|
--- |
|
tags: |
|
- mteb |
|
- sentence-similarity |
|
- sentence-transformers |
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- Sentence Transformers |
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model-index: |
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- name: gte-large-zh |
|
results: |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/AFQMC |
|
name: MTEB AFQMC |
|
config: default |
|
split: validation |
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revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 48.94131905219026 |
|
- type: cos_sim_spearman |
|
value: 54.58261199731436 |
|
- type: euclidean_pearson |
|
value: 52.73929210805982 |
|
- type: euclidean_spearman |
|
value: 54.582632097533676 |
|
- type: manhattan_pearson |
|
value: 52.73123295724949 |
|
- type: manhattan_spearman |
|
value: 54.572941830465794 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/ATEC |
|
name: MTEB ATEC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 47.292931669579005 |
|
- type: cos_sim_spearman |
|
value: 54.601019783506466 |
|
- type: euclidean_pearson |
|
value: 54.61393532658173 |
|
- type: euclidean_spearman |
|
value: 54.60101865708542 |
|
- type: manhattan_pearson |
|
value: 54.59369555606305 |
|
- type: manhattan_spearman |
|
value: 54.601098593646036 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
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name: MTEB AmazonReviewsClassification (zh) |
|
config: zh |
|
split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 47.233999999999995 |
|
- type: f1 |
|
value: 45.68998446563349 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/BQ |
|
name: MTEB BQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 62.55033151404683 |
|
- type: cos_sim_spearman |
|
value: 64.40573802644984 |
|
- type: euclidean_pearson |
|
value: 62.93453281081951 |
|
- type: euclidean_spearman |
|
value: 64.40574149035828 |
|
- type: manhattan_pearson |
|
value: 62.839969210895816 |
|
- type: manhattan_spearman |
|
value: 64.30837945045283 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/CLSClusteringP2P |
|
name: MTEB CLSClusteringP2P |
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config: default |
|
split: test |
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revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 42.098169316685045 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/CLSClusteringS2S |
|
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: 38.90716707051822 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv1-reranking |
|
name: MTEB CMedQAv1 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 86.09191911031553 |
|
- type: mrr |
|
value: 88.6747619047619 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv2-reranking |
|
name: MTEB CMedQAv2 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 86.45781885502122 |
|
- type: mrr |
|
value: 89.01591269841269 |
|
- 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: 24.215 |
|
- type: map_at_10 |
|
value: 36.498000000000005 |
|
- type: map_at_100 |
|
value: 38.409 |
|
- type: map_at_1000 |
|
value: 38.524 |
|
- type: map_at_3 |
|
value: 32.428000000000004 |
|
- type: map_at_5 |
|
value: 34.664 |
|
- type: mrr_at_1 |
|
value: 36.834 |
|
- type: mrr_at_10 |
|
value: 45.196 |
|
- type: mrr_at_100 |
|
value: 46.214 |
|
- type: mrr_at_1000 |
|
value: 46.259 |
|
- type: mrr_at_3 |
|
value: 42.631 |
|
- type: mrr_at_5 |
|
value: 44.044 |
|
- type: ndcg_at_1 |
|
value: 36.834 |
|
- type: ndcg_at_10 |
|
value: 43.146 |
|
- type: ndcg_at_100 |
|
value: 50.632999999999996 |
|
- type: ndcg_at_1000 |
|
value: 52.608999999999995 |
|
- type: ndcg_at_3 |
|
value: 37.851 |
|
- type: ndcg_at_5 |
|
value: 40.005 |
|
- type: precision_at_1 |
|
value: 36.834 |
|
- type: precision_at_10 |
|
value: 9.647 |
|
- type: precision_at_100 |
|
value: 1.574 |
|
- type: precision_at_1000 |
|
value: 0.183 |
|
- type: precision_at_3 |
|
value: 21.48 |
|
- type: precision_at_5 |
|
value: 15.649 |
|
- type: recall_at_1 |
|
value: 24.215 |
|
- type: recall_at_10 |
|
value: 54.079 |
|
- type: recall_at_100 |
|
value: 84.943 |
|
- type: recall_at_1000 |
|
value: 98.098 |
|
- type: recall_at_3 |
|
value: 38.117000000000004 |
|
- type: recall_at_5 |
|
value: 44.775999999999996 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/CMNLI |
|
name: MTEB Cmnli |
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config: default |
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split: validation |
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revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 82.51352976548407 |
|
- type: cos_sim_ap |
|
value: 89.49905141462749 |
|
- type: cos_sim_f1 |
|
value: 83.89334489486234 |
|
- type: cos_sim_precision |
|
value: 78.19761567993534 |
|
- type: cos_sim_recall |
|
value: 90.48398410100538 |
|
- type: dot_accuracy |
|
value: 82.51352976548407 |
|
- type: dot_ap |
|
value: 89.49108293121158 |
|
- type: dot_f1 |
|
value: 83.89334489486234 |
|
- type: dot_precision |
|
value: 78.19761567993534 |
|
- type: dot_recall |
|
value: 90.48398410100538 |
|
- type: euclidean_accuracy |
|
value: 82.51352976548407 |
|
- type: euclidean_ap |
|
value: 89.49904709975154 |
|
- type: euclidean_f1 |
|
value: 83.89334489486234 |
|
- type: euclidean_precision |
|
value: 78.19761567993534 |
|
- type: euclidean_recall |
|
value: 90.48398410100538 |
|
- type: manhattan_accuracy |
|
value: 82.48947684906794 |
|
- type: manhattan_ap |
|
value: 89.49231995962901 |
|
- type: manhattan_f1 |
|
value: 83.84681215233205 |
|
- type: manhattan_precision |
|
value: 77.28258726089528 |
|
- type: manhattan_recall |
|
value: 91.62964694879588 |
|
- type: max_accuracy |
|
value: 82.51352976548407 |
|
- type: max_ap |
|
value: 89.49905141462749 |
|
- type: max_f1 |
|
value: 83.89334489486234 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/CovidRetrieval |
|
name: MTEB CovidRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 78.583 |
|
- type: map_at_10 |
|
value: 85.613 |
|
- type: map_at_100 |
|
value: 85.777 |
|
- type: map_at_1000 |
|
value: 85.77900000000001 |
|
- type: map_at_3 |
|
value: 84.58 |
|
- type: map_at_5 |
|
value: 85.22800000000001 |
|
- type: mrr_at_1 |
|
value: 78.925 |
|
- type: mrr_at_10 |
|
value: 85.667 |
|
- type: mrr_at_100 |
|
value: 85.822 |
|
- type: mrr_at_1000 |
|
value: 85.824 |
|
- type: mrr_at_3 |
|
value: 84.651 |
|
- type: mrr_at_5 |
|
value: 85.299 |
|
- type: ndcg_at_1 |
|
value: 78.925 |
|
- type: ndcg_at_10 |
|
value: 88.405 |
|
- type: ndcg_at_100 |
|
value: 89.02799999999999 |
|
- type: ndcg_at_1000 |
|
value: 89.093 |
|
- type: ndcg_at_3 |
|
value: 86.393 |
|
- type: ndcg_at_5 |
|
value: 87.5 |
|
- type: precision_at_1 |
|
value: 78.925 |
|
- type: precision_at_10 |
|
value: 9.789 |
|
- type: precision_at_100 |
|
value: 1.005 |
|
- type: precision_at_1000 |
|
value: 0.101 |
|
- type: precision_at_3 |
|
value: 30.769000000000002 |
|
- type: precision_at_5 |
|
value: 19.031000000000002 |
|
- type: recall_at_1 |
|
value: 78.583 |
|
- type: recall_at_10 |
|
value: 96.891 |
|
- type: recall_at_100 |
|
value: 99.473 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 91.438 |
|
- type: recall_at_5 |
|
value: 94.152 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/DuRetrieval |
|
name: MTEB DuRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.604 |
|
- type: map_at_10 |
|
value: 77.171 |
|
- type: map_at_100 |
|
value: 80.033 |
|
- type: map_at_1000 |
|
value: 80.099 |
|
- type: map_at_3 |
|
value: 54.364000000000004 |
|
- type: map_at_5 |
|
value: 68.024 |
|
- type: mrr_at_1 |
|
value: 89.85 |
|
- type: mrr_at_10 |
|
value: 93.009 |
|
- type: mrr_at_100 |
|
value: 93.065 |
|
- type: mrr_at_1000 |
|
value: 93.068 |
|
- type: mrr_at_3 |
|
value: 92.72500000000001 |
|
- type: mrr_at_5 |
|
value: 92.915 |
|
- type: ndcg_at_1 |
|
value: 89.85 |
|
- type: ndcg_at_10 |
|
value: 85.038 |
|
- type: ndcg_at_100 |
|
value: 88.247 |
|
- type: ndcg_at_1000 |
|
value: 88.837 |
|
- type: ndcg_at_3 |
|
value: 85.20299999999999 |
|
- type: ndcg_at_5 |
|
value: 83.47 |
|
- type: precision_at_1 |
|
value: 89.85 |
|
- type: precision_at_10 |
|
value: 40.275 |
|
- type: precision_at_100 |
|
value: 4.709 |
|
- type: precision_at_1000 |
|
value: 0.486 |
|
- type: precision_at_3 |
|
value: 76.36699999999999 |
|
- type: precision_at_5 |
|
value: 63.75999999999999 |
|
- type: recall_at_1 |
|
value: 25.604 |
|
- type: recall_at_10 |
|
value: 85.423 |
|
- type: recall_at_100 |
|
value: 95.695 |
|
- type: recall_at_1000 |
|
value: 98.669 |
|
- type: recall_at_3 |
|
value: 56.737 |
|
- type: recall_at_5 |
|
value: 72.646 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/EcomRetrieval |
|
name: MTEB EcomRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 51.800000000000004 |
|
- type: map_at_10 |
|
value: 62.17 |
|
- type: map_at_100 |
|
value: 62.649 |
|
- type: map_at_1000 |
|
value: 62.663000000000004 |
|
- type: map_at_3 |
|
value: 59.699999999999996 |
|
- type: map_at_5 |
|
value: 61.23499999999999 |
|
- type: mrr_at_1 |
|
value: 51.800000000000004 |
|
- type: mrr_at_10 |
|
value: 62.17 |
|
- type: mrr_at_100 |
|
value: 62.649 |
|
- type: mrr_at_1000 |
|
value: 62.663000000000004 |
|
- type: mrr_at_3 |
|
value: 59.699999999999996 |
|
- type: mrr_at_5 |
|
value: 61.23499999999999 |
|
- type: ndcg_at_1 |
|
value: 51.800000000000004 |
|
- type: ndcg_at_10 |
|
value: 67.246 |
|
- type: ndcg_at_100 |
|
value: 69.58 |
|
- type: ndcg_at_1000 |
|
value: 69.925 |
|
- type: ndcg_at_3 |
|
value: 62.197 |
|
- type: ndcg_at_5 |
|
value: 64.981 |
|
- type: precision_at_1 |
|
value: 51.800000000000004 |
|
- type: precision_at_10 |
|
value: 8.32 |
|
- type: precision_at_100 |
|
value: 0.941 |
|
- type: precision_at_1000 |
|
value: 0.097 |
|
- type: precision_at_3 |
|
value: 23.133 |
|
- type: precision_at_5 |
|
value: 15.24 |
|
- type: recall_at_1 |
|
value: 51.800000000000004 |
|
- type: recall_at_10 |
|
value: 83.2 |
|
- type: recall_at_100 |
|
value: 94.1 |
|
- type: recall_at_1000 |
|
value: 96.8 |
|
- type: recall_at_3 |
|
value: 69.39999999999999 |
|
- type: recall_at_5 |
|
value: 76.2 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/IFlyTek-classification |
|
name: MTEB IFlyTek |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 49.60369372835706 |
|
- type: f1 |
|
value: 38.24016248875209 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/JDReview-classification |
|
name: MTEB JDReview |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 86.71669793621012 |
|
- type: ap |
|
value: 55.75807094995178 |
|
- type: f1 |
|
value: 81.59033162805417 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/LCQMC |
|
name: MTEB LCQMC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 69.50947272908907 |
|
- type: cos_sim_spearman |
|
value: 74.40054474949213 |
|
- type: euclidean_pearson |
|
value: 73.53007373987617 |
|
- type: euclidean_spearman |
|
value: 74.40054474732082 |
|
- type: manhattan_pearson |
|
value: 73.51396571849736 |
|
- type: manhattan_spearman |
|
value: 74.38395696630835 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/Mmarco-reranking |
|
name: MTEB MMarcoReranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 31.188333827724108 |
|
- type: mrr |
|
value: 29.84801587301587 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MMarcoRetrieval |
|
name: MTEB MMarcoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 64.685 |
|
- type: map_at_10 |
|
value: 73.803 |
|
- type: map_at_100 |
|
value: 74.153 |
|
- type: map_at_1000 |
|
value: 74.167 |
|
- type: map_at_3 |
|
value: 71.98 |
|
- type: map_at_5 |
|
value: 73.21600000000001 |
|
- type: mrr_at_1 |
|
value: 66.891 |
|
- type: mrr_at_10 |
|
value: 74.48700000000001 |
|
- type: mrr_at_100 |
|
value: 74.788 |
|
- type: mrr_at_1000 |
|
value: 74.801 |
|
- type: mrr_at_3 |
|
value: 72.918 |
|
- type: mrr_at_5 |
|
value: 73.965 |
|
- type: ndcg_at_1 |
|
value: 66.891 |
|
- type: ndcg_at_10 |
|
value: 77.534 |
|
- type: ndcg_at_100 |
|
value: 79.106 |
|
- type: ndcg_at_1000 |
|
value: 79.494 |
|
- type: ndcg_at_3 |
|
value: 74.13499999999999 |
|
- type: ndcg_at_5 |
|
value: 76.20700000000001 |
|
- type: precision_at_1 |
|
value: 66.891 |
|
- type: precision_at_10 |
|
value: 9.375 |
|
- type: precision_at_100 |
|
value: 1.0170000000000001 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_3 |
|
value: 27.932000000000002 |
|
- type: precision_at_5 |
|
value: 17.86 |
|
- type: recall_at_1 |
|
value: 64.685 |
|
- type: recall_at_10 |
|
value: 88.298 |
|
- type: recall_at_100 |
|
value: 95.426 |
|
- type: recall_at_1000 |
|
value: 98.48700000000001 |
|
- type: recall_at_3 |
|
value: 79.44200000000001 |
|
- type: recall_at_5 |
|
value: 84.358 |
|
- 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: 73.30531271015468 |
|
- type: f1 |
|
value: 70.88091430578575 |
|
- 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: 75.7128446536651 |
|
- type: f1 |
|
value: 75.06125593532262 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MedicalRetrieval |
|
name: MTEB MedicalRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 52.7 |
|
- type: map_at_10 |
|
value: 59.532 |
|
- type: map_at_100 |
|
value: 60.085 |
|
- type: map_at_1000 |
|
value: 60.126000000000005 |
|
- type: map_at_3 |
|
value: 57.767 |
|
- type: map_at_5 |
|
value: 58.952000000000005 |
|
- type: mrr_at_1 |
|
value: 52.900000000000006 |
|
- type: mrr_at_10 |
|
value: 59.648999999999994 |
|
- type: mrr_at_100 |
|
value: 60.20100000000001 |
|
- type: mrr_at_1000 |
|
value: 60.242 |
|
- type: mrr_at_3 |
|
value: 57.882999999999996 |
|
- type: mrr_at_5 |
|
value: 59.068 |
|
- type: ndcg_at_1 |
|
value: 52.7 |
|
- type: ndcg_at_10 |
|
value: 62.883 |
|
- type: ndcg_at_100 |
|
value: 65.714 |
|
- type: ndcg_at_1000 |
|
value: 66.932 |
|
- type: ndcg_at_3 |
|
value: 59.34700000000001 |
|
- type: ndcg_at_5 |
|
value: 61.486 |
|
- type: precision_at_1 |
|
value: 52.7 |
|
- type: precision_at_10 |
|
value: 7.340000000000001 |
|
- type: precision_at_100 |
|
value: 0.8699999999999999 |
|
- type: precision_at_1000 |
|
value: 0.097 |
|
- type: precision_at_3 |
|
value: 21.3 |
|
- type: precision_at_5 |
|
value: 13.819999999999999 |
|
- type: recall_at_1 |
|
value: 52.7 |
|
- type: recall_at_10 |
|
value: 73.4 |
|
- type: recall_at_100 |
|
value: 87.0 |
|
- type: recall_at_1000 |
|
value: 96.8 |
|
- type: recall_at_3 |
|
value: 63.9 |
|
- type: recall_at_5 |
|
value: 69.1 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/MultilingualSentiment-classification |
|
name: MTEB MultilingualSentiment |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 76.47666666666667 |
|
- type: f1 |
|
value: 76.4808576632057 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/OCNLI |
|
name: MTEB Ocnli |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 77.58527341635084 |
|
- type: cos_sim_ap |
|
value: 79.32131557636497 |
|
- type: cos_sim_f1 |
|
value: 80.51948051948052 |
|
- type: cos_sim_precision |
|
value: 71.7948717948718 |
|
- type: cos_sim_recall |
|
value: 91.65786694825766 |
|
- type: dot_accuracy |
|
value: 77.58527341635084 |
|
- type: dot_ap |
|
value: 79.32131557636497 |
|
- type: dot_f1 |
|
value: 80.51948051948052 |
|
- type: dot_precision |
|
value: 71.7948717948718 |
|
- type: dot_recall |
|
value: 91.65786694825766 |
|
- type: euclidean_accuracy |
|
value: 77.58527341635084 |
|
- type: euclidean_ap |
|
value: 79.32131557636497 |
|
- type: euclidean_f1 |
|
value: 80.51948051948052 |
|
- type: euclidean_precision |
|
value: 71.7948717948718 |
|
- type: euclidean_recall |
|
value: 91.65786694825766 |
|
- type: manhattan_accuracy |
|
value: 77.15213860314023 |
|
- type: manhattan_ap |
|
value: 79.26178519246496 |
|
- type: manhattan_f1 |
|
value: 80.22028453418999 |
|
- type: manhattan_precision |
|
value: 70.94155844155844 |
|
- type: manhattan_recall |
|
value: 92.29144667370645 |
|
- type: max_accuracy |
|
value: 77.58527341635084 |
|
- type: max_ap |
|
value: 79.32131557636497 |
|
- type: max_f1 |
|
value: 80.51948051948052 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/OnlineShopping-classification |
|
name: MTEB OnlineShopping |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 92.68 |
|
- type: ap |
|
value: 90.78652757815115 |
|
- type: f1 |
|
value: 92.67153098230253 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/PAWSX |
|
name: MTEB PAWSX |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 35.301730226895955 |
|
- type: cos_sim_spearman |
|
value: 38.54612530948101 |
|
- type: euclidean_pearson |
|
value: 39.02831131230217 |
|
- type: euclidean_spearman |
|
value: 38.54612530948101 |
|
- type: manhattan_pearson |
|
value: 39.04765584936325 |
|
- type: manhattan_spearman |
|
value: 38.54455759013173 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/QBQTC |
|
name: MTEB QBQTC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 32.27907454729754 |
|
- type: cos_sim_spearman |
|
value: 33.35945567162729 |
|
- type: euclidean_pearson |
|
value: 31.997628193815725 |
|
- type: euclidean_spearman |
|
value: 33.3592386340529 |
|
- type: manhattan_pearson |
|
value: 31.97117833750544 |
|
- type: manhattan_spearman |
|
value: 33.30857326127779 |
|
- 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: 62.53712784446981 |
|
- type: cos_sim_spearman |
|
value: 62.975074386224286 |
|
- type: euclidean_pearson |
|
value: 61.791207731290854 |
|
- type: euclidean_spearman |
|
value: 62.975073716988064 |
|
- type: manhattan_pearson |
|
value: 62.63850653150875 |
|
- type: manhattan_spearman |
|
value: 63.56640346497343 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/STSB |
|
name: MTEB STSB |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 79.52067424748047 |
|
- type: cos_sim_spearman |
|
value: 79.68425102631514 |
|
- type: euclidean_pearson |
|
value: 79.27553959329275 |
|
- type: euclidean_spearman |
|
value: 79.68450427089856 |
|
- type: manhattan_pearson |
|
value: 79.21584650471131 |
|
- type: manhattan_spearman |
|
value: 79.6419242840243 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/T2Reranking |
|
name: MTEB T2Reranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 65.8563449629786 |
|
- type: mrr |
|
value: 75.82550832339254 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/T2Retrieval |
|
name: MTEB T2Retrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.889999999999997 |
|
- type: map_at_10 |
|
value: 72.878 |
|
- type: map_at_100 |
|
value: 76.737 |
|
- type: map_at_1000 |
|
value: 76.836 |
|
- type: map_at_3 |
|
value: 52.738 |
|
- type: map_at_5 |
|
value: 63.726000000000006 |
|
- type: mrr_at_1 |
|
value: 89.35600000000001 |
|
- type: mrr_at_10 |
|
value: 92.622 |
|
- type: mrr_at_100 |
|
value: 92.692 |
|
- type: mrr_at_1000 |
|
value: 92.694 |
|
- type: mrr_at_3 |
|
value: 92.13799999999999 |
|
- type: mrr_at_5 |
|
value: 92.452 |
|
- type: ndcg_at_1 |
|
value: 89.35600000000001 |
|
- type: ndcg_at_10 |
|
value: 81.932 |
|
- type: ndcg_at_100 |
|
value: 86.351 |
|
- type: ndcg_at_1000 |
|
value: 87.221 |
|
- type: ndcg_at_3 |
|
value: 84.29100000000001 |
|
- type: ndcg_at_5 |
|
value: 82.279 |
|
- type: precision_at_1 |
|
value: 89.35600000000001 |
|
- type: precision_at_10 |
|
value: 39.511 |
|
- type: precision_at_100 |
|
value: 4.901 |
|
- type: precision_at_1000 |
|
value: 0.513 |
|
- type: precision_at_3 |
|
value: 72.62100000000001 |
|
- type: precision_at_5 |
|
value: 59.918000000000006 |
|
- type: recall_at_1 |
|
value: 27.889999999999997 |
|
- type: recall_at_10 |
|
value: 80.636 |
|
- type: recall_at_100 |
|
value: 94.333 |
|
- type: recall_at_1000 |
|
value: 98.39099999999999 |
|
- type: recall_at_3 |
|
value: 54.797 |
|
- type: recall_at_5 |
|
value: 67.824 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/TNews-classification |
|
name: MTEB TNews |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 51.979000000000006 |
|
- type: f1 |
|
value: 50.35658238894168 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringP2P |
|
name: MTEB ThuNewsClusteringP2P |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 68.36477832710159 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringS2S |
|
name: MTEB ThuNewsClusteringS2S |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 62.92080622759053 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/VideoRetrieval |
|
name: MTEB VideoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 59.3 |
|
- type: map_at_10 |
|
value: 69.299 |
|
- type: map_at_100 |
|
value: 69.669 |
|
- type: map_at_1000 |
|
value: 69.682 |
|
- type: map_at_3 |
|
value: 67.583 |
|
- type: map_at_5 |
|
value: 68.57799999999999 |
|
- type: mrr_at_1 |
|
value: 59.3 |
|
- type: mrr_at_10 |
|
value: 69.299 |
|
- type: mrr_at_100 |
|
value: 69.669 |
|
- type: mrr_at_1000 |
|
value: 69.682 |
|
- type: mrr_at_3 |
|
value: 67.583 |
|
- type: mrr_at_5 |
|
value: 68.57799999999999 |
|
- type: ndcg_at_1 |
|
value: 59.3 |
|
- type: ndcg_at_10 |
|
value: 73.699 |
|
- type: ndcg_at_100 |
|
value: 75.626 |
|
- type: ndcg_at_1000 |
|
value: 75.949 |
|
- type: ndcg_at_3 |
|
value: 70.18900000000001 |
|
- type: ndcg_at_5 |
|
value: 71.992 |
|
- type: precision_at_1 |
|
value: 59.3 |
|
- type: precision_at_10 |
|
value: 8.73 |
|
- type: precision_at_100 |
|
value: 0.9650000000000001 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 25.900000000000002 |
|
- type: precision_at_5 |
|
value: 16.42 |
|
- type: recall_at_1 |
|
value: 59.3 |
|
- type: recall_at_10 |
|
value: 87.3 |
|
- type: recall_at_100 |
|
value: 96.5 |
|
- type: recall_at_1000 |
|
value: 99.0 |
|
- type: recall_at_3 |
|
value: 77.7 |
|
- type: recall_at_5 |
|
value: 82.1 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/waimai-classification |
|
name: MTEB Waimai |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 88.36999999999999 |
|
- type: ap |
|
value: 73.29590829222836 |
|
- type: f1 |
|
value: 86.74250506247606 |
|
language: |
|
- en |
|
license: mit |
|
--- |
|
|
|
# gte-large-zh |
|
|
|
General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281) |
|
|
|
The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer different sizes of models for both Chinese and English Languages. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. |
|
|
|
## Model List |
|
|
|
| Models | Language | Max Sequence Length | Dimension | Model Size | |
|
|:-----: | :-----: |:-----: |:-----: |:-----: | |
|
|[GTE-large-zh](https://huggingface.co/thenlper/gte-large-zh) | Chinese | 512 | 1024 | 0.67GB | |
|
|[GTE-base-zh](https://huggingface.co/thenlper/gte-base-zh) | Chinese | 512 | 512 | 0.21GB | |
|
|[GTE-small-zh](https://huggingface.co/thenlper/gte-small-zh) | Chinese | 512 | 512 | 0.10GB | |
|
|[GTE-large](https://huggingface.co/thenlper/gte-large) | English | 512 | 1024 | 0.67GB | |
|
|[GTE-base](https://huggingface.co/thenlper/gte-base) | English | 512 | 512 | 0.21GB | |
|
|[GTE-small](https://huggingface.co/thenlper/gte-small) | English | 512 | 384 | 0.10GB | |
|
|
|
## Metrics |
|
|
|
We compared the performance of the GTE models with other popular text embedding models on the MTEB (CMTEB for Chinese language) benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard). |
|
|
|
- Evaluation results on CMTEB |
|
|
|
| Model | Model Size (GB) | Embedding Dimensions | Sequence Length | Average (35 datasets) | Classification (9 datasets) | Clustering (4 datasets) | Pair Classification (2 datasets) | Reranking (4 datasets) | Retrieval (8 datasets) | STS (8 datasets) | |
|
| ------------------- | -------------- | -------------------- | ---------------- | --------------------- | ------------------------------------ | ------------------------------ | --------------------------------------- | ------------------------------ | ---------------------------- | ------------------------ | |
|
| **gte-large-zh** | 0.65 | 1024 | 512 | **66.72** | 71.34 | 53.07 | 81.14 | 67.42 | 72.49 | 57.82 | |
|
| gte-base-zh | 0.20 | 768 | 512 | 65.92 | 71.26 | 53.86 | 80.44 | 67.00 | 71.71 | 55.96 | |
|
| stella-large-zh-v2 | 0.65 | 1024 | 1024 | 65.13 | 69.05 | 49.16 | 82.68 | 66.41 | 70.14 | 58.66 | |
|
| stella-large-zh | 0.65 | 1024 | 1024 | 64.54 | 67.62 | 48.65 | 78.72 | 65.98 | 71.02 | 58.3 | |
|
| bge-large-zh-v1.5 | 1.3 | 1024 | 512 | 64.53 | 69.13 | 48.99 | 81.6 | 65.84 | 70.46 | 56.25 | |
|
| stella-base-zh-v2 | 0.21 | 768 | 1024 | 64.36 | 68.29 | 49.4 | 79.96 | 66.1 | 70.08 | 56.92 | |
|
| stella-base-zh | 0.21 | 768 | 1024 | 64.16 | 67.77 | 48.7 | 76.09 | 66.95 | 71.07 | 56.54 | |
|
| piccolo-large-zh | 0.65 | 1024 | 512 | 64.11 | 67.03 | 47.04 | 78.38 | 65.98 | 70.93 | 58.02 | |
|
| piccolo-base-zh | 0.2 | 768 | 512 | 63.66 | 66.98 | 47.12 | 76.61 | 66.68 | 71.2 | 55.9 | |
|
| gte-small-zh | 0.1 | 512 | 512 | 60.04 | 64.35 | 48.95 | 69.99 | 66.21 | 65.50 | 49.72 | |
|
| bge-small-zh-v1.5 | 0.1 | 512 | 512 | 57.82 | 63.96 | 44.18 | 70.4 | 60.92 | 61.77 | 49.1 | |
|
| m3e-base | 0.41 | 768 | 512 | 57.79 | 67.52 | 47.68 | 63.99 | 59.54| 56.91 | 50.47 | |
|
|text-embedding-ada-002(openai) | - | 1536| 8192 | 53.02 | 64.31 | 45.68 | 69.56 | 54.28 | 52.0 | 43.35 | |
|
|
|
|
|
## Usage |
|
|
|
Code example |
|
|
|
```python |
|
import torch.nn.functional as F |
|
from torch import Tensor |
|
from transformers import AutoTokenizer, AutoModel |
|
|
|
input_texts = [ |
|
"中国的首都是哪里", |
|
"你喜欢去哪里旅游", |
|
"北京", |
|
"今天中午吃什么" |
|
] |
|
|
|
tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-large-zh") |
|
model = AutoModel.from_pretrained("thenlper/gte-large-zh") |
|
|
|
# Tokenize the input texts |
|
batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') |
|
|
|
outputs = model(**batch_dict) |
|
embeddings = outputs.last_hidden_state[:, 0] |
|
|
|
# (Optionally) normalize embeddings |
|
embeddings = F.normalize(embeddings, p=2, dim=1) |
|
scores = (embeddings[:1] @ embeddings[1:].T) * 100 |
|
print(scores.tolist()) |
|
``` |
|
|
|
Use with sentence-transformers: |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
from sentence_transformers.util import cos_sim |
|
|
|
sentences = ['That is a happy person', 'That is a very happy person'] |
|
|
|
model = SentenceTransformer('thenlper/gte-large-zh') |
|
embeddings = model.encode(sentences) |
|
print(cos_sim(embeddings[0], embeddings[1])) |
|
``` |
|
|
|
### Limitation |
|
|
|
This model exclusively caters to Chinese texts, and any lengthy texts will be truncated to a maximum of 512 tokens. |
|
|
|
### Citation |
|
|
|
If you find our paper or models helpful, please consider citing them as follows: |
|
|
|
``` |
|
@article{li2023towards, |
|
title={Towards general text embeddings with multi-stage contrastive learning}, |
|
author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan}, |
|
journal={arXiv preprint arXiv:2308.03281}, |
|
year={2023} |
|
} |
|
``` |
|
|
