metadata
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
model-index:
- name: stella-large-zh-v2
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 47.34436411023816
- type: cos_sim_spearman
value: 49.947084806624545
- type: euclidean_pearson
value: 48.128834319004824
- type: euclidean_spearman
value: 49.947064694876815
- type: manhattan_pearson
value: 48.083561270166484
- type: manhattan_spearman
value: 49.90207128584442
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 50.97998570817664
- type: cos_sim_spearman
value: 53.11852606980578
- type: euclidean_pearson
value: 55.12610520736481
- type: euclidean_spearman
value: 53.11852832108405
- type: manhattan_pearson
value: 55.10299116717361
- type: manhattan_spearman
value: 53.11304196536268
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 40.81799999999999
- type: f1
value: 39.022194031906444
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 62.83544115057508
- type: cos_sim_spearman
value: 65.53509404838948
- type: euclidean_pearson
value: 64.08198144850084
- type: euclidean_spearman
value: 65.53509404760305
- type: manhattan_pearson
value: 64.08808420747272
- type: manhattan_spearman
value: 65.54907862648346
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 39.95428546140963
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 38.18454393512963
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map
value: 85.4453602559479
- type: mrr
value: 88.1418253968254
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map
value: 85.82731720256984
- type: mrr
value: 88.53230158730159
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 24.459
- type: map_at_10
value: 36.274
- type: map_at_100
value: 38.168
- type: map_at_1000
value: 38.292
- type: map_at_3
value: 32.356
- type: map_at_5
value: 34.499
- type: mrr_at_1
value: 37.584
- type: mrr_at_10
value: 45.323
- type: mrr_at_100
value: 46.361999999999995
- type: mrr_at_1000
value: 46.412
- type: mrr_at_3
value: 42.919000000000004
- type: mrr_at_5
value: 44.283
- type: ndcg_at_1
value: 37.584
- type: ndcg_at_10
value: 42.63
- type: ndcg_at_100
value: 50.114000000000004
- type: ndcg_at_1000
value: 52.312000000000005
- type: ndcg_at_3
value: 37.808
- type: ndcg_at_5
value: 39.711999999999996
- type: precision_at_1
value: 37.584
- type: precision_at_10
value: 9.51
- type: precision_at_100
value: 1.554
- type: precision_at_1000
value: 0.183
- type: precision_at_3
value: 21.505
- type: precision_at_5
value: 15.514
- type: recall_at_1
value: 24.459
- type: recall_at_10
value: 52.32
- type: recall_at_100
value: 83.423
- type: recall_at_1000
value: 98.247
- type: recall_at_3
value: 37.553
- type: recall_at_5
value: 43.712
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 77.7269993986771
- type: cos_sim_ap
value: 86.8488070512359
- type: cos_sim_f1
value: 79.32095490716179
- type: cos_sim_precision
value: 72.6107226107226
- type: cos_sim_recall
value: 87.39770867430443
- type: dot_accuracy
value: 77.7269993986771
- type: dot_ap
value: 86.84218333157476
- type: dot_f1
value: 79.32095490716179
- type: dot_precision
value: 72.6107226107226
- type: dot_recall
value: 87.39770867430443
- type: euclidean_accuracy
value: 77.7269993986771
- type: euclidean_ap
value: 86.84880910178296
- type: euclidean_f1
value: 79.32095490716179
- type: euclidean_precision
value: 72.6107226107226
- type: euclidean_recall
value: 87.39770867430443
- type: manhattan_accuracy
value: 77.82321106434155
- type: manhattan_ap
value: 86.8152244713786
- type: manhattan_f1
value: 79.43262411347519
- type: manhattan_precision
value: 72.5725338491296
- type: manhattan_recall
value: 87.72504091653029
- type: max_accuracy
value: 77.82321106434155
- type: max_ap
value: 86.84880910178296
- type: max_f1
value: 79.43262411347519
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 68.862
- type: map_at_10
value: 77.106
- type: map_at_100
value: 77.455
- type: map_at_1000
value: 77.459
- type: map_at_3
value: 75.457
- type: map_at_5
value: 76.254
- type: mrr_at_1
value: 69.125
- type: mrr_at_10
value: 77.13799999999999
- type: mrr_at_100
value: 77.488
- type: mrr_at_1000
value: 77.492
- type: mrr_at_3
value: 75.606
- type: mrr_at_5
value: 76.29599999999999
- type: ndcg_at_1
value: 69.02000000000001
- type: ndcg_at_10
value: 80.81099999999999
- type: ndcg_at_100
value: 82.298
- type: ndcg_at_1000
value: 82.403
- type: ndcg_at_3
value: 77.472
- type: ndcg_at_5
value: 78.892
- type: precision_at_1
value: 69.02000000000001
- type: precision_at_10
value: 9.336
- type: precision_at_100
value: 0.9990000000000001
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 27.924
- type: precision_at_5
value: 17.492
- type: recall_at_1
value: 68.862
- type: recall_at_10
value: 92.308
- type: recall_at_100
value: 98.84100000000001
- type: recall_at_1000
value: 99.684
- type: recall_at_3
value: 83.193
- type: recall_at_5
value: 86.617
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 25.063999999999997
- type: map_at_10
value: 78.02
- type: map_at_100
value: 81.022
- type: map_at_1000
value: 81.06
- type: map_at_3
value: 53.613
- type: map_at_5
value: 68.008
- type: mrr_at_1
value: 87.8
- type: mrr_at_10
value: 91.827
- type: mrr_at_100
value: 91.913
- type: mrr_at_1000
value: 91.915
- type: mrr_at_3
value: 91.508
- type: mrr_at_5
value: 91.758
- type: ndcg_at_1
value: 87.8
- type: ndcg_at_10
value: 85.753
- type: ndcg_at_100
value: 88.82900000000001
- type: ndcg_at_1000
value: 89.208
- type: ndcg_at_3
value: 84.191
- type: ndcg_at_5
value: 83.433
- type: precision_at_1
value: 87.8
- type: precision_at_10
value: 41.33
- type: precision_at_100
value: 4.8
- type: precision_at_1000
value: 0.48900000000000005
- type: precision_at_3
value: 75.767
- type: precision_at_5
value: 64.25999999999999
- type: recall_at_1
value: 25.063999999999997
- type: recall_at_10
value: 87.357
- type: recall_at_100
value: 97.261
- type: recall_at_1000
value: 99.309
- type: recall_at_3
value: 56.259
- type: recall_at_5
value: 73.505
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 46.800000000000004
- type: map_at_10
value: 56.898
- type: map_at_100
value: 57.567
- type: map_at_1000
value: 57.593
- type: map_at_3
value: 54.167
- type: map_at_5
value: 55.822
- type: mrr_at_1
value: 46.800000000000004
- type: mrr_at_10
value: 56.898
- type: mrr_at_100
value: 57.567
- type: mrr_at_1000
value: 57.593
- type: mrr_at_3
value: 54.167
- type: mrr_at_5
value: 55.822
- type: ndcg_at_1
value: 46.800000000000004
- type: ndcg_at_10
value: 62.07
- type: ndcg_at_100
value: 65.049
- type: ndcg_at_1000
value: 65.666
- type: ndcg_at_3
value: 56.54
- type: ndcg_at_5
value: 59.492999999999995
- type: precision_at_1
value: 46.800000000000004
- type: precision_at_10
value: 7.84
- type: precision_at_100
value: 0.9169999999999999
- type: precision_at_1000
value: 0.096
- type: precision_at_3
value: 21.133
- type: precision_at_5
value: 14.099999999999998
- type: recall_at_1
value: 46.800000000000004
- type: recall_at_10
value: 78.4
- type: recall_at_100
value: 91.7
- type: recall_at_1000
value: 96.39999999999999
- type: recall_at_3
value: 63.4
- type: recall_at_5
value: 70.5
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 47.98768757214313
- type: f1
value: 35.23884426992269
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.97936210131333
- type: ap
value: 56.292679530375736
- type: f1
value: 81.87001614762136
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 71.17149643620844
- type: cos_sim_spearman
value: 77.48040046337948
- type: euclidean_pearson
value: 76.32337539923347
- type: euclidean_spearman
value: 77.4804004621894
- type: manhattan_pearson
value: 76.33275226275444
- type: manhattan_spearman
value: 77.48979843086128
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 27.966807589556826
- type: mrr
value: 26.92023809523809
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 66.15100000000001
- type: map_at_10
value: 75.048
- type: map_at_100
value: 75.374
- type: map_at_1000
value: 75.386
- type: map_at_3
value: 73.26700000000001
- type: map_at_5
value: 74.39
- type: mrr_at_1
value: 68.381
- type: mrr_at_10
value: 75.644
- type: mrr_at_100
value: 75.929
- type: mrr_at_1000
value: 75.93900000000001
- type: mrr_at_3
value: 74.1
- type: mrr_at_5
value: 75.053
- type: ndcg_at_1
value: 68.381
- type: ndcg_at_10
value: 78.669
- type: ndcg_at_100
value: 80.161
- type: ndcg_at_1000
value: 80.46799999999999
- type: ndcg_at_3
value: 75.3
- type: ndcg_at_5
value: 77.172
- type: precision_at_1
value: 68.381
- type: precision_at_10
value: 9.48
- type: precision_at_100
value: 1.023
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 28.299999999999997
- type: precision_at_5
value: 17.98
- type: recall_at_1
value: 66.15100000000001
- type: recall_at_10
value: 89.238
- type: recall_at_100
value: 96.032
- type: recall_at_1000
value: 98.437
- type: recall_at_3
value: 80.318
- type: recall_at_5
value: 84.761
- 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: 68.26160053799597
- type: f1
value: 65.96949453305112
- 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: 73.12037659717554
- type: f1
value: 72.69052407105445
- 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: 56.489999999999995
- type: map_at_100
value: 57.007
- type: map_at_1000
value: 57.06400000000001
- type: map_at_3
value: 55.25
- type: map_at_5
value: 55.93
- type: mrr_at_1
value: 50.3
- type: mrr_at_10
value: 56.591
- type: mrr_at_100
value: 57.108000000000004
- type: mrr_at_1000
value: 57.165
- type: mrr_at_3
value: 55.35
- type: mrr_at_5
value: 56.03
- type: ndcg_at_1
value: 50.1
- type: ndcg_at_10
value: 59.419999999999995
- type: ndcg_at_100
value: 62.28900000000001
- type: ndcg_at_1000
value: 63.9
- type: ndcg_at_3
value: 56.813
- type: ndcg_at_5
value: 58.044
- type: precision_at_1
value: 50.1
- type: precision_at_10
value: 6.859999999999999
- type: precision_at_100
value: 0.828
- type: precision_at_1000
value: 0.096
- type: precision_at_3
value: 20.433
- type: precision_at_5
value: 12.86
- type: recall_at_1
value: 50.1
- type: recall_at_10
value: 68.60000000000001
- type: recall_at_100
value: 82.8
- type: recall_at_1000
value: 95.7
- type: recall_at_3
value: 61.3
- type: recall_at_5
value: 64.3
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 73.41000000000001
- type: f1
value: 72.87768282499509
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 73.4163508391987
- type: cos_sim_ap
value: 78.51058998215277
- type: cos_sim_f1
value: 75.3875968992248
- type: cos_sim_precision
value: 69.65085049239033
- type: cos_sim_recall
value: 82.15417106652588
- type: dot_accuracy
value: 73.4163508391987
- type: dot_ap
value: 78.51058998215277
- type: dot_f1
value: 75.3875968992248
- type: dot_precision
value: 69.65085049239033
- type: dot_recall
value: 82.15417106652588
- type: euclidean_accuracy
value: 73.4163508391987
- type: euclidean_ap
value: 78.51058998215277
- type: euclidean_f1
value: 75.3875968992248
- type: euclidean_precision
value: 69.65085049239033
- type: euclidean_recall
value: 82.15417106652588
- type: manhattan_accuracy
value: 73.03735787763942
- type: manhattan_ap
value: 78.4190891700083
- type: manhattan_f1
value: 75.32592950265573
- type: manhattan_precision
value: 69.3950177935943
- type: manhattan_recall
value: 82.36536430834214
- type: max_accuracy
value: 73.4163508391987
- type: max_ap
value: 78.51058998215277
- type: max_f1
value: 75.3875968992248
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 91.81000000000002
- type: ap
value: 89.35809579688139
- type: f1
value: 91.79220350456818
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 30.10755999973859
- type: cos_sim_spearman
value: 36.221732138848864
- type: euclidean_pearson
value: 36.41120179336658
- type: euclidean_spearman
value: 36.221731188009436
- type: manhattan_pearson
value: 36.34865300346968
- type: manhattan_spearman
value: 36.17696483080459
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 36.778975708100226
- type: cos_sim_spearman
value: 38.733929926753724
- type: euclidean_pearson
value: 37.13383498228113
- type: euclidean_spearman
value: 38.73374886550868
- type: manhattan_pearson
value: 37.175732896552404
- type: manhattan_spearman
value: 38.74120541657908
- 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: 65.97095922825076
- type: cos_sim_spearman
value: 68.87452938308421
- type: euclidean_pearson
value: 67.23101642424429
- type: euclidean_spearman
value: 68.87452938308421
- type: manhattan_pearson
value: 67.29909334410189
- type: manhattan_spearman
value: 68.89807985930508
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 78.98860630733722
- type: cos_sim_spearman
value: 79.36601601355665
- type: euclidean_pearson
value: 78.77295944956447
- type: euclidean_spearman
value: 79.36585127278974
- type: manhattan_pearson
value: 78.82060736131619
- type: manhattan_spearman
value: 79.4395526421926
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 66.40501824507894
- type: mrr
value: 76.18463933756757
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 27.095000000000002
- type: map_at_10
value: 76.228
- type: map_at_100
value: 79.865
- type: map_at_1000
value: 79.935
- type: map_at_3
value: 53.491
- type: map_at_5
value: 65.815
- type: mrr_at_1
value: 89.554
- type: mrr_at_10
value: 92.037
- type: mrr_at_100
value: 92.133
- type: mrr_at_1000
value: 92.137
- type: mrr_at_3
value: 91.605
- type: mrr_at_5
value: 91.88
- type: ndcg_at_1
value: 89.554
- type: ndcg_at_10
value: 83.866
- type: ndcg_at_100
value: 87.566
- type: ndcg_at_1000
value: 88.249
- type: ndcg_at_3
value: 85.396
- type: ndcg_at_5
value: 83.919
- type: precision_at_1
value: 89.554
- type: precision_at_10
value: 41.792
- type: precision_at_100
value: 4.997
- type: precision_at_1000
value: 0.515
- type: precision_at_3
value: 74.795
- type: precision_at_5
value: 62.675000000000004
- type: recall_at_1
value: 27.095000000000002
- type: recall_at_10
value: 82.694
- type: recall_at_100
value: 94.808
- type: recall_at_1000
value: 98.30600000000001
- type: recall_at_3
value: 55.156000000000006
- type: recall_at_5
value: 69.19
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 51.929
- type: f1
value: 50.16876489927282
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 61.404157724658894
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 57.11418384351802
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 52.1
- type: map_at_10
value: 62.956999999999994
- type: map_at_100
value: 63.502
- type: map_at_1000
value: 63.51599999999999
- type: map_at_3
value: 60.75000000000001
- type: map_at_5
value: 62.195
- type: mrr_at_1
value: 52
- type: mrr_at_10
value: 62.907000000000004
- type: mrr_at_100
value: 63.452
- type: mrr_at_1000
value: 63.466
- type: mrr_at_3
value: 60.699999999999996
- type: mrr_at_5
value: 62.144999999999996
- type: ndcg_at_1
value: 52.1
- type: ndcg_at_10
value: 67.93299999999999
- type: ndcg_at_100
value: 70.541
- type: ndcg_at_1000
value: 70.91300000000001
- type: ndcg_at_3
value: 63.468
- type: ndcg_at_5
value: 66.08800000000001
- type: precision_at_1
value: 52.1
- type: precision_at_10
value: 8.34
- type: precision_at_100
value: 0.955
- type: precision_at_1000
value: 0.098
- type: precision_at_3
value: 23.767
- type: precision_at_5
value: 15.540000000000001
- type: recall_at_1
value: 52.1
- type: recall_at_10
value: 83.39999999999999
- type: recall_at_100
value: 95.5
- type: recall_at_1000
value: 98.4
- type: recall_at_3
value: 71.3
- type: recall_at_5
value: 77.7
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 87.12
- type: ap
value: 70.85284793227382
- type: f1
value: 85.55420883566512
stella model
新闻 | News
[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。
Release stella-base-zh-v2 and stella-large-zh-v2. The 2 models have better performance
and do not need any prefix text.
[2023-09-11] 开源stella-base-zh和stella-large-zh
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-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，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-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 |
Reproduce our results
Codes:
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)
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：
# 对于短对短数据集，下面是通用的使用方式
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
developing...
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