upload file

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+img/matryoshka-small.gif filter=lfs diff=lfs merge=lfs -text

1_Pooling/config.json

@@ -0,0 +1,7 @@
+{
+    "word_embedding_dimension": 1024,
+    "pooling_mode_cls_token": false,
+    "pooling_mode_mean_tokens": true,
+    "pooling_mode_max_tokens": false,
+    "pooling_mode_mean_sqrt_len_tokens": false
+   }

2_Dense/config.json

@@ -0,0 +1,6 @@
+{
+ "in_features": 1024,
+ "out_features": 1792,
+ "bias": true,
+ "activation_function": "torch.nn.modules.linear.Identity"
+}

2_Dense/pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a9d3b5ed4c0c36109bb0f29fecd7450fb603d10dd58a0ee82811d1667ec83291
+size 3674687

README.md

@@ -1,3 +1,1189 @@
 ---
-license: apache-2.0
+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: 54.03219651150428
+    - 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
+      name: MTEB ATEC
+      config: default
+      split: test
+      revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
+    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
+      value: 61.167825359334515
+    - type: manhattan_spearman
+      value: 57.94540903298445
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (zh)
+      config: zh
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 48.556
+    - type: f1
+      value: 46.61852566163211
+  - task:
+      type: STS
+    dataset:
+      type: C-MTEB/BQ
+      name: MTEB BQ
+      config: default
+      split: test
+      revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
+    metrics:
+    - 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
+      value: 70.34572662526428
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringP2P
+      name: MTEB CLSClusteringP2P
+      config: default
+      split: test
+      revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
+    metrics:
+    - type: v_measure
+      value: 46.54685387179774
+  - task:
+      type: Clustering
+    dataset:
+      type: C-MTEB/CLSClusteringS2S
+      name: MTEB CLSClusteringS2S
+      config: default
+      split: test
+      revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
+    metrics:
+    - type: v_measure
+      value: 44.45602575811581
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv1-reranking
+      name: MTEB CMedQAv1
+      config: default
+      split: test
+      revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
+    metrics:
+    - type: map
+      value: 88.4576468720639
+    - type: mrr
+      value: 90.90595238095237
+  - task:
+      type: Reranking
+    dataset:
+      type: C-MTEB/CMedQAv2-reranking
+      name: MTEB CMedQAv2
+      config: default
+      split: test
+      revision: 23d186750531a14a0357ca22cd92d712fd512ea0
+    metrics:
+    - type: map
+      value: 88.71413673867269
+    - type: mrr
+      value: 91.19265873015873
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CmedqaRetrieval
+      name: MTEB CmedqaRetrieval
+      config: default
+      split: dev
+      revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
+    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
+      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
+      value: 1.625
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 23.114
+    - type: precision_at_5
+      value: 17.024
+    - type: recall_at_1
+      value: 26.825
+    - type: recall_at_10
+      value: 57.909
+    - type: recall_at_100
+      value: 87.375
+    - type: recall_at_1000
+      value: 98.695
+    - type: recall_at_3
+      value: 40.754000000000005
+    - type: recall_at_5
+      value: 48.472
+  - task:
+      type: PairClassification
+    dataset:
+      type: C-MTEB/CMNLI
+      name: MTEB Cmnli
+      config: default
+      split: validation
+      revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
+    metrics:
+    - type: cos_sim_accuracy
+      value: 83.4155141310884
+    - type: cos_sim_ap
+      value: 90.49006000181046
+    - type: cos_sim_f1
+      value: 84.28797826579125
+    - type: cos_sim_precision
+      value: 81.69848584595128
+    - type: cos_sim_recall
+      value: 87.04699555763385
+    - type: dot_accuracy
+      value: 83.40348767288035
+    - type: dot_ap
+      value: 90.50667776818787
+    - type: dot_f1
+      value: 84.31853669417802
+    - type: dot_precision
+      value: 80.61420345489442
+    - type: dot_recall
+      value: 88.379705400982
+    - type: euclidean_accuracy
+      value: 83.43956704750451
+    - type: euclidean_ap
+      value: 90.48869698176196
+    - type: euclidean_f1
+      value: 84.32616081540203
+    - type: euclidean_precision
+      value: 81.77026136613222
+    - type: euclidean_recall
+      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
+      value: 90.50667776818787
+    - type: max_f1
+      value: 84.42325158946412
+  - task:
+      type: Retrieval
+    dataset:
+      type: C-MTEB/CovidRetrieval
+      name: MTEB CovidRetrieval
+      config: default
+      split: dev
+      revision: 1271c7809071a13532e05f25fb53511ffce77117
+    metrics:
+    - 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
+      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：
+
+![matryoshka-small](./img/matryoshka-small.gif)
+
+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)
+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
+import torch
+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 = torch.nn.functional.normalize(embeddings, p=2, dim=1)
+print(embeddings.shape)
+# => (2, 1024)
+
+```

config.json

@@ -0,0 +1,31 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "directionality": "bidi",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 1024,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "pooler_fc_size": 768,
+  "pooler_num_attention_heads": 12,
+  "pooler_num_fc_layers": 3,
+  "pooler_size_per_head": 128,
+  "pooler_type": "first_token_transform",
+  "position_embedding_type": "absolute",
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.28.0",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 21128
+}

modules.json

@@ -0,0 +1,20 @@
+[
+    {
+     "idx": 0,
+     "name": "0",
+     "path": "",
+     "type": "sentence_transformers.models.Transformer"
+    },
+    {
+     "idx": 1,
+     "name": "1",
+     "path": "1_Pooling",
+     "type": "sentence_transformers.models.Pooling"
+    },
+    {
+     "idx": 2,
+     "name": "2",
+     "path": "2_Dense",
+     "type": "sentence_transformers.models.Dense"
+    }
+   ]

pytorch_model.bin

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6791d0a1cee50f2d5d3f5a1092ef7e72750ad64bf48830d9deb017e8584c2941
+size 652228333

result/acge_text_embedding_a10_bf16/AFQMC.json

@@ -0,0 +1,20 @@
+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "AFQMC",
+  "mteb_version": "1.1.1",
+  "validation": {
+    "cos_sim": {
+      "pearson": 0.5403313115451808,
+      "spearman": 0.5880630275663453
+    },
+    "euclidean": {
+      "pearson": 0.5746843921090328,
+      "spearman": 0.5880155813915751
+    },
+    "evaluation_time": 2.86,
+    "manhattan": {
+      "pearson": 0.5745948298354011,
+      "spearman": 0.5879717540772654
+    }
+  }
+}

result/acge_text_embedding_a10_bf16/ATEC.json

@@ -0,0 +1,20 @@
+{
+  "dataset_revision": null,
+  "mteb_dataset_name": "ATEC",
+  "mteb_version": "1.1.1",
+  "test": {
+    "cos_sim": {
+      "pearson": 0.5352343534213784,
+      "spearman": 0.5794595170176331
+    },
+    "euclidean": {
+      "pearson": 0.6116662835482146,
+      "spearman": 0.5794590216330153
+    },
+    "evaluation_time": 15.75,
+    "manhattan": {
+      "pearson": 0.6116423136556863,
+      "spearman": 0.5794474364498415
+    }
+  }
+}

result/acge_text_embedding_a10_bf16/AmazonReviewsClassification.json

@@ -0,0 +1,25 @@
+{
+  "dataset_revision": "1399c76144fd37290681b995c656ef9b2e06e26d",
+  "mteb_dataset_name": "AmazonReviewsClassification",
+  "mteb_version": "1.1.1",
+  "test": {
+    "evaluation_time": 16.78,
+    "zh": {
+      "accuracy": 0.48536,
+      "accuracy_stderr": 0.016551688735594317,
+      "f1": 0.4659652987641795,
+      "f1_stderr": 0.016438251092234995,
+      "main_score": 0.48536
+    }
+  },
+  "validation": {
+    "evaluation_time": 22.66,
+    "zh": {
+      "accuracy": 0.4768,
+      "accuracy_stderr": 0.01374394412095741,
+      "f1": 0.4575978790615031,
+      "f1_stderr": 0.011966200426813917,
+      "main_score": 0.4768
+    }
+  }
+}

result/acge_text_embedding_a10_bf16/BQ.json

@@ -0,0 +1,20 @@
+{
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+}

result/acge_text_embedding_a10_bf16/CLSClusteringP2P.json

@@ -0,0 +1,10 @@
+{
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result/acge_text_embedding_a10_bf16/CLSClusteringS2S.json

@@ -0,0 +1,10 @@
+{
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result/acge_text_embedding_a10_bf16/CMedQAv1.json

@@ -0,0 +1,10 @@
+{
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result/acge_text_embedding_a10_bf16/CMedQAv2.json

@@ -0,0 +1,10 @@
+{
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result/acge_text_embedding_a10_bf16/CmedqaRetrieval.json

@@ -0,0 +1,38 @@
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+}

result/acge_text_embedding_a10_bf16/Cmnli.json

@@ -0,0 +1,49 @@
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+}

result/acge_text_embedding_a10_bf16/CovidRetrieval.json

@@ -0,0 +1,38 @@
+{
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+}

result/acge_text_embedding_a10_bf16/DuRetrieval.json

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+{
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result/acge_text_embedding_a10_bf16/EcomRetrieval.json

@@ -0,0 +1,38 @@
+{
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+}

result/acge_text_embedding_a10_bf16/IFlyTek.json

@@ -0,0 +1,13 @@
+{
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result/acge_text_embedding_a10_bf16/JDReview.json

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+{
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result/acge_text_embedding_a10_bf16/LCQMC.json

@@ -0,0 +1,20 @@
+{
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result/acge_text_embedding_a10_bf16/MMarcoReranking.json

@@ -0,0 +1,10 @@
+{
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result/acge_text_embedding_a10_bf16/MMarcoRetrieval.json

@@ -0,0 +1,38 @@
+{
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+}

result/acge_text_embedding_a10_bf16/MassiveIntentClassification.json

@@ -0,0 +1,25 @@
+{
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result/acge_text_embedding_a10_bf16/MassiveScenarioClassification.json

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+{
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+}

result/acge_text_embedding_a10_bf16/MedicalRetrieval.json

@@ -0,0 +1,38 @@
+{
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+}

result/acge_text_embedding_a10_bf16/MultilingualSentiment.json

@@ -0,0 +1,13 @@
+{
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+}

result/acge_text_embedding_a10_bf16/Ocnli.json

@@ -0,0 +1,49 @@
+{
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+}

result/acge_text_embedding_a10_bf16/OnlineShopping.json

@@ -0,0 +1,15 @@
+{
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+}

result/acge_text_embedding_a10_bf16/PAWSX.json

@@ -0,0 +1,20 @@
+{
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+  "mteb_dataset_name": "PAWSX",
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+}

result/acge_text_embedding_a10_bf16/QBQTC.json

@@ -0,0 +1,20 @@
+{
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+  "mteb_dataset_name": "QBQTC",
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+}

result/acge_text_embedding_a10_bf16/STS22.json

@@ -0,0 +1,22 @@
+{
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+  "mteb_dataset_name": "STS22",
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+}

result/acge_text_embedding_a10_bf16/STSB.json

@@ -0,0 +1,20 @@
+{
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+}

result/acge_text_embedding_a10_bf16/T2Reranking.json

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+{
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+}

result/acge_text_embedding_a10_bf16/T2Retrieval.json

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+{
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+}

result/acge_text_embedding_a10_bf16/TNews.json

@@ -0,0 +1,13 @@
+{
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+  "mteb_dataset_name": "TNews",
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+}

result/acge_text_embedding_a10_bf16/ThuNewsClusteringP2P.json

@@ -0,0 +1,10 @@
+{
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+  "mteb_dataset_name": "ThuNewsClusteringP2P",
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+}

result/acge_text_embedding_a10_bf16/ThuNewsClusteringS2S.json

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+{
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+  "mteb_dataset_name": "ThuNewsClusteringS2S",
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+}

result/acge_text_embedding_a10_bf16/VideoRetrieval.json

@@ -0,0 +1,38 @@
+{
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+    "recall_at_3": 0.779,
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+}

result/acge_text_embedding_a10_bf16/Waimai.json

@@ -0,0 +1,15 @@
+{
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+}

result/acge_text_embedding_bf16/AFQMC.json

@@ -0,0 +1,20 @@
+{
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+}

result/acge_text_embedding_bf16/ATEC.json

@@ -0,0 +1,20 @@
+{
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+  "mteb_dataset_name": "ATEC",
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+  }
+}

result/acge_text_embedding_bf16/AmazonReviewsClassification.json

@@ -0,0 +1,25 @@
+{
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+  "mteb_dataset_name": "AmazonReviewsClassification",
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+    "zh": {
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+  },
+  "validation": {
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+    "zh": {
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+    }
+  }
+}

result/acge_text_embedding_bf16/BQ.json

@@ -0,0 +1,20 @@
+{
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+  "mteb_dataset_name": "BQ",
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+}

result/acge_text_embedding_bf16/CLSClusteringP2P.json

@@ -0,0 +1,10 @@
+{
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+  "mteb_dataset_name": "CLSClusteringP2P",
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+}

result/acge_text_embedding_bf16/CLSClusteringS2S.json

@@ -0,0 +1,10 @@
+{
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+  "mteb_dataset_name": "CLSClusteringS2S",
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+}