metadata
tags:
- mteb
model-index:
- name: zpoint_large_embedding_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: 56.52479321107392
- type: cos_sim_spearman
value: 60.72175935031135
- type: euclidean_pearson
value: 59.40990657564856
- type: euclidean_spearman
value: 60.72175934804556
- type: manhattan_pearson
value: 59.4134322847349
- type: manhattan_spearman
value: 60.724413114688225
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 56.492631347325464
- type: cos_sim_spearman
value: 58.765171687177656
- type: euclidean_pearson
value: 63.236364373113844
- type: euclidean_spearman
value: 58.765171686714865
- type: manhattan_pearson
value: 63.22241814845751
- type: manhattan_spearman
value: 58.762780342648234
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.72
- type: f1
value: 46.588683657317084
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 73.07779128771674
- type: cos_sim_spearman
value: 75.03682691328844
- type: euclidean_pearson
value: 73.68098259699073
- type: euclidean_spearman
value: 75.03683037648963
- type: manhattan_pearson
value: 73.66963332679124
- type: manhattan_spearman
value: 75.02269337817758
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 58.2897067752906
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 48.79170511177673
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 91.10738371185181
- type: mrr
value: 92.82496031746031
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 90.06959035874831
- type: mrr
value: 92.00789682539683
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 27.132
- type: map_at_10
value: 40.400999999999996
- type: map_at_100
value: 42.246
- type: map_at_1000
value: 42.351
- type: map_at_3
value: 35.94
- type: map_at_5
value: 38.527
- type: mrr_at_1
value: 41.285
- type: mrr_at_10
value: 49.474000000000004
- type: mrr_at_100
value: 50.4
- type: mrr_at_1000
value: 50.438
- type: mrr_at_3
value: 46.891
- type: mrr_at_5
value: 48.353
- type: ndcg_at_1
value: 41.285
- type: ndcg_at_10
value: 47.159
- type: ndcg_at_100
value: 54.163
- type: ndcg_at_1000
value: 55.921
- type: ndcg_at_3
value: 41.678
- type: ndcg_at_5
value: 44.069
- type: precision_at_1
value: 41.285
- type: precision_at_10
value: 10.468
- type: precision_at_100
value: 1.611
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 23.648
- type: precision_at_5
value: 17.229
- type: recall_at_1
value: 27.132
- type: recall_at_10
value: 57.977999999999994
- type: recall_at_100
value: 86.88
- type: recall_at_1000
value: 98.586
- type: recall_at_3
value: 41.487
- type: recall_at_5
value: 48.79
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 86.06133493686109
- type: cos_sim_ap
value: 92.54288511740305
- type: cos_sim_f1
value: 86.85572811163628
- type: cos_sim_precision
value: 83.72748969407681
- type: cos_sim_recall
value: 90.22679448211363
- type: dot_accuracy
value: 86.06133493686109
- type: dot_ap
value: 92.53922591080917
- type: dot_f1
value: 86.85572811163628
- type: dot_precision
value: 83.72748969407681
- type: dot_recall
value: 90.22679448211363
- type: euclidean_accuracy
value: 86.06133493686109
- type: euclidean_ap
value: 92.54287994398305
- type: euclidean_f1
value: 86.85572811163628
- type: euclidean_precision
value: 83.72748969407681
- type: euclidean_recall
value: 90.22679448211363
- type: manhattan_accuracy
value: 86.01322910402887
- type: manhattan_ap
value: 92.53060255301997
- type: manhattan_f1
value: 86.81441683456458
- type: manhattan_precision
value: 83.27249302125833
- type: manhattan_recall
value: 90.67103109656301
- type: max_accuracy
value: 86.06133493686109
- type: max_ap
value: 92.54288511740305
- type: max_f1
value: 86.85572811163628
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 78.899
- type: map_at_10
value: 86.232
- type: map_at_100
value: 86.331
- type: map_at_1000
value: 86.332
- type: map_at_3
value: 85.256
- type: map_at_5
value: 85.883
- type: mrr_at_1
value: 79.347
- type: mrr_at_10
value: 86.252
- type: mrr_at_100
value: 86.342
- type: mrr_at_1000
value: 86.343
- type: mrr_at_3
value: 85.283
- type: mrr_at_5
value: 85.91
- type: ndcg_at_1
value: 79.347
- type: ndcg_at_10
value: 89.143
- type: ndcg_at_100
value: 89.541
- type: ndcg_at_1000
value: 89.58
- type: ndcg_at_3
value: 87.227
- type: ndcg_at_5
value: 88.31400000000001
- type: precision_at_1
value: 79.347
- type: precision_at_10
value: 9.905
- type: precision_at_100
value: 1.0070000000000001
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 31.261
- type: precision_at_5
value: 19.305
- type: recall_at_1
value: 78.899
- type: recall_at_10
value: 97.99799999999999
- type: recall_at_100
value: 99.684
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 92.808
- type: recall_at_5
value: 95.46900000000001
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 27.107999999999997
- type: map_at_10
value: 82.525
- type: map_at_100
value: 85.168
- type: map_at_1000
value: 85.194
- type: map_at_3
value: 57.74399999999999
- type: map_at_5
value: 72.53699999999999
- type: mrr_at_1
value: 92.30000000000001
- type: mrr_at_10
value: 94.705
- type: mrr_at_100
value: 94.76599999999999
- type: mrr_at_1000
value: 94.76599999999999
- type: mrr_at_3
value: 94.55
- type: mrr_at_5
value: 94.64
- type: ndcg_at_1
value: 92.30000000000001
- type: ndcg_at_10
value: 89.23100000000001
- type: ndcg_at_100
value: 91.556
- type: ndcg_at_1000
value: 91.81700000000001
- type: ndcg_at_3
value: 88.558
- type: ndcg_at_5
value: 87.316
- type: precision_at_1
value: 92.30000000000001
- type: precision_at_10
value: 42.38
- type: precision_at_100
value: 4.818
- type: precision_at_1000
value: 0.488
- type: precision_at_3
value: 79.14999999999999
- type: precision_at_5
value: 66.63
- type: recall_at_1
value: 27.107999999999997
- type: recall_at_10
value: 89.914
- type: recall_at_100
value: 97.658
- type: recall_at_1000
value: 99.00099999999999
- type: recall_at_3
value: 59.673
- type: recall_at_5
value: 76.437
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 55.00000000000001
- type: map_at_10
value: 65.57600000000001
- type: map_at_100
value: 66.096
- type: map_at_1000
value: 66.103
- type: map_at_3
value: 63.217
- type: map_at_5
value: 64.562
- type: mrr_at_1
value: 55.00000000000001
- type: mrr_at_10
value: 65.57600000000001
- type: mrr_at_100
value: 66.096
- type: mrr_at_1000
value: 66.103
- type: mrr_at_3
value: 63.217
- type: mrr_at_5
value: 64.562
- type: ndcg_at_1
value: 55.00000000000001
- type: ndcg_at_10
value: 70.74000000000001
- type: ndcg_at_100
value: 73.001
- type: ndcg_at_1000
value: 73.223
- type: ndcg_at_3
value: 65.837
- type: ndcg_at_5
value: 68.264
- type: precision_at_1
value: 55.00000000000001
- type: precision_at_10
value: 8.7
- type: precision_at_100
value: 0.97
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 24.467
- type: precision_at_5
value: 15.86
- type: recall_at_1
value: 55.00000000000001
- type: recall_at_10
value: 87
- type: recall_at_100
value: 97
- type: recall_at_1000
value: 98.8
- type: recall_at_3
value: 73.4
- type: recall_at_5
value: 79.3
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 51.696806464024625
- type: f1
value: 40.02655259854763
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 88.87429643527206
- type: ap
value: 59.89821610336161
- type: f1
value: 83.98100504939507
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 72.59510783330644
- type: cos_sim_spearman
value: 79.75022839599451
- type: euclidean_pearson
value: 79.54475341768782
- type: euclidean_spearman
value: 79.75021730266204
- type: manhattan_pearson
value: 79.53741020350834
- type: manhattan_spearman
value: 79.74152434784455
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 38.86925357762224
- type: mrr
value: 38.17460317460318
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 68.731
- type: map_at_10
value: 78.52
- type: map_at_100
value: 78.792
- type: map_at_1000
value: 78.797
- type: map_at_3
value: 76.586
- type: map_at_5
value: 77.876
- type: mrr_at_1
value: 71.003
- type: mrr_at_10
value: 79.03
- type: mrr_at_100
value: 79.27
- type: mrr_at_1000
value: 79.274
- type: mrr_at_3
value: 77.373
- type: mrr_at_5
value: 78.46600000000001
- type: ndcg_at_1
value: 71.003
- type: ndcg_at_10
value: 82.381
- type: ndcg_at_100
value: 83.504
- type: ndcg_at_1000
value: 83.627
- type: ndcg_at_3
value: 78.78699999999999
- type: ndcg_at_5
value: 80.94
- type: precision_at_1
value: 71.003
- type: precision_at_10
value: 9.961
- type: precision_at_100
value: 1.05
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 29.694
- type: precision_at_5
value: 18.963
- type: recall_at_1
value: 68.731
- type: recall_at_10
value: 93.697
- type: recall_at_100
value: 98.546
- type: recall_at_1000
value: 99.515
- type: recall_at_3
value: 84.328
- type: recall_at_5
value: 89.42
- 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: 76.79219905850707
- type: f1
value: 73.15228001501512
- 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: 84.9562878278413
- type: f1
value: 84.0910677219451
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 57.8
- type: map_at_10
value: 64.732
- type: map_at_100
value: 65.315
- type: map_at_1000
value: 65.347
- type: map_at_3
value: 63.14999999999999
- type: map_at_5
value: 63.934999999999995
- type: mrr_at_1
value: 57.99999999999999
- type: mrr_at_10
value: 64.852
- type: mrr_at_100
value: 65.435
- type: mrr_at_1000
value: 65.467
- type: mrr_at_3
value: 63.266999999999996
- type: mrr_at_5
value: 64.072
- type: ndcg_at_1
value: 57.8
- type: ndcg_at_10
value: 68.14
- type: ndcg_at_100
value: 71.04899999999999
- type: ndcg_at_1000
value: 71.856
- type: ndcg_at_3
value: 64.813
- type: ndcg_at_5
value: 66.241
- type: precision_at_1
value: 57.8
- type: precision_at_10
value: 7.89
- type: precision_at_100
value: 0.927
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 23.200000000000003
- type: precision_at_5
value: 14.62
- type: recall_at_1
value: 57.8
- type: recall_at_10
value: 78.9
- type: recall_at_100
value: 92.7
- type: recall_at_1000
value: 99
- type: recall_at_3
value: 69.6
- type: recall_at_5
value: 73.1
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 79.22333333333333
- type: f1
value: 79.01276765455862
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 85.32755820249052
- type: cos_sim_ap
value: 90.56118966152913
- type: cos_sim_f1
value: 86.28428927680798
- type: cos_sim_precision
value: 81.75803402646503
- type: cos_sim_recall
value: 91.34107708553326
- type: dot_accuracy
value: 85.32755820249052
- type: dot_ap
value: 90.56120405888693
- type: dot_f1
value: 86.28428927680798
- type: dot_precision
value: 81.75803402646503
- type: dot_recall
value: 91.34107708553326
- type: euclidean_accuracy
value: 85.32755820249052
- type: euclidean_ap
value: 90.56118966152913
- type: euclidean_f1
value: 86.28428927680798
- type: euclidean_precision
value: 81.75803402646503
- type: euclidean_recall
value: 91.34107708553326
- type: manhattan_accuracy
value: 85.43584190579317
- type: manhattan_ap
value: 90.52296007826511
- type: manhattan_f1
value: 86.42099949520444
- type: manhattan_precision
value: 82.7852998065764
- type: manhattan_recall
value: 90.3907074973601
- type: max_accuracy
value: 85.43584190579317
- type: max_ap
value: 90.56120405888693
- type: max_f1
value: 86.42099949520444
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 94.87999999999998
- type: ap
value: 93.12892276945414
- type: f1
value: 94.86921245385685
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 38.4367277229591
- type: cos_sim_spearman
value: 45.942712312151656
- type: euclidean_pearson
value: 44.96055989566686
- type: euclidean_spearman
value: 45.94279939044163
- type: manhattan_pearson
value: 44.979762134562925
- type: manhattan_spearman
value: 45.96004430328375
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 41.45428416733968
- type: cos_sim_spearman
value: 43.462057455255845
- type: euclidean_pearson
value: 38.20089604291246
- type: euclidean_spearman
value: 43.46288438624811
- type: manhattan_pearson
value: 38.175045608320694
- type: manhattan_spearman
value: 43.468885824666344
- 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: 65.61911213187778
- type: cos_sim_spearman
value: 66.70525921118497
- type: euclidean_pearson
value: 65.35554462551515
- type: euclidean_spearman
value: 66.70525921118497
- type: manhattan_pearson
value: 65.25174169329627
- type: manhattan_spearman
value: 66.6550752269368
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 81.27160581568329
- type: cos_sim_spearman
value: 83.34482829304406
- type: euclidean_pearson
value: 82.98079434913451
- type: euclidean_spearman
value: 83.34503180775212
- type: manhattan_pearson
value: 82.95256917013506
- type: manhattan_spearman
value: 83.31034894907503
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 69.29054152015013
- type: mrr
value: 79.73472208788729
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 27
- type: map_at_10
value: 75.871
- type: map_at_100
value: 79.664
- type: map_at_1000
value: 79.725
- type: map_at_3
value: 53.14
- type: map_at_5
value: 65.365
- type: mrr_at_1
value: 88.642
- type: mrr_at_10
value: 91.732
- type: mrr_at_100
value: 91.818
- type: mrr_at_1000
value: 91.821
- type: mrr_at_3
value: 91.217
- type: mrr_at_5
value: 91.561
- type: ndcg_at_1
value: 88.642
- type: ndcg_at_10
value: 83.815
- type: ndcg_at_100
value: 87.689
- type: ndcg_at_1000
value: 88.266
- type: ndcg_at_3
value: 84.807
- type: ndcg_at_5
value: 83.53699999999999
- type: precision_at_1
value: 88.642
- type: precision_at_10
value: 41.725
- type: precision_at_100
value: 5.024
- type: precision_at_1000
value: 0.516
- type: precision_at_3
value: 74.10600000000001
- type: precision_at_5
value: 62.192
- type: recall_at_1
value: 27
- type: recall_at_10
value: 83.292
- type: recall_at_100
value: 95.66799999999999
- type: recall_at_1000
value: 98.56
- type: recall_at_3
value: 55.111
- type: recall_at_5
value: 69.327
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 54.346
- type: f1
value: 52.302508458396055
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 72.47709523787981
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 69.35293863978707
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 64.60000000000001
- type: map_at_10
value: 75.683
- type: map_at_100
value: 75.961
- type: map_at_1000
value: 75.96199999999999
- type: map_at_3
value: 74.083
- type: map_at_5
value: 75.03800000000001
- type: mrr_at_1
value: 64.60000000000001
- type: mrr_at_10
value: 75.683
- type: mrr_at_100
value: 75.961
- type: mrr_at_1000
value: 75.96199999999999
- type: mrr_at_3
value: 74.083
- type: mrr_at_5
value: 75.03800000000001
- type: ndcg_at_1
value: 64.60000000000001
- type: ndcg_at_10
value: 80.26299999999999
- type: ndcg_at_100
value: 81.487
- type: ndcg_at_1000
value: 81.5
- type: ndcg_at_3
value: 77.003
- type: ndcg_at_5
value: 78.708
- type: precision_at_1
value: 64.60000000000001
- type: precision_at_10
value: 9.43
- type: precision_at_100
value: 0.997
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 28.467
- type: precision_at_5
value: 17.9
- type: recall_at_1
value: 64.60000000000001
- type: recall_at_10
value: 94.3
- type: recall_at_100
value: 99.7
- type: recall_at_1000
value: 99.8
- type: recall_at_3
value: 85.39999999999999
- type: recall_at_5
value: 89.5
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 89.36
- type: ap
value: 75.26507519569006
- type: f1
value: 87.89845508858562
language:
- zh
license: mit
library_name: sentence-transformers
ZPoint Large Embedding for Chinese
[2024-06-04] Release zpoint_large_embedding_zh, and upload model weight to huggingface [2024-06-05] Add training details
Training Details
Base Model
- We chose Stella as our base model.
Training Data
- Hard negative samping
- For retrieval task, We sampled 10 hard negative passages/answers from top50-top200 related passages/answers for each query.
- For classification/clustering tasks, we sampled 5 hard negative samples from other classes/cluster for each sample.
- For classification/clustering tasks, we also used the category names of each class and cluster as positive and negative samples.
- Data synthesis by LLM (Qwen1.5-72B)
- For retrieval tasks, we used LLM to rewrite each query, generating five different rewritten results.
- For retrieval tasks, we also generated five new queries for some documents by LLM.
- For non-retrieval tasks, we used LLM to rewrite the queries, generating five rewritten results for each query.
- Finally, total amount of synthesized data is about 30 million.
- Collect more data for retrieval-type tasks
- We constructed a dataset of approximately 100 million training samples through collection, machine translation, and LLM synthesis. This dataset includes data from various fields such as healthcare, law, electricity, automotive, and 3C (Consumer Electronics).
- miracl/miracl
- FreedomIntelligence/Huatuo26M-Lite
- PaddlePaddle/dureader_robust C-MTEB test filtered
- THUIR/T2Ranking C-MTEB test filtered
- Shitao/bge-reranker-data
- Shitao/MLDR
- ...
Training loss
- Multi-Task loss like Piccolo
- Matryoshka Representation Learning
Example
from sentence_transformers import SentenceTransformer
sentences1 = ["这个产品真垃圾"]
sentences2 = ["我太喜欢这个产品了"]
model = SentenceTransformer('iampanda/zpoint_large_embedding_zh')
embeddings_1 = model.encode(sentences1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)