|
--- |
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tags: |
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- mteb |
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base_model: mixedbread-ai/mxbai-embed-mini-v1 |
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library_name: sentence-transformers |
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model-index: |
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- name: mxbai-embed-xsmall-v1 |
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results: |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: arguana |
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name: MTEB ArguAna |
|
config: default |
|
split: test |
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revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 25.18 |
|
- type: ndcg_at_3 |
|
value: 39.22 |
|
- type: ndcg_at_5 |
|
value: 43.93 |
|
- type: ndcg_at_10 |
|
value: 49.58 |
|
- type: ndcg_at_30 |
|
value: 53.41 |
|
- type: ndcg_at_100 |
|
value: 54.11 |
|
- type: map_at_1 |
|
value: 25.18 |
|
- type: map_at_3 |
|
value: 35.66 |
|
- type: map_at_5 |
|
value: 38.25 |
|
- type: map_at_10 |
|
value: 40.58 |
|
- type: map_at_30 |
|
value: 41.6 |
|
- type: map_at_100 |
|
value: 41.69 |
|
- type: recall_at_1 |
|
value: 25.18 |
|
- type: recall_at_3 |
|
value: 49.57 |
|
- type: recall_at_5 |
|
value: 61.09 |
|
- type: recall_at_10 |
|
value: 78.59 |
|
- type: recall_at_30 |
|
value: 94.03 |
|
- type: recall_at_100 |
|
value: 97.94 |
|
- type: precision_at_1 |
|
value: 25.18 |
|
- type: precision_at_3 |
|
value: 16.52 |
|
- type: precision_at_5 |
|
value: 12.22 |
|
- type: precision_at_10 |
|
value: 7.86 |
|
- type: precision_at_30 |
|
value: 3.13 |
|
- type: precision_at_100 |
|
value: 0.98 |
|
- type: accuracy_at_3 |
|
value: 49.57 |
|
- type: accuracy_at_5 |
|
value: 61.09 |
|
- type: accuracy_at_10 |
|
value: 78.59 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
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name: MTEB CQADupstackAndroidRetrieval |
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config: default |
|
split: test |
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revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 44.35 |
|
- type: ndcg_at_3 |
|
value: 49.64 |
|
- type: ndcg_at_5 |
|
value: 51.73 |
|
- type: ndcg_at_10 |
|
value: 54.82 |
|
- type: ndcg_at_30 |
|
value: 57.64 |
|
- type: ndcg_at_100 |
|
value: 59.77 |
|
- type: map_at_1 |
|
value: 36.26 |
|
- type: map_at_3 |
|
value: 44.35 |
|
- type: map_at_5 |
|
value: 46.26 |
|
- type: map_at_10 |
|
value: 48.24 |
|
- type: map_at_30 |
|
value: 49.34 |
|
- type: map_at_100 |
|
value: 49.75 |
|
- type: recall_at_1 |
|
value: 36.26 |
|
- type: recall_at_3 |
|
value: 51.46 |
|
- type: recall_at_5 |
|
value: 57.78 |
|
- type: recall_at_10 |
|
value: 66.5 |
|
- type: recall_at_30 |
|
value: 77.19 |
|
- type: recall_at_100 |
|
value: 87.53 |
|
- type: precision_at_1 |
|
value: 44.35 |
|
- type: precision_at_3 |
|
value: 23.65 |
|
- type: precision_at_5 |
|
value: 16.88 |
|
- type: precision_at_10 |
|
value: 10.7 |
|
- type: precision_at_30 |
|
value: 4.53 |
|
- type: precision_at_100 |
|
value: 1.65 |
|
- type: accuracy_at_3 |
|
value: 60.51 |
|
- type: accuracy_at_5 |
|
value: 67.67 |
|
- type: accuracy_at_10 |
|
value: 74.68 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackEnglishRetrieval |
|
config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 39.43 |
|
- type: ndcg_at_3 |
|
value: 44.13 |
|
- type: ndcg_at_5 |
|
value: 46.06 |
|
- type: ndcg_at_10 |
|
value: 48.31 |
|
- type: ndcg_at_30 |
|
value: 51.06 |
|
- type: ndcg_at_100 |
|
value: 53.07 |
|
- type: map_at_1 |
|
value: 31.27 |
|
- type: map_at_3 |
|
value: 39.07 |
|
- type: map_at_5 |
|
value: 40.83 |
|
- type: map_at_10 |
|
value: 42.23 |
|
- type: map_at_30 |
|
value: 43.27 |
|
- type: map_at_100 |
|
value: 43.66 |
|
- type: recall_at_1 |
|
value: 31.27 |
|
- type: recall_at_3 |
|
value: 45.89 |
|
- type: recall_at_5 |
|
value: 51.44 |
|
- type: recall_at_10 |
|
value: 58.65 |
|
- type: recall_at_30 |
|
value: 69.12 |
|
- type: recall_at_100 |
|
value: 78.72 |
|
- type: precision_at_1 |
|
value: 39.43 |
|
- type: precision_at_3 |
|
value: 21.61 |
|
- type: precision_at_5 |
|
value: 15.34 |
|
- type: precision_at_10 |
|
value: 9.27 |
|
- type: precision_at_30 |
|
value: 4.01 |
|
- type: precision_at_100 |
|
value: 1.52 |
|
- type: accuracy_at_3 |
|
value: 55.48 |
|
- type: accuracy_at_5 |
|
value: 60.76 |
|
- type: accuracy_at_10 |
|
value: 67.45 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
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name: MTEB CQADupstackGamingRetrieval |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 45.58 |
|
- type: ndcg_at_3 |
|
value: 52.68 |
|
- type: ndcg_at_5 |
|
value: 55.28 |
|
- type: ndcg_at_10 |
|
value: 57.88 |
|
- type: ndcg_at_30 |
|
value: 60.6 |
|
- type: ndcg_at_100 |
|
value: 62.03 |
|
- type: map_at_1 |
|
value: 39.97 |
|
- type: map_at_3 |
|
value: 49.06 |
|
- type: map_at_5 |
|
value: 50.87 |
|
- type: map_at_10 |
|
value: 52.2 |
|
- type: map_at_30 |
|
value: 53.06 |
|
- type: map_at_100 |
|
value: 53.28 |
|
- type: recall_at_1 |
|
value: 39.97 |
|
- type: recall_at_3 |
|
value: 57.4 |
|
- type: recall_at_5 |
|
value: 63.83 |
|
- type: recall_at_10 |
|
value: 71.33 |
|
- type: recall_at_30 |
|
value: 81.81 |
|
- type: recall_at_100 |
|
value: 89.0 |
|
- type: precision_at_1 |
|
value: 45.58 |
|
- type: precision_at_3 |
|
value: 23.55 |
|
- type: precision_at_5 |
|
value: 16.01 |
|
- type: precision_at_10 |
|
value: 9.25 |
|
- type: precision_at_30 |
|
value: 3.67 |
|
- type: precision_at_100 |
|
value: 1.23 |
|
- type: accuracy_at_3 |
|
value: 62.76 |
|
- type: accuracy_at_5 |
|
value: 68.84 |
|
- type: accuracy_at_10 |
|
value: 75.8 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackGisRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 27.35 |
|
- type: ndcg_at_3 |
|
value: 34.23 |
|
- type: ndcg_at_5 |
|
value: 37.1 |
|
- type: ndcg_at_10 |
|
value: 40.26 |
|
- type: ndcg_at_30 |
|
value: 43.54 |
|
- type: ndcg_at_100 |
|
value: 45.9 |
|
- type: map_at_1 |
|
value: 25.28 |
|
- type: map_at_3 |
|
value: 31.68 |
|
- type: map_at_5 |
|
value: 33.38 |
|
- type: map_at_10 |
|
value: 34.79 |
|
- type: map_at_30 |
|
value: 35.67 |
|
- type: map_at_100 |
|
value: 35.96 |
|
- type: recall_at_1 |
|
value: 25.28 |
|
- type: recall_at_3 |
|
value: 38.95 |
|
- type: recall_at_5 |
|
value: 45.82 |
|
- type: recall_at_10 |
|
value: 55.11 |
|
- type: recall_at_30 |
|
value: 68.13 |
|
- type: recall_at_100 |
|
value: 80.88 |
|
- type: precision_at_1 |
|
value: 27.35 |
|
- type: precision_at_3 |
|
value: 14.65 |
|
- type: precision_at_5 |
|
value: 10.44 |
|
- type: precision_at_10 |
|
value: 6.37 |
|
- type: precision_at_30 |
|
value: 2.65 |
|
- type: precision_at_100 |
|
value: 0.97 |
|
- type: accuracy_at_3 |
|
value: 42.15 |
|
- type: accuracy_at_5 |
|
value: 49.15 |
|
- type: accuracy_at_10 |
|
value: 58.53 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackMathematicaRetrieval |
|
config: default |
|
split: test |
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revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 18.91 |
|
- type: ndcg_at_3 |
|
value: 24.37 |
|
- type: ndcg_at_5 |
|
value: 26.11 |
|
- type: ndcg_at_10 |
|
value: 29.37 |
|
- type: ndcg_at_30 |
|
value: 33.22 |
|
- type: ndcg_at_100 |
|
value: 35.73 |
|
- type: map_at_1 |
|
value: 15.23 |
|
- type: map_at_3 |
|
value: 21.25 |
|
- type: map_at_5 |
|
value: 22.38 |
|
- type: map_at_10 |
|
value: 23.86 |
|
- type: map_at_30 |
|
value: 24.91 |
|
- type: map_at_100 |
|
value: 25.24 |
|
- type: recall_at_1 |
|
value: 15.23 |
|
- type: recall_at_3 |
|
value: 28.28 |
|
- type: recall_at_5 |
|
value: 32.67 |
|
- type: recall_at_10 |
|
value: 42.23 |
|
- type: recall_at_30 |
|
value: 56.87 |
|
- type: recall_at_100 |
|
value: 69.44 |
|
- type: precision_at_1 |
|
value: 18.91 |
|
- type: precision_at_3 |
|
value: 11.9 |
|
- type: precision_at_5 |
|
value: 8.48 |
|
- type: precision_at_10 |
|
value: 5.63 |
|
- type: precision_at_30 |
|
value: 2.64 |
|
- type: precision_at_100 |
|
value: 1.02 |
|
- type: accuracy_at_3 |
|
value: 33.95 |
|
- type: accuracy_at_5 |
|
value: 38.81 |
|
- type: accuracy_at_10 |
|
value: 49.13 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackPhysicsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 36.96 |
|
- type: ndcg_at_3 |
|
value: 42.48 |
|
- type: ndcg_at_5 |
|
value: 44.57 |
|
- type: ndcg_at_10 |
|
value: 47.13 |
|
- type: ndcg_at_30 |
|
value: 50.65 |
|
- type: ndcg_at_100 |
|
value: 53.14 |
|
- type: map_at_1 |
|
value: 30.1 |
|
- type: map_at_3 |
|
value: 37.97 |
|
- type: map_at_5 |
|
value: 39.62 |
|
- type: map_at_10 |
|
value: 41.06 |
|
- type: map_at_30 |
|
value: 42.13 |
|
- type: map_at_100 |
|
value: 42.53 |
|
- type: recall_at_1 |
|
value: 30.1 |
|
- type: recall_at_3 |
|
value: 45.98 |
|
- type: recall_at_5 |
|
value: 51.58 |
|
- type: recall_at_10 |
|
value: 59.24 |
|
- type: recall_at_30 |
|
value: 72.47 |
|
- type: recall_at_100 |
|
value: 84.53 |
|
- type: precision_at_1 |
|
value: 36.96 |
|
- type: precision_at_3 |
|
value: 20.5 |
|
- type: precision_at_5 |
|
value: 14.4 |
|
- type: precision_at_10 |
|
value: 8.62 |
|
- type: precision_at_30 |
|
value: 3.67 |
|
- type: precision_at_100 |
|
value: 1.38 |
|
- type: accuracy_at_3 |
|
value: 54.09 |
|
- type: accuracy_at_5 |
|
value: 60.25 |
|
- type: accuracy_at_10 |
|
value: 67.37 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackProgrammersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 28.65 |
|
- type: ndcg_at_3 |
|
value: 34.3 |
|
- type: ndcg_at_5 |
|
value: 36.8 |
|
- type: ndcg_at_10 |
|
value: 39.92 |
|
- type: ndcg_at_30 |
|
value: 42.97 |
|
- type: ndcg_at_100 |
|
value: 45.45 |
|
- type: map_at_1 |
|
value: 23.35 |
|
- type: map_at_3 |
|
value: 30.36 |
|
- type: map_at_5 |
|
value: 32.15 |
|
- type: map_at_10 |
|
value: 33.74 |
|
- type: map_at_30 |
|
value: 34.69 |
|
- type: map_at_100 |
|
value: 35.02 |
|
- type: recall_at_1 |
|
value: 23.35 |
|
- type: recall_at_3 |
|
value: 37.71 |
|
- type: recall_at_5 |
|
value: 44.23 |
|
- type: recall_at_10 |
|
value: 53.6 |
|
- type: recall_at_30 |
|
value: 64.69 |
|
- type: recall_at_100 |
|
value: 77.41 |
|
- type: precision_at_1 |
|
value: 28.65 |
|
- type: precision_at_3 |
|
value: 16.74 |
|
- type: precision_at_5 |
|
value: 12.21 |
|
- type: precision_at_10 |
|
value: 7.61 |
|
- type: precision_at_30 |
|
value: 3.29 |
|
- type: precision_at_100 |
|
value: 1.22 |
|
- type: accuracy_at_3 |
|
value: 44.86 |
|
- type: accuracy_at_5 |
|
value: 52.4 |
|
- type: accuracy_at_10 |
|
value: 61.07 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackStatsRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 26.07 |
|
- type: ndcg_at_3 |
|
value: 31.62 |
|
- type: ndcg_at_5 |
|
value: 33.23 |
|
- type: ndcg_at_10 |
|
value: 35.62 |
|
- type: ndcg_at_30 |
|
value: 38.41 |
|
- type: ndcg_at_100 |
|
value: 40.81 |
|
- type: map_at_1 |
|
value: 22.96 |
|
- type: map_at_3 |
|
value: 28.85 |
|
- type: map_at_5 |
|
value: 29.97 |
|
- type: map_at_10 |
|
value: 31.11 |
|
- type: map_at_30 |
|
value: 31.86 |
|
- type: map_at_100 |
|
value: 32.15 |
|
- type: recall_at_1 |
|
value: 22.96 |
|
- type: recall_at_3 |
|
value: 35.14 |
|
- type: recall_at_5 |
|
value: 39.22 |
|
- type: recall_at_10 |
|
value: 46.52 |
|
- type: recall_at_30 |
|
value: 57.58 |
|
- type: recall_at_100 |
|
value: 70.57 |
|
- type: precision_at_1 |
|
value: 26.07 |
|
- type: precision_at_3 |
|
value: 14.11 |
|
- type: precision_at_5 |
|
value: 9.69 |
|
- type: precision_at_10 |
|
value: 5.81 |
|
- type: precision_at_30 |
|
value: 2.45 |
|
- type: precision_at_100 |
|
value: 0.92 |
|
- type: accuracy_at_3 |
|
value: 39.42 |
|
- type: accuracy_at_5 |
|
value: 43.41 |
|
- type: accuracy_at_10 |
|
value: 50.92 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackTexRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 21.78 |
|
- type: ndcg_at_3 |
|
value: 25.74 |
|
- type: ndcg_at_5 |
|
value: 27.86 |
|
- type: ndcg_at_10 |
|
value: 30.3 |
|
- type: ndcg_at_30 |
|
value: 33.51 |
|
- type: ndcg_at_100 |
|
value: 36.12 |
|
- type: map_at_1 |
|
value: 17.63 |
|
- type: map_at_3 |
|
value: 22.7 |
|
- type: map_at_5 |
|
value: 24.14 |
|
- type: map_at_10 |
|
value: 25.31 |
|
- type: map_at_30 |
|
value: 26.22 |
|
- type: map_at_100 |
|
value: 26.56 |
|
- type: recall_at_1 |
|
value: 17.63 |
|
- type: recall_at_3 |
|
value: 28.37 |
|
- type: recall_at_5 |
|
value: 33.99 |
|
- type: recall_at_10 |
|
value: 41.23 |
|
- type: recall_at_30 |
|
value: 53.69 |
|
- type: recall_at_100 |
|
value: 67.27 |
|
- type: precision_at_1 |
|
value: 21.78 |
|
- type: precision_at_3 |
|
value: 12.41 |
|
- type: precision_at_5 |
|
value: 9.07 |
|
- type: precision_at_10 |
|
value: 5.69 |
|
- type: precision_at_30 |
|
value: 2.61 |
|
- type: precision_at_100 |
|
value: 1.03 |
|
- type: accuracy_at_3 |
|
value: 33.62 |
|
- type: accuracy_at_5 |
|
value: 39.81 |
|
- type: accuracy_at_10 |
|
value: 47.32 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackUnixRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 30.97 |
|
- type: ndcg_at_3 |
|
value: 36.13 |
|
- type: ndcg_at_5 |
|
value: 39.0 |
|
- type: ndcg_at_10 |
|
value: 41.78 |
|
- type: ndcg_at_30 |
|
value: 44.96 |
|
- type: ndcg_at_100 |
|
value: 47.52 |
|
- type: map_at_1 |
|
value: 26.05 |
|
- type: map_at_3 |
|
value: 32.77 |
|
- type: map_at_5 |
|
value: 34.6 |
|
- type: map_at_10 |
|
value: 35.93 |
|
- type: map_at_30 |
|
value: 36.88 |
|
- type: map_at_100 |
|
value: 37.22 |
|
- type: recall_at_1 |
|
value: 26.05 |
|
- type: recall_at_3 |
|
value: 40.0 |
|
- type: recall_at_5 |
|
value: 47.34 |
|
- type: recall_at_10 |
|
value: 55.34 |
|
- type: recall_at_30 |
|
value: 67.08 |
|
- type: recall_at_100 |
|
value: 80.2 |
|
- type: precision_at_1 |
|
value: 30.97 |
|
- type: precision_at_3 |
|
value: 16.6 |
|
- type: precision_at_5 |
|
value: 12.03 |
|
- type: precision_at_10 |
|
value: 7.3 |
|
- type: precision_at_30 |
|
value: 3.08 |
|
- type: precision_at_100 |
|
value: 1.15 |
|
- type: accuracy_at_3 |
|
value: 45.62 |
|
- type: accuracy_at_5 |
|
value: 53.64 |
|
- type: accuracy_at_10 |
|
value: 61.66 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWebmastersRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 29.64 |
|
- type: ndcg_at_3 |
|
value: 35.49 |
|
- type: ndcg_at_5 |
|
value: 37.77 |
|
- type: ndcg_at_10 |
|
value: 40.78 |
|
- type: ndcg_at_30 |
|
value: 44.59 |
|
- type: ndcg_at_100 |
|
value: 46.97 |
|
- type: map_at_1 |
|
value: 24.77 |
|
- type: map_at_3 |
|
value: 31.33 |
|
- type: map_at_5 |
|
value: 32.95 |
|
- type: map_at_10 |
|
value: 34.47 |
|
- type: map_at_30 |
|
value: 35.7 |
|
- type: map_at_100 |
|
value: 36.17 |
|
- type: recall_at_1 |
|
value: 24.77 |
|
- type: recall_at_3 |
|
value: 38.16 |
|
- type: recall_at_5 |
|
value: 44.1 |
|
- type: recall_at_10 |
|
value: 53.31 |
|
- type: recall_at_30 |
|
value: 68.43 |
|
- type: recall_at_100 |
|
value: 80.24 |
|
- type: precision_at_1 |
|
value: 29.64 |
|
- type: precision_at_3 |
|
value: 16.8 |
|
- type: precision_at_5 |
|
value: 12.21 |
|
- type: precision_at_10 |
|
value: 7.83 |
|
- type: precision_at_30 |
|
value: 3.89 |
|
- type: precision_at_100 |
|
value: 1.63 |
|
- type: accuracy_at_3 |
|
value: 45.45 |
|
- type: accuracy_at_5 |
|
value: 51.58 |
|
- type: accuracy_at_10 |
|
value: 61.07 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackWordpressRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 23.47 |
|
- type: ndcg_at_3 |
|
value: 27.98 |
|
- type: ndcg_at_5 |
|
value: 30.16 |
|
- type: ndcg_at_10 |
|
value: 32.97 |
|
- type: ndcg_at_30 |
|
value: 36.3 |
|
- type: ndcg_at_100 |
|
value: 38.47 |
|
- type: map_at_1 |
|
value: 21.63 |
|
- type: map_at_3 |
|
value: 26.02 |
|
- type: map_at_5 |
|
value: 27.32 |
|
- type: map_at_10 |
|
value: 28.51 |
|
- type: map_at_30 |
|
value: 29.39 |
|
- type: map_at_100 |
|
value: 29.66 |
|
- type: recall_at_1 |
|
value: 21.63 |
|
- type: recall_at_3 |
|
value: 31.47 |
|
- type: recall_at_5 |
|
value: 36.69 |
|
- type: recall_at_10 |
|
value: 44.95 |
|
- type: recall_at_30 |
|
value: 58.2 |
|
- type: recall_at_100 |
|
value: 69.83 |
|
- type: precision_at_1 |
|
value: 23.47 |
|
- type: precision_at_3 |
|
value: 11.71 |
|
- type: precision_at_5 |
|
value: 8.32 |
|
- type: precision_at_10 |
|
value: 5.23 |
|
- type: precision_at_30 |
|
value: 2.29 |
|
- type: precision_at_100 |
|
value: 0.86 |
|
- type: accuracy_at_3 |
|
value: 34.01 |
|
- type: accuracy_at_5 |
|
value: 39.37 |
|
- type: accuracy_at_10 |
|
value: 48.24 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: climate-fever |
|
name: MTEB ClimateFEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 19.8 |
|
- type: ndcg_at_3 |
|
value: 17.93 |
|
- type: ndcg_at_5 |
|
value: 19.39 |
|
- type: ndcg_at_10 |
|
value: 22.42 |
|
- type: ndcg_at_30 |
|
value: 26.79 |
|
- type: ndcg_at_100 |
|
value: 29.84 |
|
- type: map_at_1 |
|
value: 9.09 |
|
- type: map_at_3 |
|
value: 12.91 |
|
- type: map_at_5 |
|
value: 14.12 |
|
- type: map_at_10 |
|
value: 15.45 |
|
- type: map_at_30 |
|
value: 16.73 |
|
- type: map_at_100 |
|
value: 17.21 |
|
- type: recall_at_1 |
|
value: 9.09 |
|
- type: recall_at_3 |
|
value: 16.81 |
|
- type: recall_at_5 |
|
value: 20.9 |
|
- type: recall_at_10 |
|
value: 27.65 |
|
- type: recall_at_30 |
|
value: 41.23 |
|
- type: recall_at_100 |
|
value: 53.57 |
|
- type: precision_at_1 |
|
value: 19.8 |
|
- type: precision_at_3 |
|
value: 13.36 |
|
- type: precision_at_5 |
|
value: 10.33 |
|
- type: precision_at_10 |
|
value: 7.15 |
|
- type: precision_at_30 |
|
value: 3.66 |
|
- type: precision_at_100 |
|
value: 1.49 |
|
- type: accuracy_at_3 |
|
value: 36.22 |
|
- type: accuracy_at_5 |
|
value: 44.1 |
|
- type: accuracy_at_10 |
|
value: 55.11 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: dbpedia-entity |
|
name: MTEB DBPedia |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 42.75 |
|
- type: ndcg_at_3 |
|
value: 35.67 |
|
- type: ndcg_at_5 |
|
value: 33.58 |
|
- type: ndcg_at_10 |
|
value: 32.19 |
|
- type: ndcg_at_30 |
|
value: 31.82 |
|
- type: ndcg_at_100 |
|
value: 35.87 |
|
- type: map_at_1 |
|
value: 7.05 |
|
- type: map_at_3 |
|
value: 10.5 |
|
- type: map_at_5 |
|
value: 12.06 |
|
- type: map_at_10 |
|
value: 14.29 |
|
- type: map_at_30 |
|
value: 17.38 |
|
- type: map_at_100 |
|
value: 19.58 |
|
- type: recall_at_1 |
|
value: 7.05 |
|
- type: recall_at_3 |
|
value: 11.89 |
|
- type: recall_at_5 |
|
value: 14.7 |
|
- type: recall_at_10 |
|
value: 19.78 |
|
- type: recall_at_30 |
|
value: 29.88 |
|
- type: recall_at_100 |
|
value: 42.4 |
|
- type: precision_at_1 |
|
value: 54.25 |
|
- type: precision_at_3 |
|
value: 39.42 |
|
- type: precision_at_5 |
|
value: 33.15 |
|
- type: precision_at_10 |
|
value: 25.95 |
|
- type: precision_at_30 |
|
value: 15.51 |
|
- type: precision_at_100 |
|
value: 7.9 |
|
- type: accuracy_at_3 |
|
value: 72.0 |
|
- type: accuracy_at_5 |
|
value: 77.75 |
|
- type: accuracy_at_10 |
|
value: 83.5 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fever |
|
name: MTEB FEVER |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 40.19 |
|
- type: ndcg_at_3 |
|
value: 50.51 |
|
- type: ndcg_at_5 |
|
value: 53.51 |
|
- type: ndcg_at_10 |
|
value: 56.45 |
|
- type: ndcg_at_30 |
|
value: 58.74 |
|
- type: ndcg_at_100 |
|
value: 59.72 |
|
- type: map_at_1 |
|
value: 37.56 |
|
- type: map_at_3 |
|
value: 46.74 |
|
- type: map_at_5 |
|
value: 48.46 |
|
- type: map_at_10 |
|
value: 49.7 |
|
- type: map_at_30 |
|
value: 50.31 |
|
- type: map_at_100 |
|
value: 50.43 |
|
- type: recall_at_1 |
|
value: 37.56 |
|
- type: recall_at_3 |
|
value: 58.28 |
|
- type: recall_at_5 |
|
value: 65.45 |
|
- type: recall_at_10 |
|
value: 74.28 |
|
- type: recall_at_30 |
|
value: 83.42 |
|
- type: recall_at_100 |
|
value: 88.76 |
|
- type: precision_at_1 |
|
value: 40.19 |
|
- type: precision_at_3 |
|
value: 20.99 |
|
- type: precision_at_5 |
|
value: 14.24 |
|
- type: precision_at_10 |
|
value: 8.12 |
|
- type: precision_at_30 |
|
value: 3.06 |
|
- type: precision_at_100 |
|
value: 0.98 |
|
- type: accuracy_at_3 |
|
value: 62.3 |
|
- type: accuracy_at_5 |
|
value: 69.94 |
|
- type: accuracy_at_10 |
|
value: 79.13 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: fiqa |
|
name: MTEB FiQA2018 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 34.41 |
|
- type: ndcg_at_3 |
|
value: 33.2 |
|
- type: ndcg_at_5 |
|
value: 34.71 |
|
- type: ndcg_at_10 |
|
value: 37.1 |
|
- type: ndcg_at_30 |
|
value: 40.88 |
|
- type: ndcg_at_100 |
|
value: 44.12 |
|
- type: map_at_1 |
|
value: 17.27 |
|
- type: map_at_3 |
|
value: 25.36 |
|
- type: map_at_5 |
|
value: 27.76 |
|
- type: map_at_10 |
|
value: 29.46 |
|
- type: map_at_30 |
|
value: 30.74 |
|
- type: map_at_100 |
|
value: 31.29 |
|
- type: recall_at_1 |
|
value: 17.27 |
|
- type: recall_at_3 |
|
value: 30.46 |
|
- type: recall_at_5 |
|
value: 36.91 |
|
- type: recall_at_10 |
|
value: 44.47 |
|
- type: recall_at_30 |
|
value: 56.71 |
|
- type: recall_at_100 |
|
value: 70.72 |
|
- type: precision_at_1 |
|
value: 34.41 |
|
- type: precision_at_3 |
|
value: 22.32 |
|
- type: precision_at_5 |
|
value: 16.91 |
|
- type: precision_at_10 |
|
value: 10.53 |
|
- type: precision_at_30 |
|
value: 4.62 |
|
- type: precision_at_100 |
|
value: 1.79 |
|
- type: accuracy_at_3 |
|
value: 50.77 |
|
- type: accuracy_at_5 |
|
value: 57.56 |
|
- type: accuracy_at_10 |
|
value: 65.12 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: hotpotqa |
|
name: MTEB HotpotQA |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 57.93 |
|
- type: ndcg_at_3 |
|
value: 44.21 |
|
- type: ndcg_at_5 |
|
value: 46.4 |
|
- type: ndcg_at_10 |
|
value: 48.37 |
|
- type: ndcg_at_30 |
|
value: 50.44 |
|
- type: ndcg_at_100 |
|
value: 51.86 |
|
- type: map_at_1 |
|
value: 28.97 |
|
- type: map_at_3 |
|
value: 36.79 |
|
- type: map_at_5 |
|
value: 38.31 |
|
- type: map_at_10 |
|
value: 39.32 |
|
- type: map_at_30 |
|
value: 39.99 |
|
- type: map_at_100 |
|
value: 40.2 |
|
- type: recall_at_1 |
|
value: 28.97 |
|
- type: recall_at_3 |
|
value: 41.01 |
|
- type: recall_at_5 |
|
value: 45.36 |
|
- type: recall_at_10 |
|
value: 50.32 |
|
- type: recall_at_30 |
|
value: 57.38 |
|
- type: recall_at_100 |
|
value: 64.06 |
|
- type: precision_at_1 |
|
value: 57.93 |
|
- type: precision_at_3 |
|
value: 27.34 |
|
- type: precision_at_5 |
|
value: 18.14 |
|
- type: precision_at_10 |
|
value: 10.06 |
|
- type: precision_at_30 |
|
value: 3.82 |
|
- type: precision_at_100 |
|
value: 1.28 |
|
- type: accuracy_at_3 |
|
value: 71.03 |
|
- type: accuracy_at_5 |
|
value: 75.14 |
|
- type: accuracy_at_10 |
|
value: 79.84 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: msmarco |
|
name: MTEB MSMARCO |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 19.74 |
|
- type: ndcg_at_3 |
|
value: 29.47 |
|
- type: ndcg_at_5 |
|
value: 32.99 |
|
- type: ndcg_at_10 |
|
value: 36.76 |
|
- type: ndcg_at_30 |
|
value: 40.52 |
|
- type: ndcg_at_100 |
|
value: 42.78 |
|
- type: map_at_1 |
|
value: 19.2 |
|
- type: map_at_3 |
|
value: 26.81 |
|
- type: map_at_5 |
|
value: 28.78 |
|
- type: map_at_10 |
|
value: 30.35 |
|
- type: map_at_30 |
|
value: 31.3 |
|
- type: map_at_100 |
|
value: 31.57 |
|
- type: recall_at_1 |
|
value: 19.2 |
|
- type: recall_at_3 |
|
value: 36.59 |
|
- type: recall_at_5 |
|
value: 45.08 |
|
- type: recall_at_10 |
|
value: 56.54 |
|
- type: recall_at_30 |
|
value: 72.05 |
|
- type: recall_at_100 |
|
value: 84.73 |
|
- type: precision_at_1 |
|
value: 19.74 |
|
- type: precision_at_3 |
|
value: 12.61 |
|
- type: precision_at_5 |
|
value: 9.37 |
|
- type: precision_at_10 |
|
value: 5.89 |
|
- type: precision_at_30 |
|
value: 2.52 |
|
- type: precision_at_100 |
|
value: 0.89 |
|
- type: accuracy_at_3 |
|
value: 37.38 |
|
- type: accuracy_at_5 |
|
value: 46.06 |
|
- type: accuracy_at_10 |
|
value: 57.62 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nq |
|
name: MTEB NQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 25.9 |
|
- type: ndcg_at_3 |
|
value: 35.97 |
|
- type: ndcg_at_5 |
|
value: 40.27 |
|
- type: ndcg_at_10 |
|
value: 44.44 |
|
- type: ndcg_at_30 |
|
value: 48.31 |
|
- type: ndcg_at_100 |
|
value: 50.14 |
|
- type: map_at_1 |
|
value: 23.03 |
|
- type: map_at_3 |
|
value: 32.45 |
|
- type: map_at_5 |
|
value: 34.99 |
|
- type: map_at_10 |
|
value: 36.84 |
|
- type: map_at_30 |
|
value: 37.92 |
|
- type: map_at_100 |
|
value: 38.16 |
|
- type: recall_at_1 |
|
value: 23.03 |
|
- type: recall_at_3 |
|
value: 43.49 |
|
- type: recall_at_5 |
|
value: 53.41 |
|
- type: recall_at_10 |
|
value: 65.65 |
|
- type: recall_at_30 |
|
value: 80.79 |
|
- type: recall_at_100 |
|
value: 90.59 |
|
- type: precision_at_1 |
|
value: 25.9 |
|
- type: precision_at_3 |
|
value: 16.76 |
|
- type: precision_at_5 |
|
value: 12.54 |
|
- type: precision_at_10 |
|
value: 7.78 |
|
- type: precision_at_30 |
|
value: 3.23 |
|
- type: precision_at_100 |
|
value: 1.1 |
|
- type: accuracy_at_3 |
|
value: 47.31 |
|
- type: accuracy_at_5 |
|
value: 57.16 |
|
- type: accuracy_at_10 |
|
value: 69.09 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: nfcorpus |
|
name: MTEB NFCorpus |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 40.87 |
|
- type: ndcg_at_3 |
|
value: 36.79 |
|
- type: ndcg_at_5 |
|
value: 34.47 |
|
- type: ndcg_at_10 |
|
value: 32.05 |
|
- type: ndcg_at_30 |
|
value: 29.23 |
|
- type: ndcg_at_100 |
|
value: 29.84 |
|
- type: map_at_1 |
|
value: 5.05 |
|
- type: map_at_3 |
|
value: 8.5 |
|
- type: map_at_5 |
|
value: 9.87 |
|
- type: map_at_10 |
|
value: 11.71 |
|
- type: map_at_30 |
|
value: 13.48 |
|
- type: map_at_100 |
|
value: 14.86 |
|
- type: recall_at_1 |
|
value: 5.05 |
|
- type: recall_at_3 |
|
value: 9.55 |
|
- type: recall_at_5 |
|
value: 11.91 |
|
- type: recall_at_10 |
|
value: 16.07 |
|
- type: recall_at_30 |
|
value: 22.13 |
|
- type: recall_at_100 |
|
value: 30.7 |
|
- type: precision_at_1 |
|
value: 42.72 |
|
- type: precision_at_3 |
|
value: 34.78 |
|
- type: precision_at_5 |
|
value: 30.03 |
|
- type: precision_at_10 |
|
value: 23.93 |
|
- type: precision_at_30 |
|
value: 14.61 |
|
- type: precision_at_100 |
|
value: 7.85 |
|
- type: accuracy_at_3 |
|
value: 58.2 |
|
- type: accuracy_at_5 |
|
value: 64.09 |
|
- type: accuracy_at_10 |
|
value: 69.35 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: quora |
|
name: MTEB QuoraRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 80.62 |
|
- type: ndcg_at_3 |
|
value: 84.62 |
|
- type: ndcg_at_5 |
|
value: 86.25 |
|
- type: ndcg_at_10 |
|
value: 87.7 |
|
- type: ndcg_at_30 |
|
value: 88.63 |
|
- type: ndcg_at_100 |
|
value: 88.95 |
|
- type: map_at_1 |
|
value: 69.91 |
|
- type: map_at_3 |
|
value: 80.7 |
|
- type: map_at_5 |
|
value: 82.57 |
|
- type: map_at_10 |
|
value: 83.78 |
|
- type: map_at_30 |
|
value: 84.33 |
|
- type: map_at_100 |
|
value: 84.44 |
|
- type: recall_at_1 |
|
value: 69.91 |
|
- type: recall_at_3 |
|
value: 86.36 |
|
- type: recall_at_5 |
|
value: 90.99 |
|
- type: recall_at_10 |
|
value: 95.19 |
|
- type: recall_at_30 |
|
value: 98.25 |
|
- type: recall_at_100 |
|
value: 99.47 |
|
- type: precision_at_1 |
|
value: 80.62 |
|
- type: precision_at_3 |
|
value: 37.03 |
|
- type: precision_at_5 |
|
value: 24.36 |
|
- type: precision_at_10 |
|
value: 13.4 |
|
- type: precision_at_30 |
|
value: 4.87 |
|
- type: precision_at_100 |
|
value: 1.53 |
|
- type: accuracy_at_3 |
|
value: 92.25 |
|
- type: accuracy_at_5 |
|
value: 95.29 |
|
- type: accuracy_at_10 |
|
value: 97.74 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scidocs |
|
name: MTEB SCIDOCS |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 24.1 |
|
- type: ndcg_at_3 |
|
value: 20.18 |
|
- type: ndcg_at_5 |
|
value: 17.72 |
|
- type: ndcg_at_10 |
|
value: 21.5 |
|
- type: ndcg_at_30 |
|
value: 26.66 |
|
- type: ndcg_at_100 |
|
value: 30.95 |
|
- type: map_at_1 |
|
value: 4.88 |
|
- type: map_at_3 |
|
value: 9.09 |
|
- type: map_at_5 |
|
value: 10.99 |
|
- type: map_at_10 |
|
value: 12.93 |
|
- type: map_at_30 |
|
value: 14.71 |
|
- type: map_at_100 |
|
value: 15.49 |
|
- type: recall_at_1 |
|
value: 4.88 |
|
- type: recall_at_3 |
|
value: 11.55 |
|
- type: recall_at_5 |
|
value: 15.91 |
|
- type: recall_at_10 |
|
value: 22.82 |
|
- type: recall_at_30 |
|
value: 35.7 |
|
- type: recall_at_100 |
|
value: 50.41 |
|
- type: precision_at_1 |
|
value: 24.1 |
|
- type: precision_at_3 |
|
value: 19.0 |
|
- type: precision_at_5 |
|
value: 15.72 |
|
- type: precision_at_10 |
|
value: 11.27 |
|
- type: precision_at_30 |
|
value: 5.87 |
|
- type: precision_at_100 |
|
value: 2.49 |
|
- type: accuracy_at_3 |
|
value: 43.0 |
|
- type: accuracy_at_5 |
|
value: 51.6 |
|
- type: accuracy_at_10 |
|
value: 62.7 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: scifact |
|
name: MTEB SciFact |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 52.33 |
|
- type: ndcg_at_3 |
|
value: 61.47 |
|
- type: ndcg_at_5 |
|
value: 63.82 |
|
- type: ndcg_at_10 |
|
value: 65.81 |
|
- type: ndcg_at_30 |
|
value: 67.75 |
|
- type: ndcg_at_100 |
|
value: 68.96 |
|
- type: map_at_1 |
|
value: 50.46 |
|
- type: map_at_3 |
|
value: 58.51 |
|
- type: map_at_5 |
|
value: 60.12 |
|
- type: map_at_10 |
|
value: 61.07 |
|
- type: map_at_30 |
|
value: 61.64 |
|
- type: map_at_100 |
|
value: 61.8 |
|
- type: recall_at_1 |
|
value: 50.46 |
|
- type: recall_at_3 |
|
value: 67.81 |
|
- type: recall_at_5 |
|
value: 73.6 |
|
- type: recall_at_10 |
|
value: 79.31 |
|
- type: recall_at_30 |
|
value: 86.8 |
|
- type: recall_at_100 |
|
value: 93.5 |
|
- type: precision_at_1 |
|
value: 52.33 |
|
- type: precision_at_3 |
|
value: 24.56 |
|
- type: precision_at_5 |
|
value: 16.27 |
|
- type: precision_at_10 |
|
value: 8.9 |
|
- type: precision_at_30 |
|
value: 3.28 |
|
- type: precision_at_100 |
|
value: 1.06 |
|
- type: accuracy_at_3 |
|
value: 69.67 |
|
- type: accuracy_at_5 |
|
value: 75.0 |
|
- type: accuracy_at_10 |
|
value: 80.67 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: trec-covid |
|
name: MTEB TRECCOVID |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 57.0 |
|
- type: ndcg_at_3 |
|
value: 53.78 |
|
- type: ndcg_at_5 |
|
value: 52.62 |
|
- type: ndcg_at_10 |
|
value: 48.9 |
|
- type: ndcg_at_30 |
|
value: 44.2 |
|
- type: ndcg_at_100 |
|
value: 36.53 |
|
- type: map_at_1 |
|
value: 0.16 |
|
- type: map_at_3 |
|
value: 0.41 |
|
- type: map_at_5 |
|
value: 0.62 |
|
- type: map_at_10 |
|
value: 1.07 |
|
- type: map_at_30 |
|
value: 2.46 |
|
- type: map_at_100 |
|
value: 5.52 |
|
- type: recall_at_1 |
|
value: 0.16 |
|
- type: recall_at_3 |
|
value: 0.45 |
|
- type: recall_at_5 |
|
value: 0.72 |
|
- type: recall_at_10 |
|
value: 1.33 |
|
- type: recall_at_30 |
|
value: 3.46 |
|
- type: recall_at_100 |
|
value: 8.73 |
|
- type: precision_at_1 |
|
value: 62.0 |
|
- type: precision_at_3 |
|
value: 57.33 |
|
- type: precision_at_5 |
|
value: 56.0 |
|
- type: precision_at_10 |
|
value: 52.0 |
|
- type: precision_at_30 |
|
value: 46.2 |
|
- type: precision_at_100 |
|
value: 37.22 |
|
- type: accuracy_at_3 |
|
value: 82.0 |
|
- type: accuracy_at_5 |
|
value: 90.0 |
|
- type: accuracy_at_10 |
|
value: 92.0 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: webis-touche2020 |
|
name: MTEB Touche2020 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_1 |
|
value: 20.41 |
|
- type: ndcg_at_3 |
|
value: 17.62 |
|
- type: ndcg_at_5 |
|
value: 17.16 |
|
- type: ndcg_at_10 |
|
value: 17.09 |
|
- type: ndcg_at_30 |
|
value: 20.1 |
|
- type: ndcg_at_100 |
|
value: 26.33 |
|
- type: map_at_1 |
|
value: 2.15 |
|
- type: map_at_3 |
|
value: 3.59 |
|
- type: map_at_5 |
|
value: 5.07 |
|
- type: map_at_10 |
|
value: 6.95 |
|
- type: map_at_30 |
|
value: 9.01 |
|
- type: map_at_100 |
|
value: 10.54 |
|
- type: recall_at_1 |
|
value: 2.15 |
|
- type: recall_at_3 |
|
value: 4.5 |
|
- type: recall_at_5 |
|
value: 7.54 |
|
- type: recall_at_10 |
|
value: 12.46 |
|
- type: recall_at_30 |
|
value: 21.9 |
|
- type: recall_at_100 |
|
value: 36.58 |
|
- type: precision_at_1 |
|
value: 22.45 |
|
- type: precision_at_3 |
|
value: 19.05 |
|
- type: precision_at_5 |
|
value: 17.55 |
|
- type: precision_at_10 |
|
value: 15.51 |
|
- type: precision_at_30 |
|
value: 10.07 |
|
- type: precision_at_100 |
|
value: 5.57 |
|
- type: accuracy_at_3 |
|
value: 42.86 |
|
- type: accuracy_at_5 |
|
value: 53.06 |
|
- type: accuracy_at_10 |
|
value: 69.39 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: BeIR/cqadupstack |
|
name: MTEB CQADupstackRetrieval |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: ndcg_at_10 |
|
value: 41.59 |
|
license: apache-2.0 |
|
language: |
|
- en |
|
pipeline_tag: feature-extraction |
|
--- |
|
|
|
|
|
<p align="center"> |
|
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|
</p> |
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|
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<p align="center"> |
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<b>The crispy sentence embedding family from <a href="https://mixedbread.ai"><b>Mixedbread</b></a>.</b> |
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</p> |
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|
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# mixedbread-ai/mxbai-embed-xsmall-v1 |
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|
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This model is an open-source English embedding model developed by [Mixedbread](https://mixedbread.ai). It's built upon [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) and trained with the [AnglE loss](https://arxiv.org/abs/2309.12871) and [Espresso](https://arxiv.org/abs/2402.14776). Read more details in our [blog post](https://www.mixedbread.ai/blog/mxbai-embed-xsmall-v1). |
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**In a bread loaf**: |
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- State-of-the-art performance |
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- Supports both [binary quantization and Matryoshka Representation Learning (MRL)](#binary-quantization-and-matryoshka). |
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- Optimized for retrieval tasks |
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|
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## Performance |
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## Binary Quantization and Matryoshka |
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Our model supports both [binary quantization](https://www.mixedbread.ai/blog/binary-quantization) and [Matryoshka Representation Learning (MRL)](https://www.mixedbread.ai/blog/mxbai-embed-2d-large-v1), allowing for significant efficiency gains: |
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- Binary quantization: Retains 93.9% of performance while increasing efficiency by a factor of 32 |
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- MRL: A 33% reduction in vector size still leaves 96.2% of model performance |
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These optimizations can lead to substantial reductions in infrastructure costs for cloud computing and vector databases. Read more [here](https://www.mixedbread.ai/blog/binary-mrl). |
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## Quickstart |
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Here are several ways to produce German sentence embeddings using our model. |
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<details> |
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<summary> angle-emb </summary> |
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```bash |
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pip install -U angle-emb |
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``` |
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```python |
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from angle_emb import AnglE |
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from angle_emb.utils import cosine_similarity |
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|
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# 1. Specify preferred dimensions |
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dimensions = 384 |
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# 2. Load model and set pooling strategy to avg |
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model = AnglE.from_pretrained( |
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"mixedbread-ai/mxbai-embed-xsmall-v1", |
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pooling_strategy='avg').cuda() |
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|
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query = 'A man is eating a piece of bread' |
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docs = [ |
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query, |
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"A man is eating food.", |
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"A man is eating pasta.", |
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"The girl is carrying a baby.", |
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"A man is riding a horse.", |
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] |
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# 3. Encode |
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embeddings = model.encode(docs, embedding_size=dimensions) |
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for doc, emb in zip(docs[1:], embeddings[1:]): |
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print(f'{query} ||| {doc}', cosine_similarity(embeddings[0], emb)) |
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``` |
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</details> |
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<details> |
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<summary> Sentence Transformers </summary> |
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```bash |
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python -m pip install -U sentence-transformers |
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``` |
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|
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```python |
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from sentence_transformers import SentenceTransformer |
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from sentence_transformers.util import cos_sim |
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|
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# 1. Specify preferred dimensions |
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dimensions = 384 |
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|
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# 2. Load model |
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model = SentenceTransformer("mixedbread-ai/mxbai-embed-xsmall-v1", truncate_dim=dimensions) |
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query = 'A man is eating a piece of bread' |
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docs = [ |
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query, |
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"A man is eating food.", |
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"A man is eating pasta.", |
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"The girl is carrying a baby.", |
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"A man is riding a horse.", |
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] |
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# 3. Encode |
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embeddings = model.encode(docs) |
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similarities = cos_sim(embeddings[0], embeddings[1:]) |
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print('similarities:', similarities) |
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``` |
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</details> |
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|
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<details> |
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<summary> transformers </summary> |
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|
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```bash |
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pip install -U transformers |
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``` |
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|
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```python |
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from typing import Dict |
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|
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import torch |
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import numpy as np |
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from transformers import AutoModel, AutoTokenizer |
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from sentence_transformers.util import cos_sim |
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def pooling(outputs: torch.Tensor, inputs: Dict) -> np.ndarray: |
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outputs = torch.sum( |
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outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"]) |
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return outputs.detach().cpu().numpy() |
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|
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# 1. Load model |
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model_id = 'mixedbread-ai/mxbai-embed-xsmall-v1' |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModel.from_pretrained(model_id).cuda() |
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query = 'A man is eating a piece of bread' |
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docs = [ |
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query, |
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"A man is eating food.", |
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"A man is eating pasta.", |
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"The girl is carrying a baby.", |
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"A man is riding a horse.", |
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] |
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|
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# 2. Encode |
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inputs = tokenizer(docs, padding=True, return_tensors='pt') |
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for k, v in inputs.items(): |
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inputs[k] = v.cuda() |
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outputs = model(**inputs).last_hidden_state |
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embeddings = pooling(outputs, inputs) |
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# 3. Compute similarity scores |
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similarities = cos_sim(embeddings[0], embeddings[1:]) |
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print('similarities:', similarities) |
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``` |
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</details> |
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<details> |
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<summary>Batched API</summary> |
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```bash |
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python -m pip install batched |
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``` |
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|
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```python |
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import uvicorn |
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import batched |
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from fastapi import FastAPI |
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from fastapi.responses import ORJSONResponse |
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from sentence_transformers import SentenceTransformer |
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from pydantic import BaseModel |
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app = FastAPI() |
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model = SentenceTransformer('mixedbread-ai/mxbai-embed-xsmall-v1') |
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model.encode = batched.aio.dynamically(model.encode) |
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class EmbeddingsRequest(BaseModel): |
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input: str | list[str] |
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@app.post("/embeddings") |
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async def embeddings(request: EmbeddingsRequest): |
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return ORJSONResponse({"embeddings": await model.encode(request.input)}) |
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|
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if __name__ == "__main__": |
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uvicorn.run(app, host="0.0.0.0", port=8000) |
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``` |
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</details> |
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## Community |
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Join our [discord community](https://www.mixedbread.ai/redirects/discord) to share your feedback and thoughts. We're here to help and always happy to discuss the exciting field of machine learning! |
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## License |
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Apache 2.0 |
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## Citation |
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```bibtex |
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@online{xsmall2024mxbai, |
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title={Every Byte Matters: Introducing mxbai-embed-xsmall-v1}, |
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author={Sean Lee and Julius Lipp and Rui Huang and Darius Koenig}, |
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year={2024}, |
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url={https://www.mixedbread.ai/blog/mxbai-embed-xsmall-v1}, |
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} |
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``` |
