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+---
+tags:
+- mteb
+- llama-cpp
+- gguf-my-repo
+library_name: sentence-transformers
+base_model: lier007/xiaobu-embedding-v2
+model-index:
+- name: piccolo-embedding_mixed2
+  results:
+  - task:
+      type: STS
+    dataset:
+      name: MTEB AFQMC
+      type: C-MTEB/AFQMC
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 56.918538280469875
+    - type: cos_sim_spearman
+      value: 60.95597435855258
+    - type: euclidean_pearson
+      value: 59.73821610051437
+    - type: euclidean_spearman
+      value: 60.956778530262454
+    - type: manhattan_pearson
+      value: 59.739675774225475
+    - type: manhattan_spearman
+      value: 60.95243600302903
+  - task:
+      type: STS
+    dataset:
+      name: MTEB ATEC
+      type: C-MTEB/ATEC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 56.79417977023184
+    - type: cos_sim_spearman
+      value: 58.80984726256814
+    - type: euclidean_pearson
+      value: 63.42225182281334
+    - type: euclidean_spearman
+      value: 58.80957930593542
+    - type: manhattan_pearson
+      value: 63.41128425333986
+    - type: manhattan_spearman
+      value: 58.80784321716389
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB AmazonReviewsClassification (zh)
+      type: mteb/amazon_reviews_multi
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 50.074000000000005
+    - type: f1
+      value: 47.11468271375511
+  - task:
+      type: STS
+    dataset:
+      name: MTEB BQ
+      type: C-MTEB/BQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 73.3412976021806
+    - type: cos_sim_spearman
+      value: 75.0799965464816
+    - type: euclidean_pearson
+      value: 73.7874729086686
+    - type: euclidean_spearman
+      value: 75.07910973646369
+    - type: manhattan_pearson
+      value: 73.7716616949607
+    - type: manhattan_spearman
+      value: 75.06089549008017
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB CLSClusteringP2P
+      type: C-MTEB/CLSClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 60.4206935177474
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB CLSClusteringS2S
+      type: C-MTEB/CLSClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 49.53654617222264
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB CMedQAv1
+      type: C-MTEB/CMedQAv1-reranking
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 90.96386786978509
+    - type: mrr
+      value: 92.8897619047619
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB CMedQAv2
+      type: C-MTEB/CMedQAv2-reranking
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 90.41014127763198
+    - type: mrr
+      value: 92.45039682539682
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB CmedqaRetrieval
+      type: C-MTEB/CmedqaRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.901999999999997
+    - type: map_at_10
+      value: 40.321
+    - type: map_at_100
+      value: 42.176
+    - type: map_at_1000
+      value: 42.282
+    - type: map_at_3
+      value: 35.882
+    - type: map_at_5
+      value: 38.433
+    - type: mrr_at_1
+      value: 40.910000000000004
+    - type: mrr_at_10
+      value: 49.309999999999995
+    - type: mrr_at_100
+      value: 50.239
+    - type: mrr_at_1000
+      value: 50.278
+    - type: mrr_at_3
+      value: 46.803
+    - type: mrr_at_5
+      value: 48.137
+    - type: ndcg_at_1
+      value: 40.785
+    - type: ndcg_at_10
+      value: 47.14
+    - type: ndcg_at_100
+      value: 54.156000000000006
+    - type: ndcg_at_1000
+      value: 55.913999999999994
+    - type: ndcg_at_3
+      value: 41.669
+    - type: ndcg_at_5
+      value: 43.99
+    - type: precision_at_1
+      value: 40.785
+    - type: precision_at_10
+      value: 10.493
+    - type: precision_at_100
+      value: 1.616
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 23.723
+    - type: precision_at_5
+      value: 17.249
+    - type: recall_at_1
+      value: 26.901999999999997
+    - type: recall_at_10
+      value: 58.25
+    - type: recall_at_100
+      value: 87.10900000000001
+    - type: recall_at_1000
+      value: 98.804
+    - type: recall_at_3
+      value: 41.804
+    - type: recall_at_5
+      value: 48.884
+  - task:
+      type: PairClassification
+    dataset:
+      name: MTEB Cmnli
+      type: C-MTEB/CMNLI
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.42212868310283
+    - type: cos_sim_ap
+      value: 92.83788702972741
+    - type: cos_sim_f1
+      value: 87.08912233141307
+    - type: cos_sim_precision
+      value: 84.24388111888112
+    - type: cos_sim_recall
+      value: 90.13327098433481
+    - type: dot_accuracy
+      value: 86.44618159951895
+    - type: dot_ap
+      value: 92.81146275060858
+    - type: dot_f1
+      value: 87.06857911250562
+    - type: dot_precision
+      value: 83.60232408005164
+    - type: dot_recall
+      value: 90.83469721767594
+    - type: euclidean_accuracy
+      value: 86.42212868310283
+    - type: euclidean_ap
+      value: 92.83805700492603
+    - type: euclidean_f1
+      value: 87.08803611738148
+    - type: euclidean_precision
+      value: 84.18066768492254
+    - type: euclidean_recall
+      value: 90.20341360766892
+    - type: manhattan_accuracy
+      value: 86.28983764281419
+    - type: manhattan_ap
+      value: 92.82818970981005
+    - type: manhattan_f1
+      value: 87.12625521832335
+    - type: manhattan_precision
+      value: 84.19101613606628
+    - type: manhattan_recall
+      value: 90.27355623100304
+    - type: max_accuracy
+      value: 86.44618159951895
+    - type: max_ap
+      value: 92.83805700492603
+    - type: max_f1
+      value: 87.12625521832335
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB CovidRetrieval
+      type: C-MTEB/CovidRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 79.215
+    - type: map_at_10
+      value: 86.516
+    - type: map_at_100
+      value: 86.6
+    - type: map_at_1000
+      value: 86.602
+    - type: map_at_3
+      value: 85.52
+    - type: map_at_5
+      value: 86.136
+    - type: mrr_at_1
+      value: 79.663
+    - type: mrr_at_10
+      value: 86.541
+    - type: mrr_at_100
+      value: 86.625
+    - type: mrr_at_1000
+      value: 86.627
+    - type: mrr_at_3
+      value: 85.564
+    - type: mrr_at_5
+      value: 86.15899999999999
+    - type: ndcg_at_1
+      value: 79.663
+    - type: ndcg_at_10
+      value: 89.399
+    - type: ndcg_at_100
+      value: 89.727
+    - type: ndcg_at_1000
+      value: 89.781
+    - type: ndcg_at_3
+      value: 87.402
+    - type: ndcg_at_5
+      value: 88.479
+    - type: precision_at_1
+      value: 79.663
+    - type: precision_at_10
+      value: 9.926
+    - type: precision_at_100
+      value: 1.006
+    - type: precision_at_1000
+      value: 0.101
+    - type: precision_at_3
+      value: 31.226
+    - type: precision_at_5
+      value: 19.283
+    - type: recall_at_1
+      value: 79.215
+    - type: recall_at_10
+      value: 98.209
+    - type: recall_at_100
+      value: 99.579
+    - type: recall_at_1000
+      value: 100
+    - type: recall_at_3
+      value: 92.703
+    - type: recall_at_5
+      value: 95.364
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB DuRetrieval
+      type: C-MTEB/DuRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.391
+    - type: map_at_10
+      value: 82.82000000000001
+    - type: map_at_100
+      value: 85.5
+    - type: map_at_1000
+      value: 85.533
+    - type: map_at_3
+      value: 57.802
+    - type: map_at_5
+      value: 72.82600000000001
+    - type: mrr_at_1
+      value: 92.80000000000001
+    - type: mrr_at_10
+      value: 94.83500000000001
+    - type: mrr_at_100
+      value: 94.883
+    - type: mrr_at_1000
+      value: 94.884
+    - type: mrr_at_3
+      value: 94.542
+    - type: mrr_at_5
+      value: 94.729
+    - type: ndcg_at_1
+      value: 92.7
+    - type: ndcg_at_10
+      value: 89.435
+    - type: ndcg_at_100
+      value: 91.78699999999999
+    - type: ndcg_at_1000
+      value: 92.083
+    - type: ndcg_at_3
+      value: 88.595
+    - type: ndcg_at_5
+      value: 87.53
+    - type: precision_at_1
+      value: 92.7
+    - type: precision_at_10
+      value: 42.4
+    - type: precision_at_100
+      value: 4.823
+    - type: precision_at_1000
+      value: 0.48900000000000005
+    - type: precision_at_3
+      value: 79.133
+    - type: precision_at_5
+      value: 66.8
+    - type: recall_at_1
+      value: 27.391
+    - type: recall_at_10
+      value: 90.069
+    - type: recall_at_100
+      value: 97.875
+    - type: recall_at_1000
+      value: 99.436
+    - type: recall_at_3
+      value: 59.367999999999995
+    - type: recall_at_5
+      value: 76.537
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB EcomRetrieval
+      type: C-MTEB/EcomRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 54.800000000000004
+    - type: map_at_10
+      value: 65.289
+    - type: map_at_100
+      value: 65.845
+    - type: map_at_1000
+      value: 65.853
+    - type: map_at_3
+      value: 62.766999999999996
+    - type: map_at_5
+      value: 64.252
+    - type: mrr_at_1
+      value: 54.800000000000004
+    - type: mrr_at_10
+      value: 65.255
+    - type: mrr_at_100
+      value: 65.81700000000001
+    - type: mrr_at_1000
+      value: 65.824
+    - type: mrr_at_3
+      value: 62.683
+    - type: mrr_at_5
+      value: 64.248
+    - type: ndcg_at_1
+      value: 54.800000000000004
+    - type: ndcg_at_10
+      value: 70.498
+    - type: ndcg_at_100
+      value: 72.82300000000001
+    - type: ndcg_at_1000
+      value: 73.053
+    - type: ndcg_at_3
+      value: 65.321
+    - type: ndcg_at_5
+      value: 67.998
+    - type: precision_at_1
+      value: 54.800000000000004
+    - type: precision_at_10
+      value: 8.690000000000001
+    - type: precision_at_100
+      value: 0.97
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 24.233
+    - type: precision_at_5
+      value: 15.840000000000002
+    - type: recall_at_1
+      value: 54.800000000000004
+    - type: recall_at_10
+      value: 86.9
+    - type: recall_at_100
+      value: 97
+    - type: recall_at_1000
+      value: 98.9
+    - type: recall_at_3
+      value: 72.7
+    - type: recall_at_5
+      value: 79.2
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB IFlyTek
+      type: C-MTEB/IFlyTek-classification
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 51.758368603308966
+    - type: f1
+      value: 40.249503783871596
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB JDReview
+      type: C-MTEB/JDReview-classification
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 89.08067542213884
+    - type: ap
+      value: 60.31281895139249
+    - type: f1
+      value: 84.20883153932607
+  - task:
+      type: STS
+    dataset:
+      name: MTEB LCQMC
+      type: C-MTEB/LCQMC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 74.04193577551248
+    - type: cos_sim_spearman
+      value: 79.81875884845549
+    - type: euclidean_pearson
+      value: 80.02581187503708
+    - type: euclidean_spearman
+      value: 79.81877215060574
+    - type: manhattan_pearson
+      value: 80.01767830530258
+    - type: manhattan_spearman
+      value: 79.81178852172727
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB MMarcoReranking
+      type: C-MTEB/Mmarco-reranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 39.90939429947956
+    - type: mrr
+      value: 39.71071428571429
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB MMarcoRetrieval
+      type: C-MTEB/MMarcoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 68.485
+    - type: map_at_10
+      value: 78.27199999999999
+    - type: map_at_100
+      value: 78.54100000000001
+    - type: map_at_1000
+      value: 78.546
+    - type: map_at_3
+      value: 76.339
+    - type: map_at_5
+      value: 77.61099999999999
+    - type: mrr_at_1
+      value: 70.80199999999999
+    - type: mrr_at_10
+      value: 78.901
+    - type: mrr_at_100
+      value: 79.12400000000001
+    - type: mrr_at_1000
+      value: 79.128
+    - type: mrr_at_3
+      value: 77.237
+    - type: mrr_at_5
+      value: 78.323
+    - type: ndcg_at_1
+      value: 70.759
+    - type: ndcg_at_10
+      value: 82.191
+    - type: ndcg_at_100
+      value: 83.295
+    - type: ndcg_at_1000
+      value: 83.434
+    - type: ndcg_at_3
+      value: 78.57600000000001
+    - type: ndcg_at_5
+      value: 80.715
+    - type: precision_at_1
+      value: 70.759
+    - type: precision_at_10
+      value: 9.951
+    - type: precision_at_100
+      value: 1.049
+    - type: precision_at_1000
+      value: 0.106
+    - type: precision_at_3
+      value: 29.660999999999998
+    - type: precision_at_5
+      value: 18.94
+    - type: recall_at_1
+      value: 68.485
+    - type: recall_at_10
+      value: 93.65
+    - type: recall_at_100
+      value: 98.434
+    - type: recall_at_1000
+      value: 99.522
+    - type: recall_at_3
+      value: 84.20100000000001
+    - type: recall_at_5
+      value: 89.261
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB MassiveIntentClassification (zh-CN)
+      type: mteb/amazon_massive_intent
+      config: zh-CN
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 77.45460659045055
+    - type: f1
+      value: 73.84987702455533
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB MassiveScenarioClassification (zh-CN)
+      type: mteb/amazon_massive_scenario
+      config: zh-CN
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 85.29926025554808
+    - type: f1
+      value: 84.40636286569843
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB MedicalRetrieval
+      type: C-MTEB/MedicalRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 57.599999999999994
+    - type: map_at_10
+      value: 64.691
+    - type: map_at_100
+      value: 65.237
+    - type: map_at_1000
+      value: 65.27
+    - type: map_at_3
+      value: 62.733000000000004
+    - type: map_at_5
+      value: 63.968
+    - type: mrr_at_1
+      value: 58.099999999999994
+    - type: mrr_at_10
+      value: 64.952
+    - type: mrr_at_100
+      value: 65.513
+    - type: mrr_at_1000
+      value: 65.548
+    - type: mrr_at_3
+      value: 63
+    - type: mrr_at_5
+      value: 64.235
+    - type: ndcg_at_1
+      value: 57.599999999999994
+    - type: ndcg_at_10
+      value: 68.19
+    - type: ndcg_at_100
+      value: 70.98400000000001
+    - type: ndcg_at_1000
+      value: 71.811
+    - type: ndcg_at_3
+      value: 64.276
+    - type: ndcg_at_5
+      value: 66.47999999999999
+    - type: precision_at_1
+      value: 57.599999999999994
+    - type: precision_at_10
+      value: 7.920000000000001
+    - type: precision_at_100
+      value: 0.9259999999999999
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 22.900000000000002
+    - type: precision_at_5
+      value: 14.799999999999999
+    - type: recall_at_1
+      value: 57.599999999999994
+    - type: recall_at_10
+      value: 79.2
+    - type: recall_at_100
+      value: 92.60000000000001
+    - type: recall_at_1000
+      value: 99
+    - type: recall_at_3
+      value: 68.7
+    - type: recall_at_5
+      value: 74
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB MultilingualSentiment
+      type: C-MTEB/MultilingualSentiment-classification
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 79.45
+    - type: f1
+      value: 79.25610578280538
+  - task:
+      type: PairClassification
+    dataset:
+      name: MTEB Ocnli
+      type: C-MTEB/OCNLI
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.43584190579317
+    - type: cos_sim_ap
+      value: 90.89979725191012
+    - type: cos_sim_f1
+      value: 86.48383937316358
+    - type: cos_sim_precision
+      value: 80.6392694063927
+    - type: cos_sim_recall
+      value: 93.24181626187962
+    - type: dot_accuracy
+      value: 85.38170005414185
+    - type: dot_ap
+      value: 90.87532457866699
+    - type: dot_f1
+      value: 86.48383937316358
+    - type: dot_precision
+      value: 80.6392694063927
+    - type: dot_recall
+      value: 93.24181626187962
+    - type: euclidean_accuracy
+      value: 85.43584190579317
+    - type: euclidean_ap
+      value: 90.90126652086121
+    - type: euclidean_f1
+      value: 86.48383937316358
+    - type: euclidean_precision
+      value: 80.6392694063927
+    - type: euclidean_recall
+      value: 93.24181626187962
+    - type: manhattan_accuracy
+      value: 85.43584190579317
+    - type: manhattan_ap
+      value: 90.87896997853466
+    - type: manhattan_f1
+      value: 86.47581441263573
+    - type: manhattan_precision
+      value: 81.18628359592215
+    - type: manhattan_recall
+      value: 92.5026399155227
+    - type: max_accuracy
+      value: 85.43584190579317
+    - type: max_ap
+      value: 90.90126652086121
+    - type: max_f1
+      value: 86.48383937316358
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB OnlineShopping
+      type: C-MTEB/OnlineShopping-classification
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 94.9
+    - type: ap
+      value: 93.1468223150745
+    - type: f1
+      value: 94.88918689508299
+  - task:
+      type: STS
+    dataset:
+      name: MTEB PAWSX
+      type: C-MTEB/PAWSX
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 40.4831743182905
+    - type: cos_sim_spearman
+      value: 47.4163675550491
+    - type: euclidean_pearson
+      value: 46.456319899274924
+    - type: euclidean_spearman
+      value: 47.41567079730661
+    - type: manhattan_pearson
+      value: 46.48561639930895
+    - type: manhattan_spearman
+      value: 47.447721653461215
+  - task:
+      type: STS
+    dataset:
+      name: MTEB QBQTC
+      type: C-MTEB/QBQTC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 42.96423587663398
+    - type: cos_sim_spearman
+      value: 45.13742225167858
+    - type: euclidean_pearson
+      value: 39.275452114075435
+    - type: euclidean_spearman
+      value: 45.137763540967406
+    - type: manhattan_pearson
+      value: 39.24797626417764
+    - type: manhattan_spearman
+      value: 45.13817773119268
+  - task:
+      type: STS
+    dataset:
+      name: MTEB STS22 (zh)
+      type: mteb/sts22-crosslingual-sts
+      config: zh
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 66.26687809086202
+    - type: cos_sim_spearman
+      value: 66.9569145816897
+    - type: euclidean_pearson
+      value: 65.72390780809788
+    - type: euclidean_spearman
+      value: 66.95406938095539
+    - type: manhattan_pearson
+      value: 65.6220809000381
+    - type: manhattan_spearman
+      value: 66.88531036320953
+  - task:
+      type: STS
+    dataset:
+      name: MTEB STSB
+      type: C-MTEB/STSB
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.30831700726195
+    - type: cos_sim_spearman
+      value: 82.05184068558792
+    - type: euclidean_pearson
+      value: 81.73198597791563
+    - type: euclidean_spearman
+      value: 82.05326103582206
+    - type: manhattan_pearson
+      value: 81.70886400949136
+    - type: manhattan_spearman
+      value: 82.03473274756037
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB T2Reranking
+      type: C-MTEB/T2Reranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 69.03398835347575
+    - type: mrr
+      value: 79.9212528613341
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB T2Retrieval
+      type: C-MTEB/T2Retrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.515
+    - type: map_at_10
+      value: 77.40599999999999
+    - type: map_at_100
+      value: 81.087
+    - type: map_at_1000
+      value: 81.148
+    - type: map_at_3
+      value: 54.327000000000005
+    - type: map_at_5
+      value: 66.813
+    - type: mrr_at_1
+      value: 89.764
+    - type: mrr_at_10
+      value: 92.58
+    - type: mrr_at_100
+      value: 92.663
+    - type: mrr_at_1000
+      value: 92.666
+    - type: mrr_at_3
+      value: 92.15299999999999
+    - type: mrr_at_5
+      value: 92.431
+    - type: ndcg_at_1
+      value: 89.777
+    - type: ndcg_at_10
+      value: 85.013
+    - type: ndcg_at_100
+      value: 88.62100000000001
+    - type: ndcg_at_1000
+      value: 89.184
+    - type: ndcg_at_3
+      value: 86.19200000000001
+    - type: ndcg_at_5
+      value: 84.909
+    - type: precision_at_1
+      value: 89.777
+    - type: precision_at_10
+      value: 42.218
+    - type: precision_at_100
+      value: 5.032
+    - type: precision_at_1000
+      value: 0.517
+    - type: precision_at_3
+      value: 75.335
+    - type: precision_at_5
+      value: 63.199000000000005
+    - type: recall_at_1
+      value: 27.515
+    - type: recall_at_10
+      value: 84.258
+    - type: recall_at_100
+      value: 95.908
+    - type: recall_at_1000
+      value: 98.709
+    - type: recall_at_3
+      value: 56.189
+    - type: recall_at_5
+      value: 70.50800000000001
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB TNews
+      type: C-MTEB/TNews-classification
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 54.635999999999996
+    - type: f1
+      value: 52.63073912739558
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB ThuNewsClusteringP2P
+      type: C-MTEB/ThuNewsClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 78.75676284855221
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB ThuNewsClusteringS2S
+      type: C-MTEB/ThuNewsClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 71.95583733802839
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB VideoRetrieval
+      type: C-MTEB/VideoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 64.9
+    - type: map_at_10
+      value: 75.622
+    - type: map_at_100
+      value: 75.93900000000001
+    - type: map_at_1000
+      value: 75.93900000000001
+    - type: map_at_3
+      value: 73.933
+    - type: map_at_5
+      value: 74.973
+    - type: mrr_at_1
+      value: 65
+    - type: mrr_at_10
+      value: 75.676
+    - type: mrr_at_100
+      value: 75.994
+    - type: mrr_at_1000
+      value: 75.994
+    - type: mrr_at_3
+      value: 74.05000000000001
+    - type: mrr_at_5
+      value: 75.03999999999999
+    - type: ndcg_at_1
+      value: 64.9
+    - type: ndcg_at_10
+      value: 80.08999999999999
+    - type: ndcg_at_100
+      value: 81.44500000000001
+    - type: ndcg_at_1000
+      value: 81.45599999999999
+    - type: ndcg_at_3
+      value: 76.688
+    - type: ndcg_at_5
+      value: 78.53
+    - type: precision_at_1
+      value: 64.9
+    - type: precision_at_10
+      value: 9.379999999999999
+    - type: precision_at_100
+      value: 0.997
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 28.199999999999996
+    - type: precision_at_5
+      value: 17.8
+    - type: recall_at_1
+      value: 64.9
+    - type: recall_at_10
+      value: 93.8
+    - type: recall_at_100
+      value: 99.7
+    - type: recall_at_1000
+      value: 99.8
+    - type: recall_at_3
+      value: 84.6
+    - type: recall_at_5
+      value: 89
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB Waimai
+      type: C-MTEB/waimai-classification
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 89.34
+    - type: ap
+      value: 75.20638024616892
+    - type: f1
+      value: 87.88648489072128
+---
+
+# lagoon999/xiaobu-embedding-v2-Q8_0-GGUF
+This model was converted to GGUF format from [`lier007/xiaobu-embedding-v2`](https://huggingface.co/lier007/xiaobu-embedding-v2) 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/lier007/xiaobu-embedding-v2) 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 lagoon999/xiaobu-embedding-v2-Q8_0-GGUF --hf-file xiaobu-embedding-v2-q8_0.gguf -p "The meaning to life and the universe is"
+```
+
+### Server:
+```bash
+llama-server --hf-repo lagoon999/xiaobu-embedding-v2-Q8_0-GGUF --hf-file xiaobu-embedding-v2-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 lagoon999/xiaobu-embedding-v2-Q8_0-GGUF --hf-file xiaobu-embedding-v2-q8_0.gguf -p "The meaning to life and the universe is"
+```
+or 
+```
+./llama-server --hf-repo lagoon999/xiaobu-embedding-v2-Q8_0-GGUF --hf-file xiaobu-embedding-v2-q8_0.gguf -c 2048
+```