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README.md

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+tags:
+- mteb
+- sentence-similarity
+- sentence-transformers
+- Sentence Transformers
+model-index:
+- name: gte-small-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: 35.80906032378281
+    - type: cos_sim_spearman
+      value: 36.688967176174415
+    - type: euclidean_pearson
+      value: 35.70701955438158
+    - type: euclidean_spearman
+      value: 36.6889470691436
+    - type: manhattan_pearson
+      value: 35.832741768286944
+    - type: manhattan_spearman
+      value: 36.831888591957195
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/ATEC
+      name: MTEB ATEC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 44.667266488330384
+    - type: cos_sim_spearman
+      value: 45.77390794946174
+    - type: euclidean_pearson
+      value: 48.14272832901943
+    - type: euclidean_spearman
+      value: 45.77390569666109
+    - type: manhattan_pearson
+      value: 48.187667158563094
+    - type: manhattan_spearman
+      value: 45.80979161966117
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (zh)
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 38.690000000000005
+    - type: f1
+      value: 36.868257131984016
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/BQ
+      name: MTEB BQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 49.03674224607541
+    - type: cos_sim_spearman
+      value: 49.63568854885055
+    - type: euclidean_pearson
+      value: 49.47441886441355
+    - type: euclidean_spearman
+      value: 49.63567815431205
+    - type: manhattan_pearson
+      value: 49.76480072909559
+    - type: manhattan_spearman
+      value: 49.977789367288224
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringP2P
+      name: MTEB CLSClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 39.538126779019755
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringS2S
+      name: MTEB CLSClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 37.333105487031766
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv1-reranking
+      name: MTEB CMedQAv1
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 86.08142426347963
+    - type: mrr
+      value: 88.04269841269841
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv2-reranking
+      name: MTEB CMedQAv2
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map
+      value: 87.25694119382474
+    - type: mrr
+      value: 89.36853174603175
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CmedqaRetrieval
+      name: MTEB CmedqaRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.913999999999998
+    - type: map_at_10
+      value: 35.913000000000004
+    - type: map_at_100
+      value: 37.836
+    - type: map_at_1000
+      value: 37.952000000000005
+    - type: map_at_3
+      value: 31.845000000000002
+    - type: map_at_5
+      value: 34.0
+    - type: mrr_at_1
+      value: 36.884
+    - type: mrr_at_10
+      value: 44.872
+    - type: mrr_at_100
+      value: 45.899
+    - type: mrr_at_1000
+      value: 45.945
+    - type: mrr_at_3
+      value: 42.331
+    - type: mrr_at_5
+      value: 43.674
+    - type: ndcg_at_1
+      value: 36.884
+    - type: ndcg_at_10
+      value: 42.459
+    - type: ndcg_at_100
+      value: 50.046
+    - type: ndcg_at_1000
+      value: 52.092000000000006
+    - type: ndcg_at_3
+      value: 37.225
+    - type: ndcg_at_5
+      value: 39.2
+    - type: precision_at_1
+      value: 36.884
+    - type: precision_at_10
+      value: 9.562
+    - type: precision_at_100
+      value: 1.572
+    - type: precision_at_1000
+      value: 0.183
+    - type: precision_at_3
+      value: 21.122
+    - type: precision_at_5
+      value: 15.274
+    - type: recall_at_1
+      value: 23.913999999999998
+    - type: recall_at_10
+      value: 52.891999999999996
+    - type: recall_at_100
+      value: 84.328
+    - type: recall_at_1000
+      value: 98.168
+    - type: recall_at_3
+      value: 37.095
+    - type: recall_at_5
+      value: 43.396
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/CMNLI
+      name: MTEB Cmnli
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 68.91160553217077
+    - type: cos_sim_ap
+      value: 76.45769658379533
+    - type: cos_sim_f1
+      value: 72.07988702844463
+    - type: cos_sim_precision
+      value: 63.384779137839274
+    - type: cos_sim_recall
+      value: 83.53986439092822
+    - type: dot_accuracy
+      value: 68.91160553217077
+    - type: dot_ap
+      value: 76.47279917239219
+    - type: dot_f1
+      value: 72.07988702844463
+    - type: dot_precision
+      value: 63.384779137839274
+    - type: dot_recall
+      value: 83.53986439092822
+    - type: euclidean_accuracy
+      value: 68.91160553217077
+    - type: euclidean_ap
+      value: 76.45768544225383
+    - type: euclidean_f1
+      value: 72.07988702844463
+    - type: euclidean_precision
+      value: 63.384779137839274
+    - type: euclidean_recall
+      value: 83.53986439092822
+    - type: manhattan_accuracy
+      value: 69.21226698737222
+    - type: manhattan_ap
+      value: 76.6623683693766
+    - type: manhattan_f1
+      value: 72.14058164628506
+    - type: manhattan_precision
+      value: 64.35643564356435
+    - type: manhattan_recall
+      value: 82.06686930091185
+    - type: max_accuracy
+      value: 69.21226698737222
+    - type: max_ap
+      value: 76.6623683693766
+    - type: max_f1
+      value: 72.14058164628506
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CovidRetrieval
+      name: MTEB CovidRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 48.419000000000004
+    - type: map_at_10
+      value: 57.367999999999995
+    - type: map_at_100
+      value: 58.081
+    - type: map_at_1000
+      value: 58.108000000000004
+    - type: map_at_3
+      value: 55.251
+    - type: map_at_5
+      value: 56.53399999999999
+    - type: mrr_at_1
+      value: 48.472
+    - type: mrr_at_10
+      value: 57.359
+    - type: mrr_at_100
+      value: 58.055
+    - type: mrr_at_1000
+      value: 58.082
+    - type: mrr_at_3
+      value: 55.303999999999995
+    - type: mrr_at_5
+      value: 56.542
+    - type: ndcg_at_1
+      value: 48.472
+    - type: ndcg_at_10
+      value: 61.651999999999994
+    - type: ndcg_at_100
+      value: 65.257
+    - type: ndcg_at_1000
+      value: 65.977
+    - type: ndcg_at_3
+      value: 57.401
+    - type: ndcg_at_5
+      value: 59.681
+    - type: precision_at_1
+      value: 48.472
+    - type: precision_at_10
+      value: 7.576
+    - type: precision_at_100
+      value: 0.932
+    - type: precision_at_1000
+      value: 0.099
+    - type: precision_at_3
+      value: 21.25
+    - type: precision_at_5
+      value: 13.888
+    - type: recall_at_1
+      value: 48.419000000000004
+    - type: recall_at_10
+      value: 74.97399999999999
+    - type: recall_at_100
+      value: 92.202
+    - type: recall_at_1000
+      value: 97.893
+    - type: recall_at_3
+      value: 63.541000000000004
+    - type: recall_at_5
+      value: 68.994
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/DuRetrieval
+      name: MTEB DuRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.328
+    - type: map_at_10
+      value: 69.11
+    - type: map_at_100
+      value: 72.47
+    - type: map_at_1000
+      value: 72.54599999999999
+    - type: map_at_3
+      value: 46.938
+    - type: map_at_5
+      value: 59.56
+    - type: mrr_at_1
+      value: 81.35
+    - type: mrr_at_10
+      value: 87.066
+    - type: mrr_at_100
+      value: 87.212
+    - type: mrr_at_1000
+      value: 87.21799999999999
+    - type: mrr_at_3
+      value: 86.558
+    - type: mrr_at_5
+      value: 86.931
+    - type: ndcg_at_1
+      value: 81.35
+    - type: ndcg_at_10
+      value: 78.568
+    - type: ndcg_at_100
+      value: 82.86099999999999
+    - type: ndcg_at_1000
+      value: 83.628
+    - type: ndcg_at_3
+      value: 76.716
+    - type: ndcg_at_5
+      value: 75.664
+    - type: precision_at_1
+      value: 81.35
+    - type: precision_at_10
+      value: 38.545
+    - type: precision_at_100
+      value: 4.657
+    - type: precision_at_1000
+      value: 0.484
+    - type: precision_at_3
+      value: 69.18299999999999
+    - type: precision_at_5
+      value: 58.67
+    - type: recall_at_1
+      value: 22.328
+    - type: recall_at_10
+      value: 80.658
+    - type: recall_at_100
+      value: 94.093
+    - type: recall_at_1000
+      value: 98.137
+    - type: recall_at_3
+      value: 50.260000000000005
+    - type: recall_at_5
+      value: 66.045
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/EcomRetrieval
+      name: MTEB EcomRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 43.1
+    - type: map_at_10
+      value: 52.872
+    - type: map_at_100
+      value: 53.556000000000004
+    - type: map_at_1000
+      value: 53.583000000000006
+    - type: map_at_3
+      value: 50.14999999999999
+    - type: map_at_5
+      value: 51.925
+    - type: mrr_at_1
+      value: 43.1
+    - type: mrr_at_10
+      value: 52.872
+    - type: mrr_at_100
+      value: 53.556000000000004
+    - type: mrr_at_1000
+      value: 53.583000000000006
+    - type: mrr_at_3
+      value: 50.14999999999999
+    - type: mrr_at_5
+      value: 51.925
+    - type: ndcg_at_1
+      value: 43.1
+    - type: ndcg_at_10
+      value: 57.907
+    - type: ndcg_at_100
+      value: 61.517999999999994
+    - type: ndcg_at_1000
+      value: 62.175000000000004
+    - type: ndcg_at_3
+      value: 52.425
+    - type: ndcg_at_5
+      value: 55.631
+    - type: precision_at_1
+      value: 43.1
+    - type: precision_at_10
+      value: 7.380000000000001
+    - type: precision_at_100
+      value: 0.9129999999999999
+    - type: precision_at_1000
+      value: 0.096
+    - type: precision_at_3
+      value: 19.667
+    - type: precision_at_5
+      value: 13.36
+    - type: recall_at_1
+      value: 43.1
+    - type: recall_at_10
+      value: 73.8
+    - type: recall_at_100
+      value: 91.3
+    - type: recall_at_1000
+      value: 96.39999999999999
+    - type: recall_at_3
+      value: 59.0
+    - type: recall_at_5
+      value: 66.8
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/IFlyTek-classification
+      name: MTEB IFlyTek
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 41.146594844170835
+    - type: f1
+      value: 28.544218732704845
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/JDReview-classification
+      name: MTEB JDReview
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 82.83302063789868
+    - type: ap
+      value: 48.881798834997056
+    - type: f1
+      value: 77.28655923994657
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/LCQMC
+      name: MTEB LCQMC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 66.05467125345538
+    - type: cos_sim_spearman
+      value: 72.71921060562211
+    - type: euclidean_pearson
+      value: 71.28539457113986
+    - type: euclidean_spearman
+      value: 72.71920173126693
+    - type: manhattan_pearson
+      value: 71.23750818174456
+    - type: manhattan_spearman
+      value: 72.61025268693467
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/Mmarco-reranking
+      name: MTEB MMarcoReranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 26.127712982639483
+    - type: mrr
+      value: 24.87420634920635
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MMarcoRetrieval
+      name: MTEB MMarcoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 62.517
+    - type: map_at_10
+      value: 71.251
+    - type: map_at_100
+      value: 71.647
+    - type: map_at_1000
+      value: 71.665
+    - type: map_at_3
+      value: 69.28
+    - type: map_at_5
+      value: 70.489
+    - type: mrr_at_1
+      value: 64.613
+    - type: mrr_at_10
+      value: 71.89
+    - type: mrr_at_100
+      value: 72.243
+    - type: mrr_at_1000
+      value: 72.259
+    - type: mrr_at_3
+      value: 70.138
+    - type: mrr_at_5
+      value: 71.232
+    - type: ndcg_at_1
+      value: 64.613
+    - type: ndcg_at_10
+      value: 75.005
+    - type: ndcg_at_100
+      value: 76.805
+    - type: ndcg_at_1000
+      value: 77.281
+    - type: ndcg_at_3
+      value: 71.234
+    - type: ndcg_at_5
+      value: 73.294
+    - type: precision_at_1
+      value: 64.613
+    - type: precision_at_10
+      value: 9.142
+    - type: precision_at_100
+      value: 1.004
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 26.781
+    - type: precision_at_5
+      value: 17.149
+    - type: recall_at_1
+      value: 62.517
+    - type: recall_at_10
+      value: 85.997
+    - type: recall_at_100
+      value: 94.18299999999999
+    - type: recall_at_1000
+      value: 97.911
+    - type: recall_at_3
+      value: 75.993
+    - type: recall_at_5
+      value: 80.88300000000001
+  - 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: 59.27706792199058
+    - type: f1
+      value: 56.77545011902468
+  - 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: 66.47948890383321
+    - type: f1
+      value: 66.4502180376861
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/MedicalRetrieval
+      name: MTEB MedicalRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 54.2
+    - type: map_at_10
+      value: 59.858
+    - type: map_at_100
+      value: 60.46
+    - type: map_at_1000
+      value: 60.507
+    - type: map_at_3
+      value: 58.416999999999994
+    - type: map_at_5
+      value: 59.331999999999994
+    - type: mrr_at_1
+      value: 54.2
+    - type: mrr_at_10
+      value: 59.862
+    - type: mrr_at_100
+      value: 60.463
+    - type: mrr_at_1000
+      value: 60.51
+    - type: mrr_at_3
+      value: 58.416999999999994
+    - type: mrr_at_5
+      value: 59.352000000000004
+    - type: ndcg_at_1
+      value: 54.2
+    - type: ndcg_at_10
+      value: 62.643
+    - type: ndcg_at_100
+      value: 65.731
+    - type: ndcg_at_1000
+      value: 67.096
+    - type: ndcg_at_3
+      value: 59.727
+    - type: ndcg_at_5
+      value: 61.375
+    - type: precision_at_1
+      value: 54.2
+    - type: precision_at_10
+      value: 7.140000000000001
+    - type: precision_at_100
+      value: 0.8619999999999999
+    - type: precision_at_1000
+      value: 0.097
+    - type: precision_at_3
+      value: 21.166999999999998
+    - type: precision_at_5
+      value: 13.5
+    - type: recall_at_1
+      value: 54.2
+    - type: recall_at_10
+      value: 71.39999999999999
+    - type: recall_at_100
+      value: 86.2
+    - type: recall_at_1000
+      value: 97.2
+    - type: recall_at_3
+      value: 63.5
+    - type: recall_at_5
+      value: 67.5
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/MultilingualSentiment-classification
+      name: MTEB MultilingualSentiment
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 68.19666666666666
+    - type: f1
+      value: 67.58581661416034
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/OCNLI
+      name: MTEB Ocnli
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: cos_sim_accuracy
+      value: 60.530590146182995
+    - type: cos_sim_ap
+      value: 63.53656091243922
+    - type: cos_sim_f1
+      value: 68.09929603556874
+    - type: cos_sim_precision
+      value: 52.45433789954338
+    - type: cos_sim_recall
+      value: 97.04329461457233
+    - type: dot_accuracy
+      value: 60.530590146182995
+    - type: dot_ap
+      value: 63.53660452157237
+    - type: dot_f1
+      value: 68.09929603556874
+    - type: dot_precision
+      value: 52.45433789954338
+    - type: dot_recall
+      value: 97.04329461457233
+    - type: euclidean_accuracy
+      value: 60.530590146182995
+    - type: euclidean_ap
+      value: 63.53678735855631
+    - type: euclidean_f1
+      value: 68.09929603556874
+    - type: euclidean_precision
+      value: 52.45433789954338
+    - type: euclidean_recall
+      value: 97.04329461457233
+    - type: manhattan_accuracy
+      value: 60.47644829453167
+    - type: manhattan_ap
+      value: 63.5622508250315
+    - type: manhattan_f1
+      value: 68.1650700073692
+    - type: manhattan_precision
+      value: 52.34861346915677
+    - type: manhattan_recall
+      value: 97.67687434002113
+    - type: max_accuracy
+      value: 60.530590146182995
+    - type: max_ap
+      value: 63.5622508250315
+    - type: max_f1
+      value: 68.1650700073692
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/OnlineShopping-classification
+      name: MTEB OnlineShopping
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 89.13
+    - type: ap
+      value: 87.21879260137172
+    - type: f1
+      value: 89.12359325300508
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/PAWSX
+      name: MTEB PAWSX
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 12.035577637900758
+    - type: cos_sim_spearman
+      value: 12.76524190663864
+    - type: euclidean_pearson
+      value: 14.4012689427106
+    - type: euclidean_spearman
+      value: 12.765328992583608
+    - type: manhattan_pearson
+      value: 14.458505202938946
+    - type: manhattan_spearman
+      value: 12.763238700117896
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/QBQTC
+      name: MTEB QBQTC
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 34.809415339934006
+    - type: cos_sim_spearman
+      value: 36.96728615916954
+    - type: euclidean_pearson
+      value: 35.56113673772396
+    - type: euclidean_spearman
+      value: 36.96842963389308
+    - type: manhattan_pearson
+      value: 35.5447066178264
+    - type: manhattan_spearman
+      value: 36.97514513480951
+  - 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: 66.39448692338551
+    - type: cos_sim_spearman
+      value: 66.72211526923901
+    - type: euclidean_pearson
+      value: 65.72981824553035
+    - type: euclidean_spearman
+      value: 66.72211526923901
+    - type: manhattan_pearson
+      value: 65.52315559414296
+    - type: manhattan_spearman
+      value: 66.61931702511545
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/STSB
+      name: MTEB STSB
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: cos_sim_pearson
+      value: 76.73608064460915
+    - type: cos_sim_spearman
+      value: 76.51424826130031
+    - type: euclidean_pearson
+      value: 76.17930213372487
+    - type: euclidean_spearman
+      value: 76.51342756283478
+    - type: manhattan_pearson
+      value: 75.87085607319342
+    - type: manhattan_spearman
+      value: 76.22676341477134
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/T2Reranking
+      name: MTEB T2Reranking
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map
+      value: 65.38779931543048
+    - type: mrr
+      value: 74.79313763420059
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/T2Retrieval
+      name: MTEB T2Retrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.131999999999998
+    - type: map_at_10
+      value: 69.131
+    - type: map_at_100
+      value: 72.943
+    - type: map_at_1000
+      value: 73.045
+    - type: map_at_3
+      value: 48.847
+    - type: map_at_5
+      value: 59.842
+    - type: mrr_at_1
+      value: 85.516
+    - type: mrr_at_10
+      value: 88.863
+    - type: mrr_at_100
+      value: 88.996
+    - type: mrr_at_1000
+      value: 89.00099999999999
+    - type: mrr_at_3
+      value: 88.277
+    - type: mrr_at_5
+      value: 88.64800000000001
+    - type: ndcg_at_1
+      value: 85.516
+    - type: ndcg_at_10
+      value: 78.122
+    - type: ndcg_at_100
+      value: 82.673
+    - type: ndcg_at_1000
+      value: 83.707
+    - type: ndcg_at_3
+      value: 80.274
+    - type: ndcg_at_5
+      value: 78.405
+    - type: precision_at_1
+      value: 85.516
+    - type: precision_at_10
+      value: 38.975
+    - type: precision_at_100
+      value: 4.833
+    - type: precision_at_1000
+      value: 0.509
+    - type: precision_at_3
+      value: 70.35
+    - type: precision_at_5
+      value: 58.638
+    - type: recall_at_1
+      value: 25.131999999999998
+    - type: recall_at_10
+      value: 76.848
+    - type: recall_at_100
+      value: 91.489
+    - type: recall_at_1000
+      value: 96.709
+    - type: recall_at_3
+      value: 50.824000000000005
+    - type: recall_at_5
+      value: 63.89
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/TNews-classification
+      name: MTEB TNews
+      config: default
+      split: validation
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 49.65
+    - type: f1
+      value: 47.66791473245483
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringP2P
+      name: MTEB ThuNewsClusteringP2P
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 63.78843565968542
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/ThuNewsClusteringS2S
+      name: MTEB ThuNewsClusteringS2S
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: v_measure
+      value: 55.14095244943176
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/VideoRetrieval
+      name: MTEB VideoRetrieval
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 53.800000000000004
+    - type: map_at_10
+      value: 63.312000000000005
+    - type: map_at_100
+      value: 63.93600000000001
+    - type: map_at_1000
+      value: 63.955
+    - type: map_at_3
+      value: 61.283
+    - type: map_at_5
+      value: 62.553000000000004
+    - type: mrr_at_1
+      value: 53.800000000000004
+    - type: mrr_at_10
+      value: 63.312000000000005
+    - type: mrr_at_100
+      value: 63.93600000000001
+    - type: mrr_at_1000
+      value: 63.955
+    - type: mrr_at_3
+      value: 61.283
+    - type: mrr_at_5
+      value: 62.553000000000004
+    - type: ndcg_at_1
+      value: 53.800000000000004
+    - type: ndcg_at_10
+      value: 67.693
+    - type: ndcg_at_100
+      value: 70.552
+    - type: ndcg_at_1000
+      value: 71.06099999999999
+    - type: ndcg_at_3
+      value: 63.632
+    - type: ndcg_at_5
+      value: 65.90899999999999
+    - type: precision_at_1
+      value: 53.800000000000004
+    - type: precision_at_10
+      value: 8.129999999999999
+    - type: precision_at_100
+      value: 0.943
+    - type: precision_at_1000
+      value: 0.098
+    - type: precision_at_3
+      value: 23.467
+    - type: precision_at_5
+      value: 15.18
+    - type: recall_at_1
+      value: 53.800000000000004
+    - type: recall_at_10
+      value: 81.3
+    - type: recall_at_100
+      value: 94.3
+    - type: recall_at_1000
+      value: 98.3
+    - type: recall_at_3
+      value: 70.39999999999999
+    - type: recall_at_5
+      value: 75.9
+  - task:
+      type: Classification
+    dataset:
+      type: C-MTEB/waimai-classification
+      name: MTEB Waimai
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: accuracy
+      value: 84.96000000000001
+    - type: ap
+      value: 66.89917287702019
+    - type: f1
+      value: 83.0239988458119
+language:
+- en
 license: mit
 ---
+
+# gte-small-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 | 512 | 0.21GB |
+|[GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.10GB |
+|[GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 0.67GB |
+|[GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB |
+|[GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB |
+
+## 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).
+
+- Evaluation results on CMTEB 
+
+| Model               | Model Size (GB) | Embedding Dimensions | Sequence Length | Average (35 datasets) | Classification  (9 datasets)         | Clustering (4 datasets)        | Pair Classification        (2 datasets) | Reranking (4 datasets)         | Retrieval  (8 datasets)      | STS         (8 datasets) |
+| ------------------- | -------------- | -------------------- | ---------------- | --------------------- | ------------------------------------ | ------------------------------ | --------------------------------------- | ------------------------------ | ---------------------------- | ------------------------ |
+| **gte-large-zh** | 0.65           | 1024                  | 512              | **66.72**                 | 71.34                                | 53.07                        | 81.14                                   | 67.42                          | 72.49                        | 57.82                  |
+| gte-base-zh         | 0.20         | 768                  | 512              | 65.92                 | 71.26                               | 53.86                          | 80.44                                  | 67.00                          | 71.71                        | 55.96                    |
+| stella-large-zh-v2  | 0.65           | 1024                 | 1024             | 65.13                 | 69.05                                | 49.16                          | 82.68                                   | 66.41                          | 70.14                        | 58.66                    |
+| stella-large-zh     | 0.65           | 1024                 | 1024             | 64.54                 | 67.62                                | 48.65                          | 78.72                                   | 65.98                          | 71.02                        | 58.3                     |
+| bge-large-zh-v1.5   | 1.3            | 1024                 | 512              | 64.53                 | 69.13                                | 48.99                          | 81.6                                    | 65.84                          | 70.46                        | 56.25                    |
+| stella-base-zh-v2   | 0.21           | 768                  | 1024             | 64.36                 | 68.29                                | 49.4                           | 79.96                                   | 66.1                           | 70.08                        | 56.92                    |
+| stella-base-zh      | 0.21           | 768                  | 1024             | 64.16                 | 67.77                                | 48.7                           | 76.09                                   | 66.95                          | 71.07                        | 56.54                    |
+| piccolo-large-zh    | 0.65           | 1024                 | 512              | 64.11                 | 67.03                                | 47.04                          | 78.38                                   | 65.98                          | 70.93                        | 58.02                    |
+| piccolo-base-zh     | 0.2            | 768                  | 512              | 63.66                 | 66.98                                | 47.12                          | 76.61                                   | 66.68                          | 71.2                         | 55.9                     |
+| gte-small-zh         | 0.1           | 512                  | 512              | 60.04                 | 64.35                                | 48.95                          | 69.99                                   | 66.21                          | 65.50                        | 49.72                    |
+| bge-small-zh-v1.5     | 0.1           | 512                  | 512              | 57.82                 | 63.96                                | 44.18                          | 70.4                                   | 60.92                          | 61.77                        | 49.1                    |
+| m3e-base | 0.41 | 768 | 512 | 57.79 | 67.52 | 47.68 | 63.99 | 59.54| 56.91 | 50.47 | 
+|text-embedding-ada-002(openai) | - | 1536| 8192 | 53.02 | 64.31 | 45.68 | 69.56 | 54.28 | 52.0 | 43.35 |
+
+
+## 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-small-zh")
+model = AutoModel.from_pretrained("thenlper/gte-small-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 = ['That is a happy person', 'That is a very happy person']
+
+model = SentenceTransformer('thenlper/gte-small-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}
+}
+```