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README.md

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+---
+tags:
+- mteb
+- llama-cpp
+- gguf-my-repo
+language:
+- zh
+license: cc-by-nc-4.0
+library_name: sentence-transformers
+base_model: TencentBAC/Conan-embedding-v1
+model-index:
+- name: conan-embedding
+  results:
+  - task:
+      type: STS
+    dataset:
+      name: MTEB AFQMC
+      type: C-MTEB/AFQMC
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 56.613572467148856
+    - type: cos_sim_spearman
+      value: 60.66446211824284
+    - type: euclidean_pearson
+      value: 58.42080485872613
+    - type: euclidean_spearman
+      value: 59.82750030458164
+    - type: manhattan_pearson
+      value: 58.39885271199772
+    - type: manhattan_spearman
+      value: 59.817749720366734
+  - task:
+      type: STS
+    dataset:
+      name: MTEB ATEC
+      type: C-MTEB/ATEC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 56.60530380552331
+    - type: cos_sim_spearman
+      value: 58.63822441736707
+    - type: euclidean_pearson
+      value: 62.18551665180664
+    - type: euclidean_spearman
+      value: 58.23168804495912
+    - type: manhattan_pearson
+      value: 62.17191480770053
+    - type: manhattan_spearman
+      value: 58.22556219601401
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB AmazonReviewsClassification (zh)
+      type: mteb/amazon_reviews_multi
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 50.308
+    - type: f1
+      value: 46.927458607895126
+  - task:
+      type: STS
+    dataset:
+      name: MTEB BQ
+      type: C-MTEB/BQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 72.6472074172711
+    - type: cos_sim_spearman
+      value: 74.50748447236577
+    - type: euclidean_pearson
+      value: 72.51833296451854
+    - type: euclidean_spearman
+      value: 73.9898922606105
+    - type: manhattan_pearson
+      value: 72.50184948939338
+    - type: manhattan_spearman
+      value: 73.97797921509638
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB CLSClusteringP2P
+      type: C-MTEB/CLSClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 60.63545326048343
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB CLSClusteringS2S
+      type: C-MTEB/CLSClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 52.64834762325994
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB CMedQAv1
+      type: C-MTEB/CMedQAv1-reranking
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 91.38528814655234
+    - type: mrr
+      value: 93.35857142857144
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB CMedQAv2
+      type: C-MTEB/CMedQAv2-reranking
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 89.72084678877096
+    - type: mrr
+      value: 91.74380952380953
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB CmedqaRetrieval
+      type: C-MTEB/CmedqaRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.987
+    - type: map_at_10
+      value: 40.675
+    - type: map_at_100
+      value: 42.495
+    - type: map_at_1000
+      value: 42.596000000000004
+    - type: map_at_3
+      value: 36.195
+    - type: map_at_5
+      value: 38.704
+    - type: mrr_at_1
+      value: 41.21
+    - type: mrr_at_10
+      value: 49.816
+    - type: mrr_at_100
+      value: 50.743
+    - type: mrr_at_1000
+      value: 50.77700000000001
+    - type: mrr_at_3
+      value: 47.312
+    - type: mrr_at_5
+      value: 48.699999999999996
+    - type: ndcg_at_1
+      value: 41.21
+    - type: ndcg_at_10
+      value: 47.606
+    - type: ndcg_at_100
+      value: 54.457
+    - type: ndcg_at_1000
+      value: 56.16100000000001
+    - type: ndcg_at_3
+      value: 42.108000000000004
+    - type: ndcg_at_5
+      value: 44.393
+    - type: precision_at_1
+      value: 41.21
+    - type: precision_at_10
+      value: 10.593
+    - type: precision_at_100
+      value: 1.609
+    - type: precision_at_1000
+      value: 0.183
+    - type: precision_at_3
+      value: 23.881
+    - type: precision_at_5
+      value: 17.339
+    - type: recall_at_1
+      value: 26.987
+    - type: recall_at_10
+      value: 58.875
+    - type: recall_at_100
+      value: 87.023
+    - type: recall_at_1000
+      value: 98.328
+    - type: recall_at_3
+      value: 42.265
+    - type: recall_at_5
+      value: 49.334
+  - task:
+      type: PairClassification
+    dataset:
+      name: MTEB Cmnli
+      type: C-MTEB/CMNLI
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.91701743836441
+    - type: cos_sim_ap
+      value: 92.53650618807644
+    - type: cos_sim_f1
+      value: 86.80265975431082
+    - type: cos_sim_precision
+      value: 83.79025239338556
+    - type: cos_sim_recall
+      value: 90.039747486556
+    - type: dot_accuracy
+      value: 77.17378232110643
+    - type: dot_ap
+      value: 85.40244368166546
+    - type: dot_f1
+      value: 79.03038001481951
+    - type: dot_precision
+      value: 72.20502901353966
+    - type: dot_recall
+      value: 87.2808043020809
+    - type: euclidean_accuracy
+      value: 84.65423932651834
+    - type: euclidean_ap
+      value: 91.47775530034588
+    - type: euclidean_f1
+      value: 85.64471499723298
+    - type: euclidean_precision
+      value: 81.31567885666246
+    - type: euclidean_recall
+      value: 90.46060322656068
+    - type: manhattan_accuracy
+      value: 84.58208057726999
+    - type: manhattan_ap
+      value: 91.46228709402014
+    - type: manhattan_f1
+      value: 85.6631626034444
+    - type: manhattan_precision
+      value: 82.10075026795283
+    - type: manhattan_recall
+      value: 89.5487491232172
+    - type: max_accuracy
+      value: 85.91701743836441
+    - type: max_ap
+      value: 92.53650618807644
+    - type: max_f1
+      value: 86.80265975431082
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB CovidRetrieval
+      type: C-MTEB/CovidRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 83.693
+    - type: map_at_10
+      value: 90.098
+    - type: map_at_100
+      value: 90.145
+    - type: map_at_1000
+      value: 90.146
+    - type: map_at_3
+      value: 89.445
+    - type: map_at_5
+      value: 89.935
+    - type: mrr_at_1
+      value: 83.878
+    - type: mrr_at_10
+      value: 90.007
+    - type: mrr_at_100
+      value: 90.045
+    - type: mrr_at_1000
+      value: 90.046
+    - type: mrr_at_3
+      value: 89.34
+    - type: mrr_at_5
+      value: 89.835
+    - type: ndcg_at_1
+      value: 84.089
+    - type: ndcg_at_10
+      value: 92.351
+    - type: ndcg_at_100
+      value: 92.54599999999999
+    - type: ndcg_at_1000
+      value: 92.561
+    - type: ndcg_at_3
+      value: 91.15299999999999
+    - type: ndcg_at_5
+      value: 91.968
+    - type: precision_at_1
+      value: 84.089
+    - type: precision_at_10
+      value: 10.011000000000001
+    - type: precision_at_100
+      value: 1.009
+    - type: precision_at_1000
+      value: 0.101
+    - type: precision_at_3
+      value: 32.28
+    - type: precision_at_5
+      value: 19.789
+    - type: recall_at_1
+      value: 83.693
+    - type: recall_at_10
+      value: 99.05199999999999
+    - type: recall_at_100
+      value: 99.895
+    - type: recall_at_1000
+      value: 100
+    - type: recall_at_3
+      value: 95.917
+    - type: recall_at_5
+      value: 97.893
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB DuRetrieval
+      type: C-MTEB/DuRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.924
+    - type: map_at_10
+      value: 81.392
+    - type: map_at_100
+      value: 84.209
+    - type: map_at_1000
+      value: 84.237
+    - type: map_at_3
+      value: 56.998000000000005
+    - type: map_at_5
+      value: 71.40100000000001
+    - type: mrr_at_1
+      value: 91.75
+    - type: mrr_at_10
+      value: 94.45
+    - type: mrr_at_100
+      value: 94.503
+    - type: mrr_at_1000
+      value: 94.505
+    - type: mrr_at_3
+      value: 94.258
+    - type: mrr_at_5
+      value: 94.381
+    - type: ndcg_at_1
+      value: 91.75
+    - type: ndcg_at_10
+      value: 88.53
+    - type: ndcg_at_100
+      value: 91.13900000000001
+    - type: ndcg_at_1000
+      value: 91.387
+    - type: ndcg_at_3
+      value: 87.925
+    - type: ndcg_at_5
+      value: 86.461
+    - type: precision_at_1
+      value: 91.75
+    - type: precision_at_10
+      value: 42.05
+    - type: precision_at_100
+      value: 4.827
+    - type: precision_at_1000
+      value: 0.48900000000000005
+    - type: precision_at_3
+      value: 78.55
+    - type: precision_at_5
+      value: 65.82000000000001
+    - type: recall_at_1
+      value: 26.924
+    - type: recall_at_10
+      value: 89.338
+    - type: recall_at_100
+      value: 97.856
+    - type: recall_at_1000
+      value: 99.11
+    - type: recall_at_3
+      value: 59.202999999999996
+    - type: recall_at_5
+      value: 75.642
+  - 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: 65.613
+    - type: map_at_100
+      value: 66.185
+    - type: map_at_1000
+      value: 66.191
+    - type: map_at_3
+      value: 62.8
+    - type: map_at_5
+      value: 64.535
+    - type: mrr_at_1
+      value: 54.800000000000004
+    - type: mrr_at_10
+      value: 65.613
+    - type: mrr_at_100
+      value: 66.185
+    - type: mrr_at_1000
+      value: 66.191
+    - type: mrr_at_3
+      value: 62.8
+    - type: mrr_at_5
+      value: 64.535
+    - type: ndcg_at_1
+      value: 54.800000000000004
+    - type: ndcg_at_10
+      value: 70.991
+    - type: ndcg_at_100
+      value: 73.434
+    - type: ndcg_at_1000
+      value: 73.587
+    - type: ndcg_at_3
+      value: 65.324
+    - type: ndcg_at_5
+      value: 68.431
+    - type: precision_at_1
+      value: 54.800000000000004
+    - type: precision_at_10
+      value: 8.790000000000001
+    - type: precision_at_100
+      value: 0.9860000000000001
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 24.2
+    - type: precision_at_5
+      value: 16.02
+    - type: recall_at_1
+      value: 54.800000000000004
+    - type: recall_at_10
+      value: 87.9
+    - type: recall_at_100
+      value: 98.6
+    - type: recall_at_1000
+      value: 99.8
+    - type: recall_at_3
+      value: 72.6
+    - type: recall_at_5
+      value: 80.10000000000001
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB IFlyTek
+      type: C-MTEB/IFlyTek-classification
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 51.94305502116199
+    - type: f1
+      value: 39.82197338426721
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB JDReview
+      type: C-MTEB/JDReview-classification
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 90.31894934333957
+    - type: ap
+      value: 63.89821836499594
+    - type: f1
+      value: 85.93687177603624
+  - task:
+      type: STS
+    dataset:
+      name: MTEB LCQMC
+      type: C-MTEB/LCQMC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 73.18906216730208
+    - type: cos_sim_spearman
+      value: 79.44570226735877
+    - type: euclidean_pearson
+      value: 78.8105072242798
+    - type: euclidean_spearman
+      value: 79.15605680863212
+    - type: manhattan_pearson
+      value: 78.80576507484064
+    - type: manhattan_spearman
+      value: 79.14625534068364
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB MMarcoReranking
+      type: C-MTEB/Mmarco-reranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 41.58107192600853
+    - type: mrr
+      value: 41.37063492063492
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB MMarcoRetrieval
+      type: C-MTEB/MMarcoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 68.33
+    - type: map_at_10
+      value: 78.261
+    - type: map_at_100
+      value: 78.522
+    - type: map_at_1000
+      value: 78.527
+    - type: map_at_3
+      value: 76.236
+    - type: map_at_5
+      value: 77.557
+    - type: mrr_at_1
+      value: 70.602
+    - type: mrr_at_10
+      value: 78.779
+    - type: mrr_at_100
+      value: 79.00500000000001
+    - type: mrr_at_1000
+      value: 79.01
+    - type: mrr_at_3
+      value: 77.037
+    - type: mrr_at_5
+      value: 78.157
+    - type: ndcg_at_1
+      value: 70.602
+    - type: ndcg_at_10
+      value: 82.254
+    - type: ndcg_at_100
+      value: 83.319
+    - type: ndcg_at_1000
+      value: 83.449
+    - type: ndcg_at_3
+      value: 78.46
+    - type: ndcg_at_5
+      value: 80.679
+    - type: precision_at_1
+      value: 70.602
+    - type: precision_at_10
+      value: 9.989
+    - type: precision_at_100
+      value: 1.05
+    - type: precision_at_1000
+      value: 0.106
+    - type: precision_at_3
+      value: 29.598999999999997
+    - type: precision_at_5
+      value: 18.948
+    - type: recall_at_1
+      value: 68.33
+    - type: recall_at_10
+      value: 94.00800000000001
+    - type: recall_at_100
+      value: 98.589
+    - type: recall_at_1000
+      value: 99.60799999999999
+    - type: recall_at_3
+      value: 84.057
+    - type: recall_at_5
+      value: 89.32900000000001
+  - 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: 78.13718897108272
+    - type: f1
+      value: 74.07613180855328
+  - 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: 86.20040349697376
+    - type: f1
+      value: 85.05282136519973
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB MedicalRetrieval
+      type: C-MTEB/MedicalRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 56.8
+    - type: map_at_10
+      value: 64.199
+    - type: map_at_100
+      value: 64.89
+    - type: map_at_1000
+      value: 64.917
+    - type: map_at_3
+      value: 62.383
+    - type: map_at_5
+      value: 63.378
+    - type: mrr_at_1
+      value: 56.8
+    - type: mrr_at_10
+      value: 64.199
+    - type: mrr_at_100
+      value: 64.89
+    - type: mrr_at_1000
+      value: 64.917
+    - type: mrr_at_3
+      value: 62.383
+    - type: mrr_at_5
+      value: 63.378
+    - type: ndcg_at_1
+      value: 56.8
+    - type: ndcg_at_10
+      value: 67.944
+    - type: ndcg_at_100
+      value: 71.286
+    - type: ndcg_at_1000
+      value: 71.879
+    - type: ndcg_at_3
+      value: 64.163
+    - type: ndcg_at_5
+      value: 65.96600000000001
+    - type: precision_at_1
+      value: 56.8
+    - type: precision_at_10
+      value: 7.9799999999999995
+    - type: precision_at_100
+      value: 0.954
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 23.1
+    - type: precision_at_5
+      value: 14.74
+    - type: recall_at_1
+      value: 56.8
+    - type: recall_at_10
+      value: 79.80000000000001
+    - type: recall_at_100
+      value: 95.39999999999999
+    - type: recall_at_1000
+      value: 99.8
+    - type: recall_at_3
+      value: 69.3
+    - type: recall_at_5
+      value: 73.7
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB MultilingualSentiment
+      type: C-MTEB/MultilingualSentiment-classification
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 78.57666666666667
+    - type: f1
+      value: 78.23373528202681
+  - 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: 90.76665640338129
+    - type: cos_sim_f1
+      value: 86.5021770682148
+    - type: cos_sim_precision
+      value: 79.82142857142858
+    - type: cos_sim_recall
+      value: 94.40337909186906
+    - type: dot_accuracy
+      value: 78.66811044937737
+    - type: dot_ap
+      value: 85.84084363880804
+    - type: dot_f1
+      value: 80.10075566750629
+    - type: dot_precision
+      value: 76.58959537572254
+    - type: dot_recall
+      value: 83.9493136219641
+    - type: euclidean_accuracy
+      value: 84.46128857606931
+    - type: euclidean_ap
+      value: 88.62351100230491
+    - type: euclidean_f1
+      value: 85.7709469509172
+    - type: euclidean_precision
+      value: 80.8411214953271
+    - type: euclidean_recall
+      value: 91.34107708553326
+    - type: manhattan_accuracy
+      value: 84.51543042772063
+    - type: manhattan_ap
+      value: 88.53975607870393
+    - type: manhattan_f1
+      value: 85.75697211155378
+    - type: manhattan_precision
+      value: 81.14985862393968
+    - type: manhattan_recall
+      value: 90.91869060190075
+    - type: max_accuracy
+      value: 85.43584190579317
+    - type: max_ap
+      value: 90.76665640338129
+    - type: max_f1
+      value: 86.5021770682148
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB OnlineShopping
+      type: C-MTEB/OnlineShopping-classification
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 95.06999999999998
+    - type: ap
+      value: 93.45104559324996
+    - type: f1
+      value: 95.06036329426092
+  - task:
+      type: STS
+    dataset:
+      name: MTEB PAWSX
+      type: C-MTEB/PAWSX
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 40.01998290519605
+    - type: cos_sim_spearman
+      value: 46.5989769986853
+    - type: euclidean_pearson
+      value: 45.37905883182924
+    - type: euclidean_spearman
+      value: 46.22213849806378
+    - type: manhattan_pearson
+      value: 45.40925124776211
+    - type: manhattan_spearman
+      value: 46.250705124226386
+  - task:
+      type: STS
+    dataset:
+      name: MTEB QBQTC
+      type: C-MTEB/QBQTC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 42.719516197112526
+    - type: cos_sim_spearman
+      value: 44.57507789581106
+    - type: euclidean_pearson
+      value: 35.73062264160721
+    - type: euclidean_spearman
+      value: 40.473523909913695
+    - type: manhattan_pearson
+      value: 35.69868964086357
+    - type: manhattan_spearman
+      value: 40.46349925372903
+  - 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: 62.340118285801104
+    - type: cos_sim_spearman
+      value: 67.72781908620632
+    - type: euclidean_pearson
+      value: 63.161965746091596
+    - type: euclidean_spearman
+      value: 67.36825684340769
+    - type: manhattan_pearson
+      value: 63.089863788261425
+    - type: manhattan_spearman
+      value: 67.40868898995384
+  - task:
+      type: STS
+    dataset:
+      name: MTEB STSB
+      type: C-MTEB/STSB
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.1646360962365
+    - type: cos_sim_spearman
+      value: 81.24426700767087
+    - type: euclidean_pearson
+      value: 79.43826409936123
+    - type: euclidean_spearman
+      value: 79.71787965300125
+    - type: manhattan_pearson
+      value: 79.43377784961737
+    - type: manhattan_spearman
+      value: 79.69348376886967
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB T2Reranking
+      type: C-MTEB/T2Reranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 68.35595092507496
+    - type: mrr
+      value: 79.00244892585788
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB T2Retrieval
+      type: C-MTEB/T2Retrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.588
+    - type: map_at_10
+      value: 75.327
+    - type: map_at_100
+      value: 79.095
+    - type: map_at_1000
+      value: 79.163
+    - type: map_at_3
+      value: 52.637
+    - type: map_at_5
+      value: 64.802
+    - type: mrr_at_1
+      value: 88.103
+    - type: mrr_at_10
+      value: 91.29899999999999
+    - type: mrr_at_100
+      value: 91.408
+    - type: mrr_at_1000
+      value: 91.411
+    - type: mrr_at_3
+      value: 90.801
+    - type: mrr_at_5
+      value: 91.12700000000001
+    - type: ndcg_at_1
+      value: 88.103
+    - type: ndcg_at_10
+      value: 83.314
+    - type: ndcg_at_100
+      value: 87.201
+    - type: ndcg_at_1000
+      value: 87.83999999999999
+    - type: ndcg_at_3
+      value: 84.408
+    - type: ndcg_at_5
+      value: 83.078
+    - type: precision_at_1
+      value: 88.103
+    - type: precision_at_10
+      value: 41.638999999999996
+    - type: precision_at_100
+      value: 5.006
+    - type: precision_at_1000
+      value: 0.516
+    - type: precision_at_3
+      value: 73.942
+    - type: precision_at_5
+      value: 62.056
+    - type: recall_at_1
+      value: 26.588
+    - type: recall_at_10
+      value: 82.819
+    - type: recall_at_100
+      value: 95.334
+    - type: recall_at_1000
+      value: 98.51299999999999
+    - type: recall_at_3
+      value: 54.74
+    - type: recall_at_5
+      value: 68.864
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB TNews
+      type: C-MTEB/TNews-classification
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 55.029
+    - type: f1
+      value: 53.043617905026764
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB ThuNewsClusteringP2P
+      type: C-MTEB/ThuNewsClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 77.83675116835911
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB ThuNewsClusteringS2S
+      type: C-MTEB/ThuNewsClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 74.19701455865277
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB VideoRetrieval
+      type: C-MTEB/VideoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 64.7
+    - type: map_at_10
+      value: 75.593
+    - type: map_at_100
+      value: 75.863
+    - type: map_at_1000
+      value: 75.863
+    - type: map_at_3
+      value: 73.63300000000001
+    - type: map_at_5
+      value: 74.923
+    - type: mrr_at_1
+      value: 64.7
+    - type: mrr_at_10
+      value: 75.593
+    - type: mrr_at_100
+      value: 75.863
+    - type: mrr_at_1000
+      value: 75.863
+    - type: mrr_at_3
+      value: 73.63300000000001
+    - type: mrr_at_5
+      value: 74.923
+    - type: ndcg_at_1
+      value: 64.7
+    - type: ndcg_at_10
+      value: 80.399
+    - type: ndcg_at_100
+      value: 81.517
+    - type: ndcg_at_1000
+      value: 81.517
+    - type: ndcg_at_3
+      value: 76.504
+    - type: ndcg_at_5
+      value: 78.79899999999999
+    - type: precision_at_1
+      value: 64.7
+    - type: precision_at_10
+      value: 9.520000000000001
+    - type: precision_at_100
+      value: 1
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 28.266999999999996
+    - type: precision_at_5
+      value: 18.060000000000002
+    - type: recall_at_1
+      value: 64.7
+    - type: recall_at_10
+      value: 95.19999999999999
+    - type: recall_at_100
+      value: 100
+    - type: recall_at_1000
+      value: 100
+    - type: recall_at_3
+      value: 84.8
+    - type: recall_at_5
+      value: 90.3
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB Waimai
+      type: C-MTEB/waimai-classification
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 89.69999999999999
+    - type: ap
+      value: 75.91371640164184
+    - type: f1
+      value: 88.34067777698694
+---
+
+# Hoshino-Yumetsuki/Conan-embedding-v1-Q4_K_M-GGUF
+This model was converted to GGUF format from [`TencentBAC/Conan-embedding-v1`](https://huggingface.co/TencentBAC/Conan-embedding-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/TencentBAC/Conan-embedding-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 Hoshino-Yumetsuki/Conan-embedding-v1-Q4_K_M-GGUF --hf-file conan-embedding-v1-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+
+### Server:
+```bash
+llama-server --hf-repo Hoshino-Yumetsuki/Conan-embedding-v1-Q4_K_M-GGUF --hf-file conan-embedding-v1-q4_k_m.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 Hoshino-Yumetsuki/Conan-embedding-v1-Q4_K_M-GGUF --hf-file conan-embedding-v1-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+or 
+```
+./llama-server --hf-repo Hoshino-Yumetsuki/Conan-embedding-v1-Q4_K_M-GGUF --hf-file conan-embedding-v1-q4_k_m.gguf -c 2048
+```