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+---
+base_model: aspire/acge_text_embedding
+pipeline_tag: sentence-similarity
+tags:
+- mteb
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- llama-cpp
+- gguf-my-repo
+model-index:
+- name: acge_text_embedding
+  results:
+  - task:
+      type: STS
+    dataset:
+      name: MTEB AFQMC
+      type: C-MTEB/AFQMC
+      config: default
+      split: validation
+      revision: b44c3b011063adb25877c13823db83bb193913c4
+    metrics:
+    - type: cos_sim_pearson
+      value: 54.03434872650919
+    - type: cos_sim_spearman
+      value: 58.80730796688325
+    - type: euclidean_pearson
+      value: 57.47231387497989
+    - type: euclidean_spearman
+      value: 58.80775026351807
+    - type: manhattan_pearson
+      value: 57.46332720141574
+    - type: manhattan_spearman
+      value: 58.80196022940078
+  - task:
+      type: STS
+    dataset:
+      name: MTEB ATEC
+      type: C-MTEB/ATEC
+      config: default
+      split: test
+      revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
+    metrics:
+    - type: cos_sim_pearson
+      value: 53.52621290548175
+    - type: cos_sim_spearman
+      value: 57.945227768312144
+    - type: euclidean_pearson
+      value: 61.17041394151802
+    - type: euclidean_spearman
+      value: 57.94553287835657
+    - type: manhattan_pearson
+      value: 61.168327500057885
+    - type: manhattan_spearman
+      value: 57.94477516925043
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB AmazonReviewsClassification (zh)
+      type: mteb/amazon_reviews_multi
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 48.538000000000004
+    - type: f1
+      value: 46.59920995594044
+  - task:
+      type: STS
+    dataset:
+      name: MTEB BQ
+      type: C-MTEB/BQ
+      config: default
+      split: test
+      revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
+    metrics:
+    - type: cos_sim_pearson
+      value: 68.27529991817154
+    - type: cos_sim_spearman
+      value: 70.37095914176643
+    - type: euclidean_pearson
+      value: 69.42690712802727
+    - type: euclidean_spearman
+      value: 70.37017971889912
+    - type: manhattan_pearson
+      value: 69.40264877917839
+    - type: manhattan_spearman
+      value: 70.34786744049524
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB CLSClusteringP2P
+      type: C-MTEB/CLSClusteringP2P
+      config: default
+      split: test
+      revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
+    metrics:
+    - type: v_measure
+      value: 47.08027536192709
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB CLSClusteringS2S
+      type: C-MTEB/CLSClusteringS2S
+      config: default
+      split: test
+      revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
+    metrics:
+    - type: v_measure
+      value: 44.0526024940363
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB CMedQAv1
+      type: C-MTEB/CMedQAv1-reranking
+      config: default
+      split: test
+      revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
+    metrics:
+    - type: map
+      value: 88.65974993133156
+    - type: mrr
+      value: 90.64761904761905
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB CMedQAv2
+      type: C-MTEB/CMedQAv2-reranking
+      config: default
+      split: test
+      revision: 23d186750531a14a0357ca22cd92d712fd512ea0
+    metrics:
+    - type: map
+      value: 88.90396838907245
+    - type: mrr
+      value: 90.90932539682541
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB CmedqaRetrieval
+      type: C-MTEB/CmedqaRetrieval
+      config: default
+      split: dev
+      revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
+    metrics:
+    - type: map_at_1
+      value: 26.875
+    - type: map_at_10
+      value: 39.995999999999995
+    - type: map_at_100
+      value: 41.899
+    - type: map_at_1000
+      value: 42.0
+    - type: map_at_3
+      value: 35.414
+    - type: map_at_5
+      value: 38.019
+    - type: mrr_at_1
+      value: 40.635
+    - type: mrr_at_10
+      value: 48.827
+    - type: mrr_at_100
+      value: 49.805
+    - type: mrr_at_1000
+      value: 49.845
+    - type: mrr_at_3
+      value: 46.145
+    - type: mrr_at_5
+      value: 47.693999999999996
+    - type: ndcg_at_1
+      value: 40.635
+    - type: ndcg_at_10
+      value: 46.78
+    - type: ndcg_at_100
+      value: 53.986999999999995
+    - type: ndcg_at_1000
+      value: 55.684
+    - type: ndcg_at_3
+      value: 41.018
+    - type: ndcg_at_5
+      value: 43.559
+    - type: precision_at_1
+      value: 40.635
+    - type: precision_at_10
+      value: 10.427999999999999
+    - type: precision_at_100
+      value: 1.625
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 23.139000000000003
+    - type: precision_at_5
+      value: 17.004
+    - type: recall_at_1
+      value: 26.875
+    - type: recall_at_10
+      value: 57.887
+    - type: recall_at_100
+      value: 87.408
+    - type: recall_at_1000
+      value: 98.721
+    - type: recall_at_3
+      value: 40.812
+    - type: recall_at_5
+      value: 48.397
+  - task:
+      type: PairClassification
+    dataset:
+      name: MTEB Cmnli
+      type: C-MTEB/CMNLI
+      config: default
+      split: validation
+      revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
+    metrics:
+    - type: cos_sim_accuracy
+      value: 83.43956704750451
+    - type: cos_sim_ap
+      value: 90.49172854352659
+    - type: cos_sim_f1
+      value: 84.28475486903963
+    - type: cos_sim_precision
+      value: 80.84603822203135
+    - type: cos_sim_recall
+      value: 88.02899228431144
+    - type: dot_accuracy
+      value: 83.43956704750451
+    - type: dot_ap
+      value: 90.46317132695233
+    - type: dot_f1
+      value: 84.28794294628929
+    - type: dot_precision
+      value: 80.51948051948052
+    - type: dot_recall
+      value: 88.4264671498714
+    - type: euclidean_accuracy
+      value: 83.43956704750451
+    - type: euclidean_ap
+      value: 90.49171785256486
+    - type: euclidean_f1
+      value: 84.28235820561584
+    - type: euclidean_precision
+      value: 80.8022308022308
+    - type: euclidean_recall
+      value: 88.07575403320084
+    - type: manhattan_accuracy
+      value: 83.55983162958509
+    - type: manhattan_ap
+      value: 90.48046779812815
+    - type: manhattan_f1
+      value: 84.45354259069714
+    - type: manhattan_precision
+      value: 82.21877767936226
+    - type: manhattan_recall
+      value: 86.81318681318682
+    - type: max_accuracy
+      value: 83.55983162958509
+    - type: max_ap
+      value: 90.49172854352659
+    - type: max_f1
+      value: 84.45354259069714
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB CovidRetrieval
+      type: C-MTEB/CovidRetrieval
+      config: default
+      split: dev
+      revision: 1271c7809071a13532e05f25fb53511ffce77117
+    metrics:
+    - type: map_at_1
+      value: 68.54599999999999
+    - type: map_at_10
+      value: 77.62400000000001
+    - type: map_at_100
+      value: 77.886
+    - type: map_at_1000
+      value: 77.89
+    - type: map_at_3
+      value: 75.966
+    - type: map_at_5
+      value: 76.995
+    - type: mrr_at_1
+      value: 68.915
+    - type: mrr_at_10
+      value: 77.703
+    - type: mrr_at_100
+      value: 77.958
+    - type: mrr_at_1000
+      value: 77.962
+    - type: mrr_at_3
+      value: 76.08
+    - type: mrr_at_5
+      value: 77.118
+    - type: ndcg_at_1
+      value: 68.809
+    - type: ndcg_at_10
+      value: 81.563
+    - type: ndcg_at_100
+      value: 82.758
+    - type: ndcg_at_1000
+      value: 82.864
+    - type: ndcg_at_3
+      value: 78.29
+    - type: ndcg_at_5
+      value: 80.113
+    - type: precision_at_1
+      value: 68.809
+    - type: precision_at_10
+      value: 9.463000000000001
+    - type: precision_at_100
+      value: 1.001
+    - type: precision_at_1000
+      value: 0.101
+    - type: precision_at_3
+      value: 28.486
+    - type: precision_at_5
+      value: 18.019
+    - type: recall_at_1
+      value: 68.54599999999999
+    - type: recall_at_10
+      value: 93.625
+    - type: recall_at_100
+      value: 99.05199999999999
+    - type: recall_at_1000
+      value: 99.895
+    - type: recall_at_3
+      value: 84.879
+    - type: recall_at_5
+      value: 89.252
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB DuRetrieval
+      type: C-MTEB/DuRetrieval
+      config: default
+      split: dev
+      revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
+    metrics:
+    - type: map_at_1
+      value: 25.653
+    - type: map_at_10
+      value: 79.105
+    - type: map_at_100
+      value: 81.902
+    - type: map_at_1000
+      value: 81.947
+    - type: map_at_3
+      value: 54.54599999999999
+    - type: map_at_5
+      value: 69.226
+    - type: mrr_at_1
+      value: 89.35
+    - type: mrr_at_10
+      value: 92.69
+    - type: mrr_at_100
+      value: 92.77
+    - type: mrr_at_1000
+      value: 92.774
+    - type: mrr_at_3
+      value: 92.425
+    - type: mrr_at_5
+      value: 92.575
+    - type: ndcg_at_1
+      value: 89.35
+    - type: ndcg_at_10
+      value: 86.55199999999999
+    - type: ndcg_at_100
+      value: 89.35300000000001
+    - type: ndcg_at_1000
+      value: 89.782
+    - type: ndcg_at_3
+      value: 85.392
+    - type: ndcg_at_5
+      value: 84.5
+    - type: precision_at_1
+      value: 89.35
+    - type: precision_at_10
+      value: 41.589999999999996
+    - type: precision_at_100
+      value: 4.781
+    - type: precision_at_1000
+      value: 0.488
+    - type: precision_at_3
+      value: 76.683
+    - type: precision_at_5
+      value: 65.06
+    - type: recall_at_1
+      value: 25.653
+    - type: recall_at_10
+      value: 87.64999999999999
+    - type: recall_at_100
+      value: 96.858
+    - type: recall_at_1000
+      value: 99.13300000000001
+    - type: recall_at_3
+      value: 56.869
+    - type: recall_at_5
+      value: 74.024
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB EcomRetrieval
+      type: C-MTEB/EcomRetrieval
+      config: default
+      split: dev
+      revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
+    metrics:
+    - type: map_at_1
+      value: 52.1
+    - type: map_at_10
+      value: 62.629999999999995
+    - type: map_at_100
+      value: 63.117000000000004
+    - type: map_at_1000
+      value: 63.134
+    - type: map_at_3
+      value: 60.267
+    - type: map_at_5
+      value: 61.777
+    - type: mrr_at_1
+      value: 52.1
+    - type: mrr_at_10
+      value: 62.629999999999995
+    - type: mrr_at_100
+      value: 63.117000000000004
+    - type: mrr_at_1000
+      value: 63.134
+    - type: mrr_at_3
+      value: 60.267
+    - type: mrr_at_5
+      value: 61.777
+    - type: ndcg_at_1
+      value: 52.1
+    - type: ndcg_at_10
+      value: 67.596
+    - type: ndcg_at_100
+      value: 69.95
+    - type: ndcg_at_1000
+      value: 70.33500000000001
+    - type: ndcg_at_3
+      value: 62.82600000000001
+    - type: ndcg_at_5
+      value: 65.546
+    - type: precision_at_1
+      value: 52.1
+    - type: precision_at_10
+      value: 8.309999999999999
+    - type: precision_at_100
+      value: 0.941
+    - type: precision_at_1000
+      value: 0.097
+    - type: precision_at_3
+      value: 23.400000000000002
+    - type: precision_at_5
+      value: 15.36
+    - type: recall_at_1
+      value: 52.1
+    - type: recall_at_10
+      value: 83.1
+    - type: recall_at_100
+      value: 94.1
+    - type: recall_at_1000
+      value: 97.0
+    - type: recall_at_3
+      value: 70.19999999999999
+    - type: recall_at_5
+      value: 76.8
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB IFlyTek
+      type: C-MTEB/IFlyTek-classification
+      config: default
+      split: validation
+      revision: 421605374b29664c5fc098418fe20ada9bd55f8a
+    metrics:
+    - type: accuracy
+      value: 51.773759138130046
+    - type: f1
+      value: 40.341407912920054
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB JDReview
+      type: C-MTEB/JDReview-classification
+      config: default
+      split: test
+      revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
+    metrics:
+    - type: accuracy
+      value: 86.69793621013133
+    - type: ap
+      value: 55.46718958939327
+    - type: f1
+      value: 81.48228915952436
+  - task:
+      type: STS
+    dataset:
+      name: MTEB LCQMC
+      type: C-MTEB/LCQMC
+      config: default
+      split: test
+      revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
+    metrics:
+    - type: cos_sim_pearson
+      value: 71.1397780205448
+    - type: cos_sim_spearman
+      value: 78.17368193033309
+    - type: euclidean_pearson
+      value: 77.4849177602368
+    - type: euclidean_spearman
+      value: 78.17369079663212
+    - type: manhattan_pearson
+      value: 77.47344305182406
+    - type: manhattan_spearman
+      value: 78.16454335155387
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB MMarcoReranking
+      type: C-MTEB/Mmarco-reranking
+      config: default
+      split: dev
+      revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
+    metrics:
+    - type: map
+      value: 27.76160559006673
+    - type: mrr
+      value: 28.02420634920635
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB MMarcoRetrieval
+      type: C-MTEB/MMarcoRetrieval
+      config: default
+      split: dev
+      revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
+    metrics:
+    - type: map_at_1
+      value: 65.661
+    - type: map_at_10
+      value: 74.752
+    - type: map_at_100
+      value: 75.091
+    - type: map_at_1000
+      value: 75.104
+    - type: map_at_3
+      value: 72.997
+    - type: map_at_5
+      value: 74.119
+    - type: mrr_at_1
+      value: 67.923
+    - type: mrr_at_10
+      value: 75.376
+    - type: mrr_at_100
+      value: 75.673
+    - type: mrr_at_1000
+      value: 75.685
+    - type: mrr_at_3
+      value: 73.856
+    - type: mrr_at_5
+      value: 74.82799999999999
+    - type: ndcg_at_1
+      value: 67.923
+    - type: ndcg_at_10
+      value: 78.424
+    - type: ndcg_at_100
+      value: 79.95100000000001
+    - type: ndcg_at_1000
+      value: 80.265
+    - type: ndcg_at_3
+      value: 75.101
+    - type: ndcg_at_5
+      value: 76.992
+    - type: precision_at_1
+      value: 67.923
+    - type: precision_at_10
+      value: 9.474
+    - type: precision_at_100
+      value: 1.023
+    - type: precision_at_1000
+      value: 0.105
+    - type: precision_at_3
+      value: 28.319
+    - type: precision_at_5
+      value: 17.986
+    - type: recall_at_1
+      value: 65.661
+    - type: recall_at_10
+      value: 89.09899999999999
+    - type: recall_at_100
+      value: 96.023
+    - type: recall_at_1000
+      value: 98.455
+    - type: recall_at_3
+      value: 80.314
+    - type: recall_at_5
+      value: 84.81
+  - 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: 75.86751849361131
+    - type: f1
+      value: 73.04918450508
+  - 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: 78.4364492266308
+    - type: f1
+      value: 78.120686034844
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB MedicalRetrieval
+      type: C-MTEB/MedicalRetrieval
+      config: default
+      split: dev
+      revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
+    metrics:
+    - type: map_at_1
+      value: 55.00000000000001
+    - type: map_at_10
+      value: 61.06399999999999
+    - type: map_at_100
+      value: 61.622
+    - type: map_at_1000
+      value: 61.663000000000004
+    - type: map_at_3
+      value: 59.583
+    - type: map_at_5
+      value: 60.373
+    - type: mrr_at_1
+      value: 55.2
+    - type: mrr_at_10
+      value: 61.168
+    - type: mrr_at_100
+      value: 61.726000000000006
+    - type: mrr_at_1000
+      value: 61.767
+    - type: mrr_at_3
+      value: 59.683
+    - type: mrr_at_5
+      value: 60.492999999999995
+    - type: ndcg_at_1
+      value: 55.00000000000001
+    - type: ndcg_at_10
+      value: 64.098
+    - type: ndcg_at_100
+      value: 67.05
+    - type: ndcg_at_1000
+      value: 68.262
+    - type: ndcg_at_3
+      value: 61.00600000000001
+    - type: ndcg_at_5
+      value: 62.439
+    - type: precision_at_1
+      value: 55.00000000000001
+    - type: precision_at_10
+      value: 7.37
+    - type: precision_at_100
+      value: 0.881
+    - type: precision_at_1000
+      value: 0.098
+    - type: precision_at_3
+      value: 21.7
+    - type: precision_at_5
+      value: 13.719999999999999
+    - type: recall_at_1
+      value: 55.00000000000001
+    - type: recall_at_10
+      value: 73.7
+    - type: recall_at_100
+      value: 88.1
+    - type: recall_at_1000
+      value: 97.8
+    - type: recall_at_3
+      value: 65.10000000000001
+    - type: recall_at_5
+      value: 68.60000000000001
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB MultilingualSentiment
+      type: C-MTEB/MultilingualSentiment-classification
+      config: default
+      split: validation
+      revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
+    metrics:
+    - type: accuracy
+      value: 77.52666666666667
+    - type: f1
+      value: 77.49784731367215
+  - task:
+      type: PairClassification
+    dataset:
+      name: MTEB Ocnli
+      type: C-MTEB/OCNLI
+      config: default
+      split: validation
+      revision: 66e76a618a34d6d565d5538088562851e6daa7ec
+    metrics:
+    - type: cos_sim_accuracy
+      value: 81.10449377368705
+    - type: cos_sim_ap
+      value: 85.17742765935606
+    - type: cos_sim_f1
+      value: 83.00094966761633
+    - type: cos_sim_precision
+      value: 75.40983606557377
+    - type: cos_sim_recall
+      value: 92.29144667370645
+    - type: dot_accuracy
+      value: 81.10449377368705
+    - type: dot_ap
+      value: 85.17143850809614
+    - type: dot_f1
+      value: 83.01707779886148
+    - type: dot_precision
+      value: 75.36606373815677
+    - type: dot_recall
+      value: 92.39704329461456
+    - type: euclidean_accuracy
+      value: 81.10449377368705
+    - type: euclidean_ap
+      value: 85.17856775343333
+    - type: euclidean_f1
+      value: 83.00094966761633
+    - type: euclidean_precision
+      value: 75.40983606557377
+    - type: euclidean_recall
+      value: 92.29144667370645
+    - type: manhattan_accuracy
+      value: 81.05035192203573
+    - type: manhattan_ap
+      value: 85.14464459395809
+    - type: manhattan_f1
+      value: 82.96155671570953
+    - type: manhattan_precision
+      value: 75.3448275862069
+    - type: manhattan_recall
+      value: 92.29144667370645
+    - type: max_accuracy
+      value: 81.10449377368705
+    - type: max_ap
+      value: 85.17856775343333
+    - type: max_f1
+      value: 83.01707779886148
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB OnlineShopping
+      type: C-MTEB/OnlineShopping-classification
+      config: default
+      split: test
+      revision: e610f2ebd179a8fda30ae534c3878750a96db120
+    metrics:
+    - type: accuracy
+      value: 93.71000000000001
+    - type: ap
+      value: 91.83202232349356
+    - type: f1
+      value: 93.69900560334331
+  - task:
+      type: STS
+    dataset:
+      name: MTEB PAWSX
+      type: C-MTEB/PAWSX
+      config: default
+      split: test
+      revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
+    metrics:
+    - type: cos_sim_pearson
+      value: 39.175047651512415
+    - type: cos_sim_spearman
+      value: 45.51434675777896
+    - type: euclidean_pearson
+      value: 44.864110004132286
+    - type: euclidean_spearman
+      value: 45.516433048896076
+    - type: manhattan_pearson
+      value: 44.87153627706517
+    - type: manhattan_spearman
+      value: 45.52862617925012
+  - task:
+      type: STS
+    dataset:
+      name: MTEB QBQTC
+      type: C-MTEB/QBQTC
+      config: default
+      split: test
+      revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
+    metrics:
+    - type: cos_sim_pearson
+      value: 34.249579701429084
+    - type: cos_sim_spearman
+      value: 37.30903127368978
+    - type: euclidean_pearson
+      value: 35.129438425253355
+    - type: euclidean_spearman
+      value: 37.308544018709085
+    - type: manhattan_pearson
+      value: 35.08936153503652
+    - type: manhattan_spearman
+      value: 37.25582901077839
+  - task:
+      type: STS
+    dataset:
+      name: MTEB STS22 (zh)
+      type: mteb/sts22-crosslingual-sts
+      config: zh
+      split: test
+      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
+    metrics:
+    - type: cos_sim_pearson
+      value: 61.29309637460004
+    - type: cos_sim_spearman
+      value: 65.85136090376717
+    - type: euclidean_pearson
+      value: 64.04783990953557
+    - type: euclidean_spearman
+      value: 65.85036859610366
+    - type: manhattan_pearson
+      value: 63.995852552712186
+    - type: manhattan_spearman
+      value: 65.86508416749417
+  - task:
+      type: STS
+    dataset:
+      name: MTEB STSB
+      type: C-MTEB/STSB
+      config: default
+      split: test
+      revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.5595940455587
+    - type: cos_sim_spearman
+      value: 82.72654634579749
+    - type: euclidean_pearson
+      value: 82.4892721061365
+    - type: euclidean_spearman
+      value: 82.72678504228253
+    - type: manhattan_pearson
+      value: 82.4770861422454
+    - type: manhattan_spearman
+      value: 82.71137469783162
+  - task:
+      type: Reranking
+    dataset:
+      name: MTEB T2Reranking
+      type: C-MTEB/T2Reranking
+      config: default
+      split: dev
+      revision: 76631901a18387f85eaa53e5450019b87ad58ef9
+    metrics:
+    - type: map
+      value: 66.6159547610527
+    - type: mrr
+      value: 76.35739406347057
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB T2Retrieval
+      type: C-MTEB/T2Retrieval
+      config: default
+      split: dev
+      revision: 8731a845f1bf500a4f111cf1070785c793d10e64
+    metrics:
+    - type: map_at_1
+      value: 27.878999999999998
+    - type: map_at_10
+      value: 77.517
+    - type: map_at_100
+      value: 81.139
+    - type: map_at_1000
+      value: 81.204
+    - type: map_at_3
+      value: 54.728
+    - type: map_at_5
+      value: 67.128
+    - type: mrr_at_1
+      value: 90.509
+    - type: mrr_at_10
+      value: 92.964
+    - type: mrr_at_100
+      value: 93.045
+    - type: mrr_at_1000
+      value: 93.048
+    - type: mrr_at_3
+      value: 92.551
+    - type: mrr_at_5
+      value: 92.81099999999999
+    - type: ndcg_at_1
+      value: 90.509
+    - type: ndcg_at_10
+      value: 85.075
+    - type: ndcg_at_100
+      value: 88.656
+    - type: ndcg_at_1000
+      value: 89.25699999999999
+    - type: ndcg_at_3
+      value: 86.58200000000001
+    - type: ndcg_at_5
+      value: 85.138
+    - type: precision_at_1
+      value: 90.509
+    - type: precision_at_10
+      value: 42.05
+    - type: precision_at_100
+      value: 5.013999999999999
+    - type: precision_at_1000
+      value: 0.516
+    - type: precision_at_3
+      value: 75.551
+    - type: precision_at_5
+      value: 63.239999999999995
+    - type: recall_at_1
+      value: 27.878999999999998
+    - type: recall_at_10
+      value: 83.941
+    - type: recall_at_100
+      value: 95.568
+    - type: recall_at_1000
+      value: 98.55000000000001
+    - type: recall_at_3
+      value: 56.374
+    - type: recall_at_5
+      value: 70.435
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB TNews
+      type: C-MTEB/TNews-classification
+      config: default
+      split: validation
+      revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
+    metrics:
+    - type: accuracy
+      value: 53.687
+    - type: f1
+      value: 51.86911933364655
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB ThuNewsClusteringP2P
+      type: C-MTEB/ThuNewsClusteringP2P
+      config: default
+      split: test
+      revision: 5798586b105c0434e4f0fe5e767abe619442cf93
+    metrics:
+    - type: v_measure
+      value: 74.65887489872564
+  - task:
+      type: Clustering
+    dataset:
+      name: MTEB ThuNewsClusteringS2S
+      type: C-MTEB/ThuNewsClusteringS2S
+      config: default
+      split: test
+      revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
+    metrics:
+    - type: v_measure
+      value: 69.00410995984436
+  - task:
+      type: Retrieval
+    dataset:
+      name: MTEB VideoRetrieval
+      type: C-MTEB/VideoRetrieval
+      config: default
+      split: dev
+      revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
+    metrics:
+    - type: map_at_1
+      value: 59.4
+    - type: map_at_10
+      value: 69.214
+    - type: map_at_100
+      value: 69.72699999999999
+    - type: map_at_1000
+      value: 69.743
+    - type: map_at_3
+      value: 67.717
+    - type: map_at_5
+      value: 68.782
+    - type: mrr_at_1
+      value: 59.4
+    - type: mrr_at_10
+      value: 69.214
+    - type: mrr_at_100
+      value: 69.72699999999999
+    - type: mrr_at_1000
+      value: 69.743
+    - type: mrr_at_3
+      value: 67.717
+    - type: mrr_at_5
+      value: 68.782
+    - type: ndcg_at_1
+      value: 59.4
+    - type: ndcg_at_10
+      value: 73.32300000000001
+    - type: ndcg_at_100
+      value: 75.591
+    - type: ndcg_at_1000
+      value: 75.98700000000001
+    - type: ndcg_at_3
+      value: 70.339
+    - type: ndcg_at_5
+      value: 72.246
+    - type: precision_at_1
+      value: 59.4
+    - type: precision_at_10
+      value: 8.59
+    - type: precision_at_100
+      value: 0.96
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 25.967000000000002
+    - type: precision_at_5
+      value: 16.5
+    - type: recall_at_1
+      value: 59.4
+    - type: recall_at_10
+      value: 85.9
+    - type: recall_at_100
+      value: 96.0
+    - type: recall_at_1000
+      value: 99.1
+    - type: recall_at_3
+      value: 77.9
+    - type: recall_at_5
+      value: 82.5
+  - task:
+      type: Classification
+    dataset:
+      name: MTEB Waimai
+      type: C-MTEB/waimai-classification
+      config: default
+      split: test
+      revision: 339287def212450dcaa9df8c22bf93e9980c7023
+    metrics:
+    - type: accuracy
+      value: 88.53
+    - type: ap
+      value: 73.56216166534062
+    - type: f1
+      value: 87.06093694294485
+---
+
+# bnightning/acge_text_embedding-Q4_K_M-GGUF
+This model was converted to GGUF format from [`aspire/acge_text_embedding`](https://huggingface.co/aspire/acge_text_embedding) 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/aspire/acge_text_embedding) 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 bnightning/acge_text_embedding-Q4_K_M-GGUF --hf-file acge_text_embedding-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+
+### Server:
+```bash
+llama-server --hf-repo bnightning/acge_text_embedding-Q4_K_M-GGUF --hf-file acge_text_embedding-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 bnightning/acge_text_embedding-Q4_K_M-GGUF --hf-file acge_text_embedding-q4_k_m.gguf -p "The meaning to life and the universe is"
+```
+or 
+```
+./llama-server --hf-repo bnightning/acge_text_embedding-Q4_K_M-GGUF --hf-file acge_text_embedding-q4_k_m.gguf -c 2048
+```