--- tags: - mteb - llama-cpp - gguf-my-repo base_model: mixedbread-ai/mxbai-embed-xsmall-v1 library_name: sentence-transformers license: apache-2.0 language: - en pipeline_tag: feature-extraction 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 --- # twine-network/mxbai-embed-xsmall-v1-Q8_0-GGUF This model was converted to GGUF format from [`mixedbread-ai/mxbai-embed-xsmall-v1`](https://huggingface.co/mixedbread-ai/mxbai-embed-xsmall-v1) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space. Refer to the [original model card](https://huggingface.co/mixedbread-ai/mxbai-embed-xsmall-v1) for more details on the model. ## Use with llama.cpp Install llama.cpp through brew (works on Mac and Linux) ```bash brew install llama.cpp ``` Invoke the llama.cpp server or the CLI. ### CLI: ```bash llama-cli --hf-repo twine-network/mxbai-embed-xsmall-v1-Q8_0-GGUF --hf-file mxbai-embed-xsmall-v1-q8_0.gguf -p "The meaning to life and the universe is" ``` ### Server: ```bash llama-server --hf-repo twine-network/mxbai-embed-xsmall-v1-Q8_0-GGUF --hf-file mxbai-embed-xsmall-v1-q8_0.gguf -c 2048 ``` Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well. Step 1: Clone llama.cpp from GitHub. ``` git clone https://github.com/ggerganov/llama.cpp ``` Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux). ``` cd llama.cpp && LLAMA_CURL=1 make ``` Step 3: Run inference through the main binary. ``` ./llama-cli --hf-repo twine-network/mxbai-embed-xsmall-v1-Q8_0-GGUF --hf-file mxbai-embed-xsmall-v1-q8_0.gguf -p "The meaning to life and the universe is" ``` or ``` ./llama-server --hf-repo twine-network/mxbai-embed-xsmall-v1-Q8_0-GGUF --hf-file mxbai-embed-xsmall-v1-q8_0.gguf -c 2048 ```