From c19140442b046b1570cb8ed1831b97ddb3dfd776 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Thu, 30 Jul 2026 00:32:52 +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: DMetaSoul/sbert-chinese-general-v1 Source: Original Platform --- .gitattributes | 28 + 1_Pooling/config.json | 7 + README.md | 1180 ++ config.json | 31 + config_sentence_transformers.json | 7 + dmetasoul-v2.jpeg | Bin 0 -> 373843 bytes dmetasoul.png | Bin 0 -> 352460 bytes modules.json | 14 + pytorch_model.bin | 3 + sentence_bert_config.json | 4 + special_tokens_map.json | 1 + tokenizer.json | 21290 ++++++++++++++++++++++++++++ tokenizer_config.json | 1 + vocab.txt | 21128 +++++++++++++++++++++++++++ 14 files changed, 43694 insertions(+) create mode 100644 .gitattributes create mode 100644 1_Pooling/config.json create mode 100644 README.md create mode 100644 config.json create mode 100644 config_sentence_transformers.json create mode 100644 dmetasoul-v2.jpeg create mode 100644 dmetasoul.png create mode 100644 modules.json create mode 100644 pytorch_model.bin create mode 100644 sentence_bert_config.json create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 vocab.txt diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..76cfa96 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,28 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zstandard filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +pytorch_model.bin filter=lfs diff=lfs merge=lfs -text diff --git a/1_Pooling/config.json b/1_Pooling/config.json new file mode 100644 index 0000000..4e09f29 --- /dev/null +++ b/1_Pooling/config.json @@ -0,0 +1,7 @@ +{ + "word_embedding_dimension": 768, + "pooling_mode_cls_token": false, + "pooling_mode_mean_tokens": true, + "pooling_mode_max_tokens": false, + "pooling_mode_mean_sqrt_len_tokens": false +} \ No newline at end of file diff --git a/README.md b/README.md new file mode 100644 index 0000000..0c595e7 --- /dev/null +++ b/README.md @@ -0,0 +1,1180 @@ +--- +pipeline_tag: sentence-similarity +tags: +- sentence-transformers +- feature-extraction +- sentence-similarity +- transformers +- semantic-search +- chinese +- mteb +model-index: +- name: sbert-chinese-general-v1 + results: + - task: + type: STS + dataset: + type: C-MTEB/AFQMC + name: MTEB AFQMC + config: default + split: validation + revision: None + metrics: + - type: cos_sim_pearson + value: 22.293919432958074 + - type: cos_sim_spearman + value: 22.56718923553609 + - type: euclidean_pearson + value: 22.525656322797026 + - type: euclidean_spearman + value: 22.56718923553609 + - type: manhattan_pearson + value: 22.501773028824065 + - type: manhattan_spearman + value: 22.536992587828397 + - task: + type: STS + dataset: + type: C-MTEB/ATEC + name: MTEB ATEC + config: default + split: test + revision: None + metrics: + - type: cos_sim_pearson + value: 30.33575274463879 + - type: cos_sim_spearman + value: 30.298708742167772 + - type: euclidean_pearson + value: 32.33094743729218 + - type: euclidean_spearman + value: 30.298710993858734 + - type: manhattan_pearson + value: 32.31155376195945 + - type: manhattan_spearman + value: 30.267669681690744 + - task: + type: Classification + dataset: + type: mteb/amazon_reviews_multi + name: MTEB AmazonReviewsClassification (zh) + config: zh + split: test + revision: 1399c76144fd37290681b995c656ef9b2e06e26d + metrics: + - type: accuracy + value: 37.507999999999996 + - type: f1 + value: 36.436808400753286 + - task: + type: STS + dataset: + type: C-MTEB/BQ + name: MTEB BQ + config: default + split: test + revision: None + metrics: + - type: cos_sim_pearson + value: 41.493256724214255 + - type: cos_sim_spearman + value: 40.98395961967895 + - type: euclidean_pearson + value: 41.12345737966565 + - type: euclidean_spearman + value: 40.983959619555996 + - type: manhattan_pearson + value: 41.02584539471014 + - type: manhattan_spearman + value: 40.87549513383032 + - task: + type: BitextMining + dataset: + type: mteb/bucc-bitext-mining + name: MTEB BUCC (zh-en) + config: zh-en + split: test + revision: d51519689f32196a32af33b075a01d0e7c51e252 + metrics: + - type: accuracy + value: 9.794628751974724 + - type: f1 + value: 9.350535369492716 + - type: precision + value: 9.179392662804986 + - type: recall + value: 9.794628751974724 + - task: + type: Clustering + dataset: + type: C-MTEB/CLSClusteringP2P + name: MTEB CLSClusteringP2P + config: default + split: test + revision: None + metrics: + - type: v_measure + value: 34.984726547788284 + - task: + type: Clustering + dataset: + type: C-MTEB/CLSClusteringS2S + name: MTEB CLSClusteringS2S + config: default + split: test + revision: None + metrics: + - type: v_measure + value: 27.81945732281589 + - task: + type: Reranking + dataset: + type: C-MTEB/CMedQAv1-reranking + name: MTEB CMedQAv1 + config: default + split: test + revision: None + metrics: + - type: map + value: 53.06586280826805 + - type: mrr + value: 59.58781746031746 + - task: + type: Reranking + dataset: + type: C-MTEB/CMedQAv2-reranking + name: MTEB CMedQAv2 + config: default + split: test + revision: None + metrics: + - type: map + value: 52.83635946154306 + - type: mrr + value: 59.315079365079356 + - task: + type: Retrieval + dataset: + type: C-MTEB/CmedqaRetrieval + name: MTEB CmedqaRetrieval + config: default + split: dev + revision: None + metrics: + - type: map_at_1 + value: 5.721 + - type: map_at_10 + value: 8.645 + - type: map_at_100 + value: 9.434 + - type: map_at_1000 + value: 9.586 + - type: map_at_3 + value: 7.413 + - type: map_at_5 + value: 8.05 + - type: mrr_at_1 + value: 9.626999999999999 + - type: mrr_at_10 + value: 13.094 + - type: mrr_at_100 + value: 13.854 + - type: mrr_at_1000 + value: 13.958 + - type: mrr_at_3 + value: 11.724 + - type: mrr_at_5 + value: 12.409 + - type: ndcg_at_1 + value: 9.626999999999999 + - type: ndcg_at_10 + value: 11.35 + - type: ndcg_at_100 + value: 15.593000000000002 + - type: ndcg_at_1000 + value: 19.619 + - type: ndcg_at_3 + value: 9.317 + - type: ndcg_at_5 + value: 10.049 + - type: precision_at_1 + value: 9.626999999999999 + - type: precision_at_10 + value: 2.796 + - type: precision_at_100 + value: 0.629 + - type: precision_at_1000 + value: 0.11800000000000001 + - type: precision_at_3 + value: 5.476 + - type: precision_at_5 + value: 4.1209999999999996 + - type: recall_at_1 + value: 5.721 + - type: recall_at_10 + value: 15.190000000000001 + - type: recall_at_100 + value: 33.633 + - type: recall_at_1000 + value: 62.019999999999996 + - type: recall_at_3 + value: 9.099 + - type: recall_at_5 + value: 11.423 + - task: + type: PairClassification + dataset: + type: C-MTEB/CMNLI + name: MTEB Cmnli + config: default + split: validation + revision: None + metrics: + - type: cos_sim_accuracy + value: 77.36620565243535 + - type: cos_sim_ap + value: 85.92291866877001 + - type: cos_sim_f1 + value: 78.19390231037029 + - type: cos_sim_precision + value: 71.24183006535948 + - type: cos_sim_recall + value: 86.64952069207388 + - type: dot_accuracy + value: 77.36620565243535 + - type: dot_ap + value: 85.94113738490068 + - type: dot_f1 + value: 78.19390231037029 + - type: dot_precision + value: 71.24183006535948 + - type: dot_recall + value: 86.64952069207388 + - type: euclidean_accuracy + value: 77.36620565243535 + - type: euclidean_ap + value: 85.92291893444687 + - type: euclidean_f1 + value: 78.19390231037029 + - type: euclidean_precision + value: 71.24183006535948 + - type: euclidean_recall + value: 86.64952069207388 + - type: manhattan_accuracy + value: 77.29404690318701 + - type: manhattan_ap + value: 85.88284362100919 + - type: manhattan_f1 + value: 78.17836812144213 + - type: manhattan_precision + value: 71.18448838548666 + - type: manhattan_recall + value: 86.69628244096329 + - type: max_accuracy + value: 77.36620565243535 + - type: max_ap + value: 85.94113738490068 + - type: max_f1 + value: 78.19390231037029 + - task: + type: Retrieval + dataset: + type: C-MTEB/CovidRetrieval + name: MTEB CovidRetrieval + config: default + split: dev + revision: None + metrics: + - type: map_at_1 + value: 26.976 + - type: map_at_10 + value: 35.18 + - type: map_at_100 + value: 35.921 + - type: map_at_1000 + value: 35.998999999999995 + - type: map_at_3 + value: 32.763 + - type: map_at_5 + value: 34.165 + - type: mrr_at_1 + value: 26.976 + - type: mrr_at_10 + value: 35.234 + - type: mrr_at_100 + value: 35.939 + - type: mrr_at_1000 + value: 36.016 + - type: mrr_at_3 + value: 32.771 + - type: mrr_at_5 + value: 34.172999999999995 + - type: ndcg_at_1 + value: 26.976 + - type: ndcg_at_10 + value: 39.635 + - type: ndcg_at_100 + value: 43.54 + - type: ndcg_at_1000 + value: 45.723 + - type: ndcg_at_3 + value: 34.652 + - type: ndcg_at_5 + value: 37.186 + - type: precision_at_1 + value: 26.976 + - type: precision_at_10 + value: 5.406 + - type: precision_at_100 + value: 0.736 + - type: precision_at_1000 + value: 0.091 + - type: precision_at_3 + value: 13.418 + - type: precision_at_5 + value: 9.293999999999999 + - type: recall_at_1 + value: 26.976 + - type: recall_at_10 + value: 53.766999999999996 + - type: recall_at_100 + value: 72.761 + - type: recall_at_1000 + value: 90.148 + - type: recall_at_3 + value: 40.095 + - type: recall_at_5 + value: 46.233000000000004 + - task: + type: Retrieval + dataset: + type: C-MTEB/DuRetrieval + name: MTEB DuRetrieval + config: default + split: dev + revision: None + metrics: + - type: map_at_1 + value: 11.285 + - type: map_at_10 + value: 30.259000000000004 + - type: map_at_100 + value: 33.772000000000006 + - type: map_at_1000 + value: 34.037 + - type: map_at_3 + value: 21.038999999999998 + - type: map_at_5 + value: 25.939 + - type: mrr_at_1 + value: 45.1 + - type: mrr_at_10 + value: 55.803999999999995 + - type: mrr_at_100 + value: 56.301 + - type: mrr_at_1000 + value: 56.330999999999996 + - type: mrr_at_3 + value: 53.333 + - type: mrr_at_5 + value: 54.798 + - type: ndcg_at_1 + value: 45.1 + - type: ndcg_at_10 + value: 41.156 + - type: ndcg_at_100 + value: 49.518 + - type: ndcg_at_1000 + value: 52.947 + - type: ndcg_at_3 + value: 39.708 + - type: ndcg_at_5 + value: 38.704 + - type: precision_at_1 + value: 45.1 + - type: precision_at_10 + value: 20.75 + - type: precision_at_100 + value: 3.424 + - type: precision_at_1000 + value: 0.42700000000000005 + - type: precision_at_3 + value: 35.632999999999996 + - type: precision_at_5 + value: 30.080000000000002 + - type: recall_at_1 + value: 11.285 + - type: recall_at_10 + value: 43.242000000000004 + - type: recall_at_100 + value: 68.604 + - type: recall_at_1000 + value: 85.904 + - type: recall_at_3 + value: 24.404 + - type: recall_at_5 + value: 32.757 + - task: + type: Retrieval + dataset: + type: C-MTEB/EcomRetrieval + name: MTEB EcomRetrieval + config: default + split: dev + revision: None + metrics: + - type: map_at_1 + value: 21 + - type: map_at_10 + value: 28.364 + - type: map_at_100 + value: 29.199 + - type: map_at_1000 + value: 29.265 + - type: map_at_3 + value: 25.717000000000002 + - type: map_at_5 + value: 27.311999999999998 + - type: mrr_at_1 + value: 21 + - type: mrr_at_10 + value: 28.364 + - type: mrr_at_100 + value: 29.199 + - type: mrr_at_1000 + value: 29.265 + - type: mrr_at_3 + value: 25.717000000000002 + - type: mrr_at_5 + value: 27.311999999999998 + - type: ndcg_at_1 + value: 21 + - type: ndcg_at_10 + value: 32.708 + - type: ndcg_at_100 + value: 37.184 + - type: ndcg_at_1000 + value: 39.273 + - type: ndcg_at_3 + value: 27.372000000000003 + - type: ndcg_at_5 + value: 30.23 + - type: precision_at_1 + value: 21 + - type: precision_at_10 + value: 4.66 + - type: precision_at_100 + value: 0.685 + - type: precision_at_1000 + value: 0.086 + - type: precision_at_3 + value: 10.732999999999999 + - type: precision_at_5 + value: 7.82 + - type: recall_at_1 + value: 21 + - type: recall_at_10 + value: 46.6 + - type: recall_at_100 + value: 68.5 + - type: recall_at_1000 + value: 85.6 + - type: recall_at_3 + value: 32.2 + - type: recall_at_5 + value: 39.1 + - task: + type: Classification + dataset: + type: C-MTEB/IFlyTek-classification + name: MTEB IFlyTek + config: default + split: validation + revision: None + metrics: + - type: accuracy + value: 44.878799538283964 + - type: f1 + value: 33.84678310261366 + - task: + type: Classification + dataset: + type: C-MTEB/JDReview-classification + name: MTEB JDReview + config: default + split: test + revision: None + metrics: + - type: accuracy + value: 82.1951219512195 + - type: ap + value: 46.78292030042397 + - type: f1 + value: 76.20482468514128 + - task: + type: STS + dataset: + type: C-MTEB/LCQMC + name: MTEB LCQMC + config: default + split: test + revision: None + metrics: + - type: cos_sim_pearson + value: 62.84331627244547 + - type: cos_sim_spearman + value: 68.39990265073726 + - type: euclidean_pearson + value: 66.87431827169324 + - type: euclidean_spearman + value: 68.39990264979167 + - type: manhattan_pearson + value: 66.89702078900328 + - type: manhattan_spearman + value: 68.42107302159141 + - task: + type: Reranking + dataset: + type: C-MTEB/Mmarco-reranking + name: MTEB MMarcoReranking + config: default + split: dev + revision: None + metrics: + - type: map + value: 9.28600891904827 + - type: mrr + value: 8.057936507936509 + - task: + type: Retrieval + dataset: + type: C-MTEB/MMarcoRetrieval + name: MTEB MMarcoRetrieval + config: default + split: dev + revision: None + metrics: + - type: map_at_1 + value: 22.820999999999998 + - type: map_at_10 + value: 30.44 + - type: map_at_100 + value: 31.35 + - type: map_at_1000 + value: 31.419000000000004 + - type: map_at_3 + value: 28.134999999999998 + - type: map_at_5 + value: 29.482000000000003 + - type: mrr_at_1 + value: 23.782 + - type: mrr_at_10 + value: 31.141999999999996 + - type: mrr_at_100 + value: 32.004 + - type: mrr_at_1000 + value: 32.068000000000005 + - type: mrr_at_3 + value: 28.904000000000003 + - type: mrr_at_5 + value: 30.214999999999996 + - type: ndcg_at_1 + value: 23.782 + - type: ndcg_at_10 + value: 34.625 + - type: ndcg_at_100 + value: 39.226 + - type: ndcg_at_1000 + value: 41.128 + - type: ndcg_at_3 + value: 29.968 + - type: ndcg_at_5 + value: 32.35 + - type: precision_at_1 + value: 23.782 + - type: precision_at_10 + value: 4.994 + - type: precision_at_100 + value: 0.736 + - type: precision_at_1000 + value: 0.09 + - type: precision_at_3 + value: 12.13 + - type: precision_at_5 + value: 8.495999999999999 + - type: recall_at_1 + value: 22.820999999999998 + - type: recall_at_10 + value: 47.141 + - type: recall_at_100 + value: 68.952 + - type: recall_at_1000 + value: 83.985 + - type: recall_at_3 + value: 34.508 + - type: recall_at_5 + value: 40.232 + - 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: 57.343644922663074 + - type: f1 + value: 56.744802953803486 + - 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: 62.363819771351714 + - type: f1 + value: 62.15920863434656 + - task: + type: Retrieval + dataset: + type: C-MTEB/MedicalRetrieval + name: MTEB MedicalRetrieval + config: default + split: dev + revision: None + metrics: + - type: map_at_1 + value: 14.6 + - type: map_at_10 + value: 18.231 + - type: map_at_100 + value: 18.744 + - type: map_at_1000 + value: 18.811 + - type: map_at_3 + value: 17.133000000000003 + - type: map_at_5 + value: 17.663 + - type: mrr_at_1 + value: 14.6 + - type: mrr_at_10 + value: 18.231 + - type: mrr_at_100 + value: 18.744 + - type: mrr_at_1000 + value: 18.811 + - type: mrr_at_3 + value: 17.133000000000003 + - type: mrr_at_5 + value: 17.663 + - type: ndcg_at_1 + value: 14.6 + - type: ndcg_at_10 + value: 20.349 + - type: ndcg_at_100 + value: 23.204 + - type: ndcg_at_1000 + value: 25.44 + - type: ndcg_at_3 + value: 17.995 + - type: ndcg_at_5 + value: 18.945999999999998 + - type: precision_at_1 + value: 14.6 + - type: precision_at_10 + value: 2.7199999999999998 + - type: precision_at_100 + value: 0.414 + - type: precision_at_1000 + value: 0.06 + - type: precision_at_3 + value: 6.833 + - type: precision_at_5 + value: 4.5600000000000005 + - type: recall_at_1 + value: 14.6 + - type: recall_at_10 + value: 27.200000000000003 + - type: recall_at_100 + value: 41.4 + - type: recall_at_1000 + value: 60 + - type: recall_at_3 + value: 20.5 + - type: recall_at_5 + value: 22.8 + - task: + type: Classification + dataset: + type: C-MTEB/MultilingualSentiment-classification + name: MTEB MultilingualSentiment + config: default + split: validation + revision: None + metrics: + - type: accuracy + value: 66.58333333333333 + - type: f1 + value: 66.26700927460007 + - task: + type: PairClassification + dataset: + type: C-MTEB/OCNLI + name: MTEB Ocnli + config: default + split: validation + revision: None + metrics: + - type: cos_sim_accuracy + value: 72.00866269626421 + - type: cos_sim_ap + value: 77.00520104243304 + - type: cos_sim_f1 + value: 74.39303710490151 + - type: cos_sim_precision + value: 65.69579288025889 + - type: cos_sim_recall + value: 85.74445617740233 + - type: dot_accuracy + value: 72.00866269626421 + - type: dot_ap + value: 77.00520104243304 + - type: dot_f1 + value: 74.39303710490151 + - type: dot_precision + value: 65.69579288025889 + - type: dot_recall + value: 85.74445617740233 + - type: euclidean_accuracy + value: 72.00866269626421 + - type: euclidean_ap + value: 77.00520104243304 + - type: euclidean_f1 + value: 74.39303710490151 + - type: euclidean_precision + value: 65.69579288025889 + - type: euclidean_recall + value: 85.74445617740233 + - type: manhattan_accuracy + value: 72.1710882512182 + - type: manhattan_ap + value: 77.00551017913976 + - type: manhattan_f1 + value: 74.23423423423424 + - type: manhattan_precision + value: 64.72898664571878 + - type: manhattan_recall + value: 87.0116156282999 + - type: max_accuracy + value: 72.1710882512182 + - type: max_ap + value: 77.00551017913976 + - type: max_f1 + value: 74.39303710490151 + - task: + type: Classification + dataset: + type: C-MTEB/OnlineShopping-classification + name: MTEB OnlineShopping + config: default + split: test + revision: None + metrics: + - type: accuracy + value: 88.19000000000001 + - type: ap + value: 85.13415594781077 + - type: f1 + value: 88.17344156114062 + - task: + type: STS + dataset: + type: C-MTEB/PAWSX + name: MTEB PAWSX + config: default + split: test + revision: None + metrics: + - type: cos_sim_pearson + value: 13.70522140998517 + - type: cos_sim_spearman + value: 15.07546667334743 + - type: euclidean_pearson + value: 17.49511420225285 + - type: euclidean_spearman + value: 15.093970931789618 + - type: manhattan_pearson + value: 17.44069961390521 + - type: manhattan_spearman + value: 15.076029291596962 + - task: + type: STS + dataset: + type: C-MTEB/QBQTC + name: MTEB QBQTC + config: default + split: test + revision: None + metrics: + - type: cos_sim_pearson + value: 26.835294224547155 + - type: cos_sim_spearman + value: 27.920204597498856 + - type: euclidean_pearson + value: 26.153796707702803 + - type: euclidean_spearman + value: 27.920971379720548 + - type: manhattan_pearson + value: 26.21954147857523 + - type: manhattan_spearman + value: 27.996860049937478 + - task: + type: STS + dataset: + type: mteb/sts22-crosslingual-sts + name: MTEB STS22 (zh) + config: zh + split: test + revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 + metrics: + - type: cos_sim_pearson + value: 55.15901259718581 + - type: cos_sim_spearman + value: 61.57967880874167 + - type: euclidean_pearson + value: 53.83523291596683 + - type: euclidean_spearman + value: 61.57967880874167 + - type: manhattan_pearson + value: 54.99971428907956 + - type: manhattan_spearman + value: 61.61229543613867 + - task: + type: STS + dataset: + type: mteb/sts22-crosslingual-sts + name: MTEB STS22 (zh-en) + config: zh-en + split: test + revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 + metrics: + - type: cos_sim_pearson + value: 34.20930208460845 + - type: cos_sim_spearman + value: 33.879011104224524 + - type: euclidean_pearson + value: 35.08526425284862 + - type: euclidean_spearman + value: 33.879011104224524 + - type: manhattan_pearson + value: 35.509419089701275 + - type: manhattan_spearman + value: 33.30035487147621 + - task: + type: STS + dataset: + type: C-MTEB/STSB + name: MTEB STSB + config: default + split: test + revision: None + metrics: + - type: cos_sim_pearson + value: 82.30068282185835 + - type: cos_sim_spearman + value: 82.16763221361724 + - type: euclidean_pearson + value: 80.52772752433374 + - type: euclidean_spearman + value: 82.16797037220333 + - type: manhattan_pearson + value: 80.51093859500105 + - type: manhattan_spearman + value: 82.17643310049654 + - task: + type: Reranking + dataset: + type: C-MTEB/T2Reranking + name: MTEB T2Reranking + config: default + split: dev + revision: None + metrics: + - type: map + value: 65.14113035189213 + - type: mrr + value: 74.9589270937443 + - task: + type: Retrieval + dataset: + type: C-MTEB/T2Retrieval + name: MTEB T2Retrieval + config: default + split: dev + revision: None + metrics: + - type: map_at_1 + value: 12.013 + - type: map_at_10 + value: 30.885 + - type: map_at_100 + value: 34.643 + - type: map_at_1000 + value: 34.927 + - type: map_at_3 + value: 21.901 + - type: map_at_5 + value: 26.467000000000002 + - type: mrr_at_1 + value: 49.623 + - type: mrr_at_10 + value: 58.05200000000001 + - type: mrr_at_100 + value: 58.61300000000001 + - type: mrr_at_1000 + value: 58.643 + - type: mrr_at_3 + value: 55.947 + - type: mrr_at_5 + value: 57.229 + - type: ndcg_at_1 + value: 49.623 + - type: ndcg_at_10 + value: 41.802 + - type: ndcg_at_100 + value: 49.975 + - type: ndcg_at_1000 + value: 53.504 + - type: ndcg_at_3 + value: 43.515 + - type: ndcg_at_5 + value: 41.576 + - type: precision_at_1 + value: 49.623 + - type: precision_at_10 + value: 22.052 + - type: precision_at_100 + value: 3.6450000000000005 + - type: precision_at_1000 + value: 0.45399999999999996 + - type: precision_at_3 + value: 38.616 + - type: precision_at_5 + value: 31.966 + - type: recall_at_1 + value: 12.013 + - type: recall_at_10 + value: 41.891 + - type: recall_at_100 + value: 67.096 + - type: recall_at_1000 + value: 84.756 + - type: recall_at_3 + value: 24.695 + - type: recall_at_5 + value: 32.09 + - task: + type: Classification + dataset: + type: C-MTEB/TNews-classification + name: MTEB TNews + config: default + split: validation + revision: None + metrics: + - type: accuracy + value: 39.800999999999995 + - type: f1 + value: 38.5345899934575 + - task: + type: Clustering + dataset: + type: C-MTEB/ThuNewsClusteringP2P + name: MTEB ThuNewsClusteringP2P + config: default + split: test + revision: None + metrics: + - type: v_measure + value: 40.16574242797479 + - task: + type: Clustering + dataset: + type: C-MTEB/ThuNewsClusteringS2S + name: MTEB ThuNewsClusteringS2S + config: default + split: test + revision: None + metrics: + - type: v_measure + value: 24.232617974671754 + - task: + type: Retrieval + dataset: + type: C-MTEB/VideoRetrieval + name: MTEB VideoRetrieval + config: default + split: dev + revision: None + metrics: + - type: map_at_1 + value: 24.6 + - type: map_at_10 + value: 31.328 + - type: map_at_100 + value: 32.088 + - type: map_at_1000 + value: 32.164 + - type: map_at_3 + value: 29.133 + - type: map_at_5 + value: 30.358 + - type: mrr_at_1 + value: 24.6 + - type: mrr_at_10 + value: 31.328 + - type: mrr_at_100 + value: 32.088 + - type: mrr_at_1000 + value: 32.164 + - type: mrr_at_3 + value: 29.133 + - type: mrr_at_5 + value: 30.358 + - type: ndcg_at_1 + value: 24.6 + - type: ndcg_at_10 + value: 35.150999999999996 + - type: ndcg_at_100 + value: 39.024 + - type: ndcg_at_1000 + value: 41.157 + - type: ndcg_at_3 + value: 30.637999999999998 + - type: ndcg_at_5 + value: 32.833 + - type: precision_at_1 + value: 24.6 + - type: precision_at_10 + value: 4.74 + - type: precision_at_100 + value: 0.66 + - type: precision_at_1000 + value: 0.083 + - type: precision_at_3 + value: 11.667 + - type: precision_at_5 + value: 8.06 + - type: recall_at_1 + value: 24.6 + - type: recall_at_10 + value: 47.4 + - type: recall_at_100 + value: 66 + - type: recall_at_1000 + value: 83 + - type: recall_at_3 + value: 35 + - type: recall_at_5 + value: 40.300000000000004 + - task: + type: Classification + dataset: + type: C-MTEB/waimai-classification + name: MTEB Waimai + config: default + split: test + revision: None + metrics: + - type: accuracy + value: 83.96000000000001 + - type: ap + value: 65.11027167433211 + - type: f1 + value: 82.03549710974653 +license: apache-2.0 +language: +- zh +--- + +# DMetaSoul/sbert-chinese-general-v1 + +此模型基于 [bert-base-chinese](https://huggingface.co/bert-base-chinese) 版本 BERT 模型，在 NLI、PAWS-X、PKU-Paraphrase-Bank、STS 等语义相似数据集上进行训练，适用于**通用语义匹配**场景（此模型在 Chinese-STS 任务上效果较好，但在其它任务上效果并非最优，存在一定过拟合风险），比如文本特征抽取、文本向量聚类、文本语义搜索等业务场景。 + +注：此模型的[轻量化版本](https://huggingface.co/DMetaSoul/sbert-chinese-general-v1-distill)，也已经开源啦！ + +# Usage + +## 1. Sentence-Transformers + +通过 [sentence-transformers](https://www.SBERT.net) 框架来使用该模型，首先进行安装： + +``` +pip install -U sentence-transformers +``` + +然后使用下面的代码来载入该模型并进行文本表征向量的提取： + +```python +from sentence_transformers import SentenceTransformer +sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"] + +model = SentenceTransformer('DMetaSoul/sbert-chinese-general-v1') +embeddings = model.encode(sentences) +print(embeddings) +``` + +## 2. HuggingFace Transformers + +如果不想使用 [sentence-transformers](https://www.SBERT.net) 的话，也可以通过 HuggingFace Transformers 来载入该模型并进行文本向量抽取： + +```python +from transformers import AutoTokenizer, AutoModel +import torch + + +#Mean Pooling - Take attention mask into account for correct averaging +def mean_pooling(model_output, attention_mask): + token_embeddings = model_output[0] #First element of model_output contains all token embeddings + input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() + return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) + + +# Sentences we want sentence embeddings for +sentences = ["我的儿子！他猛然间喊道，我的儿子在哪儿？", "我的儿子呢！他突然喊道，我的儿子在哪里？"] + +# Load model from HuggingFace Hub +tokenizer = AutoTokenizer.from_pretrained('DMetaSoul/sbert-chinese-general-v1') +model = AutoModel.from_pretrained('DMetaSoul/sbert-chinese-general-v1') + +# Tokenize sentences +encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') + +# Compute token embeddings +with torch.no_grad(): + model_output = model(**encoded_input) + +# Perform pooling. In this case, mean pooling. +sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask']) + +print("Sentence embeddings:") +print(sentence_embeddings) +``` + +## Evaluation + +该模型在公开的几个语义匹配数据集上进行了评测，计算了向量相似度跟真实标签之间的相关性系数： + +| | **csts_dev** | **csts_test** | **afqmc** | **lcqmc** | **bqcorpus** | **pawsx** | **xiaobu** | +| ------------ | ------------ | ------------- | --------- | --------- | ------------ | --------- | ---------- | +| **spearman** | 84.54% | 82.17% | 23.80% | 65.94% | 45.52% | 11.52% | 48.51% | + +## Citing & Authors + +E-mail: xiaowenbin@dmetasoul.com \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..e918588 --- /dev/null +++ b/config.json @@ -0,0 +1,31 @@ +{ + "_name_or_path": "releases/sbert-chinese-general-v1/", + "architectures": [ + "BertModel" + ], + "attention_probs_dropout_prob": 0.1, + "classifier_dropout": null, + "directionality": "bidi", + "hidden_act": "gelu", + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-12, + "max_position_embeddings": 512, + "model_type": "bert", + "num_attention_heads": 12, + "num_hidden_layers": 12, + "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": "float32", + "transformers_version": "4.16.0", + "type_vocab_size": 2, + "use_cache": true, + "vocab_size": 21128 +} diff --git a/config_sentence_transformers.json b/config_sentence_transformers.json new file mode 100644 index 0000000..748893f --- /dev/null +++ b/config_sentence_transformers.json @@ -0,0 +1,7 @@ +{ + "__version__": { + "sentence_transformers": "2.1.0", + "transformers": "4.16.0", + "pytorch": "1.10.2" + } +} \ No newline at end of file diff --git a/dmetasoul-v2.jpeg b/dmetasoul-v2.jpeg 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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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