|
--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
|
- mteb |
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model-index: |
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- name: stella-base-zh-v3-1792d |
|
results: |
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- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/AFQMC |
|
name: MTEB AFQMC |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 54.5145388936202 |
|
- type: cos_sim_spearman |
|
value: 59.223125058197134 |
|
- type: euclidean_pearson |
|
value: 57.819377838734695 |
|
- type: euclidean_spearman |
|
value: 59.22310494948463 |
|
- type: manhattan_pearson |
|
value: 57.44029759610327 |
|
- type: manhattan_spearman |
|
value: 58.88336250854381 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/ATEC |
|
name: MTEB ATEC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 54.544243591344866 |
|
- type: cos_sim_spearman |
|
value: 58.43052988038229 |
|
- type: euclidean_pearson |
|
value: 62.1608405146189 |
|
- type: euclidean_spearman |
|
value: 58.43052762862396 |
|
- type: manhattan_pearson |
|
value: 61.88443779892169 |
|
- type: manhattan_spearman |
|
value: 58.26899143609596 |
|
- 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: 46.343999999999994 |
|
- type: f1 |
|
value: 44.46931958420461 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/BQ |
|
name: MTEB BQ |
|
config: default |
|
split: test |
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revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 68.52081000538426 |
|
- type: cos_sim_spearman |
|
value: 70.44089935351529 |
|
- type: euclidean_pearson |
|
value: 69.24671010626395 |
|
- type: euclidean_spearman |
|
value: 70.44090281761693 |
|
- type: manhattan_pearson |
|
value: 69.00737718109357 |
|
- type: manhattan_spearman |
|
value: 70.24344902456502 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/CLSClusteringP2P |
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name: MTEB CLSClusteringP2P |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 42.86119436460332 |
|
- 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 |
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metrics: |
|
- type: v_measure |
|
value: 39.97521728440642 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv1-reranking |
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name: MTEB CMedQAv1 |
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config: default |
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split: test |
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revision: None |
|
metrics: |
|
- type: map |
|
value: 88.34151862240452 |
|
- type: mrr |
|
value: 90.40380952380953 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv2-reranking |
|
name: MTEB CMedQAv2 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 89.06288758814637 |
|
- type: mrr |
|
value: 90.91285714285713 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/CmedqaRetrieval |
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name: MTEB CmedqaRetrieval |
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config: default |
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split: dev |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.651000000000003 |
|
- type: map_at_10 |
|
value: 38.576 |
|
- type: map_at_100 |
|
value: 40.534 |
|
- type: map_at_1000 |
|
value: 40.64 |
|
- type: map_at_3 |
|
value: 34.016000000000005 |
|
- type: map_at_5 |
|
value: 36.675999999999995 |
|
- type: mrr_at_1 |
|
value: 39.06 |
|
- type: mrr_at_10 |
|
value: 47.278 |
|
- type: mrr_at_100 |
|
value: 48.272999999999996 |
|
- type: mrr_at_1000 |
|
value: 48.314 |
|
- type: mrr_at_3 |
|
value: 44.461 |
|
- type: mrr_at_5 |
|
value: 46.107 |
|
- type: ndcg_at_1 |
|
value: 39.06 |
|
- type: ndcg_at_10 |
|
value: 45.384 |
|
- type: ndcg_at_100 |
|
value: 52.796 |
|
- type: ndcg_at_1000 |
|
value: 54.55 |
|
- type: ndcg_at_3 |
|
value: 39.497 |
|
- type: ndcg_at_5 |
|
value: 42.189 |
|
- type: precision_at_1 |
|
value: 39.06 |
|
- type: precision_at_10 |
|
value: 10.17 |
|
- type: precision_at_100 |
|
value: 1.6179999999999999 |
|
- type: precision_at_1000 |
|
value: 0.184 |
|
- type: precision_at_3 |
|
value: 22.247 |
|
- type: precision_at_5 |
|
value: 16.529 |
|
- type: recall_at_1 |
|
value: 25.651000000000003 |
|
- type: recall_at_10 |
|
value: 56.82899999999999 |
|
- type: recall_at_100 |
|
value: 87.134 |
|
- type: recall_at_1000 |
|
value: 98.709 |
|
- type: recall_at_3 |
|
value: 39.461 |
|
- type: recall_at_5 |
|
value: 47.329 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/CMNLI |
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name: MTEB Cmnli |
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config: default |
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split: validation |
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revision: None |
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metrics: |
|
- type: cos_sim_accuracy |
|
value: 83.1870114251353 |
|
- type: cos_sim_ap |
|
value: 90.42393852164342 |
|
- type: cos_sim_f1 |
|
value: 84.10685985963323 |
|
- type: cos_sim_precision |
|
value: 81.5229317533465 |
|
- type: cos_sim_recall |
|
value: 86.85994856207621 |
|
- type: dot_accuracy |
|
value: 83.1870114251353 |
|
- type: dot_ap |
|
value: 90.41339758845682 |
|
- type: dot_f1 |
|
value: 84.10685985963323 |
|
- type: dot_precision |
|
value: 81.5229317533465 |
|
- type: dot_recall |
|
value: 86.85994856207621 |
|
- type: euclidean_accuracy |
|
value: 83.1870114251353 |
|
- type: euclidean_ap |
|
value: 90.42393581056393 |
|
- type: euclidean_f1 |
|
value: 84.10685985963323 |
|
- type: euclidean_precision |
|
value: 81.5229317533465 |
|
- type: euclidean_recall |
|
value: 86.85994856207621 |
|
- type: manhattan_accuracy |
|
value: 82.77811184606134 |
|
- type: manhattan_ap |
|
value: 90.18115714681704 |
|
- type: manhattan_f1 |
|
value: 83.75083130126357 |
|
- type: manhattan_precision |
|
value: 79.62065331928345 |
|
- type: manhattan_recall |
|
value: 88.33294365209258 |
|
- type: max_accuracy |
|
value: 83.1870114251353 |
|
- type: max_ap |
|
value: 90.42393852164342 |
|
- type: max_f1 |
|
value: 84.10685985963323 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/CovidRetrieval |
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name: MTEB CovidRetrieval |
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config: default |
|
split: dev |
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revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 68.388 |
|
- type: map_at_10 |
|
value: 76.819 |
|
- type: map_at_100 |
|
value: 77.153 |
|
- type: map_at_1000 |
|
value: 77.16 |
|
- type: map_at_3 |
|
value: 74.98700000000001 |
|
- type: map_at_5 |
|
value: 76.101 |
|
- type: mrr_at_1 |
|
value: 68.599 |
|
- type: mrr_at_10 |
|
value: 76.844 |
|
- type: mrr_at_100 |
|
value: 77.168 |
|
- type: mrr_at_1000 |
|
value: 77.17500000000001 |
|
- type: mrr_at_3 |
|
value: 75.044 |
|
- type: mrr_at_5 |
|
value: 76.208 |
|
- type: ndcg_at_1 |
|
value: 68.599 |
|
- type: ndcg_at_10 |
|
value: 80.613 |
|
- type: ndcg_at_100 |
|
value: 82.017 |
|
- type: ndcg_at_1000 |
|
value: 82.19300000000001 |
|
- type: ndcg_at_3 |
|
value: 76.956 |
|
- type: ndcg_at_5 |
|
value: 78.962 |
|
- type: precision_at_1 |
|
value: 68.599 |
|
- type: precision_at_10 |
|
value: 9.336 |
|
- type: precision_at_100 |
|
value: 0.996 |
|
- type: precision_at_1000 |
|
value: 0.101 |
|
- type: precision_at_3 |
|
value: 27.678000000000004 |
|
- type: precision_at_5 |
|
value: 17.619 |
|
- type: recall_at_1 |
|
value: 68.388 |
|
- type: recall_at_10 |
|
value: 92.36 |
|
- type: recall_at_100 |
|
value: 98.52499999999999 |
|
- type: recall_at_1000 |
|
value: 99.895 |
|
- type: recall_at_3 |
|
value: 82.53399999999999 |
|
- type: recall_at_5 |
|
value: 87.355 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/DuRetrieval |
|
name: MTEB DuRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.1 |
|
- type: map_at_10 |
|
value: 77.71000000000001 |
|
- type: map_at_100 |
|
value: 80.638 |
|
- type: map_at_1000 |
|
value: 80.679 |
|
- type: map_at_3 |
|
value: 53.187 |
|
- type: map_at_5 |
|
value: 67.735 |
|
- type: mrr_at_1 |
|
value: 87.8 |
|
- type: mrr_at_10 |
|
value: 91.8 |
|
- type: mrr_at_100 |
|
value: 91.893 |
|
- type: mrr_at_1000 |
|
value: 91.89500000000001 |
|
- type: mrr_at_3 |
|
value: 91.51700000000001 |
|
- type: mrr_at_5 |
|
value: 91.704 |
|
- type: ndcg_at_1 |
|
value: 87.8 |
|
- type: ndcg_at_10 |
|
value: 85.55 |
|
- type: ndcg_at_100 |
|
value: 88.626 |
|
- type: ndcg_at_1000 |
|
value: 89.021 |
|
- type: ndcg_at_3 |
|
value: 83.94 |
|
- type: ndcg_at_5 |
|
value: 83.259 |
|
- type: precision_at_1 |
|
value: 87.8 |
|
- type: precision_at_10 |
|
value: 41.295 |
|
- type: precision_at_100 |
|
value: 4.781 |
|
- type: precision_at_1000 |
|
value: 0.488 |
|
- type: precision_at_3 |
|
value: 75.3 |
|
- type: precision_at_5 |
|
value: 64.13 |
|
- type: recall_at_1 |
|
value: 25.1 |
|
- type: recall_at_10 |
|
value: 87.076 |
|
- type: recall_at_100 |
|
value: 97.095 |
|
- type: recall_at_1000 |
|
value: 99.129 |
|
- type: recall_at_3 |
|
value: 56.013999999999996 |
|
- type: recall_at_5 |
|
value: 73.2 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/EcomRetrieval |
|
name: MTEB EcomRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 53.300000000000004 |
|
- type: map_at_10 |
|
value: 63.01 |
|
- type: map_at_100 |
|
value: 63.574 |
|
- type: map_at_1000 |
|
value: 63.587 |
|
- type: map_at_3 |
|
value: 60.783 |
|
- type: map_at_5 |
|
value: 62.098 |
|
- type: mrr_at_1 |
|
value: 53.300000000000004 |
|
- type: mrr_at_10 |
|
value: 63.01 |
|
- type: mrr_at_100 |
|
value: 63.574 |
|
- type: mrr_at_1000 |
|
value: 63.587 |
|
- type: mrr_at_3 |
|
value: 60.783 |
|
- type: mrr_at_5 |
|
value: 62.098 |
|
- type: ndcg_at_1 |
|
value: 53.300000000000004 |
|
- type: ndcg_at_10 |
|
value: 67.876 |
|
- type: ndcg_at_100 |
|
value: 70.434 |
|
- type: ndcg_at_1000 |
|
value: 70.753 |
|
- type: ndcg_at_3 |
|
value: 63.275000000000006 |
|
- type: ndcg_at_5 |
|
value: 65.654 |
|
- type: precision_at_1 |
|
value: 53.300000000000004 |
|
- type: precision_at_10 |
|
value: 8.32 |
|
- type: precision_at_100 |
|
value: 0.9480000000000001 |
|
- type: precision_at_1000 |
|
value: 0.097 |
|
- type: precision_at_3 |
|
value: 23.5 |
|
- type: precision_at_5 |
|
value: 15.260000000000002 |
|
- type: recall_at_1 |
|
value: 53.300000000000004 |
|
- type: recall_at_10 |
|
value: 83.2 |
|
- type: recall_at_100 |
|
value: 94.8 |
|
- type: recall_at_1000 |
|
value: 97.3 |
|
- type: recall_at_3 |
|
value: 70.5 |
|
- type: recall_at_5 |
|
value: 76.3 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/IFlyTek-classification |
|
name: MTEB IFlyTek |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 49.92689495959984 |
|
- type: f1 |
|
value: 37.784780470986625 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/JDReview-classification |
|
name: MTEB JDReview |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 86.26641651031895 |
|
- type: ap |
|
value: 54.50750244841821 |
|
- type: f1 |
|
value: 80.94927946681523 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/LCQMC |
|
name: MTEB LCQMC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 72.3980811478615 |
|
- type: cos_sim_spearman |
|
value: 78.26906056425528 |
|
- type: euclidean_pearson |
|
value: 77.87705501225068 |
|
- type: euclidean_spearman |
|
value: 78.26905834518651 |
|
- type: manhattan_pearson |
|
value: 77.77154630197 |
|
- type: manhattan_spearman |
|
value: 78.1940918602169 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/Mmarco-reranking |
|
name: MTEB MMarcoReranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 27.48003475319453 |
|
- type: mrr |
|
value: 26.400793650793652 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MMarcoRetrieval |
|
name: MTEB MMarcoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 64.373 |
|
- type: map_at_10 |
|
value: 73.604 |
|
- type: map_at_100 |
|
value: 73.953 |
|
- type: map_at_1000 |
|
value: 73.965 |
|
- type: map_at_3 |
|
value: 71.70100000000001 |
|
- type: map_at_5 |
|
value: 72.859 |
|
- type: mrr_at_1 |
|
value: 66.676 |
|
- type: mrr_at_10 |
|
value: 74.248 |
|
- type: mrr_at_100 |
|
value: 74.56099999999999 |
|
- type: mrr_at_1000 |
|
value: 74.572 |
|
- type: mrr_at_3 |
|
value: 72.59100000000001 |
|
- type: mrr_at_5 |
|
value: 73.592 |
|
- type: ndcg_at_1 |
|
value: 66.676 |
|
- type: ndcg_at_10 |
|
value: 77.417 |
|
- type: ndcg_at_100 |
|
value: 79.006 |
|
- type: ndcg_at_1000 |
|
value: 79.334 |
|
- type: ndcg_at_3 |
|
value: 73.787 |
|
- type: ndcg_at_5 |
|
value: 75.74 |
|
- type: precision_at_1 |
|
value: 66.676 |
|
- type: precision_at_10 |
|
value: 9.418 |
|
- type: precision_at_100 |
|
value: 1.0210000000000001 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_3 |
|
value: 27.832 |
|
- type: precision_at_5 |
|
value: 17.736 |
|
- type: recall_at_1 |
|
value: 64.373 |
|
- type: recall_at_10 |
|
value: 88.565 |
|
- type: recall_at_100 |
|
value: 95.789 |
|
- type: recall_at_1000 |
|
value: 98.355 |
|
- type: recall_at_3 |
|
value: 78.914 |
|
- type: recall_at_5 |
|
value: 83.56 |
|
- 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: 72.0544720914593 |
|
- type: f1 |
|
value: 69.61749470345791 |
|
- 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: 75.30262273032953 |
|
- type: f1 |
|
value: 75.05097671215634 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MedicalRetrieval |
|
name: MTEB MedicalRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 55.1 |
|
- type: map_at_10 |
|
value: 61.284000000000006 |
|
- type: map_at_100 |
|
value: 61.794000000000004 |
|
- type: map_at_1000 |
|
value: 61.838 |
|
- type: map_at_3 |
|
value: 59.75 |
|
- type: map_at_5 |
|
value: 60.64000000000001 |
|
- type: mrr_at_1 |
|
value: 55.300000000000004 |
|
- type: mrr_at_10 |
|
value: 61.38400000000001 |
|
- type: mrr_at_100 |
|
value: 61.894000000000005 |
|
- type: mrr_at_1000 |
|
value: 61.938 |
|
- type: mrr_at_3 |
|
value: 59.85 |
|
- type: mrr_at_5 |
|
value: 60.74 |
|
- type: ndcg_at_1 |
|
value: 55.1 |
|
- type: ndcg_at_10 |
|
value: 64.345 |
|
- type: ndcg_at_100 |
|
value: 67.148 |
|
- type: ndcg_at_1000 |
|
value: 68.36 |
|
- type: ndcg_at_3 |
|
value: 61.182 |
|
- type: ndcg_at_5 |
|
value: 62.808 |
|
- type: precision_at_1 |
|
value: 55.1 |
|
- type: precision_at_10 |
|
value: 7.3999999999999995 |
|
- type: precision_at_100 |
|
value: 0.8789999999999999 |
|
- type: precision_at_1000 |
|
value: 0.098 |
|
- type: precision_at_3 |
|
value: 21.767 |
|
- type: precision_at_5 |
|
value: 13.86 |
|
- type: recall_at_1 |
|
value: 55.1 |
|
- type: recall_at_10 |
|
value: 74 |
|
- type: recall_at_100 |
|
value: 87.9 |
|
- type: recall_at_1000 |
|
value: 97.5 |
|
- type: recall_at_3 |
|
value: 65.3 |
|
- type: recall_at_5 |
|
value: 69.3 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/MultilingualSentiment-classification |
|
name: MTEB MultilingualSentiment |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 76.21666666666667 |
|
- type: f1 |
|
value: 76.03732395559548 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/OCNLI |
|
name: MTEB Ocnli |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 81.8083378451543 |
|
- type: cos_sim_ap |
|
value: 85.43050139514027 |
|
- type: cos_sim_f1 |
|
value: 83.25969563082965 |
|
- type: cos_sim_precision |
|
value: 77.79816513761469 |
|
- type: cos_sim_recall |
|
value: 89.54593453009504 |
|
- type: dot_accuracy |
|
value: 81.8083378451543 |
|
- type: dot_ap |
|
value: 85.43050139514027 |
|
- type: dot_f1 |
|
value: 83.25969563082965 |
|
- type: dot_precision |
|
value: 77.79816513761469 |
|
- type: dot_recall |
|
value: 89.54593453009504 |
|
- type: euclidean_accuracy |
|
value: 81.8083378451543 |
|
- type: euclidean_ap |
|
value: 85.43050139514027 |
|
- type: euclidean_f1 |
|
value: 83.25969563082965 |
|
- type: euclidean_precision |
|
value: 77.79816513761469 |
|
- type: euclidean_recall |
|
value: 89.54593453009504 |
|
- type: manhattan_accuracy |
|
value: 81.53762858689767 |
|
- type: manhattan_ap |
|
value: 84.90556637024838 |
|
- type: manhattan_f1 |
|
value: 82.90258449304174 |
|
- type: manhattan_precision |
|
value: 78.30985915492957 |
|
- type: manhattan_recall |
|
value: 88.0675818373812 |
|
- type: max_accuracy |
|
value: 81.8083378451543 |
|
- type: max_ap |
|
value: 85.43050139514027 |
|
- type: max_f1 |
|
value: 83.25969563082965 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/OnlineShopping-classification |
|
name: MTEB OnlineShopping |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 93.53 |
|
- type: ap |
|
value: 91.62070655043128 |
|
- type: f1 |
|
value: 93.51908163199477 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/PAWSX |
|
name: MTEB PAWSX |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 38.451787103814375 |
|
- type: cos_sim_spearman |
|
value: 43.97299462643919 |
|
- type: euclidean_pearson |
|
value: 43.63298716626501 |
|
- type: euclidean_spearman |
|
value: 43.973080252178576 |
|
- type: manhattan_pearson |
|
value: 43.37465277323481 |
|
- type: manhattan_spearman |
|
value: 43.71981281220414 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/QBQTC |
|
name: MTEB QBQTC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 37.75882451277358 |
|
- type: cos_sim_spearman |
|
value: 40.0244327844802 |
|
- type: euclidean_pearson |
|
value: 38.11050875514246 |
|
- type: euclidean_spearman |
|
value: 40.02440987254504 |
|
- type: manhattan_pearson |
|
value: 38.03186803221696 |
|
- type: manhattan_spearman |
|
value: 39.757452890246775 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (zh) |
|
config: zh |
|
split: test |
|
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 65.9133992390713 |
|
- type: cos_sim_spearman |
|
value: 66.4894937647578 |
|
- type: euclidean_pearson |
|
value: 66.19047142189935 |
|
- type: euclidean_spearman |
|
value: 66.4894937647578 |
|
- type: manhattan_pearson |
|
value: 66.6960935896136 |
|
- type: manhattan_spearman |
|
value: 66.88179996508133 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/STSB |
|
name: MTEB STSB |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.55099417946924 |
|
- type: cos_sim_spearman |
|
value: 83.05000687568048 |
|
- type: euclidean_pearson |
|
value: 82.62744668792926 |
|
- type: euclidean_spearman |
|
value: 83.05000687568048 |
|
- type: manhattan_pearson |
|
value: 82.6543207325763 |
|
- type: manhattan_spearman |
|
value: 83.06852715971705 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/T2Reranking |
|
name: MTEB T2Reranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 66.48634798223672 |
|
- type: mrr |
|
value: 76.30158461488861 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/T2Retrieval |
|
name: MTEB T2Retrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.483999999999998 |
|
- type: map_at_10 |
|
value: 76.848 |
|
- type: map_at_100 |
|
value: 80.541 |
|
- type: map_at_1000 |
|
value: 80.607 |
|
- type: map_at_3 |
|
value: 54.111 |
|
- type: map_at_5 |
|
value: 66.46300000000001 |
|
- type: mrr_at_1 |
|
value: 90.045 |
|
- type: mrr_at_10 |
|
value: 92.552 |
|
- type: mrr_at_100 |
|
value: 92.642 |
|
- type: mrr_at_1000 |
|
value: 92.645 |
|
- type: mrr_at_3 |
|
value: 92.134 |
|
- type: mrr_at_5 |
|
value: 92.391 |
|
- type: ndcg_at_1 |
|
value: 90.045 |
|
- type: ndcg_at_10 |
|
value: 84.504 |
|
- type: ndcg_at_100 |
|
value: 88.23100000000001 |
|
- type: ndcg_at_1000 |
|
value: 88.85300000000001 |
|
- type: ndcg_at_3 |
|
value: 85.992 |
|
- type: ndcg_at_5 |
|
value: 84.548 |
|
- type: precision_at_1 |
|
value: 90.045 |
|
- type: precision_at_10 |
|
value: 41.91 |
|
- type: precision_at_100 |
|
value: 5.017 |
|
- type: precision_at_1000 |
|
value: 0.516 |
|
- type: precision_at_3 |
|
value: 75.15899999999999 |
|
- type: precision_at_5 |
|
value: 62.958000000000006 |
|
- type: recall_at_1 |
|
value: 27.483999999999998 |
|
- type: recall_at_10 |
|
value: 83.408 |
|
- type: recall_at_100 |
|
value: 95.514 |
|
- type: recall_at_1000 |
|
value: 98.65 |
|
- type: recall_at_3 |
|
value: 55.822 |
|
- type: recall_at_5 |
|
value: 69.868 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/TNews-classification |
|
name: MTEB TNews |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 53.196 |
|
- type: f1 |
|
value: 51.51679244513836 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringP2P |
|
name: MTEB ThuNewsClusteringP2P |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 67.87592101539063 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringS2S |
|
name: MTEB ThuNewsClusteringS2S |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 62.4675464095125 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/VideoRetrieval |
|
name: MTEB VideoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 57.9 |
|
- type: map_at_10 |
|
value: 68.099 |
|
- type: map_at_100 |
|
value: 68.55499999999999 |
|
- type: map_at_1000 |
|
value: 68.566 |
|
- type: map_at_3 |
|
value: 66.4 |
|
- type: map_at_5 |
|
value: 67.46 |
|
- type: mrr_at_1 |
|
value: 57.9 |
|
- type: mrr_at_10 |
|
value: 68.099 |
|
- type: mrr_at_100 |
|
value: 68.55499999999999 |
|
- type: mrr_at_1000 |
|
value: 68.566 |
|
- type: mrr_at_3 |
|
value: 66.4 |
|
- type: mrr_at_5 |
|
value: 67.46 |
|
- type: ndcg_at_1 |
|
value: 57.9 |
|
- type: ndcg_at_10 |
|
value: 72.555 |
|
- type: ndcg_at_100 |
|
value: 74.715 |
|
- type: ndcg_at_1000 |
|
value: 75.034 |
|
- type: ndcg_at_3 |
|
value: 69.102 |
|
- type: ndcg_at_5 |
|
value: 71.004 |
|
- type: precision_at_1 |
|
value: 57.9 |
|
- type: precision_at_10 |
|
value: 8.63 |
|
- type: precision_at_100 |
|
value: 0.963 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 25.633 |
|
- type: precision_at_5 |
|
value: 16.3 |
|
- type: recall_at_1 |
|
value: 57.9 |
|
- type: recall_at_10 |
|
value: 86.3 |
|
- type: recall_at_100 |
|
value: 96.3 |
|
- type: recall_at_1000 |
|
value: 98.9 |
|
- type: recall_at_3 |
|
value: 76.9 |
|
- type: recall_at_5 |
|
value: 81.5 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/waimai-classification |
|
name: MTEB Waimai |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 87.27000000000001 |
|
- type: ap |
|
value: 71.10883470119464 |
|
- type: f1 |
|
value: 85.76618863591946 |
|
license: mit |
|
--- |
|
|
|
**新闻 | News** |
|
|
|
**[2024-04-06]** 开源[puff](https://huggingface.co/infgrad/puff-base-v1)系列模型，**专门针对检索和语义匹配任务，更多的考虑泛化性和私有通用测试集效果，向量维度可变，中英双语**。 |
|
|
|
**[2024-02-27]** 开源stella-mrl-large-zh-v3.5-1792d模型，支持**向量可变维度**。 |
|
|
|
**[2024-02-17]** 开源stella v3系列、dialogue编码模型和相关训练数据。 |
|
|
|
**[2023-10-19]** 开源stella-base-en-v2 使用简单，**不需要任何前缀文本**。 |
|
|
|
**[2023-10-12]** 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，**不需要任何前缀文本**。 |
|
|
|
**[2023-09-11]** 开源stella-base-zh和stella-large-zh |
|
|
|
欢迎去[本人主页](https://huggingface.co/infgrad)查看最新模型，并提出您的宝贵意见！ |
|
|
|
# 1 开源清单 |
|
|
|
本次开源2个通用向量编码模型和一个针对dialogue进行编码的向量模型，同时开源全量160万对话重写数据集和20万的难负例的检索数据集。 |
|
|
|
**开源模型：** |
|
|
|
| ModelName | ModelSize | MaxTokens | EmbeddingDimensions | Language | Scenario | C-MTEB Score | |
|
|---------------------------------------------------------------------------------------------------------------|-----------|-----------|---------------------|----------|----------|--------------| |
|
| [infgrad/stella-base-zh-v3-1792d](https://huggingface.co/infgrad/stella-base-zh-v3-1792d) | 0.4GB | 512 | 1792 | zh-CN | 通用文本 | 67.96 | |
|
| [infgrad/stella-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-large-zh-v3-1792d) | 1.3GB | 512 | 1792 | zh-CN | 通用文本 | 68.48 | |
|
| [infgrad/stella-dialogue-large-zh-v3-1792d](https://huggingface.co/infgrad/stella-dialogue-large-zh-v3-1792d) | 1.3GB | 512 | 1792 | zh-CN | **对话文本** | 不适用 | |
|
|
|
**开源数据：** |
|
|
|
1. [全量对话重写数据集](https://huggingface.co/datasets/infgrad/dialogue_rewrite_llm) 约160万 |
|
2. [部分带有难负例的检索数据集](https://huggingface.co/datasets/infgrad/retrieval_data_llm) 约20万 |
|
|
|
上述数据集均使用LLM构造，欢迎各位贡献数据集。 |
|
|
|
# 2 使用方法 |
|
|
|
## 2.1 通用编码模型使用方法 |
|
|
|
直接SentenceTransformer加载即可： |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
model = SentenceTransformer("infgrad/stella-base-zh-v3-1792d") |
|
# model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d") |
|
vectors = model.encode(["text1", "text2"]) |
|
``` |
|
|
|
## 2.2 dialogue编码模型使用方法 |
|
|
|
**使用场景：** |
|
**在一段对话中，需要根据用户语句去检索相关文本，但是对话中的用户语句存在大量的指代和省略，导致直接使用通用编码模型效果不好， |
|
可以使用本项目的专门的dialogue编码模型进行编码** |
|
|
|
**使用要点：** |
|
|
|
1. 对dialogue进行编码时，dialogue中的每个utterance需要是如下格式：`"{ROLE}: {TEXT}"`，然后使用`[SEP]` join一下 |
|
2. 整个对话都要送入模型进行编码，如果长度不够就删掉早期的对话，**编码后的向量本质是对话中最后一句话的重写版本的向量！！** |
|
3. 对话用stella-dialogue-large-zh-v3-1792d编码，被检索文本使用stella-large-zh-v3-1792d进行编码，所以本场景是需要2个编码模型的 |
|
|
|
如果对使用方法还有疑惑，请到下面章节阅读该模型是如何训练的。 |
|
|
|
使用示例： |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
dial_model = SentenceTransformer("infgrad/stella-dialogue-large-zh-v3-1792d") |
|
general_model = SentenceTransformer("infgrad/stella-large-zh-v3-1792d") |
|
# dialogue = ["张三: 吃饭吗", "李四: 等会去"] |
|
dialogue = ["A: 最近去打篮球了吗", "B: 没有"] |
|
corpus = ["B没打篮球是因为受伤了。", "B没有打乒乓球"] |
|
last_utterance_vector = dial_model.encode(["[SEP]".join(dialogue)], normalize_embeddings=True) |
|
corpus_vectors = general_model.encode(corpus, normalize_embeddings=True) |
|
# 计算相似度 |
|
sims = (last_utterance_vector * corpus_vectors).sum(axis=1) |
|
print(sims) |
|
``` |
|
|
|
# 3 通用编码模型训练技巧分享 |
|
|
|
## hard negative |
|
|
|
难负例挖掘也是个经典的trick了，几乎总能提升效果 |
|
|
|
## dropout-1d |
|
|
|
dropout已经是深度学习的标配，我们可以稍微改造下使其更适合句向量的训练。 |
|
我们在训练时会尝试让每一个token-embedding都可以表征整个句子，而在推理时使用mean_pooling从而达到类似模型融合的效果。 |
|
具体操作是在mean_pooling时加入dropout_1d，torch代码如下： |
|
|
|
```python |
|
vector_dropout = nn.Dropout1d(0.3) # 算力有限，试了0.3和0.5 两个参数，其中0.3更优 |
|
last_hidden_state = bert_model(...)[0] |
|
last_hidden = last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0) |
|
last_hidden = vector_dropout(last_hidden) |
|
vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] |
|
``` |
|
|
|
# 4 dialogue编码模型细节 |
|
|
|
## 4.1 为什么需要一个dialogue编码模型？ |
|
|
|
参见本人历史文章：https://www.zhihu.com/pin/1674913544847077376 |
|
|
|
## 4.2 训练数据 |
|
|
|
单条数据示例： |
|
|
|
```json |
|
{ |
|
"dialogue": [ |
|
"A: 最近去打篮球了吗", |
|
"B: 没有" |
|
], |
|
"last_utterance_rewrite": "B: 我最近没有去打篮球" |
|
} |
|
``` |
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## 4.3 训练Loss |
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``` |
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loss = cosine_loss( dial_model.encode(dialogue), existing_model.encode(last_utterance_rewrite) ) |
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``` |
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dial_model就是要被训练的模型，本人是以stella-large-zh-v3-1792d作为base-model进行继续训练的 |
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existing_model就是现有训练好的**通用编码模型**，本人使用的是stella-large-zh-v3-1792d |
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已开源dialogue-embedding的全量训练数据，理论上可以复现本模型效果。 |
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Loss下降情况： |
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<div align="center"> |
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<img src="dial_loss.png" alt="icon" width="2000px"/> |
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</div> |
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## 4.4 效果 |
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目前还没有专门测试集，本人简单测试了下是有效果的，部分测试结果见文件`dial_retrieval_test.xlsx`。 |
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# 5 后续TODO |
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1. 更多的dial-rewrite数据 |
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2. 不同EmbeddingDimensions的编码模型 |
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# 6 FAQ |
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Q: 为什么向量维度是1792？\ |
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A: 最初考虑发布768、1024，768+768，1024+1024，1024+768维度，但是时间有限，先做了1792就只发布1792维度的模型。理论上维度越高效果越好。 |
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Q: 如何复现CMTEB效果？\ |
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A: SentenceTransformer加载后直接用官方评测脚本就行，注意对于Classification任务向量需要先normalize一下 |
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Q: 复现的CMTEB效果和本文不一致？\ |
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A: 聚类不一致正常，官方评测代码没有设定seed，其他不一致建议检查代码或联系本人。 |
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Q: 如何选择向量模型？\ |
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A: 没有免费的午餐，在自己测试集上试试，本人推荐bge、e5和stella. |
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Q: 长度为什么只有512，能否更长？\ |
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A: 可以但没必要，长了效果普遍不好，这是当前训练方法和数据导致的，几乎无解，建议长文本还是走分块。 |
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Q: 训练资源和算力？\ |
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A: 亿级别的数据，单卡A100要一个月起步 |
