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README.md

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+tags:
+- mteb
+- sentence-similarity
+- sentence-transformers
+- Sentence Transformers
+model-index:
+- name: gte-base-zh
+  results:
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/AFQMC
+      name: MTEB AFQMC
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 44.45621572456527
+    - type: cos_sim_spearman
+      value: 49.06500895667604
+    - type: euclidean_pearson
+      value: 47.55002064096053
+    - type: euclidean_spearman
+      value: 49.06500895667604
+    - type: manhattan_pearson
+      value: 47.429900262366715
+    - type: manhattan_spearman
+      value: 48.95704890278774
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/ATEC
+      name: MTEB ATEC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 44.31699346653116
+    - type: cos_sim_spearman
+      value: 50.83133156721432
+    - type: euclidean_pearson
+      value: 51.36086517946001
+    - type: euclidean_spearman
+      value: 50.83132818894256
+    - type: manhattan_pearson
+      value: 51.255926461574084
+    - type: manhattan_spearman
+      value: 50.73460147395406
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (zh)
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 45.818000000000005
+    - type: f1
+      value: 43.998253644678144
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/BQ
+      name: MTEB BQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 63.47477451918581
+    - type: cos_sim_spearman
+      value: 65.49832607366159
+    - type: euclidean_pearson
+      value: 64.11399760832107
+    - type: euclidean_spearman
+      value: 65.49832260877398
+    - type: manhattan_pearson
+      value: 64.02541311484639
+    - type: manhattan_spearman
+      value: 65.42436057501452
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringP2P
+      name: MTEB CLSClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 42.58046835435111
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringS2S
+      name: MTEB CLSClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 40.42134173217685
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv1-reranking
+      name: MTEB CMedQAv1
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 86.79079943923792
+    - type: mrr
+      value: 88.81341269841269
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv2-reranking
+      name: MTEB CMedQAv2
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 87.20186031249037
+    - type: mrr
+      value: 89.46551587301587
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CmedqaRetrieval
+      name: MTEB CmedqaRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.098
+    - type: map_at_10
+      value: 37.759
+    - type: map_at_100
+      value: 39.693
+    - type: map_at_1000
+      value: 39.804
+    - type: map_at_3
+      value: 33.477000000000004
+    - type: map_at_5
+      value: 35.839
+    - type: mrr_at_1
+      value: 38.06
+    - type: mrr_at_10
+      value: 46.302
+    - type: mrr_at_100
+      value: 47.370000000000005
+    - type: mrr_at_1000
+      value: 47.412
+    - type: mrr_at_3
+      value: 43.702999999999996
+    - type: mrr_at_5
+      value: 45.213
+    - type: ndcg_at_1
+      value: 38.06
+    - type: ndcg_at_10
+      value: 44.375
+    - type: ndcg_at_100
+      value: 51.849999999999994
+    - type: ndcg_at_1000
+      value: 53.725
+    - type: ndcg_at_3
+      value: 38.97
+    - type: ndcg_at_5
+      value: 41.193000000000005
+    - type: precision_at_1
+      value: 38.06
+    - type: precision_at_10
+      value: 9.934999999999999
+    - type: precision_at_100
+      value: 1.599
+    - type: precision_at_1000
+      value: 0.183
+    - type: precision_at_3
+      value: 22.072
+    - type: precision_at_5
+      value: 16.089000000000002
+    - type: recall_at_1
+      value: 25.098
+    - type: recall_at_10
+      value: 55.264
+    - type: recall_at_100
+      value: 85.939
+    - type: recall_at_1000
+      value: 98.44800000000001
+    - type: recall_at_3
+      value: 39.122
+    - type: recall_at_5
+      value: 45.948
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/CMNLI
+      name: MTEB Cmnli
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 78.02766085387853
+    - type: cos_sim_ap
+      value: 85.59982802559004
+    - type: cos_sim_f1
+      value: 79.57103418984921
+    - type: cos_sim_precision
+      value: 72.88465279128575
+    - type: cos_sim_recall
+      value: 87.60813654430676
+    - type: dot_accuracy
+      value: 78.02766085387853
+    - type: dot_ap
+      value: 85.59604477360719
+    - type: dot_f1
+      value: 79.57103418984921
+    - type: dot_precision
+      value: 72.88465279128575
+    - type: dot_recall
+      value: 87.60813654430676
+    - type: euclidean_accuracy
+      value: 78.02766085387853
+    - type: euclidean_ap
+      value: 85.59982802559004
+    - type: euclidean_f1
+      value: 79.57103418984921
+    - type: euclidean_precision
+      value: 72.88465279128575
+    - type: euclidean_recall
+      value: 87.60813654430676
+    - type: manhattan_accuracy
+      value: 77.9795550210463
+    - type: manhattan_ap
+      value: 85.58042267497707
+    - type: manhattan_f1
+      value: 79.40344001741781
+    - type: manhattan_precision
+      value: 74.29211652067632
+    - type: manhattan_recall
+      value: 85.27004909983633
+    - type: max_accuracy
+      value: 78.02766085387853
+    - type: max_ap
+      value: 85.59982802559004
+    - type: max_f1
+      value: 79.57103418984921
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CovidRetrieval
+      name: MTEB CovidRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 62.144
+    - type: map_at_10
+      value: 71.589
+    - type: map_at_100
+      value: 72.066
+    - type: map_at_1000
+      value: 72.075
+    - type: map_at_3
+      value: 69.916
+    - type: map_at_5
+      value: 70.806
+    - type: mrr_at_1
+      value: 62.275999999999996
+    - type: mrr_at_10
+      value: 71.57
+    - type: mrr_at_100
+      value: 72.048
+    - type: mrr_at_1000
+      value: 72.057
+    - type: mrr_at_3
+      value: 69.89800000000001
+    - type: mrr_at_5
+      value: 70.84700000000001
+    - type: ndcg_at_1
+      value: 62.381
+    - type: ndcg_at_10
+      value: 75.74
+    - type: ndcg_at_100
+      value: 77.827
+    - type: ndcg_at_1000
+      value: 78.044
+    - type: ndcg_at_3
+      value: 72.307
+    - type: ndcg_at_5
+      value: 73.91499999999999
+    - type: precision_at_1
+      value: 62.381
+    - type: precision_at_10
+      value: 8.946
+    - type: precision_at_100
+      value: 0.988
+    - type: precision_at_1000
+      value: 0.101
+    - type: precision_at_3
+      value: 26.554
+    - type: precision_at_5
+      value: 16.733
+    - type: recall_at_1
+      value: 62.144
+    - type: recall_at_10
+      value: 88.567
+    - type: recall_at_100
+      value: 97.84
+    - type: recall_at_1000
+      value: 99.473
+    - type: recall_at_3
+      value: 79.083
+    - type: recall_at_5
+      value: 83.035
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/DuRetrieval
+      name: MTEB DuRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.665
+    - type: map_at_10
+      value: 74.91600000000001
+    - type: map_at_100
+      value: 77.981
+    - type: map_at_1000
+      value: 78.032
+    - type: map_at_3
+      value: 51.015
+    - type: map_at_5
+      value: 64.681
+    - type: mrr_at_1
+      value: 86.5
+    - type: mrr_at_10
+      value: 90.78399999999999
+    - type: mrr_at_100
+      value: 90.859
+    - type: mrr_at_1000
+      value: 90.863
+    - type: mrr_at_3
+      value: 90.375
+    - type: mrr_at_5
+      value: 90.66199999999999
+    - type: ndcg_at_1
+      value: 86.5
+    - type: ndcg_at_10
+      value: 83.635
+    - type: ndcg_at_100
+      value: 86.926
+    - type: ndcg_at_1000
+      value: 87.425
+    - type: ndcg_at_3
+      value: 81.28999999999999
+    - type: ndcg_at_5
+      value: 80.549
+    - type: precision_at_1
+      value: 86.5
+    - type: precision_at_10
+      value: 40.544999999999995
+    - type: precision_at_100
+      value: 4.748
+    - type: precision_at_1000
+      value: 0.48700000000000004
+    - type: precision_at_3
+      value: 72.68299999999999
+    - type: precision_at_5
+      value: 61.86000000000001
+    - type: recall_at_1
+      value: 24.665
+    - type: recall_at_10
+      value: 85.72
+    - type: recall_at_100
+      value: 96.116
+    - type: recall_at_1000
+      value: 98.772
+    - type: recall_at_3
+      value: 53.705999999999996
+    - type: recall_at_5
+      value: 70.42699999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/EcomRetrieval
+      name: MTEB EcomRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 54.0
+    - type: map_at_10
+      value: 64.449
+    - type: map_at_100
+      value: 64.937
+    - type: map_at_1000
+      value: 64.946
+    - type: map_at_3
+      value: 61.85000000000001
+    - type: map_at_5
+      value: 63.525
+    - type: mrr_at_1
+      value: 54.0
+    - type: mrr_at_10
+      value: 64.449
+    - type: mrr_at_100
+      value: 64.937
+    - type: mrr_at_1000
+      value: 64.946
+    - type: mrr_at_3
+      value: 61.85000000000001
+    - type: mrr_at_5
+      value: 63.525
+    - type: ndcg_at_1
+      value: 54.0
+    - type: ndcg_at_10
+      value: 69.56400000000001
+    - type: ndcg_at_100
+      value: 71.78999999999999
+    - type: ndcg_at_1000
+      value: 72.021
+    - type: ndcg_at_3
+      value: 64.334
+    - type: ndcg_at_5
+      value: 67.368
+    - type: precision_at_1
+      value: 54.0
+    - type: precision_at_10
+      value: 8.559999999999999
+    - type: precision_at_100
+      value: 0.9570000000000001
+    - type: precision_at_1000
+      value: 0.098
+    - type: precision_at_3
+      value: 23.833
+    - type: precision_at_5
+      value: 15.78
+    - type: recall_at_1
+      value: 54.0
+    - type: recall_at_10
+      value: 85.6
+    - type: recall_at_100
+      value: 95.7
+    - type: recall_at_1000
+      value: 97.5
+    - type: recall_at_3
+      value: 71.5
+    - type: recall_at_5
+      value: 78.9
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/IFlyTek-classification
+      name: MTEB IFlyTek
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 48.61869949980762
+    - type: f1
+      value: 36.49337336098832
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/JDReview-classification
+      name: MTEB JDReview
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 85.94746716697938
+    - type: ap
+      value: 53.75927589310753
+    - type: f1
+      value: 80.53821597736138
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/LCQMC
+      name: MTEB LCQMC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 68.77445518082875
+    - type: cos_sim_spearman
+      value: 74.05909185405268
+    - type: euclidean_pearson
+      value: 72.92870557009725
+    - type: euclidean_spearman
+      value: 74.05909628639644
+    - type: manhattan_pearson
+      value: 72.92072580598351
+    - type: manhattan_spearman
+      value: 74.0304390211741
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/Mmarco-reranking
+      name: MTEB MMarcoReranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 27.643607073221975
+    - type: mrr
+      value: 26.646825396825395
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MMarcoRetrieval
+      name: MTEB MMarcoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 65.10000000000001
+    - type: map_at_10
+      value: 74.014
+    - type: map_at_100
+      value: 74.372
+    - type: map_at_1000
+      value: 74.385
+    - type: map_at_3
+      value: 72.179
+    - type: map_at_5
+      value: 73.37700000000001
+    - type: mrr_at_1
+      value: 67.364
+    - type: mrr_at_10
+      value: 74.68
+    - type: mrr_at_100
+      value: 74.992
+    - type: mrr_at_1000
+      value: 75.003
+    - type: mrr_at_3
+      value: 73.054
+    - type: mrr_at_5
+      value: 74.126
+    - type: ndcg_at_1
+      value: 67.364
+    - type: ndcg_at_10
+      value: 77.704
+    - type: ndcg_at_100
+      value: 79.29899999999999
+    - type: ndcg_at_1000
+      value: 79.637
+    - type: ndcg_at_3
+      value: 74.232
+    - type: ndcg_at_5
+      value: 76.264
+    - type: precision_at_1
+      value: 67.364
+    - type: precision_at_10
+      value: 9.397
+    - type: precision_at_100
+      value: 1.019
+    - type: precision_at_1000
+      value: 0.105
+    - type: precision_at_3
+      value: 27.942
+    - type: precision_at_5
+      value: 17.837
+    - type: recall_at_1
+      value: 65.10000000000001
+    - type: recall_at_10
+      value: 88.416
+    - type: recall_at_100
+      value: 95.61
+    - type: recall_at_1000
+      value: 98.261
+    - type: recall_at_3
+      value: 79.28
+    - type: recall_at_5
+      value: 84.108
+  - 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: 73.315400134499
+    - type: f1
+      value: 70.81060697693198
+  - 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: 76.78883658372563
+    - type: f1
+      value: 76.21512438791976
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MedicalRetrieval
+      name: MTEB MedicalRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 55.300000000000004
+    - type: map_at_10
+      value: 61.879
+    - type: map_at_100
+      value: 62.434
+    - type: map_at_1000
+      value: 62.476
+    - type: map_at_3
+      value: 60.417
+    - type: map_at_5
+      value: 61.297000000000004
+    - type: mrr_at_1
+      value: 55.400000000000006
+    - type: mrr_at_10
+      value: 61.92100000000001
+    - type: mrr_at_100
+      value: 62.476
+    - type: mrr_at_1000
+      value: 62.517999999999994
+    - type: mrr_at_3
+      value: 60.483
+    - type: mrr_at_5
+      value: 61.338
+    - type: ndcg_at_1
+      value: 55.300000000000004
+    - type: ndcg_at_10
+      value: 64.937
+    - type: ndcg_at_100
+      value: 67.848
+    - type: ndcg_at_1000
+      value: 68.996
+    - type: ndcg_at_3
+      value: 61.939
+    - type: ndcg_at_5
+      value: 63.556999999999995
+    - type: precision_at_1
+      value: 55.300000000000004
+    - type: precision_at_10
+      value: 7.449999999999999
+    - type: precision_at_100
+      value: 0.886
+    - type: precision_at_1000
+      value: 0.098
+    - type: precision_at_3
+      value: 22.1
+    - type: precision_at_5
+      value: 14.06
+    - type: recall_at_1
+      value: 55.300000000000004
+    - type: recall_at_10
+      value: 74.5
+    - type: recall_at_100
+      value: 88.6
+    - type: recall_at_1000
+      value: 97.7
+    - type: recall_at_3
+      value: 66.3
+    - type: recall_at_5
+      value: 70.3
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/MultilingualSentiment-classification
+      name: MTEB MultilingualSentiment
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 75.79
+    - type: f1
+      value: 75.58944709087194
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/OCNLI
+      name: MTEB Ocnli
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 71.5755278830536
+    - type: cos_sim_ap
+      value: 75.27777388526098
+    - type: cos_sim_f1
+      value: 75.04604051565377
+    - type: cos_sim_precision
+      value: 66.53061224489795
+    - type: cos_sim_recall
+      value: 86.06124604012672
+    - type: dot_accuracy
+      value: 71.5755278830536
+    - type: dot_ap
+      value: 75.27765883143745
+    - type: dot_f1
+      value: 75.04604051565377
+    - type: dot_precision
+      value: 66.53061224489795
+    - type: dot_recall
+      value: 86.06124604012672
+    - type: euclidean_accuracy
+      value: 71.5755278830536
+    - type: euclidean_ap
+      value: 75.27762982049899
+    - type: euclidean_f1
+      value: 75.04604051565377
+    - type: euclidean_precision
+      value: 66.53061224489795
+    - type: euclidean_recall
+      value: 86.06124604012672
+    - type: manhattan_accuracy
+      value: 71.41310232809963
+    - type: manhattan_ap
+      value: 75.11908556317425
+    - type: manhattan_f1
+      value: 75.0118091639112
+    - type: manhattan_precision
+      value: 67.86324786324786
+    - type: manhattan_recall
+      value: 83.84371700105596
+    - type: max_accuracy
+      value: 71.5755278830536
+    - type: max_ap
+      value: 75.27777388526098
+    - type: max_f1
+      value: 75.04604051565377
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/OnlineShopping-classification
+      name: MTEB OnlineShopping
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 93.36
+    - type: ap
+      value: 91.66871784150999
+    - type: f1
+      value: 93.35216314755989
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/PAWSX
+      name: MTEB PAWSX
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 24.21926662784366
+    - type: cos_sim_spearman
+      value: 27.969680921064644
+    - type: euclidean_pearson
+      value: 28.75506415195721
+    - type: euclidean_spearman
+      value: 27.969593815056058
+    - type: manhattan_pearson
+      value: 28.90608040712011
+    - type: manhattan_spearman
+      value: 28.07097299964309
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/QBQTC
+      name: MTEB QBQTC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 33.4112661812038
+    - type: cos_sim_spearman
+      value: 35.192765228905174
+    - type: euclidean_pearson
+      value: 33.57803958232971
+    - type: euclidean_spearman
+      value: 35.19270413260232
+    - type: manhattan_pearson
+      value: 33.75933288702631
+    - type: manhattan_spearman
+      value: 35.362780488430126
+  - 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: 62.178764479940206
+    - type: cos_sim_spearman
+      value: 63.644049344272155
+    - type: euclidean_pearson
+      value: 61.97852518030118
+    - type: euclidean_spearman
+      value: 63.644049344272155
+    - type: manhattan_pearson
+      value: 62.3931275533103
+    - type: manhattan_spearman
+      value: 63.68720814152202
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/STSB
+      name: MTEB STSB
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.09847341753118
+    - type: cos_sim_spearman
+      value: 81.46211495319093
+    - type: euclidean_pearson
+      value: 80.97905808856734
+    - type: euclidean_spearman
+      value: 81.46177732221445
+    - type: manhattan_pearson
+      value: 80.8737913286308
+    - type: manhattan_spearman
+      value: 81.41142532907402
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/T2Reranking
+      name: MTEB T2Reranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 66.36295416100998
+    - type: mrr
+      value: 76.42041058129412
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/T2Retrieval
+      name: MTEB T2Retrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.898
+    - type: map_at_10
+      value: 75.089
+    - type: map_at_100
+      value: 78.786
+    - type: map_at_1000
+      value: 78.86
+    - type: map_at_3
+      value: 52.881
+    - type: map_at_5
+      value: 64.881
+    - type: mrr_at_1
+      value: 88.984
+    - type: mrr_at_10
+      value: 91.681
+    - type: mrr_at_100
+      value: 91.77300000000001
+    - type: mrr_at_1000
+      value: 91.777
+    - type: mrr_at_3
+      value: 91.205
+    - type: mrr_at_5
+      value: 91.486
+    - type: ndcg_at_1
+      value: 88.984
+    - type: ndcg_at_10
+      value: 83.083
+    - type: ndcg_at_100
+      value: 86.955
+    - type: ndcg_at_1000
+      value: 87.665
+    - type: ndcg_at_3
+      value: 84.661
+    - type: ndcg_at_5
+      value: 83.084
+    - type: precision_at_1
+      value: 88.984
+    - type: precision_at_10
+      value: 41.311
+    - type: precision_at_100
+      value: 4.978
+    - type: precision_at_1000
+      value: 0.515
+    - type: precision_at_3
+      value: 74.074
+    - type: precision_at_5
+      value: 61.956999999999994
+    - type: recall_at_1
+      value: 26.898
+    - type: recall_at_10
+      value: 82.03200000000001
+    - type: recall_at_100
+      value: 94.593
+    - type: recall_at_1000
+      value: 98.188
+    - type: recall_at_3
+      value: 54.647999999999996
+    - type: recall_at_5
+      value: 68.394
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/TNews-classification
+      name: MTEB TNews
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 53.648999999999994
+    - type: f1
+      value: 51.87788185753318
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringP2P
+      name: MTEB ThuNewsClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 68.81293224496076
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringS2S
+      name: MTEB ThuNewsClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 63.60504270553153
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/VideoRetrieval
+      name: MTEB VideoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 59.3
+    - type: map_at_10
+      value: 69.89
+    - type: map_at_100
+      value: 70.261
+    - type: map_at_1000
+      value: 70.27
+    - type: map_at_3
+      value: 67.93299999999999
+    - type: map_at_5
+      value: 69.10300000000001
+    - type: mrr_at_1
+      value: 59.3
+    - type: mrr_at_10
+      value: 69.89
+    - type: mrr_at_100
+      value: 70.261
+    - type: mrr_at_1000
+      value: 70.27
+    - type: mrr_at_3
+      value: 67.93299999999999
+    - type: mrr_at_5
+      value: 69.10300000000001
+    - type: ndcg_at_1
+      value: 59.3
+    - type: ndcg_at_10
+      value: 74.67099999999999
+    - type: ndcg_at_100
+      value: 76.371
+    - type: ndcg_at_1000
+      value: 76.644
+    - type: ndcg_at_3
+      value: 70.678
+    - type: ndcg_at_5
+      value: 72.783
+    - type: precision_at_1
+      value: 59.3
+    - type: precision_at_10
+      value: 8.95
+    - type: precision_at_100
+      value: 0.972
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 26.200000000000003
+    - type: precision_at_5
+      value: 16.74
+    - type: recall_at_1
+      value: 59.3
+    - type: recall_at_10
+      value: 89.5
+    - type: recall_at_100
+      value: 97.2
+    - type: recall_at_1000
+      value: 99.4
+    - type: recall_at_3
+      value: 78.60000000000001
+    - type: recall_at_5
+      value: 83.7
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/waimai-classification
+      name: MTEB Waimai
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 88.07000000000001
+    - type: ap
+      value: 72.68881791758656
+    - type: f1
+      value: 86.647906274628
+language:
+- en
 license: mit
 ---
+
+# gte-large-zh
+
+General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
+
+The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer different sizes of models for both Chinese and English Languages. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
+
+## Model List
+
+| Models | Language | Max Sequence Length | Dimension | Model Size |
+|:-----: | :-----: |:-----: |:-----: |:-----: |
+|[GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 0.67GB |
+|[GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 1024 | 0.67GB |
+|[GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 1024 | 0.67GB |
+|[GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 0.67GB |
+|[GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 1024 | 0.67GB |
+|[GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 1024 | 0.67GB |
+
+
+## Metrics
+
+We compared the performance of the GTE models with other popular text embedding models on the MTEB (CMTEB for Chinese language) benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
+
+
+## Usage
+
+Code example
+
+```python
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+input_texts = [
+    "中国的首都是哪里",
+    "你喜欢去哪里旅游",
+    "北京",
+    "今天中午吃什么"
+]
+
+tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-base-zh")
+model = AutoModel.from_pretrained("thenlper/gte-base-zh")
+
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
+
+outputs = model(**batch_dict)
+embeddings = outputs.last_hidden_state[:, 0]
+ 
+# (Optionally) normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:1] @ embeddings[1:].T) * 100
+print(scores.tolist())
+```
+
+Use with sentence-transformers:
+```python
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+
+sentences = ['中国的首都是哪里', '中国的首都是北京']
+
+model = SentenceTransformer('thenlper/gte-base-zh')
+embeddings = model.encode(sentences)
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+### Limitation
+
+This model exclusively caters to Chinese texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
+
+### Citation
+
+If you find our paper or models helpful, please consider citing them as follows:
+
+```
+@misc{li2023general,
+      title={Towards General Text Embeddings with Multi-stage Contrastive Learning},
+      author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
+      year={2023},
+      eprint={2308.03281},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```