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JA
Xiaobu Embedding
X
Xiaobu Embedding
lier007によって開発
xiaobu-embeddingはマルチタスク埋め込みモデルで、テキスト類似度計算、分類、クラスタリング、検索など、さまざまな中国語自然言語処理タスクをサポートします。
ダウンロード数 147
リリース時間 : 1/9/2024
モデル概要
このモデルは中国語テキストの埋め込み表現学習に特化しており、高品質なテキストベクトルを生成でき、意味類似度計算、テキスト分類、情報検索などの多様な下流タスクに適用可能です。
モデル特徴
マルチタスクサポート
テキスト類似度計算、分類、クラスタリング、検索など、さまざまな自然言語処理タスクをサポートします。
中国語最適化
中国語テキストに特化して最適化されており、中国語の意味的特徴をより効果的に捉えることができます。
高性能
複数の中国語ベンチマークテストで優れた性能を発揮し、特に医療分野関連のタスクで顕著な成果を示しています。
モデル能力
テキスト埋め込み
意味類似度計算
テキスト分類
テキストクラスタリング
情報検索
再ランキング
使用事例
医療分野
医療QA検索
医療関連質問の検索と回答に使用
CMedQA検索タスクでMAP@10が37.604を達成
医療文書再ランキング
医療関連文書の関連性再ランキング
CMedQAv2再ランキングタスクでMAPが87.57を達成
電子商取引
商品レビュー分類
商品レビューの感情・主題分類
京東レビュー分類タスクで精度86.74%を達成
商品検索
ECプラットフォームの商品検索機能
EcomRetrievalタスクでMAP@10が63.14を達成
汎用NLP
テキスト類似度計算
2つのテキスト間の意味的類似度を計算
STSBタスクでコサイン類似度ピアソン79.75を達成
テキスト分類
テキストの多クラス分類
iFlyTek分類タスクで精度49.74%を達成
tags:
- mteb model-index:
- name: xiaobu-embedding
results:
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson value: 49.37874132528482
- type: cos_sim_spearman value: 54.84722470052176
- type: euclidean_pearson value: 53.0495882931575
- type: euclidean_spearman value: 54.847727301700665
- type: manhattan_pearson value: 53.0632140838278
- type: manhattan_spearman value: 54.8744258024692
- task:
type: STS
dataset:
type: C-MTEB/ATEC
name: MTEB ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 48.15992903013723
- type: cos_sim_spearman value: 55.13198035464577
- type: euclidean_pearson value: 55.435876753245715
- type: euclidean_spearman value: 55.13215936702871
- type: manhattan_pearson value: 55.41429518223402
- type: manhattan_spearman value: 55.13363087679285
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (zh)
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 46.722
- type: f1 value: 45.039340641893205
- task:
type: STS
dataset:
type: C-MTEB/BQ
name: MTEB BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 63.517830355554224
- type: cos_sim_spearman value: 65.57007801018649
- type: euclidean_pearson value: 64.05153340906585
- type: euclidean_spearman value: 65.5696865661119
- type: manhattan_pearson value: 63.95710619755406
- type: manhattan_spearman value: 65.48565785379489
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringP2P
name: MTEB CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure value: 43.24046498507819
- task:
type: Clustering
dataset:
type: C-MTEB/CLSClusteringS2S
name: MTEB CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure value: 41.22618199372116
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv1-reranking
name: MTEB CMedQAv1
config: default
split: test
revision: None
metrics:
- type: map value: 87.12213224673621
- type: mrr value: 89.57150793650794
- task:
type: Reranking
dataset:
type: C-MTEB/CMedQAv2-reranking
name: MTEB CMedQAv2
config: default
split: test
revision: None
metrics:
- type: map value: 87.57290061886421
- type: mrr value: 90.19202380952382
- task:
type: Retrieval
dataset:
type: C-MTEB/CmedqaRetrieval
name: MTEB CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 25.22
- type: map_at_10 value: 37.604
- type: map_at_100 value: 39.501
- type: map_at_1000 value: 39.614
- type: map_at_3 value: 33.378
- type: map_at_5 value: 35.774
- type: mrr_at_1 value: 38.385000000000005
- type: mrr_at_10 value: 46.487
- type: mrr_at_100 value: 47.504999999999995
- type: mrr_at_1000 value: 47.548
- type: mrr_at_3 value: 43.885999999999996
- type: mrr_at_5 value: 45.373000000000005
- type: ndcg_at_1 value: 38.385000000000005
- type: ndcg_at_10 value: 44.224999999999994
- type: ndcg_at_100 value: 51.637
- type: ndcg_at_1000 value: 53.55799999999999
- type: ndcg_at_3 value: 38.845
- type: ndcg_at_5 value: 41.163
- type: precision_at_1 value: 38.385000000000005
- type: precision_at_10 value: 9.812
- type: precision_at_100 value: 1.58
- type: precision_at_1000 value: 0.183
- type: precision_at_3 value: 21.88
- type: precision_at_5 value: 15.974
- type: recall_at_1 value: 25.22
- type: recall_at_10 value: 54.897
- type: recall_at_100 value: 85.469
- type: recall_at_1000 value: 98.18599999999999
- type: recall_at_3 value: 38.815
- type: recall_at_5 value: 45.885
- task:
type: PairClassification
dataset:
type: C-MTEB/CMNLI
name: MTEB Cmnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy value: 83.22309079975948
- type: cos_sim_ap value: 89.94833400328307
- type: cos_sim_f1 value: 84.39319055464031
- type: cos_sim_precision value: 79.5774647887324
- type: cos_sim_recall value: 89.82931961655366
- type: dot_accuracy value: 83.22309079975948
- type: dot_ap value: 89.95618559578415
- type: dot_f1 value: 84.41173239591345
- type: dot_precision value: 79.61044343141317
- type: dot_recall value: 89.82931961655366
- type: euclidean_accuracy value: 83.23511725796753
- type: euclidean_ap value: 89.94836342787318
- type: euclidean_f1 value: 84.40550133096718
- type: euclidean_precision value: 80.29120067524794
- type: euclidean_recall value: 88.9642272620996
- type: manhattan_accuracy value: 83.23511725796753
- type: manhattan_ap value: 89.9450103956978
- type: manhattan_f1 value: 84.44444444444444
- type: manhattan_precision value: 80.09647651006712
- type: manhattan_recall value: 89.29155950432546
- type: max_accuracy value: 83.23511725796753
- type: max_ap value: 89.95618559578415
- type: max_f1 value: 84.44444444444444
- task:
type: Retrieval
dataset:
type: C-MTEB/CovidRetrieval
name: MTEB CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 76.87
- type: map_at_10 value: 84.502
- type: map_at_100 value: 84.615
- type: map_at_1000 value: 84.617
- type: map_at_3 value: 83.127
- type: map_at_5 value: 83.99600000000001
- type: mrr_at_1 value: 77.02799999999999
- type: mrr_at_10 value: 84.487
- type: mrr_at_100 value: 84.59299999999999
- type: mrr_at_1000 value: 84.59400000000001
- type: mrr_at_3 value: 83.193
- type: mrr_at_5 value: 83.994
- type: ndcg_at_1 value: 77.134
- type: ndcg_at_10 value: 87.68599999999999
- type: ndcg_at_100 value: 88.17099999999999
- type: ndcg_at_1000 value: 88.21
- type: ndcg_at_3 value: 84.993
- type: ndcg_at_5 value: 86.519
- type: precision_at_1 value: 77.134
- type: precision_at_10 value: 9.841999999999999
- type: precision_at_100 value: 1.006
- type: precision_at_1000 value: 0.101
- type: precision_at_3 value: 30.313000000000002
- type: precision_at_5 value: 18.945999999999998
- type: recall_at_1 value: 76.87
- type: recall_at_10 value: 97.418
- type: recall_at_100 value: 99.579
- type: recall_at_1000 value: 99.895
- type: recall_at_3 value: 90.227
- type: recall_at_5 value: 93.888
- task:
type: Retrieval
dataset:
type: C-MTEB/DuRetrieval
name: MTEB DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 25.941
- type: map_at_10 value: 78.793
- type: map_at_100 value: 81.57799999999999
- type: map_at_1000 value: 81.626
- type: map_at_3 value: 54.749
- type: map_at_5 value: 69.16
- type: mrr_at_1 value: 90.45
- type: mrr_at_10 value: 93.406
- type: mrr_at_100 value: 93.453
- type: mrr_at_1000 value: 93.45700000000001
- type: mrr_at_3 value: 93.10000000000001
- type: mrr_at_5 value: 93.27499999999999
- type: ndcg_at_1 value: 90.45
- type: ndcg_at_10 value: 86.44500000000001
- type: ndcg_at_100 value: 89.28399999999999
- type: ndcg_at_1000 value: 89.739
- type: ndcg_at_3 value: 85.62100000000001
- type: ndcg_at_5 value: 84.441
- type: precision_at_1 value: 90.45
- type: precision_at_10 value: 41.19
- type: precision_at_100 value: 4.761
- type: precision_at_1000 value: 0.48700000000000004
- type: precision_at_3 value: 76.583
- type: precision_at_5 value: 64.68
- type: recall_at_1 value: 25.941
- type: recall_at_10 value: 87.443
- type: recall_at_100 value: 96.54
- type: recall_at_1000 value: 98.906
- type: recall_at_3 value: 56.947
- type: recall_at_5 value: 73.714
- task:
type: Retrieval
dataset:
type: C-MTEB/EcomRetrieval
name: MTEB EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 52.900000000000006
- type: map_at_10 value: 63.144
- type: map_at_100 value: 63.634
- type: map_at_1000 value: 63.644999999999996
- type: map_at_3 value: 60.817
- type: map_at_5 value: 62.202
- type: mrr_at_1 value: 52.900000000000006
- type: mrr_at_10 value: 63.144
- type: mrr_at_100 value: 63.634
- type: mrr_at_1000 value: 63.644999999999996
- type: mrr_at_3 value: 60.817
- type: mrr_at_5 value: 62.202
- type: ndcg_at_1 value: 52.900000000000006
- type: ndcg_at_10 value: 68.042
- type: ndcg_at_100 value: 70.417
- type: ndcg_at_1000 value: 70.722
- type: ndcg_at_3 value: 63.287000000000006
- type: ndcg_at_5 value: 65.77
- type: precision_at_1 value: 52.900000000000006
- type: precision_at_10 value: 8.34
- type: precision_at_100 value: 0.9450000000000001
- type: precision_at_1000 value: 0.097
- type: precision_at_3 value: 23.467
- type: precision_at_5 value: 15.28
- type: recall_at_1 value: 52.900000000000006
- type: recall_at_10 value: 83.39999999999999
- type: recall_at_100 value: 94.5
- type: recall_at_1000 value: 96.89999999999999
- type: recall_at_3 value: 70.39999999999999
- type: recall_at_5 value: 76.4
- task:
type: Classification
dataset:
type: C-MTEB/IFlyTek-classification
name: MTEB IFlyTek
config: default
split: validation
revision: None
metrics:
- type: accuracy value: 49.74220854174683
- type: f1 value: 38.01399980618159
- task:
type: Classification
dataset:
type: C-MTEB/JDReview-classification
name: MTEB JDReview
config: default
split: test
revision: None
metrics:
- type: accuracy value: 86.73545966228893
- type: ap value: 55.72394235169542
- type: f1 value: 81.58550390953492
- task:
type: STS
dataset:
type: C-MTEB/LCQMC
name: MTEB LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 69.96711977441642
- type: cos_sim_spearman value: 75.54747609685569
- type: euclidean_pearson value: 74.62663478056035
- type: euclidean_spearman value: 75.54761576699639
- type: manhattan_pearson value: 74.60983904582241
- type: manhattan_spearman value: 75.52758938061503
- task:
type: Reranking
dataset:
type: C-MTEB/Mmarco-reranking
name: MTEB MMarcoReranking
config: default
split: dev
revision: None
metrics:
- type: map value: 28.076927649720986
- type: mrr value: 26.98015873015873
- task:
type: Retrieval
dataset:
type: C-MTEB/MMarcoRetrieval
name: MTEB MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 65.58
- type: map_at_10 value: 74.763
- type: map_at_100 value: 75.077
- type: map_at_1000 value: 75.091
- type: map_at_3 value: 72.982
- type: map_at_5 value: 74.155
- type: mrr_at_1 value: 67.822
- type: mrr_at_10 value: 75.437
- type: mrr_at_100 value: 75.702
- type: mrr_at_1000 value: 75.715
- type: mrr_at_3 value: 73.91799999999999
- type: mrr_at_5 value: 74.909
- type: ndcg_at_1 value: 67.822
- type: ndcg_at_10 value: 78.472
- type: ndcg_at_100 value: 79.891
- type: ndcg_at_1000 value: 80.262
- type: ndcg_at_3 value: 75.138
- type: ndcg_at_5 value: 77.094
- type: precision_at_1 value: 67.822
- type: precision_at_10 value: 9.474
- type: precision_at_100 value: 1.019
- type: precision_at_1000 value: 0.105
- type: precision_at_3 value: 28.281
- type: precision_at_5 value: 18.017
- type: recall_at_1 value: 65.58
- type: recall_at_10 value: 89.18599999999999
- type: recall_at_100 value: 95.64399999999999
- type: recall_at_1000 value: 98.541
- type: recall_at_3 value: 80.455
- type: recall_at_5 value: 85.063
- 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: 72.86819098856758
- type: f1 value: 70.25369778283451
- 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: 75.46738399462004
- type: f1 value: 75.02466838130249
- task:
type: Retrieval
dataset:
type: C-MTEB/MedicalRetrieval
name: MTEB MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 53.300000000000004
- type: map_at_10 value: 60.072
- type: map_at_100 value: 60.618
- type: map_at_1000 value: 60.659
- type: map_at_3 value: 58.550000000000004
- type: map_at_5 value: 59.425
- type: mrr_at_1 value: 53.5
- type: mrr_at_10 value: 60.187999999999995
- type: mrr_at_100 value: 60.73499999999999
- type: mrr_at_1000 value: 60.775999999999996
- type: mrr_at_3 value: 58.667
- type: mrr_at_5 value: 59.541999999999994
- type: ndcg_at_1 value: 53.300000000000004
- type: ndcg_at_10 value: 63.376999999999995
- type: ndcg_at_100 value: 66.24600000000001
- type: ndcg_at_1000 value: 67.408
- type: ndcg_at_3 value: 60.211000000000006
- type: ndcg_at_5 value: 61.781
- type: precision_at_1 value: 53.300000000000004
- type: precision_at_10 value: 7.380000000000001
- type: precision_at_100 value: 0.877
- type: precision_at_1000 value: 0.097
- type: precision_at_3 value: 21.667
- type: precision_at_5 value: 13.76
- type: recall_at_1 value: 53.300000000000004
- type: recall_at_10 value: 73.8
- type: recall_at_100 value: 87.7
- type: recall_at_1000 value: 97.0
- type: recall_at_3 value: 65.0
- type: recall_at_5 value: 68.8
- task:
type: Classification
dataset:
type: C-MTEB/MultilingualSentiment-classification
name: MTEB MultilingualSentiment
config: default
split: validation
revision: None
metrics:
- type: accuracy value: 76.27666666666667
- type: f1 value: 76.31280038435165
- task:
type: PairClassification
dataset:
type: C-MTEB/OCNLI
name: MTEB Ocnli
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy value: 78.72225230102869
- type: cos_sim_ap value: 80.63941899467723
- type: cos_sim_f1 value: 80.52190121155638
- type: cos_sim_precision value: 72.06005004170142
- type: cos_sim_recall value: 91.23548046462513
- type: dot_accuracy value: 78.72225230102869
- type: dot_ap value: 80.63913939812744
- type: dot_f1 value: 80.51948051948052
- type: dot_precision value: 71.7948717948718
- type: dot_recall value: 91.65786694825766
- type: euclidean_accuracy value: 78.72225230102869
- type: euclidean_ap value: 80.64403797436798
- type: euclidean_f1 value: 80.52190121155638
- type: euclidean_precision value: 72.06005004170142
- type: euclidean_recall value: 91.23548046462513
- type: manhattan_accuracy value: 78.18083378451544
- type: manhattan_ap value: 80.5241189302444
- type: manhattan_f1 value: 80.43478260869566
- type: manhattan_precision value: 72.7972626176219
- type: manhattan_recall value: 89.86272439281943
- type: max_accuracy value: 78.72225230102869
- type: max_ap value: 80.64403797436798
- type: max_f1 value: 80.52190121155638
- task:
type: Classification
dataset:
type: C-MTEB/OnlineShopping-classification
name: MTEB OnlineShopping
config: default
split: test
revision: None
metrics:
- type: accuracy value: 92.49000000000001
- type: ap value: 90.66330807324402
- type: f1 value: 92.48245049107115
- task:
type: STS
dataset:
type: C-MTEB/PAWSX
name: MTEB PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 33.6275431596535
- type: cos_sim_spearman value: 37.865700050451494
- type: euclidean_pearson value: 38.1050665279388
- type: euclidean_spearman value: 37.864125056066364
- type: manhattan_pearson value: 38.11206873232881
- type: manhattan_spearman value: 37.852977098473936
- task:
type: STS
dataset:
type: C-MTEB/QBQTC
name: MTEB QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 32.137955501231104
- type: cos_sim_spearman value: 33.68610910423116
- type: euclidean_pearson value: 32.155444753547926
- type: euclidean_spearman value: 33.685799252964124
- type: manhattan_pearson value: 32.14490855334317
- type: manhattan_spearman value: 33.656549820048554
- 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: 63.63884916818661
- type: cos_sim_spearman value: 64.3217581571435
- type: euclidean_pearson value: 63.475760085926055
- type: euclidean_spearman value: 64.31638169371887
- type: manhattan_pearson value: 64.39677572604752
- type: manhattan_spearman value: 64.85585019406021
- task:
type: STS
dataset:
type: C-MTEB/STSB
name: MTEB STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson value: 79.74698333415277
- type: cos_sim_spearman value: 81.1850043859317
- type: euclidean_pearson value: 80.94512578669881
- type: euclidean_spearman value: 81.18825478390181
- type: manhattan_pearson value: 80.88114336824758
- type: manhattan_spearman value: 81.12266715583868
- task:
type: Reranking
dataset:
type: C-MTEB/T2Reranking
name: MTEB T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map value: 66.59971552953814
- type: mrr value: 76.42177408088038
- task:
type: Retrieval
dataset:
type: C-MTEB/T2Retrieval
name: MTEB T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 28.825
- type: map_at_10 value: 77.48899999999999
- type: map_at_100 value: 81.144
- type: map_at_1000 value: 81.216
- type: map_at_3 value: 55.435
- type: map_at_5 value: 67.496
- type: mrr_at_1 value: 91.377
- type: mrr_at_10 value: 94.062
- type: mrr_at_100 value: 94.122
- type: mrr_at_1000 value: 94.123
- type: mrr_at_3 value: 93.709
- type: mrr_at_5 value: 93.932
- type: ndcg_at_1 value: 91.377
- type: ndcg_at_10 value: 85.44800000000001
- type: ndcg_at_100 value: 89.11099999999999
- type: ndcg_at_1000 value: 89.752
- type: ndcg_at_3 value: 87.262
- type: ndcg_at_5 value: 85.668
- type: precision_at_1 value: 91.377
- type: precision_at_10 value: 41.525
- type: precision_at_100 value: 4.989
- type: precision_at_1000 value: 0.516
- type: precision_at_3 value: 75.452
- type: precision_at_5 value: 62.785000000000004
- type: recall_at_1 value: 28.825
- type: recall_at_10 value: 84.202
- type: recall_at_100 value: 95.768
- type: recall_at_1000 value: 98.791
- type: recall_at_3 value: 57.284
- type: recall_at_5 value: 71.071
- task:
type: Classification
dataset:
type: C-MTEB/TNews-classification
name: MTEB TNews
config: default
split: validation
revision: None
metrics:
- type: accuracy value: 52.160000000000004
- type: f1 value: 50.49492950548829
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringP2P
name: MTEB ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure value: 70.06019845009966
- task:
type: Clustering
dataset:
type: C-MTEB/ThuNewsClusteringS2S
name: MTEB ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure value: 63.9370959228245
- task:
type: Retrieval
dataset:
type: C-MTEB/VideoRetrieval
name: MTEB VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1 value: 60.0
- type: map_at_10 value: 69.362
- type: map_at_100 value: 69.819
- type: map_at_1000 value: 69.833
- type: map_at_3 value: 67.783
- type: map_at_5 value: 68.71300000000001
- type: mrr_at_1 value: 60.0
- type: mrr_at_10 value: 69.362
- type: mrr_at_100 value: 69.819
- type: mrr_at_1000 value: 69.833
- type: mrr_at_3 value: 67.783
- type: mrr_at_5 value: 68.71300000000001
- type: ndcg_at_1 value: 60.0
- type: ndcg_at_10 value: 73.59400000000001
- type: ndcg_at_100 value: 75.734
- type: ndcg_at_1000 value: 76.049
- type: ndcg_at_3 value: 70.33
- type: ndcg_at_5 value: 72.033
- type: precision_at_1 value: 60.0
- type: precision_at_10 value: 8.67
- type: precision_at_100 value: 0.9650000000000001
- type: precision_at_1000 value: 0.099
- type: precision_at_3 value: 25.900000000000002
- type: precision_at_5 value: 16.38
- type: recall_at_1 value: 60.0
- type: recall_at_10 value: 86.7
- type: recall_at_100 value: 96.5
- type: recall_at_1000 value: 98.9
- type: recall_at_3 value: 77.7
- type: recall_at_5 value: 81.89999999999999
- task:
type: Classification
dataset:
type: C-MTEB/waimai-classification
name: MTEB Waimai
config: default
split: test
revision: None
metrics:
- type: accuracy value: 88.36
- type: ap value: 73.25144216855439
- type: f1 value: 86.75076261442027
- task:
type: STS
dataset:
type: C-MTEB/AFQMC
name: MTEB AFQMC
config: default
split: validation
revision: None
metrics:
xiaobu-embedding
模型：基于GTE模型[1]多任务微调。
数据：闲聊类Query-Query、知识类Query-Doc、BGE开源Query-Doc[2]；清洗正例，挖掘中等难度负例；累计6M(质量更重要)。
Usage (Sentence-Transformers)
pip install -U sentence-transformers
相似度计算：
from sentence_transformers import SentenceTransformer
sentences_1 = ["样例数据-1", "样例数据-2"]
sentences_2 = ["样例数据-3", "样例数据-4"]
model = SentenceTransformer('lier007/xiaobu-embedding')
embeddings_1 = model.encode(sentences_1, normalize_embeddings=True)
embeddings_2 = model.encode(sentences_2, normalize_embeddings=True)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)
Evaluation
参考BGE中文CMTEB评估[2]
Finetune
参考BGE微调模块[2]
Reference
- https://huggingface.co/thenlper/gte-large-zh
- https://github.com/FlagOpen/FlagEmbedding
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