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--- |
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pipeline_tag: sentence-similarity |
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tags: |
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- mteb |
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- sentence-transformers |
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- feature-extraction |
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- sentence-similarity |
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model-index: |
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- name: acge_text_embedding |
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results: |
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- task: |
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type: STS |
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dataset: |
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type: C-MTEB/AFQMC |
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name: MTEB AFQMC |
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config: default |
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split: validation |
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revision: b44c3b011063adb25877c13823db83bb193913c4 |
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metrics: |
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- type: cos_sim_pearson |
|
value: 54.03219651150428 |
|
- type: cos_sim_spearman |
|
value: 58.80567952355933 |
|
- type: euclidean_pearson |
|
value: 57.47052075207808 |
|
- type: euclidean_spearman |
|
value: 58.80429232297114 |
|
- type: manhattan_pearson |
|
value: 57.46163912433917 |
|
- type: manhattan_spearman |
|
value: 58.797778532121 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/ATEC |
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name: MTEB ATEC |
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config: default |
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split: test |
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revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 |
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metrics: |
|
- type: cos_sim_pearson |
|
value: 53.523171963746854 |
|
- type: cos_sim_spearman |
|
value: 57.94610819724817 |
|
- type: euclidean_pearson |
|
value: 61.16974418403869 |
|
- type: euclidean_spearman |
|
value: 57.94681861980281 |
|
- type: manhattan_pearson |
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value: 61.167825359334515 |
|
- type: manhattan_spearman |
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value: 57.94540903298445 |
|
- task: |
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type: Classification |
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dataset: |
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type: mteb/amazon_reviews_multi |
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name: MTEB AmazonReviewsClassification (zh) |
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config: zh |
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split: test |
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revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
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metrics: |
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- type: accuracy |
|
value: 48.556 |
|
- type: f1 |
|
value: 46.61852566163211 |
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- task: |
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type: STS |
|
dataset: |
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type: C-MTEB/BQ |
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name: MTEB BQ |
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config: default |
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split: test |
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revision: e3dda5e115e487b39ec7e618c0c6a29137052a55 |
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metrics: |
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- type: cos_sim_pearson |
|
value: 68.26963267181252 |
|
- type: cos_sim_spearman |
|
value: 70.36696156869363 |
|
- type: euclidean_pearson |
|
value: 69.42591718370763 |
|
- type: euclidean_spearman |
|
value: 70.3677583116469 |
|
- type: manhattan_pearson |
|
value: 69.40127857737215 |
|
- type: manhattan_spearman |
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value: 70.34572662526428 |
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- task: |
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type: Clustering |
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dataset: |
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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: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 |
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metrics: |
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- type: v_measure |
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value: 46.54685387179774 |
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- task: |
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type: Clustering |
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dataset: |
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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: e458b3f5414b62b7f9f83499ac1f5497ae2e869f |
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metrics: |
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- type: v_measure |
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value: 44.45602575811581 |
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- task: |
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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: 8d7f1e942507dac42dc58017c1a001c3717da7df |
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metrics: |
|
- type: map |
|
value: 88.4576468720639 |
|
- type: mrr |
|
value: 90.90595238095237 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/CMedQAv2-reranking |
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name: MTEB CMedQAv2 |
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config: default |
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split: test |
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revision: 23d186750531a14a0357ca22cd92d712fd512ea0 |
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metrics: |
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- type: map |
|
value: 88.71413673867269 |
|
- type: mrr |
|
value: 91.19265873015873 |
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- task: |
|
type: Retrieval |
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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: cd540c506dae1cf9e9a59c3e06f42030d54e7301 |
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metrics: |
|
- type: map_at_1 |
|
value: 26.825 |
|
- type: map_at_10 |
|
value: 39.959 |
|
- type: map_at_100 |
|
value: 41.861 |
|
- type: map_at_1000 |
|
value: 41.963 |
|
- type: map_at_3 |
|
value: 35.357 |
|
- type: map_at_5 |
|
value: 38.001000000000005 |
|
- type: mrr_at_1 |
|
value: 40.585 |
|
- type: mrr_at_10 |
|
value: 48.802 |
|
- type: mrr_at_100 |
|
value: 49.779 |
|
- type: mrr_at_1000 |
|
value: 49.819 |
|
- type: mrr_at_3 |
|
value: 46.095000000000006 |
|
- type: mrr_at_5 |
|
value: 47.678 |
|
- type: ndcg_at_1 |
|
value: 40.585 |
|
- type: ndcg_at_10 |
|
value: 46.758 |
|
- type: ndcg_at_100 |
|
value: 53.957 |
|
- type: ndcg_at_1000 |
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value: 55.656000000000006 |
|
- type: ndcg_at_3 |
|
value: 40.961 |
|
- type: ndcg_at_5 |
|
value: 43.564 |
|
- type: precision_at_1 |
|
value: 40.585 |
|
- type: precision_at_10 |
|
value: 10.424999999999999 |
|
- type: precision_at_100 |
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value: 1.625 |
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- type: precision_at_1000 |
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value: 0.184 |
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- type: precision_at_3 |
|
value: 23.114 |
|
- type: precision_at_5 |
|
value: 17.024 |
|
- type: recall_at_1 |
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value: 26.825 |
|
- type: recall_at_10 |
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value: 57.909 |
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- type: recall_at_100 |
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value: 87.375 |
|
- type: recall_at_1000 |
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value: 98.695 |
|
- type: recall_at_3 |
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value: 40.754000000000005 |
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- type: recall_at_5 |
|
value: 48.472 |
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- task: |
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type: PairClassification |
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dataset: |
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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: 41bc36f332156f7adc9e38f53777c959b2ae9766 |
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metrics: |
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- type: cos_sim_accuracy |
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value: 83.4155141310884 |
|
- type: cos_sim_ap |
|
value: 90.49006000181046 |
|
- type: cos_sim_f1 |
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value: 84.28797826579125 |
|
- type: cos_sim_precision |
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value: 81.69848584595128 |
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- type: cos_sim_recall |
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value: 87.04699555763385 |
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- type: dot_accuracy |
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value: 83.40348767288035 |
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- type: dot_ap |
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value: 90.50667776818787 |
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- type: dot_f1 |
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value: 84.31853669417802 |
|
- type: dot_precision |
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value: 80.61420345489442 |
|
- type: dot_recall |
|
value: 88.379705400982 |
|
- type: euclidean_accuracy |
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value: 83.43956704750451 |
|
- type: euclidean_ap |
|
value: 90.48869698176196 |
|
- type: euclidean_f1 |
|
value: 84.32616081540203 |
|
- type: euclidean_precision |
|
value: 81.77026136613222 |
|
- type: euclidean_recall |
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value: 87.04699555763385 |
|
- type: manhattan_accuracy |
|
value: 83.55983162958509 |
|
- type: manhattan_ap |
|
value: 90.47972486190912 |
|
- type: manhattan_f1 |
|
value: 84.42325158946412 |
|
- type: manhattan_precision |
|
value: 82.0569410726109 |
|
- type: manhattan_recall |
|
value: 86.93009118541033 |
|
- type: max_accuracy |
|
value: 83.55983162958509 |
|
- type: max_ap |
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value: 90.50667776818787 |
|
- type: max_f1 |
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value: 84.42325158946412 |
|
- task: |
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type: Retrieval |
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dataset: |
|
type: C-MTEB/CovidRetrieval |
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name: MTEB CovidRetrieval |
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config: default |
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split: dev |
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revision: 1271c7809071a13532e05f25fb53511ffce77117 |
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metrics: |
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- type: map_at_1 |
|
value: 67.597 |
|
- type: map_at_10 |
|
value: 76.545 |
|
- type: map_at_100 |
|
value: 76.893 |
|
- type: map_at_1000 |
|
value: 76.897 |
|
- type: map_at_3 |
|
value: 74.807 |
|
- type: map_at_5 |
|
value: 75.895 |
|
- type: mrr_at_1 |
|
value: 67.861 |
|
- type: mrr_at_10 |
|
value: 76.545 |
|
- type: mrr_at_100 |
|
value: 76.893 |
|
- type: mrr_at_1000 |
|
value: 76.897 |
|
- type: mrr_at_3 |
|
value: 74.886 |
|
- type: mrr_at_5 |
|
value: 75.934 |
|
- type: ndcg_at_1 |
|
value: 67.861 |
|
- type: ndcg_at_10 |
|
value: 80.417 |
|
- type: ndcg_at_100 |
|
value: 81.928 |
|
- type: ndcg_at_1000 |
|
value: 82.038 |
|
- type: ndcg_at_3 |
|
value: 77.025 |
|
- type: ndcg_at_5 |
|
value: 78.94099999999999 |
|
- type: precision_at_1 |
|
value: 67.861 |
|
- type: precision_at_10 |
|
value: 9.336 |
|
- type: precision_at_100 |
|
value: 1.001 |
|
- type: precision_at_1000 |
|
value: 0.101 |
|
- type: precision_at_3 |
|
value: 27.959 |
|
- type: precision_at_5 |
|
value: 17.745 |
|
- type: recall_at_1 |
|
value: 67.597 |
|
- type: recall_at_10 |
|
value: 92.308 |
|
- type: recall_at_100 |
|
value: 99.05199999999999 |
|
- type: recall_at_1000 |
|
value: 99.895 |
|
- type: recall_at_3 |
|
value: 83.325 |
|
- type: recall_at_5 |
|
value: 87.908 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/DuRetrieval |
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name: MTEB DuRetrieval |
|
config: default |
|
split: dev |
|
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced |
|
metrics: |
|
- type: map_at_1 |
|
value: 25.574 |
|
- type: map_at_10 |
|
value: 78.493 |
|
- type: map_at_100 |
|
value: 81.384 |
|
- type: map_at_1000 |
|
value: 81.429 |
|
- type: map_at_3 |
|
value: 54.107000000000006 |
|
- type: map_at_5 |
|
value: 68.755 |
|
- type: mrr_at_1 |
|
value: 89.2 |
|
- type: mrr_at_10 |
|
value: 92.567 |
|
- type: mrr_at_100 |
|
value: 92.642 |
|
- type: mrr_at_1000 |
|
value: 92.646 |
|
- type: mrr_at_3 |
|
value: 92.258 |
|
- type: mrr_at_5 |
|
value: 92.458 |
|
- type: ndcg_at_1 |
|
value: 89.2 |
|
- type: ndcg_at_10 |
|
value: 86.084 |
|
- type: ndcg_at_100 |
|
value: 89.053 |
|
- type: ndcg_at_1000 |
|
value: 89.484 |
|
- type: ndcg_at_3 |
|
value: 84.898 |
|
- type: ndcg_at_5 |
|
value: 84.078 |
|
- type: precision_at_1 |
|
value: 89.2 |
|
- type: precision_at_10 |
|
value: 41.345 |
|
- type: precision_at_100 |
|
value: 4.779 |
|
- type: precision_at_1000 |
|
value: 0.488 |
|
- type: precision_at_3 |
|
value: 76.167 |
|
- type: precision_at_5 |
|
value: 64.7 |
|
- type: recall_at_1 |
|
value: 25.574 |
|
- type: recall_at_10 |
|
value: 87.153 |
|
- type: recall_at_100 |
|
value: 96.829 |
|
- type: recall_at_1000 |
|
value: 99.11999999999999 |
|
- type: recall_at_3 |
|
value: 56.421 |
|
- type: recall_at_5 |
|
value: 73.7 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/EcomRetrieval |
|
name: MTEB EcomRetrieval |
|
config: default |
|
split: dev |
|
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 |
|
metrics: |
|
- type: map_at_1 |
|
value: 52.0 |
|
- type: map_at_10 |
|
value: 62.553000000000004 |
|
- type: map_at_100 |
|
value: 63.048 |
|
- type: map_at_1000 |
|
value: 63.065000000000005 |
|
- type: map_at_3 |
|
value: 60.233000000000004 |
|
- type: map_at_5 |
|
value: 61.712999999999994 |
|
- type: mrr_at_1 |
|
value: 52.0 |
|
- type: mrr_at_10 |
|
value: 62.553000000000004 |
|
- type: mrr_at_100 |
|
value: 63.048 |
|
- type: mrr_at_1000 |
|
value: 63.065000000000005 |
|
- type: mrr_at_3 |
|
value: 60.233000000000004 |
|
- type: mrr_at_5 |
|
value: 61.712999999999994 |
|
- type: ndcg_at_1 |
|
value: 52.0 |
|
- type: ndcg_at_10 |
|
value: 67.51599999999999 |
|
- type: ndcg_at_100 |
|
value: 69.896 |
|
- type: ndcg_at_1000 |
|
value: 70.281 |
|
- type: ndcg_at_3 |
|
value: 62.82600000000001 |
|
- type: ndcg_at_5 |
|
value: 65.498 |
|
- type: precision_at_1 |
|
value: 52.0 |
|
- type: precision_at_10 |
|
value: 8.3 |
|
- type: precision_at_100 |
|
value: 0.941 |
|
- type: precision_at_1000 |
|
value: 0.097 |
|
- type: precision_at_3 |
|
value: 23.433 |
|
- type: precision_at_5 |
|
value: 15.36 |
|
- type: recall_at_1 |
|
value: 52.0 |
|
- type: recall_at_10 |
|
value: 83.0 |
|
- type: recall_at_100 |
|
value: 94.1 |
|
- type: recall_at_1000 |
|
value: 97.0 |
|
- type: recall_at_3 |
|
value: 70.3 |
|
- type: recall_at_5 |
|
value: 76.8 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/IFlyTek-classification |
|
name: MTEB IFlyTek |
|
config: default |
|
split: validation |
|
revision: 421605374b29664c5fc098418fe20ada9bd55f8a |
|
metrics: |
|
- type: accuracy |
|
value: 51.76606387071951 |
|
- type: f1 |
|
value: 40.25725744367441 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/JDReview-classification |
|
name: MTEB JDReview |
|
config: default |
|
split: test |
|
revision: b7c64bd89eb87f8ded463478346f76731f07bf8b |
|
metrics: |
|
- type: accuracy |
|
value: 86.69793621013133 |
|
- type: ap |
|
value: 55.46718958939327 |
|
- type: f1 |
|
value: 81.48228915952436 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/LCQMC |
|
name: MTEB LCQMC |
|
config: default |
|
split: test |
|
revision: 17f9b096f80380fce5ed12a9be8be7784b337daf |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 71.13755846688528 |
|
- type: cos_sim_spearman |
|
value: 78.17322744116031 |
|
- type: euclidean_pearson |
|
value: 77.48740502819294 |
|
- type: euclidean_spearman |
|
value: 78.17553979551616 |
|
- type: manhattan_pearson |
|
value: 77.47671561749276 |
|
- type: manhattan_spearman |
|
value: 78.16780681181362 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/Mmarco-reranking |
|
name: MTEB MMarcoReranking |
|
config: default |
|
split: dev |
|
revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6 |
|
metrics: |
|
- type: map |
|
value: 27.054392822906316 |
|
- type: mrr |
|
value: 29.001190476190473 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MMarcoRetrieval |
|
name: MTEB MMarcoRetrieval |
|
config: default |
|
split: dev |
|
revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 |
|
metrics: |
|
- type: map_at_1 |
|
value: 65.62599999999999 |
|
- type: map_at_10 |
|
value: 74.749 |
|
- type: map_at_100 |
|
value: 75.091 |
|
- type: map_at_1000 |
|
value: 75.103 |
|
- type: map_at_3 |
|
value: 73.007 |
|
- type: map_at_5 |
|
value: 74.124 |
|
- type: mrr_at_1 |
|
value: 67.894 |
|
- type: mrr_at_10 |
|
value: 75.374 |
|
- type: mrr_at_100 |
|
value: 75.67399999999999 |
|
- type: mrr_at_1000 |
|
value: 75.685 |
|
- type: mrr_at_3 |
|
value: 73.868 |
|
- type: mrr_at_5 |
|
value: 74.83 |
|
- type: ndcg_at_1 |
|
value: 67.894 |
|
- type: ndcg_at_10 |
|
value: 78.414 |
|
- type: ndcg_at_100 |
|
value: 79.947 |
|
- type: ndcg_at_1000 |
|
value: 80.265 |
|
- type: ndcg_at_3 |
|
value: 75.12 |
|
- type: ndcg_at_5 |
|
value: 76.999 |
|
- type: precision_at_1 |
|
value: 67.894 |
|
- type: precision_at_10 |
|
value: 9.47 |
|
- type: precision_at_100 |
|
value: 1.023 |
|
- type: precision_at_1000 |
|
value: 0.105 |
|
- type: precision_at_3 |
|
value: 28.333000000000002 |
|
- type: precision_at_5 |
|
value: 17.989 |
|
- type: recall_at_1 |
|
value: 65.62599999999999 |
|
- type: recall_at_10 |
|
value: 89.063 |
|
- type: recall_at_100 |
|
value: 95.99499999999999 |
|
- type: recall_at_1000 |
|
value: 98.455 |
|
- type: recall_at_3 |
|
value: 80.357 |
|
- type: recall_at_5 |
|
value: 84.824 |
|
- 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: 75.88433086751849 |
|
- type: f1 |
|
value: 73.06801290283882 |
|
- 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: 78.44317417619366 |
|
- type: f1 |
|
value: 78.1407925250533 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/MedicalRetrieval |
|
name: MTEB MedicalRetrieval |
|
config: default |
|
split: dev |
|
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 |
|
metrics: |
|
- type: map_at_1 |
|
value: 54.900000000000006 |
|
- type: map_at_10 |
|
value: 61.0 |
|
- type: map_at_100 |
|
value: 61.549 |
|
- type: map_at_1000 |
|
value: 61.590999999999994 |
|
- type: map_at_3 |
|
value: 59.516999999999996 |
|
- type: map_at_5 |
|
value: 60.267 |
|
- type: mrr_at_1 |
|
value: 55.1 |
|
- type: mrr_at_10 |
|
value: 61.1 |
|
- type: mrr_at_100 |
|
value: 61.649 |
|
- type: mrr_at_1000 |
|
value: 61.690999999999995 |
|
- type: mrr_at_3 |
|
value: 59.617 |
|
- type: mrr_at_5 |
|
value: 60.367000000000004 |
|
- type: ndcg_at_1 |
|
value: 54.900000000000006 |
|
- type: ndcg_at_10 |
|
value: 64.07000000000001 |
|
- type: ndcg_at_100 |
|
value: 66.981 |
|
- type: ndcg_at_1000 |
|
value: 68.207 |
|
- type: ndcg_at_3 |
|
value: 60.955999999999996 |
|
- type: ndcg_at_5 |
|
value: 62.31100000000001 |
|
- type: precision_at_1 |
|
value: 54.900000000000006 |
|
- type: precision_at_10 |
|
value: 7.380000000000001 |
|
- type: precision_at_100 |
|
value: 0.88 |
|
- type: precision_at_1000 |
|
value: 0.098 |
|
- type: precision_at_3 |
|
value: 21.7 |
|
- type: precision_at_5 |
|
value: 13.68 |
|
- type: recall_at_1 |
|
value: 54.900000000000006 |
|
- type: recall_at_10 |
|
value: 73.8 |
|
- type: recall_at_100 |
|
value: 88.0 |
|
- type: recall_at_1000 |
|
value: 97.8 |
|
- type: recall_at_3 |
|
value: 65.10000000000001 |
|
- type: recall_at_5 |
|
value: 68.4 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/MultilingualSentiment-classification |
|
name: MTEB MultilingualSentiment |
|
config: default |
|
split: validation |
|
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a |
|
metrics: |
|
- type: accuracy |
|
value: 77.56333333333333 |
|
- type: f1 |
|
value: 77.53666660124703 |
|
- task: |
|
type: PairClassification |
|
dataset: |
|
type: C-MTEB/OCNLI |
|
name: MTEB Ocnli |
|
config: default |
|
split: validation |
|
revision: 66e76a618a34d6d565d5538088562851e6daa7ec |
|
metrics: |
|
- type: cos_sim_accuracy |
|
value: 81.10449377368705 |
|
- type: cos_sim_ap |
|
value: 85.16141108141811 |
|
- type: cos_sim_f1 |
|
value: 82.97771455666192 |
|
- type: cos_sim_precision |
|
value: 75.30120481927712 |
|
- type: cos_sim_recall |
|
value: 92.39704329461456 |
|
- type: dot_accuracy |
|
value: 81.05035192203573 |
|
- type: dot_ap |
|
value: 85.13568069803823 |
|
- type: dot_f1 |
|
value: 83.04038004750595 |
|
- type: dot_precision |
|
value: 75.47495682210709 |
|
- type: dot_recall |
|
value: 92.29144667370645 |
|
- type: euclidean_accuracy |
|
value: 81.10449377368705 |
|
- type: euclidean_ap |
|
value: 85.16341835376645 |
|
- type: euclidean_f1 |
|
value: 82.96860133206471 |
|
- type: euclidean_precision |
|
value: 75.4978354978355 |
|
- type: euclidean_recall |
|
value: 92.08025343189018 |
|
- type: manhattan_accuracy |
|
value: 81.15863562533838 |
|
- type: manhattan_ap |
|
value: 85.13388548299352 |
|
- type: manhattan_f1 |
|
value: 82.91048348492102 |
|
- type: manhattan_precision |
|
value: 75.83187390542906 |
|
- type: manhattan_recall |
|
value: 91.4466737064414 |
|
- type: max_accuracy |
|
value: 81.15863562533838 |
|
- type: max_ap |
|
value: 85.16341835376645 |
|
- type: max_f1 |
|
value: 83.04038004750595 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/OnlineShopping-classification |
|
name: MTEB OnlineShopping |
|
config: default |
|
split: test |
|
revision: e610f2ebd179a8fda30ae534c3878750a96db120 |
|
metrics: |
|
- type: accuracy |
|
value: 93.75 |
|
- type: ap |
|
value: 91.8757063139003 |
|
- type: f1 |
|
value: 93.73901896028437 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/PAWSX |
|
name: MTEB PAWSX |
|
config: default |
|
split: test |
|
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 39.15831534609524 |
|
- type: cos_sim_spearman |
|
value: 45.4969633673045 |
|
- type: euclidean_pearson |
|
value: 44.848515043386826 |
|
- type: euclidean_spearman |
|
value: 45.50184060659851 |
|
- type: manhattan_pearson |
|
value: 44.855618769134786 |
|
- type: manhattan_spearman |
|
value: 45.521349632021 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/QBQTC |
|
name: MTEB QBQTC |
|
config: default |
|
split: test |
|
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 34.240063381471685 |
|
- type: cos_sim_spearman |
|
value: 37.29810568951238 |
|
- type: euclidean_pearson |
|
value: 35.114630288288694 |
|
- type: euclidean_spearman |
|
value: 37.29224953963422 |
|
- type: manhattan_pearson |
|
value: 35.07429582481541 |
|
- type: manhattan_spearman |
|
value: 37.24006222876743 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: mteb/sts22-crosslingual-sts |
|
name: MTEB STS22 (zh) |
|
config: zh |
|
split: test |
|
revision: eea2b4fe26a775864c896887d910b76a8098ad3f |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 61.839386292911634 |
|
- type: cos_sim_spearman |
|
value: 67.05632097771566 |
|
- type: euclidean_pearson |
|
value: 65.72031356075829 |
|
- type: euclidean_spearman |
|
value: 67.05823973191457 |
|
- type: manhattan_pearson |
|
value: 65.66073527177826 |
|
- type: manhattan_spearman |
|
value: 67.04221791481658 |
|
- task: |
|
type: STS |
|
dataset: |
|
type: C-MTEB/STSB |
|
name: MTEB STSB |
|
config: default |
|
split: test |
|
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0 |
|
metrics: |
|
- type: cos_sim_pearson |
|
value: 81.56195178204662 |
|
- type: cos_sim_spearman |
|
value: 82.73033434099031 |
|
- type: euclidean_pearson |
|
value: 82.49605254478311 |
|
- type: euclidean_spearman |
|
value: 82.72004995354247 |
|
- type: manhattan_pearson |
|
value: 82.48358662476731 |
|
- type: manhattan_spearman |
|
value: 82.70676710419983 |
|
- task: |
|
type: Reranking |
|
dataset: |
|
type: C-MTEB/T2Reranking |
|
name: MTEB T2Reranking |
|
config: default |
|
split: dev |
|
revision: 76631901a18387f85eaa53e5450019b87ad58ef9 |
|
metrics: |
|
- type: map |
|
value: 65.9012655137193 |
|
- type: mrr |
|
value: 75.97216177150165 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/T2Retrieval |
|
name: MTEB T2Retrieval |
|
config: default |
|
split: dev |
|
revision: 8731a845f1bf500a4f111cf1070785c793d10e64 |
|
metrics: |
|
- type: map_at_1 |
|
value: 27.057 |
|
- type: map_at_10 |
|
value: 75.29299999999999 |
|
- type: map_at_100 |
|
value: 79.098 |
|
- type: map_at_1000 |
|
value: 79.172 |
|
- type: map_at_3 |
|
value: 53.049 |
|
- type: map_at_5 |
|
value: 65.103 |
|
- type: mrr_at_1 |
|
value: 88.822 |
|
- type: mrr_at_10 |
|
value: 91.721 |
|
- type: mrr_at_100 |
|
value: 91.814 |
|
- type: mrr_at_1000 |
|
value: 91.818 |
|
- type: mrr_at_3 |
|
value: 91.213 |
|
- type: mrr_at_5 |
|
value: 91.544 |
|
- type: ndcg_at_1 |
|
value: 88.822 |
|
- type: ndcg_at_10 |
|
value: 83.269 |
|
- type: ndcg_at_100 |
|
value: 87.259 |
|
- type: ndcg_at_1000 |
|
value: 87.938 |
|
- type: ndcg_at_3 |
|
value: 84.678 |
|
- type: ndcg_at_5 |
|
value: 83.231 |
|
- type: precision_at_1 |
|
value: 88.822 |
|
- type: precision_at_10 |
|
value: 41.297 |
|
- type: precision_at_100 |
|
value: 4.994 |
|
- type: precision_at_1000 |
|
value: 0.515 |
|
- type: precision_at_3 |
|
value: 73.933 |
|
- type: precision_at_5 |
|
value: 61.885 |
|
- type: recall_at_1 |
|
value: 27.057 |
|
- type: recall_at_10 |
|
value: 82.33200000000001 |
|
- type: recall_at_100 |
|
value: 95.065 |
|
- type: recall_at_1000 |
|
value: 98.466 |
|
- type: recall_at_3 |
|
value: 54.872 |
|
- type: recall_at_5 |
|
value: 68.814 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/TNews-classification |
|
name: MTEB TNews |
|
config: default |
|
split: validation |
|
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4 |
|
metrics: |
|
- type: accuracy |
|
value: 53.690000000000005 |
|
- type: f1 |
|
value: 51.87306088948137 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringP2P |
|
name: MTEB ThuNewsClusteringP2P |
|
config: default |
|
split: test |
|
revision: 5798586b105c0434e4f0fe5e767abe619442cf93 |
|
metrics: |
|
- type: v_measure |
|
value: 73.76590442198115 |
|
- task: |
|
type: Clustering |
|
dataset: |
|
type: C-MTEB/ThuNewsClusteringS2S |
|
name: MTEB ThuNewsClusteringS2S |
|
config: default |
|
split: test |
|
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d |
|
metrics: |
|
- type: v_measure |
|
value: 68.61875345658028 |
|
- task: |
|
type: Retrieval |
|
dataset: |
|
type: C-MTEB/VideoRetrieval |
|
name: MTEB VideoRetrieval |
|
config: default |
|
split: dev |
|
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 |
|
metrics: |
|
- type: map_at_1 |
|
value: 59.4 |
|
- type: map_at_10 |
|
value: 69.19 |
|
- type: map_at_100 |
|
value: 69.711 |
|
- type: map_at_1000 |
|
value: 69.72699999999999 |
|
- type: map_at_3 |
|
value: 67.717 |
|
- type: map_at_5 |
|
value: 68.742 |
|
- type: mrr_at_1 |
|
value: 59.4 |
|
- type: mrr_at_10 |
|
value: 69.19 |
|
- type: mrr_at_100 |
|
value: 69.711 |
|
- type: mrr_at_1000 |
|
value: 69.72699999999999 |
|
- type: mrr_at_3 |
|
value: 67.717 |
|
- type: mrr_at_5 |
|
value: 68.742 |
|
- type: ndcg_at_1 |
|
value: 59.4 |
|
- type: ndcg_at_10 |
|
value: 73.28099999999999 |
|
- type: ndcg_at_100 |
|
value: 75.575 |
|
- type: ndcg_at_1000 |
|
value: 75.971 |
|
- type: ndcg_at_3 |
|
value: 70.339 |
|
- type: ndcg_at_5 |
|
value: 72.16799999999999 |
|
- type: precision_at_1 |
|
value: 59.4 |
|
- type: precision_at_10 |
|
value: 8.58 |
|
- type: precision_at_100 |
|
value: 0.96 |
|
- type: precision_at_1000 |
|
value: 0.099 |
|
- type: precision_at_3 |
|
value: 25.967000000000002 |
|
- type: precision_at_5 |
|
value: 16.46 |
|
- type: recall_at_1 |
|
value: 59.4 |
|
- type: recall_at_10 |
|
value: 85.8 |
|
- type: recall_at_100 |
|
value: 96.0 |
|
- type: recall_at_1000 |
|
value: 99.1 |
|
- type: recall_at_3 |
|
value: 77.9 |
|
- type: recall_at_5 |
|
value: 82.3 |
|
- task: |
|
type: Classification |
|
dataset: |
|
type: C-MTEB/waimai-classification |
|
name: MTEB Waimai |
|
config: default |
|
split: test |
|
revision: 339287def212450dcaa9df8c22bf93e9980c7023 |
|
metrics: |
|
- type: accuracy |
|
value: 88.56000000000002 |
|
- type: ap |
|
value: 73.62152033132061 |
|
- type: f1 |
|
value: 87.0916916405758 |
|
--- |
|
## acge model |
|
|
|
acge是一个通用的文本编码模型，是一个可变长度的向量化模型，使用了[Matryoshka Representation Learning](https://arxiv.org/abs/2205.13147)，如图所示： |
|
|
|
 |
|
|
|
建议使用的维度为1024或者1792 |
|
|
|
|
|
| Model Name | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? | |
|
|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:| |
|
| acge-text-embedding | 0.65 | [1024, 1792] | 1024 | Chinese | NO | |
|
|
|
|
|
## Metric |
|
|
|
#### C-MTEB leaderboard (Chinese) |
|
|
|
测试的时候因为数据的随机性、显卡、推理的数据类型导致每次推理的结果不一致，我总共测试了4次，不同的显卡(A10 A100)，不同的数据类型，测试结果放在了result文件夹中，选取了一个精度最低的测试作为最终的精度测试。 |
|
|
|
| Model Name | GPU | tensor-type | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) | |
|
|:------------------:|:---------------:|:---------:|:---------------:|:------------:|:------------------:|:--------------:|:-----------------------:|:-------------:|:-------------:|:-------:|:-------:|:-------:| |
|
| acge_text_embedding | NVIDIA TESLA A10 | bfloat16 | 0.65 | 1792 | 1024 | 68.91 | 72.76 | 58.22 | 87.82 | 67.67 | 72.48 | 62.24 | |
|
| acge_text_embedding | NVIDIA TESLA A100 | bfloat16 | 0.65 | 1792 | 1024 | 68.91 | 72.77 | 58.35 | 87.82 | 67.53 | 72.48 | 62.24 | |
|
| acge_text_embedding | NVIDIA TESLA A100 | float16 | 0.65 | 1792 | 1024 | 68.99 | 72.76 | 58.68 | 87.84 | 67.89 | 72.49 | 62.24 | |
|
| acge_text_embedding | NVIDIA TESLA A100 | float32 | 0.65 | 1792 | 1024 | 68.98 | 72.76 | 58.58 | 87.83 | 67.91 | 72.49 | 62.24 | |
|
|
|
#### Reproduce our results |
|
|
|
**C-MTEB:** |
|
|
|
```python |
|
import torch |
|
import argparse |
|
import functools |
|
from C_MTEB.tasks import * |
|
from typing import List, Dict |
|
from sentence_transformers import SentenceTransformer |
|
from mteb import MTEB, DRESModel |
|
|
|
|
|
class RetrievalModel(DRESModel): |
|
def __init__(self, encoder, **kwargs): |
|
self.encoder = encoder |
|
|
|
def encode_queries(self, queries: List[str], **kwargs) -> np.ndarray: |
|
input_texts = ['{}'.format(q) for q in queries] |
|
return self._do_encode(input_texts) |
|
|
|
def encode_corpus(self, corpus: List[Dict[str, str]], **kwargs) -> np.ndarray: |
|
input_texts = ['{} {}'.format(doc.get('title', ''), doc['text']).strip() for doc in corpus] |
|
input_texts = ['{}'.format(t) for t in input_texts] |
|
return self._do_encode(input_texts) |
|
|
|
@torch.no_grad() |
|
def _do_encode(self, input_texts: List[str]) -> np.ndarray: |
|
return self.encoder.encode( |
|
sentences=input_texts, |
|
batch_size=512, |
|
normalize_embeddings=True, |
|
convert_to_numpy=True |
|
) |
|
|
|
|
|
def get_args(): |
|
parser = argparse.ArgumentParser() |
|
parser.add_argument('--model_name_or_path', default="acge_text_embedding", type=str) |
|
parser.add_argument('--task_type', default=None, type=str) |
|
parser.add_argument('--pooling_method', default='cls', type=str) |
|
parser.add_argument('--output_dir', default='zh_results', |
|
type=str, help='output directory') |
|
parser.add_argument('--max_len', default=1024, type=int, help='max length') |
|
return parser.parse_args() |
|
|
|
|
|
if __name__ == '__main__': |
|
args = get_args() |
|
encoder = SentenceTransformer(args.model_name_or_path).half() |
|
encoder.encode = functools.partial(encoder.encode, normalize_embeddings=True) |
|
encoder.max_seq_length = int(args.max_len) |
|
|
|
task_names = [t.description["name"] for t in MTEB(task_types=args.task_type, |
|
task_langs=['zh', 'zh-CN']).tasks] |
|
TASKS_WITH_PROMPTS = ["T2Retrieval", "MMarcoRetrieval", "DuRetrieval", "CovidRetrieval", "CmedqaRetrieval", |
|
"EcomRetrieval", "MedicalRetrieval", "VideoRetrieval"] |
|
for task in task_names: |
|
evaluation = MTEB(tasks=[task], task_langs=['zh', 'zh-CN']) |
|
if task in TASKS_WITH_PROMPTS: |
|
evaluation.run(RetrievalModel(encoder), output_folder=args.output_dir, overwrite_results=False) |
|
else: |
|
evaluation.run(encoder, output_folder=args.output_dir, overwrite_results=False) |
|
|
|
|
|
``` |
|
|
|
|
|
## Usage |
|
|
|
#### acge 中文系列模型 |
|
|
|
在sentence-transformer库中的使用方法： |
|
|
|
```python |
|
from sentence_transformers import SentenceTransformer |
|
|
|
sentences = ["数据1", "数据2"] |
|
model = SentenceTransformer('acge_text_embedding') |
|
print(model.max_seq_length) |
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embeddings_1 = model.encode(sentences, normalize_embeddings=True) |
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embeddings_2 = model.encode(sentences, normalize_embeddings=True) |
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similarity = embeddings_1 @ embeddings_2.T |
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print(similarity) |
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``` |
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在sentence-transformer库中的使用方法，选取不同的维度： |
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|
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```python |
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import torch |
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from sentence_transformers import SentenceTransformer |
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|
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sentences = ["数据1", "数据2"] |
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model = SentenceTransformer('acge_text_embedding') |
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embeddings = model.encode(sentences, normalize_embeddings=False) |
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matryoshka_dim = 1024 |
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embeddings = embeddings[..., :matryoshka_dim] # Shrink the embedding dimensions |
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embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1) |
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print(embeddings.shape) |
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# => (2, 1024) |
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|
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``` |
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|
