|
--- |
|
pipeline_tag: sentence-similarity |
|
tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
|
- mteb |
|
model-index: |
|
- name: Dmeta-embedding |
|
results: |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/AFQMC |
|
name: MTEB AFQMC |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 65.60825224706932 |
|
- type: cos_sim_spearman |
|
value: 71.12862586297193 |
|
- type: euclidean_pearson |
|
value: 70.18130275750404 |
|
- type: euclidean_spearman |
|
value: 71.12862586297193 |
|
- type: manhattan_pearson |
|
value: 70.14470398075396 |
|
- type: manhattan_spearman |
|
value: 71.05226975911737 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/ATEC |
|
name: MTEB ATEC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 65.52386345655479 |
|
- type: cos_sim_spearman |
|
value: 64.64245253181382 |
|
- type: euclidean_pearson |
|
value: 73.20157662981914 |
|
- type: euclidean_spearman |
|
value: 64.64245253178956 |
|
- type: manhattan_pearson |
|
value: 73.22837571756348 |
|
- type: manhattan_spearman |
|
value: 64.62632334391418 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (zh) |
|
config: zh |
|
split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 44.925999999999995 |
|
- type: f1 |
|
value: 42.82555191308971 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/BQ |
|
name: MTEB BQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 71.35236446393156 |
|
- type: cos_sim_spearman |
|
value: 72.29629643702184 |
|
- type: euclidean_pearson |
|
value: 70.94570179874498 |
|
- type: euclidean_spearman |
|
value: 72.29629297226953 |
|
- type: manhattan_pearson |
|
value: 70.84463025501125 |
|
- type: manhattan_spearman |
|
value: 72.24527021975821 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/CLSClusteringP2P |
|
name: MTEB CLSClusteringP2P |
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config: default |
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split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 40.24232916894152 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/CLSClusteringS2S |
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name: MTEB CLSClusteringS2S |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 39.167806226929706 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv1-reranking |
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name: MTEB CMedQAv1 |
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config: default |
|
split: test |
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revision: None |
|
metrics: |
|
- type: map |
|
value: 88.48837920106357 |
|
- type: mrr |
|
value: 90.36861111111111 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv2-reranking |
|
name: MTEB CMedQAv2 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 89.17878171657071 |
|
- type: mrr |
|
value: 91.35805555555555 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/CmedqaRetrieval |
|
name: MTEB CmedqaRetrieval |
|
config: default |
|
split: dev |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.751 |
|
- type: map_at_10 |
|
value: 38.946 |
|
- type: map_at_100 |
|
value: 40.855000000000004 |
|
- type: map_at_1000 |
|
value: 40.953 |
|
- type: map_at_3 |
|
value: 34.533 |
|
- type: map_at_5 |
|
value: 36.905 |
|
- type: mrr_at_1 |
|
value: 39.235 |
|
- type: mrr_at_10 |
|
value: 47.713 |
|
- type: mrr_at_100 |
|
value: 48.71 |
|
- type: mrr_at_1000 |
|
value: 48.747 |
|
- type: mrr_at_3 |
|
value: 45.086 |
|
- type: mrr_at_5 |
|
value: 46.498 |
|
- type: ndcg_at_1 |
|
value: 39.235 |
|
- type: ndcg_at_10 |
|
value: 45.831 |
|
- type: ndcg_at_100 |
|
value: 53.162 |
|
- type: ndcg_at_1000 |
|
value: 54.800000000000004 |
|
- type: ndcg_at_3 |
|
value: 40.188 |
|
- type: ndcg_at_5 |
|
value: 42.387 |
|
- type: precision_at_1 |
|
value: 39.235 |
|
- type: precision_at_10 |
|
value: 10.273 |
|
- type: precision_at_100 |
|
value: 1.627 |
|
- type: precision_at_1000 |
|
value: 0.183 |
|
- type: precision_at_3 |
|
value: 22.772000000000002 |
|
- type: precision_at_5 |
|
value: 16.524 |
|
- type: recall_at_1 |
|
value: 25.751 |
|
- type: recall_at_10 |
|
value: 57.411 |
|
- type: recall_at_100 |
|
value: 87.44 |
|
- type: recall_at_1000 |
|
value: 98.386 |
|
- type: recall_at_3 |
|
value: 40.416000000000004 |
|
- type: recall_at_5 |
|
value: 47.238 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/CMNLI |
|
name: MTEB Cmnli |
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config: default |
|
split: validation |
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revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 83.59591100420926 |
|
- type: cos_sim_ap |
|
value: 90.65538153970263 |
|
- type: cos_sim_f1 |
|
value: 84.76466651795673 |
|
- type: cos_sim_precision |
|
value: 81.04073363190446 |
|
- type: cos_sim_recall |
|
value: 88.84732288987608 |
|
- type: dot_accuracy |
|
value: 83.59591100420926 |
|
- type: dot_ap |
|
value: 90.64355541781003 |
|
- type: dot_f1 |
|
value: 84.76466651795673 |
|
- type: dot_precision |
|
value: 81.04073363190446 |
|
- type: dot_recall |
|
value: 88.84732288987608 |
|
- type: euclidean_accuracy |
|
value: 83.59591100420926 |
|
- type: euclidean_ap |
|
value: 90.6547878194287 |
|
- type: euclidean_f1 |
|
value: 84.76466651795673 |
|
- type: euclidean_precision |
|
value: 81.04073363190446 |
|
- type: euclidean_recall |
|
value: 88.84732288987608 |
|
- type: manhattan_accuracy |
|
value: 83.51172579675286 |
|
- type: manhattan_ap |
|
value: 90.59941589844144 |
|
- type: manhattan_f1 |
|
value: 84.51827242524917 |
|
- type: manhattan_precision |
|
value: 80.28613507258574 |
|
- type: manhattan_recall |
|
value: 89.22141688099134 |
|
- type: max_accuracy |
|
value: 83.59591100420926 |
|
- type: max_ap |
|
value: 90.65538153970263 |
|
- type: max_f1 |
|
value: 84.76466651795673 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/CovidRetrieval |
|
name: MTEB CovidRetrieval |
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config: default |
|
split: dev |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 63.251000000000005 |
|
- type: map_at_10 |
|
value: 72.442 |
|
- type: map_at_100 |
|
value: 72.79299999999999 |
|
- type: map_at_1000 |
|
value: 72.80499999999999 |
|
- type: map_at_3 |
|
value: 70.293 |
|
- type: map_at_5 |
|
value: 71.571 |
|
- type: mrr_at_1 |
|
value: 63.541000000000004 |
|
- type: mrr_at_10 |
|
value: 72.502 |
|
- type: mrr_at_100 |
|
value: 72.846 |
|
- type: mrr_at_1000 |
|
value: 72.858 |
|
- type: mrr_at_3 |
|
value: 70.39 |
|
- type: mrr_at_5 |
|
value: 71.654 |
|
- type: ndcg_at_1 |
|
value: 63.541000000000004 |
|
- type: ndcg_at_10 |
|
value: 76.774 |
|
- type: ndcg_at_100 |
|
value: 78.389 |
|
- type: ndcg_at_1000 |
|
value: 78.678 |
|
- type: ndcg_at_3 |
|
value: 72.47 |
|
- type: ndcg_at_5 |
|
value: 74.748 |
|
- type: precision_at_1 |
|
value: 63.541000000000004 |
|
- type: precision_at_10 |
|
value: 9.115 |
|
- type: precision_at_100 |
|
value: 0.9860000000000001 |
|
- type: precision_at_1000 |
|
value: 0.101 |
|
- type: precision_at_3 |
|
value: 26.379 |
|
- type: precision_at_5 |
|
value: 16.965 |
|
- type: recall_at_1 |
|
value: 63.251000000000005 |
|
- type: recall_at_10 |
|
value: 90.253 |
|
- type: recall_at_100 |
|
value: 97.576 |
|
- type: recall_at_1000 |
|
value: 99.789 |
|
- type: recall_at_3 |
|
value: 78.635 |
|
- type: recall_at_5 |
|
value: 84.141 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/DuRetrieval |
|
name: MTEB DuRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 23.597 |
|
- type: map_at_10 |
|
value: 72.411 |
|
- type: map_at_100 |
|
value: 75.58500000000001 |
|
- type: map_at_1000 |
|
value: 75.64800000000001 |
|
- type: map_at_3 |
|
value: 49.61 |
|
- type: map_at_5 |
|
value: 62.527 |
|
- type: mrr_at_1 |
|
value: 84.65 |
|
- type: mrr_at_10 |
|
value: 89.43900000000001 |
|
- type: mrr_at_100 |
|
value: 89.525 |
|
- type: mrr_at_1000 |
|
value: 89.529 |
|
- type: mrr_at_3 |
|
value: 89 |
|
- type: mrr_at_5 |
|
value: 89.297 |
|
- type: ndcg_at_1 |
|
value: 84.65 |
|
- type: ndcg_at_10 |
|
value: 81.47 |
|
- type: ndcg_at_100 |
|
value: 85.198 |
|
- type: ndcg_at_1000 |
|
value: 85.828 |
|
- type: ndcg_at_3 |
|
value: 79.809 |
|
- type: ndcg_at_5 |
|
value: 78.55 |
|
- type: precision_at_1 |
|
value: 84.65 |
|
- type: precision_at_10 |
|
value: 39.595 |
|
- type: precision_at_100 |
|
value: 4.707 |
|
- type: precision_at_1000 |
|
value: 0.485 |
|
- type: precision_at_3 |
|
value: 71.61699999999999 |
|
- type: precision_at_5 |
|
value: 60.45 |
|
- type: recall_at_1 |
|
value: 23.597 |
|
- type: recall_at_10 |
|
value: 83.34 |
|
- type: recall_at_100 |
|
value: 95.19800000000001 |
|
- type: recall_at_1000 |
|
value: 98.509 |
|
- type: recall_at_3 |
|
value: 52.744 |
|
- type: recall_at_5 |
|
value: 68.411 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/EcomRetrieval |
|
name: MTEB EcomRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 53.1 |
|
- type: map_at_10 |
|
value: 63.359 |
|
- type: map_at_100 |
|
value: 63.9 |
|
- type: map_at_1000 |
|
value: 63.909000000000006 |
|
- type: map_at_3 |
|
value: 60.95 |
|
- type: map_at_5 |
|
value: 62.305 |
|
- type: mrr_at_1 |
|
value: 53.1 |
|
- type: mrr_at_10 |
|
value: 63.359 |
|
- type: mrr_at_100 |
|
value: 63.9 |
|
- type: mrr_at_1000 |
|
value: 63.909000000000006 |
|
- type: mrr_at_3 |
|
value: 60.95 |
|
- type: mrr_at_5 |
|
value: 62.305 |
|
- type: ndcg_at_1 |
|
value: 53.1 |
|
- type: ndcg_at_10 |
|
value: 68.418 |
|
- type: ndcg_at_100 |
|
value: 70.88499999999999 |
|
- type: ndcg_at_1000 |
|
value: 71.135 |
|
- type: ndcg_at_3 |
|
value: 63.50599999999999 |
|
- type: ndcg_at_5 |
|
value: 65.92 |
|
- type: precision_at_1 |
|
value: 53.1 |
|
- type: precision_at_10 |
|
value: 8.43 |
|
- type: precision_at_100 |
|
value: 0.955 |
|
- type: precision_at_1000 |
|
value: 0.098 |
|
- type: precision_at_3 |
|
value: 23.633000000000003 |
|
- type: precision_at_5 |
|
value: 15.340000000000002 |
|
- type: recall_at_1 |
|
value: 53.1 |
|
- type: recall_at_10 |
|
value: 84.3 |
|
- type: recall_at_100 |
|
value: 95.5 |
|
- type: recall_at_1000 |
|
value: 97.5 |
|
- type: recall_at_3 |
|
value: 70.89999999999999 |
|
- type: recall_at_5 |
|
value: 76.7 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/IFlyTek-classification |
|
name: MTEB IFlyTek |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 48.303193535975375 |
|
- type: f1 |
|
value: 35.96559358693866 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/JDReview-classification |
|
name: MTEB JDReview |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 85.06566604127579 |
|
- type: ap |
|
value: 52.0596483757231 |
|
- type: f1 |
|
value: 79.5196835127668 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/LCQMC |
|
name: MTEB LCQMC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 74.48499423626059 |
|
- type: cos_sim_spearman |
|
value: 78.75806756061169 |
|
- type: euclidean_pearson |
|
value: 78.47917601852879 |
|
- type: euclidean_spearman |
|
value: 78.75807199272622 |
|
- type: manhattan_pearson |
|
value: 78.40207586289772 |
|
- type: manhattan_spearman |
|
value: 78.6911776964119 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/Mmarco-reranking |
|
name: MTEB MMarcoReranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 24.75987466552363 |
|
- type: mrr |
|
value: 23.40515873015873 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MMarcoRetrieval |
|
name: MTEB MMarcoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 58.026999999999994 |
|
- type: map_at_10 |
|
value: 67.50699999999999 |
|
- type: map_at_100 |
|
value: 67.946 |
|
- type: map_at_1000 |
|
value: 67.96600000000001 |
|
- type: map_at_3 |
|
value: 65.503 |
|
- type: map_at_5 |
|
value: 66.649 |
|
- type: mrr_at_1 |
|
value: 60.20100000000001 |
|
- type: mrr_at_10 |
|
value: 68.271 |
|
- type: mrr_at_100 |
|
value: 68.664 |
|
- type: mrr_at_1000 |
|
value: 68.682 |
|
- type: mrr_at_3 |
|
value: 66.47800000000001 |
|
- type: mrr_at_5 |
|
value: 67.499 |
|
- type: ndcg_at_1 |
|
value: 60.20100000000001 |
|
- type: ndcg_at_10 |
|
value: 71.697 |
|
- type: ndcg_at_100 |
|
value: 73.736 |
|
- type: ndcg_at_1000 |
|
value: 74.259 |
|
- type: ndcg_at_3 |
|
value: 67.768 |
|
- type: ndcg_at_5 |
|
value: 69.72 |
|
- type: precision_at_1 |
|
value: 60.20100000000001 |
|
- type: precision_at_10 |
|
value: 8.927999999999999 |
|
- type: precision_at_100 |
|
value: 0.9950000000000001 |
|
- type: precision_at_1000 |
|
value: 0.104 |
|
- type: precision_at_3 |
|
value: 25.883 |
|
- type: precision_at_5 |
|
value: 16.55 |
|
- type: recall_at_1 |
|
value: 58.026999999999994 |
|
- type: recall_at_10 |
|
value: 83.966 |
|
- type: recall_at_100 |
|
value: 93.313 |
|
- type: recall_at_1000 |
|
value: 97.426 |
|
- type: recall_at_3 |
|
value: 73.342 |
|
- type: recall_at_5 |
|
value: 77.997 |
|
- 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: 71.1600537995965 |
|
- type: f1 |
|
value: 68.8126216609964 |
|
- 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: 73.54068594485541 |
|
- type: f1 |
|
value: 73.46845879869848 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MedicalRetrieval |
|
name: MTEB MedicalRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 54.900000000000006 |
|
- type: map_at_10 |
|
value: 61.363 |
|
- type: map_at_100 |
|
value: 61.924 |
|
- type: map_at_1000 |
|
value: 61.967000000000006 |
|
- type: map_at_3 |
|
value: 59.767 |
|
- type: map_at_5 |
|
value: 60.802 |
|
- type: mrr_at_1 |
|
value: 55.1 |
|
- type: mrr_at_10 |
|
value: 61.454 |
|
- type: mrr_at_100 |
|
value: 62.016000000000005 |
|
- type: mrr_at_1000 |
|
value: 62.059 |
|
- type: mrr_at_3 |
|
value: 59.882999999999996 |
|
- type: mrr_at_5 |
|
value: 60.893 |
|
- type: ndcg_at_1 |
|
value: 54.900000000000006 |
|
- type: ndcg_at_10 |
|
value: 64.423 |
|
- type: ndcg_at_100 |
|
value: 67.35900000000001 |
|
- type: ndcg_at_1000 |
|
value: 68.512 |
|
- type: ndcg_at_3 |
|
value: 61.224000000000004 |
|
- type: ndcg_at_5 |
|
value: 63.083 |
|
- type: precision_at_1 |
|
value: 54.900000000000006 |
|
- type: precision_at_10 |
|
value: 7.3999999999999995 |
|
- type: precision_at_100 |
|
value: 0.882 |
|
- type: precision_at_1000 |
|
value: 0.097 |
|
- type: precision_at_3 |
|
value: 21.8 |
|
- type: precision_at_5 |
|
value: 13.98 |
|
- type: recall_at_1 |
|
value: 54.900000000000006 |
|
- type: recall_at_10 |
|
value: 74 |
|
- type: recall_at_100 |
|
value: 88.2 |
|
- type: recall_at_1000 |
|
value: 97.3 |
|
- type: recall_at_3 |
|
value: 65.4 |
|
- type: recall_at_5 |
|
value: 69.89999999999999 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/MultilingualSentiment-classification |
|
name: MTEB MultilingualSentiment |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 75.15666666666667 |
|
- type: f1 |
|
value: 74.8306375354435 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/OCNLI |
|
name: MTEB Ocnli |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 83.10774228478614 |
|
- type: cos_sim_ap |
|
value: 87.17679348388666 |
|
- type: cos_sim_f1 |
|
value: 84.59302325581395 |
|
- type: cos_sim_precision |
|
value: 78.15577439570276 |
|
- type: cos_sim_recall |
|
value: 92.18585005279832 |
|
- type: dot_accuracy |
|
value: 83.10774228478614 |
|
- type: dot_ap |
|
value: 87.17679348388666 |
|
- type: dot_f1 |
|
value: 84.59302325581395 |
|
- type: dot_precision |
|
value: 78.15577439570276 |
|
- type: dot_recall |
|
value: 92.18585005279832 |
|
- type: euclidean_accuracy |
|
value: 83.10774228478614 |
|
- type: euclidean_ap |
|
value: 87.17679348388666 |
|
- type: euclidean_f1 |
|
value: 84.59302325581395 |
|
- type: euclidean_precision |
|
value: 78.15577439570276 |
|
- type: euclidean_recall |
|
value: 92.18585005279832 |
|
- type: manhattan_accuracy |
|
value: 82.67460747157553 |
|
- type: manhattan_ap |
|
value: 86.94296334435238 |
|
- type: manhattan_f1 |
|
value: 84.32327166504382 |
|
- type: manhattan_precision |
|
value: 78.22944896115628 |
|
- type: manhattan_recall |
|
value: 91.4466737064414 |
|
- type: max_accuracy |
|
value: 83.10774228478614 |
|
- type: max_ap |
|
value: 87.17679348388666 |
|
- type: max_f1 |
|
value: 84.59302325581395 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/OnlineShopping-classification |
|
name: MTEB OnlineShopping |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 93.24999999999999 |
|
- type: ap |
|
value: 90.98617641063584 |
|
- type: f1 |
|
value: 93.23447883650289 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/PAWSX |
|
name: MTEB PAWSX |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 41.071417937737856 |
|
- type: cos_sim_spearman |
|
value: 45.049199344455424 |
|
- type: euclidean_pearson |
|
value: 44.913450096830786 |
|
- type: euclidean_spearman |
|
value: 45.05733424275291 |
|
- type: manhattan_pearson |
|
value: 44.881623825912065 |
|
- type: manhattan_spearman |
|
value: 44.989923561416596 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/QBQTC |
|
name: MTEB QBQTC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 41.38238052689359 |
|
- type: cos_sim_spearman |
|
value: 42.61949690594399 |
|
- type: euclidean_pearson |
|
value: 40.61261500356766 |
|
- type: euclidean_spearman |
|
value: 42.619626605620724 |
|
- type: manhattan_pearson |
|
value: 40.8886109204474 |
|
- type: manhattan_spearman |
|
value: 42.75791523010463 |
|
- 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: 62.10977863727196 |
|
- type: cos_sim_spearman |
|
value: 63.843727112473225 |
|
- type: euclidean_pearson |
|
value: 63.25133487817196 |
|
- type: euclidean_spearman |
|
value: 63.843727112473225 |
|
- type: manhattan_pearson |
|
value: 63.58749018644103 |
|
- type: manhattan_spearman |
|
value: 63.83820575456674 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/STSB |
|
name: MTEB STSB |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 79.30616496720054 |
|
- type: cos_sim_spearman |
|
value: 80.767935782436 |
|
- type: euclidean_pearson |
|
value: 80.4160642670106 |
|
- type: euclidean_spearman |
|
value: 80.76820284024356 |
|
- type: manhattan_pearson |
|
value: 80.27318714580251 |
|
- type: manhattan_spearman |
|
value: 80.61030164164964 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/T2Reranking |
|
name: MTEB T2Reranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 66.26242871142425 |
|
- type: mrr |
|
value: 76.20689863623174 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/T2Retrieval |
|
name: MTEB T2Retrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.240999999999996 |
|
- type: map_at_10 |
|
value: 73.009 |
|
- type: map_at_100 |
|
value: 76.893 |
|
- type: map_at_1000 |
|
value: 76.973 |
|
- type: map_at_3 |
|
value: 51.339 |
|
- type: map_at_5 |
|
value: 63.003 |
|
- type: mrr_at_1 |
|
value: 87.458 |
|
- type: mrr_at_10 |
|
value: 90.44 |
|
- type: mrr_at_100 |
|
value: 90.558 |
|
- type: mrr_at_1000 |
|
value: 90.562 |
|
- type: mrr_at_3 |
|
value: 89.89 |
|
- type: mrr_at_5 |
|
value: 90.231 |
|
- type: ndcg_at_1 |
|
value: 87.458 |
|
- type: ndcg_at_10 |
|
value: 81.325 |
|
- type: ndcg_at_100 |
|
value: 85.61999999999999 |
|
- type: ndcg_at_1000 |
|
value: 86.394 |
|
- type: ndcg_at_3 |
|
value: 82.796 |
|
- type: ndcg_at_5 |
|
value: 81.219 |
|
- type: precision_at_1 |
|
value: 87.458 |
|
- type: precision_at_10 |
|
value: 40.534 |
|
- type: precision_at_100 |
|
value: 4.96 |
|
- type: precision_at_1000 |
|
value: 0.514 |
|
- type: precision_at_3 |
|
value: 72.444 |
|
- type: precision_at_5 |
|
value: 60.601000000000006 |
|
- type: recall_at_1 |
|
value: 26.240999999999996 |
|
- type: recall_at_10 |
|
value: 80.42 |
|
- type: recall_at_100 |
|
value: 94.118 |
|
- type: recall_at_1000 |
|
value: 98.02199999999999 |
|
- type: recall_at_3 |
|
value: 53.174 |
|
- type: recall_at_5 |
|
value: 66.739 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/TNews-classification |
|
name: MTEB TNews |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 52.40899999999999 |
|
- type: f1 |
|
value: 50.68532128056062 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringP2P |
|
name: MTEB ThuNewsClusteringP2P |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 65.57616085176686 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringS2S |
|
name: MTEB ThuNewsClusteringS2S |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 58.844999922904925 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/VideoRetrieval |
|
name: MTEB VideoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 58.4 |
|
- type: map_at_10 |
|
value: 68.64 |
|
- type: map_at_100 |
|
value: 69.062 |
|
- type: map_at_1000 |
|
value: 69.073 |
|
- type: map_at_3 |
|
value: 66.567 |
|
- type: map_at_5 |
|
value: 67.89699999999999 |
|
- type: mrr_at_1 |
|
value: 58.4 |
|
- type: mrr_at_10 |
|
value: 68.64 |
|
- type: mrr_at_100 |
|
value: 69.062 |
|
- type: mrr_at_1000 |
|
value: 69.073 |
|
- type: mrr_at_3 |
|
value: 66.567 |
|
- type: mrr_at_5 |
|
value: 67.89699999999999 |
|
- type: ndcg_at_1 |
|
value: 58.4 |
|
- type: ndcg_at_10 |
|
value: 73.30600000000001 |
|
- type: ndcg_at_100 |
|
value: 75.276 |
|
- type: ndcg_at_1000 |
|
value: 75.553 |
|
- type: ndcg_at_3 |
|
value: 69.126 |
|
- type: ndcg_at_5 |
|
value: 71.519 |
|
- type: precision_at_1 |
|
value: 58.4 |
|
- type: precision_at_10 |
|
value: 8.780000000000001 |
|
- type: precision_at_100 |
|
value: 0.968 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 25.5 |
|
- type: precision_at_5 |
|
value: 16.46 |
|
- type: recall_at_1 |
|
value: 58.4 |
|
- type: recall_at_10 |
|
value: 87.8 |
|
- type: recall_at_100 |
|
value: 96.8 |
|
- type: recall_at_1000 |
|
value: 99 |
|
- type: recall_at_3 |
|
value: 76.5 |
|
- type: recall_at_5 |
|
value: 82.3 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/waimai-classification |
|
name: MTEB Waimai |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 86.21000000000001 |
|
- type: ap |
|
value: 69.17460264576461 |
|
- type: f1 |
|
value: 84.68032984659226 |
|
license: apache-2.0 |
|
language: |
|
- zh |
|
- en |
|
--- |
|
|
|
<div align="center"> |
|
<img src="logo.png" alt="icon" width="100px"/> |
|
</div> |
|
|
|
<h1 align="center">Dmeta-embedding</h1> |
|
|
|
<h4 align="center"> |
|
<p> |
|
<a href=#usage>用法</a> | |
|
<a href="#evaluation">评测</a> | |
|
<a href=#faq>FAQ</a> | |
|
<a href="#contact">联系</a> | |
|
<a href="#license">版权（免费商用）</a> |
|
<p> |
|
</h4> |
|
|
|
Dmeta-embedding 是一款跨领域、跨任务、开箱即用的中文 Embedding 模型，适用于搜索、问答、智能客服、LLM+RAG 等各种业务场景。 |
|
|
|
优势特点如下： |
|
|
|
- 多任务、场景泛化性能优异，目前已取得 [MTEB](https://huggingface.co/spaces/mteb/leaderboard) 中文榜单第二成绩（2024.01.25） |
|
- 模型参数大小仅 400MB，对比参数量超过 GB 级模型，可以极大降低推理成本 |
|
- 支持上下文窗口长度达到 1024，对于长文本检索、RAG 等场景更适配 |
|
|
|
## Usage |
|
|
|
目前模型支持通过 [Sentence-Transformers](#sentence-transformers), [Langchain](#langchain), [Huggingface Transformers](#huggingface-transformers) 等主流框架进行推理，具体用法参考各个框架的示例。 |
|
|
|
### Sentence-Transformers |
|
|
|
Dmeta-embedding 模型支持通过 [sentence-transformers](https://www.SBERT.net) 来加载推理： |
|
|
|
``` |
|
pip install -U sentence-transformers |
|
``` |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"] |
|
texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"] |
|
|
|
model = SentenceTransformer('DMetaSoul/Dmeta-embedding') |
|
embs1 = model.encode(texts1, normalize_embeddings=True) |
|
embs2 = model.encode(texts2, normalize_embeddings=True) |
|
|
|
# 计算两两相似度 |
|
similarity = embs1 @ embs2.T |
|
print(similarity) |
|
|
|
# 获取 texts1[i] 对应的最相似 texts2[j] |
|
for i in range(len(texts1)): |
|
scores = [] |
|
for j in range(len(texts2)): |
|
scores.append([texts2[j], similarity[i][j]]) |
|
scores = sorted(scores, key=lambda x:x[1], reverse=True) |
|
|
|
print(f"查询文本：{texts1[i]}") |
|
for text2, score in scores: |
|
print(f"相似文本：{text2}，打分：{score}") |
|
print() |
|
``` |
|
|
|
示例输出如下： |
|
|
|
``` |
|
查询文本：胡子长得太快怎么办？ |
|
相似文本：胡子长得快怎么办？，打分：0.9535336494445801 |
|
相似文本：怎样使胡子不浓密！，打分：0.6776421070098877 |
|
相似文本：香港买手表哪里好，打分：0.2297907918691635 |
|
相似文本：在杭州手机到哪里买，打分：0.11386542022228241 |
|
|
|
查询文本：在香港哪里买手表好 |
|
相似文本：香港买手表哪里好，打分：0.9843372106552124 |
|
相似文本：在杭州手机到哪里买，打分：0.45211508870124817 |
|
相似文本：胡子长得快怎么办？，打分：0.19985519349575043 |
|
相似文本：怎样使胡子不浓密！，打分：0.18558596074581146 |
|
``` |
|
|
|
### Langchain |
|
|
|
Dmeta-embedding 模型支持通过 LLM 工具框架 [langchain](https://www.langchain.com/) 来加载推理： |
|
|
|
``` |
|
pip install -U langchain |
|
``` |
|
|
|
```python |
|
import torch |
|
import numpy as np |
|
from langchain.embeddings import HuggingFaceEmbeddings |
|
|
|
model_name = "DMetaSoul/Dmeta-embedding" |
|
model_kwargs = {'device': 'cuda' if torch.cuda.is_available() else 'cpu'} |
|
encode_kwargs = {'normalize_embeddings': True} # set True to compute cosine similarity |
|
|
|
model = HuggingFaceEmbeddings( |
|
model_name=model_name, |
|
model_kwargs=model_kwargs, |
|
encode_kwargs=encode_kwargs, |
|
) |
|
|
|
texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"] |
|
texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"] |
|
|
|
embs1 = model.embed_documents(texts1) |
|
embs2 = model.embed_documents(texts2) |
|
embs1, embs2 = np.array(embs1), np.array(embs2) |
|
|
|
# 计算两两相似度 |
|
similarity = embs1 @ embs2.T |
|
print(similarity) |
|
|
|
# 获取 texts1[i] 对应的最相似 texts2[j] |
|
for i in range(len(texts1)): |
|
scores = [] |
|
for j in range(len(texts2)): |
|
scores.append([texts2[j], similarity[i][j]]) |
|
scores = sorted(scores, key=lambda x:x[1], reverse=True) |
|
|
|
print(f"查询文本：{texts1[i]}") |
|
for text2, score in scores: |
|
print(f"相似文本：{text2}，打分：{score}") |
|
print() |
|
``` |
|
|
|
### HuggingFace Transformers |
|
|
|
Dmeta-embedding 模型支持通过 [HuggingFace Transformers](https://huggingface.co/docs/transformers/index) 框架来加载推理： |
|
|
|
``` |
|
pip install -U transformers |
|
``` |
|
|
|
```python |
|
import torch |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
|
|
def mean_pooling(model_output, attention_mask): |
|
token_embeddings = model_output[0] #First element of model_output contains all token embeddings |
|
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
|
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
|
|
|
def cls_pooling(model_output): |
|
return model_output[0][:, 0] |
|
|
|
|
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texts1 = ["胡子长得太快怎么办？", "在香港哪里买手表好"] |
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texts2 = ["胡子长得快怎么办？", "怎样使胡子不浓密！", "香港买手表哪里好", "在杭州手机到哪里买"] |
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tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/Dmeta-embedding') |
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model = AutoModel.from_pretrained('DMetaSoul/Dmeta-embedding') |
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model.eval() |
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with torch.no_grad(): |
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inputs1 = tokenizer(texts1, padding=True, truncation=True, return_tensors='pt') |
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inputs2 = tokenizer(texts2, padding=True, truncation=True, return_tensors='pt') |
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model_output1 = model(**inputs1) |
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model_output2 = model(**inputs2) |
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embs1, embs2 = cls_pooling(model_output1), cls_pooling(model_output2) |
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embs1 = torch.nn.functional.normalize(embs1, p=2, dim=1).numpy() |
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embs2 = torch.nn.functional.normalize(embs2, p=2, dim=1).numpy() |
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# 计算两两相似度 |
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similarity = embs1 @ embs2.T |
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print(similarity) |
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# 获取 texts1[i] 对应的最相似 texts2[j] |
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for i in range(len(texts1)): |
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scores = [] |
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for j in range(len(texts2)): |
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scores.append([texts2[j], similarity[i][j]]) |
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scores = sorted(scores, key=lambda x:x[1], reverse=True) |
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print(f"查询文本：{texts1[i]}") |
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for text2, score in scores: |
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print(f"相似文本：{text2}，打分：{score}") |
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print() |
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``` |
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## Evaluation |
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Dmeta-embedding 模型在 [MTEB 中文榜单](https://huggingface.co/spaces/mteb/leaderboard)取得开源第一的成绩（2024.01.25，Baichuan 榜单第一、未开源），具体关于评测数据和代码可参考 MTEB 官方[仓库](https://github.com/embeddings-benchmark/mteb)。 |
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**MTEB Chinese**: |
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该[榜单数据集](https://github.com/FlagOpen/FlagEmbedding/blob/master/C_MTEB)由智源研究院团队（BAAI）收集整理，包含 6 个经典任务共计 35 个中文数据集，涵盖了分类、检索、排序、句对、STS 等任务，是目前 Embedding 模型全方位能力评测的全球权威榜单。 |
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| Model | Vendor | Embedding dimension | Avg | Retrieval | STS | PairClassification | Classification | Reranking | Clustering | |
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|:-------------------------------------------------------------------------------------------------------- | ------ |:-------------------:|:-----:|:---------:|:-----:|:------------------:|:--------------:|:---------:|:----------:| |
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| [Dmeta-embedding](https://huggingface.co/DMetaSoul/Dmeta-embedding) | 数元灵 | 1024 | 67.51 | 70.41 | 64.09 | 88.92 | 70 | 67.17 | 50.96 | |
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| [gte-large-zh](https://huggingface.co/thenlper/gte-large-zh) | 阿里达摩院 | 1024 | 66.72 | 72.49 | 57.82 | 84.41 | 71.34 | 67.4 | 53.07 | |
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| [BAAI/bge-large-zh-v1.5](https://huggingface.co/BAAI/bge-large-zh-v1.5) | 智源 | 1024 | 64.53 | 70.46 | 56.25 | 81.6 | 69.13 | 65.84 | 48.99 | |
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| [BAAI/bge-base-zh-v1.5](https://huggingface.co/BAAI/bge-base-zh-v1.5) | 智源 | 768 | 63.13 | 69.49 | 53.72 | 79.75 | 68.07 | 65.39 | 47.53 | |
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| [text-embedding-ada-002(OpenAI)](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings) | OpenAI | 1536 | 53.02 | 52.0 | 43.35 | 69.56 | 64.31 | 54.28 | 45.68 | |
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| [text2vec-base](https://huggingface.co/shibing624/text2vec-base-chinese) | 个人 | 768 | 47.63 | 38.79 | 43.41 | 67.41 | 62.19 | 49.45 | 37.66 | |
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| [text2vec-large](https://huggingface.co/GanymedeNil/text2vec-large-chinese) | 个人 | 1024 | 47.36 | 41.94 | 44.97 | 70.86 | 60.66 | 49.16 | 30.02 | |
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## FAQ |
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<details> |
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<summary>1. 为何模型多任务、场景泛化能力优异，可开箱即用适配诸多应用场景？</summary> |
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<!-- ### 为何模型多任务、场景泛化能力优异，可开箱即用适配诸多应用场景？ --> |
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简单来说，模型优异的泛化能力来自于预训练数据的广泛和多样，以及模型优化时面向多任务场景设计了不同优化目标。 |
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具体来说，技术要点有： |
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1）首先是大规模弱标签对比学习。业界经验表明开箱即用的语言模型在 Embedding 相关任务上表现不佳，但由于监督数据标注、获取成本较高，因此大规模、高质量的弱标签学习成为一条可选技术路线。通过在互联网上论坛、新闻、问答社区、百科等半结构化数据中提取弱标签，并利用大模型进行低质过滤，得到 10 亿级别弱监督文本对数据。 |
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2）其次是高质量监督学习。我们收集整理了大规模开源标注的语句对数据集，包含百科、教育、金融、医疗、法律、新闻、学术等多个领域共计 3000 万句对样本。同时挖掘难负样本对，借助对比学习更好的进行模型优化。 |
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3）最后是检索任务针对性优化。考虑到搜索、问答以及 RAG 等场景是 Embedding 模型落地的重要应用阵地，为了增强模型跨领域、跨场景的效果性能，我们专门针对检索任务进行了模型优化，核心在于从问答、检索等数据中挖掘难负样本，借助稀疏和稠密检索等多种手段，构造百万级难负样本对数据集，显著提升了模型跨领域的检索性能。 |
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</details> |
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<details> |
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<summary>2. 模型可以商用吗？</summary> |
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<!-- ### 模型可以商用吗 --> |
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我们的开源模型基于 Apache-2.0 协议，完全支持免费商用。 |
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</details> |
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<details> |
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<summary>3. 如何复现 MTEB 评测结果？</summary> |
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<!-- ### 如何复现 MTEB 评测结果？ --> |
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我们在模型仓库中提供了脚本 mteb_eval.py，您可以直接运行此脚本来复现我们的评测结果。 |
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</details> |
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<details> |
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<summary>4. 后续规划有哪些？</summary> |
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<!-- ### 后续规划有哪些？ --> |
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我们将不断致力于为社区提供效果优异、推理轻量、多场景开箱即用的 Embedding 模型，同时我们也会将 Embedding 逐步整合到目前已经的技术生态中，跟随社区一起成长！ |
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</details> |
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## Contact |
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您如果在使用过程中，遇到任何问题，欢迎前往[讨论区](https://huggingface.co/DMetaSoul/Dmeta-embedding/discussions)建言献策。 |
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您也可以联系我们：赵中昊 <zhongh@dmetasoul.com>, 肖文斌 <xiaowenbin@dmetasoul.com>, 孙凯 <sunkai@dmetasoul.com> |
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## License |
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Dmeta-embedding 模型采用 Apache-2.0 License，开源模型可以进行免费商用私有部署。 |
