metadata
tags:
- mteb
model-index:
- name: stella-large-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: 51.61327712288466
- type: cos_sim_spearman
value: 54.48753880097122
- type: euclidean_pearson
value: 52.68387289931342
- type: euclidean_spearman
value: 54.48753879487172
- type: manhattan_pearson
value: 52.635406372350026
- type: manhattan_spearman
value: 54.447390526317044
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 53.39178036427897
- type: cos_sim_spearman
value: 54.450028472876134
- type: euclidean_pearson
value: 56.87300033777842
- type: euclidean_spearman
value: 54.45002622056799
- type: manhattan_pearson
value: 56.84326996138951
- type: manhattan_spearman
value: 54.433880144849375
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.574000000000005
- type: f1
value: 38.87775700245793
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 60.80957921870066
- type: cos_sim_spearman
value: 62.37707350882237
- type: euclidean_pearson
value: 61.29032932843765
- type: euclidean_spearman
value: 62.37707350713817
- type: manhattan_pearson
value: 61.23028102541801
- type: manhattan_spearman
value: 62.31280056582247
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 40.27066616318565
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 37.503323644484716
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 84.69295191328456
- type: mrr
value: 87.08992063492063
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 85.22650690364465
- type: mrr
value: 87.72158730158729
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.54
- type: map_at_10
value: 35.591
- type: map_at_100
value: 37.549
- type: map_at_1000
value: 37.663000000000004
- type: map_at_3
value: 31.405
- type: map_at_5
value: 33.792
- type: mrr_at_1
value: 36.359
- type: mrr_at_10
value: 44.624
- type: mrr_at_100
value: 45.660000000000004
- type: mrr_at_1000
value: 45.707
- type: mrr_at_3
value: 42.002
- type: mrr_at_5
value: 43.535000000000004
- type: ndcg_at_1
value: 36.359
- type: ndcg_at_10
value: 42.28
- type: ndcg_at_100
value: 49.997
- type: ndcg_at_1000
value: 51.966
- type: ndcg_at_3
value: 36.851
- type: ndcg_at_5
value: 39.249
- type: precision_at_1
value: 36.359
- type: precision_at_10
value: 9.542
- type: precision_at_100
value: 1.582
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 20.913999999999998
- type: precision_at_5
value: 15.404000000000002
- type: recall_at_1
value: 23.54
- type: recall_at_10
value: 53.005
- type: recall_at_100
value: 85.085
- type: recall_at_1000
value: 98.21
- type: recall_at_3
value: 36.944
- type: recall_at_5
value: 44.137
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 76.16355983162958
- type: cos_sim_ap
value: 85.14228023901842
- type: cos_sim_f1
value: 77.86752827140549
- type: cos_sim_precision
value: 72.18450479233228
- type: cos_sim_recall
value: 84.5218611176058
- type: dot_accuracy
value: 76.16355983162958
- type: dot_ap
value: 85.16266644596179
- type: dot_f1
value: 77.86752827140549
- type: dot_precision
value: 72.18450479233228
- type: dot_recall
value: 84.5218611176058
- type: euclidean_accuracy
value: 76.16355983162958
- type: euclidean_ap
value: 85.14227717790371
- type: euclidean_f1
value: 77.86752827140549
- type: euclidean_precision
value: 72.18450479233228
- type: euclidean_recall
value: 84.5218611176058
- type: manhattan_accuracy
value: 75.99518941671678
- type: manhattan_ap
value: 85.10764940972825
- type: manhattan_f1
value: 77.80804694048618
- type: manhattan_precision
value: 70.49553825707233
- type: manhattan_recall
value: 86.81318681318682
- type: max_accuracy
value: 76.16355983162958
- type: max_ap
value: 85.16266644596179
- type: max_f1
value: 77.86752827140549
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 73.762
- type: map_at_10
value: 81.76299999999999
- type: map_at_100
value: 81.974
- type: map_at_1000
value: 81.977
- type: map_at_3
value: 80.23400000000001
- type: map_at_5
value: 81.189
- type: mrr_at_1
value: 74.18299999999999
- type: mrr_at_10
value: 81.792
- type: mrr_at_100
value: 81.994
- type: mrr_at_1000
value: 81.997
- type: mrr_at_3
value: 80.277
- type: mrr_at_5
value: 81.221
- type: ndcg_at_1
value: 74.078
- type: ndcg_at_10
value: 85.195
- type: ndcg_at_100
value: 86.041
- type: ndcg_at_1000
value: 86.111
- type: ndcg_at_3
value: 82.171
- type: ndcg_at_5
value: 83.90100000000001
- type: precision_at_1
value: 74.078
- type: precision_at_10
value: 9.684
- type: precision_at_100
value: 1.004
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 29.470000000000002
- type: precision_at_5
value: 18.567
- type: recall_at_1
value: 73.762
- type: recall_at_10
value: 95.785
- type: recall_at_100
value: 99.368
- type: recall_at_1000
value: 99.895
- type: recall_at_3
value: 87.724
- type: recall_at_5
value: 91.93900000000001
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 25.911
- type: map_at_10
value: 80.656
- type: map_at_100
value: 83.446
- type: map_at_1000
value: 83.485
- type: map_at_3
value: 55.998000000000005
- type: map_at_5
value: 70.577
- type: mrr_at_1
value: 90.14999999999999
- type: mrr_at_10
value: 93.35900000000001
- type: mrr_at_100
value: 93.419
- type: mrr_at_1000
value: 93.423
- type: mrr_at_3
value: 93.133
- type: mrr_at_5
value: 93.26100000000001
- type: ndcg_at_1
value: 90.14999999999999
- type: ndcg_at_10
value: 87.806
- type: ndcg_at_100
value: 90.4
- type: ndcg_at_1000
value: 90.776
- type: ndcg_at_3
value: 86.866
- type: ndcg_at_5
value: 85.619
- type: precision_at_1
value: 90.14999999999999
- type: precision_at_10
value: 42.045
- type: precision_at_100
value: 4.814
- type: precision_at_1000
value: 0.49
- type: precision_at_3
value: 78
- type: precision_at_5
value: 65.62
- type: recall_at_1
value: 25.911
- type: recall_at_10
value: 88.942
- type: recall_at_100
value: 97.56700000000001
- type: recall_at_1000
value: 99.62
- type: recall_at_3
value: 58.361
- type: recall_at_5
value: 75.126
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 46.2
- type: map_at_10
value: 56.309
- type: map_at_100
value: 56.977
- type: map_at_1000
value: 56.995
- type: map_at_3
value: 53.55
- type: map_at_5
value: 55.19
- type: mrr_at_1
value: 46.2
- type: mrr_at_10
value: 56.309
- type: mrr_at_100
value: 56.977
- type: mrr_at_1000
value: 56.995
- type: mrr_at_3
value: 53.55
- type: mrr_at_5
value: 55.19
- type: ndcg_at_1
value: 46.2
- type: ndcg_at_10
value: 61.656
- type: ndcg_at_100
value: 64.714
- type: ndcg_at_1000
value: 65.217
- type: ndcg_at_3
value: 56.022000000000006
- type: ndcg_at_5
value: 58.962
- type: precision_at_1
value: 46.2
- type: precision_at_10
value: 7.86
- type: precision_at_100
value: 0.9249999999999999
- type: precision_at_1000
value: 0.097
- type: precision_at_3
value: 21.067
- type: precision_at_5
value: 14.06
- type: recall_at_1
value: 46.2
- type: recall_at_10
value: 78.60000000000001
- type: recall_at_100
value: 92.5
- type: recall_at_1000
value: 96.5
- type: recall_at_3
value: 63.2
- type: recall_at_5
value: 70.3
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 47.03347441323585
- type: f1
value: 35.50895794566714
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.73545966228893
- type: ap
value: 55.43694740493539
- type: f1
value: 81.47218440859787
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 70.49478085579923
- type: cos_sim_spearman
value: 76.28442852235379
- type: euclidean_pearson
value: 74.90910715249527
- type: euclidean_spearman
value: 76.28443517178847
- type: manhattan_pearson
value: 74.90744903779758
- type: manhattan_spearman
value: 76.2886829916495
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 64.798
- type: map_at_10
value: 74.263
- type: map_at_100
value: 74.59
- type: map_at_1000
value: 74.601
- type: map_at_3
value: 72.382
- type: map_at_5
value: 73.59700000000001
- type: mrr_at_1
value: 67.049
- type: mrr_at_10
value: 74.86500000000001
- type: mrr_at_100
value: 75.155
- type: mrr_at_1000
value: 75.165
- type: mrr_at_3
value: 73.21600000000001
- type: mrr_at_5
value: 74.259
- type: ndcg_at_1
value: 67.049
- type: ndcg_at_10
value: 78.104
- type: ndcg_at_100
value: 79.56400000000001
- type: ndcg_at_1000
value: 79.85600000000001
- type: ndcg_at_3
value: 74.54499999999999
- type: ndcg_at_5
value: 76.587
- type: precision_at_1
value: 67.049
- type: precision_at_10
value: 9.493
- type: precision_at_100
value: 1.022
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 28.189999999999998
- type: precision_at_5
value: 18.003
- type: recall_at_1
value: 64.798
- type: recall_at_10
value: 89.328
- type: recall_at_100
value: 95.916
- type: recall_at_1000
value: 98.223
- type: recall_at_3
value: 79.93599999999999
- type: recall_at_5
value: 84.789
- 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: 64.01815736381977
- type: f1
value: 61.07806329750582
- 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: 68.94754539340954
- type: f1
value: 68.76446930296682
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 50.1
- type: map_at_10
value: 56.406
- type: map_at_100
value: 56.958
- type: map_at_1000
value: 57.007
- type: map_at_3
value: 55.083000000000006
- type: map_at_5
value: 55.952999999999996
- type: mrr_at_1
value: 50.1
- type: mrr_at_10
value: 56.401999999999994
- type: mrr_at_100
value: 56.955
- type: mrr_at_1000
value: 57.004
- type: mrr_at_3
value: 55.05
- type: mrr_at_5
value: 55.95
- type: ndcg_at_1
value: 50.1
- type: ndcg_at_10
value: 59.384
- type: ndcg_at_100
value: 62.339
- type: ndcg_at_1000
value: 63.756
- type: ndcg_at_3
value: 56.657999999999994
- type: ndcg_at_5
value: 58.267
- type: precision_at_1
value: 50.1
- type: precision_at_10
value: 6.87
- type: precision_at_100
value: 0.832
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 20.4
- type: precision_at_5
value: 13.04
- type: recall_at_1
value: 50.1
- type: recall_at_10
value: 68.7
- type: recall_at_100
value: 83.2
- type: recall_at_1000
value: 94.6
- type: recall_at_3
value: 61.199999999999996
- type: recall_at_5
value: 65.2
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 27.159122893681587
- type: mrr
value: 25.659126984126985
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 73.02666666666667
- type: f1
value: 72.47691397067602
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 67.0817541959935
- type: cos_sim_ap
value: 72.29133043915637
- type: cos_sim_f1
value: 72.71207689093188
- type: cos_sim_precision
value: 60.16597510373444
- type: cos_sim_recall
value: 91.86906019007391
- type: dot_accuracy
value: 67.0817541959935
- type: dot_ap
value: 72.29133043915637
- type: dot_f1
value: 72.71207689093188
- type: dot_precision
value: 60.16597510373444
- type: dot_recall
value: 91.86906019007391
- type: euclidean_accuracy
value: 67.0817541959935
- type: euclidean_ap
value: 72.29133043915637
- type: euclidean_f1
value: 72.71207689093188
- type: euclidean_precision
value: 60.16597510373444
- type: euclidean_recall
value: 91.86906019007391
- type: manhattan_accuracy
value: 66.91932864103953
- type: manhattan_ap
value: 72.20070509521395
- type: manhattan_f1
value: 72.52839713925118
- type: manhattan_precision
value: 60.27972027972028
- type: manhattan_recall
value: 91.02428722280888
- type: max_accuracy
value: 67.0817541959935
- type: max_ap
value: 72.29133043915637
- type: max_f1
value: 72.71207689093188
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 90.75000000000001
- type: ap
value: 87.99706544930007
- type: f1
value: 90.72973221476978
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 33.57372874898899
- type: cos_sim_spearman
value: 37.9718472605281
- type: euclidean_pearson
value: 38.52264008741102
- type: euclidean_spearman
value: 37.97184654854654
- type: manhattan_pearson
value: 38.50412571398273
- type: manhattan_spearman
value: 37.98038173979437
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 37.510457667606225
- type: cos_sim_spearman
value: 37.83522430820119
- type: euclidean_pearson
value: 36.65815519443564
- type: euclidean_spearman
value: 37.83519816393499
- type: manhattan_pearson
value: 36.66835898210608
- type: manhattan_spearman
value: 37.85390202705368
- 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: 66.9953337569138
- type: cos_sim_spearman
value: 67.27632129468024
- type: euclidean_pearson
value: 65.83716645437758
- type: euclidean_spearman
value: 67.27632129468024
- type: manhattan_pearson
value: 65.81209103940279
- type: manhattan_spearman
value: 67.26678679870099
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 75.73719311549382
- type: cos_sim_spearman
value: 75.71173848950517
- type: euclidean_pearson
value: 75.23070020894484
- type: euclidean_spearman
value: 75.71173839940812
- type: manhattan_pearson
value: 75.23517292603057
- type: manhattan_spearman
value: 75.74250916645184
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 66.8596523608508
- type: mrr
value: 76.9288884590171
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 26.618000000000002
- type: map_at_10
value: 74.884
- type: map_at_100
value: 78.65299999999999
- type: map_at_1000
value: 78.724
- type: map_at_3
value: 52.507999999999996
- type: map_at_5
value: 64.52799999999999
- type: mrr_at_1
value: 88.453
- type: mrr_at_10
value: 91.157
- type: mrr_at_100
value: 91.263
- type: mrr_at_1000
value: 91.268
- type: mrr_at_3
value: 90.672
- type: mrr_at_5
value: 90.96499999999999
- type: ndcg_at_1
value: 88.453
- type: ndcg_at_10
value: 82.759
- type: ndcg_at_100
value: 86.709
- type: ndcg_at_1000
value: 87.41499999999999
- type: ndcg_at_3
value: 84.194
- type: ndcg_at_5
value: 82.645
- type: precision_at_1
value: 88.453
- type: precision_at_10
value: 41.369
- type: precision_at_100
value: 4.9910000000000005
- type: precision_at_1000
value: 0.515
- type: precision_at_3
value: 73.79400000000001
- type: precision_at_5
value: 61.80799999999999
- type: recall_at_1
value: 26.618000000000002
- type: recall_at_10
value: 81.772
- type: recall_at_100
value: 94.55
- type: recall_at_1000
value: 98.184
- type: recall_at_3
value: 54.26499999999999
- type: recall_at_5
value: 67.963
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 50.690000000000005
- type: f1
value: 48.77079213417325
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 62.14566804144758
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 54.66890415410679
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 55.900000000000006
- type: map_at_10
value: 66.188
- type: map_at_100
value: 66.67699999999999
- type: map_at_1000
value: 66.691
- type: map_at_3
value: 64.017
- type: map_at_5
value: 65.462
- type: mrr_at_1
value: 55.800000000000004
- type: mrr_at_10
value: 66.13799999999999
- type: mrr_at_100
value: 66.62700000000001
- type: mrr_at_1000
value: 66.64099999999999
- type: mrr_at_3
value: 63.967
- type: mrr_at_5
value: 65.412
- type: ndcg_at_1
value: 55.900000000000006
- type: ndcg_at_10
value: 70.961
- type: ndcg_at_100
value: 73.22
- type: ndcg_at_1000
value: 73.583
- type: ndcg_at_3
value: 66.61
- type: ndcg_at_5
value: 69.18900000000001
- type: precision_at_1
value: 55.900000000000006
- type: precision_at_10
value: 8.58
- type: precision_at_100
value: 0.9610000000000001
- type: precision_at_1000
value: 0.099
- type: precision_at_3
value: 24.7
- type: precision_at_5
value: 16.06
- type: recall_at_1
value: 55.900000000000006
- type: recall_at_10
value: 85.8
- type: recall_at_100
value: 96.1
- type: recall_at_1000
value: 98.9
- type: recall_at_3
value: 74.1
- type: recall_at_5
value: 80.30000000000001
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.77
- type: ap
value: 70.21134107638184
- type: f1
value: 85.22521777795022
新闻 | News
[2024-04-06] 开源puff系列模型，专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语。
[2024-02-27] 开源stella-mrl-large-zh-v3.5-1792d模型，支持向量可变维度。
[2024-02-17] 开源stella v3系列、dialogue编码模型和相关训练数据。
[2023-10-19] 开源stella-base-en-v2 使用简单，不需要任何前缀文本。
[2023-10-12] 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，不需要任何前缀文本。
[2023-09-11] 开源stella-base-zh和stella-large-zh
欢迎去本人主页查看最新模型，并提出您的宝贵意见！
stella model
stella是一个通用的文本编码模型，主要有以下模型：
| Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? |
|---|---|---|---|---|---|
| stella-base-en-v2 | 0.2 | 768 | 512 | English | No |
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | Chinese | No |
| stella-base-zh-v2 | 0.2 | 768 | 1024 | Chinese | No |
| stella-large-zh | 0.65 | 1024 | 1024 | Chinese | Yes |
| stella-base-zh | 0.2 | 768 | 1024 | Chinese | Yes |
完整的训练思路和训练过程已记录在博客1和博客2，欢迎阅读讨论。
训练数据：
- 开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本
- 在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据
训练方法：
- 对比学习损失函数
- 带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例)
- EWC(Elastic Weights Consolidation)[4]
- cosent loss[5]
- 每一种类型的数据一个迭代器，分别计算loss进行更新
stella-v2在stella模型的基础上，使用了更多的训练数据，同时知识蒸馏等方法去除了前置的instruction(
比如piccolo的查询:, 结果:, e5的query:和passage:)。
初始权重：
stella-base-zh和stella-large-zh分别以piccolo-base-zh[6]和piccolo-large-zh作为基础模型，512-1024的position
embedding使用层次分解位置编码[7]进行初始化。
感谢商汤科技研究院开源的piccolo系列模型。
stella is a general-purpose text encoder, which mainly includes the following models:
| Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? |
|---|---|---|---|---|---|
| stella-base-en-v2 | 0.2 | 768 | 512 | English | No |
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | Chinese | No |
| stella-base-zh-v2 | 0.2 | 768 | 1024 | Chinese | No |
| stella-large-zh | 0.65 | 1024 | 1024 | Chinese | Yes |
| stella-base-zh | 0.2 | 768 | 1024 | Chinese | Yes |
The training data mainly includes:
- Open-source training data (wudao_base_200GB, m3e, and simclue), with a focus on selecting texts with lengths greater than 512.
- A batch of (question, paragraph) and (sentence, paragraph) data constructed on a general corpus using LLM.
The loss functions mainly include:
- Contrastive learning loss function
- Contrastive learning loss function with hard negative examples (based on bm25 and vector hard negatives)
- EWC (Elastic Weights Consolidation)
- cosent loss
Model weight initialization:
stella-base-zh and stella-large-zh use piccolo-base-zh and piccolo-large-zh as the base models, respectively, and the
512-1024 position embedding uses the initialization strategy of hierarchical decomposed position encoding.
Training strategy:
One iterator for each type of data, separately calculating the loss.
Based on stella models, stella-v2 use more training data and remove instruction by Knowledge Distillation.
Metric
C-MTEB leaderboard (Chinese)
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) |
|---|---|---|---|---|---|---|---|---|---|---|
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 |
| stella-base-zh-v2 | 0.2 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.95 | 66.1 | 70.08 | 56.92 |
| stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 |
| stella-base-zh | 0.2 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 |
MTEB leaderboard (English)
| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) |
|---|---|---|---|---|---|---|---|---|---|---|---|
| stella-base-en-v2 | 0.2 | 768 | 512 | 62.61 | 75.28 | 44.9 | 86.45 | 58.77 | 50.1 | 83.02 | 32.52 |
Reproduce our results
C-MTEB:
import torch
import numpy as np
from typing import List
from mteb import MTEB
from sentence_transformers import SentenceTransformer
class FastTextEncoder():
def __init__(self, model_name):
self.model = SentenceTransformer(model_name).cuda().half().eval()
self.model.max_seq_length = 512
def encode(
self,
input_texts: List[str],
*args,
**kwargs
):
new_sens = list(set(input_texts))
new_sens.sort(key=lambda x: len(x), reverse=True)
vecs = self.model.encode(
new_sens, normalize_embeddings=True, convert_to_numpy=True, batch_size=256
).astype(np.float32)
sen2arrid = {sen: idx for idx, sen in enumerate(new_sens)}
vecs = vecs[[sen2arrid[sen] for sen in input_texts]]
torch.cuda.empty_cache()
return vecs
if __name__ == '__main__':
model_name = "infgrad/stella-base-zh-v2"
output_folder = "zh_mteb_results/stella-base-zh-v2"
task_names = [t.description["name"] for t in MTEB(task_langs=['zh', 'zh-CN']).tasks]
model = FastTextEncoder(model_name)
for task in task_names:
MTEB(tasks=[task], task_langs=['zh', 'zh-CN']).run(model, output_folder=output_folder)
MTEB:
You can use official script to reproduce our result. scripts/run_mteb_english.py
Evaluation for long text
经过实际观察发现，C-MTEB的评测数据长度基本都是小于512的， 更致命的是那些长度大于512的文本，其重点都在前半部分 这里以CMRC2018的数据为例说明这个问题：
question: 《无双大蛇z》是谁旗下ω-force开发的动作游戏？
passage：《无双大蛇z》是光荣旗下ω-force开发的动作游戏，于2009年3月12日登陆索尼playstation3，并于2009年11月27日推......
passage长度为800多，大于512，但是对于这个question而言只需要前面40个字就足以检索，多的内容对于模型而言是一种噪声，反而降低了效果。
简言之，现有数据集的2个问题：
1）长度大于512的过少
2）即便大于512，对于检索而言也只需要前512的文本内容
导致无法准确评估模型的长文本编码能力。
为了解决这个问题，搜集了相关开源数据并使用规则进行过滤，最终整理了6份长文本测试集,他们分别是：
- CMRC2018，通用百科
- CAIL，法律阅读理解
- DRCD，繁体百科，已转简体
- Military，军工问答
- Squad，英文阅读理解，已转中文
- Multifieldqa_zh，清华的大模型长文本理解能力评测数据[9]
处理规则是选取答案在512长度之后的文本，短的测试数据会欠采样一下，长短文本占比约为1:2，所以模型既得理解短文本也得理解长文本。 除了Military数据集，我们提供了其他5个测试数据的下载地址：https://drive.google.com/file/d/1WC6EWaCbVgz-vPMDFH4TwAMkLyh5WNcN/view?usp=sharing
评测指标为Recall@5, 结果如下：
| Dataset | piccolo-base-zh | piccolo-large-zh | bge-base-zh | bge-large-zh | stella-base-zh | stella-large-zh |
|---|---|---|---|---|---|---|
| CMRC2018 | 94.34 | 93.82 | 91.56 | 93.12 | 96.08 | 95.56 |
| CAIL | 28.04 | 33.64 | 31.22 | 33.94 | 34.62 | 37.18 |
| DRCD | 78.25 | 77.9 | 78.34 | 80.26 | 86.14 | 84.58 |
| Military | 76.61 | 73.06 | 75.65 | 75.81 | 83.71 | 80.48 |
| Squad | 91.21 | 86.61 | 87.87 | 90.38 | 93.31 | 91.21 |
| Multifieldqa_zh | 81.41 | 83.92 | 83.92 | 83.42 | 79.9 | 80.4 |
| Average | 74.98 | 74.83 | 74.76 | 76.15 | 78.96 | 78.24 |
注意： 因为长文本评测数据数量稀少，所以构造时也使用了train部分，如果自行评测，请注意模型的训练数据以免数据泄露。
Usage
stella 中文系列模型
stella-base-zh 和 stella-large-zh: 本模型是在piccolo基础上训练的，因此用法和piccolo完全一致
，即在检索重排任务上给query和passage加上查询: 和结果: 。对于短短匹配不需要做任何操作。
stella-base-zh-v2 和 stella-large-zh-v2: 本模型使用简单，任何使用场景中都不需要加前缀文本。
stella中文系列模型均使用mean pooling做为文本向量。
在sentence-transformer库中的使用方法：
from sentence_transformers import SentenceTransformer
sentences = ["数据1", "数据2"]
model = SentenceTransformer('infgrad/stella-base-zh-v2')
print(model.max_seq_length)
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
直接使用transformers库：
from transformers import AutoModel, AutoTokenizer
from sklearn.preprocessing import normalize
model = AutoModel.from_pretrained('infgrad/stella-base-zh-v2')
tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-zh-v2')
sentences = ["数据1", "数据ABCDEFGH"]
batch_data = tokenizer(
batch_text_or_text_pairs=sentences,
padding="longest",
return_tensors="pt",
max_length=1024,
truncation=True,
)
attention_mask = batch_data["attention_mask"]
model_output = model(**batch_data)
last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
vectors = normalize(vectors, norm="l2", axis=1, )
print(vectors.shape) # 2,768
stella models for English
Using Sentence-Transformers:
from sentence_transformers import SentenceTransformer
sentences = ["one car come", "one car go"]
model = SentenceTransformer('infgrad/stella-base-en-v2')
print(model.max_seq_length)
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
Using HuggingFace Transformers:
from transformers import AutoModel, AutoTokenizer
from sklearn.preprocessing import normalize
model = AutoModel.from_pretrained('infgrad/stella-base-en-v2')
tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-en-v2')
sentences = ["one car come", "one car go"]
batch_data = tokenizer(
batch_text_or_text_pairs=sentences,
padding="longest",
return_tensors="pt",
max_length=512,
truncation=True,
)
attention_mask = batch_data["attention_mask"]
model_output = model(**batch_data)
last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
vectors = normalize(vectors, norm="l2", axis=1, )
print(vectors.shape) # 2,768
Training Detail
硬件： 单卡A100-80GB
环境： torch1.13.*; transformers-trainer + deepspeed + gradient-checkpointing
学习率： 1e-6
batch_size： base模型为1024，额外增加20%的难负例；large模型为768，额外增加20%的难负例
数据量： 第一版模型约100万，其中用LLM构造的数据约有200K. LLM模型大小为13b。v2系列模型到了2000万训练数据。
ToDoList
评测的稳定性： 评测过程中发现Clustering任务会和官方的结果不一致，大约有±0.0x的小差距，原因是聚类代码没有设置random_seed，差距可以忽略不计，不影响评测结论。
更高质量的长文本训练和测试数据： 训练数据多是用13b模型构造的，肯定会存在噪声。 测试数据基本都是从mrc数据整理来的，所以问题都是factoid类型，不符合真实分布。
OOD的性能： 虽然近期出现了很多向量编码模型，但是对于不是那么通用的domain，这一众模型包括stella、openai和cohere, 它们的效果均比不上BM25。
Reference
- https://www.scidb.cn/en/detail?dataSetId=c6a3fe684227415a9db8e21bac4a15ab
- https://github.com/wangyuxinwhy/uniem
- https://github.com/CLUEbenchmark/SimCLUE
- https://arxiv.org/abs/1612.00796
- https://kexue.fm/archives/8847
- https://huggingface.co/sensenova/piccolo-base-zh
- https://kexue.fm/archives/7947
- https://github.com/FlagOpen/FlagEmbedding
- https://github.com/THUDM/LongBench