|
--- |
|
tags: |
|
- mteb |
|
model-index: |
|
- name: piccolo-embedding_mixed2 |
|
results: |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/AFQMC |
|
name: MTEB AFQMC |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 56.918538280469875 |
|
- type: cos_sim_spearman |
|
value: 60.95597435855258 |
|
- type: euclidean_pearson |
|
value: 59.73821610051437 |
|
- type: euclidean_spearman |
|
value: 60.956778530262454 |
|
- type: manhattan_pearson |
|
value: 59.739675774225475 |
|
- type: manhattan_spearman |
|
value: 60.95243600302903 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/ATEC |
|
name: MTEB ATEC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 56.79417977023184 |
|
- type: cos_sim_spearman |
|
value: 58.80984726256814 |
|
- type: euclidean_pearson |
|
value: 63.42225182281334 |
|
- type: euclidean_spearman |
|
value: 58.80957930593542 |
|
- type: manhattan_pearson |
|
value: 63.41128425333986 |
|
- type: manhattan_spearman |
|
value: 58.80784321716389 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_reviews_multi |
|
name: MTEB AmazonReviewsClassification (zh) |
|
config: zh |
|
split: test |
|
revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
|
metrics: |
|
- type: accuracy |
|
value: 50.074000000000005 |
|
- type: f1 |
|
value: 47.11468271375511 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/BQ |
|
name: MTEB BQ |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 73.3412976021806 |
|
- type: cos_sim_spearman |
|
value: 75.0799965464816 |
|
- type: euclidean_pearson |
|
value: 73.7874729086686 |
|
- type: euclidean_spearman |
|
value: 75.07910973646369 |
|
- type: manhattan_pearson |
|
value: 73.7716616949607 |
|
- type: manhattan_spearman |
|
value: 75.06089549008017 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/CLSClusteringP2P |
|
name: MTEB CLSClusteringP2P |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 60.4206935177474 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/CLSClusteringS2S |
|
name: MTEB CLSClusteringS2S |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 49.53654617222264 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv1-reranking |
|
name: MTEB CMedQAv1 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 90.96386786978509 |
|
- type: mrr |
|
value: 92.8897619047619 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv2-reranking |
|
name: MTEB CMedQAv2 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 90.41014127763198 |
|
- type: mrr |
|
value: 92.45039682539682 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/CmedqaRetrieval |
|
name: MTEB CmedqaRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 26.901999999999997 |
|
- type: map_at_10 |
|
value: 40.321 |
|
- type: map_at_100 |
|
value: 42.176 |
|
- type: map_at_1000 |
|
value: 42.282 |
|
- type: map_at_3 |
|
value: 35.882 |
|
- type: map_at_5 |
|
value: 38.433 |
|
- type: mrr_at_1 |
|
value: 40.910000000000004 |
|
- type: mrr_at_10 |
|
value: 49.309999999999995 |
|
- type: mrr_at_100 |
|
value: 50.239 |
|
- type: mrr_at_1000 |
|
value: 50.278 |
|
- type: mrr_at_3 |
|
value: 46.803 |
|
- type: mrr_at_5 |
|
value: 48.137 |
|
- type: ndcg_at_1 |
|
value: 40.785 |
|
- type: ndcg_at_10 |
|
value: 47.14 |
|
- type: ndcg_at_100 |
|
value: 54.156000000000006 |
|
- type: ndcg_at_1000 |
|
value: 55.913999999999994 |
|
- type: ndcg_at_3 |
|
value: 41.669 |
|
- type: ndcg_at_5 |
|
value: 43.99 |
|
- type: precision_at_1 |
|
value: 40.785 |
|
- type: precision_at_10 |
|
value: 10.493 |
|
- type: precision_at_100 |
|
value: 1.616 |
|
- type: precision_at_1000 |
|
value: 0.184 |
|
- type: precision_at_3 |
|
value: 23.723 |
|
- type: precision_at_5 |
|
value: 17.249 |
|
- type: recall_at_1 |
|
value: 26.901999999999997 |
|
- type: recall_at_10 |
|
value: 58.25 |
|
- type: recall_at_100 |
|
value: 87.10900000000001 |
|
- type: recall_at_1000 |
|
value: 98.804 |
|
- type: recall_at_3 |
|
value: 41.804 |
|
- type: recall_at_5 |
|
value: 48.884 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/CMNLI |
|
name: MTEB Cmnli |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 86.42212868310283 |
|
- type: cos_sim_ap |
|
value: 92.83788702972741 |
|
- type: cos_sim_f1 |
|
value: 87.08912233141307 |
|
- type: cos_sim_precision |
|
value: 84.24388111888112 |
|
- type: cos_sim_recall |
|
value: 90.13327098433481 |
|
- type: dot_accuracy |
|
value: 86.44618159951895 |
|
- type: dot_ap |
|
value: 92.81146275060858 |
|
- type: dot_f1 |
|
value: 87.06857911250562 |
|
- type: dot_precision |
|
value: 83.60232408005164 |
|
- type: dot_recall |
|
value: 90.83469721767594 |
|
- type: euclidean_accuracy |
|
value: 86.42212868310283 |
|
- type: euclidean_ap |
|
value: 92.83805700492603 |
|
- type: euclidean_f1 |
|
value: 87.08803611738148 |
|
- type: euclidean_precision |
|
value: 84.18066768492254 |
|
- type: euclidean_recall |
|
value: 90.20341360766892 |
|
- type: manhattan_accuracy |
|
value: 86.28983764281419 |
|
- type: manhattan_ap |
|
value: 92.82818970981005 |
|
- type: manhattan_f1 |
|
value: 87.12625521832335 |
|
- type: manhattan_precision |
|
value: 84.19101613606628 |
|
- type: manhattan_recall |
|
value: 90.27355623100304 |
|
- type: max_accuracy |
|
value: 86.44618159951895 |
|
- type: max_ap |
|
value: 92.83805700492603 |
|
- type: max_f1 |
|
value: 87.12625521832335 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/CovidRetrieval |
|
name: MTEB CovidRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 79.215 |
|
- type: map_at_10 |
|
value: 86.516 |
|
- type: map_at_100 |
|
value: 86.6 |
|
- type: map_at_1000 |
|
value: 86.602 |
|
- type: map_at_3 |
|
value: 85.52 |
|
- type: map_at_5 |
|
value: 86.136 |
|
- type: mrr_at_1 |
|
value: 79.663 |
|
- type: mrr_at_10 |
|
value: 86.541 |
|
- type: mrr_at_100 |
|
value: 86.625 |
|
- type: mrr_at_1000 |
|
value: 86.627 |
|
- type: mrr_at_3 |
|
value: 85.564 |
|
- type: mrr_at_5 |
|
value: 86.15899999999999 |
|
- type: ndcg_at_1 |
|
value: 79.663 |
|
- type: ndcg_at_10 |
|
value: 89.399 |
|
- type: ndcg_at_100 |
|
value: 89.727 |
|
- type: ndcg_at_1000 |
|
value: 89.781 |
|
- type: ndcg_at_3 |
|
value: 87.402 |
|
- type: ndcg_at_5 |
|
value: 88.479 |
|
- type: precision_at_1 |
|
value: 79.663 |
|
- type: precision_at_10 |
|
value: 9.926 |
|
- type: precision_at_100 |
|
value: 1.006 |
|
- type: precision_at_1000 |
|
value: 0.101 |
|
- type: precision_at_3 |
|
value: 31.226 |
|
- type: precision_at_5 |
|
value: 19.283 |
|
- type: recall_at_1 |
|
value: 79.215 |
|
- type: recall_at_10 |
|
value: 98.209 |
|
- type: recall_at_100 |
|
value: 99.579 |
|
- type: recall_at_1000 |
|
value: 100 |
|
- type: recall_at_3 |
|
value: 92.703 |
|
- type: recall_at_5 |
|
value: 95.364 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/DuRetrieval |
|
name: MTEB DuRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.391 |
|
- type: map_at_10 |
|
value: 82.82000000000001 |
|
- type: map_at_100 |
|
value: 85.5 |
|
- type: map_at_1000 |
|
value: 85.533 |
|
- type: map_at_3 |
|
value: 57.802 |
|
- type: map_at_5 |
|
value: 72.82600000000001 |
|
- type: mrr_at_1 |
|
value: 92.80000000000001 |
|
- type: mrr_at_10 |
|
value: 94.83500000000001 |
|
- type: mrr_at_100 |
|
value: 94.883 |
|
- type: mrr_at_1000 |
|
value: 94.884 |
|
- type: mrr_at_3 |
|
value: 94.542 |
|
- type: mrr_at_5 |
|
value: 94.729 |
|
- type: ndcg_at_1 |
|
value: 92.7 |
|
- type: ndcg_at_10 |
|
value: 89.435 |
|
- type: ndcg_at_100 |
|
value: 91.78699999999999 |
|
- type: ndcg_at_1000 |
|
value: 92.083 |
|
- type: ndcg_at_3 |
|
value: 88.595 |
|
- type: ndcg_at_5 |
|
value: 87.53 |
|
- type: precision_at_1 |
|
value: 92.7 |
|
- type: precision_at_10 |
|
value: 42.4 |
|
- type: precision_at_100 |
|
value: 4.823 |
|
- type: precision_at_1000 |
|
value: 0.48900000000000005 |
|
- type: precision_at_3 |
|
value: 79.133 |
|
- type: precision_at_5 |
|
value: 66.8 |
|
- type: recall_at_1 |
|
value: 27.391 |
|
- type: recall_at_10 |
|
value: 90.069 |
|
- type: recall_at_100 |
|
value: 97.875 |
|
- type: recall_at_1000 |
|
value: 99.436 |
|
- type: recall_at_3 |
|
value: 59.367999999999995 |
|
- type: recall_at_5 |
|
value: 76.537 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/EcomRetrieval |
|
name: MTEB EcomRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 54.800000000000004 |
|
- type: map_at_10 |
|
value: 65.289 |
|
- type: map_at_100 |
|
value: 65.845 |
|
- type: map_at_1000 |
|
value: 65.853 |
|
- type: map_at_3 |
|
value: 62.766999999999996 |
|
- type: map_at_5 |
|
value: 64.252 |
|
- type: mrr_at_1 |
|
value: 54.800000000000004 |
|
- type: mrr_at_10 |
|
value: 65.255 |
|
- type: mrr_at_100 |
|
value: 65.81700000000001 |
|
- type: mrr_at_1000 |
|
value: 65.824 |
|
- type: mrr_at_3 |
|
value: 62.683 |
|
- type: mrr_at_5 |
|
value: 64.248 |
|
- type: ndcg_at_1 |
|
value: 54.800000000000004 |
|
- type: ndcg_at_10 |
|
value: 70.498 |
|
- type: ndcg_at_100 |
|
value: 72.82300000000001 |
|
- type: ndcg_at_1000 |
|
value: 73.053 |
|
- type: ndcg_at_3 |
|
value: 65.321 |
|
- type: ndcg_at_5 |
|
value: 67.998 |
|
- type: precision_at_1 |
|
value: 54.800000000000004 |
|
- type: precision_at_10 |
|
value: 8.690000000000001 |
|
- type: precision_at_100 |
|
value: 0.97 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 24.233 |
|
- type: precision_at_5 |
|
value: 15.840000000000002 |
|
- type: recall_at_1 |
|
value: 54.800000000000004 |
|
- type: recall_at_10 |
|
value: 86.9 |
|
- type: recall_at_100 |
|
value: 97 |
|
- type: recall_at_1000 |
|
value: 98.9 |
|
- type: recall_at_3 |
|
value: 72.7 |
|
- type: recall_at_5 |
|
value: 79.2 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/IFlyTek-classification |
|
name: MTEB IFlyTek |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 51.758368603308966 |
|
- type: f1 |
|
value: 40.249503783871596 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/JDReview-classification |
|
name: MTEB JDReview |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 89.08067542213884 |
|
- type: ap |
|
value: 60.31281895139249 |
|
- type: f1 |
|
value: 84.20883153932607 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/LCQMC |
|
name: MTEB LCQMC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 74.04193577551248 |
|
- type: cos_sim_spearman |
|
value: 79.81875884845549 |
|
- type: euclidean_pearson |
|
value: 80.02581187503708 |
|
- type: euclidean_spearman |
|
value: 79.81877215060574 |
|
- type: manhattan_pearson |
|
value: 80.01767830530258 |
|
- type: manhattan_spearman |
|
value: 79.81178852172727 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/Mmarco-reranking |
|
name: MTEB MMarcoReranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 39.90939429947956 |
|
- type: mrr |
|
value: 39.71071428571429 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MMarcoRetrieval |
|
name: MTEB MMarcoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 68.485 |
|
- type: map_at_10 |
|
value: 78.27199999999999 |
|
- type: map_at_100 |
|
value: 78.54100000000001 |
|
- type: map_at_1000 |
|
value: 78.546 |
|
- type: map_at_3 |
|
value: 76.339 |
|
- type: map_at_5 |
|
value: 77.61099999999999 |
|
- type: mrr_at_1 |
|
value: 70.80199999999999 |
|
- type: mrr_at_10 |
|
value: 78.901 |
|
- type: mrr_at_100 |
|
value: 79.12400000000001 |
|
- type: mrr_at_1000 |
|
value: 79.128 |
|
- type: mrr_at_3 |
|
value: 77.237 |
|
- type: mrr_at_5 |
|
value: 78.323 |
|
- type: ndcg_at_1 |
|
value: 70.759 |
|
- type: ndcg_at_10 |
|
value: 82.191 |
|
- type: ndcg_at_100 |
|
value: 83.295 |
|
- type: ndcg_at_1000 |
|
value: 83.434 |
|
- type: ndcg_at_3 |
|
value: 78.57600000000001 |
|
- type: ndcg_at_5 |
|
value: 80.715 |
|
- type: precision_at_1 |
|
value: 70.759 |
|
- type: precision_at_10 |
|
value: 9.951 |
|
- type: precision_at_100 |
|
value: 1.049 |
|
- type: precision_at_1000 |
|
value: 0.106 |
|
- type: precision_at_3 |
|
value: 29.660999999999998 |
|
- type: precision_at_5 |
|
value: 18.94 |
|
- type: recall_at_1 |
|
value: 68.485 |
|
- type: recall_at_10 |
|
value: 93.65 |
|
- type: recall_at_100 |
|
value: 98.434 |
|
- type: recall_at_1000 |
|
value: 99.522 |
|
- type: recall_at_3 |
|
value: 84.20100000000001 |
|
- type: recall_at_5 |
|
value: 89.261 |
|
- 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: 77.45460659045055 |
|
- type: f1 |
|
value: 73.84987702455533 |
|
- 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: 85.29926025554808 |
|
- type: f1 |
|
value: 84.40636286569843 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MedicalRetrieval |
|
name: MTEB MedicalRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 57.599999999999994 |
|
- type: map_at_10 |
|
value: 64.691 |
|
- type: map_at_100 |
|
value: 65.237 |
|
- type: map_at_1000 |
|
value: 65.27 |
|
- type: map_at_3 |
|
value: 62.733000000000004 |
|
- type: map_at_5 |
|
value: 63.968 |
|
- type: mrr_at_1 |
|
value: 58.099999999999994 |
|
- type: mrr_at_10 |
|
value: 64.952 |
|
- type: mrr_at_100 |
|
value: 65.513 |
|
- type: mrr_at_1000 |
|
value: 65.548 |
|
- type: mrr_at_3 |
|
value: 63 |
|
- type: mrr_at_5 |
|
value: 64.235 |
|
- type: ndcg_at_1 |
|
value: 57.599999999999994 |
|
- type: ndcg_at_10 |
|
value: 68.19 |
|
- type: ndcg_at_100 |
|
value: 70.98400000000001 |
|
- type: ndcg_at_1000 |
|
value: 71.811 |
|
- type: ndcg_at_3 |
|
value: 64.276 |
|
- type: ndcg_at_5 |
|
value: 66.47999999999999 |
|
- type: precision_at_1 |
|
value: 57.599999999999994 |
|
- type: precision_at_10 |
|
value: 7.920000000000001 |
|
- type: precision_at_100 |
|
value: 0.9259999999999999 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 22.900000000000002 |
|
- type: precision_at_5 |
|
value: 14.799999999999999 |
|
- type: recall_at_1 |
|
value: 57.599999999999994 |
|
- type: recall_at_10 |
|
value: 79.2 |
|
- type: recall_at_100 |
|
value: 92.60000000000001 |
|
- type: recall_at_1000 |
|
value: 99 |
|
- type: recall_at_3 |
|
value: 68.7 |
|
- type: recall_at_5 |
|
value: 74 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/MultilingualSentiment-classification |
|
name: MTEB MultilingualSentiment |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 79.45 |
|
- type: f1 |
|
value: 79.25610578280538 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/OCNLI |
|
name: MTEB Ocnli |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 85.43584190579317 |
|
- type: cos_sim_ap |
|
value: 90.89979725191012 |
|
- type: cos_sim_f1 |
|
value: 86.48383937316358 |
|
- type: cos_sim_precision |
|
value: 80.6392694063927 |
|
- type: cos_sim_recall |
|
value: 93.24181626187962 |
|
- type: dot_accuracy |
|
value: 85.38170005414185 |
|
- type: dot_ap |
|
value: 90.87532457866699 |
|
- type: dot_f1 |
|
value: 86.48383937316358 |
|
- type: dot_precision |
|
value: 80.6392694063927 |
|
- type: dot_recall |
|
value: 93.24181626187962 |
|
- type: euclidean_accuracy |
|
value: 85.43584190579317 |
|
- type: euclidean_ap |
|
value: 90.90126652086121 |
|
- type: euclidean_f1 |
|
value: 86.48383937316358 |
|
- type: euclidean_precision |
|
value: 80.6392694063927 |
|
- type: euclidean_recall |
|
value: 93.24181626187962 |
|
- type: manhattan_accuracy |
|
value: 85.43584190579317 |
|
- type: manhattan_ap |
|
value: 90.87896997853466 |
|
- type: manhattan_f1 |
|
value: 86.47581441263573 |
|
- type: manhattan_precision |
|
value: 81.18628359592215 |
|
- type: manhattan_recall |
|
value: 92.5026399155227 |
|
- type: max_accuracy |
|
value: 85.43584190579317 |
|
- type: max_ap |
|
value: 90.90126652086121 |
|
- type: max_f1 |
|
value: 86.48383937316358 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/OnlineShopping-classification |
|
name: MTEB OnlineShopping |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 94.9 |
|
- type: ap |
|
value: 93.1468223150745 |
|
- type: f1 |
|
value: 94.88918689508299 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/PAWSX |
|
name: MTEB PAWSX |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 40.4831743182905 |
|
- type: cos_sim_spearman |
|
value: 47.4163675550491 |
|
- type: euclidean_pearson |
|
value: 46.456319899274924 |
|
- type: euclidean_spearman |
|
value: 47.41567079730661 |
|
- type: manhattan_pearson |
|
value: 46.48561639930895 |
|
- type: manhattan_spearman |
|
value: 47.447721653461215 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/QBQTC |
|
name: MTEB QBQTC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 42.96423587663398 |
|
- type: cos_sim_spearman |
|
value: 45.13742225167858 |
|
- type: euclidean_pearson |
|
value: 39.275452114075435 |
|
- type: euclidean_spearman |
|
value: 45.137763540967406 |
|
- type: manhattan_pearson |
|
value: 39.24797626417764 |
|
- type: manhattan_spearman |
|
value: 45.13817773119268 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (zh) |
|
config: zh |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 66.26687809086202 |
|
- type: cos_sim_spearman |
|
value: 66.9569145816897 |
|
- type: euclidean_pearson |
|
value: 65.72390780809788 |
|
- type: euclidean_spearman |
|
value: 66.95406938095539 |
|
- type: manhattan_pearson |
|
value: 65.6220809000381 |
|
- type: manhattan_spearman |
|
value: 66.88531036320953 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/STSB |
|
name: MTEB STSB |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.30831700726195 |
|
- type: cos_sim_spearman |
|
value: 82.05184068558792 |
|
- type: euclidean_pearson |
|
value: 81.73198597791563 |
|
- type: euclidean_spearman |
|
value: 82.05326103582206 |
|
- type: manhattan_pearson |
|
value: 81.70886400949136 |
|
- type: manhattan_spearman |
|
value: 82.03473274756037 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/T2Reranking |
|
name: MTEB T2Reranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 69.03398835347575 |
|
- type: mrr |
|
value: 79.9212528613341 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/T2Retrieval |
|
name: MTEB T2Retrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.515 |
|
- type: map_at_10 |
|
value: 77.40599999999999 |
|
- type: map_at_100 |
|
value: 81.087 |
|
- type: map_at_1000 |
|
value: 81.148 |
|
- type: map_at_3 |
|
value: 54.327000000000005 |
|
- type: map_at_5 |
|
value: 66.813 |
|
- type: mrr_at_1 |
|
value: 89.764 |
|
- type: mrr_at_10 |
|
value: 92.58 |
|
- type: mrr_at_100 |
|
value: 92.663 |
|
- type: mrr_at_1000 |
|
value: 92.666 |
|
- type: mrr_at_3 |
|
value: 92.15299999999999 |
|
- type: mrr_at_5 |
|
value: 92.431 |
|
- type: ndcg_at_1 |
|
value: 89.777 |
|
- type: ndcg_at_10 |
|
value: 85.013 |
|
- type: ndcg_at_100 |
|
value: 88.62100000000001 |
|
- type: ndcg_at_1000 |
|
value: 89.184 |
|
- type: ndcg_at_3 |
|
value: 86.19200000000001 |
|
- type: ndcg_at_5 |
|
value: 84.909 |
|
- type: precision_at_1 |
|
value: 89.777 |
|
- type: precision_at_10 |
|
value: 42.218 |
|
- type: precision_at_100 |
|
value: 5.032 |
|
- type: precision_at_1000 |
|
value: 0.517 |
|
- type: precision_at_3 |
|
value: 75.335 |
|
- type: precision_at_5 |
|
value: 63.199000000000005 |
|
- type: recall_at_1 |
|
value: 27.515 |
|
- type: recall_at_10 |
|
value: 84.258 |
|
- type: recall_at_100 |
|
value: 95.908 |
|
- type: recall_at_1000 |
|
value: 98.709 |
|
- type: recall_at_3 |
|
value: 56.189 |
|
- type: recall_at_5 |
|
value: 70.50800000000001 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/TNews-classification |
|
name: MTEB TNews |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 54.635999999999996 |
|
- type: f1 |
|
value: 52.63073912739558 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringP2P |
|
name: MTEB ThuNewsClusteringP2P |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 78.75676284855221 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringS2S |
|
name: MTEB ThuNewsClusteringS2S |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 71.95583733802839 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/VideoRetrieval |
|
name: MTEB VideoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 64.9 |
|
- type: map_at_10 |
|
value: 75.622 |
|
- type: map_at_100 |
|
value: 75.93900000000001 |
|
- type: map_at_1000 |
|
value: 75.93900000000001 |
|
- type: map_at_3 |
|
value: 73.933 |
|
- type: map_at_5 |
|
value: 74.973 |
|
- type: mrr_at_1 |
|
value: 65 |
|
- type: mrr_at_10 |
|
value: 75.676 |
|
- type: mrr_at_100 |
|
value: 75.994 |
|
- type: mrr_at_1000 |
|
value: 75.994 |
|
- type: mrr_at_3 |
|
value: 74.05000000000001 |
|
- type: mrr_at_5 |
|
value: 75.03999999999999 |
|
- type: ndcg_at_1 |
|
value: 64.9 |
|
- type: ndcg_at_10 |
|
value: 80.08999999999999 |
|
- type: ndcg_at_100 |
|
value: 81.44500000000001 |
|
- type: ndcg_at_1000 |
|
value: 81.45599999999999 |
|
- type: ndcg_at_3 |
|
value: 76.688 |
|
- type: ndcg_at_5 |
|
value: 78.53 |
|
- type: precision_at_1 |
|
value: 64.9 |
|
- type: precision_at_10 |
|
value: 9.379999999999999 |
|
- type: precision_at_100 |
|
value: 0.997 |
|
- type: precision_at_1000 |
|
value: 0.1 |
|
- type: precision_at_3 |
|
value: 28.199999999999996 |
|
- type: precision_at_5 |
|
value: 17.8 |
|
- type: recall_at_1 |
|
value: 64.9 |
|
- type: recall_at_10 |
|
value: 93.8 |
|
- type: recall_at_100 |
|
value: 99.7 |
|
- type: recall_at_1000 |
|
value: 99.8 |
|
- type: recall_at_3 |
|
value: 84.6 |
|
- type: recall_at_5 |
|
value: 89 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/waimai-classification |
|
name: MTEB Waimai |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 89.34 |
|
- type: ap |
|
value: 75.20638024616892 |
|
- type: f1 |
|
value: 87.88648489072128 |
|
library_name: sentence-transformers |
|
--- |
|
# xiaobu-embedding-v2 |
|
|
|
基于piccolo-embedding[1]，主要改动如下： |
|
- 合成数据替换为xiaobu-embedding-v1[2]所积累数据 |
|
- 在circle_loss[3]视角下统一处理CMTEB的6类问题，最大优势是可充分利用原始数据集中的多个正例，其次是可一定程度上避免考虑多个不同loss之间的权重问题 |
|
|
|
## Usage (Sentence-Transformers) |
|
|
|
``` |
|
pip install -U sentence-transformers |
|
``` |
|
相似度计算： |
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
sentences_1 = ["样例数据-1", "样例数据-2"] |
|
sentences_2 = ["样例数据-3", "样例数据-4"] |
|
model = SentenceTransformer('lier007/xiaobu-embedding-v2') |
|
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True) |
|
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True) |
|
similarity = embeddings_1 @ embeddings_2.T |
|
print(similarity) |
|
``` |
|
|
|
|
|
## Reference |
|
1. https://github.com/hjq133/piccolo-embedding |
|
2. https://huggingface.co./lier007/xiaobu-embedding |
|
3. https://arxiv.org/abs/2002.10857 |
