|
--- |
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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: stella-large-zh-v3-1792d |
|
results: |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/AFQMC |
|
name: MTEB AFQMC |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 54.48093298255762 |
|
- type: cos_sim_spearman |
|
value: 59.105354109068685 |
|
- type: euclidean_pearson |
|
value: 57.761189988643444 |
|
- type: euclidean_spearman |
|
value: 59.10537421115596 |
|
- type: manhattan_pearson |
|
value: 56.94359297051431 |
|
- type: manhattan_spearman |
|
value: 58.37611109821567 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/ATEC |
|
name: MTEB ATEC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 54.39711127600595 |
|
- type: cos_sim_spearman |
|
value: 58.190191920824454 |
|
- type: euclidean_pearson |
|
value: 61.80082379352729 |
|
- type: euclidean_spearman |
|
value: 58.19018966860797 |
|
- type: manhattan_pearson |
|
value: 60.927601060396206 |
|
- type: manhattan_spearman |
|
value: 57.78832902694192 |
|
- 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: 46.31600000000001 |
|
- type: f1 |
|
value: 44.45281663598873 |
|
- 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: 69.12211326097868 |
|
- type: cos_sim_spearman |
|
value: 71.0741302039443 |
|
- type: euclidean_pearson |
|
value: 69.89070483887852 |
|
- type: euclidean_spearman |
|
value: 71.07413020351787 |
|
- type: manhattan_pearson |
|
value: 69.62345441260962 |
|
- type: manhattan_spearman |
|
value: 70.8517591280618 |
|
- 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: 41.937723608805314 |
|
- 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: 40.34373057675427 |
|
- 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: 88.98896401788376 |
|
- type: mrr |
|
value: 90.97119047619047 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv2-reranking |
|
name: MTEB CMedQAv2 |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 89.59718540244556 |
|
- type: mrr |
|
value: 91.41246031746032 |
|
- 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: 26.954 |
|
- type: map_at_10 |
|
value: 40.144999999999996 |
|
- type: map_at_100 |
|
value: 42.083999999999996 |
|
- type: map_at_1000 |
|
value: 42.181000000000004 |
|
- type: map_at_3 |
|
value: 35.709 |
|
- type: map_at_5 |
|
value: 38.141000000000005 |
|
- type: mrr_at_1 |
|
value: 40.71 |
|
- type: mrr_at_10 |
|
value: 48.93 |
|
- type: mrr_at_100 |
|
value: 49.921 |
|
- type: mrr_at_1000 |
|
value: 49.958999999999996 |
|
- type: mrr_at_3 |
|
value: 46.32 |
|
- type: mrr_at_5 |
|
value: 47.769 |
|
- type: ndcg_at_1 |
|
value: 40.71 |
|
- type: ndcg_at_10 |
|
value: 46.869 |
|
- type: ndcg_at_100 |
|
value: 54.234 |
|
- type: ndcg_at_1000 |
|
value: 55.854000000000006 |
|
- type: ndcg_at_3 |
|
value: 41.339 |
|
- type: ndcg_at_5 |
|
value: 43.594 |
|
- type: precision_at_1 |
|
value: 40.71 |
|
- type: precision_at_10 |
|
value: 10.408000000000001 |
|
- type: precision_at_100 |
|
value: 1.635 |
|
- type: precision_at_1000 |
|
value: 0.184 |
|
- type: precision_at_3 |
|
value: 23.348 |
|
- type: precision_at_5 |
|
value: 16.929 |
|
- type: recall_at_1 |
|
value: 26.954 |
|
- type: recall_at_10 |
|
value: 57.821999999999996 |
|
- type: recall_at_100 |
|
value: 88.08200000000001 |
|
- type: recall_at_1000 |
|
value: 98.83800000000001 |
|
- type: recall_at_3 |
|
value: 41.221999999999994 |
|
- type: recall_at_5 |
|
value: 48.241 |
|
- 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: 83.6680697534576 |
|
- type: cos_sim_ap |
|
value: 90.77401562455269 |
|
- type: cos_sim_f1 |
|
value: 84.68266427450101 |
|
- type: cos_sim_precision |
|
value: 81.36177547942253 |
|
- type: cos_sim_recall |
|
value: 88.28618190320317 |
|
- type: dot_accuracy |
|
value: 83.6680697534576 |
|
- type: dot_ap |
|
value: 90.76429465198817 |
|
- type: dot_f1 |
|
value: 84.68266427450101 |
|
- type: dot_precision |
|
value: 81.36177547942253 |
|
- type: dot_recall |
|
value: 88.28618190320317 |
|
- type: euclidean_accuracy |
|
value: 83.6680697534576 |
|
- type: euclidean_ap |
|
value: 90.77401909305344 |
|
- type: euclidean_f1 |
|
value: 84.68266427450101 |
|
- type: euclidean_precision |
|
value: 81.36177547942253 |
|
- type: euclidean_recall |
|
value: 88.28618190320317 |
|
- type: manhattan_accuracy |
|
value: 83.40348767288035 |
|
- type: manhattan_ap |
|
value: 90.57002020310819 |
|
- type: manhattan_f1 |
|
value: 84.51526032315978 |
|
- type: manhattan_precision |
|
value: 81.25134843581445 |
|
- type: manhattan_recall |
|
value: 88.05237315875614 |
|
- type: max_accuracy |
|
value: 83.6680697534576 |
|
- type: max_ap |
|
value: 90.77401909305344 |
|
- type: max_f1 |
|
value: 84.68266427450101 |
|
- 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: 69.705 |
|
- type: map_at_10 |
|
value: 78.648 |
|
- type: map_at_100 |
|
value: 78.888 |
|
- type: map_at_1000 |
|
value: 78.89399999999999 |
|
- type: map_at_3 |
|
value: 77.151 |
|
- type: map_at_5 |
|
value: 77.98 |
|
- type: mrr_at_1 |
|
value: 69.863 |
|
- type: mrr_at_10 |
|
value: 78.62599999999999 |
|
- type: mrr_at_100 |
|
value: 78.861 |
|
- type: mrr_at_1000 |
|
value: 78.867 |
|
- type: mrr_at_3 |
|
value: 77.204 |
|
- type: mrr_at_5 |
|
value: 78.005 |
|
- type: ndcg_at_1 |
|
value: 69.968 |
|
- type: ndcg_at_10 |
|
value: 82.44399999999999 |
|
- type: ndcg_at_100 |
|
value: 83.499 |
|
- type: ndcg_at_1000 |
|
value: 83.647 |
|
- type: ndcg_at_3 |
|
value: 79.393 |
|
- type: ndcg_at_5 |
|
value: 80.855 |
|
- type: precision_at_1 |
|
value: 69.968 |
|
- type: precision_at_10 |
|
value: 9.515 |
|
- type: precision_at_100 |
|
value: 0.9990000000000001 |
|
- type: precision_at_1000 |
|
value: 0.101 |
|
- type: precision_at_3 |
|
value: 28.802 |
|
- type: precision_at_5 |
|
value: 18.019 |
|
- type: recall_at_1 |
|
value: 69.705 |
|
- type: recall_at_10 |
|
value: 94.152 |
|
- type: recall_at_100 |
|
value: 98.84100000000001 |
|
- type: recall_at_1000 |
|
value: 100.0 |
|
- type: recall_at_3 |
|
value: 85.774 |
|
- type: recall_at_5 |
|
value: 89.252 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/DuRetrieval |
|
name: MTEB DuRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.88 |
|
- type: map_at_10 |
|
value: 79.857 |
|
- type: map_at_100 |
|
value: 82.636 |
|
- type: map_at_1000 |
|
value: 82.672 |
|
- type: map_at_3 |
|
value: 55.184 |
|
- type: map_at_5 |
|
value: 70.009 |
|
- type: mrr_at_1 |
|
value: 89.64999999999999 |
|
- type: mrr_at_10 |
|
value: 92.967 |
|
- type: mrr_at_100 |
|
value: 93.039 |
|
- type: mrr_at_1000 |
|
value: 93.041 |
|
- type: mrr_at_3 |
|
value: 92.65 |
|
- type: mrr_at_5 |
|
value: 92.86 |
|
- type: ndcg_at_1 |
|
value: 89.64999999999999 |
|
- type: ndcg_at_10 |
|
value: 87.126 |
|
- type: ndcg_at_100 |
|
value: 89.898 |
|
- type: ndcg_at_1000 |
|
value: 90.253 |
|
- type: ndcg_at_3 |
|
value: 86.012 |
|
- type: ndcg_at_5 |
|
value: 85.124 |
|
- type: precision_at_1 |
|
value: 89.64999999999999 |
|
- type: precision_at_10 |
|
value: 41.735 |
|
- type: precision_at_100 |
|
value: 4.797 |
|
- type: precision_at_1000 |
|
value: 0.488 |
|
- type: precision_at_3 |
|
value: 77.267 |
|
- type: precision_at_5 |
|
value: 65.48 |
|
- type: recall_at_1 |
|
value: 25.88 |
|
- type: recall_at_10 |
|
value: 88.28399999999999 |
|
- type: recall_at_100 |
|
value: 97.407 |
|
- type: recall_at_1000 |
|
value: 99.29299999999999 |
|
- type: recall_at_3 |
|
value: 57.38799999999999 |
|
- type: recall_at_5 |
|
value: 74.736 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/EcomRetrieval |
|
name: MTEB EcomRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 53.2 |
|
- type: map_at_10 |
|
value: 63.556000000000004 |
|
- type: map_at_100 |
|
value: 64.033 |
|
- type: map_at_1000 |
|
value: 64.044 |
|
- type: map_at_3 |
|
value: 60.983 |
|
- type: map_at_5 |
|
value: 62.588 |
|
- type: mrr_at_1 |
|
value: 53.2 |
|
- type: mrr_at_10 |
|
value: 63.556000000000004 |
|
- type: mrr_at_100 |
|
value: 64.033 |
|
- type: mrr_at_1000 |
|
value: 64.044 |
|
- type: mrr_at_3 |
|
value: 60.983 |
|
- type: mrr_at_5 |
|
value: 62.588 |
|
- type: ndcg_at_1 |
|
value: 53.2 |
|
- type: ndcg_at_10 |
|
value: 68.61699999999999 |
|
- type: ndcg_at_100 |
|
value: 70.88499999999999 |
|
- type: ndcg_at_1000 |
|
value: 71.15899999999999 |
|
- type: ndcg_at_3 |
|
value: 63.434000000000005 |
|
- type: ndcg_at_5 |
|
value: 66.301 |
|
- type: precision_at_1 |
|
value: 53.2 |
|
- type: precision_at_10 |
|
value: 8.450000000000001 |
|
- type: precision_at_100 |
|
value: 0.95 |
|
- type: precision_at_1000 |
|
value: 0.097 |
|
- type: precision_at_3 |
|
value: 23.5 |
|
- type: precision_at_5 |
|
value: 15.479999999999999 |
|
- type: recall_at_1 |
|
value: 53.2 |
|
- type: recall_at_10 |
|
value: 84.5 |
|
- type: recall_at_100 |
|
value: 95.0 |
|
- type: recall_at_1000 |
|
value: 97.1 |
|
- type: recall_at_3 |
|
value: 70.5 |
|
- type: recall_at_5 |
|
value: 77.4 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/IFlyTek-classification |
|
name: MTEB IFlyTek |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 50.63485956136976 |
|
- type: f1 |
|
value: 38.286307407751266 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/JDReview-classification |
|
name: MTEB JDReview |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 86.11632270168855 |
|
- type: ap |
|
value: 54.43932599806482 |
|
- type: f1 |
|
value: 80.85485110996076 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/LCQMC |
|
name: MTEB LCQMC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 72.47315152994804 |
|
- type: cos_sim_spearman |
|
value: 78.26531600908152 |
|
- type: euclidean_pearson |
|
value: 77.8560788714531 |
|
- type: euclidean_spearman |
|
value: 78.26531157334841 |
|
- type: manhattan_pearson |
|
value: 77.70593783974188 |
|
- type: manhattan_spearman |
|
value: 78.13880812439999 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/Mmarco-reranking |
|
name: MTEB MMarcoReranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 28.088177976572222 |
|
- type: mrr |
|
value: 27.125 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MMarcoRetrieval |
|
name: MTEB MMarcoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 66.428 |
|
- type: map_at_10 |
|
value: 75.5 |
|
- type: map_at_100 |
|
value: 75.82600000000001 |
|
- type: map_at_1000 |
|
value: 75.837 |
|
- type: map_at_3 |
|
value: 73.74300000000001 |
|
- type: map_at_5 |
|
value: 74.87 |
|
- type: mrr_at_1 |
|
value: 68.754 |
|
- type: mrr_at_10 |
|
value: 76.145 |
|
- type: mrr_at_100 |
|
value: 76.432 |
|
- type: mrr_at_1000 |
|
value: 76.442 |
|
- type: mrr_at_3 |
|
value: 74.628 |
|
- type: mrr_at_5 |
|
value: 75.612 |
|
- type: ndcg_at_1 |
|
value: 68.754 |
|
- type: ndcg_at_10 |
|
value: 79.144 |
|
- type: ndcg_at_100 |
|
value: 80.60199999999999 |
|
- type: ndcg_at_1000 |
|
value: 80.886 |
|
- type: ndcg_at_3 |
|
value: 75.81599999999999 |
|
- type: ndcg_at_5 |
|
value: 77.729 |
|
- type: precision_at_1 |
|
value: 68.754 |
|
- type: precision_at_10 |
|
value: 9.544 |
|
- type: precision_at_100 |
|
value: 1.026 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_3 |
|
value: 28.534 |
|
- type: precision_at_5 |
|
value: 18.138 |
|
- type: recall_at_1 |
|
value: 66.428 |
|
- type: recall_at_10 |
|
value: 89.716 |
|
- type: recall_at_100 |
|
value: 96.313 |
|
- type: recall_at_1000 |
|
value: 98.541 |
|
- type: recall_at_3 |
|
value: 80.923 |
|
- type: recall_at_5 |
|
value: 85.48 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_intent |
|
name: MTEB MassiveIntentClassification (zh-CN) |
|
config: zh-CN |
|
split: test |
|
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
|
metrics: |
|
- type: accuracy |
|
value: 73.27841291190316 |
|
- type: f1 |
|
value: 70.65529957574735 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: mteb/amazon_massive_scenario |
|
name: MTEB MassiveScenarioClassification (zh-CN) |
|
config: zh-CN |
|
split: test |
|
revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
|
metrics: |
|
- type: accuracy |
|
value: 76.30127774041695 |
|
- type: f1 |
|
value: 76.10358226518304 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MedicalRetrieval |
|
name: MTEB MedicalRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 56.3 |
|
- type: map_at_10 |
|
value: 62.193 |
|
- type: map_at_100 |
|
value: 62.722 |
|
- type: map_at_1000 |
|
value: 62.765 |
|
- type: map_at_3 |
|
value: 60.633 |
|
- type: map_at_5 |
|
value: 61.617999999999995 |
|
- type: mrr_at_1 |
|
value: 56.3 |
|
- type: mrr_at_10 |
|
value: 62.193 |
|
- type: mrr_at_100 |
|
value: 62.722 |
|
- type: mrr_at_1000 |
|
value: 62.765 |
|
- type: mrr_at_3 |
|
value: 60.633 |
|
- type: mrr_at_5 |
|
value: 61.617999999999995 |
|
- type: ndcg_at_1 |
|
value: 56.3 |
|
- type: ndcg_at_10 |
|
value: 65.176 |
|
- type: ndcg_at_100 |
|
value: 67.989 |
|
- type: ndcg_at_1000 |
|
value: 69.219 |
|
- type: ndcg_at_3 |
|
value: 62.014 |
|
- type: ndcg_at_5 |
|
value: 63.766 |
|
- type: precision_at_1 |
|
value: 56.3 |
|
- type: precision_at_10 |
|
value: 7.46 |
|
- type: precision_at_100 |
|
value: 0.8829999999999999 |
|
- type: precision_at_1000 |
|
value: 0.098 |
|
- type: precision_at_3 |
|
value: 22.0 |
|
- type: precision_at_5 |
|
value: 14.04 |
|
- type: recall_at_1 |
|
value: 56.3 |
|
- type: recall_at_10 |
|
value: 74.6 |
|
- type: recall_at_100 |
|
value: 88.3 |
|
- type: recall_at_1000 |
|
value: 98.1 |
|
- type: recall_at_3 |
|
value: 66.0 |
|
- type: recall_at_5 |
|
value: 70.19999999999999 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/MultilingualSentiment-classification |
|
name: MTEB MultilingualSentiment |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 76.44666666666666 |
|
- type: f1 |
|
value: 76.34548655475949 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/OCNLI |
|
name: MTEB Ocnli |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 82.34975636166757 |
|
- type: cos_sim_ap |
|
value: 85.44149338593267 |
|
- type: cos_sim_f1 |
|
value: 83.68654509610647 |
|
- type: cos_sim_precision |
|
value: 78.46580406654344 |
|
- type: cos_sim_recall |
|
value: 89.65153115100317 |
|
- type: dot_accuracy |
|
value: 82.34975636166757 |
|
- type: dot_ap |
|
value: 85.4415701376729 |
|
- type: dot_f1 |
|
value: 83.68654509610647 |
|
- type: dot_precision |
|
value: 78.46580406654344 |
|
- type: dot_recall |
|
value: 89.65153115100317 |
|
- type: euclidean_accuracy |
|
value: 82.34975636166757 |
|
- type: euclidean_ap |
|
value: 85.4415701376729 |
|
- type: euclidean_f1 |
|
value: 83.68654509610647 |
|
- type: euclidean_precision |
|
value: 78.46580406654344 |
|
- type: euclidean_recall |
|
value: 89.65153115100317 |
|
- type: manhattan_accuracy |
|
value: 81.97076340010828 |
|
- type: manhattan_ap |
|
value: 84.83614660756733 |
|
- type: manhattan_f1 |
|
value: 83.34167083541772 |
|
- type: manhattan_precision |
|
value: 79.18250950570342 |
|
- type: manhattan_recall |
|
value: 87.96198521647307 |
|
- type: max_accuracy |
|
value: 82.34975636166757 |
|
- type: max_ap |
|
value: 85.4415701376729 |
|
- type: max_f1 |
|
value: 83.68654509610647 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/OnlineShopping-classification |
|
name: MTEB OnlineShopping |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 93.24 |
|
- type: ap |
|
value: 91.3586656455605 |
|
- type: f1 |
|
value: 93.22999314249503 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/PAWSX |
|
name: MTEB PAWSX |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 39.05676042449009 |
|
- type: cos_sim_spearman |
|
value: 44.996534098358545 |
|
- type: euclidean_pearson |
|
value: 44.42418609172825 |
|
- type: euclidean_spearman |
|
value: 44.995941361058996 |
|
- type: manhattan_pearson |
|
value: 43.98118203238076 |
|
- type: manhattan_spearman |
|
value: 44.51414152788784 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/QBQTC |
|
name: MTEB QBQTC |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 36.694269474438045 |
|
- type: cos_sim_spearman |
|
value: 38.686738967031616 |
|
- type: euclidean_pearson |
|
value: 36.822540068407235 |
|
- type: euclidean_spearman |
|
value: 38.68690745429757 |
|
- type: manhattan_pearson |
|
value: 36.77180703308932 |
|
- type: manhattan_spearman |
|
value: 38.45414914148094 |
|
- 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: 65.81209017614124 |
|
- type: cos_sim_spearman |
|
value: 66.5255285833172 |
|
- type: euclidean_pearson |
|
value: 66.01848701752732 |
|
- type: euclidean_spearman |
|
value: 66.5255285833172 |
|
- type: manhattan_pearson |
|
value: 66.66433676370542 |
|
- type: manhattan_spearman |
|
value: 67.07086311480214 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/STSB |
|
name: MTEB STSB |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 80.60785761283502 |
|
- type: cos_sim_spearman |
|
value: 82.80278693241074 |
|
- type: euclidean_pearson |
|
value: 82.47573315938638 |
|
- type: euclidean_spearman |
|
value: 82.80290808593806 |
|
- type: manhattan_pearson |
|
value: 82.49682028989669 |
|
- type: manhattan_spearman |
|
value: 82.84565039346022 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/T2Reranking |
|
name: MTEB T2Reranking |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map |
|
value: 66.37886004738723 |
|
- type: mrr |
|
value: 76.08501655006394 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/T2Retrieval |
|
name: MTEB T2Retrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 28.102 |
|
- type: map_at_10 |
|
value: 78.071 |
|
- type: map_at_100 |
|
value: 81.71000000000001 |
|
- type: map_at_1000 |
|
value: 81.773 |
|
- type: map_at_3 |
|
value: 55.142 |
|
- type: map_at_5 |
|
value: 67.669 |
|
- type: mrr_at_1 |
|
value: 90.9 |
|
- type: mrr_at_10 |
|
value: 93.29499999999999 |
|
- type: mrr_at_100 |
|
value: 93.377 |
|
- type: mrr_at_1000 |
|
value: 93.379 |
|
- type: mrr_at_3 |
|
value: 92.901 |
|
- type: mrr_at_5 |
|
value: 93.152 |
|
- type: ndcg_at_1 |
|
value: 90.9 |
|
- type: ndcg_at_10 |
|
value: 85.564 |
|
- type: ndcg_at_100 |
|
value: 89.11200000000001 |
|
- type: ndcg_at_1000 |
|
value: 89.693 |
|
- type: ndcg_at_3 |
|
value: 87.024 |
|
- type: ndcg_at_5 |
|
value: 85.66 |
|
- type: precision_at_1 |
|
value: 90.9 |
|
- type: precision_at_10 |
|
value: 42.208 |
|
- type: precision_at_100 |
|
value: 5.027 |
|
- type: precision_at_1000 |
|
value: 0.517 |
|
- type: precision_at_3 |
|
value: 75.872 |
|
- type: precision_at_5 |
|
value: 63.566 |
|
- type: recall_at_1 |
|
value: 28.102 |
|
- type: recall_at_10 |
|
value: 84.44500000000001 |
|
- type: recall_at_100 |
|
value: 95.91300000000001 |
|
- type: recall_at_1000 |
|
value: 98.80799999999999 |
|
- type: recall_at_3 |
|
value: 56.772999999999996 |
|
- type: recall_at_5 |
|
value: 70.99499999999999 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/TNews-classification |
|
name: MTEB TNews |
|
config: default |
|
split: validation |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 53.10599999999999 |
|
- type: f1 |
|
value: 51.40415523558322 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringP2P |
|
name: MTEB ThuNewsClusteringP2P |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 69.6145576098232 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringS2S |
|
name: MTEB ThuNewsClusteringS2S |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: v_measure |
|
value: 63.7129548775017 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/VideoRetrieval |
|
name: MTEB VideoRetrieval |
|
config: default |
|
split: dev |
|
revision: None |
|
metrics: |
|
- type: map_at_1 |
|
value: 60.199999999999996 |
|
- type: map_at_10 |
|
value: 69.724 |
|
- type: map_at_100 |
|
value: 70.185 |
|
- type: map_at_1000 |
|
value: 70.196 |
|
- type: map_at_3 |
|
value: 67.95 |
|
- type: map_at_5 |
|
value: 69.155 |
|
- type: mrr_at_1 |
|
value: 60.199999999999996 |
|
- type: mrr_at_10 |
|
value: 69.724 |
|
- type: mrr_at_100 |
|
value: 70.185 |
|
- type: mrr_at_1000 |
|
value: 70.196 |
|
- type: mrr_at_3 |
|
value: 67.95 |
|
- type: mrr_at_5 |
|
value: 69.155 |
|
- type: ndcg_at_1 |
|
value: 60.199999999999996 |
|
- type: ndcg_at_10 |
|
value: 73.888 |
|
- type: ndcg_at_100 |
|
value: 76.02799999999999 |
|
- type: ndcg_at_1000 |
|
value: 76.344 |
|
- type: ndcg_at_3 |
|
value: 70.384 |
|
- type: ndcg_at_5 |
|
value: 72.541 |
|
- type: precision_at_1 |
|
value: 60.199999999999996 |
|
- type: precision_at_10 |
|
value: 8.67 |
|
- type: precision_at_100 |
|
value: 0.9650000000000001 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 25.8 |
|
- type: precision_at_5 |
|
value: 16.520000000000003 |
|
- type: recall_at_1 |
|
value: 60.199999999999996 |
|
- type: recall_at_10 |
|
value: 86.7 |
|
- type: recall_at_100 |
|
value: 96.5 |
|
- type: recall_at_1000 |
|
value: 99.0 |
|
- type: recall_at_3 |
|
value: 77.4 |
|
- type: recall_at_5 |
|
value: 82.6 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/waimai-classification |
|
name: MTEB Waimai |
|
config: default |
|
split: test |
|
revision: None |
|
metrics: |
|
- type: accuracy |
|
value: 88.08 |
|
- type: ap |
|
value: 72.66435456846166 |
|
- type: f1 |
|
value: 86.55995793551286 |
|
--- |
|
|
|
**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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``` |
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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 |
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