acge_text_embedding / README.md
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metadata
pipeline_tag: sentence-similarity
tags:
  - mteb
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
model-index:
  - name: acge_text_embedding
    results:
      - task:
          type: STS
        dataset:
          type: C-MTEB/AFQMC
          name: MTEB AFQMC
          config: default
          split: validation
          revision: b44c3b011063adb25877c13823db83bb193913c4
        metrics:
          - type: cos_sim_pearson
            value: 54.03434872650919
          - type: cos_sim_spearman
            value: 58.80730796688325
          - type: euclidean_pearson
            value: 57.47231387497989
          - type: euclidean_spearman
            value: 58.80775026351807
          - type: manhattan_pearson
            value: 57.46332720141574
          - type: manhattan_spearman
            value: 58.80196022940078
      - task:
          type: STS
        dataset:
          type: C-MTEB/ATEC
          name: MTEB ATEC
          config: default
          split: test
          revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
        metrics:
          - type: cos_sim_pearson
            value: 53.52621290548175
          - type: cos_sim_spearman
            value: 57.945227768312144
          - type: euclidean_pearson
            value: 61.17041394151802
          - type: euclidean_spearman
            value: 57.94553287835657
          - type: manhattan_pearson
            value: 61.168327500057885
          - type: manhattan_spearman
            value: 57.94477516925043
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (zh)
          config: zh
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 48.538000000000004
          - type: f1
            value: 46.59920995594044
      - task:
          type: STS
        dataset:
          type: C-MTEB/BQ
          name: MTEB BQ
          config: default
          split: test
          revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
        metrics:
          - type: cos_sim_pearson
            value: 68.27529991817154
          - type: cos_sim_spearman
            value: 70.37095914176643
          - type: euclidean_pearson
            value: 69.42690712802727
          - type: euclidean_spearman
            value: 70.37017971889912
          - type: manhattan_pearson
            value: 69.40264877917839
          - type: manhattan_spearman
            value: 70.34786744049524
      - task:
          type: Clustering
        dataset:
          type: C-MTEB/CLSClusteringP2P
          name: MTEB CLSClusteringP2P
          config: default
          split: test
          revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
        metrics:
          - type: v_measure
            value: 47.08027536192709
      - task:
          type: Clustering
        dataset:
          type: C-MTEB/CLSClusteringS2S
          name: MTEB CLSClusteringS2S
          config: default
          split: test
          revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
        metrics:
          - type: v_measure
            value: 44.0526024940363
      - task:
          type: Reranking
        dataset:
          type: C-MTEB/CMedQAv1-reranking
          name: MTEB CMedQAv1
          config: default
          split: test
          revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
        metrics:
          - type: map
            value: 88.65974993133156
          - type: mrr
            value: 90.64761904761905
      - task:
          type: Reranking
        dataset:
          type: C-MTEB/CMedQAv2-reranking
          name: MTEB CMedQAv2
          config: default
          split: test
          revision: 23d186750531a14a0357ca22cd92d712fd512ea0
        metrics:
          - type: map
            value: 88.90396838907245
          - type: mrr
            value: 90.90932539682541
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/CmedqaRetrieval
          name: MTEB CmedqaRetrieval
          config: default
          split: dev
          revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
        metrics:
          - type: map_at_1
            value: 26.875
          - type: map_at_10
            value: 39.995999999999995
          - type: map_at_100
            value: 41.899
          - type: map_at_1000
            value: 42
          - type: map_at_3
            value: 35.414
          - type: map_at_5
            value: 38.019
          - type: mrr_at_1
            value: 40.635
          - type: mrr_at_10
            value: 48.827
          - type: mrr_at_100
            value: 49.805
          - type: mrr_at_1000
            value: 49.845
          - type: mrr_at_3
            value: 46.145
          - type: mrr_at_5
            value: 47.693999999999996
          - type: ndcg_at_1
            value: 40.635
          - type: ndcg_at_10
            value: 46.78
          - type: ndcg_at_100
            value: 53.986999999999995
          - type: ndcg_at_1000
            value: 55.684
          - type: ndcg_at_3
            value: 41.018
          - type: ndcg_at_5
            value: 43.559
          - type: precision_at_1
            value: 40.635
          - type: precision_at_10
            value: 10.427999999999999
          - type: precision_at_100
            value: 1.625
          - type: precision_at_1000
            value: 0.184
          - type: precision_at_3
            value: 23.139000000000003
          - type: precision_at_5
            value: 17.004
          - type: recall_at_1
            value: 26.875
          - type: recall_at_10
            value: 57.887
          - type: recall_at_100
            value: 87.408
          - type: recall_at_1000
            value: 98.721
          - type: recall_at_3
            value: 40.812
          - type: recall_at_5
            value: 48.397
      - task:
          type: PairClassification
        dataset:
          type: C-MTEB/CMNLI
          name: MTEB Cmnli
          config: default
          split: validation
          revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
        metrics:
          - type: cos_sim_accuracy
            value: 83.43956704750451
          - type: cos_sim_ap
            value: 90.49172854352659
          - type: cos_sim_f1
            value: 84.28475486903963
          - type: cos_sim_precision
            value: 80.84603822203135
          - type: cos_sim_recall
            value: 88.02899228431144
          - type: dot_accuracy
            value: 83.43956704750451
          - type: dot_ap
            value: 90.46317132695233
          - type: dot_f1
            value: 84.28794294628929
          - type: dot_precision
            value: 80.51948051948052
          - type: dot_recall
            value: 88.4264671498714
          - type: euclidean_accuracy
            value: 83.43956704750451
          - type: euclidean_ap
            value: 90.49171785256486
          - type: euclidean_f1
            value: 84.28235820561584
          - type: euclidean_precision
            value: 80.8022308022308
          - type: euclidean_recall
            value: 88.07575403320084
          - type: manhattan_accuracy
            value: 83.55983162958509
          - type: manhattan_ap
            value: 90.48046779812815
          - type: manhattan_f1
            value: 84.45354259069714
          - type: manhattan_precision
            value: 82.21877767936226
          - type: manhattan_recall
            value: 86.81318681318682
          - type: max_accuracy
            value: 83.55983162958509
          - type: max_ap
            value: 90.49172854352659
          - type: max_f1
            value: 84.45354259069714
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/CovidRetrieval
          name: MTEB CovidRetrieval
          config: default
          split: dev
          revision: 1271c7809071a13532e05f25fb53511ffce77117
        metrics:
          - type: map_at_1
            value: 68.54599999999999
          - type: map_at_10
            value: 77.62400000000001
          - type: map_at_100
            value: 77.886
          - type: map_at_1000
            value: 77.89
          - type: map_at_3
            value: 75.966
          - type: map_at_5
            value: 76.995
          - type: mrr_at_1
            value: 68.915
          - type: mrr_at_10
            value: 77.703
          - type: mrr_at_100
            value: 77.958
          - type: mrr_at_1000
            value: 77.962
          - type: mrr_at_3
            value: 76.08
          - type: mrr_at_5
            value: 77.118
          - type: ndcg_at_1
            value: 68.809
          - type: ndcg_at_10
            value: 81.563
          - type: ndcg_at_100
            value: 82.758
          - type: ndcg_at_1000
            value: 82.864
          - type: ndcg_at_3
            value: 78.29
          - type: ndcg_at_5
            value: 80.113
          - type: precision_at_1
            value: 68.809
          - type: precision_at_10
            value: 9.463000000000001
          - type: precision_at_100
            value: 1.001
          - type: precision_at_1000
            value: 0.101
          - type: precision_at_3
            value: 28.486
          - type: precision_at_5
            value: 18.019
          - type: recall_at_1
            value: 68.54599999999999
          - type: recall_at_10
            value: 93.625
          - type: recall_at_100
            value: 99.05199999999999
          - type: recall_at_1000
            value: 99.895
          - type: recall_at_3
            value: 84.879
          - type: recall_at_5
            value: 89.252
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/DuRetrieval
          name: MTEB DuRetrieval
          config: default
          split: dev
          revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
        metrics:
          - type: map_at_1
            value: 25.653
          - type: map_at_10
            value: 79.105
          - type: map_at_100
            value: 81.902
          - type: map_at_1000
            value: 81.947
          - type: map_at_3
            value: 54.54599999999999
          - type: map_at_5
            value: 69.226
          - type: mrr_at_1
            value: 89.35
          - type: mrr_at_10
            value: 92.69
          - type: mrr_at_100
            value: 92.77
          - type: mrr_at_1000
            value: 92.774
          - type: mrr_at_3
            value: 92.425
          - type: mrr_at_5
            value: 92.575
          - type: ndcg_at_1
            value: 89.35
          - type: ndcg_at_10
            value: 86.55199999999999
          - type: ndcg_at_100
            value: 89.35300000000001
          - type: ndcg_at_1000
            value: 89.782
          - type: ndcg_at_3
            value: 85.392
          - type: ndcg_at_5
            value: 84.5
          - type: precision_at_1
            value: 89.35
          - type: precision_at_10
            value: 41.589999999999996
          - type: precision_at_100
            value: 4.781
          - type: precision_at_1000
            value: 0.488
          - type: precision_at_3
            value: 76.683
          - type: precision_at_5
            value: 65.06
          - type: recall_at_1
            value: 25.653
          - type: recall_at_10
            value: 87.64999999999999
          - type: recall_at_100
            value: 96.858
          - type: recall_at_1000
            value: 99.13300000000001
          - type: recall_at_3
            value: 56.869
          - type: recall_at_5
            value: 74.024
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/EcomRetrieval
          name: MTEB EcomRetrieval
          config: default
          split: dev
          revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
        metrics:
          - type: map_at_1
            value: 52.1
          - type: map_at_10
            value: 62.629999999999995
          - type: map_at_100
            value: 63.117000000000004
          - type: map_at_1000
            value: 63.134
          - type: map_at_3
            value: 60.267
          - type: map_at_5
            value: 61.777
          - type: mrr_at_1
            value: 52.1
          - type: mrr_at_10
            value: 62.629999999999995
          - type: mrr_at_100
            value: 63.117000000000004
          - type: mrr_at_1000
            value: 63.134
          - type: mrr_at_3
            value: 60.267
          - type: mrr_at_5
            value: 61.777
          - type: ndcg_at_1
            value: 52.1
          - type: ndcg_at_10
            value: 67.596
          - type: ndcg_at_100
            value: 69.95
          - type: ndcg_at_1000
            value: 70.33500000000001
          - type: ndcg_at_3
            value: 62.82600000000001
          - type: ndcg_at_5
            value: 65.546
          - type: precision_at_1
            value: 52.1
          - type: precision_at_10
            value: 8.309999999999999
          - type: precision_at_100
            value: 0.941
          - type: precision_at_1000
            value: 0.097
          - type: precision_at_3
            value: 23.400000000000002
          - type: precision_at_5
            value: 15.36
          - type: recall_at_1
            value: 52.1
          - type: recall_at_10
            value: 83.1
          - type: recall_at_100
            value: 94.1
          - type: recall_at_1000
            value: 97
          - type: recall_at_3
            value: 70.19999999999999
          - 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: 51.773759138130046
          - type: f1
            value: 40.341407912920054
      - 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: 71.1397780205448
          - type: cos_sim_spearman
            value: 78.17368193033309
          - type: euclidean_pearson
            value: 77.4849177602368
          - type: euclidean_spearman
            value: 78.17369079663212
          - type: manhattan_pearson
            value: 77.47344305182406
          - type: manhattan_spearman
            value: 78.16454335155387
      - task:
          type: Reranking
        dataset:
          type: C-MTEB/Mmarco-reranking
          name: MTEB MMarcoReranking
          config: default
          split: dev
          revision: 8e0c766dbe9e16e1d221116a3f36795fbade07f6
        metrics:
          - type: map
            value: 27.76160559006673
          - type: mrr
            value: 28.02420634920635
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/MMarcoRetrieval
          name: MTEB MMarcoRetrieval
          config: default
          split: dev
          revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
        metrics:
          - type: map_at_1
            value: 65.661
          - type: map_at_10
            value: 74.752
          - type: map_at_100
            value: 75.091
          - type: map_at_1000
            value: 75.104
          - type: map_at_3
            value: 72.997
          - type: map_at_5
            value: 74.119
          - type: mrr_at_1
            value: 67.923
          - type: mrr_at_10
            value: 75.376
          - type: mrr_at_100
            value: 75.673
          - type: mrr_at_1000
            value: 75.685
          - type: mrr_at_3
            value: 73.856
          - type: mrr_at_5
            value: 74.82799999999999
          - type: ndcg_at_1
            value: 67.923
          - type: ndcg_at_10
            value: 78.424
          - type: ndcg_at_100
            value: 79.95100000000001
          - type: ndcg_at_1000
            value: 80.265
          - type: ndcg_at_3
            value: 75.101
          - type: ndcg_at_5
            value: 76.992
          - type: precision_at_1
            value: 67.923
          - type: precision_at_10
            value: 9.474
          - type: precision_at_100
            value: 1.023
          - type: precision_at_1000
            value: 0.105
          - type: precision_at_3
            value: 28.319
          - type: precision_at_5
            value: 17.986
          - type: recall_at_1
            value: 65.661
          - type: recall_at_10
            value: 89.09899999999999
          - type: recall_at_100
            value: 96.023
          - type: recall_at_1000
            value: 98.455
          - type: recall_at_3
            value: 80.314
          - type: recall_at_5
            value: 84.81
      - 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: 75.86751849361131
          - type: f1
            value: 73.04918450508
      - 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: 78.4364492266308
          - type: f1
            value: 78.120686034844
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/MedicalRetrieval
          name: MTEB MedicalRetrieval
          config: default
          split: dev
          revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
        metrics:
          - type: map_at_1
            value: 55.00000000000001
          - type: map_at_10
            value: 61.06399999999999
          - type: map_at_100
            value: 61.622
          - type: map_at_1000
            value: 61.663000000000004
          - type: map_at_3
            value: 59.583
          - type: map_at_5
            value: 60.373
          - type: mrr_at_1
            value: 55.2
          - type: mrr_at_10
            value: 61.168
          - type: mrr_at_100
            value: 61.726000000000006
          - type: mrr_at_1000
            value: 61.767
          - type: mrr_at_3
            value: 59.683
          - type: mrr_at_5
            value: 60.492999999999995
          - type: ndcg_at_1
            value: 55.00000000000001
          - type: ndcg_at_10
            value: 64.098
          - type: ndcg_at_100
            value: 67.05
          - type: ndcg_at_1000
            value: 68.262
          - type: ndcg_at_3
            value: 61.00600000000001
          - type: ndcg_at_5
            value: 62.439
          - type: precision_at_1
            value: 55.00000000000001
          - type: precision_at_10
            value: 7.37
          - type: precision_at_100
            value: 0.881
          - type: precision_at_1000
            value: 0.098
          - type: precision_at_3
            value: 21.7
          - type: precision_at_5
            value: 13.719999999999999
          - type: recall_at_1
            value: 55.00000000000001
          - type: recall_at_10
            value: 73.7
          - type: recall_at_100
            value: 88.1
          - type: recall_at_1000
            value: 97.8
          - type: recall_at_3
            value: 65.10000000000001
          - type: recall_at_5
            value: 68.60000000000001
      - task:
          type: Classification
        dataset:
          type: C-MTEB/MultilingualSentiment-classification
          name: MTEB MultilingualSentiment
          config: default
          split: validation
          revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
        metrics:
          - type: accuracy
            value: 77.52666666666667
          - type: f1
            value: 77.49784731367215
      - 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: 85.17742765935606
          - type: cos_sim_f1
            value: 83.00094966761633
          - type: cos_sim_precision
            value: 75.40983606557377
          - type: cos_sim_recall
            value: 92.29144667370645
          - type: dot_accuracy
            value: 81.10449377368705
          - type: dot_ap
            value: 85.17143850809614
          - type: dot_f1
            value: 83.01707779886148
          - type: dot_precision
            value: 75.36606373815677
          - type: dot_recall
            value: 92.39704329461456
          - type: euclidean_accuracy
            value: 81.10449377368705
          - type: euclidean_ap
            value: 85.17856775343333
          - type: euclidean_f1
            value: 83.00094966761633
          - type: euclidean_precision
            value: 75.40983606557377
          - type: euclidean_recall
            value: 92.29144667370645
          - type: manhattan_accuracy
            value: 81.05035192203573
          - type: manhattan_ap
            value: 85.14464459395809
          - type: manhattan_f1
            value: 82.96155671570953
          - type: manhattan_precision
            value: 75.3448275862069
          - type: manhattan_recall
            value: 92.29144667370645
          - type: max_accuracy
            value: 81.10449377368705
          - type: max_ap
            value: 85.17856775343333
          - type: max_f1
            value: 83.01707779886148
      - task:
          type: Classification
        dataset:
          type: C-MTEB/OnlineShopping-classification
          name: MTEB OnlineShopping
          config: default
          split: test
          revision: e610f2ebd179a8fda30ae534c3878750a96db120
        metrics:
          - type: accuracy
            value: 93.71000000000001
          - type: ap
            value: 91.83202232349356
          - type: f1
            value: 93.69900560334331
      - task:
          type: STS
        dataset:
          type: C-MTEB/PAWSX
          name: MTEB PAWSX
          config: default
          split: test
          revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
        metrics:
          - type: cos_sim_pearson
            value: 39.175047651512415
          - type: cos_sim_spearman
            value: 45.51434675777896
          - type: euclidean_pearson
            value: 44.864110004132286
          - type: euclidean_spearman
            value: 45.516433048896076
          - type: manhattan_pearson
            value: 44.87153627706517
          - type: manhattan_spearman
            value: 45.52862617925012
      - task:
          type: STS
        dataset:
          type: C-MTEB/QBQTC
          name: MTEB QBQTC
          config: default
          split: test
          revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
        metrics:
          - type: cos_sim_pearson
            value: 34.249579701429084
          - type: cos_sim_spearman
            value: 37.30903127368978
          - type: euclidean_pearson
            value: 35.129438425253355
          - type: euclidean_spearman
            value: 37.308544018709085
          - type: manhattan_pearson
            value: 35.08936153503652
          - type: manhattan_spearman
            value: 37.25582901077839
      - 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: 61.29309637460004
          - type: cos_sim_spearman
            value: 65.85136090376717
          - type: euclidean_pearson
            value: 64.04783990953557
          - type: euclidean_spearman
            value: 65.85036859610366
          - type: manhattan_pearson
            value: 63.995852552712186
          - type: manhattan_spearman
            value: 65.86508416749417
      - task:
          type: STS
        dataset:
          type: C-MTEB/STSB
          name: MTEB STSB
          config: default
          split: test
          revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
        metrics:
          - type: cos_sim_pearson
            value: 81.5595940455587
          - type: cos_sim_spearman
            value: 82.72654634579749
          - type: euclidean_pearson
            value: 82.4892721061365
          - type: euclidean_spearman
            value: 82.72678504228253
          - type: manhattan_pearson
            value: 82.4770861422454
          - type: manhattan_spearman
            value: 82.71137469783162
      - task:
          type: Reranking
        dataset:
          type: C-MTEB/T2Reranking
          name: MTEB T2Reranking
          config: default
          split: dev
          revision: 76631901a18387f85eaa53e5450019b87ad58ef9
        metrics:
          - type: map
            value: 66.6159547610527
          - type: mrr
            value: 76.35739406347057
      - task:
          type: Retrieval
        dataset:
          type: C-MTEB/T2Retrieval
          name: MTEB T2Retrieval
          config: default
          split: dev
          revision: 8731a845f1bf500a4f111cf1070785c793d10e64
        metrics:
          - type: map_at_1
            value: 27.878999999999998
          - type: map_at_10
            value: 77.517
          - type: map_at_100
            value: 81.139
          - type: map_at_1000
            value: 81.204
          - type: map_at_3
            value: 54.728
          - type: map_at_5
            value: 67.128
          - type: mrr_at_1
            value: 90.509
          - type: mrr_at_10
            value: 92.964
          - type: mrr_at_100
            value: 93.045
          - type: mrr_at_1000
            value: 93.048
          - type: mrr_at_3
            value: 92.551
          - type: mrr_at_5
            value: 92.81099999999999
          - type: ndcg_at_1
            value: 90.509
          - type: ndcg_at_10
            value: 85.075
          - type: ndcg_at_100
            value: 88.656
          - type: ndcg_at_1000
            value: 89.25699999999999
          - type: ndcg_at_3
            value: 86.58200000000001
          - type: ndcg_at_5
            value: 85.138
          - type: precision_at_1
            value: 90.509
          - type: precision_at_10
            value: 42.05
          - type: precision_at_100
            value: 5.013999999999999
          - type: precision_at_1000
            value: 0.516
          - type: precision_at_3
            value: 75.551
          - type: precision_at_5
            value: 63.239999999999995
          - type: recall_at_1
            value: 27.878999999999998
          - type: recall_at_10
            value: 83.941
          - type: recall_at_100
            value: 95.568
          - type: recall_at_1000
            value: 98.55000000000001
          - type: recall_at_3
            value: 56.374
          - type: recall_at_5
            value: 70.435
      - task:
          type: Classification
        dataset:
          type: C-MTEB/TNews-classification
          name: MTEB TNews
          config: default
          split: validation
          revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
        metrics:
          - type: accuracy
            value: 53.687
          - type: f1
            value: 51.86911933364655
      - task:
          type: Clustering
        dataset:
          type: C-MTEB/ThuNewsClusteringP2P
          name: MTEB ThuNewsClusteringP2P
          config: default
          split: test
          revision: 5798586b105c0434e4f0fe5e767abe619442cf93
        metrics:
          - type: v_measure
            value: 74.65887489872564
      - task:
          type: Clustering
        dataset:
          type: C-MTEB/ThuNewsClusteringS2S
          name: MTEB ThuNewsClusteringS2S
          config: default
          split: test
          revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
        metrics:
          - type: v_measure
            value: 69.00410995984436
      - 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: 69.214
          - type: map_at_100
            value: 69.72699999999999
          - type: map_at_1000
            value: 69.743
          - type: map_at_3
            value: 67.717
          - type: map_at_5
            value: 68.782
          - type: mrr_at_1
            value: 59.4
          - type: mrr_at_10
            value: 69.214
          - type: mrr_at_100
            value: 69.72699999999999
          - type: mrr_at_1000
            value: 69.743
          - type: mrr_at_3
            value: 67.717
          - type: mrr_at_5
            value: 68.782
          - type: ndcg_at_1
            value: 59.4
          - type: ndcg_at_10
            value: 73.32300000000001
          - type: ndcg_at_100
            value: 75.591
          - type: ndcg_at_1000
            value: 75.98700000000001
          - type: ndcg_at_3
            value: 70.339
          - type: ndcg_at_5
            value: 72.246
          - type: precision_at_1
            value: 59.4
          - type: precision_at_10
            value: 8.59
          - 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: 16.5
          - type: recall_at_1
            value: 59.4
          - type: recall_at_10
            value: 85.9
          - type: recall_at_100
            value: 96
          - type: recall_at_1000
            value: 99.1
          - type: recall_at_3
            value: 77.9
          - type: recall_at_5
            value: 82.5
      - task:
          type: Classification
        dataset:
          type: C-MTEB/waimai-classification
          name: MTEB Waimai
          config: default
          split: test
          revision: 339287def212450dcaa9df8c22bf93e9980c7023
        metrics:
          - type: accuracy
            value: 88.53
          - type: ap
            value: 73.56216166534062
          - type: f1
            value: 87.06093694294485
icon

acge model

acge模型来自于合合信息技术团队,对外技术试用平台TextIn, github开源链接为github。合合信息是行业领先的人工智能及大数据科技企业,致力于通过智能文字识别及商业大数据领域的核心技术、C端和B端产品以及行业解决方案为全球企业和个人用户提供创新的数字化、智能化服务。

技术交流请联系yanhui_he@intsig.net,商务合作请联系simon_liu@intsig.net,可以点击图片,扫面二维码来加入我们的微信社群。想加入合合信息,做“文档解析”、“文档检索”、“文档预研”的同学可以投简历给min_du@intsig.net,也可直接添加HR微信详聊岗位内容。

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,如图所示:

matryoshka-small

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。 根据infgrad的建议,选取不用的输入的长度作为测试,Sequence Length为512时测试最佳。

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
acge_text_embedding NVIDIA TESLA A100 float16 0.65 1792 768 68.95 72.76 58.68 87.84 67.86 72.48 62.07
acge_text_embedding NVIDIA TESLA A100 float16 0.65 1792 512 69.07 72.75 58.7 87.84 67.99 72.93 62.09

Reproduce our results

C-MTEB:

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:

from sentence_transformers import SentenceTransformer

sentences = ["数据1", "数据2"]
model = SentenceTransformer('acge_text_embedding')
print(model.max_seq_length)
embeddings_1 = model.encode(sentences, normalize_embeddings=True)
embeddings_2 = model.encode(sentences, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)

在sentence-transformer库中的使用方法,选取不同的维度:

from sklearn.preprocessing import normalize
from sentence_transformers import SentenceTransformer

sentences = ["数据1", "数据2"]
model = SentenceTransformer('acge_text_embedding')
embeddings = model.encode(sentences, normalize_embeddings=False)
matryoshka_dim = 1024
embeddings = embeddings[..., :matryoshka_dim]  # Shrink the embedding dimensions
embeddings = normalize(embeddings, norm="l2", axis=1)
print(embeddings.shape)
# => (2, 1024)