From d561db1f48024f09bebce988e10d5f60742b986d Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Thu, 14 May 2026 14:59:48 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: aspire/acge_text_embedding Source: Original Platform --- .gitattributes | 36 + 1_Pooling/config.json | 7 + 2_Dense/config.json | 6 + 2_Dense/model.safetensors | 3 + 2_Dense/pytorch_model.bin | 3 + README.md | 1206 + config.json | 31 + img/hr.jpg | Bin 0 -> 163545 bytes img/logo.png | Bin 0 -> 10180 bytes img/matryoshka-small.gif | 3 + img/wx.jpg | Bin 0 -> 173821 bytes model.safetensors | 3 + modules.json | 20 + pytorch_model.bin | 3 + .../acge_text_embedding_a10_bf16/AFQMC.json | 20 + result/acge_text_embedding_a10_bf16/ATEC.json | 20 + .../AmazonReviewsClassification.json | 25 + 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diff --git a/2_Dense/config.json b/2_Dense/config.json new file mode 100644 index 0000000..fcab58c --- /dev/null +++ b/2_Dense/config.json @@ -0,0 +1,6 @@ +{ + "in_features": 1024, + "out_features": 1792, + "bias": true, + "activation_function": "torch.nn.modules.linear.Identity" +} \ No newline at end of file diff --git a/2_Dense/model.safetensors b/2_Dense/model.safetensors new file mode 100644 index 0000000..92da304 --- /dev/null +++ b/2_Dense/model.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6c49c1fc6dc5bf2d42b8c16d9f787de90dba5e57a705c67b7d67d547f895c905 +size 3673800 diff --git a/2_Dense/pytorch_model.bin b/2_Dense/pytorch_model.bin new file mode 100644 index 0000000..682fa49 --- /dev/null +++ b/2_Dense/pytorch_model.bin @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a9d3b5ed4c0c36109bb0f29fecd7450fb603d10dd58a0ee82811d1667ec83291 +size 3674687 diff --git a/README.md b/README.md new file mode 100644 index 0000000..5bfcf60 --- /dev/null +++ b/README.md @@ -0,0 +1,1206 @@ +--- +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 +--- + +
+icon +
+ + + +## 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/), github开源链接为[github](https://github.com/intsig-textin)。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： + +![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。 +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) + +``` + + + diff --git a/config.json b/config.json new file mode 100644 index 0000000..7a33b70 --- /dev/null +++ b/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 +} diff --git a/img/hr.jpg b/img/hr.jpg new file mode 100644 index 0000000000000000000000000000000000000000..0dac767c503097fde48252298efcaed6f262b4c6 GIT binary patch literal 163545 zcmbrm2S5|u);1ahMLM zC(@-?3B4DkCX^6JyTg0R_kHJ_``_>08<{W(Gqcy)v-e)lde$@2#%Ob(^SW9(S|B<) 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"识": 6399, + "诈": 6400, + "诉": 6401, + "诊": 6402, + "诋": 6403, + "词": 6404, + "诏": 6405, + "译": 6406, + "试": 6407, + "诗": 6408, + "诘": 6409, + "诙": 6410, + "诚": 6411, + "诛": 6412, + "话": 6413, + "诞": 6414, + "诟": 6415, + "诠": 6416, + "诡": 6417, + "询": 6418, + "诣": 6419, + "诤": 6420, + "该": 6421, + "详": 6422, + "诧": 6423, + "诩": 6424, + "诫": 6425, + "诬": 6426, + "语": 6427, + "误": 6428, + "诰": 6429, + "诱": 6430, + "诲": 6431, + "说": 6432, + "诵": 6433, + "诶": 6434, + "请": 6435, + "诸": 6436, + "诺": 6437, + "读": 6438, + "诽": 6439, + "课": 6440, + "诿": 6441, + "谀": 6442, + "谁": 6443, + "调": 6444, + "谄": 6445, + "谅": 6446, + "谆": 6447, + "谈": 6448, + "谊": 6449, + "谋": 6450, + "谌": 6451, + "谍": 6452, + "谎": 6453, + "谏": 6454, + "谐": 6455, + "谑": 6456, + "谒": 6457, + "谓": 6458, + "谔": 6459, + "谕": 6460, + "谗": 6461, + "谘": 6462, + "谙": 6463, + "谚": 6464, + "谛": 6465, + "谜": 6466, + "谟": 6467, + "谢": 6468, + "谣": 6469, + "谤": 6470, + "谥": 6471, + "谦": 6472, + "谧": 6473, + "谨": 6474, + "谩": 6475, + "谪": 6476, + "谬": 6477, + "谭": 6478, + "谯": 6479, + "谱": 6480, + "谲": 6481, + "谴": 6482, + "谶": 6483, + "谷": 6484, + "豁": 6485, + "豆": 6486, + "豇": 6487, + "豈": 6488, + "豉": 6489, + "豊": 6490, + "豌": 6491, + "豎": 6492, + "豐": 6493, + "豔": 6494, + "豚": 6495, + "象": 6496, + "豢": 6497, + "豪": 6498, + "豫": 6499, + "豬": 6500, + "豹": 6501, + "豺": 6502, + "貂": 6503, + "貅": 6504, + "貌": 6505, + "貓": 6506, + "貔": 6507, + "貘": 6508, + "貝": 6509, + "貞": 6510, + "負": 6511, + "財": 6512, + "貢": 6513, + "貧": 6514, + "貨": 6515, + "販": 6516, + "貪": 6517, + "貫": 6518, + "責": 6519, + "貯": 6520, + "貰": 6521, + "貳": 6522, + "貴": 6523, + "貶": 6524, + "買": 6525, + "貸": 6526, + "費": 6527, + "貼": 6528, + "貽": 6529, + "貿": 6530, + "賀": 6531, + "賁": 6532, + "賂": 6533, + "賃": 6534, + "賄": 6535, + "資": 6536, + "賈": 6537, + "賊": 6538, + "賑": 6539, + "賓": 6540, + "賜": 6541, + "賞": 6542, + "賠": 6543, + "賡": 6544, + "賢": 6545, + "賣": 6546, + "賤": 6547, + "賦": 6548, + "質": 6549, + "賬": 6550, + "賭": 6551, + "賴": 6552, + "賺": 6553, + "購": 6554, + "賽": 6555, + "贅": 6556, + "贈": 6557, + "贊": 6558, + "贍": 6559, + "贏": 6560, + "贓": 6561, + "贖": 6562, + "贛": 6563, + "贝": 6564, + "贞": 6565, + "负": 6566, + "贡": 6567, + "财": 6568, + "责": 6569, + "贤": 6570, + "败": 6571, + "账": 6572, + "货": 6573, + "质": 6574, + "贩": 6575, + "贪": 6576, + "贫": 6577, + "贬": 6578, + "购": 6579, + "贮": 6580, + "贯": 6581, + "贰": 6582, + "贱": 6583, + "贲": 6584, + "贴": 6585, + "贵": 6586, + "贷": 6587, + "贸": 6588, + "费": 6589, + "贺": 6590, + "贻": 6591, + "贼": 6592, + "贾": 6593, + "贿": 6594, + "赁": 6595, + "赂": 6596, + "赃": 6597, + "资": 6598, + "赅": 6599, + "赈": 6600, + "赊": 6601, + "赋": 6602, + "赌": 6603, + "赎": 6604, + "赏": 6605, + "赐": 6606, + "赓": 6607, + "赔": 6608, + "赖": 6609, + "赘": 6610, + "赚": 6611, + "赛": 6612, + "赝": 6613, + "赞": 6614, + "赠": 6615, + "赡": 6616, + "赢": 6617, + "赣": 6618, + "赤": 6619, + "赦": 6620, + "赧": 6621, + "赫": 6622, + "赭": 6623, + "走": 6624, + "赳": 6625, + "赴": 6626, + "赵": 6627, + "赶": 6628, + "起": 6629, + "趁": 6630, + "超": 6631, + "越": 6632, + "趋": 6633, + "趕": 6634, + "趙": 6635, + "趟": 6636, + "趣": 6637, + "趨": 6638, + "足": 6639, + "趴": 6640, + "趵": 6641, + "趸": 6642, + "趺": 6643, + "趾": 6644, + "跃": 6645, + "跄": 6646, + "跆": 6647, + "跋": 6648, + "跌": 6649, + "跎": 6650, + "跑": 6651, + "跖": 6652, + "跚": 6653, + "跛": 6654, + "距": 6655, + "跟": 6656, + "跡": 6657, + "跤": 6658, + "跨": 6659, + "跩": 6660, + "跪": 6661, + "路": 6662, + "跳": 6663, + "践": 6664, + "跷": 6665, + "跹": 6666, + "跺": 6667, + "跻": 6668, + "踉": 6669, + "踊": 6670, + "踌": 6671, + "踏": 6672, + "踐": 6673, + "踝": 6674, + "踞": 6675, + "踟": 6676, + "踢": 6677, + "踩": 6678, + "踪": 6679, + "踮": 6680, + "踱": 6681, + "踴": 6682, + "踵": 6683, + "踹": 6684, + "蹂": 6685, + "蹄": 6686, + "蹇": 6687, + "蹈": 6688, + "蹉": 6689, + "蹊": 6690, + "蹋": 6691, + "蹑": 6692, + "蹒": 6693, + "蹙": 6694, + "蹟": 6695, + "蹣": 6696, + "蹤": 6697, + "蹦": 6698, + "蹩": 6699, + "蹬": 6700, + "蹭": 6701, + "蹲": 6702, + "蹴": 6703, + "蹶": 6704, + "蹺": 6705, + "蹼": 6706, + 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"辕": 6784, + "辖": 6785, + "辗": 6786, + "辘": 6787, + "辙": 6788, + "辛": 6789, + "辜": 6790, + "辞": 6791, + "辟": 6792, + "辣": 6793, + "辦": 6794, + "辨": 6795, + "辩": 6796, + "辫": 6797, + "辭": 6798, + "辮": 6799, + "辯": 6800, + "辰": 6801, + "辱": 6802, + "農": 6803, + "边": 6804, + "辺": 6805, + "辻": 6806, + "込": 6807, + "辽": 6808, + "达": 6809, + "迁": 6810, + "迂": 6811, + "迄": 6812, + "迅": 6813, + "过": 6814, + "迈": 6815, + "迎": 6816, + "运": 6817, + "近": 6818, + "返": 6819, + "还": 6820, + "这": 6821, + "进": 6822, + "远": 6823, + "违": 6824, + "连": 6825, + "迟": 6826, + "迢": 6827, + "迤": 6828, + "迥": 6829, + "迦": 6830, + "迩": 6831, + "迪": 6832, + "迫": 6833, + "迭": 6834, + "述": 6835, + "迴": 6836, + "迷": 6837, + "迸": 6838, + "迹": 6839, + "迺": 6840, + "追": 6841, + "退": 6842, + "送": 6843, + "适": 6844, + "逃": 6845, + "逅": 6846, + "逆": 6847, + "选": 6848, + "逊": 6849, + "逍": 6850, + "透": 6851, + "逐": 6852, + "递": 6853, + "途": 6854, + "逕": 6855, + "逗": 6856, + "這": 6857, + "通": 6858, + "逛": 6859, + "逝": 6860, + 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