---
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: 42.0
- 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: 97.0
- 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: 96.0
- 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
---
## acge model
acge模型来自于[合合信息](https://www.intsig.com/)技术团队，对外技术试用平台[TextIn](https://www.textin.com/)。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，商务合作请联系，可以[点击图片](https://huggingface.co/aspire/acge_text_embedding/blob/main/img/wx.jpg)，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](https://huggingface.co/aspire/acge_text_embedding/blob/main/img/hr.jpg)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](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。
根据[infgrad](https://huggingface.co/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:**
```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)
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：
```python
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)
```