Update README.md

README.md

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 ---
-license: mit
+pipeline_tag: sentence-similarity
+tags:
+  - sentence-transformers
+  - feature-extraction
+  - sentence-similarity
+  - mteb
+model-index:
+  - name: stella-large-zh-v3-1792d
+    results:
+      - task:
+          type: STS
+        dataset:
+          type: C-MTEB/AFQMC
+          name: MTEB AFQMC
+          config: default
+          split: validation
+          revision: None
+        metrics:
+          - type: cos_sim_pearson
+            value: 54.48093298255762
+          - type: cos_sim_spearman
+            value: 59.105354109068685
+          - type: euclidean_pearson
+            value: 57.761189988643444
+          - type: euclidean_spearman
+            value: 59.10537421115596
+          - type: manhattan_pearson
+            value: 56.94359297051431
+          - type: manhattan_spearman
+            value: 58.37611109821567
+      - task:
+          type: STS
+        dataset:
+          type: C-MTEB/ATEC
+          name: MTEB ATEC
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: cos_sim_pearson
+            value: 54.39711127600595
+          - type: cos_sim_spearman
+            value: 58.190191920824454
+          - type: euclidean_pearson
+            value: 61.80082379352729
+          - type: euclidean_spearman
+            value: 58.19018966860797
+          - type: manhattan_pearson
+            value: 60.927601060396206
+          - type: manhattan_spearman
+            value: 57.78832902694192
+      - task:
+          type: Classification
+        dataset:
+          type: mteb/amazon_reviews_multi
+          name: MTEB AmazonReviewsClassification (zh)
+          config: zh
+          split: test
+          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+        metrics:
+          - type: accuracy
+            value: 46.31600000000001
+          - type: f1
+            value: 44.45281663598873
+      - task:
+          type: STS
+        dataset:
+          type: C-MTEB/BQ
+          name: MTEB BQ
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: cos_sim_pearson
+            value: 69.12211326097868
+          - type: cos_sim_spearman
+            value: 71.0741302039443
+          - type: euclidean_pearson
+            value: 69.89070483887852
+          - type: euclidean_spearman
+            value: 71.07413020351787
+          - type: manhattan_pearson
+            value: 69.62345441260962
+          - type: manhattan_spearman
+            value: 70.8517591280618
+      - task:
+          type: Clustering
+        dataset:
+          type: C-MTEB/CLSClusteringP2P
+          name: MTEB CLSClusteringP2P
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: v_measure
+            value: 41.937723608805314
+      - task:
+          type: Clustering
+        dataset:
+          type: C-MTEB/CLSClusteringS2S
+          name: MTEB CLSClusteringS2S
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: v_measure
+            value: 40.34373057675427
+      - task:
+          type: Reranking
+        dataset:
+          type: C-MTEB/CMedQAv1-reranking
+          name: MTEB CMedQAv1
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: map
+            value: 88.98896401788376
+          - type: mrr
+            value: 90.97119047619047
+      - task:
+          type: Reranking
+        dataset:
+          type: C-MTEB/CMedQAv2-reranking
+          name: MTEB CMedQAv2
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: map
+            value: 89.59718540244556
+          - type: mrr
+            value: 91.41246031746032
+      - task:
+          type: Retrieval
+        dataset:
+          type: C-MTEB/CmedqaRetrieval
+          name: MTEB CmedqaRetrieval
+          config: default
+          split: dev
+          revision: None
+        metrics:
+          - type: map_at_1
+            value: 26.954
+          - type: map_at_10
+            value: 40.144999999999996
+          - type: map_at_100
+            value: 42.083999999999996
+          - type: map_at_1000
+            value: 42.181000000000004
+          - type: map_at_3
+            value: 35.709
+          - type: map_at_5
+            value: 38.141000000000005
+          - type: mrr_at_1
+            value: 40.71
+          - type: mrr_at_10
+            value: 48.93
+          - type: mrr_at_100
+            value: 49.921
+          - type: mrr_at_1000
+            value: 49.958999999999996
+          - type: mrr_at_3
+            value: 46.32
+          - type: mrr_at_5
+            value: 47.769
+          - type: ndcg_at_1
+            value: 40.71
+          - type: ndcg_at_10
+            value: 46.869
+          - type: ndcg_at_100
+            value: 54.234
+          - type: ndcg_at_1000
+            value: 55.854000000000006
+          - type: ndcg_at_3
+            value: 41.339
+          - type: ndcg_at_5
+            value: 43.594
+          - type: precision_at_1
+            value: 40.71
+          - type: precision_at_10
+            value: 10.408000000000001
+          - type: precision_at_100
+            value: 1.635
+          - type: precision_at_1000
+            value: 0.184
+          - type: precision_at_3
+            value: 23.348
+          - type: precision_at_5
+            value: 16.929
+          - type: recall_at_1
+            value: 26.954
+          - type: recall_at_10
+            value: 57.821999999999996
+          - type: recall_at_100
+            value: 88.08200000000001
+          - type: recall_at_1000
+            value: 98.83800000000001
+          - type: recall_at_3
+            value: 41.221999999999994
+          - type: recall_at_5
+            value: 48.241
+      - task:
+          type: PairClassification
+        dataset:
+          type: C-MTEB/CMNLI
+          name: MTEB Cmnli
+          config: default
+          split: validation
+          revision: None
+        metrics:
+          - type: cos_sim_accuracy
+            value: 83.6680697534576
+          - type: cos_sim_ap
+            value: 90.77401562455269
+          - type: cos_sim_f1
+            value: 84.68266427450101
+          - type: cos_sim_precision
+            value: 81.36177547942253
+          - type: cos_sim_recall
+            value: 88.28618190320317
+          - type: dot_accuracy
+            value: 83.6680697534576
+          - type: dot_ap
+            value: 90.76429465198817
+          - type: dot_f1
+            value: 84.68266427450101
+          - type: dot_precision
+            value: 81.36177547942253
+          - type: dot_recall
+            value: 88.28618190320317
+          - type: euclidean_accuracy
+            value: 83.6680697534576
+          - type: euclidean_ap
+            value: 90.77401909305344
+          - type: euclidean_f1
+            value: 84.68266427450101
+          - type: euclidean_precision
+            value: 81.36177547942253
+          - type: euclidean_recall
+            value: 88.28618190320317
+          - type: manhattan_accuracy
+            value: 83.40348767288035
+          - type: manhattan_ap
+            value: 90.57002020310819
+          - type: manhattan_f1
+            value: 84.51526032315978
+          - type: manhattan_precision
+            value: 81.25134843581445
+          - type: manhattan_recall
+            value: 88.05237315875614
+          - type: max_accuracy
+            value: 83.6680697534576
+          - type: max_ap
+            value: 90.77401909305344
+          - type: max_f1
+            value: 84.68266427450101
+      - task:
+          type: Retrieval
+        dataset:
+          type: C-MTEB/CovidRetrieval
+          name: MTEB CovidRetrieval
+          config: default
+          split: dev
+          revision: None
+        metrics:
+          - type: map_at_1
+            value: 69.705
+          - type: map_at_10
+            value: 78.648
+          - type: map_at_100
+            value: 78.888
+          - type: map_at_1000
+            value: 78.89399999999999
+          - type: map_at_3
+            value: 77.151
+          - type: map_at_5
+            value: 77.98
+          - type: mrr_at_1
+            value: 69.863
+          - type: mrr_at_10
+            value: 78.62599999999999
+          - type: mrr_at_100
+            value: 78.861
+          - type: mrr_at_1000
+            value: 78.867
+          - type: mrr_at_3
+            value: 77.204
+          - type: mrr_at_5
+            value: 78.005
+          - type: ndcg_at_1
+            value: 69.968
+          - type: ndcg_at_10
+            value: 82.44399999999999
+          - type: ndcg_at_100
+            value: 83.499
+          - type: ndcg_at_1000
+            value: 83.647
+          - type: ndcg_at_3
+            value: 79.393
+          - type: ndcg_at_5
+            value: 80.855
+          - type: precision_at_1
+            value: 69.968
+          - type: precision_at_10
+            value: 9.515
+          - type: precision_at_100
+            value: 0.9990000000000001
+          - type: precision_at_1000
+            value: 0.101
+          - type: precision_at_3
+            value: 28.802
+          - type: precision_at_5
+            value: 18.019
+          - type: recall_at_1
+            value: 69.705
+          - type: recall_at_10
+            value: 94.152
+          - type: recall_at_100
+            value: 98.84100000000001
+          - type: recall_at_1000
+            value: 100.0
+          - type: recall_at_3
+            value: 85.774
+          - type: recall_at_5
+            value: 89.252
+      - task:
+          type: Retrieval
+        dataset:
+          type: C-MTEB/DuRetrieval
+          name: MTEB DuRetrieval
+          config: default
+          split: dev
+          revision: None
+        metrics:
+          - type: map_at_1
+            value: 25.88
+          - type: map_at_10
+            value: 79.857
+          - type: map_at_100
+            value: 82.636
+          - type: map_at_1000
+            value: 82.672
+          - type: map_at_3
+            value: 55.184
+          - type: map_at_5
+            value: 70.009
+          - type: mrr_at_1
+            value: 89.64999999999999
+          - type: mrr_at_10
+            value: 92.967
+          - type: mrr_at_100
+            value: 93.039
+          - type: mrr_at_1000
+            value: 93.041
+          - type: mrr_at_3
+            value: 92.65
+          - type: mrr_at_5
+            value: 92.86
+          - type: ndcg_at_1
+            value: 89.64999999999999
+          - type: ndcg_at_10
+            value: 87.126
+          - type: ndcg_at_100
+            value: 89.898
+          - type: ndcg_at_1000
+            value: 90.253
+          - type: ndcg_at_3
+            value: 86.012
+          - type: ndcg_at_5
+            value: 85.124
+          - type: precision_at_1
+            value: 89.64999999999999
+          - type: precision_at_10
+            value: 41.735
+          - type: precision_at_100
+            value: 4.797
+          - type: precision_at_1000
+            value: 0.488
+          - type: precision_at_3
+            value: 77.267
+          - type: precision_at_5
+            value: 65.48
+          - type: recall_at_1
+            value: 25.88
+          - type: recall_at_10
+            value: 88.28399999999999
+          - type: recall_at_100
+            value: 97.407
+          - type: recall_at_1000
+            value: 99.29299999999999
+          - type: recall_at_3
+            value: 57.38799999999999
+          - type: recall_at_5
+            value: 74.736
+      - task:
+          type: Retrieval
+        dataset:
+          type: C-MTEB/EcomRetrieval
+          name: MTEB EcomRetrieval
+          config: default
+          split: dev
+          revision: None
+        metrics:
+          - type: map_at_1
+            value: 53.2
+          - type: map_at_10
+            value: 63.556000000000004
+          - type: map_at_100
+            value: 64.033
+          - type: map_at_1000
+            value: 64.044
+          - type: map_at_3
+            value: 60.983
+          - type: map_at_5
+            value: 62.588
+          - type: mrr_at_1
+            value: 53.2
+          - type: mrr_at_10
+            value: 63.556000000000004
+          - type: mrr_at_100
+            value: 64.033
+          - type: mrr_at_1000
+            value: 64.044
+          - type: mrr_at_3
+            value: 60.983
+          - type: mrr_at_5
+            value: 62.588
+          - type: ndcg_at_1
+            value: 53.2
+          - type: ndcg_at_10
+            value: 68.61699999999999
+          - type: ndcg_at_100
+            value: 70.88499999999999
+          - type: ndcg_at_1000
+            value: 71.15899999999999
+          - type: ndcg_at_3
+            value: 63.434000000000005
+          - type: ndcg_at_5
+            value: 66.301
+          - type: precision_at_1
+            value: 53.2
+          - type: precision_at_10
+            value: 8.450000000000001
+          - type: precision_at_100
+            value: 0.95
+          - type: precision_at_1000
+            value: 0.097
+          - type: precision_at_3
+            value: 23.5
+          - type: precision_at_5
+            value: 15.479999999999999
+          - type: recall_at_1
+            value: 53.2
+          - type: recall_at_10
+            value: 84.5
+          - type: recall_at_100
+            value: 95.0
+          - type: recall_at_1000
+            value: 97.1
+          - type: recall_at_3
+            value: 70.5
+          - type: recall_at_5
+            value: 77.4
+      - task:
+          type: Classification
+        dataset:
+          type: C-MTEB/IFlyTek-classification
+          name: MTEB IFlyTek
+          config: default
+          split: validation
+          revision: None
+        metrics:
+          - type: accuracy
+            value: 50.63485956136976
+          - type: f1
+            value: 38.286307407751266
+      - task:
+          type: Classification
+        dataset:
+          type: C-MTEB/JDReview-classification
+          name: MTEB JDReview
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: accuracy
+            value: 86.11632270168855
+          - type: ap
+            value: 54.43932599806482
+          - type: f1
+            value: 80.85485110996076
+      - task:
+          type: STS
+        dataset:
+          type: C-MTEB/LCQMC
+          name: MTEB LCQMC
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: cos_sim_pearson
+            value: 72.47315152994804
+          - type: cos_sim_spearman
+            value: 78.26531600908152
+          - type: euclidean_pearson
+            value: 77.8560788714531
+          - type: euclidean_spearman
+            value: 78.26531157334841
+          - type: manhattan_pearson
+            value: 77.70593783974188
+          - type: manhattan_spearman
+            value: 78.13880812439999
+      - task:
+          type: Reranking
+        dataset:
+          type: C-MTEB/Mmarco-reranking
+          name: MTEB MMarcoReranking
+          config: default
+          split: dev
+          revision: None
+        metrics:
+          - type: map
+            value: 28.088177976572222
+          - type: mrr
+            value: 27.125
+      - task:
+          type: Retrieval
+        dataset:
+          type: C-MTEB/MMarcoRetrieval
+          name: MTEB MMarcoRetrieval
+          config: default
+          split: dev
+          revision: None
+        metrics:
+          - type: map_at_1
+            value: 66.428
+          - type: map_at_10
+            value: 75.5
+          - type: map_at_100
+            value: 75.82600000000001
+          - type: map_at_1000
+            value: 75.837
+          - type: map_at_3
+            value: 73.74300000000001
+          - type: map_at_5
+            value: 74.87
+          - type: mrr_at_1
+            value: 68.754
+          - type: mrr_at_10
+            value: 76.145
+          - type: mrr_at_100
+            value: 76.432
+          - type: mrr_at_1000
+            value: 76.442
+          - type: mrr_at_3
+            value: 74.628
+          - type: mrr_at_5
+            value: 75.612
+          - type: ndcg_at_1
+            value: 68.754
+          - type: ndcg_at_10
+            value: 79.144
+          - type: ndcg_at_100
+            value: 80.60199999999999
+          - type: ndcg_at_1000
+            value: 80.886
+          - type: ndcg_at_3
+            value: 75.81599999999999
+          - type: ndcg_at_5
+            value: 77.729
+          - type: precision_at_1
+            value: 68.754
+          - type: precision_at_10
+            value: 9.544
+          - type: precision_at_100
+            value: 1.026
+          - type: precision_at_1000
+            value: 0.105
+          - type: precision_at_3
+            value: 28.534
+          - type: precision_at_5
+            value: 18.138
+          - type: recall_at_1
+            value: 66.428
+          - type: recall_at_10
+            value: 89.716
+          - type: recall_at_100
+            value: 96.313
+          - type: recall_at_1000
+            value: 98.541
+          - type: recall_at_3
+            value: 80.923
+          - type: recall_at_5
+            value: 85.48
+      - task:
+          type: Classification
+        dataset:
+          type: mteb/amazon_massive_intent
+          name: MTEB MassiveIntentClassification (zh-CN)
+          config: zh-CN
+          split: test
+          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+        metrics:
+          - type: accuracy
+            value: 73.27841291190316
+          - type: f1
+            value: 70.65529957574735
+      - task:
+          type: Classification
+        dataset:
+          type: mteb/amazon_massive_scenario
+          name: MTEB MassiveScenarioClassification (zh-CN)
+          config: zh-CN
+          split: test
+          revision: 7d571f92784cd94a019292a1f45445077d0ef634
+        metrics:
+          - type: accuracy
+            value: 76.30127774041695
+          - type: f1
+            value: 76.10358226518304
+      - task:
+          type: Retrieval
+        dataset:
+          type: C-MTEB/MedicalRetrieval
+          name: MTEB MedicalRetrieval
+          config: default
+          split: dev
+          revision: None
+        metrics:
+          - type: map_at_1
+            value: 56.3
+          - type: map_at_10
+            value: 62.193
+          - type: map_at_100
+            value: 62.722
+          - type: map_at_1000
+            value: 62.765
+          - type: map_at_3
+            value: 60.633
+          - type: map_at_5
+            value: 61.617999999999995
+          - type: mrr_at_1
+            value: 56.3
+          - type: mrr_at_10
+            value: 62.193
+          - type: mrr_at_100
+            value: 62.722
+          - type: mrr_at_1000
+            value: 62.765
+          - type: mrr_at_3
+            value: 60.633
+          - type: mrr_at_5
+            value: 61.617999999999995
+          - type: ndcg_at_1
+            value: 56.3
+          - type: ndcg_at_10
+            value: 65.176
+          - type: ndcg_at_100
+            value: 67.989
+          - type: ndcg_at_1000
+            value: 69.219
+          - type: ndcg_at_3
+            value: 62.014
+          - type: ndcg_at_5
+            value: 63.766
+          - type: precision_at_1
+            value: 56.3
+          - type: precision_at_10
+            value: 7.46
+          - type: precision_at_100
+            value: 0.8829999999999999
+          - type: precision_at_1000
+            value: 0.098
+          - type: precision_at_3
+            value: 22.0
+          - type: precision_at_5
+            value: 14.04
+          - type: recall_at_1
+            value: 56.3
+          - type: recall_at_10
+            value: 74.6
+          - type: recall_at_100
+            value: 88.3
+          - type: recall_at_1000
+            value: 98.1
+          - type: recall_at_3
+            value: 66.0
+          - type: recall_at_5
+            value: 70.19999999999999
+      - task:
+          type: Classification
+        dataset:
+          type: C-MTEB/MultilingualSentiment-classification
+          name: MTEB MultilingualSentiment
+          config: default
+          split: validation
+          revision: None
+        metrics:
+          - type: accuracy
+            value: 76.44666666666666
+          - type: f1
+            value: 76.34548655475949
+      - task:
+          type: PairClassification
+        dataset:
+          type: C-MTEB/OCNLI
+          name: MTEB Ocnli
+          config: default
+          split: validation
+          revision: None
+        metrics:
+          - type: cos_sim_accuracy
+            value: 82.34975636166757
+          - type: cos_sim_ap
+            value: 85.44149338593267
+          - type: cos_sim_f1
+            value: 83.68654509610647
+          - type: cos_sim_precision
+            value: 78.46580406654344
+          - type: cos_sim_recall
+            value: 89.65153115100317
+          - type: dot_accuracy
+            value: 82.34975636166757
+          - type: dot_ap
+            value: 85.4415701376729
+          - type: dot_f1
+            value: 83.68654509610647
+          - type: dot_precision
+            value: 78.46580406654344
+          - type: dot_recall
+            value: 89.65153115100317
+          - type: euclidean_accuracy
+            value: 82.34975636166757
+          - type: euclidean_ap
+            value: 85.4415701376729
+          - type: euclidean_f1
+            value: 83.68654509610647
+          - type: euclidean_precision
+            value: 78.46580406654344
+          - type: euclidean_recall
+            value: 89.65153115100317
+          - type: manhattan_accuracy
+            value: 81.97076340010828
+          - type: manhattan_ap
+            value: 84.83614660756733
+          - type: manhattan_f1
+            value: 83.34167083541772
+          - type: manhattan_precision
+            value: 79.18250950570342
+          - type: manhattan_recall
+            value: 87.96198521647307
+          - type: max_accuracy
+            value: 82.34975636166757
+          - type: max_ap
+            value: 85.4415701376729
+          - type: max_f1
+            value: 83.68654509610647
+      - task:
+          type: Classification
+        dataset:
+          type: C-MTEB/OnlineShopping-classification
+          name: MTEB OnlineShopping
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: accuracy
+            value: 93.24
+          - type: ap
+            value: 91.3586656455605
+          - type: f1
+            value: 93.22999314249503
+      - task:
+          type: STS
+        dataset:
+          type: C-MTEB/PAWSX
+          name: MTEB PAWSX
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: cos_sim_pearson
+            value: 39.05676042449009
+          - type: cos_sim_spearman
+            value: 44.996534098358545
+          - type: euclidean_pearson
+            value: 44.42418609172825
+          - type: euclidean_spearman
+            value: 44.995941361058996
+          - type: manhattan_pearson
+            value: 43.98118203238076
+          - type: manhattan_spearman
+            value: 44.51414152788784
+      - task:
+          type: STS
+        dataset:
+          type: C-MTEB/QBQTC
+          name: MTEB QBQTC
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: cos_sim_pearson
+            value: 36.694269474438045
+          - type: cos_sim_spearman
+            value: 38.686738967031616
+          - type: euclidean_pearson
+            value: 36.822540068407235
+          - type: euclidean_spearman
+            value: 38.68690745429757
+          - type: manhattan_pearson
+            value: 36.77180703308932
+          - type: manhattan_spearman
+            value: 38.45414914148094
+      - task:
+          type: STS
+        dataset:
+          type: mteb/sts22-crosslingual-sts
+          name: MTEB STS22 (zh)
+          config: zh
+          split: test
+          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+        metrics:
+          - type: cos_sim_pearson
+            value: 65.81209017614124
+          - type: cos_sim_spearman
+            value: 66.5255285833172
+          - type: euclidean_pearson
+            value: 66.01848701752732
+          - type: euclidean_spearman
+            value: 66.5255285833172
+          - type: manhattan_pearson
+            value: 66.66433676370542
+          - type: manhattan_spearman
+            value: 67.07086311480214
+      - task:
+          type: STS
+        dataset:
+          type: C-MTEB/STSB
+          name: MTEB STSB
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: cos_sim_pearson
+            value: 80.60785761283502
+          - type: cos_sim_spearman
+            value: 82.80278693241074
+          - type: euclidean_pearson
+            value: 82.47573315938638
+          - type: euclidean_spearman
+            value: 82.80290808593806
+          - type: manhattan_pearson
+            value: 82.49682028989669
+          - type: manhattan_spearman
+            value: 82.84565039346022
+      - task:
+          type: Reranking
+        dataset:
+          type: C-MTEB/T2Reranking
+          name: MTEB T2Reranking
+          config: default
+          split: dev
+          revision: None
+        metrics:
+          - type: map
+            value: 66.37886004738723
+          - type: mrr
+            value: 76.08501655006394
+      - task:
+          type: Retrieval
+        dataset:
+          type: C-MTEB/T2Retrieval
+          name: MTEB T2Retrieval
+          config: default
+          split: dev
+          revision: None
+        metrics:
+          - type: map_at_1
+            value: 28.102
+          - type: map_at_10
+            value: 78.071
+          - type: map_at_100
+            value: 81.71000000000001
+          - type: map_at_1000
+            value: 81.773
+          - type: map_at_3
+            value: 55.142
+          - type: map_at_5
+            value: 67.669
+          - type: mrr_at_1
+            value: 90.9
+          - type: mrr_at_10
+            value: 93.29499999999999
+          - type: mrr_at_100
+            value: 93.377
+          - type: mrr_at_1000
+            value: 93.379
+          - type: mrr_at_3
+            value: 92.901
+          - type: mrr_at_5
+            value: 93.152
+          - type: ndcg_at_1
+            value: 90.9
+          - type: ndcg_at_10
+            value: 85.564
+          - type: ndcg_at_100
+            value: 89.11200000000001
+          - type: ndcg_at_1000
+            value: 89.693
+          - type: ndcg_at_3
+            value: 87.024
+          - type: ndcg_at_5
+            value: 85.66
+          - type: precision_at_1
+            value: 90.9
+          - type: precision_at_10
+            value: 42.208
+          - type: precision_at_100
+            value: 5.027
+          - type: precision_at_1000
+            value: 0.517
+          - type: precision_at_3
+            value: 75.872
+          - type: precision_at_5
+            value: 63.566
+          - type: recall_at_1
+            value: 28.102
+          - type: recall_at_10
+            value: 84.44500000000001
+          - type: recall_at_100
+            value: 95.91300000000001
+          - type: recall_at_1000
+            value: 98.80799999999999
+          - type: recall_at_3
+            value: 56.772999999999996
+          - type: recall_at_5
+            value: 70.99499999999999
+      - task:
+          type: Classification
+        dataset:
+          type: C-MTEB/TNews-classification
+          name: MTEB TNews
+          config: default
+          split: validation
+          revision: None
+        metrics:
+          - type: accuracy
+            value: 53.10599999999999
+          - type: f1
+            value: 51.40415523558322
+      - task:
+          type: Clustering
+        dataset:
+          type: C-MTEB/ThuNewsClusteringP2P
+          name: MTEB ThuNewsClusteringP2P
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: v_measure
+            value: 69.6145576098232
+      - task:
+          type: Clustering
+        dataset:
+          type: C-MTEB/ThuNewsClusteringS2S
+          name: MTEB ThuNewsClusteringS2S
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: v_measure
+            value: 63.7129548775017
+      - task:
+          type: Retrieval
+        dataset:
+          type: C-MTEB/VideoRetrieval
+          name: MTEB VideoRetrieval
+          config: default
+          split: dev
+          revision: None
+        metrics:
+          - type: map_at_1
+            value: 60.199999999999996
+          - type: map_at_10
+            value: 69.724
+          - type: map_at_100
+            value: 70.185
+          - type: map_at_1000
+            value: 70.196
+          - type: map_at_3
+            value: 67.95
+          - type: map_at_5
+            value: 69.155
+          - type: mrr_at_1
+            value: 60.199999999999996
+          - type: mrr_at_10
+            value: 69.724
+          - type: mrr_at_100
+            value: 70.185
+          - type: mrr_at_1000
+            value: 70.196
+          - type: mrr_at_3
+            value: 67.95
+          - type: mrr_at_5
+            value: 69.155
+          - type: ndcg_at_1
+            value: 60.199999999999996
+          - type: ndcg_at_10
+            value: 73.888
+          - type: ndcg_at_100
+            value: 76.02799999999999
+          - type: ndcg_at_1000
+            value: 76.344
+          - type: ndcg_at_3
+            value: 70.384
+          - type: ndcg_at_5
+            value: 72.541
+          - type: precision_at_1
+            value: 60.199999999999996
+          - type: precision_at_10
+            value: 8.67
+          - type: precision_at_100
+            value: 0.9650000000000001
+          - type: precision_at_1000
+            value: 0.099
+          - type: precision_at_3
+            value: 25.8
+          - type: precision_at_5
+            value: 16.520000000000003
+          - type: recall_at_1
+            value: 60.199999999999996
+          - type: recall_at_10
+            value: 86.7
+          - type: recall_at_100
+            value: 96.5
+          - type: recall_at_1000
+            value: 99.0
+          - type: recall_at_3
+            value: 77.4
+          - type: recall_at_5
+            value: 82.6
+      - task:
+          type: Classification
+        dataset:
+          type: C-MTEB/waimai-classification
+          name: MTEB Waimai
+          config: default
+          split: test
+          revision: None
+        metrics:
+          - type: accuracy
+            value: 88.08
+          - type: ap
+            value: 72.66435456846166
+          - type: f1
+            value: 86.55995793551286
 ---
+
+# 1 开源清单
+
+本次开源2个通用向量编码模型和一个针对dialogue进行编码的向量模型，同时开源全量160万对话重写数据集和20万的难负例的检索数据集。
+
+**开源模型：**
+
+| ModelName                                                                                                     | ModelSize | MaxTokens | EmbeddingDimensions | Language | Scenario | C-MTEB Score |   
+|---------------------------------------------------------------------------------------------------------------|-----------|-----------|---------------------|----------|----------|--------------|
+| [infgrad/stella-base-zh-v3-1792d](https://huggingface.co/infgrad/stella-base-zh-v3-1792d)                     | 0.4GB     | 512       | 1792                | zh-CN    | 通用文本     | 67.96        |   
+| [infgrad/stella-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-large-zh-v3-1792d)                   | 1.3GB     | 512       | 1792                | zh-CN    | 通用文本     | 68.48        |   
+| [infgrad/stella-dialogue-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-dialogue-large-zh-v3-1792d) | 1.3GB     | 512       | 1792                | zh-CN    | **对话文本** | 不适用          |
+
+**开源数据：**
+
+1. [全量对话重写数据集](https://huggingface.co/datasets/infgrad/dialogue_rewrite_llm) 约160万
+2. [部分带有难负例的检索数据集](https://huggingface.co/datasets/infgrad/retrieval_data_llm) 约20万
+
+上述数据集均使用LLM构造，欢迎各位贡献数据集。
+
+# 2 使用方法
+
+## 2.1 通用编码模型使用方法
+
+直接SentenceTransformer加载即可：
+
+```python
+from sentence_transformers import SentenceTransformer
+
+model = SentenceTransformer("infgrad/stella-base-zh-v3-1792d")
+# model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d")
+vectors = model.encode(["text1", "text2"])
+```
+
+## 2.2 dialogue编码模型使用方法
+
+**使用场景：**
+**在一段对话中，需要根据用户语句去检索相关文本，但是对话中的用户语句存在大量的指代和省略，导致直接使用通用编码模型效果不好，
+可以使用本项目的专门的dialogue编码模型进行编码**
+
+**使用要点：**
+
+1. 对dialogue进行编码时，dialogue中的每个utterance需要是如下格式：`"{ROLE}: {TEXT}"`，然后使用`[SEP]` join一下
+2. 整个对话都要送入模型进行编码，如果长度不够就删掉早期的对话，**编码后的向量本质是对话中最后一句话的重写版本的向量！！**
+3. 对话用stella-dialogue-large-zh-v3-1792d编码，被检索文本使用stella-large-zh-v3-1792d进行编码，所以本场景是需要2个编码模型的
+
+如果对使用方法还有疑惑，请到下面章节阅读该模型是如何训练的。
+
+使用示例：
+
+```python
+from sentence_transformers import SentenceTransformer
+
+dial_model = SentenceTransformer("infgrad/stella-dialogue-large-zh-v3-1792d")
+general_model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d")
+# dialogue = ["张三: 吃饭吗", "李四: 等会去"]
+dialogue = ["A: 最近去打篮球了吗", "B: 没有"]
+corpus = ["B没打篮球是因为受伤了。", "B没有打乒乓球"]
+last_utterance_vector = dial_model.encode(["[SEP]".join(dialogue)], normalize_embeddings=True)
+corpus_vectors = general_model.encode(corpus, normalize_embeddings=True)
+# 计算相似度
+sims = (last_utterance_vector * corpus_vectors).sum(axis=1)
+print(sims)
+```
+
+# 3 通用编码模型训练技巧分享
+
+## hard negative
+
+难负例挖掘也是个经典的trick了，几乎总能提升效果
+
+## dropout-1d
+
+dropout已经是深度学习的标配，我们可以稍微改造下使其更适合句向量的训练。
+我们在训练时会尝试让每一个token-embedding都可以表征整个句子，而在推理时使用mean_pooling从而达到类似模型融合的效果。
+具体操作是在mean_pooling时加入dropout_1d，torch代码如下：
+
+```python
+vector_dropout = nn.Dropout1d(0.3)  # 算力有限，试了0.3和0.5 两个参数，其中0.3更优
+last_hidden_state = bert_model(...)[0]
+last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
+last_hidden = vector_dropout(last_hidden)
+vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+```
+
+# 4 dialogue编码模型细节
+
+## 4.1 为什么需要一个dialogue编码模型？
+
+参见本人历史文章：https://www.zhihu.com/pin/1674913544847077376
+
+## 4.2 训练数据
+
+单条数据示例：
+
+```json
+{
+  "dialogue": [
+    "A: 最近去打篮球了吗",
+    "B: 没有"
+  ],
+  "last_utterance_rewrite": "B: 我最近没有去打篮球"
+}
+```
+
+## 4.3 训练Loss
+
+```
+loss = cosine_loss( dial_model.encode(dialogue), existing_model.encode(last_utterance_rewrite) )
+```
+
+dial_model就是要被训练的模型，本人是以stella-large-zh-v3-1792d作为base-model进行继续训练的
+
+existing_model就是现有训练好的**通用编码模型**，本人使用的是stella-large-zh-v3-1792d
+
+已开源dialogue-embedding的全量训练数据，理论上可以复现本模型效果。
+
+Loss下降情况：
+
+<div align="center">
+<img src="dial_loss.png" alt="icon" width="2000px"/>
+</div>
+
+## 4.4 效果
+
+目前还没有专门测试集，本人简单测试了下是有效果的，部分测试结果见文件`dial_retrieval_test.xlsx`。
+
+# 5 后续TODO
+
+1. 更多的dial-rewrite数据
+2. 不同EmbeddingDimensions的编码模型
+
+# 6 FAQ
+
+Q: 为什么向量维度是1792？\
+A: 最初考虑发布768、1024，768+768，1024+1024，1024+768维度，但是时间有限，先做了1792就只发布1792维度的模型。理论上维度越高效果越好。
+
+Q: 如何复现CMTEB效果？\
+A: SentenceTransformer加载后直接用官方评测脚本就行，注意对于Classification任务向量需要先normalize一下
+
+Q: 复现的CMTEB效果和本文不一致？\
+A: 聚类不一致正常，官方评测代码没有设定seed，其他不一致建议检查代码或联系本人。
+
+Q: 如何选择向量模型？\
+A: 没有免费的午餐，在自己测试集上试试，本人推荐bge、e5和stella.
+
+Q: 长度为什么只有512，能否更长？\
+A: 可以但没必要，长了效果普遍不好，这是当前训练方法和数据导致的，几乎无解，建议长文本还是走分块。
+
+Q: 训练资源和算力？\
+A: 亿级别的数据，单卡A100要一个月起步