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JobGTE-multilingual-base-v1

Sentence Similarity
sentence-transformers
Safetensors
feature-extraction
Generated from Trainer
dataset_size:124788
loss:GISTEmbedLoss
Eval Results
Model card Files
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JobGTE-multilingual-base-v1 / README.md
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tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:124788
- loss:GISTEmbedLoss
base_model: Alibaba-NLP/gte-multilingual-base
widget:
- source_sentence: 其他机械、设备和有形货物租赁服务代表
sentences:
- 其他机械和设备租赁服务工作人员
- 电子和电信设备及零部件物流经理
- 工业主厨
- source_sentence: 公交车司机
sentences:
- 表演灯光设计师
- 乙烯基地板安装工
- 国际巴士司机
- source_sentence: online communication manager
sentences:
- trades union official
- social media manager
- budget manager
- source_sentence: Projektmanagerin
sentences:
- Projektmanager/Projektmanagerin
- Category-Manager
- Infanterist
- source_sentence: Volksvertreter
sentences:
- Parlamentarier
- Oberbürgermeister
- Konsul
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@20
- cosine_accuracy@50
- cosine_accuracy@100
- cosine_accuracy@150
- cosine_accuracy@200
- cosine_precision@1
- cosine_precision@20
- cosine_precision@50
- cosine_precision@100
- cosine_precision@150
- cosine_precision@200
- cosine_recall@1
- cosine_recall@20
- cosine_recall@50
- cosine_recall@100
- cosine_recall@150
- cosine_recall@200
- cosine_ndcg@1
- cosine_ndcg@20
- cosine_ndcg@50
- cosine_ndcg@100
- cosine_ndcg@150
- cosine_ndcg@200
- cosine_mrr@1
- cosine_mrr@20
- cosine_mrr@50
- cosine_mrr@100
- cosine_mrr@150
- cosine_mrr@200
- cosine_map@1
- cosine_map@20
- cosine_map@50
- cosine_map@100
- cosine_map@150
- cosine_map@200
- cosine_map@500
model-index:
- name: SentenceTransformer based on Alibaba-NLP/gte-multilingual-base
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full en
type: full_en
metrics:
- type: cosine_accuracy@1
value: 0.6571428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9904761904761905
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9904761904761905
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9904761904761905
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9904761904761905
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9904761904761905
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6571428571428571
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5171428571428571
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.316
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.18895238095238095
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.13384126984126984
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.10433333333333335
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.0678253733846715
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5470006025464504
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.7399645316315758
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.8452891149669638
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8838497168796887
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9109269128757174
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6571428571428571
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6953571805621692
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.7150421121165462
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.7679394555495317
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7856911059911225
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7969632777290026
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6571428571428571
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.8138095238095239
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.8138095238095239
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.8138095238095239
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.8138095238095239
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.8138095238095239
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6571428571428571
name: Cosine Map@1
- type: cosine_map@20
value: 0.5578605627627369
name: Cosine Map@20
- type: cosine_map@50
value: 0.5471407389299809
name: Cosine Map@50
- type: cosine_map@100
value: 0.5795933384755297
name: Cosine Map@100
- type: cosine_map@150
value: 0.5874505508842796
name: Cosine Map@150
- type: cosine_map@200
value: 0.5912226659397186
name: Cosine Map@200
- type: cosine_map@500
value: 0.5952587557760031
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full es
type: full_es
metrics:
- type: cosine_accuracy@1
value: 0.12432432432432433
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 1.0
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 1.0
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 1.0
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 1.0
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 1.0
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.12432432432432433
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5718918918918919
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.3885405405405405
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.25172972972972973
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.1904864864864865
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.1521891891891892
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.0036619075252531876
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.3842245968041533
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.5640822196868902
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.6741986120580108
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.7463851968088967
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.7825399601398452
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.12432432432432433
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6139182209948354
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5873893466818746
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.6144038475288277
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.6498632077214272
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.6680602466150343
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.12432432432432433
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5581081081081081
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5581081081081081
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5581081081081081
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5581081081081081
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5581081081081081
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.12432432432432433
name: Cosine Map@1
- type: cosine_map@20
value: 0.47988875190050484
name: Cosine Map@20
- type: cosine_map@50
value: 0.4249833337950364
name: Cosine Map@50
- type: cosine_map@100
value: 0.430155652024808
name: Cosine Map@100
- type: cosine_map@150
value: 0.4458862132745998
name: Cosine Map@150
- type: cosine_map@200
value: 0.45334655744992447
name: Cosine Map@200
- type: cosine_map@500
value: 0.4656066165331343
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full de
type: full_de
metrics:
- type: cosine_accuracy@1
value: 0.2955665024630542
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9704433497536946
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9852216748768473
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9852216748768473
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9901477832512315
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9901477832512315
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.2955665024630542
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.5083743842364532
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.3654187192118227
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.24133004926108376
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.18036124794745487
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.14467980295566504
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.01108543831680986
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.3221185941380065
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.5024502430161547
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.6247617904371989
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.6829583450315939
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.7216293640715983
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.2955665024630542
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5393376062142305
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5267125529267169
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.55793511917882
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5879547828450983
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.6071252185389439
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.2955665024630542
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.5104381157401634
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.5109752961295605
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.5109752961295605
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.5110222114474118
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.5110222114474118
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.2955665024630542
name: Cosine Map@1
- type: cosine_map@20
value: 0.40097257642946377
name: Cosine Map@20
- type: cosine_map@50
value: 0.35882787401455
name: Cosine Map@50
- type: cosine_map@100
value: 0.3633182590941781
name: Cosine Map@100
- type: cosine_map@150
value: 0.3776727961080201
name: Cosine Map@150
- type: cosine_map@200
value: 0.3848401555555339
name: Cosine Map@200
- type: cosine_map@500
value: 0.3978065874082948
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: full zh
type: full_zh
metrics:
- type: cosine_accuracy@1
value: 0.6601941747572816
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9805825242718447
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9902912621359223
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9902912621359223
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9902912621359223
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9902912621359223
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6601941747572816
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.4781553398058253
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.28951456310679613
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.17572815533980585
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.12595469255663433
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.09815533980582528
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.06151358631979527
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.5107966412908705
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.6922746152164951
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.8004152884148357
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.8465065661615649
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.8770990926698364
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6601941747572816
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.6539867858378715
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.6707332209240133
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.72342020484322
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7437750875502527
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.7553648453187212
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6601941747572816
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.8037216828478965
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.8040950958426687
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.8040950958426687
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.8040950958426687
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.8040950958426687
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6601941747572816
name: Cosine Map@1
- type: cosine_map@20
value: 0.5087334164702914
name: Cosine Map@20
- type: cosine_map@50
value: 0.49260246320797585
name: Cosine Map@50
- type: cosine_map@100
value: 0.5217412166882693
name: Cosine Map@100
- type: cosine_map@150
value: 0.529859818130126
name: Cosine Map@150
- type: cosine_map@200
value: 0.533378795921413
name: Cosine Map@200
- type: cosine_map@500
value: 0.5386011712914499
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix es
type: mix_es
metrics:
- type: cosine_accuracy@1
value: 0.7280291211648466
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9599583983359334
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9791991679667187
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9942797711908476
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9958398335933437
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9973998959958398
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.7280291211648466
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.12433697347893914
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.05145085803432139
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.02625065002600105
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.017621771537528162
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.013283931357254294
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.28133620582918556
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.9183394002426764
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.9499306638932224
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.9700901369388107
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.9767724042295025
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9818166059975733
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.7280291211648466
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.8043549768911603
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.81295852465432
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.817339429558165
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.8186380742931886
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.8195485984235017
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.7280291211648466
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.7968549154271433
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.7974653825839162
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.7976914864910069
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.7977044635908871
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.7977139196654446
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.7280291211648466
name: Cosine Map@1
- type: cosine_map@20
value: 0.7350836192117531
name: Cosine Map@20
- type: cosine_map@50
value: 0.7374205090112232
name: Cosine Map@50
- type: cosine_map@100
value: 0.737988888492803
name: Cosine Map@100
- type: cosine_map@150
value: 0.7381133157945164
name: Cosine Map@150
- type: cosine_map@200
value: 0.7381788581828236
name: Cosine Map@200
- type: cosine_map@500
value: 0.7382854440643231
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix de
type: mix_de
metrics:
- type: cosine_accuracy@1
value: 0.6703068122724909
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 0.9505980239209568
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 0.9776391055642226
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 0.9864794591783671
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 0.9932397295891836
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 0.9947997919916797
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.6703068122724909
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.1251690067602704
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.052282891315652634
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.026729069162766517
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.01799965331946611
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.013541341653666149
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.25235742763043856
name: Cosine Recall@1
- type: cosine_recall@20
value: 0.9095857167620037
name: Cosine Recall@20
- type: cosine_recall@50
value: 0.9482405962905183
name: Cosine Recall@50
- type: cosine_recall@100
value: 0.96845207141619
name: Cosine Recall@100
- type: cosine_recall@150
value: 0.9781591263650546
name: Cosine Recall@150
- type: cosine_recall@200
value: 0.9810192407696308
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.6703068122724909
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.7735712514376322
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.7843644592705362
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.7889444470773866
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.7908660087982327
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.791403470160319
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.6703068122724909
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.7520307321055828
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.7529374175534339
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.7530616872072472
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.7531202644382351
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.7531293951311296
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.6703068122724909
name: Cosine Map@1
- type: cosine_map@20
value: 0.6967639778693541
name: Cosine Map@20
- type: cosine_map@50
value: 0.699575457224443
name: Cosine Map@50
- type: cosine_map@100
value: 0.70027844357658
name: Cosine Map@100
- type: cosine_map@150
value: 0.7004487000056766
name: Cosine Map@150
- type: cosine_map@200
value: 0.7004863395843564
name: Cosine Map@200
- type: cosine_map@500
value: 0.7005835771389989
name: Cosine Map@500
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: mix zh
type: mix_zh
metrics:
- type: cosine_accuracy@1
value: 0.19084763390535622
name: Cosine Accuracy@1
- type: cosine_accuracy@20
value: 1.0
name: Cosine Accuracy@20
- type: cosine_accuracy@50
value: 1.0
name: Cosine Accuracy@50
- type: cosine_accuracy@100
value: 1.0
name: Cosine Accuracy@100
- type: cosine_accuracy@150
value: 1.0
name: Cosine Accuracy@150
- type: cosine_accuracy@200
value: 1.0
name: Cosine Accuracy@200
- type: cosine_precision@1
value: 0.19084763390535622
name: Cosine Precision@1
- type: cosine_precision@20
value: 0.15439417576703063
name: Cosine Precision@20
- type: cosine_precision@50
value: 0.0617576703068123
name: Cosine Precision@50
- type: cosine_precision@100
value: 0.03087883515340615
name: Cosine Precision@100
- type: cosine_precision@150
value: 0.020585890102270757
name: Cosine Precision@150
- type: cosine_precision@200
value: 0.015439417576703075
name: Cosine Precision@200
- type: cosine_recall@1
value: 0.06137978852487433
name: Cosine Recall@1
- type: cosine_recall@20
value: 1.0
name: Cosine Recall@20
- type: cosine_recall@50
value: 1.0
name: Cosine Recall@50
- type: cosine_recall@100
value: 1.0
name: Cosine Recall@100
- type: cosine_recall@150
value: 1.0
name: Cosine Recall@150
- type: cosine_recall@200
value: 1.0
name: Cosine Recall@200
- type: cosine_ndcg@1
value: 0.19084763390535622
name: Cosine Ndcg@1
- type: cosine_ndcg@20
value: 0.5474303590499686
name: Cosine Ndcg@20
- type: cosine_ndcg@50
value: 0.5474303590499686
name: Cosine Ndcg@50
- type: cosine_ndcg@100
value: 0.5474303590499686
name: Cosine Ndcg@100
- type: cosine_ndcg@150
value: 0.5474303590499686
name: Cosine Ndcg@150
- type: cosine_ndcg@200
value: 0.5474303590499686
name: Cosine Ndcg@200
- type: cosine_mrr@1
value: 0.19084763390535622
name: Cosine Mrr@1
- type: cosine_mrr@20
value: 0.4093433087972877
name: Cosine Mrr@20
- type: cosine_mrr@50
value: 0.4093433087972877
name: Cosine Mrr@50
- type: cosine_mrr@100
value: 0.4093433087972877
name: Cosine Mrr@100
- type: cosine_mrr@150
value: 0.4093433087972877
name: Cosine Mrr@150
- type: cosine_mrr@200
value: 0.4093433087972877
name: Cosine Mrr@200
- type: cosine_map@1
value: 0.19084763390535622
name: Cosine Map@1
- type: cosine_map@20
value: 0.32981711891302556
name: Cosine Map@20
- type: cosine_map@50
value: 0.32981711891302556
name: Cosine Map@50
- type: cosine_map@100
value: 0.32981711891302556
name: Cosine Map@100
- type: cosine_map@150
value: 0.32981711891302556
name: Cosine Map@150
- type: cosine_map@200
value: 0.32981711891302556
name: Cosine Map@200
- type: cosine_map@500
value: 0.32981711891302556
name: Cosine Map@500
---
# Job - Job matching Alibaba-NLP/gte-multilingual-base (v1)
Top performing model on [TalentCLEF 2025](https://talentclef.github.io/talentclef/) Task A. Use it for multilingual job title matching
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Alibaba-NLP/gte-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) <!-- at revision 9fdd4ee8bba0e2808a34e0e739576f6740d2b225 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- full_en
- full_de
- full_es
- full_zh
- mix
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: NewModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("pj-mathematician/JobGTE-multilingual-base-v1")
# Run inference
sentences = [
'Volksvertreter',
'Parlamentarier',
'Oberbürgermeister',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `full_en`, `full_es`, `full_de`, `full_zh`, `mix_es`, `mix_de` and `mix_zh`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | full_en | full_es | full_de | full_zh | mix_es | mix_de | mix_zh |
|:---------------------|:----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.6571 | 0.1243 | 0.2956 | 0.6602 | 0.728 | 0.6703 | 0.1908 |
| cosine_accuracy@20 | 0.9905 | 1.0 | 0.9704 | 0.9806 | 0.96 | 0.9506 | 1.0 |
| cosine_accuracy@50 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9792 | 0.9776 | 1.0 |
| cosine_accuracy@100 | 0.9905 | 1.0 | 0.9852 | 0.9903 | 0.9943 | 0.9865 | 1.0 |
| cosine_accuracy@150 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9958 | 0.9932 | 1.0 |
| cosine_accuracy@200 | 0.9905 | 1.0 | 0.9901 | 0.9903 | 0.9974 | 0.9948 | 1.0 |
| cosine_precision@1 | 0.6571 | 0.1243 | 0.2956 | 0.6602 | 0.728 | 0.6703 | 0.1908 |
| cosine_precision@20 | 0.5171 | 0.5719 | 0.5084 | 0.4782 | 0.1243 | 0.1252 | 0.1544 |
| cosine_precision@50 | 0.316 | 0.3885 | 0.3654 | 0.2895 | 0.0515 | 0.0523 | 0.0618 |
| cosine_precision@100 | 0.189 | 0.2517 | 0.2413 | 0.1757 | 0.0263 | 0.0267 | 0.0309 |
| cosine_precision@150 | 0.1338 | 0.1905 | 0.1804 | 0.126 | 0.0176 | 0.018 | 0.0206 |
| cosine_precision@200 | 0.1043 | 0.1522 | 0.1447 | 0.0982 | 0.0133 | 0.0135 | 0.0154 |
| cosine_recall@1 | 0.0678 | 0.0037 | 0.0111 | 0.0615 | 0.2813 | 0.2524 | 0.0614 |
| cosine_recall@20 | 0.547 | 0.3842 | 0.3221 | 0.5108 | 0.9183 | 0.9096 | 1.0 |
| cosine_recall@50 | 0.74 | 0.5641 | 0.5025 | 0.6923 | 0.9499 | 0.9482 | 1.0 |
| cosine_recall@100 | 0.8453 | 0.6742 | 0.6248 | 0.8004 | 0.9701 | 0.9685 | 1.0 |
| cosine_recall@150 | 0.8838 | 0.7464 | 0.683 | 0.8465 | 0.9768 | 0.9782 | 1.0 |
| cosine_recall@200 | 0.9109 | 0.7825 | 0.7216 | 0.8771 | 0.9818 | 0.981 | 1.0 |
| cosine_ndcg@1 | 0.6571 | 0.1243 | 0.2956 | 0.6602 | 0.728 | 0.6703 | 0.1908 |
| cosine_ndcg@20 | 0.6954 | 0.6139 | 0.5393 | 0.654 | 0.8044 | 0.7736 | 0.5474 |
| cosine_ndcg@50 | 0.715 | 0.5874 | 0.5267 | 0.6707 | 0.813 | 0.7844 | 0.5474 |
| cosine_ndcg@100 | 0.7679 | 0.6144 | 0.5579 | 0.7234 | 0.8173 | 0.7889 | 0.5474 |
| cosine_ndcg@150 | 0.7857 | 0.6499 | 0.588 | 0.7438 | 0.8186 | 0.7909 | 0.5474 |
| **cosine_ndcg@200** | **0.797** | **0.6681** | **0.6071** | **0.7554** | **0.8195** | **0.7914** | **0.5474** |
| cosine_mrr@1 | 0.6571 | 0.1243 | 0.2956 | 0.6602 | 0.728 | 0.6703 | 0.1908 |
| cosine_mrr@20 | 0.8138 | 0.5581 | 0.5104 | 0.8037 | 0.7969 | 0.752 | 0.4093 |
| cosine_mrr@50 | 0.8138 | 0.5581 | 0.511 | 0.8041 | 0.7975 | 0.7529 | 0.4093 |
| cosine_mrr@100 | 0.8138 | 0.5581 | 0.511 | 0.8041 | 0.7977 | 0.7531 | 0.4093 |
| cosine_mrr@150 | 0.8138 | 0.5581 | 0.511 | 0.8041 | 0.7977 | 0.7531 | 0.4093 |
| cosine_mrr@200 | 0.8138 | 0.5581 | 0.511 | 0.8041 | 0.7977 | 0.7531 | 0.4093 |
| cosine_map@1 | 0.6571 | 0.1243 | 0.2956 | 0.6602 | 0.728 | 0.6703 | 0.1908 |
| cosine_map@20 | 0.5579 | 0.4799 | 0.401 | 0.5087 | 0.7351 | 0.6968 | 0.3298 |
| cosine_map@50 | 0.5471 | 0.425 | 0.3588 | 0.4926 | 0.7374 | 0.6996 | 0.3298 |
| cosine_map@100 | 0.5796 | 0.4302 | 0.3633 | 0.5217 | 0.738 | 0.7003 | 0.3298 |
| cosine_map@150 | 0.5875 | 0.4459 | 0.3777 | 0.5299 | 0.7381 | 0.7004 | 0.3298 |
| cosine_map@200 | 0.5912 | 0.4533 | 0.3848 | 0.5334 | 0.7382 | 0.7005 | 0.3298 |
| cosine_map@500 | 0.5953 | 0.4656 | 0.3978 | 0.5386 | 0.7383 | 0.7006 | 0.3298 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
<details><summary>full_en</summary>
#### full_en
* Dataset: full_en
* Size: 28,880 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 5.68 tokens</li><li>max: 11 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 5.76 tokens</li><li>max: 12 tokens</li></ul> |
* Samples:
| anchor | positive |
|:-----------------------------------------|:-----------------------------------------|
| <code>air commodore</code> | <code>flight lieutenant</code> |
| <code>command and control officer</code> | <code>flight officer</code> |
| <code>air commodore</code> | <code>command and control officer</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_de</summary>
#### full_de
* Dataset: full_de
* Size: 23,023 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 7.99 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.19 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| anchor | positive |
|:----------------------------------|:-----------------------------------------------------|
| <code>Staffelkommandantin</code> | <code>Kommodore</code> |
| <code>Luftwaffenoffizierin</code> | <code>Luftwaffenoffizier/Luftwaffenoffizierin</code> |
| <code>Staffelkommandantin</code> | <code>Luftwaffenoffizierin</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_es</summary>
#### full_es
* Dataset: full_es
* Size: 20,724 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 3 tokens</li><li>mean: 9.13 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 3 tokens</li><li>mean: 8.84 tokens</li><li>max: 32 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------|:-------------------------------------------|
| <code>jefe de escuadrón</code> | <code>instructor</code> |
| <code>comandante de aeronave</code> | <code>instructor de simulador</code> |
| <code>instructor</code> | <code>oficial del Ejército del Aire</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>full_zh</summary>
#### full_zh
* Dataset: full_zh
* Size: 30,401 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 7.15 tokens</li><li>max: 14 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 7.46 tokens</li><li>max: 21 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------|:---------------------|
| <code>技术总监</code> | <code>技术和运营总监</code> |
| <code>技术总监</code> | <code>技术主管</code> |
| <code>技术总监</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
<details><summary>mix</summary>
#### mix
* Dataset: mix
* Size: 21,760 training samples
* Columns: <code>anchor</code> and <code>positive</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive |
|:--------|:---------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 2 tokens</li><li>mean: 6.71 tokens</li><li>max: 19 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 7.69 tokens</li><li>max: 19 tokens</li></ul> |
* Samples:
| anchor | positive |
|:------------------------------------------|:----------------------------------------------------------------|
| <code>technical manager</code> | <code>Technischer Direktor für Bühne, Film und Fernsehen</code> |
| <code>head of technical</code> | <code>directora técnica</code> |
| <code>head of technical department</code> | <code>技术艺术总监</code> |
* Loss: [<code>GISTEmbedLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#gistembedloss) with these parameters:
```json
{'guide': SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
), 'temperature': 0.01, 'margin_strategy': 'absolute', 'margin': 0.0}
```
</details>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 2
- `num_train_epochs`: 5
- `warmup_ratio`: 0.05
- `log_on_each_node`: False
- `fp16`: True
- `dataloader_num_workers`: 4
- `ddp_find_unused_parameters`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: False
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 4
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: True
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | full_en_cosine_ndcg@200 | full_es_cosine_ndcg@200 | full_de_cosine_ndcg@200 | full_zh_cosine_ndcg@200 | mix_es_cosine_ndcg@200 | mix_de_cosine_ndcg@200 | mix_zh_cosine_ndcg@200 |
|:------:|:----:|:-------------:|:-----------------------:|:-----------------------:|:-----------------------:|:-----------------------:|:----------------------:|:----------------------:|:----------------------:|
| -1 | -1 | - | 0.7447 | 0.6125 | 0.5378 | 0.7240 | 0.7029 | 0.6345 | 0.5531 |
| 0.0010 | 1 | 3.4866 | - | - | - | - | - | - | - |
| 0.1027 | 100 | 2.5431 | - | - | - | - | - | - | - |
| 0.2053 | 200 | 1.4536 | 0.7993 | 0.6633 | 0.5974 | 0.7642 | 0.7567 | 0.7011 | 0.5498 |
| 0.3080 | 300 | 1.1018 | - | - | - | - | - | - | - |
| 0.4107 | 400 | 0.9184 | 0.7925 | 0.6586 | 0.6058 | 0.7587 | 0.7749 | 0.7278 | 0.5486 |
| 0.5133 | 500 | 0.8973 | - | - | - | - | - | - | - |
| 0.6160 | 600 | 0.7309 | 0.7951 | 0.6671 | 0.6096 | 0.7708 | 0.7793 | 0.7339 | 0.5525 |
| 0.7187 | 700 | 0.7297 | - | - | - | - | - | - | - |
| 0.8214 | 800 | 0.7281 | 0.7929 | 0.6711 | 0.6088 | 0.7645 | 0.7899 | 0.7444 | 0.5479 |
| 0.9240 | 900 | 0.6607 | - | - | - | - | - | - | - |
| 1.0267 | 1000 | 0.6075 | 0.7915 | 0.6659 | 0.6088 | 0.7665 | 0.7968 | 0.7588 | 0.5482 |
| 1.1294 | 1100 | 0.4553 | - | - | - | - | - | - | - |
| 1.2320 | 1200 | 0.4775 | 0.7979 | 0.6696 | 0.6033 | 0.7669 | 0.7959 | 0.7624 | 0.5484 |
| 1.3347 | 1300 | 0.4838 | - | - | - | - | - | - | - |
| 1.4374 | 1400 | 0.4912 | 0.7973 | 0.6757 | 0.6112 | 0.7656 | 0.7978 | 0.7650 | 0.5487 |
| 1.5400 | 1500 | 0.4732 | - | - | - | - | - | - | - |
| 1.6427 | 1600 | 0.5269 | 0.8031 | 0.6723 | 0.6108 | 0.7654 | 0.8008 | 0.7660 | 0.5492 |
| 1.7454 | 1700 | 0.4822 | - | - | - | - | - | - | - |
| 1.8480 | 1800 | 0.5072 | 0.7962 | 0.6668 | 0.6051 | 0.7592 | 0.8001 | 0.7714 | 0.5486 |
| 1.9507 | 1900 | 0.4709 | - | - | - | - | - | - | - |
| 2.0544 | 2000 | 0.3772 | 0.7940 | 0.6647 | 0.6037 | 0.7579 | 0.8064 | 0.7732 | 0.5479 |
| 2.1571 | 2100 | 0.3982 | - | - | - | - | - | - | - |
| 2.2598 | 2200 | 0.3073 | 0.7969 | 0.6652 | 0.6005 | 0.7625 | 0.8054 | 0.7734 | 0.5493 |
| 2.3624 | 2300 | 0.383 | - | - | - | - | - | - | - |
| 2.4651 | 2400 | 0.3687 | 0.7925 | 0.6690 | 0.5987 | 0.7583 | 0.8081 | 0.7735 | 0.5477 |
| 2.5678 | 2500 | 0.3472 | - | - | - | - | - | - | - |
| 2.6704 | 2600 | 0.3557 | 0.7956 | 0.6758 | 0.6019 | 0.7659 | 0.8082 | 0.7767 | 0.5491 |
| 2.7731 | 2700 | 0.3527 | - | - | - | - | - | - | - |
| 2.8758 | 2800 | 0.3446 | 0.7945 | 0.6719 | 0.6020 | 0.7616 | 0.8124 | 0.7818 | 0.5496 |
| 2.9784 | 2900 | 0.3566 | - | - | - | - | - | - | - |
| 3.0821 | 3000 | 0.3252 | 0.7948 | 0.6682 | 0.6025 | 0.7617 | 0.8152 | 0.7848 | 0.5516 |
| 3.1848 | 3100 | 0.2968 | - | - | - | - | - | - | - |
| 3.2875 | 3200 | 0.2962 | 0.7953 | 0.6717 | 0.6086 | 0.7613 | 0.8110 | 0.7824 | 0.5482 |
| 3.3901 | 3300 | 0.3084 | - | - | - | - | - | - | - |
| 3.4928 | 3400 | 0.2909 | 0.7940 | 0.6634 | 0.6023 | 0.7615 | 0.8138 | 0.7822 | 0.5457 |
| 3.5955 | 3500 | 0.2964 | - | - | - | - | - | - | - |
| 3.6982 | 3600 | 0.3193 | 0.7960 | 0.6635 | 0.6070 | 0.7534 | 0.8164 | 0.7844 | 0.5467 |
| 3.8008 | 3700 | 0.3514 | - | - | - | - | - | - | - |
| 3.9035 | 3800 | 0.3147 | 0.7973 | 0.6696 | 0.6125 | 0.7616 | 0.8176 | 0.7885 | 0.5469 |
| 4.0062 | 3900 | 0.2738 | - | - | - | - | - | - | - |
| 4.1088 | 4000 | 0.2842 | 0.7960 | 0.6672 | 0.6082 | 0.7536 | 0.8174 | 0.7891 | 0.5479 |
| 4.2115 | 4100 | 0.2739 | - | - | - | - | - | - | - |
| 4.3142 | 4200 | 0.2704 | 0.7979 | 0.6681 | 0.6111 | 0.7540 | 0.8180 | 0.7891 | 0.5476 |
| 4.4168 | 4300 | 0.2529 | - | - | - | - | - | - | - |
| 4.5195 | 4400 | 0.272 | 0.7968 | 0.6685 | 0.6087 | 0.7564 | 0.8185 | 0.7901 | 0.5476 |
| 4.6222 | 4500 | 0.3 | - | - | - | - | - | - | - |
| 4.7248 | 4600 | 0.2598 | 0.7972 | 0.6675 | 0.6072 | 0.7556 | 0.8190 | 0.7909 | 0.5478 |
| 4.8275 | 4700 | 0.3101 | - | - | - | - | - | - | - |
| 4.9302 | 4800 | 0.2524 | 0.7970 | 0.6681 | 0.6071 | 0.7554 | 0.8195 | 0.7914 | 0.5474 |
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### GISTEmbedLoss
```bibtex
@misc{solatorio2024gistembed,
title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
author={Aivin V. Solatorio},
year={2024},
eprint={2402.16829},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
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