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cnmoro
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static-retrieval-distilbert-ptbr

Sentence Similarity
sentence-transformers
Safetensors
Portuguese
feature-extraction
Generated from Trainer
dataset_size:25863649
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
Inference Endpoints
Model card Files Community
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---
language:
- pt
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:25863649
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: google o que causa urina turva
sentences:
- Um cagoule, cagoul, kagoule ou kagool (do francês cagoule significa balaclava)
é o termo inglês britânico para um leve (geralmente sem forro), capa de chuva
à prova de intempéries ou anorak com um capuz, que muitas vezes vem no joelho.
O equivalente inglês canadense é quebra-vento ou K-Way".
- Causas da Urina Nublada. 1 Infecção da bexiga (Cisite) A infecção da bexiga é
uma infecção da bexiga, geralmente causada por bactérias ou, raramente, por Candida.
2 Desidratação é a perda excessiva de água corporal. 3 Gonorreia Em Mulheres Gonorreia
é uma infecção bacteriana transmitida durante o contato sexual".
- infecção vaginal ou desidratação. Se a urina é mais leitosa na aparência, isso
pode ser devido à presença de bactérias, muco, gordura ou glóbulos vermelhos ou
brancos. A propósito, a urina â-saudável deve ser amarela pálida ou de cor palha
na aparência. Se a sua urina cheira. Engraçado. É mais provável devido a algo
que você comeu".
- source_sentence: como viver a vida sem depressão
sentences:
- a depressão resulta em uma perda da qualidade de vida. Por definição, um transtorno
depressivo prejudica sua capacidade de funcionar adequadamente em seu trabalho,
participar adequadamente de relacionamentos com os outros e de atender adequadamente
às suas atividades de vida diária".
- Mantém o controle de seus sentimentos e atividades. Quando você se sente mais
deprimido, você pode começar a se afastar de atividades que você normalmente faz,
como ir para a aula ou trabalhar, visitar amigos, fazer exercícios e até mesmo
tomar banho. Você também pode começar a se sentir pior ou ter sintomas mais graves
de depressão".
- EUA Embaixadores e outras agências para sincronizar planos e executar atividades
de informação e influenciar (IIA) em toda a gama de operações militares. 4o Grupo
MIS (A) ".
- source_sentence: o que faz tadasana significa
sentences:
- 'Esta é uma refeição vegetariana (VGML) que também é preparado chinês ou oriental-estilo.
Vegetarian Lacto-Ovo Refeição (VLML) Esta é uma refeição vegetariana que também
pode conter ovos e produtos lácteos. Contém um ou mais destes ingredientes: legumes,
frutas frescas, ovos, produtos lácteos e leguminosas. Não contém qualquer tipo
de peixe ou carne".'
- Tadasana, com 'tada' que significa 'montanha', é considerado como uma das posturas
mais benéficas na ioga. Embora pareça ser bastante simples, uma pessoa tem que
passar por muita prática para alcançar a postura perfeita de tadasana. Acredita-se
que a asana também fornece benefícios físicos, mas mentais".
- 'Alafia: Uma saudação, como olá com o significado de boa saúde ou paz (como shalom).
Fanga: Uma dança de boas-vindas tradicional. Muitas vezes é escrito como funga.Ashe:
(Pronuncia-se ah-shay) O Yoruba acredita que a cinza é uma força básica que emana
do Criador que une todas as coisas vivas e não-viveres.lafia: Uma saudação, como
olá com o significado de boa saúde ou paz (como shalom). Fanga: Uma dança de boas-vindas
tradicional. Muitas vezes é escrito como funga".'
- source_sentence: qual é a coisa voando sobre a cidade esmeralda
sentences:
- '" Maior aeroporto principal para Chincoteague, Virgínia: O principal aeroporto
mais próximo de Chincoteague, Virginia é Salisbury-Ocean City Wicomico Regional
Airport (SBY / KSBY). Este aeroporto fica em Salisbury, Maryland e fica a 47 milhas
do centro de Chincoteague, VA. Se você está procurando voos domésticos para SBY,
verifique as companhias aéreas que voam para SBY".'
- 1 The Emerald City aparece no filme The Wizard of Oz (1939). 2 The Emerald City
aparece em The Wizard of Oz série. 3 Depois que a Bruxa Malvada do Ocidente é
ressuscitada por seus leais Macacos Voadores, ela lança um feitiço na Cidade Esmeralda
que o mancha".
- Isso dá a Esmeralda o valor adicional da boa sorte, da providência e como uma
ponte entre a mente humana e os escritos Divinos. Onde quer que haja alguém impactando
a mente e o espírito da humanidade de maneiras profundas, é provável que você
encontre a Esmeralda na imagem. Esmeralda vem sob o domínio da deusa Vênus".
- source_sentence: o que ajuda a síndrome de ibs
sentences:
- óleo de hortelã-revestida com antecérico é amplamente utilizado para a síndrome
do intestino irritável. Tem a intenção de reduzir a dor abdominal e inchaço da
síndrome do intestino irritável. Peppermint é considerada uma erva carminativa,
o que significa que é usado para eliminar o excesso de gás nos intestinos. Embora
novas pesquisas sejam necessárias, estudos preliminares indicam que pode aliviar
os sintomas da SII".
- diarreia ou prisão de ventre que não responde ao tratamento domiciliar".
- Este tipo de halva é feito por fritar farinha (como sêmola) em óleo, misturando-o
em um roux, e depois cozinhá-lo com um xarope açucarado. Esta variedade é popular
na Grécia, Azerbaijão, Irã, Turquia, Somália, índia, Paquistão e Afeganistão".
datasets:
- cnmoro/AllTripletsMsMarco-PTBR
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: SentenceTransformer
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoClimateFEVER
type: NanoClimateFEVER
metrics:
- type: cosine_accuracy@1
value: 0.16
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.34
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.38
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.08800000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.056000000000000015
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.07233333333333332
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12233333333333335
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.169
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.21633333333333332
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.17347962524637853
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.22666666666666668
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.13734138567741627
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: cosine_accuracy@1
value: 0.48
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.7
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.82
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.86
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.48
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.43999999999999995
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.408
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.35999999999999993
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.035316726913150166
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10434144077897482
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.15231964640086332
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.2237637244339288
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4246552618150319
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6176666666666667
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.31123449548810894
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFEVER
type: NanoFEVER
metrics:
- type: cosine_accuracy@1
value: 0.32
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.58
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.72
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.82
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.32
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15200000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08799999999999997
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.2866666666666666
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.5466666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.6933333333333332
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.79
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.541603756700773
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4797777777777777
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4632721572721572
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: cosine_accuracy@1
value: 0.16
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.26
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.32
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.38
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.16
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.052000000000000005
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.047079365079365075
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.12374603174603176
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1498015873015873
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.19921428571428573
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.14911410247271004
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2208571428571429
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.10914868671112705
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: cosine_accuracy@1
value: 0.5
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.68
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.76
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.86
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.5
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.29333333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19599999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.122
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.25
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.44
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.49
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.61
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5140251570207169
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.6078333333333333
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4296608736936407
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: cosine_accuracy@1
value: 0.08
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.32
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.10666666666666665
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.09200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05800000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.32
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.46
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.58
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.31757857296738545
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2347460317460317
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.24643617899193362
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: cosine_accuracy@1
value: 0.26
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.42
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.48
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.26
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22666666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.2
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.14800000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.039136679314288055
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.07088473736441431
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.08854886067737688
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.09738297754672119
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.20662886108023884
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.33716666666666667
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.08492712298780619
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: cosine_accuracy@1
value: 0.06
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.12
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.18
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.3
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.06
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.039999999999999994
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.036000000000000004
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.030000000000000006
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.06
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.11
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.17
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.27
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.14834320225800574
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.11593650793650795
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12214508911612589
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoQuoraRetrieval
type: NanoQuoraRetrieval
metrics:
- type: cosine_accuracy@1
value: 0.7
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.84
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.94
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30666666666666664
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.21199999999999997
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11399999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.644
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.7613333333333333
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.848
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.902
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.796606045632188
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7831666666666668
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7555666834462891
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: cosine_accuracy@1
value: 0.18
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.38
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.48
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.58
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.18
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.16666666666666663
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09000000000000002
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.03866666666666667
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.10466666666666667
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.1456666666666667
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.18566666666666667
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.1754827925505982
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.2969126984126984
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.12469236976328293
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoArguAna
type: NanoArguAna
metrics:
- type: cosine_accuracy@1
value: 0.08
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.28
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.36
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.54
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.08
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.09333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.07200000000000001
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05400000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.08
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.28
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.36
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.54
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.2899394224946307
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.21268253968253967
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.22184431538753369
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoSciFact
type: NanoSciFact
metrics:
- type: cosine_accuracy@1
value: 0.34
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.44
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.46
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.52
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.34
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.15333333333333332
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.096
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.05600000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.34
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.43
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.44
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.495
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.4215626178273768
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3998571428571428
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.4072112112025905
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: NanoTouche2020
type: NanoTouche2020
metrics:
- type: cosine_accuracy@1
value: 0.30612244897959184
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4897959183673469
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.6122448979591837
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.7959183673469388
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.30612244897959184
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2857142857142857
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.28571428571428575
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.2714285714285714
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.017318112827283315
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.04934081962696573
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.08015471400681852
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.1539608360137575
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.28125127808062544
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4444930353093618
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.1901047659008045
name: Cosine Map@100
- task:
type: nano-beir
name: Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: cosine_accuracy@1
value: 0.2789324960753532
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.4438304552590267
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.5286342229199373
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.6181475667189953
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.2789324960753532
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1927472527472527
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.15767032967032968
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.11534065934065933
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15311673467698103
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2664086945781836
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3266788314143574
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.40487090951605337
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.3415592843189738
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.3829048366599387
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.27719887197221665
name: Cosine Map@100
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained on the [all_triplets_ms_marco-ptbr](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR) dataset. It maps sentences & paragraphs to a 512-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co./unknown) -->
- **Maximum Sequence Length:** inf tokens
- **Output Dimensionality:** 512 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [all_triplets_ms_marco-ptbr](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR)
- **Language:** pt
<!-- - **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): StaticEmbedding(
(embedding): EmbeddingBag(29794, 512, mode='mean')
)
)
```
## 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("cnmoro/static-retrieval-distilbert-ptbr")
# Run inference
sentences = [
'o que ajuda a síndrome de ibs',
'óleo de hortelã-revestida com antecérico é amplamente utilizado para a síndrome do intestino irritável. Tem a intenção de reduzir a dor abdominal e inchaço da síndrome do intestino irritável. Peppermint é considerada uma erva carminativa, o que significa que é usado para eliminar o excesso de gás nos intestinos. Embora novas pesquisas sejam necessárias, estudos preliminares indicam que pode aliviar os sintomas da SII".',
'diarreia ou prisão de ventre que não responde ao tratamento domiciliar".',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 512]
# 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: `NanoClimateFEVER`, `NanoDBPedia`, `NanoFEVER`, `NanoFiQA2018`, `NanoHotpotQA`, `NanoMSMARCO`, `NanoNFCorpus`, `NanoNQ`, `NanoQuoraRetrieval`, `NanoSCIDOCS`, `NanoArguAna`, `NanoSciFact` and `NanoTouche2020`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | NanoClimateFEVER | NanoDBPedia | NanoFEVER | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNFCorpus | NanoNQ | NanoQuoraRetrieval | NanoSCIDOCS | NanoArguAna | NanoSciFact | NanoTouche2020 |
|:--------------------|:-----------------|:------------|:-----------|:-------------|:-------------|:------------|:-------------|:-----------|:-------------------|:------------|:------------|:------------|:---------------|
| cosine_accuracy@1 | 0.16 | 0.48 | 0.32 | 0.16 | 0.5 | 0.08 | 0.26 | 0.06 | 0.7 | 0.18 | 0.08 | 0.34 | 0.3061 |
| cosine_accuracy@3 | 0.26 | 0.7 | 0.58 | 0.26 | 0.68 | 0.32 | 0.42 | 0.12 | 0.84 | 0.38 | 0.28 | 0.44 | 0.4898 |
| cosine_accuracy@5 | 0.34 | 0.82 | 0.72 | 0.32 | 0.76 | 0.46 | 0.46 | 0.18 | 0.9 | 0.48 | 0.36 | 0.46 | 0.6122 |
| cosine_accuracy@10 | 0.38 | 0.86 | 0.82 | 0.38 | 0.86 | 0.58 | 0.48 | 0.3 | 0.94 | 0.58 | 0.54 | 0.52 | 0.7959 |
| cosine_precision@1 | 0.16 | 0.48 | 0.32 | 0.16 | 0.5 | 0.08 | 0.26 | 0.06 | 0.7 | 0.18 | 0.08 | 0.34 | 0.3061 |
| cosine_precision@3 | 0.1 | 0.44 | 0.2 | 0.0933 | 0.2933 | 0.1067 | 0.2267 | 0.04 | 0.3067 | 0.1667 | 0.0933 | 0.1533 | 0.2857 |
| cosine_precision@5 | 0.088 | 0.408 | 0.152 | 0.072 | 0.196 | 0.092 | 0.2 | 0.036 | 0.212 | 0.14 | 0.072 | 0.096 | 0.2857 |
| cosine_precision@10 | 0.056 | 0.36 | 0.088 | 0.052 | 0.122 | 0.058 | 0.148 | 0.03 | 0.114 | 0.09 | 0.054 | 0.056 | 0.2714 |
| cosine_recall@1 | 0.0723 | 0.0353 | 0.2867 | 0.0471 | 0.25 | 0.08 | 0.0391 | 0.06 | 0.644 | 0.0387 | 0.08 | 0.34 | 0.0173 |
| cosine_recall@3 | 0.1223 | 0.1043 | 0.5467 | 0.1237 | 0.44 | 0.32 | 0.0709 | 0.11 | 0.7613 | 0.1047 | 0.28 | 0.43 | 0.0493 |
| cosine_recall@5 | 0.169 | 0.1523 | 0.6933 | 0.1498 | 0.49 | 0.46 | 0.0885 | 0.17 | 0.848 | 0.1457 | 0.36 | 0.44 | 0.0802 |
| cosine_recall@10 | 0.2163 | 0.2238 | 0.79 | 0.1992 | 0.61 | 0.58 | 0.0974 | 0.27 | 0.902 | 0.1857 | 0.54 | 0.495 | 0.154 |
| **cosine_ndcg@10** | **0.1735** | **0.4247** | **0.5416** | **0.1491** | **0.514** | **0.3176** | **0.2066** | **0.1483** | **0.7966** | **0.1755** | **0.2899** | **0.4216** | **0.2813** |
| cosine_mrr@10 | 0.2267 | 0.6177 | 0.4798 | 0.2209 | 0.6078 | 0.2347 | 0.3372 | 0.1159 | 0.7832 | 0.2969 | 0.2127 | 0.3999 | 0.4445 |
| cosine_map@100 | 0.1373 | 0.3112 | 0.4633 | 0.1091 | 0.4297 | 0.2464 | 0.0849 | 0.1221 | 0.7556 | 0.1247 | 0.2218 | 0.4072 | 0.1901 |
#### Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>NanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.NanoBEIREvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.2789 |
| cosine_accuracy@3 | 0.4438 |
| cosine_accuracy@5 | 0.5286 |
| cosine_accuracy@10 | 0.6181 |
| cosine_precision@1 | 0.2789 |
| cosine_precision@3 | 0.1927 |
| cosine_precision@5 | 0.1577 |
| cosine_precision@10 | 0.1153 |
| cosine_recall@1 | 0.1531 |
| cosine_recall@3 | 0.2664 |
| cosine_recall@5 | 0.3267 |
| cosine_recall@10 | 0.4049 |
| **cosine_ndcg@10** | **0.3416** |
| cosine_mrr@10 | 0.3829 |
| cosine_map@100 | 0.2772 |
<!--
## 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 Dataset
#### all_triplets_ms_marco-ptbr
* Dataset: [all_triplets_ms_marco-ptbr](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR) at [f934503](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR/tree/f934503cfbb69901217f12c87f28767354e597ea)
* Size: 25,863,649 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 characters</li><li>mean: 35.31 characters</li><li>max: 105 characters</li></ul> | <ul><li>min: 31 characters</li><li>mean: 356.8 characters</li><li>max: 1050 characters</li></ul> | <ul><li>min: 13 characters</li><li>mean: 359.92 characters</li><li>max: 1153 characters</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>partes mais quentes da califórnia em dezembro</code> | <code>as melhores praias da Califórnia para o clima quente do inverno estão ao longo da costa sul, particularmente as margens viradas para o sul. As temperaturas mais quentes acontecem em Avila Beach, Long Beach e Laguna Beach, onde os dias se dem até pelo menos 67 graus F (19 C) em média em dezembro e janeiro".</code> | <code>Outros destinos da ilha do Caribe com uma combinação de clima quente e não muita chuva em dezembro incluem Kingston, Jamaica (87 F), St. Kitts (85 F) e Nassau, Bahamas (79 F). Nos EUA continentais, o clima de férias mais quente em dezembro é mais frequentemente a Flórida. Tente afundar seus dedos na areia branca quente e macia de Nápoles e Sarasota, dois dos nossos locais de férias de inverno românticos da Flórida da Costa do Golfo da Flórida".</code> |
| <code>definição de anosmia</code> | <code>Anosmia (/aen-É-zmiÉ/) A sÉ-zmiÉ é a incapacidade de perceber o odor ou a falta de funcionamento da autaraction a perda do sentido.</code> | <code>Anemia é um termo médico que se refere a um número reduzido de glóbulos vermelhos circulantes (RBC), hemoglobina (Hb), ou ambos. Não é uma doença específica, mas sim o resultado de algum outro processo de doença ou condição.nemia é um termo médico referindo-se a um número reduzido de glóbulos vermelhos circulantes (RBC), hemoglobina (Hb), ou ambos. Não é uma doença específica, mas sim o resultado de algum outro processo ou condição de doença".</code> |
| <code>can fêmeas obter hemofilia</code> | <code>uma fêmea que herda um afetado x cromossomo torna-se um portador de hemofilia que ela pode passar o gene afetado para seus filhos, além de uma mulher que é um portador às vezes pode ter sintomas de hemofilia na verdade alguns médicos descrevem essas mulheres como tendo mulheres leves que carregam o gene da hemofilia que carregam o gene da hemofilia e têm quaisquer sintomas do transtorno deve ser verificado e cuidado por um provedor de saúde de boa qualidade cuidados médicos e enfermeiros que podem evitar que os problemas sérios que saibam que muitos.</code> | <code>Hemofilia é um X ligado ou sexo ligado a doença hereditária que significa que o defeito é realizado no cromossomo X. As fêmeas têm dois cromossomos X e os machos têm um cromossomo X e um cromossomo Y. O cromossomo X, que carrega o gene da hemofilia em homens, faz com que Fator VIII ou Fator IX esteja ausente ou deficiente (nível baixo). Cada criança de um portador de hemofilia tem 50% de chance de ser afetada pela hemofilia; seja ter hemofilia para um macho ou ser portadora de uma mulher".</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
512,
384,
256,
128,
64,
32,
16,
8
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### all_triplets_ms_marco-ptbr
* Dataset: [all_triplets_ms_marco-ptbr](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR) at [f934503](https://huggingface.co./datasets/cnmoro/AllTripletsMsMarco-PTBR/tree/f934503cfbb69901217f12c87f28767354e597ea)
* Size: 527,832 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 characters</li><li>mean: 36.15 characters</li><li>max: 193 characters</li></ul> | <ul><li>min: 20 characters</li><li>mean: 360.3 characters</li><li>max: 1097 characters</li></ul> | <ul><li>min: 14 characters</li><li>mean: 365.67 characters</li><li>max: 1145 characters</li></ul> |
* Samples:
| anchor | positive | negative |
|:----------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>diferença entre o ovo cozido duro e o ovo escalfado</code> | <code>o ovo é escalfado (ou cozido) quando o branco é cozido e a gema ainda é escorrendo, um ovo cozido duro é cozido em sua casca por 7 a 8 minutos até que seja cozido sólido todo o caminho. Carmen D 4 anos atrás. Os polegares para cima. 0".</code> | <code>mexidos, escalfados, fritos ou cozidos, e dado todas essas variações, a questão de longa duração que eles podem ser armazenados com segurança é uma boa a considerar. Uma bactéria chamada Salmonella enteritidis pode estar presente dentro da gema, mas ovos duros os torna seguros para comer".</code> |
| <code>quando você pode coletar segurança social se deficientes</code> | <code>Como a Segurança Social pagará benefícios de invalidez a uma pessoa com deficiência é determinada pela data em que você apresentou sua reivindicação de deficiência ao se candidatar à segurança social e/ou incapacidade da SSI.</code> | <code>Se for esse o caso, você não terá mais direito a benefícios de Deficiência da Segurança Social, mas você pode ter direito a benefícios de aposentadoria da Previdência Social uma vez que você atinja a idade de 65 anos. Se você decidir voltar ao trabalho seus benefícios não vai parar imediatamente. Você pode ganhar renda em uma base de â-trialâ para até nove meses antes de seus benefícios de Deficiência Social são revogados. Se você tentar voltar ao trabalho e descobrir que você é incapaz de lidar com isso, seus Benefícios de Segurança Social não terminará.ou pode ganhar renda em uma base de âtrialâ por até nove meses antes de seus benefícios de deficientes de segurança social são revogados. Se você tentar voltar ao trabalho e descobrir que não consegue lidar com isso, seus Benefícios de Segurança Social não terminarão".</code> |
| <code>número de contato da sede da união ocidental</code> | <code>número de telefone da União Ocidental. O número e as etapas abaixo são votados no 1 de 4 por mais de 7190 clientes da Western Union. 800-999-9660. Suporte telefônico da Western Union. Leia as principais etapas e dicas abaixo. Eles chamam você em vez dissoNão esperando em espera. Free.ress 1 e continue pressionando 0. Este número de telefone é popular entre outros clientes da Western Union, mas não se esqueça de seguir os 6 passos mais abaixo".</code> | <code>Neste artigo eu listei o número de telefone de serviço ao cliente Western Union essencial e o número de telefone de contato e números gratuitos para a Western Union. Western Union operando em muitos países, então eu listei números de telefone de atendimento ao cliente internacional Western Union. Se você é o cliente da Western Union e gosta de saber informações sobre produtos e serviços da Western Union, basta usar os seguintes números".</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
512,
384,
256,
128,
64,
32,
16,
8
],
"matryoshka_weights": [
1,
1,
1,
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 512
- `per_device_eval_batch_size`: 512
- `learning_rate`: 0.2
- `num_train_epochs`: 5
- `warmup_ratio`: 0.1
- `bf16`: True
#### 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`: 512
- `per_device_eval_batch_size`: 512
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 0.2
- `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`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `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`: True
- `fp16`: False
- `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`: False
- `dataloader_num_workers`: 0
- `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}
- `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`: None
- `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
- `dispatch_batches`: None
- `split_batches`: 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`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | NanoClimateFEVER_cosine_ndcg@10 | NanoDBPedia_cosine_ndcg@10 | NanoFEVER_cosine_ndcg@10 | NanoFiQA2018_cosine_ndcg@10 | NanoHotpotQA_cosine_ndcg@10 | NanoMSMARCO_cosine_ndcg@10 | NanoNFCorpus_cosine_ndcg@10 | NanoNQ_cosine_ndcg@10 | NanoQuoraRetrieval_cosine_ndcg@10 | NanoSCIDOCS_cosine_ndcg@10 | NanoArguAna_cosine_ndcg@10 | NanoSciFact_cosine_ndcg@10 | NanoTouche2020_cosine_ndcg@10 | NanoBEIR_mean_cosine_ndcg@10 |
|:------:|:------:|:-------------:|:---------------:|:-------------------------------:|:--------------------------:|:------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------------:|:---------------------:|:---------------------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-----------------------------:|:----------------------------:|
| 0.0000 | 1 | 66.3307 | - | - | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 0.0198 | 1000 | 42.3936 | 27.4352 | 0.1314 | 0.3901 | 0.4362 | 0.0856 | 0.4261 | 0.2743 | 0.1524 | 0.1226 | 0.7497 | 0.1547 | 0.1544 | 0.4066 | 0.2984 | 0.2910 |
| 0.0396 | 2000 | 21.4189 | 17.5353 | 0.1443 | 0.4301 | 0.5087 | 0.1281 | 0.4315 | 0.2600 | 0.1859 | 0.1462 | 0.7842 | 0.1978 | 0.1944 | 0.4489 | 0.3432 | 0.3233 |
| 0.0594 | 3000 | 15.8675 | 14.6976 | 0.1579 | 0.4524 | 0.5459 | 0.1350 | 0.4307 | 0.2972 | 0.1980 | 0.1443 | 0.7807 | 0.1921 | 0.2016 | 0.4302 | 0.3561 | 0.3325 |
| 0.0792 | 4000 | 14.0655 | 13.5888 | 0.1803 | 0.4522 | 0.5321 | 0.1402 | 0.4479 | 0.2982 | 0.1914 | 0.1912 | 0.7992 | 0.2001 | 0.2143 | 0.4502 | 0.3432 | 0.3416 |
| 0.0990 | 5000 | 13.2932 | 13.0002 | 0.1926 | 0.4523 | 0.5118 | 0.1607 | 0.4451 | 0.3059 | 0.2048 | 0.2168 | 0.7903 | 0.1974 | 0.2387 | 0.4653 | 0.3520 | 0.3487 |
| 0.1188 | 6000 | 12.8258 | 12.6530 | 0.1998 | 0.4510 | 0.5437 | 0.1296 | 0.4506 | 0.3335 | 0.2100 | 0.1894 | 0.8074 | 0.1761 | 0.2423 | 0.4456 | 0.3688 | 0.3498 |
| 0.1386 | 7000 | 12.5101 | 12.3932 | 0.1775 | 0.4638 | 0.4978 | 0.1503 | 0.4547 | 0.3197 | 0.2037 | 0.1864 | 0.8178 | 0.1757 | 0.1987 | 0.4518 | 0.3382 | 0.3412 |
| 0.1584 | 8000 | 12.2601 | 12.1873 | 0.1884 | 0.4794 | 0.5263 | 0.1668 | 0.4764 | 0.3603 | 0.2115 | 0.1673 | 0.7835 | 0.1720 | 0.2266 | 0.4534 | 0.3535 | 0.3512 |
| 0.1782 | 9000 | 12.0884 | 12.0142 | 0.2139 | 0.4735 | 0.5170 | 0.1598 | 0.4498 | 0.3448 | 0.2002 | 0.1983 | 0.7901 | 0.1651 | 0.2351 | 0.4458 | 0.3240 | 0.3475 |
| 0.1980 | 10000 | 11.9352 | 11.8797 | 0.2123 | 0.4813 | 0.5146 | 0.1452 | 0.5095 | 0.3642 | 0.1983 | 0.1637 | 0.8041 | 0.1699 | 0.2384 | 0.4545 | 0.3198 | 0.3520 |
| 0.2178 | 11000 | 11.8034 | 11.7615 | 0.1776 | 0.4579 | 0.5237 | 0.1673 | 0.4808 | 0.3068 | 0.2009 | 0.1828 | 0.8173 | 0.1706 | 0.2572 | 0.4408 | 0.3205 | 0.3465 |
| 0.2376 | 12000 | 11.6906 | 11.6589 | 0.1789 | 0.4593 | 0.5512 | 0.1341 | 0.4894 | 0.3340 | 0.2106 | 0.1811 | 0.8192 | 0.1773 | 0.2381 | 0.4480 | 0.3209 | 0.3494 |
| 0.2573 | 13000 | 11.5868 | 11.5586 | 0.1877 | 0.4648 | 0.5137 | 0.1494 | 0.4939 | 0.3212 | 0.2193 | 0.2025 | 0.8120 | 0.1640 | 0.2452 | 0.4258 | 0.3561 | 0.3504 |
| 0.2771 | 14000 | 11.4752 | 11.4752 | 0.1938 | 0.4411 | 0.5186 | 0.1418 | 0.4839 | 0.3411 | 0.2106 | 0.1688 | 0.8217 | 0.1744 | 0.2768 | 0.4688 | 0.3384 | 0.3523 |
| 0.2969 | 15000 | 11.4299 | 11.3873 | 0.1989 | 0.4501 | 0.5109 | 0.1309 | 0.5037 | 0.3280 | 0.2040 | 0.1649 | 0.8035 | 0.1707 | 0.2549 | 0.4714 | 0.3308 | 0.3479 |
| 0.3167 | 16000 | 11.3369 | 11.3173 | 0.1880 | 0.4666 | 0.4988 | 0.1430 | 0.5086 | 0.3385 | 0.2054 | 0.1786 | 0.8181 | 0.1712 | 0.2766 | 0.4555 | 0.3220 | 0.3516 |
| 0.3365 | 17000 | 11.2737 | 11.2503 | 0.1748 | 0.4673 | 0.4849 | 0.1485 | 0.4902 | 0.3567 | 0.2160 | 0.1501 | 0.8059 | 0.1659 | 0.2476 | 0.4728 | 0.3121 | 0.3456 |
| 0.3563 | 18000 | 11.2138 | 11.1802 | 0.1738 | 0.4619 | 0.5408 | 0.1426 | 0.4986 | 0.3427 | 0.2193 | 0.1594 | 0.7995 | 0.1597 | 0.2567 | 0.4331 | 0.3140 | 0.3463 |
| 0.3761 | 19000 | 11.1662 | 11.1250 | 0.1625 | 0.4522 | 0.5313 | 0.1419 | 0.5093 | 0.3499 | 0.1982 | 0.1713 | 0.8000 | 0.1693 | 0.2332 | 0.4799 | 0.3353 | 0.3488 |
| 0.3959 | 20000 | 11.0674 | 11.0633 | 0.1627 | 0.4608 | 0.5167 | 0.1368 | 0.5025 | 0.3653 | 0.2090 | 0.1743 | 0.8166 | 0.1670 | 0.2281 | 0.4614 | 0.3408 | 0.3494 |
| 0.4157 | 21000 | 11.0251 | 11.0233 | 0.1730 | 0.4695 | 0.4854 | 0.1417 | 0.5211 | 0.3393 | 0.2246 | 0.1477 | 0.8146 | 0.1692 | 0.2148 | 0.4584 | 0.3356 | 0.3458 |
| 0.4355 | 22000 | 10.9932 | 10.9695 | 0.1709 | 0.4630 | 0.5161 | 0.1400 | 0.4945 | 0.3507 | 0.2226 | 0.1585 | 0.8103 | 0.1595 | 0.2355 | 0.4325 | 0.3343 | 0.3453 |
| 0.4553 | 23000 | 10.9327 | 10.9186 | 0.1803 | 0.4509 | 0.5341 | 0.1454 | 0.5241 | 0.3485 | 0.2032 | 0.1480 | 0.8056 | 0.1634 | 0.2206 | 0.4557 | 0.3266 | 0.3466 |
| 0.4751 | 24000 | 10.8936 | 10.8830 | 0.1891 | 0.4450 | 0.5202 | 0.1485 | 0.5006 | 0.3427 | 0.2079 | 0.1639 | 0.8115 | 0.1731 | 0.2213 | 0.4269 | 0.3424 | 0.3456 |
| 0.4949 | 25000 | 10.8654 | 10.8392 | 0.1610 | 0.4479 | 0.5524 | 0.1547 | 0.5002 | 0.3377 | 0.2128 | 0.1802 | 0.7996 | 0.1937 | 0.2240 | 0.4506 | 0.3097 | 0.3480 |
| 0.5147 | 26000 | 10.8168 | 10.7826 | 0.1784 | 0.4558 | 0.5211 | 0.1482 | 0.5099 | 0.3531 | 0.2165 | 0.1456 | 0.8090 | 0.1782 | 0.2367 | 0.4240 | 0.3251 | 0.3463 |
| 0.5345 | 27000 | 10.7554 | 10.7164 | 0.1841 | 0.4593 | 0.5183 | 0.1377 | 0.4843 | 0.3469 | 0.2066 | 0.1632 | 0.8099 | 0.1818 | 0.2779 | 0.4305 | 0.3270 | 0.3483 |
| 0.5543 | 28000 | 10.6605 | 10.6510 | 0.1780 | 0.4566 | 0.5328 | 0.1439 | 0.4923 | 0.3519 | 0.2152 | 0.1507 | 0.8060 | 0.1838 | 0.2585 | 0.4256 | 0.3147 | 0.3469 |
| 0.5741 | 29000 | 10.6202 | 10.5959 | 0.1866 | 0.4668 | 0.5370 | 0.1553 | 0.5118 | 0.3699 | 0.2265 | 0.1553 | 0.8090 | 0.1732 | 0.2614 | 0.4287 | 0.3193 | 0.3539 |
| 0.5939 | 30000 | 10.5399 | 10.5401 | 0.1862 | 0.4593 | 0.5237 | 0.1510 | 0.5273 | 0.3353 | 0.2101 | 0.1594 | 0.8092 | 0.1709 | 0.2643 | 0.4308 | 0.3199 | 0.3498 |
| 0.6137 | 31000 | 10.5212 | 10.4866 | 0.2000 | 0.4547 | 0.5131 | 0.1450 | 0.5213 | 0.3341 | 0.2136 | 0.1518 | 0.8094 | 0.1726 | 0.2911 | 0.4246 | 0.3388 | 0.3516 |
| 0.6335 | 32000 | 10.4767 | 10.4375 | 0.1873 | 0.4487 | 0.5162 | 0.1377 | 0.5186 | 0.3463 | 0.2184 | 0.1711 | 0.8087 | 0.1769 | 0.2871 | 0.4441 | 0.3297 | 0.3531 |
| 0.6533 | 33000 | 10.4247 | 10.4089 | 0.1949 | 0.4572 | 0.5322 | 0.1524 | 0.5286 | 0.3309 | 0.2204 | 0.1464 | 0.8006 | 0.1765 | 0.2727 | 0.4314 | 0.3323 | 0.3520 |
| 0.6731 | 34000 | 10.389 | 10.3680 | 0.1867 | 0.4628 | 0.5265 | 0.1369 | 0.5196 | 0.3411 | 0.2224 | 0.1597 | 0.8003 | 0.1702 | 0.2678 | 0.4386 | 0.3163 | 0.3499 |
| 0.6929 | 35000 | 10.3299 | 10.3354 | 0.1937 | 0.4614 | 0.5042 | 0.1430 | 0.5215 | 0.3416 | 0.2159 | 0.1488 | 0.8101 | 0.1764 | 0.2601 | 0.4525 | 0.3192 | 0.3499 |
| 0.7127 | 36000 | 10.3103 | 10.3054 | 0.1764 | 0.4555 | 0.5281 | 0.1577 | 0.5291 | 0.3338 | 0.2049 | 0.1483 | 0.7980 | 0.1660 | 0.2626 | 0.4153 | 0.3137 | 0.3453 |
| 0.7325 | 37000 | 10.2869 | 10.2670 | 0.1703 | 0.4488 | 0.5188 | 0.1560 | 0.5200 | 0.3370 | 0.2118 | 0.1513 | 0.8108 | 0.1671 | 0.2853 | 0.4057 | 0.3102 | 0.3456 |
| 0.7523 | 38000 | 10.2414 | 10.2453 | 0.1713 | 0.4556 | 0.5400 | 0.1568 | 0.5228 | 0.3359 | 0.2081 | 0.1624 | 0.8063 | 0.1636 | 0.2644 | 0.4413 | 0.3117 | 0.3492 |
| 0.7720 | 39000 | 10.231 | 10.2169 | 0.1595 | 0.4577 | 0.5599 | 0.1510 | 0.5195 | 0.3300 | 0.2070 | 0.1635 | 0.8145 | 0.1615 | 0.2846 | 0.4269 | 0.3236 | 0.3507 |
| 0.7918 | 40000 | 10.2115 | 10.1964 | 0.1734 | 0.4621 | 0.5414 | 0.1481 | 0.5300 | 0.3438 | 0.2072 | 0.1712 | 0.8062 | 0.1639 | 0.2815 | 0.4122 | 0.3000 | 0.3493 |
| 0.8116 | 41000 | 10.1947 | 10.1671 | 0.1712 | 0.4559 | 0.5450 | 0.1523 | 0.5145 | 0.3392 | 0.2198 | 0.1588 | 0.7927 | 0.1734 | 0.2826 | 0.4281 | 0.3014 | 0.3488 |
| 0.8314 | 42000 | 10.1666 | 10.1581 | 0.1648 | 0.4464 | 0.5555 | 0.1639 | 0.5014 | 0.3477 | 0.2099 | 0.1443 | 0.7988 | 0.1640 | 0.2784 | 0.4482 | 0.2983 | 0.3478 |
| 0.8512 | 43000 | 10.1528 | 10.1265 | 0.1789 | 0.4437 | 0.5328 | 0.1525 | 0.5266 | 0.3369 | 0.2016 | 0.1561 | 0.8097 | 0.1742 | 0.2863 | 0.4503 | 0.3008 | 0.3500 |
| 0.8710 | 44000 | 10.1054 | 10.1122 | 0.1716 | 0.4542 | 0.5310 | 0.1610 | 0.5359 | 0.3454 | 0.2022 | 0.1725 | 0.7948 | 0.1666 | 0.2840 | 0.4246 | 0.3149 | 0.3507 |
| 0.8908 | 45000 | 10.0878 | 10.0890 | 0.1729 | 0.4489 | 0.5533 | 0.1561 | 0.5401 | 0.3413 | 0.2135 | 0.1510 | 0.7989 | 0.1735 | 0.2950 | 0.4348 | 0.3202 | 0.3538 |
| 0.9106 | 46000 | 10.0875 | 10.0730 | 0.1776 | 0.4550 | 0.5499 | 0.1563 | 0.5313 | 0.3357 | 0.2084 | 0.1578 | 0.8058 | 0.1739 | 0.2976 | 0.4468 | 0.3176 | 0.3549 |
| 0.9304 | 47000 | 10.0615 | 10.0561 | 0.1816 | 0.4569 | 0.5310 | 0.1583 | 0.5279 | 0.3332 | 0.2058 | 0.1532 | 0.7976 | 0.1727 | 0.2813 | 0.4513 | 0.3146 | 0.3512 |
| 0.9502 | 48000 | 10.0378 | 10.0374 | 0.1916 | 0.4558 | 0.5242 | 0.1552 | 0.5368 | 0.3518 | 0.2050 | 0.1617 | 0.8065 | 0.1736 | 0.2898 | 0.4268 | 0.3109 | 0.3531 |
| 0.9700 | 49000 | 10.0393 | 10.0283 | 0.1809 | 0.4542 | 0.5319 | 0.1594 | 0.5240 | 0.3329 | 0.2070 | 0.1595 | 0.7998 | 0.1670 | 0.2885 | 0.4522 | 0.3204 | 0.3521 |
| 0.9898 | 50000 | 10.0035 | 10.0112 | 0.1721 | 0.4495 | 0.5200 | 0.1548 | 0.5294 | 0.3514 | 0.2124 | 0.1597 | 0.8063 | 0.1798 | 0.2785 | 0.4479 | 0.3322 | 0.3534 |
| 1.0096 | 51000 | 9.9575 | 10.0040 | 0.1737 | 0.4476 | 0.5422 | 0.1527 | 0.5345 | 0.3513 | 0.2076 | 0.1513 | 0.8071 | 0.1681 | 0.2715 | 0.4547 | 0.3149 | 0.3521 |
| 1.0294 | 52000 | 9.9083 | 9.9996 | 0.1668 | 0.4530 | 0.5315 | 0.1645 | 0.5212 | 0.3375 | 0.2168 | 0.1458 | 0.8046 | 0.1720 | 0.2746 | 0.4432 | 0.3234 | 0.3504 |
| 1.0492 | 53000 | 9.9229 | 9.9895 | 0.1777 | 0.4434 | 0.5348 | 0.1601 | 0.5158 | 0.3390 | 0.2130 | 0.1461 | 0.8014 | 0.1717 | 0.2808 | 0.4546 | 0.3161 | 0.3504 |
| 1.0690 | 54000 | 9.884 | 9.9758 | 0.1797 | 0.4507 | 0.5372 | 0.1685 | 0.5202 | 0.3398 | 0.2174 | 0.1739 | 0.7949 | 0.1744 | 0.2944 | 0.4334 | 0.3191 | 0.3541 |
| 1.0888 | 55000 | 9.9108 | 9.9650 | 0.1780 | 0.4458 | 0.5249 | 0.1510 | 0.5190 | 0.3492 | 0.2222 | 0.1639 | 0.7968 | 0.1895 | 0.2878 | 0.4251 | 0.3153 | 0.3514 |
| 1.1086 | 56000 | 9.9019 | 9.9556 | 0.1893 | 0.4465 | 0.5368 | 0.1514 | 0.5131 | 0.3384 | 0.2151 | 0.1609 | 0.8029 | 0.1886 | 0.2993 | 0.4280 | 0.3223 | 0.3533 |
| 1.1284 | 57000 | 9.8931 | 9.9392 | 0.1837 | 0.4409 | 0.5381 | 0.1632 | 0.5254 | 0.3332 | 0.2046 | 0.1470 | 0.8067 | 0.1915 | 0.2797 | 0.4167 | 0.3212 | 0.3501 |
| 1.1482 | 58000 | 9.8714 | 9.9229 | 0.1731 | 0.4440 | 0.5289 | 0.1477 | 0.5073 | 0.3257 | 0.2063 | 0.1631 | 0.8079 | 0.1844 | 0.3001 | 0.4391 | 0.3194 | 0.3498 |
| 1.1680 | 59000 | 9.885 | 9.9159 | 0.1756 | 0.4498 | 0.5274 | 0.1580 | 0.5156 | 0.3227 | 0.2101 | 0.1470 | 0.8042 | 0.1783 | 0.3026 | 0.4215 | 0.3237 | 0.3490 |
| 1.1878 | 60000 | 9.8824 | 9.9016 | 0.1794 | 0.4512 | 0.5261 | 0.1523 | 0.5093 | 0.3427 | 0.1964 | 0.1468 | 0.8029 | 0.1756 | 0.2898 | 0.4325 | 0.3173 | 0.3479 |
| 1.2076 | 61000 | 9.8846 | 9.8969 | 0.1768 | 0.4518 | 0.5452 | 0.1643 | 0.5087 | 0.3471 | 0.2004 | 0.1509 | 0.7959 | 0.1847 | 0.2954 | 0.4386 | 0.3099 | 0.3515 |
| 1.2274 | 62000 | 9.8534 | 9.8831 | 0.1848 | 0.4532 | 0.5422 | 0.1583 | 0.5177 | 0.3546 | 0.2087 | 0.1546 | 0.7985 | 0.1815 | 0.3024 | 0.4335 | 0.3285 | 0.3553 |
| 1.2472 | 63000 | 9.8494 | 9.8759 | 0.1776 | 0.4490 | 0.5305 | 0.1641 | 0.5138 | 0.3517 | 0.2043 | 0.1474 | 0.8040 | 0.1809 | 0.2947 | 0.4252 | 0.3183 | 0.3509 |
| 1.2670 | 64000 | 9.8514 | 9.8639 | 0.1820 | 0.4553 | 0.5386 | 0.1569 | 0.5055 | 0.3442 | 0.2116 | 0.1396 | 0.7949 | 0.1807 | 0.2820 | 0.4225 | 0.3154 | 0.3484 |
| 1.2867 | 65000 | 9.8341 | 9.8563 | 0.1772 | 0.4507 | 0.5300 | 0.1579 | 0.5072 | 0.3392 | 0.2067 | 0.1529 | 0.7961 | 0.1825 | 0.2874 | 0.4215 | 0.3195 | 0.3484 |
| 1.3065 | 66000 | 9.8417 | 9.8492 | 0.1784 | 0.4557 | 0.5251 | 0.1598 | 0.5011 | 0.3324 | 0.2183 | 0.1566 | 0.7928 | 0.1821 | 0.2873 | 0.4181 | 0.3153 | 0.3479 |
| 1.3263 | 67000 | 9.8081 | 9.8369 | 0.1831 | 0.4488 | 0.5360 | 0.1681 | 0.5046 | 0.3317 | 0.2064 | 0.1467 | 0.8013 | 0.1738 | 0.2887 | 0.4381 | 0.3043 | 0.3486 |
| 1.3461 | 68000 | 9.8001 | 9.8274 | 0.1842 | 0.4563 | 0.5387 | 0.1647 | 0.5080 | 0.3174 | 0.2089 | 0.1595 | 0.7964 | 0.1705 | 0.2918 | 0.4187 | 0.3054 | 0.3477 |
| 1.3659 | 69000 | 9.8059 | 9.8159 | 0.1827 | 0.4570 | 0.5528 | 0.1715 | 0.5207 | 0.3289 | 0.2046 | 0.1543 | 0.8094 | 0.1757 | 0.2839 | 0.4281 | 0.3025 | 0.3517 |
| 1.3857 | 70000 | 9.7848 | 9.8117 | 0.1656 | 0.4547 | 0.5381 | 0.1562 | 0.5091 | 0.3233 | 0.2127 | 0.1539 | 0.8000 | 0.1722 | 0.2885 | 0.4168 | 0.3091 | 0.3462 |
| 1.4055 | 71000 | 9.7847 | 9.8049 | 0.1786 | 0.4499 | 0.5495 | 0.1675 | 0.5194 | 0.3180 | 0.2133 | 0.1587 | 0.8025 | 0.1588 | 0.2895 | 0.4224 | 0.3056 | 0.3487 |
| 1.4253 | 72000 | 9.7587 | 9.7976 | 0.1706 | 0.4562 | 0.5425 | 0.1530 | 0.5283 | 0.3356 | 0.2125 | 0.1564 | 0.8055 | 0.1660 | 0.2939 | 0.4219 | 0.3005 | 0.3495 |
| 1.4451 | 73000 | 9.7652 | 9.7898 | 0.1787 | 0.4479 | 0.5406 | 0.1539 | 0.5281 | 0.3291 | 0.2088 | 0.1438 | 0.8058 | 0.1767 | 0.2938 | 0.4115 | 0.2960 | 0.3473 |
| 1.4649 | 74000 | 9.7507 | 9.7830 | 0.1746 | 0.4394 | 0.5426 | 0.1647 | 0.5201 | 0.3290 | 0.2131 | 0.1507 | 0.8039 | 0.1643 | 0.2856 | 0.4510 | 0.3030 | 0.3494 |
| 1.4847 | 75000 | 9.7412 | 9.7757 | 0.1701 | 0.4386 | 0.5244 | 0.1639 | 0.5140 | 0.3218 | 0.2111 | 0.1542 | 0.8086 | 0.1714 | 0.2765 | 0.4224 | 0.2973 | 0.3442 |
| 1.5045 | 76000 | 9.7412 | 9.7727 | 0.1823 | 0.4477 | 0.5337 | 0.1544 | 0.5117 | 0.3381 | 0.2074 | 0.1605 | 0.8079 | 0.1710 | 0.2820 | 0.4325 | 0.2996 | 0.3484 |
| 1.5243 | 77000 | 9.7475 | 9.7626 | 0.1743 | 0.4423 | 0.5343 | 0.1511 | 0.5142 | 0.3224 | 0.2124 | 0.1567 | 0.8076 | 0.1802 | 0.2946 | 0.4303 | 0.3044 | 0.3481 |
| 1.5441 | 78000 | 9.7512 | 9.7590 | 0.1737 | 0.4406 | 0.5323 | 0.1535 | 0.5102 | 0.3419 | 0.2099 | 0.1476 | 0.8058 | 0.1626 | 0.2877 | 0.4073 | 0.3015 | 0.3442 |
| 1.5639 | 79000 | 9.7406 | 9.7501 | 0.1735 | 0.4472 | 0.5189 | 0.1639 | 0.5148 | 0.3232 | 0.2065 | 0.1555 | 0.8015 | 0.1698 | 0.2826 | 0.4320 | 0.3047 | 0.3457 |
| 1.5837 | 80000 | 9.7409 | 9.7426 | 0.1799 | 0.4405 | 0.5225 | 0.1627 | 0.5158 | 0.3487 | 0.2051 | 0.1608 | 0.8079 | 0.1657 | 0.2857 | 0.4469 | 0.3014 | 0.3495 |
| 1.6035 | 81000 | 9.7125 | 9.7399 | 0.1781 | 0.4402 | 0.5230 | 0.1564 | 0.5153 | 0.3439 | 0.2167 | 0.1622 | 0.8070 | 0.1706 | 0.3040 | 0.4512 | 0.3071 | 0.3520 |
| 1.6233 | 82000 | 9.7164 | 9.7319 | 0.1806 | 0.4485 | 0.5317 | 0.1486 | 0.5220 | 0.3353 | 0.2087 | 0.1604 | 0.8033 | 0.1783 | 0.2899 | 0.4178 | 0.3025 | 0.3483 |
| 1.6431 | 83000 | 9.7203 | 9.7257 | 0.1766 | 0.4513 | 0.5120 | 0.1581 | 0.5108 | 0.3375 | 0.2084 | 0.1635 | 0.8085 | 0.1682 | 0.2904 | 0.4334 | 0.2932 | 0.3471 |
| 1.6629 | 84000 | 9.7035 | 9.7229 | 0.1759 | 0.4447 | 0.5391 | 0.1555 | 0.5104 | 0.3369 | 0.2067 | 0.1584 | 0.8036 | 0.1754 | 0.2943 | 0.4266 | 0.3032 | 0.3485 |
| 1.6827 | 85000 | 9.7277 | 9.7206 | 0.1757 | 0.4401 | 0.5229 | 0.1540 | 0.5188 | 0.3448 | 0.2070 | 0.1521 | 0.8078 | 0.1731 | 0.2967 | 0.4287 | 0.2984 | 0.3477 |
| 1.7025 | 86000 | 9.6992 | 9.7184 | 0.1849 | 0.4403 | 0.5276 | 0.1598 | 0.5196 | 0.3342 | 0.2110 | 0.1585 | 0.8119 | 0.1790 | 0.2887 | 0.4211 | 0.3067 | 0.3495 |
| 1.7223 | 87000 | 9.6789 | 9.7084 | 0.1744 | 0.4400 | 0.5367 | 0.1572 | 0.5068 | 0.3289 | 0.2088 | 0.1622 | 0.8087 | 0.1750 | 0.2886 | 0.4340 | 0.3095 | 0.3485 |
| 1.7421 | 88000 | 9.6939 | 9.7020 | 0.1736 | 0.4400 | 0.5423 | 0.1644 | 0.5125 | 0.3339 | 0.2064 | 0.1643 | 0.8052 | 0.1869 | 0.2921 | 0.4120 | 0.3091 | 0.3494 |
| 1.7619 | 89000 | 9.661 | 9.6965 | 0.1651 | 0.4404 | 0.5433 | 0.1625 | 0.5234 | 0.3362 | 0.2103 | 0.1682 | 0.8052 | 0.1797 | 0.2823 | 0.4291 | 0.3052 | 0.3501 |
| 1.7816 | 90000 | 9.6624 | 9.6919 | 0.1689 | 0.4438 | 0.5317 | 0.1496 | 0.5125 | 0.3421 | 0.2056 | 0.1643 | 0.8078 | 0.1750 | 0.3034 | 0.4187 | 0.3003 | 0.3480 |
| 1.8014 | 91000 | 9.666 | 9.6855 | 0.1719 | 0.4468 | 0.5395 | 0.1572 | 0.5188 | 0.3430 | 0.2032 | 0.1506 | 0.8065 | 0.1795 | 0.2888 | 0.4185 | 0.2940 | 0.3476 |
| 1.8212 | 92000 | 9.6715 | 9.6823 | 0.1703 | 0.4456 | 0.5311 | 0.1568 | 0.5193 | 0.3530 | 0.2046 | 0.1635 | 0.7988 | 0.1758 | 0.2951 | 0.4236 | 0.2994 | 0.3490 |
| 1.8410 | 93000 | 9.6597 | 9.6800 | 0.1703 | 0.4491 | 0.5255 | 0.1622 | 0.5194 | 0.3491 | 0.2137 | 0.1444 | 0.8062 | 0.1728 | 0.3083 | 0.4199 | 0.3070 | 0.3498 |
| 1.8608 | 94000 | 9.6594 | 9.6740 | 0.1668 | 0.4469 | 0.5233 | 0.1536 | 0.5194 | 0.3396 | 0.2077 | 0.1586 | 0.8095 | 0.1809 | 0.2895 | 0.4238 | 0.3000 | 0.3477 |
| 1.8806 | 95000 | 9.6565 | 9.6647 | 0.1738 | 0.4461 | 0.5312 | 0.1502 | 0.5392 | 0.3444 | 0.2074 | 0.1555 | 0.8063 | 0.1823 | 0.2979 | 0.4282 | 0.3023 | 0.3511 |
| 1.9004 | 96000 | 9.6476 | 9.6640 | 0.1759 | 0.4456 | 0.5433 | 0.1565 | 0.5318 | 0.3470 | 0.2149 | 0.1548 | 0.8047 | 0.1717 | 0.3024 | 0.4359 | 0.2953 | 0.3523 |
| 1.9202 | 97000 | 9.6588 | 9.6563 | 0.1815 | 0.4449 | 0.5431 | 0.1617 | 0.5267 | 0.3460 | 0.2061 | 0.1557 | 0.8068 | 0.1667 | 0.2997 | 0.4463 | 0.3066 | 0.3532 |
| 1.9400 | 98000 | 9.6232 | 9.6491 | 0.1769 | 0.4426 | 0.5411 | 0.1562 | 0.5255 | 0.3430 | 0.2074 | 0.1534 | 0.8108 | 0.1686 | 0.2991 | 0.4395 | 0.2915 | 0.3504 |
| 1.9598 | 99000 | 9.6412 | 9.6446 | 0.1722 | 0.4434 | 0.5368 | 0.1652 | 0.5236 | 0.3378 | 0.1998 | 0.1533 | 0.8043 | 0.1670 | 0.3053 | 0.4498 | 0.2899 | 0.3499 |
| 1.9796 | 100000 | 9.6418 | 9.6400 | 0.1740 | 0.4444 | 0.5379 | 0.1635 | 0.5284 | 0.3340 | 0.2038 | 0.1682 | 0.8013 | 0.1780 | 0.3077 | 0.4224 | 0.2877 | 0.3501 |
| 1.9994 | 101000 | 9.6363 | 9.6378 | 0.1784 | 0.4439 | 0.5349 | 0.1626 | 0.5273 | 0.3432 | 0.2168 | 0.1602 | 0.8028 | 0.1797 | 0.2987 | 0.4336 | 0.2999 | 0.3525 |
| 2.0192 | 102000 | 9.5424 | 9.6456 | 0.1817 | 0.4450 | 0.5436 | 0.1563 | 0.5333 | 0.3374 | 0.2124 | 0.1551 | 0.8045 | 0.1767 | 0.2880 | 0.4329 | 0.2923 | 0.3507 |
| 2.0390 | 103000 | 9.5632 | 9.6461 | 0.1818 | 0.4505 | 0.5405 | 0.1566 | 0.5251 | 0.3387 | 0.2047 | 0.1533 | 0.7995 | 0.1697 | 0.2860 | 0.4399 | 0.2936 | 0.3492 |
| 2.0588 | 104000 | 9.5526 | 9.6401 | 0.1775 | 0.4386 | 0.5245 | 0.1471 | 0.5212 | 0.3383 | 0.2110 | 0.1548 | 0.8061 | 0.1663 | 0.2945 | 0.4264 | 0.2995 | 0.3466 |
| 2.0786 | 105000 | 9.5694 | 9.6374 | 0.1915 | 0.4489 | 0.5283 | 0.1506 | 0.5276 | 0.3393 | 0.2016 | 0.1498 | 0.8045 | 0.1723 | 0.2938 | 0.4376 | 0.3007 | 0.3497 |
| 2.0984 | 106000 | 9.5772 | 9.6314 | 0.1728 | 0.4530 | 0.5356 | 0.1605 | 0.5278 | 0.3358 | 0.2061 | 0.1503 | 0.8050 | 0.1734 | 0.3016 | 0.4274 | 0.2991 | 0.3499 |
| 2.1182 | 107000 | 9.5735 | 9.6322 | 0.1711 | 0.4380 | 0.5450 | 0.1618 | 0.5333 | 0.3462 | 0.2026 | 0.1591 | 0.8057 | 0.1711 | 0.3005 | 0.4159 | 0.2984 | 0.3499 |
| 2.1380 | 108000 | 9.5764 | 9.6262 | 0.1738 | 0.4547 | 0.5394 | 0.1548 | 0.5330 | 0.3372 | 0.2003 | 0.1589 | 0.8026 | 0.1768 | 0.2914 | 0.4384 | 0.2877 | 0.3499 |
| 2.1578 | 109000 | 9.5918 | 9.6217 | 0.1699 | 0.4404 | 0.5272 | 0.1469 | 0.5248 | 0.3483 | 0.2020 | 0.1507 | 0.8006 | 0.1771 | 0.2851 | 0.4183 | 0.3009 | 0.3456 |
| 2.1776 | 110000 | 9.5565 | 9.6192 | 0.1700 | 0.4443 | 0.5291 | 0.1477 | 0.5296 | 0.3409 | 0.2072 | 0.1530 | 0.8042 | 0.1752 | 0.2823 | 0.4203 | 0.2976 | 0.3463 |
| 2.1974 | 111000 | 9.5725 | 9.6153 | 0.1733 | 0.4434 | 0.5258 | 0.1499 | 0.5215 | 0.3397 | 0.1976 | 0.1544 | 0.8031 | 0.1830 | 0.2749 | 0.4255 | 0.2939 | 0.3451 |
| 2.2172 | 112000 | 9.552 | 9.6102 | 0.1765 | 0.4440 | 0.5258 | 0.1539 | 0.5315 | 0.3397 | 0.1998 | 0.1561 | 0.8026 | 0.1833 | 0.2790 | 0.4262 | 0.2914 | 0.3469 |
| 2.2370 | 113000 | 9.5574 | 9.6062 | 0.1810 | 0.4425 | 0.5363 | 0.1573 | 0.5344 | 0.3341 | 0.2008 | 0.1549 | 0.8016 | 0.1767 | 0.2808 | 0.4411 | 0.2972 | 0.3491 |
| 2.2568 | 114000 | 9.5671 | 9.6021 | 0.1837 | 0.4423 | 0.5330 | 0.1547 | 0.5164 | 0.3357 | 0.2062 | 0.1572 | 0.7990 | 0.1733 | 0.2852 | 0.4280 | 0.2894 | 0.3465 |
| 2.2766 | 115000 | 9.5393 | 9.6005 | 0.1857 | 0.4413 | 0.5339 | 0.1639 | 0.5091 | 0.3312 | 0.2057 | 0.1547 | 0.8018 | 0.1820 | 0.2761 | 0.4236 | 0.2909 | 0.3462 |
| 2.2963 | 116000 | 9.5581 | 9.5972 | 0.1807 | 0.4443 | 0.5454 | 0.1488 | 0.5168 | 0.3191 | 0.2154 | 0.1558 | 0.8021 | 0.1770 | 0.2949 | 0.4140 | 0.2945 | 0.3468 |
| 2.3161 | 117000 | 9.5702 | 9.5921 | 0.1804 | 0.4424 | 0.5471 | 0.1499 | 0.5147 | 0.3227 | 0.2109 | 0.1461 | 0.8018 | 0.1783 | 0.3053 | 0.4120 | 0.2889 | 0.3462 |
| 2.3359 | 118000 | 9.5395 | 9.5915 | 0.1756 | 0.4371 | 0.5301 | 0.1582 | 0.5210 | 0.3224 | 0.2090 | 0.1507 | 0.7967 | 0.1780 | 0.2988 | 0.4034 | 0.2933 | 0.3442 |
| 2.3557 | 119000 | 9.5434 | 9.5855 | 0.1735 | 0.4458 | 0.5441 | 0.1566 | 0.5253 | 0.3281 | 0.2098 | 0.1517 | 0.7965 | 0.1736 | 0.3016 | 0.4166 | 0.2859 | 0.3468 |
| 2.3755 | 120000 | 9.5444 | 9.5812 | 0.1709 | 0.4490 | 0.5432 | 0.1534 | 0.5174 | 0.3308 | 0.2043 | 0.1503 | 0.7965 | 0.1748 | 0.2895 | 0.4206 | 0.2802 | 0.3447 |
| 2.3953 | 121000 | 9.5562 | 9.5739 | 0.1779 | 0.4413 | 0.5380 | 0.1467 | 0.5184 | 0.3371 | 0.2057 | 0.1511 | 0.7974 | 0.1821 | 0.2815 | 0.4202 | 0.2856 | 0.3448 |
| 2.4151 | 122000 | 9.5334 | 9.5738 | 0.1802 | 0.4385 | 0.5357 | 0.1537 | 0.5149 | 0.3361 | 0.2151 | 0.1503 | 0.7975 | 0.1836 | 0.3001 | 0.4133 | 0.2822 | 0.3463 |
| 2.4349 | 123000 | 9.5202 | 9.5696 | 0.1697 | 0.4451 | 0.5411 | 0.1493 | 0.5216 | 0.3337 | 0.2116 | 0.1488 | 0.7965 | 0.1804 | 0.2903 | 0.4231 | 0.2908 | 0.3463 |
| 2.4547 | 124000 | 9.5296 | 9.5683 | 0.1711 | 0.4556 | 0.5306 | 0.1466 | 0.5181 | 0.3235 | 0.2141 | 0.1570 | 0.7965 | 0.1785 | 0.2984 | 0.4201 | 0.2929 | 0.3464 |
| 2.4745 | 125000 | 9.5399 | 9.5660 | 0.1791 | 0.4487 | 0.5275 | 0.1417 | 0.5264 | 0.3305 | 0.2209 | 0.1596 | 0.7977 | 0.1770 | 0.3013 | 0.4271 | 0.2833 | 0.3478 |
| 2.4943 | 126000 | 9.5583 | 9.5641 | 0.1708 | 0.4400 | 0.5341 | 0.1489 | 0.5198 | 0.3291 | 0.2107 | 0.1515 | 0.8003 | 0.1784 | 0.3049 | 0.4282 | 0.2871 | 0.3465 |
| 2.5141 | 127000 | 9.5252 | 9.5618 | 0.1756 | 0.4424 | 0.5408 | 0.1577 | 0.5209 | 0.3244 | 0.2130 | 0.1526 | 0.8015 | 0.1785 | 0.3094 | 0.4217 | 0.2849 | 0.3480 |
| 2.5339 | 128000 | 9.5122 | 9.5577 | 0.1748 | 0.4405 | 0.5383 | 0.1501 | 0.5188 | 0.3305 | 0.2102 | 0.1446 | 0.8041 | 0.1804 | 0.3074 | 0.4184 | 0.2943 | 0.3471 |
| 2.5537 | 129000 | 9.5237 | 9.5523 | 0.1754 | 0.4396 | 0.5369 | 0.1509 | 0.5269 | 0.3246 | 0.2117 | 0.1458 | 0.8026 | 0.1799 | 0.2997 | 0.4153 | 0.2947 | 0.3465 |
| 2.5735 | 130000 | 9.5257 | 9.5510 | 0.1705 | 0.4365 | 0.5369 | 0.1560 | 0.5302 | 0.3310 | 0.2087 | 0.1559 | 0.8015 | 0.1832 | 0.3070 | 0.4243 | 0.2955 | 0.3490 |
| 2.5933 | 131000 | 9.5407 | 9.5489 | 0.1704 | 0.4386 | 0.5350 | 0.1495 | 0.5323 | 0.3302 | 0.2123 | 0.1565 | 0.8012 | 0.1846 | 0.3027 | 0.4278 | 0.2997 | 0.3493 |
| 2.6131 | 132000 | 9.5339 | 9.5449 | 0.1693 | 0.4445 | 0.5416 | 0.1621 | 0.5170 | 0.3186 | 0.2105 | 0.1551 | 0.8018 | 0.1799 | 0.2952 | 0.4263 | 0.2969 | 0.3476 |
| 2.6329 | 133000 | 9.5095 | 9.5399 | 0.1697 | 0.4392 | 0.5416 | 0.1545 | 0.5140 | 0.3332 | 0.2090 | 0.1557 | 0.7995 | 0.1758 | 0.2920 | 0.4202 | 0.3030 | 0.3467 |
| 2.6527 | 134000 | 9.5319 | 9.5397 | 0.1743 | 0.4370 | 0.5427 | 0.1635 | 0.5250 | 0.3231 | 0.2076 | 0.1504 | 0.8012 | 0.1767 | 0.2909 | 0.4205 | 0.2920 | 0.3465 |
| 2.6725 | 135000 | 9.5018 | 9.5376 | 0.1698 | 0.4358 | 0.5316 | 0.1600 | 0.5249 | 0.3199 | 0.2058 | 0.1496 | 0.8012 | 0.1859 | 0.2939 | 0.4150 | 0.2945 | 0.3452 |
| 2.6923 | 136000 | 9.4906 | 9.5338 | 0.1762 | 0.4350 | 0.5308 | 0.1525 | 0.5226 | 0.3315 | 0.2108 | 0.1667 | 0.7995 | 0.1809 | 0.2830 | 0.4364 | 0.2952 | 0.3478 |
| 2.7121 | 137000 | 9.4951 | 9.5307 | 0.1745 | 0.4356 | 0.5385 | 0.1482 | 0.5183 | 0.3339 | 0.2103 | 0.1658 | 0.7995 | 0.1786 | 0.2899 | 0.4205 | 0.2943 | 0.3468 |
| 2.7319 | 138000 | 9.498 | 9.5292 | 0.1710 | 0.4353 | 0.5363 | 0.1504 | 0.5278 | 0.3377 | 0.2045 | 0.1586 | 0.7981 | 0.1885 | 0.2882 | 0.4145 | 0.2996 | 0.3470 |
| 2.7517 | 139000 | 9.5133 | 9.5262 | 0.1705 | 0.4336 | 0.5352 | 0.1514 | 0.5250 | 0.3233 | 0.2091 | 0.1604 | 0.8016 | 0.1854 | 0.2837 | 0.4188 | 0.2966 | 0.3457 |
| 2.7715 | 140000 | 9.4934 | 9.5222 | 0.1740 | 0.4378 | 0.5279 | 0.1539 | 0.5199 | 0.3302 | 0.2128 | 0.1554 | 0.7989 | 0.1799 | 0.2885 | 0.4224 | 0.3013 | 0.3464 |
| 2.7913 | 141000 | 9.4993 | 9.5188 | 0.1754 | 0.4353 | 0.5209 | 0.1504 | 0.5287 | 0.3284 | 0.2128 | 0.1503 | 0.7972 | 0.1853 | 0.2851 | 0.4239 | 0.2956 | 0.3453 |
| 2.8110 | 142000 | 9.498 | 9.5188 | 0.1763 | 0.4313 | 0.5328 | 0.1514 | 0.5203 | 0.3260 | 0.2068 | 0.1603 | 0.8016 | 0.1812 | 0.3041 | 0.4303 | 0.2892 | 0.3470 |
| 2.8308 | 143000 | 9.477 | 9.5174 | 0.1749 | 0.4281 | 0.5437 | 0.1515 | 0.5096 | 0.3183 | 0.2025 | 0.1524 | 0.7963 | 0.1897 | 0.2938 | 0.4315 | 0.2872 | 0.3446 |
| 2.8506 | 144000 | 9.483 | 9.5132 | 0.1768 | 0.4279 | 0.5361 | 0.1424 | 0.5181 | 0.3307 | 0.2046 | 0.1506 | 0.7969 | 0.1834 | 0.2965 | 0.4301 | 0.2885 | 0.3448 |
| 2.8704 | 145000 | 9.478 | 9.5092 | 0.1870 | 0.4299 | 0.5334 | 0.1450 | 0.5128 | 0.3299 | 0.2035 | 0.1488 | 0.7981 | 0.1792 | 0.3008 | 0.4289 | 0.2886 | 0.3451 |
| 2.8902 | 146000 | 9.4904 | 9.5053 | 0.1759 | 0.4279 | 0.5370 | 0.1438 | 0.5218 | 0.3271 | 0.2077 | 0.1537 | 0.7995 | 0.1847 | 0.2832 | 0.4269 | 0.2891 | 0.3445 |
| 2.9100 | 147000 | 9.4787 | 9.5035 | 0.1744 | 0.4281 | 0.5437 | 0.1597 | 0.5050 | 0.3377 | 0.2044 | 0.1499 | 0.8003 | 0.1898 | 0.2915 | 0.4273 | 0.2928 | 0.3465 |
| 2.9298 | 148000 | 9.4861 | 9.5041 | 0.1801 | 0.4294 | 0.5303 | 0.1586 | 0.5067 | 0.3178 | 0.2086 | 0.1492 | 0.8030 | 0.1803 | 0.2837 | 0.4160 | 0.2972 | 0.3431 |
| 2.9496 | 149000 | 9.4736 | 9.5001 | 0.1758 | 0.4249 | 0.5350 | 0.1515 | 0.5103 | 0.3258 | 0.2128 | 0.1463 | 0.7983 | 0.1785 | 0.2847 | 0.4281 | 0.2936 | 0.3435 |
| 2.9694 | 150000 | 9.4847 | 9.4980 | 0.1742 | 0.4305 | 0.5362 | 0.1524 | 0.5215 | 0.3250 | 0.2097 | 0.1485 | 0.8016 | 0.1768 | 0.2911 | 0.4228 | 0.2946 | 0.3450 |
| 2.9892 | 151000 | 9.4756 | 9.4948 | 0.1694 | 0.4270 | 0.5333 | 0.1575 | 0.5128 | 0.3191 | 0.2116 | 0.1445 | 0.8015 | 0.1736 | 0.2908 | 0.4215 | 0.2889 | 0.3424 |
| 3.0090 | 152000 | 9.4206 | 9.4949 | 0.1751 | 0.4243 | 0.5332 | 0.1432 | 0.5094 | 0.3172 | 0.2100 | 0.1442 | 0.7981 | 0.1763 | 0.2852 | 0.4310 | 0.2880 | 0.3412 |
| 3.0288 | 153000 | 9.3728 | 9.4973 | 0.1746 | 0.4330 | 0.5332 | 0.1447 | 0.5212 | 0.3211 | 0.2142 | 0.1493 | 0.7968 | 0.1803 | 0.2964 | 0.4287 | 0.2886 | 0.3448 |
| 3.0486 | 154000 | 9.3962 | 9.5003 | 0.1815 | 0.4325 | 0.5341 | 0.1456 | 0.5162 | 0.3300 | 0.2175 | 0.1431 | 0.7971 | 0.1806 | 0.3010 | 0.4328 | 0.2892 | 0.3462 |
| 3.0684 | 155000 | 9.3975 | 9.4988 | 0.1784 | 0.4276 | 0.5391 | 0.1478 | 0.5187 | 0.3271 | 0.2212 | 0.1457 | 0.7987 | 0.1832 | 0.3011 | 0.4305 | 0.2866 | 0.3466 |
| 3.0882 | 156000 | 9.411 | 9.4975 | 0.1728 | 0.4266 | 0.5301 | 0.1505 | 0.5208 | 0.3275 | 0.2191 | 0.1461 | 0.7994 | 0.1829 | 0.3012 | 0.4289 | 0.2916 | 0.3460 |
| 3.1080 | 157000 | 9.3958 | 9.4955 | 0.1796 | 0.4283 | 0.5375 | 0.1498 | 0.5186 | 0.3409 | 0.2209 | 0.1503 | 0.7985 | 0.1816 | 0.3024 | 0.4372 | 0.2875 | 0.3487 |
| 3.1278 | 158000 | 9.4203 | 9.4925 | 0.1699 | 0.4338 | 0.5324 | 0.1454 | 0.5078 | 0.3324 | 0.2152 | 0.1480 | 0.7990 | 0.1780 | 0.2957 | 0.4364 | 0.2849 | 0.3445 |
| 3.1476 | 159000 | 9.416 | 9.4913 | 0.1751 | 0.4325 | 0.5301 | 0.1498 | 0.5152 | 0.3270 | 0.2179 | 0.1491 | 0.7964 | 0.1782 | 0.3020 | 0.4285 | 0.2878 | 0.3454 |
| 3.1674 | 160000 | 9.4133 | 9.4867 | 0.1757 | 0.4320 | 0.5334 | 0.1528 | 0.5177 | 0.3264 | 0.2153 | 0.1443 | 0.7896 | 0.1784 | 0.2946 | 0.4276 | 0.2933 | 0.3447 |
| 3.1872 | 161000 | 9.4188 | 9.4860 | 0.1780 | 0.4300 | 0.5357 | 0.1486 | 0.5096 | 0.3295 | 0.2221 | 0.1479 | 0.7915 | 0.1780 | 0.2941 | 0.4224 | 0.2920 | 0.3446 |
| 3.2070 | 162000 | 9.4297 | 9.4831 | 0.1826 | 0.4291 | 0.5338 | 0.1520 | 0.5032 | 0.3359 | 0.2204 | 0.1488 | 0.7951 | 0.1759 | 0.2946 | 0.4272 | 0.2887 | 0.3452 |
| 3.2268 | 163000 | 9.4151 | 9.4808 | 0.1779 | 0.4341 | 0.5256 | 0.1517 | 0.5141 | 0.3407 | 0.2200 | 0.1460 | 0.7973 | 0.1854 | 0.2971 | 0.4191 | 0.2903 | 0.3461 |
| 3.2466 | 164000 | 9.4185 | 9.4781 | 0.1748 | 0.4358 | 0.5368 | 0.1409 | 0.5137 | 0.3376 | 0.2139 | 0.1414 | 0.7974 | 0.1759 | 0.3024 | 0.4214 | 0.2890 | 0.3447 |
| 3.2664 | 165000 | 9.4227 | 9.4763 | 0.1771 | 0.4319 | 0.5236 | 0.1389 | 0.5143 | 0.3389 | 0.2091 | 0.1515 | 0.7960 | 0.1800 | 0.2955 | 0.4286 | 0.2896 | 0.3442 |
| 3.2862 | 166000 | 9.4049 | 9.4711 | 0.1804 | 0.4312 | 0.5264 | 0.1449 | 0.5098 | 0.3393 | 0.2083 | 0.1505 | 0.7963 | 0.1811 | 0.2918 | 0.4278 | 0.2897 | 0.3444 |
| 3.3059 | 167000 | 9.4249 | 9.4675 | 0.1788 | 0.4297 | 0.5298 | 0.1395 | 0.5121 | 0.3463 | 0.2096 | 0.1455 | 0.7975 | 0.1810 | 0.3020 | 0.4351 | 0.2882 | 0.3458 |
| 3.3257 | 168000 | 9.4047 | 9.4667 | 0.1660 | 0.4296 | 0.5296 | 0.1427 | 0.5152 | 0.3488 | 0.2093 | 0.1458 | 0.7975 | 0.1830 | 0.3008 | 0.4352 | 0.2869 | 0.3454 |
| 3.3455 | 169000 | 9.4124 | 9.4663 | 0.1661 | 0.4260 | 0.5325 | 0.1439 | 0.5171 | 0.3550 | 0.2122 | 0.1444 | 0.7975 | 0.1833 | 0.2994 | 0.4352 | 0.2891 | 0.3463 |
| 3.3653 | 170000 | 9.416 | 9.4636 | 0.1729 | 0.4248 | 0.5424 | 0.1578 | 0.5146 | 0.3521 | 0.2078 | 0.1463 | 0.7975 | 0.1783 | 0.3047 | 0.4292 | 0.2883 | 0.3474 |
| 3.3851 | 171000 | 9.4139 | 9.4593 | 0.1732 | 0.4275 | 0.5390 | 0.1517 | 0.5233 | 0.3433 | 0.2079 | 0.1477 | 0.7975 | 0.1750 | 0.3052 | 0.4285 | 0.2865 | 0.3466 |
| 3.4049 | 172000 | 9.3927 | 9.4585 | 0.1771 | 0.4279 | 0.5339 | 0.1522 | 0.5226 | 0.3456 | 0.2095 | 0.1468 | 0.7981 | 0.1791 | 0.3029 | 0.4300 | 0.2851 | 0.3470 |
| 3.4247 | 173000 | 9.4008 | 9.4560 | 0.1753 | 0.4289 | 0.5344 | 0.1606 | 0.5179 | 0.3410 | 0.2068 | 0.1467 | 0.7975 | 0.1796 | 0.2984 | 0.4294 | 0.2869 | 0.3464 |
| 3.4445 | 174000 | 9.403 | 9.4545 | 0.1730 | 0.4337 | 0.5372 | 0.1535 | 0.5230 | 0.3296 | 0.2030 | 0.1470 | 0.8010 | 0.1802 | 0.3080 | 0.4243 | 0.2879 | 0.3463 |
| 3.4643 | 175000 | 9.414 | 9.4498 | 0.1678 | 0.4330 | 0.5383 | 0.1588 | 0.5134 | 0.3348 | 0.2050 | 0.1472 | 0.7984 | 0.1794 | 0.2980 | 0.4165 | 0.2876 | 0.3445 |
| 3.4841 | 176000 | 9.4006 | 9.4484 | 0.1726 | 0.4367 | 0.5311 | 0.1571 | 0.5167 | 0.3191 | 0.2092 | 0.1517 | 0.7975 | 0.1840 | 0.2968 | 0.4212 | 0.2904 | 0.3449 |
| 3.5039 | 177000 | 9.4065 | 9.4452 | 0.1722 | 0.4347 | 0.5311 | 0.1524 | 0.5210 | 0.3324 | 0.2061 | 0.1525 | 0.7964 | 0.1810 | 0.3090 | 0.4310 | 0.2895 | 0.3469 |
| 3.5237 | 178000 | 9.4145 | 9.4411 | 0.1763 | 0.4360 | 0.5279 | 0.1571 | 0.5112 | 0.3257 | 0.2094 | 0.1505 | 0.7969 | 0.1768 | 0.2963 | 0.4288 | 0.2883 | 0.3447 |
| 3.5435 | 179000 | 9.4052 | 9.4404 | 0.1757 | 0.4367 | 0.5292 | 0.1549 | 0.5200 | 0.3348 | 0.2107 | 0.1527 | 0.7961 | 0.1808 | 0.2873 | 0.4250 | 0.2871 | 0.3455 |
| 3.5633 | 180000 | 9.412 | 9.4392 | 0.1723 | 0.4337 | 0.5354 | 0.1531 | 0.5181 | 0.3348 | 0.2092 | 0.1480 | 0.7967 | 0.1786 | 0.2877 | 0.4227 | 0.2907 | 0.3447 |
| 3.5831 | 181000 | 9.4105 | 9.4377 | 0.1747 | 0.4308 | 0.5334 | 0.1572 | 0.5188 | 0.3348 | 0.2101 | 0.1480 | 0.7967 | 0.1753 | 0.2894 | 0.4294 | 0.2895 | 0.3452 |
| 3.6029 | 182000 | 9.3904 | 9.4336 | 0.1703 | 0.4358 | 0.5354 | 0.1524 | 0.5229 | 0.3283 | 0.2227 | 0.1488 | 0.7999 | 0.1768 | 0.2954 | 0.4290 | 0.2889 | 0.3467 |
| 3.6227 | 183000 | 9.3784 | 9.4310 | 0.1743 | 0.4311 | 0.5379 | 0.1437 | 0.5182 | 0.3264 | 0.2198 | 0.1490 | 0.7999 | 0.1758 | 0.3012 | 0.4294 | 0.2889 | 0.3458 |
| 3.6425 | 184000 | 9.3762 | 9.4288 | 0.1713 | 0.4345 | 0.5362 | 0.1506 | 0.5136 | 0.3186 | 0.2107 | 0.1491 | 0.7973 | 0.1751 | 0.3018 | 0.4282 | 0.2898 | 0.3444 |
| 3.6623 | 185000 | 9.3958 | 9.4268 | 0.1757 | 0.4290 | 0.5420 | 0.1503 | 0.5158 | 0.3175 | 0.2067 | 0.1465 | 0.7938 | 0.1772 | 0.3020 | 0.4219 | 0.2921 | 0.3439 |
| 3.6821 | 186000 | 9.4056 | 9.4261 | 0.1790 | 0.4308 | 0.5388 | 0.1454 | 0.5162 | 0.3200 | 0.2096 | 0.1400 | 0.7949 | 0.1699 | 0.2988 | 0.4235 | 0.2867 | 0.3426 |
| 3.7019 | 187000 | 9.3616 | 9.4244 | 0.1797 | 0.4279 | 0.5428 | 0.1499 | 0.5173 | 0.3252 | 0.2150 | 0.1405 | 0.7975 | 0.1758 | 0.2900 | 0.4287 | 0.2862 | 0.3443 |
| 3.7217 | 188000 | 9.3864 | 9.4239 | 0.1794 | 0.4288 | 0.5447 | 0.1474 | 0.5225 | 0.3273 | 0.2210 | 0.1467 | 0.7975 | 0.1710 | 0.2966 | 0.4361 | 0.2804 | 0.3461 |
| 3.7415 | 189000 | 9.3842 | 9.4199 | 0.1765 | 0.4295 | 0.5306 | 0.1450 | 0.5176 | 0.3190 | 0.2218 | 0.1461 | 0.7961 | 0.1753 | 0.2959 | 0.4284 | 0.2843 | 0.3436 |
| 3.7613 | 190000 | 9.3888 | 9.4186 | 0.1770 | 0.4281 | 0.5369 | 0.1451 | 0.5140 | 0.3171 | 0.2173 | 0.1408 | 0.7953 | 0.1774 | 0.2887 | 0.4271 | 0.2833 | 0.3422 |
| 3.7811 | 191000 | 9.3769 | 9.4163 | 0.1777 | 0.4291 | 0.5417 | 0.1411 | 0.5150 | 0.3176 | 0.2103 | 0.1474 | 0.7959 | 0.1813 | 0.3013 | 0.4268 | 0.2757 | 0.3431 |
| 3.8009 | 192000 | 9.3643 | 9.4151 | 0.1773 | 0.4275 | 0.5396 | 0.1483 | 0.5170 | 0.3236 | 0.2100 | 0.1482 | 0.7959 | 0.1796 | 0.2993 | 0.4274 | 0.2766 | 0.3439 |
| 3.8206 | 193000 | 9.376 | 9.4128 | 0.1707 | 0.4300 | 0.5431 | 0.1422 | 0.5139 | 0.3277 | 0.2144 | 0.1472 | 0.7959 | 0.1823 | 0.2945 | 0.4283 | 0.2821 | 0.3440 |
| 3.8404 | 194000 | 9.396 | 9.4102 | 0.1727 | 0.4280 | 0.5418 | 0.1486 | 0.5137 | 0.3242 | 0.2071 | 0.1470 | 0.7959 | 0.1800 | 0.3001 | 0.4280 | 0.2843 | 0.3440 |
| 3.8602 | 195000 | 9.3662 | 9.4087 | 0.1741 | 0.4273 | 0.5371 | 0.1451 | 0.5116 | 0.3185 | 0.2101 | 0.1455 | 0.7959 | 0.1810 | 0.2940 | 0.4278 | 0.2840 | 0.3424 |
| 3.8800 | 196000 | 9.3727 | 9.4067 | 0.1704 | 0.4271 | 0.5393 | 0.1411 | 0.5099 | 0.3165 | 0.2047 | 0.1508 | 0.7967 | 0.1848 | 0.2946 | 0.4281 | 0.2838 | 0.3421 |
| 3.8998 | 197000 | 9.3805 | 9.4048 | 0.1716 | 0.4254 | 0.5416 | 0.1477 | 0.5192 | 0.3154 | 0.2098 | 0.1468 | 0.7953 | 0.1827 | 0.2920 | 0.4280 | 0.2874 | 0.3433 |
| 3.9196 | 198000 | 9.3799 | 9.4033 | 0.1687 | 0.4278 | 0.5393 | 0.1472 | 0.5146 | 0.3219 | 0.2083 | 0.1479 | 0.7961 | 0.1838 | 0.2918 | 0.4275 | 0.2860 | 0.3432 |
| 3.9394 | 199000 | 9.3702 | 9.3999 | 0.1681 | 0.4306 | 0.5401 | 0.1476 | 0.5098 | 0.3233 | 0.2112 | 0.1470 | 0.7975 | 0.1816 | 0.2926 | 0.4278 | 0.2814 | 0.3430 |
| 3.9592 | 200000 | 9.3646 | 9.3988 | 0.1701 | 0.4321 | 0.5401 | 0.1484 | 0.5107 | 0.3227 | 0.2135 | 0.1465 | 0.7980 | 0.1815 | 0.2930 | 0.4335 | 0.2858 | 0.3443 |
| 3.9790 | 201000 | 9.3559 | 9.3963 | 0.1696 | 0.4319 | 0.5418 | 0.1475 | 0.5135 | 0.3218 | 0.2117 | 0.1484 | 0.7975 | 0.1821 | 0.2856 | 0.4270 | 0.2853 | 0.3434 |
| 3.9988 | 202000 | 9.3566 | 9.3950 | 0.1743 | 0.4284 | 0.5432 | 0.1398 | 0.5092 | 0.3236 | 0.2113 | 0.1481 | 0.7980 | 0.1822 | 0.2784 | 0.4330 | 0.2827 | 0.3425 |
| 4.0186 | 203000 | 9.2801 | 9.3988 | 0.1709 | 0.4305 | 0.5418 | 0.1357 | 0.5223 | 0.3149 | 0.2129 | 0.1513 | 0.7975 | 0.1804 | 0.2873 | 0.4349 | 0.2820 | 0.3433 |
| 4.0384 | 204000 | 9.3024 | 9.3985 | 0.1745 | 0.4305 | 0.5418 | 0.1451 | 0.5189 | 0.3159 | 0.2081 | 0.1501 | 0.7975 | 0.1795 | 0.2869 | 0.4284 | 0.2828 | 0.3431 |
| 4.0582 | 205000 | 9.2953 | 9.3992 | 0.1743 | 0.4278 | 0.5418 | 0.1327 | 0.5162 | 0.3145 | 0.2110 | 0.1498 | 0.7975 | 0.1843 | 0.2818 | 0.4289 | 0.2825 | 0.3418 |
| 4.0780 | 206000 | 9.2922 | 9.4003 | 0.1731 | 0.4283 | 0.5416 | 0.1391 | 0.5180 | 0.3166 | 0.2110 | 0.1498 | 0.7972 | 0.1801 | 0.2796 | 0.4289 | 0.2830 | 0.3420 |
| 4.0978 | 207000 | 9.2851 | 9.3996 | 0.1740 | 0.4294 | 0.5416 | 0.1410 | 0.5147 | 0.3155 | 0.2134 | 0.1560 | 0.7975 | 0.1822 | 0.2880 | 0.4303 | 0.2820 | 0.3435 |
| 4.1176 | 208000 | 9.2913 | 9.3978 | 0.1740 | 0.4325 | 0.5416 | 0.1350 | 0.5131 | 0.3156 | 0.2129 | 0.1554 | 0.7975 | 0.1800 | 0.2876 | 0.4303 | 0.2856 | 0.3432 |
| 4.1374 | 209000 | 9.298 | 9.3966 | 0.1732 | 0.4274 | 0.5430 | 0.1387 | 0.5219 | 0.3139 | 0.2145 | 0.1507 | 0.7975 | 0.1779 | 0.2870 | 0.4275 | 0.2852 | 0.3430 |
| 4.1572 | 210000 | 9.2952 | 9.3943 | 0.1761 | 0.4262 | 0.5430 | 0.1433 | 0.5226 | 0.3231 | 0.2128 | 0.1561 | 0.7980 | 0.1806 | 0.2871 | 0.4282 | 0.2865 | 0.3449 |
| 4.1770 | 211000 | 9.3193 | 9.3924 | 0.1741 | 0.4269 | 0.5430 | 0.1331 | 0.5218 | 0.3256 | 0.2140 | 0.1503 | 0.7980 | 0.1786 | 0.2869 | 0.4284 | 0.2843 | 0.3435 |
| 4.1968 | 212000 | 9.297 | 9.3912 | 0.1744 | 0.4278 | 0.5428 | 0.1427 | 0.5217 | 0.3267 | 0.2138 | 0.1488 | 0.7980 | 0.1794 | 0.2806 | 0.4278 | 0.2831 | 0.3437 |
| 4.2166 | 213000 | 9.2984 | 9.3891 | 0.1797 | 0.4297 | 0.5430 | 0.1428 | 0.5236 | 0.3251 | 0.2128 | 0.1495 | 0.7980 | 0.1762 | 0.2791 | 0.4272 | 0.2859 | 0.3440 |
| 4.2364 | 214000 | 9.306 | 9.3881 | 0.1818 | 0.4275 | 0.5436 | 0.1457 | 0.5215 | 0.3244 | 0.2120 | 0.1498 | 0.7980 | 0.1812 | 0.2801 | 0.4278 | 0.2835 | 0.3444 |
| 4.2562 | 215000 | 9.3029 | 9.3861 | 0.1807 | 0.4290 | 0.5436 | 0.1413 | 0.5206 | 0.3244 | 0.2166 | 0.1481 | 0.7980 | 0.1829 | 0.2860 | 0.4275 | 0.2847 | 0.3449 |
| 4.2760 | 216000 | 9.2965 | 9.3848 | 0.1769 | 0.4280 | 0.5430 | 0.1471 | 0.5209 | 0.3251 | 0.2128 | 0.1555 | 0.7975 | 0.1794 | 0.2739 | 0.4270 | 0.2827 | 0.3438 |
| 4.2958 | 217000 | 9.3171 | 9.3828 | 0.1796 | 0.4285 | 0.5430 | 0.1438 | 0.5209 | 0.3231 | 0.2133 | 0.1490 | 0.7975 | 0.1819 | 0.2858 | 0.4270 | 0.2793 | 0.3440 |
| 4.3155 | 218000 | 9.3181 | 9.3824 | 0.1794 | 0.4262 | 0.5430 | 0.1496 | 0.5241 | 0.3243 | 0.2147 | 0.1481 | 0.7975 | 0.1794 | 0.2812 | 0.4275 | 0.2818 | 0.3444 |
| 4.3353 | 219000 | 9.2952 | 9.3794 | 0.1766 | 0.4265 | 0.5432 | 0.1412 | 0.5223 | 0.3243 | 0.2098 | 0.1475 | 0.7975 | 0.1777 | 0.2851 | 0.4328 | 0.2784 | 0.3433 |
| 4.3551 | 220000 | 9.32 | 9.3776 | 0.1739 | 0.4261 | 0.5432 | 0.1362 | 0.5258 | 0.3257 | 0.2106 | 0.1470 | 0.7980 | 0.1782 | 0.2815 | 0.4268 | 0.2787 | 0.3424 |
| 4.3749 | 221000 | 9.2999 | 9.3758 | 0.1767 | 0.4297 | 0.5432 | 0.1395 | 0.5175 | 0.3252 | 0.2126 | 0.1489 | 0.7980 | 0.1778 | 0.2865 | 0.4210 | 0.2801 | 0.3428 |
| 4.3947 | 222000 | 9.2954 | 9.3750 | 0.1783 | 0.4261 | 0.5432 | 0.1397 | 0.5220 | 0.3244 | 0.2116 | 0.1496 | 0.7980 | 0.1797 | 0.2929 | 0.4268 | 0.2786 | 0.3439 |
| 4.4145 | 223000 | 9.2944 | 9.3726 | 0.1795 | 0.4275 | 0.5432 | 0.1395 | 0.5172 | 0.3236 | 0.2130 | 0.1488 | 0.7971 | 0.1785 | 0.2921 | 0.4273 | 0.2796 | 0.3436 |
| 4.4343 | 224000 | 9.2851 | 9.3714 | 0.1794 | 0.4251 | 0.5432 | 0.1395 | 0.5172 | 0.3227 | 0.2136 | 0.1488 | 0.7975 | 0.1780 | 0.2921 | 0.4268 | 0.2788 | 0.3433 |
| 4.4541 | 225000 | 9.2856 | 9.3694 | 0.1761 | 0.4257 | 0.5432 | 0.1408 | 0.5218 | 0.3227 | 0.2116 | 0.1486 | 0.7971 | 0.1800 | 0.2935 | 0.4270 | 0.2794 | 0.3437 |
| 4.4739 | 226000 | 9.2967 | 9.3676 | 0.1792 | 0.4256 | 0.5418 | 0.1372 | 0.5200 | 0.3230 | 0.2100 | 0.1492 | 0.7967 | 0.1774 | 0.2939 | 0.4270 | 0.2803 | 0.3432 |
| 4.4937 | 227000 | 9.3019 | 9.3670 | 0.1798 | 0.4253 | 0.5430 | 0.1397 | 0.5200 | 0.3147 | 0.2063 | 0.1481 | 0.7967 | 0.1779 | 0.2946 | 0.4210 | 0.2792 | 0.3420 |
| 4.5135 | 228000 | 9.2938 | 9.3655 | 0.1795 | 0.4258 | 0.5423 | 0.1397 | 0.5192 | 0.3139 | 0.2094 | 0.1487 | 0.7967 | 0.1775 | 0.2943 | 0.4210 | 0.2780 | 0.3420 |
| 4.5333 | 229000 | 9.306 | 9.3643 | 0.1772 | 0.4251 | 0.5432 | 0.1393 | 0.5235 | 0.3148 | 0.2079 | 0.1487 | 0.7998 | 0.1798 | 0.2917 | 0.4216 | 0.2768 | 0.3423 |
| 4.5531 | 230000 | 9.3057 | 9.3631 | 0.1726 | 0.4250 | 0.5423 | 0.1393 | 0.5241 | 0.3148 | 0.2080 | 0.1483 | 0.7967 | 0.1795 | 0.2923 | 0.4216 | 0.2771 | 0.3417 |
| 4.5729 | 231000 | 9.3069 | 9.3615 | 0.1757 | 0.4240 | 0.5421 | 0.1500 | 0.5226 | 0.3171 | 0.2093 | 0.1481 | 0.7980 | 0.1783 | 0.2920 | 0.4216 | 0.2784 | 0.3429 |
| 4.5927 | 232000 | 9.3003 | 9.3604 | 0.1752 | 0.4255 | 0.5421 | 0.1498 | 0.5226 | 0.3185 | 0.2096 | 0.1478 | 0.7980 | 0.1801 | 0.2920 | 0.4216 | 0.2783 | 0.3432 |
| 4.6125 | 233000 | 9.3042 | 9.3594 | 0.1748 | 0.4243 | 0.5407 | 0.1453 | 0.5263 | 0.3185 | 0.2098 | 0.1472 | 0.7972 | 0.1796 | 0.2918 | 0.4216 | 0.2797 | 0.3428 |
| 4.6323 | 234000 | 9.3079 | 9.3573 | 0.1749 | 0.4256 | 0.5407 | 0.1428 | 0.5242 | 0.3185 | 0.2096 | 0.1536 | 0.7975 | 0.1793 | 0.2920 | 0.4273 | 0.2815 | 0.3437 |
| 4.6521 | 235000 | 9.284 | 9.3566 | 0.1729 | 0.4256 | 0.5407 | 0.1455 | 0.5253 | 0.3190 | 0.2079 | 0.1487 | 0.7975 | 0.1801 | 0.2936 | 0.4273 | 0.2812 | 0.3435 |
| 4.6719 | 236000 | 9.2916 | 9.3550 | 0.1755 | 0.4270 | 0.5416 | 0.1447 | 0.5216 | 0.3190 | 0.2081 | 0.1487 | 0.7975 | 0.1797 | 0.2869 | 0.4273 | 0.2823 | 0.3431 |
| 4.6917 | 237000 | 9.2871 | 9.3537 | 0.1733 | 0.4263 | 0.5421 | 0.1447 | 0.5246 | 0.3190 | 0.2097 | 0.1492 | 0.7980 | 0.1779 | 0.2917 | 0.4273 | 0.2786 | 0.3433 |
| 4.7115 | 238000 | 9.3105 | 9.3519 | 0.1729 | 0.4248 | 0.5430 | 0.1372 | 0.5194 | 0.3176 | 0.2096 | 0.1492 | 0.7980 | 0.1803 | 0.2917 | 0.4273 | 0.2799 | 0.3424 |
| 4.7313 | 239000 | 9.2935 | 9.3506 | 0.1731 | 0.4241 | 0.5421 | 0.1447 | 0.5194 | 0.3176 | 0.2078 | 0.1483 | 0.7975 | 0.1780 | 0.2903 | 0.4273 | 0.2797 | 0.3423 |
| 4.7511 | 240000 | 9.283 | 9.3497 | 0.1730 | 0.4257 | 0.5421 | 0.1388 | 0.5149 | 0.3176 | 0.2079 | 0.1486 | 0.7975 | 0.1779 | 0.2906 | 0.4273 | 0.2809 | 0.3417 |
| 4.7709 | 241000 | 9.2994 | 9.3486 | 0.1733 | 0.4257 | 0.5421 | 0.1388 | 0.5194 | 0.3176 | 0.2093 | 0.1486 | 0.7959 | 0.1798 | 0.2903 | 0.4216 | 0.2785 | 0.3416 |
| 4.7907 | 242000 | 9.2784 | 9.3475 | 0.1734 | 0.4245 | 0.5421 | 0.1433 | 0.5149 | 0.3176 | 0.2078 | 0.1486 | 0.7966 | 0.1780 | 0.2899 | 0.4200 | 0.2797 | 0.3413 |
| 4.8105 | 243000 | 9.2968 | 9.3466 | 0.1751 | 0.4245 | 0.5421 | 0.1388 | 0.5149 | 0.3176 | 0.2083 | 0.1486 | 0.7980 | 0.1779 | 0.2906 | 0.4273 | 0.2768 | 0.3416 |
| 4.8302 | 244000 | 9.2829 | 9.3455 | 0.1751 | 0.4245 | 0.5421 | 0.1446 | 0.5149 | 0.3176 | 0.2096 | 0.1486 | 0.7959 | 0.1778 | 0.2899 | 0.4273 | 0.2782 | 0.3420 |
| 4.8500 | 245000 | 9.2787 | 9.3449 | 0.1739 | 0.4245 | 0.5421 | 0.1446 | 0.5149 | 0.3176 | 0.2085 | 0.1486 | 0.7961 | 0.1779 | 0.2899 | 0.4273 | 0.2794 | 0.3420 |
| 4.8698 | 246000 | 9.2856 | 9.3439 | 0.1735 | 0.4247 | 0.5421 | 0.1491 | 0.5149 | 0.3176 | 0.2081 | 0.1483 | 0.7961 | 0.1779 | 0.2899 | 0.4216 | 0.2806 | 0.3419 |
| 4.8896 | 247000 | 9.2754 | 9.3433 | 0.1735 | 0.4247 | 0.5421 | 0.1490 | 0.5149 | 0.3176 | 0.2083 | 0.1483 | 0.7966 | 0.1779 | 0.2897 | 0.4216 | 0.2810 | 0.3419 |
| 4.9094 | 248000 | 9.2706 | 9.3427 | 0.1735 | 0.4247 | 0.5421 | 0.1491 | 0.5140 | 0.3176 | 0.2066 | 0.1487 | 0.7959 | 0.1774 | 0.2899 | 0.4216 | 0.2825 | 0.3418 |
| 4.9292 | 249000 | 9.3004 | 9.3422 | 0.1735 | 0.4247 | 0.5416 | 0.1491 | 0.5140 | 0.3176 | 0.2066 | 0.1487 | 0.7975 | 0.1774 | 0.2899 | 0.4216 | 0.2811 | 0.3418 |
| 4.9490 | 250000 | 9.2861 | 9.3417 | 0.1735 | 0.4247 | 0.5416 | 0.1491 | 0.5140 | 0.3176 | 0.2066 | 0.1487 | 0.7961 | 0.1774 | 0.2899 | 0.4216 | 0.2811 | 0.3417 |
| 4.9688 | 251000 | 9.2583 | 9.3412 | 0.1735 | 0.4247 | 0.5416 | 0.1491 | 0.5140 | 0.3176 | 0.2066 | 0.1487 | 0.7966 | 0.1755 | 0.2899 | 0.4216 | 0.2813 | 0.3416 |
| 4.9886 | 252000 | 9.2786 | 9.3411 | 0.1735 | 0.4247 | 0.5416 | 0.1491 | 0.5140 | 0.3176 | 0.2066 | 0.1483 | 0.7966 | 0.1755 | 0.2899 | 0.4216 | 0.2813 | 0.3416 |
</details>
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.3.1
- Transformers: 4.48.0
- PyTorch: 2.5.1+cu124
- Accelerate: 1.2.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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