commit modelcard

README.md

@@ -1,6 +1,1207 @@
----
-datasets:
-- teknium/OpenHermes-2.5
-language:
-- en
----
+```yaml
+tags:
+- mteb
+model-index:
+- name: jionglin-embedding
+  results:
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/AFQMC
+      name: MTEB AFQMC
+      config: default
+      split: validation
+      revision: b44c3b011063adb25877c13823db83bb193913c4
+    metrics:
+    - type: cos_sim_pearson
+      value: 53.66919706568301
+    - type: cos_sim_spearman
+      value: 53.84074348656974
+    - type: euclidean_pearson
+      value: 53.58226184439896
+    - type: euclidean_spearman
+      value: 53.84074348656974
+    - type: manhattan_pearson
+      value: 53.64565834381205
+    - type: manhattan_spearman
+      value: 53.75526003581371
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/ATEC
+      name: MTEB ATEC
+      config: default
+      split: test
+      revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
+    metrics:
+    - type: cos_sim_pearson
+      value: 58.123744893539495
+    - type: cos_sim_spearman
+      value: 54.44277675493291
+    - type: euclidean_pearson
+      value: 61.20550691770944
+    - type: euclidean_spearman
+      value: 54.44277225170509
+    - type: manhattan_pearson
+      value: 60.57835645653918
+    - type: manhattan_spearman
+      value: 54.46153709699013
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (zh)
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 29.746
+    - type: f1
+      value: 29.039321522193585
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/BQ
+      name: MTEB BQ
+      config: default
+      split: test
+      revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
+    metrics:
+    - type: cos_sim_pearson
+      value: 70.7026320728244
+    - type: cos_sim_spearman
+      value: 70.57218534128499
+    - type: euclidean_pearson
+      value: 69.28488221289881
+    - type: euclidean_spearman
+      value: 70.57218534192015
+    - type: manhattan_pearson
+      value: 69.65344674392082
+    - type: manhattan_spearman
+      value: 70.64136691477553
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringP2P
+      name: MTEB CLSClusteringP2P
+      config: default
+      split: test
+      revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
+    metrics:
+    - type: v_measure
+      value: 38.87791994762536
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringS2S
+      name: MTEB CLSClusteringS2S
+      config: default
+      split: test
+      revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
+    metrics:
+    - type: v_measure
+      value: 39.09103599244803
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv1-reranking
+      name: MTEB CMedQAv1
+      config: default
+      split: test
+      revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
+    metrics:
+    - type: map
+      value: 80.40249793910444
+    - type: mrr
+      value: 82.96805555555555
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv2-reranking
+      name: MTEB CMedQAv2
+      config: default
+      split: test
+      revision: 23d186750531a14a0357ca22cd92d712fd512ea0
+    metrics:
+    - type: map
+      value: 80.39046823499085
+    - type: mrr
+      value: 83.22674603174602
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CmedqaRetrieval
+      name: MTEB CmedqaRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.715000000000002
+    - type: map_at_10
+      value: 24.651
+    - type: map_at_100
+      value: 26.478
+    - type: map_at_1000
+      value: 26.648
+    - type: map_at_3
+      value: 21.410999999999998
+    - type: map_at_5
+      value: 23.233
+    - type: mrr_at_1
+      value: 24.806
+    - type: mrr_at_10
+      value: 32.336
+    - type: mrr_at_100
+      value: 33.493
+    - type: mrr_at_1000
+      value: 33.568999999999996
+    - type: mrr_at_3
+      value: 29.807
+    - type: mrr_at_5
+      value: 31.294
+    - type: ndcg_at_1
+      value: 24.806
+    - type: ndcg_at_10
+      value: 30.341
+    - type: ndcg_at_100
+      value: 38.329
+    - type: ndcg_at_1000
+      value: 41.601
+    - type: ndcg_at_3
+      value: 25.655
+    - type: ndcg_at_5
+      value: 27.758
+    - type: precision_at_1
+      value: 24.806
+    - type: precision_at_10
+      value: 7.119000000000001
+    - type: precision_at_100
+      value: 1.3679999999999999
+    - type: precision_at_1000
+      value: 0.179
+    - type: precision_at_3
+      value: 14.787
+    - type: precision_at_5
+      value: 11.208
+    - type: recall_at_1
+      value: 15.715000000000002
+    - type: recall_at_10
+      value: 39.519999999999996
+    - type: recall_at_100
+      value: 73.307
+    - type: recall_at_1000
+      value: 95.611
+    - type: recall_at_3
+      value: 26.026
+    - type: recall_at_5
+      value: 32.027
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/CMNLI
+      name: MTEB Cmnli
+      config: default
+      split: validation
+      revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
+    metrics:
+    - type: cos_sim_accuracy
+      value: 66.89116055321708
+    - type: cos_sim_ap
+      value: 75.66575745519994
+    - type: cos_sim_f1
+      value: 70.2448775612194
+    - type: cos_sim_precision
+      value: 61.347765363128495
+    - type: cos_sim_recall
+      value: 82.16039279869068
+    - type: dot_accuracy
+      value: 66.89116055321708
+    - type: dot_ap
+      value: 75.68262052264197
+    - type: dot_f1
+      value: 70.2448775612194
+    - type: dot_precision
+      value: 61.347765363128495
+    - type: dot_recall
+      value: 82.16039279869068
+    - type: euclidean_accuracy
+      value: 66.89116055321708
+    - type: euclidean_ap
+      value: 75.66576722188334
+    - type: euclidean_f1
+      value: 70.2448775612194
+    - type: euclidean_precision
+      value: 61.347765363128495
+    - type: euclidean_recall
+      value: 82.16039279869068
+    - type: manhattan_accuracy
+      value: 67.03547805171377
+    - type: manhattan_ap
+      value: 75.78816934864089
+    - type: manhattan_f1
+      value: 70.35407081416284
+    - type: manhattan_precision
+      value: 61.4752665617899
+    - type: manhattan_recall
+      value: 82.23053542202479
+    - type: max_accuracy
+      value: 67.03547805171377
+    - type: max_ap
+      value: 75.78816934864089
+    - type: max_f1
+      value: 70.35407081416284
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CovidRetrieval
+      name: MTEB CovidRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 41.57
+    - type: map_at_10
+      value: 52.932
+    - type: map_at_100
+      value: 53.581999999999994
+    - type: map_at_1000
+      value: 53.61900000000001
+    - type: map_at_3
+      value: 50.066
+    - type: map_at_5
+      value: 51.735
+    - type: mrr_at_1
+      value: 41.623
+    - type: mrr_at_10
+      value: 52.964999999999996
+    - type: mrr_at_100
+      value: 53.6
+    - type: mrr_at_1000
+      value: 53.637
+    - type: mrr_at_3
+      value: 50.158
+    - type: mrr_at_5
+      value: 51.786
+    - type: ndcg_at_1
+      value: 41.623
+    - type: ndcg_at_10
+      value: 58.55200000000001
+    - type: ndcg_at_100
+      value: 61.824999999999996
+    - type: ndcg_at_1000
+      value: 62.854
+    - type: ndcg_at_3
+      value: 52.729000000000006
+    - type: ndcg_at_5
+      value: 55.696999999999996
+    - type: precision_at_1
+      value: 41.623
+    - type: precision_at_10
+      value: 7.692
+    - type: precision_at_100
+      value: 0.927
+    - type: precision_at_1000
+      value: 0.101
+    - type: precision_at_3
+      value: 20.162
+    - type: precision_at_5
+      value: 13.572000000000001
+    - type: recall_at_1
+      value: 41.57
+    - type: recall_at_10
+      value: 76.185
+    - type: recall_at_100
+      value: 91.728
+    - type: recall_at_1000
+      value: 99.895
+    - type: recall_at_3
+      value: 60.27400000000001
+    - type: recall_at_5
+      value: 67.46600000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/DuRetrieval
+      name: MTEB DuRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.071
+    - type: map_at_10
+      value: 65.093
+    - type: map_at_100
+      value: 69.097
+    - type: map_at_1000
+      value: 69.172
+    - type: map_at_3
+      value: 44.568000000000005
+    - type: map_at_5
+      value: 56.016999999999996
+    - type: mrr_at_1
+      value: 76.35
+    - type: mrr_at_10
+      value: 83.721
+    - type: mrr_at_100
+      value: 83.899
+    - type: mrr_at_1000
+      value: 83.904
+    - type: mrr_at_3
+      value: 82.958
+    - type: mrr_at_5
+      value: 83.488
+    - type: ndcg_at_1
+      value: 76.35
+    - type: ndcg_at_10
+      value: 75.05199999999999
+    - type: ndcg_at_100
+      value: 80.596
+    - type: ndcg_at_1000
+      value: 81.394
+    - type: ndcg_at_3
+      value: 73.298
+    - type: ndcg_at_5
+      value: 72.149
+    - type: precision_at_1
+      value: 76.35
+    - type: precision_at_10
+      value: 36.96
+    - type: precision_at_100
+      value: 4.688
+    - type: precision_at_1000
+      value: 0.48700000000000004
+    - type: precision_at_3
+      value: 66.2
+    - type: precision_at_5
+      value: 55.81
+    - type: recall_at_1
+      value: 21.071
+    - type: recall_at_10
+      value: 77.459
+    - type: recall_at_100
+      value: 94.425
+    - type: recall_at_1000
+      value: 98.631
+    - type: recall_at_3
+      value: 48.335
+    - type: recall_at_5
+      value: 63.227999999999994
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/EcomRetrieval
+      name: MTEB EcomRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 36.3
+    - type: map_at_10
+      value: 46.888999999999996
+    - type: map_at_100
+      value: 47.789
+    - type: map_at_1000
+      value: 47.827999999999996
+    - type: map_at_3
+      value: 43.85
+    - type: map_at_5
+      value: 45.58
+    - type: mrr_at_1
+      value: 36.3
+    - type: mrr_at_10
+      value: 46.888999999999996
+    - type: mrr_at_100
+      value: 47.789
+    - type: mrr_at_1000
+      value: 47.827999999999996
+    - type: mrr_at_3
+      value: 43.85
+    - type: mrr_at_5
+      value: 45.58
+    - type: ndcg_at_1
+      value: 36.3
+    - type: ndcg_at_10
+      value: 52.539
+    - type: ndcg_at_100
+      value: 56.882
+    - type: ndcg_at_1000
+      value: 57.841
+    - type: ndcg_at_3
+      value: 46.303
+    - type: ndcg_at_5
+      value: 49.406
+    - type: precision_at_1
+      value: 36.3
+    - type: precision_at_10
+      value: 7.049999999999999
+    - type: precision_at_100
+      value: 0.907
+    - type: precision_at_1000
+      value: 0.098
+    - type: precision_at_3
+      value: 17.8
+    - type: precision_at_5
+      value: 12.18
+    - type: recall_at_1
+      value: 36.3
+    - type: recall_at_10
+      value: 70.5
+    - type: recall_at_100
+      value: 90.7
+    - type: recall_at_1000
+      value: 98.1
+    - type: recall_at_3
+      value: 53.400000000000006
+    - type: recall_at_5
+      value: 60.9
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/IFlyTek-classification
+      name: MTEB IFlyTek
+      config: default
+      split: validation
+      revision: 421605374b29664c5fc098418fe20ada9bd55f8a
+    metrics:
+    - type: accuracy
+      value: 50.927279722970376
+    - type: f1
+      value: 39.57514582425314
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/JDReview-classification
+      name: MTEB JDReview
+      config: default
+      split: test
+      revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
+    metrics:
+    - type: accuracy
+      value: 84.93433395872421
+    - type: ap
+      value: 50.35046267230439
+    - type: f1
+      value: 78.76452515604298
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/LCQMC
+      name: MTEB LCQMC
+      config: default
+      split: test
+      revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
+    metrics:
+    - type: cos_sim_pearson
+      value: 67.40319768112933
+    - type: cos_sim_spearman
+      value: 74.9867527749418
+    - type: euclidean_pearson
+      value: 74.08762625643878
+    - type: euclidean_spearman
+      value: 74.98675720634276
+    - type: manhattan_pearson
+      value: 73.86303861791671
+    - type: manhattan_spearman
+      value: 75.0594224188492
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/Mmarco-reranking
+      name: MTEB MMarcoReranking
+      config: default
+      split: dev
+      revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
+    metrics:
+    - type: map
+      value: 18.860945903258536
+    - type: mrr
+      value: 17.686507936507937
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MMarcoRetrieval
+      name: MTEB MMarcoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 49.16
+    - type: map_at_10
+      value: 57.992
+    - type: map_at_100
+      value: 58.638
+    - type: map_at_1000
+      value: 58.67
+    - type: map_at_3
+      value: 55.71
+    - type: map_at_5
+      value: 57.04900000000001
+    - type: mrr_at_1
+      value: 50.989
+    - type: mrr_at_10
+      value: 58.814
+    - type: mrr_at_100
+      value: 59.401
+    - type: mrr_at_1000
+      value: 59.431
+    - type: mrr_at_3
+      value: 56.726
+    - type: mrr_at_5
+      value: 57.955
+    - type: ndcg_at_1
+      value: 50.989
+    - type: ndcg_at_10
+      value: 62.259
+    - type: ndcg_at_100
+      value: 65.347
+    - type: ndcg_at_1000
+      value: 66.231
+    - type: ndcg_at_3
+      value: 57.78
+    - type: ndcg_at_5
+      value: 60.09100000000001
+    - type: precision_at_1
+      value: 50.989
+    - type: precision_at_10
+      value: 7.9479999999999995
+    - type: precision_at_100
+      value: 0.951
+    - type: precision_at_1000
+      value: 0.10200000000000001
+    - type: precision_at_3
+      value: 22.087
+    - type: precision_at_5
+      value: 14.479000000000001
+    - type: recall_at_1
+      value: 49.16
+    - type: recall_at_10
+      value: 74.792
+    - type: recall_at_100
+      value: 89.132
+    - type: recall_at_1000
+      value: 96.13199999999999
+    - type: recall_at_3
+      value: 62.783
+    - type: recall_at_5
+      value: 68.26100000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MedicalRetrieval
+      name: MTEB MedicalRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 40.5
+    - type: map_at_10
+      value: 46.892
+    - type: map_at_100
+      value: 47.579
+    - type: map_at_1000
+      value: 47.648
+    - type: map_at_3
+      value: 45.367000000000004
+    - type: map_at_5
+      value: 46.182
+    - type: mrr_at_1
+      value: 40.6
+    - type: mrr_at_10
+      value: 46.942
+    - type: mrr_at_100
+      value: 47.629
+    - type: mrr_at_1000
+      value: 47.698
+    - type: mrr_at_3
+      value: 45.417
+    - type: mrr_at_5
+      value: 46.232
+    - type: ndcg_at_1
+      value: 40.5
+    - type: ndcg_at_10
+      value: 50.078
+    - type: ndcg_at_100
+      value: 53.635999999999996
+    - type: ndcg_at_1000
+      value: 55.696999999999996
+    - type: ndcg_at_3
+      value: 46.847
+    - type: ndcg_at_5
+      value: 48.323
+    - type: precision_at_1
+      value: 40.5
+    - type: precision_at_10
+      value: 6.02
+    - type: precision_at_100
+      value: 0.773
+    - type: precision_at_1000
+      value: 0.094
+    - type: precision_at_3
+      value: 17.033
+    - type: precision_at_5
+      value: 10.94
+    - type: recall_at_1
+      value: 40.5
+    - type: recall_at_10
+      value: 60.199999999999996
+    - type: recall_at_100
+      value: 77.3
+    - type: recall_at_1000
+      value: 94.0
+    - type: recall_at_3
+      value: 51.1
+    - type: recall_at_5
+      value: 54.7
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/MultilingualSentiment-classification
+      name: MTEB MultilingualSentiment
+      config: default
+      split: validation
+      revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
+    metrics:
+    - type: accuracy
+      value: 55.90333333333333
+    - type: f1
+      value: 55.291185234519546
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/OCNLI
+      name: MTEB Ocnli
+      config: default
+      split: validation
+      revision: 66e76a618a34d6d565d5538088562851e6daa7ec
+    metrics:
+    - type: cos_sim_accuracy
+      value: 59.01461829994585
+    - type: cos_sim_ap
+      value: 61.84829541140869
+    - type: cos_sim_f1
+      value: 67.94150731158605
+    - type: cos_sim_precision
+      value: 52.674418604651166
+    - type: cos_sim_recall
+      value: 95.67053854276664
+    - type: dot_accuracy
+      value: 59.01461829994585
+    - type: dot_ap
+      value: 61.84829541140869
+    - type: dot_f1
+      value: 67.94150731158605
+    - type: dot_precision
+      value: 52.674418604651166
+    - type: dot_recall
+      value: 95.67053854276664
+    - type: euclidean_accuracy
+      value: 59.01461829994585
+    - type: euclidean_ap
+      value: 61.84829541140869
+    - type: euclidean_f1
+      value: 67.94150731158605
+    - type: euclidean_precision
+      value: 52.674418604651166
+    - type: euclidean_recall
+      value: 95.67053854276664
+    - type: manhattan_accuracy
+      value: 59.06876015159719
+    - type: manhattan_ap
+      value: 61.91217952354554
+    - type: manhattan_f1
+      value: 67.89059572873735
+    - type: manhattan_precision
+      value: 52.613240418118465
+    - type: manhattan_recall
+      value: 95.67053854276664
+    - type: max_accuracy
+      value: 59.06876015159719
+    - type: max_ap
+      value: 61.91217952354554
+    - type: max_f1
+      value: 67.94150731158605
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/OnlineShopping-classification
+      name: MTEB OnlineShopping
+      config: default
+      split: test
+      revision: e610f2ebd179a8fda30ae534c3878750a96db120
+    metrics:
+    - type: accuracy
+      value: 82.53
+    - type: ap
+      value: 77.67591637020448
+    - type: f1
+      value: 82.39976599130478
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/PAWSX
+      name: MTEB PAWSX
+      config: default
+      split: test
+      revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
+    metrics:
+    - type: cos_sim_pearson
+      value: 55.76388035743312
+    - type: cos_sim_spearman
+      value: 58.34768166139753
+    - type: euclidean_pearson
+      value: 57.971763429924074
+    - type: euclidean_spearman
+      value: 58.34750745303424
+    - type: manhattan_pearson
+      value: 58.044053497280245
+    - type: manhattan_spearman
+      value: 58.61627719613188
+  - task:
+      type: PairClassification
+    dataset:
+      type: paws-x
+      name: MTEB PawsX (zh)
+      config: zh
+      split: test
+      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+    metrics:
+    - type: cos_sim_accuracy
+      value: 75.75
+    - type: cos_sim_ap
+      value: 78.80617392926526
+    - type: cos_sim_f1
+      value: 75.92417061611374
+    - type: cos_sim_precision
+      value: 65.87171052631578
+    - type: cos_sim_recall
+      value: 89.59731543624162
+    - type: dot_accuracy
+      value: 75.75
+    - type: dot_ap
+      value: 78.83768586994135
+    - type: dot_f1
+      value: 75.92417061611374
+    - type: dot_precision
+      value: 65.87171052631578
+    - type: dot_recall
+      value: 89.59731543624162
+    - type: euclidean_accuracy
+      value: 75.75
+    - type: euclidean_ap
+      value: 78.80617392926526
+    - type: euclidean_f1
+      value: 75.92417061611374
+    - type: euclidean_precision
+      value: 65.87171052631578
+    - type: euclidean_recall
+      value: 89.59731543624162
+    - type: manhattan_accuracy
+      value: 75.75
+    - type: manhattan_ap
+      value: 78.98640478955386
+    - type: manhattan_f1
+      value: 75.92954990215264
+    - type: manhattan_precision
+      value: 67.47826086956522
+    - type: manhattan_recall
+      value: 86.80089485458613
+    - type: max_accuracy
+      value: 75.75
+    - type: max_ap
+      value: 78.98640478955386
+    - type: max_f1
+      value: 75.92954990215264
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/QBQTC
+      name: MTEB QBQTC
+      config: default
+      split: test
+      revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
+    metrics:
+    - type: cos_sim_pearson
+      value: 74.40348414238575
+    - type: cos_sim_spearman
+      value: 71.452270332177
+    - type: euclidean_pearson
+      value: 72.62509231589097
+    - type: euclidean_spearman
+      value: 71.45228258458943
+    - type: manhattan_pearson
+      value: 73.03846856200839
+    - type: manhattan_spearman
+      value: 71.43673225319574
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (zh)
+      config: zh
+      split: test
+      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
+    metrics:
+    - type: cos_sim_pearson
+      value: 75.38335474357001
+    - type: cos_sim_spearman
+      value: 74.92262892309807
+    - type: euclidean_pearson
+      value: 73.93451693251345
+    - type: euclidean_spearman
+      value: 74.92262892309807
+    - type: manhattan_pearson
+      value: 74.55911294300788
+    - type: manhattan_spearman
+      value: 74.89436791272614
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/STSB
+      name: MTEB STSB
+      config: default
+      split: test
+      revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.01687361650126
+    - type: cos_sim_spearman
+      value: 82.74413230806265
+    - type: euclidean_pearson
+      value: 81.50177295189083
+    - type: euclidean_spearman
+      value: 82.74413230806265
+    - type: manhattan_pearson
+      value: 81.90798387028589
+    - type: manhattan_spearman
+      value: 82.65064251275778
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/T2Reranking
+      name: MTEB T2Reranking
+      config: default
+      split: dev
+      revision: 76631901a18387f85eaa53e5450019b87ad58ef9
+    metrics:
+    - type: map
+      value: 66.25459669294304
+    - type: mrr
+      value: 76.76845224661744
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/T2Retrieval
+      name: MTEB T2Retrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.515
+    - type: map_at_10
+      value: 63.63999999999999
+    - type: map_at_100
+      value: 67.67
+    - type: map_at_1000
+      value: 67.792
+    - type: map_at_3
+      value: 44.239
+    - type: map_at_5
+      value: 54.54599999999999
+    - type: mrr_at_1
+      value: 79.752
+    - type: mrr_at_10
+      value: 83.525
+    - type: mrr_at_100
+      value: 83.753
+    - type: mrr_at_1000
+      value: 83.763
+    - type: mrr_at_3
+      value: 82.65599999999999
+    - type: mrr_at_5
+      value: 83.192
+    - type: ndcg_at_1
+      value: 79.752
+    - type: ndcg_at_10
+      value: 72.699
+    - type: ndcg_at_100
+      value: 78.145
+    - type: ndcg_at_1000
+      value: 79.481
+    - type: ndcg_at_3
+      value: 74.401
+    - type: ndcg_at_5
+      value: 72.684
+    - type: precision_at_1
+      value: 79.752
+    - type: precision_at_10
+      value: 37.163000000000004
+    - type: precision_at_100
+      value: 4.769
+    - type: precision_at_1000
+      value: 0.508
+    - type: precision_at_3
+      value: 65.67399999999999
+    - type: precision_at_5
+      value: 55.105000000000004
+    - type: recall_at_1
+      value: 22.515
+    - type: recall_at_10
+      value: 71.816
+    - type: recall_at_100
+      value: 89.442
+    - type: recall_at_1000
+      value: 96.344
+    - type: recall_at_3
+      value: 46.208
+    - type: recall_at_5
+      value: 58.695
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/TNews-classification
+      name: MTEB TNews
+      config: default
+      split: validation
+      revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
+    metrics:
+    - type: accuracy
+      value: 55.077999999999996
+    - type: f1
+      value: 53.2447237349446
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringP2P
+      name: MTEB ThuNewsClusteringP2P
+      config: default
+      split: test
+      revision: 5798586b105c0434e4f0fe5e767abe619442cf93
+    metrics:
+    - type: v_measure
+      value: 59.50582115422618
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringS2S
+      name: MTEB ThuNewsClusteringS2S
+      config: default
+      split: test
+      revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
+    metrics:
+    - type: v_measure
+      value: 54.71907850412647
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/VideoRetrieval
+      name: MTEB VideoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 49.4
+    - type: map_at_10
+      value: 59.245999999999995
+    - type: map_at_100
+      value: 59.811
+    - type: map_at_1000
+      value: 59.836
+    - type: map_at_3
+      value: 56.733
+    - type: map_at_5
+      value: 58.348
+    - type: mrr_at_1
+      value: 49.4
+    - type: mrr_at_10
+      value: 59.245999999999995
+    - type: mrr_at_100
+      value: 59.811
+    - type: mrr_at_1000
+      value: 59.836
+    - type: mrr_at_3
+      value: 56.733
+    - type: mrr_at_5
+      value: 58.348
+    - type: ndcg_at_1
+      value: 49.4
+    - type: ndcg_at_10
+      value: 64.08
+    - type: ndcg_at_100
+      value: 67.027
+    - type: ndcg_at_1000
+      value: 67.697
+    - type: ndcg_at_3
+      value: 58.995
+    - type: ndcg_at_5
+      value: 61.891
+    - type: precision_at_1
+      value: 49.4
+    - type: precision_at_10
+      value: 7.93
+    - type: precision_at_100
+      value: 0.935
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 21.833
+    - type: precision_at_5
+      value: 14.499999999999998
+    - type: recall_at_1
+      value: 49.4
+    - type: recall_at_10
+      value: 79.3
+    - type: recall_at_100
+      value: 93.5
+    - type: recall_at_1000
+      value: 98.8
+    - type: recall_at_3
+      value: 65.5
+    - type: recall_at_5
+      value: 72.5
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/waimai-classification
+      name: MTEB Waimai
+      config: default
+      split: test
+      revision: 339287def212450dcaa9df8c22bf93e9980c7023
+    metrics:
+    - type: accuracy
+      value: 81.16
+    - type: ap
+      value: 60.864524843400616
+    - type: f1
+      value: 79.41246877404483
+
+```
+
+ZNV Embedding utilizes a 6B LLM (Large Language Model) for embedding, achieving excellent embedding results.
+
+In a single inference, we used two prompts to extract two different embeddings for a sentence, and then concatenated them.
+
+Model usage method:
+
+
+1. Define ZNVEmbeddingModel
+```python
+import os
+from transformers import (
+    LlamaForCausalLM,
+    LlamaTokenizer, AutoConfig,
+)
+import torch
+import torch.nn.functional as F
+import numpy as np
+
+
+class ZNVEmbeddingModel(torch.nn.Module):
+    def __init__(self, model_name_or_path):
+        super(ZNVEmbeddingModel, self).__init__()
+        self.prompt_prefix = "阅读下文，然后答题\n"
+        self.prompt_suffixes = ["\n1.一个字总结上文的意思是:",
+                                "\n2.上文深层次的意思是:"]
+        self.hidden_size = 4096
+        self.model_name_or_path = model_name_or_path
+        self.linear_suffixes = torch.nn.ModuleList(
+            [torch.nn.Linear(self.hidden_size, self.hidden_size//len(self.prompt_suffixes))
+             for _ in range(len(self.prompt_suffixes))])
+        self.tokenizer, self.llama = self.load_llama()
+
+        self.tanh = torch.nn.Tanh()
+        self.suffixes_ids = []
+        self.suffixes_ids_len = []
+        self.suffixes_len = 0
+        for suffix in self.prompt_suffixes:
+            ids = self.tokenizer(suffix, return_tensors="pt")["input_ids"].tolist()[0]
+            self.suffixes_ids += ids
+            self.suffixes_ids_len.append(len(ids))
+            self.suffixes_len += len(ids)
+
+        self.suffixes_ones = torch.ones(self.suffixes_len)
+        self.suffixes_ids = torch.tensor(self.suffixes_ids)
+
+        linear_file = os.path.join(model_name_or_path, "linears")
+        load_layers = torch.load(linear_file)
+        model_state = self.state_dict()
+        model_state.update(load_layers)
+        self.load_state_dict(model_state, strict=False)
+
+    def load_llama(self):
+        llm_path = os.path.join(self.model_name_or_path)
+        config = AutoConfig.from_pretrained(llm_path)
+        tokenizer = LlamaTokenizer.from_pretrained(self.model_name_or_path)
+        tokenizer.padding_side = "left"
+        model = LlamaForCausalLM.from_pretrained(
+            llm_path,
+            config=config,
+            low_cpu_mem_usage=True
+        )
+        model.config.use_cache = False
+        return tokenizer, model
+
+    def forward(self, sentences):
+        prompts_embeddings = []
+        sentences = [self.prompt_prefix + s for s in sentences]
+        inputs = self.tokenizer(sentences, max_length=256, padding=True, truncation=True,
+                                return_tensors='pt')
+        attention_mask = inputs["attention_mask"]
+        input_ids = inputs["input_ids"]
+        batch_size = len(sentences)
+        suffixes_ones = self.suffixes_ones.unsqueeze(0)
+        suffixes_ones = suffixes_ones.repeat(batch_size, 1)
+        device = next(self.parameters()).device
+        attention_mask = torch.cat([attention_mask, suffixes_ones], dim=-1).to(device)
+
+        suffixes_ids = self.suffixes_ids.unsqueeze(0)
+        suffixes_ids = suffixes_ids.repeat(batch_size, 1)
+        input_ids = torch.cat([input_ids, suffixes_ids], dim=-1).to(device)
+        last_hidden_state = self.llama.base_model.base_model(attention_mask=attention_mask, input_ids=input_ids).last_hidden_state
+        index = -1
+        for i in range(len(self.suffixes_ids_len)):
+            embedding = last_hidden_state[:, index, :]
+            embedding = self.linear_suffixes[i](embedding)
+            prompts_embeddings.append(embedding)
+            index -= self.suffixes_ids_len[-i-1]
+
+        output_embedding = torch.cat(prompts_embeddings, dim=-1)
+        output_embedding = self.tanh(output_embedding)
+        output_embedding = F.normalize(output_embedding, p=2, dim=1)
+        return output_embedding
+
+    def encode(self, sentences, batch_size=10, **kwargs):
+        size = len(sentences)
+        embeddings = None
+        handled = 0
+        while handled < size:
+            tokens = sentences[handled:handled + batch_size]
+            output_embeddings = self.forward(tokens)
+            result = output_embeddings.cpu().numpy()
+            handled += result.shape[0]
+            if embeddings is not None:
+                embeddings = np.concatenate((embeddings, result), axis=0)
+            else:
+                embeddings = result
+        return embeddings
+```
+
+		
+2. Use ZNVEmbeddingModel for Embedding.
+```python
+znv_model = ZNVEmbeddingModel("your_model_path")
+znv_model.eval()
+with torch.no_grad():
+    output = znv_model(["请问你的电话号码是多少？","可以告诉我你的手机号吗？"])
+    cos_sim = F.cosine_similarity(output[0],output[1],dim=0)
+    print(cos_sim)
+```
+