall_at_1000 |
|
value: 0.95219 |
|
- type: recall_at_3 |
|
value: 0.43245 |
|
- type: recall_at_5 |
|
value: 0.49931 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
type: mteb/cqadupstack-webmasters |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.28409 |
|
- type: map_at_10 |
|
value: 0.37621 |
|
- type: map_at_100 |
|
value: 0.39233 |
|
- type: map_at_1000 |
|
value: 0.39471 |
|
- type: map_at_3 |
|
value: 0.34337 |
|
- type: map_at_5 |
|
value: 0.35985 |
|
- type: mrr_at_1 |
|
value: 0.33794 |
|
- type: mrr_at_10 |
|
value: 0.42349 |
|
- type: mrr_at_100 |
|
value: 0.43196 |
|
- type: mrr_at_1000 |
|
value: 0.43237 |
|
- type: mrr_at_3 |
|
value: 0.39526 |
|
- type: mrr_at_5 |
|
value: 0.41087 |
|
- type: ndcg_at_1 |
|
value: 0.33794 |
|
- type: ndcg_at_10 |
|
value: 0.43832 |
|
- type: ndcg_at_100 |
|
value: 0.49514 |
|
- type: ndcg_at_1000 |
|
value: 0.51742 |
|
- type: ndcg_at_3 |
|
value: 0.38442 |
|
- type: ndcg_at_5 |
|
value: 0.40737 |
|
- type: precision_at_1 |
|
value: 0.33794 |
|
- type: precision_at_10 |
|
value: 0.08597 |
|
- type: precision_at_100 |
|
value: 0.01652 |
|
- type: precision_at_1000 |
|
value: 0.00251 |
|
- type: precision_at_3 |
|
value: 0.17787 |
|
- type: precision_at_5 |
|
value: 0.13241 |
|
- type: recall_at_1 |
|
value: 0.28409 |
|
- type: recall_at_10 |
|
value: 0.55388 |
|
- type: recall_at_100 |
|
value: 0.81517 |
|
- type: recall_at_1000 |
|
value: 0.95038 |
|
- type: recall_at_3 |
|
value: 0.40133 |
|
- type: recall_at_5 |
|
value: 0.45913 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB CQADupstackWordpressRetrieval |
|
type: mteb/cqadupstack-wordpress |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.24067 |
|
- type: map_at_10 |
|
value: 0.32184 |
|
- type: map_at_100 |
|
value: 0.33357 |
|
- type: map_at_1000 |
|
value: 0.33458 |
|
- type: map_at_3 |
|
value: 0.29492 |
|
- type: map_at_5 |
|
value: 0.3111 |
|
- type: mrr_at_1 |
|
value: 0.26248 |
|
- type: mrr_at_10 |
|
value: 0.34149 |
|
- type: mrr_at_100 |
|
value: 0.35189 |
|
- type: mrr_at_1000 |
|
value: 0.35251 |
|
- type: mrr_at_3 |
|
value: 0.31639 |
|
- type: mrr_at_5 |
|
value: 0.33182 |
|
- type: ndcg_at_1 |
|
value: 0.26248 |
|
- type: ndcg_at_10 |
|
value: 0.36889 |
|
- type: ndcg_at_100 |
|
value: 0.42426 |
|
- type: ndcg_at_1000 |
|
value: 0.44745 |
|
- type: ndcg_at_3 |
|
value: 0.31799 |
|
- type: ndcg_at_5 |
|
value: 0.34563 |
|
- type: precision_at_1 |
|
value: 0.26248 |
|
- type: precision_at_10 |
|
value: 0.05712 |
|
- type: precision_at_100 |
|
value: 0.00915 |
|
- type: precision_at_1000 |
|
value: 0.00123 |
|
- type: precision_at_3 |
|
value: 0.13309 |
|
- type: precision_at_5 |
|
value: 0.09649 |
|
- type: recall_at_1 |
|
value: 0.24067 |
|
- type: recall_at_10 |
|
value: 0.49344 |
|
- type: recall_at_100 |
|
value: 0.7412 |
|
- type: recall_at_1000 |
|
value: 0.91276 |
|
- type: recall_at_3 |
|
value: 0.36272 |
|
- type: recall_at_5 |
|
value: 0.4277 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB DBPedia |
|
type: mteb/dbpedia |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.08651 |
|
- type: map_at_10 |
|
value: 0.17628 |
|
- type: map_at_100 |
|
value: 0.23354 |
|
- type: map_at_1000 |
|
value: 0.24827 |
|
- type: map_at_3 |
|
value: 0.1351 |
|
- type: map_at_5 |
|
value: 0.15468 |
|
- type: mrr_at_1 |
|
value: 0.645 |
|
- type: mrr_at_10 |
|
value: 0.71989 |
|
- type: mrr_at_100 |
|
value: 0.72332 |
|
- type: mrr_at_1000 |
|
value: 0.72346 |
|
- type: mrr_at_3 |
|
value: 0.7025 |
|
- type: mrr_at_5 |
|
value: 0.71275 |
|
- type: ndcg_at_1 |
|
value: 0.51375 |
|
- type: ndcg_at_10 |
|
value: 0.3596 |
|
- type: ndcg_at_100 |
|
value: 0.39878 |
|
- type: ndcg_at_1000 |
|
value: 0.47931 |
|
- type: ndcg_at_3 |
|
value: 0.41275 |
|
- type: ndcg_at_5 |
|
value: 0.38297 |
|
- type: precision_at_1 |
|
value: 0.645 |
|
- type: precision_at_10 |
|
value: 0.2745 |
|
- type: precision_at_100 |
|
value: 0.08405 |
|
- type: precision_at_1000 |
|
value: 0.01923 |
|
- type: precision_at_3 |
|
value: 0.44417 |
|
- type: precision_at_5 |
|
value: 0.366 |
|
- type: recall_at_1 |
|
value: 0.08651 |
|
- type: recall_at_10 |
|
value: 0.22416 |
|
- type: recall_at_100 |
|
value: 0.46381 |
|
- type: recall_at_1000 |
|
value: 0.71557 |
|
- type: recall_at_3 |
|
value: 0.14847 |
|
- type: recall_at_5 |
|
value: 0.1804 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB FEVER |
|
type: mteb/fever |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.73211 |
|
- type: map_at_10 |
|
value: 0.81463 |
|
- type: map_at_100 |
|
value: 0.81622 |
|
- type: map_at_1000 |
|
value: 0.81634 |
|
- type: map_at_3 |
|
value: 0.805 |
|
- type: map_at_5 |
|
value: 0.81134 |
|
- type: mrr_at_1 |
|
value: 0.79088 |
|
- type: mrr_at_10 |
|
value: 0.86943 |
|
- type: mrr_at_100 |
|
value: 0.87017 |
|
- type: mrr_at_1000 |
|
value: 0.87018 |
|
- type: mrr_at_3 |
|
value: 0.86154 |
|
- type: mrr_at_5 |
|
value: 0.867 |
|
- type: ndcg_at_1 |
|
value: 0.79088 |
|
- type: ndcg_at_10 |
|
value: 0.85528 |
|
- type: ndcg_at_100 |
|
value: 0.86134 |
|
- type: ndcg_at_1000 |
|
value: 0.86367 |
|
- type: ndcg_at_3 |
|
value: 0.83943 |
|
- type: ndcg_at_5 |
|
value: 0.84878 |
|
- type: precision_at_1 |
|
value: 0.79088 |
|
- type: precision_at_10 |
|
value: 0.10132 |
|
- type: precision_at_100 |
|
value: 0.01055 |
|
- type: precision_at_1000 |
|
value: 0.00109 |
|
- type: precision_at_3 |
|
value: 0.31963 |
|
- type: precision_at_5 |
|
value: 0.19769 |
|
- type: recall_at_1 |
|
value: 0.73211 |
|
- type: recall_at_10 |
|
value: 0.92797 |
|
- type: recall_at_100 |
|
value: 0.95263 |
|
- type: recall_at_1000 |
|
value: 0.96738 |
|
- type: recall_at_3 |
|
value: 0.88328 |
|
- type: recall_at_5 |
|
value: 0.90821 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB FiQA2018 |
|
type: mteb/fiqa |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.18311 |
|
- type: map_at_10 |
|
value: 0.29201 |
|
- type: map_at_100 |
|
value: 0.3093 |
|
- type: map_at_1000 |
|
value: 0.31116 |
|
- type: map_at_3 |
|
value: 0.24778 |
|
- type: map_at_5 |
|
value: 0.27453 |
|
- type: mrr_at_1 |
|
value: 0.35494 |
|
- type: mrr_at_10 |
|
value: 0.44489 |
|
- type: mrr_at_100 |
|
value: 0.4532 |
|
- type: mrr_at_1000 |
|
value: 0.45369 |
|
- type: mrr_at_3 |
|
value: 0.41667 |
|
- type: mrr_at_5 |
|
value: 0.43418 |
|
- type: ndcg_at_1 |
|
value: 0.35494 |
|
- type: ndcg_at_10 |
|
value: 0.36868 |
|
- type: ndcg_at_100 |
|
value: 0.43463 |
|
- type: ndcg_at_1000 |
|
value: 0.46766 |
|
- type: ndcg_at_3 |
|
value: 0.32305 |
|
- type: ndcg_at_5 |
|
value: 0.34332 |
|
- type: precision_at_1 |
|
value: 0.35494 |
|
- type: precision_at_10 |
|
value: 0.10324 |
|
- type: precision_at_100 |
|
value: 0.01707 |
|
- type: precision_at_1000 |
|
value: 0.00229 |
|
- type: precision_at_3 |
|
value: 0.21142 |
|
- type: precision_at_5 |
|
value: 0.16327 |
|
- type: recall_at_1 |
|
value: 0.18311 |
|
- type: recall_at_10 |
|
value: 0.43881 |
|
- type: recall_at_100 |
|
value: 0.68593 |
|
- type: recall_at_1000 |
|
value: 0.8855 |
|
- type: recall_at_3 |
|
value: 0.28824 |
|
- type: recall_at_5 |
|
value: 0.36178 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB HotpotQA |
|
type: mteb/hotpotqa |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.36766 |
|
- type: map_at_10 |
|
value: 0.53639 |
|
- type: map_at_100 |
|
value: 0.54532 |
|
- type: map_at_1000 |
|
value: 0.54608 |
|
- type: map_at_3 |
|
value: 0.50427 |
|
- type: map_at_5 |
|
value: 0.5245 |
|
- type: mrr_at_1 |
|
value: 0.73531 |
|
- type: mrr_at_10 |
|
value: 0.80104 |
|
- type: mrr_at_100 |
|
value: 0.80341 |
|
- type: mrr_at_1000 |
|
value: 0.80351 |
|
- type: mrr_at_3 |
|
value: 0.78949 |
|
- type: mrr_at_5 |
|
value: 0.79729 |
|
- type: ndcg_at_1 |
|
value: 0.73531 |
|
- type: ndcg_at_10 |
|
value: 0.62918 |
|
- type: ndcg_at_100 |
|
value: 0.66056 |
|
- type: ndcg_at_1000 |
|
value: 0.67554 |
|
- type: ndcg_at_3 |
|
value: 0.58247 |
|
- type: ndcg_at_5 |
|
value: 0.60905 |
|
- type: precision_at_1 |
|
value: 0.73531 |
|
- type: precision_at_10 |
|
value: 0.1302 |
|
- type: precision_at_100 |
|
value: 0.01546 |
|
- type: precision_at_1000 |
|
value: 0.00175 |
|
- type: precision_at_3 |
|
value: 0.36556 |
|
- type: precision_at_5 |
|
value: 0.24032 |
|
- type: recall_at_1 |
|
value: 0.36766 |
|
- type: recall_at_10 |
|
value: 0.65098 |
|
- type: recall_at_100 |
|
value: 0.77306 |
|
- type: recall_at_1000 |
|
value: 0.87252 |
|
- type: recall_at_3 |
|
value: 0.54835 |
|
- type: recall_at_5 |
|
value: 0.60081 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB MSMARCO |
|
type: mteb/msmarco |
|
config: default |
|
split: dev |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.14654 |
|
- type: map_at_10 |
|
value: 0.2472 |
|
- type: map_at_100 |
|
value: 0.25994 |
|
- type: map_at_1000 |
|
value: 0.26067 |
|
- type: map_at_3 |
|
value: 0.21234 |
|
- type: map_at_5 |
|
value: 0.2319 |
|
- type: mrr_at_1 |
|
value: 0.15086 |
|
- type: mrr_at_10 |
|
value: 0.25184 |
|
- type: mrr_at_100 |
|
value: 0.26422 |
|
- type: mrr_at_1000 |
|
value: 0.26489 |
|
- type: mrr_at_3 |
|
value: 0.21731 |
|
- type: mrr_at_5 |
|
value: 0.23674 |
|
- type: ndcg_at_1 |
|
value: 0.15086 |
|
- type: ndcg_at_10 |
|
value: 0.30711 |
|
- type: ndcg_at_100 |
|
value: 0.37221 |
|
- type: ndcg_at_1000 |
|
value: 0.39133 |
|
- type: ndcg_at_3 |
|
value: 0.23567 |
|
- type: ndcg_at_5 |
|
value: 0.27066 |
|
- type: precision_at_1 |
|
value: 0.15086 |
|
- type: precision_at_10 |
|
value: 0.05132 |
|
- type: precision_at_100 |
|
value: 0.00845 |
|
- type: precision_at_1000 |
|
value: 0.00101 |
|
- type: precision_at_3 |
|
value: 0.10277 |
|
- type: precision_at_5 |
|
value: 0.07923 |
|
- type: recall_at_1 |
|
value: 0.14654 |
|
- type: recall_at_10 |
|
value: 0.49341 |
|
- type: recall_at_100 |
|
value: 0.80224 |
|
- type: recall_at_1000 |
|
value: 0.95037 |
|
- type: recall_at_3 |
|
value: 0.29862 |
|
- type: recall_at_5 |
|
value: 0.38274 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB NFCorpus |
|
type: mteb/nfcorpus |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.05452 |
|
- type: map_at_10 |
|
value: 0.12758 |
|
- type: map_at_100 |
|
value: 0.1593 |
|
- type: map_at_1000 |
|
value: 0.17422 |
|
- type: map_at_3 |
|
value: 0.0945 |
|
- type: map_at_5 |
|
value: 0.1092 |
|
- type: mrr_at_1 |
|
value: 0.43963 |
|
- type: mrr_at_10 |
|
value: 0.53237 |
|
- type: mrr_at_100 |
|
value: 0.53777 |
|
- type: mrr_at_1000 |
|
value: 0.53822 |
|
- type: mrr_at_3 |
|
value: 0.51445 |
|
- type: mrr_at_5 |
|
value: 0.52466 |
|
- type: ndcg_at_1 |
|
value: 0.41486 |
|
- type: ndcg_at_10 |
|
value: 0.33737 |
|
- type: ndcg_at_100 |
|
value: 0.30886 |
|
- type: ndcg_at_1000 |
|
value: 0.40018 |
|
- type: ndcg_at_3 |
|
value: 0.39324 |
|
- type: ndcg_at_5 |
|
value: 0.36949 |
|
- type: precision_at_1 |
|
value: 0.43344 |
|
- type: precision_at_10 |
|
value: 0.24799 |
|
- type: precision_at_100 |
|
value: 0.07895 |
|
- type: precision_at_1000 |
|
value: 0.02091 |
|
- type: precision_at_3 |
|
value: 0.37152 |
|
- type: precision_at_5 |
|
value: 0.31703 |
|
- type: recall_at_1 |
|
value: 0.05452 |
|
- type: recall_at_10 |
|
value: 0.1712 |
|
- type: recall_at_100 |
|
value: 0.30719 |
|
- type: recall_at_1000 |
|
value: 0.62766 |
|
- type: recall_at_3 |
|
value: 0.10733 |
|
- type: recall_at_5 |
|
value: 0.13553 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB NQ |
|
type: mteb/nq |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.29022 |
|
- type: map_at_10 |
|
value: 0.4373 |
|
- type: map_at_100 |
|
value: 0.44849 |
|
- type: map_at_1000 |
|
value: 0.44877 |
|
- type: map_at_3 |
|
value: 0.39045 |
|
- type: map_at_5 |
|
value: 0.4186 |
|
- type: mrr_at_1 |
|
value: 0.32793 |
|
- type: mrr_at_10 |
|
value: 0.46243 |
|
- type: mrr_at_100 |
|
value: 0.47083 |
|
- type: mrr_at_1000 |
|
value: 0.47101 |
|
- type: mrr_at_3 |
|
value: 0.42261 |
|
- type: mrr_at_5 |
|
value: 0.44775 |
|
- type: ndcg_at_1 |
|
value: 0.32793 |
|
- type: ndcg_at_10 |
|
value: 0.51631 |
|
- type: ndcg_at_100 |
|
value: 0.56287 |
|
- type: ndcg_at_1000 |
|
value: 0.56949 |
|
- type: ndcg_at_3 |
|
value: 0.42782 |
|
- type: ndcg_at_5 |
|
value: 0.47554 |
|
- type: precision_at_1 |
|
value: 0.32793 |
|
- type: precision_at_10 |
|
value: 0.08737 |
|
- type: precision_at_100 |
|
value: 0.01134 |
|
- type: precision_at_1000 |
|
value: 0.0012 |
|
- type: precision_at_3 |
|
value: 0.19583 |
|
- type: precision_at_5 |
|
value: 0.14484 |
|
- type: recall_at_1 |
|
value: 0.29022 |
|
- type: recall_at_10 |
|
value: 0.73325 |
|
- type: recall_at_100 |
|
value: 0.93455 |
|
- type: recall_at_1000 |
|
value: 0.98414 |
|
- type: recall_at_3 |
|
value: 0.50406 |
|
- type: recall_at_5 |
|
value: 0.6145 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB QuoraRetrieval |
|
type: mteb/quora |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.68941 |
|
- type: map_at_10 |
|
value: 0.82641 |
|
- type: map_at_100 |
|
value: 0.83317 |
|
- type: map_at_1000 |
|
value: 0.83337 |
|
- type: map_at_3 |
|
value: 0.79604 |
|
- type: map_at_5 |
|
value: 0.81525 |
|
- type: mrr_at_1 |
|
value: 0.7935 |
|
- type: mrr_at_10 |
|
value: 0.85969 |
|
- type: mrr_at_100 |
|
value: 0.86094 |
|
- type: mrr_at_1000 |
|
value: 0.86095 |
|
- type: mrr_at_3 |
|
value: 0.84852 |
|
- type: mrr_at_5 |
|
value: 0.85627 |
|
- type: ndcg_at_1 |
|
value: 0.7936 |
|
- type: ndcg_at_10 |
|
value: 0.86687 |
|
- type: ndcg_at_100 |
|
value: 0.88094 |
|
- type: ndcg_at_1000 |
|
value: 0.88243 |
|
- type: ndcg_at_3 |
|
value: 0.83538 |
|
- type: ndcg_at_5 |
|
value: 0.85308 |
|
- type: precision_at_1 |
|
value: 0.7936 |
|
- type: precision_at_10 |
|
value: 0.13145 |
|
- type: precision_at_100 |
|
value: 0.01517 |
|
- type: precision_at_1000 |
|
value: 0.00156 |
|
- type: precision_at_3 |
|
value: 0.36353 |
|
- type: precision_at_5 |
|
value: 0.24044 |
|
- type: recall_at_1 |
|
value: 0.68941 |
|
- type: recall_at_10 |
|
value: 0.94407 |
|
- type: recall_at_100 |
|
value: 0.99226 |
|
- type: recall_at_1000 |
|
value: 0.99958 |
|
- type: recall_at_3 |
|
value: 0.85502 |
|
- type: recall_at_5 |
|
value: 0.90372 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB SCIDOCS |
|
type: mteb/scidocs |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.04988 |
|
- type: map_at_10 |
|
value: 0.13553 |
|
- type: map_at_100 |
|
value: 0.16136 |
|
- type: map_at_1000 |
|
value: 0.16512 |
|
- type: map_at_3 |
|
value: 0.09439 |
|
- type: map_at_5 |
|
value: 0.1146 |
|
- type: mrr_at_1 |
|
value: 0.246 |
|
- type: mrr_at_10 |
|
value: 0.36792 |
|
- type: mrr_at_100 |
|
value: 0.37973 |
|
- type: mrr_at_1000 |
|
value: 0.38011 |
|
- type: mrr_at_3 |
|
value: 0.33117 |
|
- type: mrr_at_5 |
|
value: 0.35172 |
|
- type: ndcg_at_1 |
|
value: 0.246 |
|
- type: ndcg_at_10 |
|
value: 0.22542 |
|
- type: ndcg_at_100 |
|
value: 0.32326 |
|
- type: ndcg_at_1000 |
|
value: 0.3828 |
|
- type: ndcg_at_3 |
|
value: 0.20896 |
|
- type: ndcg_at_5 |
|
value: 0.18497 |
|
- type: precision_at_1 |
|
value: 0.246 |
|
- type: precision_at_10 |
|
value: 0.1194 |
|
- type: precision_at_100 |
|
value: 0.02616 |
|
- type: precision_at_1000 |
|
value: 0.00404 |
|
- type: precision_at_3 |
|
value: 0.198 |
|
- type: precision_at_5 |
|
value: 0.1654 |
|
- type: recall_at_1 |
|
value: 0.04988 |
|
- type: recall_at_10 |
|
value: 0.24212 |
|
- type: recall_at_100 |
|
value: 0.53105 |
|
- type: recall_at_1000 |
|
value: 0.82022 |
|
- type: recall_at_3 |
|
value: 0.12047 |
|
- type: recall_at_5 |
|
value: 0.16777 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB SciFact |
|
type: mteb/scifact |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.56578 |
|
- type: map_at_10 |
|
value: 0.66725 |
|
- type: map_at_100 |
|
value: 0.67379 |
|
- type: map_at_1000 |
|
value: 0.674 |
|
- type: map_at_3 |
|
value: 0.63416 |
|
- type: map_at_5 |
|
value: 0.6577 |
|
- type: mrr_at_1 |
|
value: 0.59333 |
|
- type: mrr_at_10 |
|
value: 0.67533 |
|
- type: mrr_at_100 |
|
value: 0.68062 |
|
- type: mrr_at_1000 |
|
value: 0.68082 |
|
- type: mrr_at_3 |
|
value: 0.64944 |
|
- type: mrr_at_5 |
|
value: 0.66928 |
|
- type: ndcg_at_1 |
|
value: 0.59333 |
|
- type: ndcg_at_10 |
|
value: 0.7127 |
|
- type: ndcg_at_100 |
|
value: 0.73889 |
|
- type: ndcg_at_1000 |
|
value: 0.7441 |
|
- type: ndcg_at_3 |
|
value: 0.65793 |
|
- type: ndcg_at_5 |
|
value: 0.69429 |
|
- type: precision_at_1 |
|
value: 0.59333 |
|
- type: precision_at_10 |
|
value: 0.096 |
|
- type: precision_at_100 |
|
value: 0.01087 |
|
- type: precision_at_1000 |
|
value: 0.00113 |
|
- type: precision_at_3 |
|
value: 0.25556 |
|
- type: precision_at_5 |
|
value: 0.17667 |
|
- type: recall_at_1 |
|
value: 0.56578 |
|
- type: recall_at_10 |
|
value: 0.842 |
|
- type: recall_at_100 |
|
value: 0.95667 |
|
- type: recall_at_1000 |
|
value: 0.99667 |
|
- type: recall_at_3 |
|
value: 0.70072 |
|
- type: recall_at_5 |
|
value: 0.79011 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB Touche2020 |
|
type: mteb/touche2020 |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.01976 |
|
- type: map_at_10 |
|
value: 0.09688 |
|
- type: map_at_100 |
|
value: 0.15117 |
|
- type: map_at_1000 |
|
value: 0.16769 |
|
- type: map_at_3 |
|
value: 0.04589 |
|
- type: map_at_5 |
|
value: 0.06556 |
|
- type: mrr_at_1 |
|
value: 0.26531 |
|
- type: mrr_at_10 |
|
value: 0.43863 |
|
- type: mrr_at_100 |
|
value: 0.44767 |
|
- type: mrr_at_1000 |
|
value: 0.44767 |
|
- type: mrr_at_3 |
|
value: 0.39116 |
|
- type: mrr_at_5 |
|
value: 0.41156 |
|
- type: ndcg_at_1 |
|
value: 0.23469 |
|
- type: ndcg_at_10 |
|
value: 0.24029 |
|
- type: ndcg_at_100 |
|
value: 0.34425 |
|
- type: ndcg_at_1000 |
|
value: 0.46907 |
|
- type: ndcg_at_3 |
|
value: 0.25522 |
|
- type: ndcg_at_5 |
|
value: 0.24333 |
|
- type: precision_at_1 |
|
value: 0.26531 |
|
- type: precision_at_10 |
|
value: 0.22449 |
|
- type: precision_at_100 |
|
value: 0.07122 |
|
- type: precision_at_1000 |
|
value: 0.01527 |
|
- type: precision_at_3 |
|
value: 0.27891 |
|
- type: precision_at_5 |
|
value: 0.25714 |
|
- type: recall_at_1 |
|
value: 0.01976 |
|
- type: recall_at_10 |
|
value: 0.16633 |
|
- type: recall_at_100 |
|
value: 0.4561 |
|
- type: recall_at_1000 |
|
value: 0.82481 |
|
- type: recall_at_3 |
|
value: 0.06101 |
|
- type: recall_at_5 |
|
value: 0.0968 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
name: MTEB TRECCOVID |
|
type: mteb/trec-covid |
|
config: default |
|
split: test |
|
metrics: |
|
- type: map_at_1 |
|
value: 0.00211 |
|
- type: map_at_10 |
|
value: 0.01526 |
|
- type: map_at_100 |
|
value: 0.08863 |
|
- type: map_at_1000 |
|
value: 0.23162 |
|
- type: map_at_3 |
|
value: 0.00555 |
|
- type: map_at_5 |
|
value: 0.00873 |
|
- type: mrr_at_1 |
|
value: 0.76 |
|
- type: mrr_at_10 |
|
value: 0.8485 |
|
- type: mrr_at_100 |
|
value: 0.8485 |
|
- type: mrr_at_1000 |
|
value: 0.8485 |
|
- type: mrr_at_3 |
|
value: 0.84 |
|
- type: mrr_at_5 |
|
value: 0.844 |
|
- type: ndcg_at_1 |
|
value: 0.7 |
|
- type: ndcg_at_10 |
|
value: 0.63098 |
|
- type: ndcg_at_100 |
|
value: 0.49847 |
|
- type: ndcg_at_1000 |
|
value: 0.48395 |
|
- type: ndcg_at_3 |
|
value: 0.68704 |
|
- type: ndcg_at_5 |
|
value: 0.67533 |
|
- type: precision_at_1 |
|
value: 0.76 |
|
- type: precision_at_10 |
|
value: 0.66 |
|
- type: precision_at_100 |
|
value: 0.5134 |
|
- type: precision_at_1000 |
|
value: 0.2168 |
|
- type: precision_at_3 |
|
value: 0.72667 |
|
- type: precision_at_5 |
|
value: 0.716 |
|
- type: recall_at_1 |
|
value: 0.00211 |
|
- type: recall_at_10 |
|
value: 0.01748 |
|
- type: recall_at_100 |
|
value: 0.12448 |
|
- type: recall_at_1000 |
|
value: 0.46795 |
|
- type: recall_at_3 |
|
value: 0.00593 |
|
- type: recall_at_5 |
|
value: 0.00962 |
|
--- |
|
|
|
## Llamacpp Static Quantizations of granite-embedding-30m-english |
|
|
|
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b4341">b4341</a> for quantization. |
|
|
|
Original model: https://huggingface.co/ibm-granite/granite-embedding-30m-english |
|
|
|
Run them in [LM Studio](https://lmstudio.ai/) |
|
|
|
## Prompt format |
|
|
|
No prompt format found, check original model page |
|
|
|
## Download a file (not the whole branch) from below: |
|
|
|
| Filename | Quant type | File Size | Split | Description | |
|
| -------- | ---------- | --------- | ----- | ----------- | |
|
| [granite-embedding-30m-english-f16.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-f16.gguf) | f16 | 0.06GB | false | Full F16 weights. | |
|
| [granite-embedding-30m-english-Q8_0.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q8_0.gguf) | Q8_0 | 0.03GB | false | Extremely high quality, generally unneeded but max available quant. | |
|
| [granite-embedding-30m-english-Q6_K_L.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q6_K_L.gguf) | Q6_K_L | 0.03GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. | |
|
| [granite-embedding-30m-english-Q6_K.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q6_K.gguf) | Q6_K | 0.03GB | false | Very high quality, near perfect, *recommended*. | |
|
| [granite-embedding-30m-english-Q5_K_L.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q5_K_L.gguf) | Q5_K_L | 0.03GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. | |
|
| [granite-embedding-30m-english-Q5_K_M.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q5_K_M.gguf) | Q5_K_M | 0.03GB | false | High quality, *recommended*. | |
|
| [granite-embedding-30m-english-Q5_K_S.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q5_K_S.gguf) | Q5_K_S | 0.03GB | false | High quality, *recommended*. | |
|
| [granite-embedding-30m-english-Q4_K_L.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q4_K_L.gguf) | Q4_K_L | 0.03GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. | |
|
| [granite-embedding-30m-english-Q4_K_M.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q4_K_M.gguf) | Q4_K_M | 0.03GB | false | Good quality, default size for most use cases, *recommended*. | |
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| [granite-embedding-30m-english-Q4_K_S.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q4_K_S.gguf) | Q4_K_S | 0.03GB | false | Slightly lower quality with more space savings, *recommended*. | |
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| [granite-embedding-30m-english-Q4_0.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q4_0.gguf) | Q4_0 | 0.03GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. | |
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| [granite-embedding-30m-english-IQ4_NL.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-IQ4_NL.gguf) | IQ4_NL | 0.03GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. | |
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| [granite-embedding-30m-english-IQ4_XS.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-IQ4_XS.gguf) | IQ4_XS | 0.03GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. | |
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| [granite-embedding-30m-english-Q3_K_XL.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q3_K_XL.gguf) | Q3_K_XL | 0.03GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. | |
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| [granite-embedding-30m-english-Q3_K_L.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-Q3_K_L.gguf) | Q3_K_L | 0.03GB | false | Lower quality but usable, good for low RAM availability. | |
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| [granite-embedding-30m-english-IQ3_M.gguf](https://huggingface.co/bartowski/granite-embedding-30m-english-GGUF/blob/main/granite-embedding-30m-english-IQ3_M.gguf) | IQ3_M | 0.03GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. | |
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## Embed/output weights |
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Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to. |
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## Downloading using huggingface-cli |
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<details> |
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<summary>Click to view download instructions</summary> |
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First, make sure you have hugginface-cli installed: |
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``` |
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pip install -U "huggingface_hub[cli]" |
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``` |
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Then, you can target the specific file you want: |
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``` |
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huggingface-cli download bartowski/granite-embedding-30m-english-GGUF --include "granite-embedding-30m-english-Q4_K_M.gguf" --local-dir ./ |
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``` |
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If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run: |
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``` |
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huggingface-cli download bartowski/granite-embedding-30m-english-GGUF --include "granite-embedding-30m-english-Q8_0/*" --local-dir ./ |
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``` |
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You can either specify a new local-dir (granite-embedding-30m-english-Q8_0) or download them all in place (./) |
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</details> |
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## ARM/AVX information |
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Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass. |
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Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly. |
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As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0. |
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Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase. |
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<details> |
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<summary>Click to view Q4_0_X_X information (deprecated</summary> |
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I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking. |
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<details> |
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<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary> |
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| model | size | params | backend | threads | test | t/s | % (vs Q4_0) | |
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| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% | |
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| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% | |
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| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% | |
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| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% | |
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Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation |
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</details> |
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</details> |
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## Which file should I choose? |
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<details> |
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<summary>Click here for details</summary> |
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A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9) |
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The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have. |
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If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM. |
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If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total. |
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Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'. |
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If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M. |
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If you want to get more into the weeds, you can check out this extremely useful feature chart: |
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[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix) |
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But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size. |
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These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide. |
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The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm. |
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</details> |
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## Credits |
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Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset. |
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Thank you ZeroWw for the inspiration to experiment with embed/output. |
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Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski |
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