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EN
Stella Mrl Large Zh V3.5 1792d
S
Stella Mrl Large Zh V3.5 1792d
Developed by dunzhang
A model focused on Chinese sentence similarity calculation and feature extraction, excelling on multiple Chinese text evaluation benchmarks
Downloads 154.20k
Release Time : 2/27/2024
Model Overview
This model is a Chinese text embedding model based on the sentence transformer architecture, primarily used for sentence similarity calculation, feature extraction, and related text tasks
Model Features
Chinese Optimization
Specially optimized for Chinese text processing, achieving excellent performance on multiple Chinese evaluation benchmarks
Multi-task Support
Supports various NLP tasks including sentence similarity calculation, feature extraction, and text classification
High Performance
Achieved outstanding performance metrics on multiple Chinese evaluation benchmarks including C-MTEB
Model Capabilities
Sentence similarity calculation
Text feature extraction
Text classification
Text clustering
Information retrieval
Question answering reranking
Use Cases
Intelligent Customer Service
Question Similarity Matching
Identify similarity between user questions and knowledge base questions
Achieved over 89% accuracy on reranking tasks in CMedQA dataset
Text Analysis
Text Clustering
Automatically group similar documents
Achieved over 40% V-measure on CLSClustering task
Information Retrieval
Document Retrieval
Return relevant documents based on queries
Achieved 68.97% first-position accuracy on CovidRetrieval task
pipeline_tag: sentence-similarity tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb model-index:
- name: stella-mrl-large-zh-v3.5-1792d
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson value: 54.33822814973567
- type: cos_sim_spearman value: 58.85457316132848
- type: euclidean_pearson value: 57.57048145477383
- type: euclidean_spearman value: 58.854593263425095
- type: manhattan_pearson value: 57.55884028558309
- type: manhattan_spearman value: 58.84474216217465
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 54.219652875381875
- type: cos_sim_spearman value: 58.079506691583546
- type: euclidean_pearson value: 61.646366330471736
- type: euclidean_spearman value: 58.07951006894859
- type: manhattan_pearson value: 61.64460832085762
- type: manhattan_spearman value: 58.08054699349972
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 46.593999999999994
- type: f1 value: 44.73150848183217
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 69.16841007040091
- type: cos_sim_spearman value: 71.04760904227217
- type: euclidean_pearson value: 69.95126084376611
- type: euclidean_spearman value: 71.04760904184589
- type: manhattan_pearson value: 69.92512024129407
- type: manhattan_spearman value: 71.02613161257672
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure value: 43.032332399653306
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure value: 40.41603958793544
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map value: 89.33487924447584
- type: mrr value: 91.34623015873017
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map value: 89.17795270698021
- type: mrr value: 91.0956746031746
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 26.809
- type: map_at_10 value: 39.906000000000006
- type: map_at_100 value: 41.858000000000004
- type: map_at_1000 value: 41.954
- type: map_at_3 value: 35.435
- type: map_at_5 value: 37.978
- type: mrr_at_1 value: 40.660000000000004
- type: mrr_at_10 value: 48.787000000000006
- type: mrr_at_100 value: 49.796
- type: mrr_at_1000 value: 49.832
- type: mrr_at_3 value: 46.166000000000004
- type: mrr_at_5 value: 47.675
- type: ndcg_at_1 value: 40.660000000000004
- type: ndcg_at_10 value: 46.614
- type: ndcg_at_100 value: 54.037
- type: ndcg_at_1000 value: 55.654
- type: ndcg_at_3 value: 41.032000000000004
- type: ndcg_at_5 value: 43.464999999999996
- type: precision_at_1 value: 40.660000000000004
- type: precision_at_10 value: 10.35
- type: precision_at_100 value: 1.6340000000000001
- type: precision_at_1000 value: 0.184
- type: precision_at_3 value: 23.122
- type: precision_at_5 value: 16.944
- type: recall_at_1 value: 26.809
- type: recall_at_10 value: 57.474000000000004
- type: recall_at_100 value: 87.976
- type: recall_at_1000 value: 98.74199999999999
- type: recall_at_3 value: 40.819
- type: recall_at_5 value: 48.175000000000004
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy value: 83.4996993385448
- type: cos_sim_ap value: 90.66238348446467
- type: cos_sim_f1 value: 84.39077936333699
- type: cos_sim_precision value: 79.53651975998345
- type: cos_sim_recall value: 89.87608136544307
- type: dot_accuracy value: 83.4996993385448
- type: dot_ap value: 90.64660919236363
- type: dot_f1 value: 84.39077936333699
- type: dot_precision value: 79.53651975998345
- type: dot_recall value: 89.87608136544307
- type: euclidean_accuracy value: 83.4996993385448
- type: euclidean_ap value: 90.66238269557765
- type: euclidean_f1 value: 84.39077936333699
- type: euclidean_precision value: 79.53651975998345
- type: euclidean_recall value: 89.87608136544307
- type: manhattan_accuracy value: 83.35538184004811
- type: manhattan_ap value: 90.6446013420276
- type: manhattan_f1 value: 84.37465196569775
- type: manhattan_precision value: 80.5614632071459
- type: manhattan_recall value: 88.56675239653963
- type: max_accuracy value: 83.4996993385448
- type: max_ap value: 90.66238348446467
- type: max_f1 value: 84.39077936333699
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 68.967
- type: map_at_10 value: 77.95299999999999
- type: map_at_100 value: 78.213
- type: map_at_1000 value: 78.21900000000001
- type: map_at_3 value: 76.30799999999999
- type: map_at_5 value: 77.316
- type: mrr_at_1 value: 69.125
- type: mrr_at_10 value: 77.886
- type: mrr_at_100 value: 78.141
- type: mrr_at_1000 value: 78.147
- type: mrr_at_3 value: 76.291
- type: mrr_at_5 value: 77.29700000000001
- type: ndcg_at_1 value: 69.231
- type: ndcg_at_10 value: 81.867
- type: ndcg_at_100 value: 82.982
- type: ndcg_at_1000 value: 83.12
- type: ndcg_at_3 value: 78.592
- type: ndcg_at_5 value: 80.39
- type: precision_at_1 value: 69.231
- type: precision_at_10 value: 9.494
- type: precision_at_100 value: 0.9990000000000001
- type: precision_at_1000 value: 0.101
- type: precision_at_3 value: 28.591
- type: precision_at_5 value: 18.061
- type: recall_at_1 value: 68.967
- type: recall_at_10 value: 93.941
- type: recall_at_100 value: 98.84100000000001
- type: recall_at_1000 value: 99.895
- type: recall_at_3 value: 85.142
- type: recall_at_5 value: 89.46300000000001
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 25.824
- type: map_at_10 value: 79.396
- type: map_at_100 value: 82.253
- type: map_at_1000 value: 82.295
- type: map_at_3 value: 54.83
- type: map_at_5 value: 69.536
- type: mrr_at_1 value: 89.7
- type: mrr_at_10 value: 92.929
- type: mrr_at_100 value: 93.013
- type: mrr_at_1000 value: 93.015
- type: mrr_at_3 value: 92.658
- type: mrr_at_5 value: 92.841
- type: ndcg_at_1 value: 89.7
- type: ndcg_at_10 value: 86.797
- type: ndcg_at_100 value: 89.652
- type: ndcg_at_1000 value: 90.047
- type: ndcg_at_3 value: 85.651
- type: ndcg_at_5 value: 84.747
- type: precision_at_1 value: 89.7
- type: precision_at_10 value: 41.61
- type: precision_at_100 value: 4.788
- type: precision_at_1000 value: 0.488
- type: precision_at_3 value: 76.833
- type: precision_at_5 value: 65.14
- type: recall_at_1 value: 25.824
- type: recall_at_10 value: 87.896
- type: recall_at_100 value: 97.221
- type: recall_at_1000 value: 99.29599999999999
- type: recall_at_3 value: 57.178
- type: recall_at_5 value: 74.348
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 52.5
- type: map_at_10 value: 63.04
- type: map_at_100 value: 63.548
- type: map_at_1000 value: 63.56
- type: map_at_3 value: 60.483
- type: map_at_5 value: 62.22800000000001
- type: mrr_at_1 value: 52.5
- type: mrr_at_10 value: 63.04
- type: mrr_at_100 value: 63.548
- type: mrr_at_1000 value: 63.56
- type: mrr_at_3 value: 60.483
- type: mrr_at_5 value: 62.22800000000001
- type: ndcg_at_1 value: 52.5
- type: ndcg_at_10 value: 68.099
- type: ndcg_at_100 value: 70.48400000000001
- type: ndcg_at_1000 value: 70.769
- type: ndcg_at_3 value: 63.01
- type: ndcg_at_5 value: 66.148
- type: precision_at_1 value: 52.5
- type: precision_at_10 value: 8.39
- type: precision_at_100 value: 0.9490000000000001
- type: precision_at_1000 value: 0.097
- type: precision_at_3 value: 23.433
- type: precision_at_5 value: 15.58
- type: recall_at_1 value: 52.5
- type: recall_at_10 value: 83.89999999999999
- type: recall_at_100 value: 94.89999999999999
- type: recall_at_1000 value: 97.1
- type: recall_at_3 value: 70.3
- type: recall_at_5 value: 77.9
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy value: 50.742593305117346
- type: f1 value: 38.7451988564002
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy value: 86.09756097560977
- type: ap value: 54.39255221143281
- type: f1 value: 80.8326851537251
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 72.32408066246728
- type: cos_sim_spearman value: 78.25773378380241
- type: euclidean_pearson value: 77.87824677060661
- type: euclidean_spearman value: 78.25773599854358
- type: manhattan_pearson value: 77.86648277798515
- type: manhattan_spearman value: 78.24642917155661
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map value: 28.846601097874608
- type: mrr value: 27.902777777777775
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 66.533
- type: map_at_10 value: 75.58399999999999
- type: map_at_100 value: 75.91
- type: map_at_1000 value: 75.921
- type: map_at_3 value: 73.847
- type: map_at_5 value: 74.929
- type: mrr_at_1 value: 68.854
- type: mrr_at_10 value: 76.20700000000001
- type: mrr_at_100 value: 76.498
- type: mrr_at_1000 value: 76.508
- type: mrr_at_3 value: 74.71600000000001
- type: mrr_at_5 value: 75.653
- type: ndcg_at_1 value: 68.854
- type: ndcg_at_10 value: 79.209
- type: ndcg_at_100 value: 80.67
- type: ndcg_at_1000 value: 80.95
- type: ndcg_at_3 value: 75.923
- type: ndcg_at_5 value: 77.74799999999999
- type: precision_at_1 value: 68.854
- type: precision_at_10 value: 9.547
- type: precision_at_100 value: 1.027
- type: precision_at_1000 value: 0.105
- type: precision_at_3 value: 28.582
- type: precision_at_5 value: 18.112000000000002
- type: recall_at_1 value: 66.533
- type: recall_at_10 value: 89.736
- type: recall_at_100 value: 96.34
- type: recall_at_1000 value: 98.52
- type: recall_at_3 value: 81.047
- type: recall_at_5 value: 85.38900000000001
- 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: 73.27841291190316
- type: f1 value: 70.82287701665152
- 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: 76.20040349697376
- type: f1 value: 75.92782428878164
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 56.39999999999999
- type: map_at_10 value: 62.122
- type: map_at_100 value: 62.692
- type: map_at_1000 value: 62.739
- type: map_at_3 value: 60.617
- type: map_at_5 value: 61.582
- type: mrr_at_1 value: 56.39999999999999
- type: mrr_at_10 value: 62.125
- type: mrr_at_100 value: 62.696
- type: mrr_at_1000 value: 62.742
- type: mrr_at_3 value: 60.617
- type: mrr_at_5 value: 61.602000000000004
- type: ndcg_at_1 value: 56.39999999999999
- type: ndcg_at_10 value: 64.986
- type: ndcg_at_100 value: 67.889
- type: ndcg_at_1000 value: 69.16499999999999
- type: ndcg_at_3 value: 61.951
- type: ndcg_at_5 value: 63.685
- type: precision_at_1 value: 56.39999999999999
- type: precision_at_10 value: 7.3999999999999995
- type: precision_at_100 value: 0.8789999999999999
- type: precision_at_1000 value: 0.098
- type: precision_at_3 value: 21.933
- type: precision_at_5 value: 14.000000000000002
- type: recall_at_1 value: 56.39999999999999
- type: recall_at_10 value: 74
- type: recall_at_100 value: 87.9
- type: recall_at_1000 value: 98
- type: recall_at_3 value: 65.8
- type: recall_at_5 value: 70
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy value: 76.64
- type: f1 value: 76.5446299028248
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy value: 82.34975636166757
- type: cos_sim_ap value: 85.51352392694149
- type: cos_sim_f1 value: 83.53057199211045
- type: cos_sim_precision value: 78.35337650323775
- type: cos_sim_recall value: 89.44033790918691
- type: dot_accuracy value: 82.34975636166757
- type: dot_ap value: 85.51347115601486
- type: dot_f1 value: 83.53057199211045
- type: dot_precision value: 78.35337650323775
- type: dot_recall value: 89.44033790918691
- type: euclidean_accuracy value: 82.34975636166757
- type: euclidean_ap value: 85.51352392694149
- type: euclidean_f1 value: 83.53057199211045
- type: euclidean_precision value: 78.35337650323775
- type: euclidean_recall value: 89.44033790918691
- type: manhattan_accuracy value: 82.34975636166757
- type: manhattan_ap value: 85.48313896880585
- type: manhattan_f1 value: 83.52414136386261
- type: manhattan_precision value: 79.00188323917138
- type: manhattan_recall value: 88.59556494192185
- type: max_accuracy value: 82.34975636166757
- type: max_ap value: 85.51352392694149
- type: max_f1 value: 83.53057199211045
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy value: 93.39
- type: ap value: 91.62127505252761
- type: f1 value: 93.38126146765326
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 39.69424895486595
- type: cos_sim_spearman value: 45.357868735202885
- type: euclidean_pearson value: 44.85027304963503
- type: euclidean_spearman value: 45.356945176162064
- type: manhattan_pearson value: 44.866080721344744
- type: manhattan_spearman value: 45.37053172312661
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 37.03908089465844
- type: cos_sim_spearman value: 38.98314179826781
- type: euclidean_pearson value: 37.189386019789545
- type: euclidean_spearman value: 38.98311189555396
- type: manhattan_pearson value: 37.14695118899785
- type: manhattan_spearman value: 38.94957261261034
- 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: 65.08396305098712
- type: cos_sim_spearman value: 66.26346934994216
- type: euclidean_pearson value: 65.56501615370941
- type: euclidean_spearman value: 66.26346934994216
- type: manhattan_pearson value: 65.47984748172154
- type: manhattan_spearman value: 66.25326746119808
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 80.95965207330296
- type: cos_sim_spearman value: 82.96149593569953
- type: euclidean_pearson value: 82.67125448003975
- type: euclidean_spearman value: 82.96141174550262
- type: manhattan_pearson value: 82.64660468206361
- type: manhattan_spearman value: 82.91756025324656
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map value: 66.43391960680063
- type: mrr value: 76.078440855015
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 28.29
- type: map_at_10 value: 78.441
- type: map_at_100 value: 82.043
- type: map_at_1000 value: 82.10499999999999
- type: map_at_3 value: 55.448
- type: map_at_5 value: 67.982
- type: mrr_at_1 value: 91.18
- type: mrr_at_10 value: 93.498
- type: mrr_at_100 value: 93.57
- type: mrr_at_1000 value: 93.572
- type: mrr_at_3 value: 93.112
- type: mrr_at_5 value: 93.351
- type: ndcg_at_1 value: 91.18
- type: ndcg_at_10 value: 85.849
- type: ndcg_at_100 value: 89.32600000000001
- type: ndcg_at_1000 value: 89.9
- type: ndcg_at_3 value: 87.333
- type: ndcg_at_5 value: 85.91499999999999
- type: precision_at_1 value: 91.18
- type: precision_at_10 value: 42.315000000000005
- type: precision_at_100 value: 5.029
- type: precision_at_1000 value: 0.517
- type: precision_at_3 value: 76.12400000000001
- type: precision_at_5 value: 63.690000000000005
- type: recall_at_1 value: 28.29
- type: recall_at_10 value: 84.679
- type: recall_at_100 value: 95.952
- type: recall_at_1000 value: 98.821
- type: recall_at_3 value: 56.987
- type: recall_at_5 value: 71.15599999999999
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy value: 53.09799999999999
- type: f1 value: 51.397192036892314
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure value: 70.59693805158501
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure value: 63.21127290121542
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 61.3
- type: map_at_10 value: 70.658
- type: map_at_100 value: 71.096
- type: map_at_1000 value: 71.108
- type: map_at_3 value: 69.15
- type: map_at_5 value: 70.125
- type: mrr_at_1 value: 61.3
- type: mrr_at_10 value: 70.658
- type: mrr_at_100 value: 71.096
- type: mrr_at_1000 value: 71.108
- type: mrr_at_3 value: 69.15
- type: mrr_at_5 value: 70.125
- type: ndcg_at_1 value: 61.3
- type: ndcg_at_10 value: 74.71
- type: ndcg_at_100 value: 76.783
- type: ndcg_at_1000 value: 77.09899999999999
- type: ndcg_at_3 value: 71.634
- type: ndcg_at_5 value: 73.399
- type: precision_at_1 value: 61.3
- type: precision_at_10 value: 8.72
- type: precision_at_100 value: 0.967
- type: precision_at_1000 value: 0.099
- type: precision_at_3 value: 26.267000000000003
- type: precision_at_5 value: 16.619999999999997
- type: recall_at_1 value: 61.3
- type: recall_at_10 value: 87.2
- type: recall_at_100 value: 96.7
- type: recall_at_1000 value: 99.2
- type: recall_at_3 value: 78.8
- type: recall_at_5 value: 83.1
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy value: 88.01
- type: ap value: 72.51537272974005
- type: f1 value: 86.49546025793478 license: mit
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
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！
1 开源模型
本次开源stella-mrl-large-zh-v3.5-1792d模型， 本模型是在stella-large-zh-v3-1792d的基础上使用MRL方法训练而成。 其主要特点是可变的向量维度。
2 使用方法
from sentence_transformers import SentenceTransformer
from sklearn.preprocessing import normalize
model = SentenceTransformer("infgrad/stella-mrl-large-zh-v3.5-1792d")
# 注意先不要normalize! 选取前n维后再normalize
vectors = model.encode(["text1", "text2"], normalize_embeddings=False)
print(vectors.shape) # shape is [2,1792]
# n_dims越大效果越好，但是时空消耗就越大。建议维度选取128的倍数，因为是这么训练的
n_dims = 768
cut_vecs = normalize(vectors[:, :n_dims])
3 不同向量维度的CMTEB得分
stella-mrl-large-zh-v3.5-1792d_1024 代表取前1024维。整体趋势是维度越大效果越好。
| Model | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | CMTEB-Score |
|---|---|---|---|---|---|---|---|
| stella-mrl-large-zh-v3.5-1792d_128 | 70.01 | 62.17 | 87.99 | 70.67 | 66.77 | 53.55 | 67.16 |
| stella-mrl-large-zh-v3.5-1792d_256 | 72.19 | 62.41 | 88.09 | 71.22 | 68.32 | 53.38 | 68.02 |
| stella-mrl-large-zh-v3.5-1792d_384 | 72.77 | 62.43 | 88.26 | 71.34 | 68.31 | 53.87 | 68.25 |
| stella-mrl-large-zh-v3.5-1792d_512 | 73.11 | 62.45 | 88.16 | 71.46 | 68.32 | 53.28 | 68.29 |
| stella-mrl-large-zh-v3.5-1792d_640 | 73.27 | 62.49 | 88.21 | 71.46 | 68.69 | 53.63 | 68.42 |
| stella-mrl-large-zh-v3.5-1792d_768 | 73.38 | 62.5 | 88.19 | 71.49 | 68.64 | 53.77 | 68.47 |
| stella-mrl-large-zh-v3.5-1792d_896 | 73.37 | 62.5 | 88.14 | 71.51 | 68.44 | 54.13 | 68.49 |
| stella-mrl-large-zh-v3.5-1792d_1024 | 73.43 | 62.51 | 88.16 | 71.52 | 68.59 | 53.43 | 68.44 |
| stella-mrl-large-zh-v3.5-1792d_1152 | 73.46 | 62.49 | 88.16 | 71.57 | 68.55 | 53.67 | 68.49 |
| stella-mrl-large-zh-v3.5-1792d_1280 | 73.48 | 62.51 | 88.12 | 71.55 | 68.44 | 53.74 | 68.48 |
| stella-mrl-large-zh-v3.5-1792d_1408 | 73.48 | 62.51 | 88.14 | 71.58 | 68.46 | 53.69 | 68.48 |
| stella-mrl-large-zh-v3.5-1792d_1536 | 73.49 | 62.5 | 88.11 | 71.55 | 68.5 | 54.06 | 68.52 |
| stella-mrl-large-zh-v3.5-1792d_1664 | 73.56 | 62.49 | 88.06 | 71.56 | 68.47 | 54.28 | 68.56 |
| stella-mrl-large-zh-v3.5-1792d_1792 | 73.51 | 62.48 | 88.09 | 71.56 | 68.45 | 54.39 | 68.56 |
上述表格中stella-mrl-large-zh-v3.5-1792d_1792的得分为68.56和榜单68.55得分不一致，原因和权重类型有关，小差异请忽略不计。
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