metadata
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
- 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
- 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
Converted and quantized thenlper/gte-small-zh ONNX model for use with transformer.js
Usage reference
Convert your models to ONNX
gte-small-zh
General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning
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 | Chinese | 512 | 1024 | 0.67GB |
| GTE-base-zh | Chinese | 512 | 512 | 0.21GB |
| GTE-small-zh | Chinese | 512 | 512 | 0.10GB |
| GTE-large | English | 512 | 1024 | 0.67GB |
| GTE-base | English | 512 | 512 | 0.21GB |
| 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.
- 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
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:
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:
@article{li2023towards,
title={Towards general text embeddings with multi-stage contrastive learning},
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
journal={arXiv preprint arXiv:2308.03281},
year={2023}
}