sentence-transformers onnx safetensors openvino gguf bert mteb feature-extraction en arxiv:2309.12871 arxiv:2402.14776 base_model:mixedbread-ai/mxbai-embed-xsmall-v1 base_model:quantized:mixedbread-ai/mxbai-embed-xsmall-v1 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: mixedbread-ai/mxbai-embed-mini-v1
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- mteb
model-index:
- name: mxbai-embed-xsmall-v1
  results:
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      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:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - 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
    - type: ndcg_at_10
      value: 41.59
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      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:
      name: MTEB DBPedia
      type: dbpedia-entity
      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:
      name: MTEB FEVER
      type: 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:
      name: MTEB FiQA2018
      type: fiqa
      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:
      name: MTEB HotpotQA
      type: 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:
      name: MTEB MSMARCO
      type: 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:
      name: MTEB NQ
      type: 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:
      name: MTEB NFCorpus
      type: 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:
      name: MTEB QuoraRetrieval
      type: quora
      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:
      name: MTEB SCIDOCS
      type: 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:
      name: MTEB SciFact
      type: 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:
      name: MTEB TRECCOVID
      type: trec-covid
      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:
      name: MTEB Touche2020
      type: webis-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
---


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</p>

<p align="center">
  <b>The crispy sentence embedding family from <a href="https://mixedbread.com"><b>Mixedbread</b></a>.</b>
</p>

<p align="center">
<sup> 🍞 Looking for a simple end-to-end retrieval solution? Meet Omni, our multimodal and multilingual model. <a href="https://mixedbread.com"><b>Get in touch for access.</a> </sup>
</p>

# mixedbread-ai/mxbai-embed-xsmall-v1

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).

**In a bread loaf**:
- State-of-the-art performance
- Supports both [binary quantization and Matryoshka Representation Learning (MRL)](#binary-quantization-and-matryoshka).
- Optimized for retrieval tasks
- 4096 context support

## Performance


## Binary Quantization and Matryoshka

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:

- Binary quantization: Retains 93.9% of performance while increasing efficiency by a factor of 32
- MRL: A 33% reduction in vector size still leaves 96.2% of model performance

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).

## Quickstart

Here are several ways to produce German sentence embeddings using our model. 

<details>
  <summary> angle-emb </summary>
  
  ```bash
  pip install -U angle-emb
  ```
  
  ```python
  from angle_emb import AnglE
  from angle_emb.utils import cosine_similarity
  
  # 1. Specify preferred dimensions
  dimensions = 384
  
  # 2. Load model and set pooling strategy to avg
  model = AnglE.from_pretrained(
      "mixedbread-ai/mxbai-embed-xsmall-v1",
      pooling_strategy='avg').cuda()
  
  query = 'A man is eating a piece of bread'
  
  docs = [
      query,
      "A man is eating food.",
      "A man is eating pasta.",
      "The girl is carrying a baby.",
      "A man is riding a horse.",
  ]
  
  # 3. Encode
  embeddings = model.encode(docs, embedding_size=dimensions)
  
  for doc, emb in zip(docs[1:], embeddings[1:]):
      print(f'{query} ||| {doc}', cosine_similarity(embeddings[0], emb))
  ```
</details>

<details>
  <summary> Sentence Transformers </summary>
  
  ```bash
  python -m pip install -U sentence-transformers
  ```
  
  ```python
  from sentence_transformers import SentenceTransformer
  from sentence_transformers.util import cos_sim
  
  # 1. Specify preferred dimensions
  dimensions = 384
  
  # 2. Load model
  model = SentenceTransformer("mixedbread-ai/mxbai-embed-xsmall-v1", truncate_dim=dimensions)
  
  query = 'A man is eating a piece of bread'
  
  docs = [
      query,
      "A man is eating food.",
      "A man is eating pasta.",
      "The girl is carrying a baby.",
      "A man is riding a horse.",
  ]
  
  
  # 3. Encode
  embeddings = model.encode(docs)
  
  similarities = cos_sim(embeddings[0], embeddings[1:])
  print('similarities:', similarities)
  ```
</details>

<details>
  <summary> transformers </summary>
  
  ```bash
  pip install -U transformers
  ```
  
  ```python
  from typing import Dict
  
  import torch
  import numpy as np
  from transformers import AutoModel, AutoTokenizer
  from sentence_transformers.util import cos_sim
  
  def pooling(outputs: torch.Tensor, inputs: Dict) -> np.ndarray:
      outputs = torch.sum(
        outputs * inputs["attention_mask"][:, :, None], dim=1) / torch.sum(inputs["attention_mask"])
      return outputs.detach().cpu().numpy()
  
  # 1. Load model
  model_id = 'mixedbread-ai/mxbai-embed-xsmall-v1'
  tokenizer = AutoTokenizer.from_pretrained(model_id)
  model = AutoModel.from_pretrained(model_id).cuda()
  
  query = 'A man is eating a piece of bread'
  
  docs = [
      query,
      "A man is eating food.",
      "A man is eating pasta.",
      "The girl is carrying a baby.",
      "A man is riding a horse.",
  ]
  
  # 2. Encode
  inputs = tokenizer(docs, padding=True, return_tensors='pt')
  for k, v in inputs.items():
      inputs[k] = v.cuda()
  outputs = model(**inputs).last_hidden_state
  embeddings = pooling(outputs, inputs)
  
  # 3. Compute similarity scores
  similarities = cos_sim(embeddings[0], embeddings[1:])
  print('similarities:', similarities)
  ```
</details>

<details>
  <summary>Batched API</summary>
  
  ```bash
  python -m pip install batched
  ```
  
  ```python
  import uvicorn
  import batched
  from fastapi import FastAPI
  from fastapi.responses import ORJSONResponse
  from sentence_transformers import SentenceTransformer
  from pydantic import BaseModel
   
  app = FastAPI()
   
  model = SentenceTransformer('mixedbread-ai/mxbai-embed-xsmall-v1')
  model.encode = batched.aio.dynamically(model.encode)
   
  class EmbeddingsRequest(BaseModel):
      input: str | list[str]
   
  @app.post("/embeddings")
  async def embeddings(request: EmbeddingsRequest):
      return ORJSONResponse({"embeddings": await model.encode(request.input)})
   
  if __name__ == "__main__":
      uvicorn.run(app, host="0.0.0.0", port=8000)
  ```
</details>

## Community

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!

## License

Apache 2.0

## Citation

```bibtex
@online{xsmall2024mxbai,
  title={Every Byte Matters: Introducing mxbai-embed-xsmall-v1},
  author={Sean Lee and Julius Lipp and Rui Huang and Darius Koenig},
  year={2024},
  url={https://www.mixedbread.ai/blog/mxbai-embed-xsmall-v1},
}
```