diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,3636 @@ +--- +language: +- ko +license: apache-2.0 +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:265311 +- loss:MatryoshkaLoss +- loss:MinMaxDistillKLDivLoss +base_model: whybe-choi/Qwen3-VL-Embedding-2B-ko-vdr-preview-v0.7 +widget: +- source_sentence: 불법드론 대응 기술 개발 사업 저고도소형드론식별관리기반조성 사업 간 기술 범위 차이 + sentences: + - data/images/ko/ko-vdr-public/8623.png + - data/images/ko/ko-vdr-public/5774.png + - data/images/ko/ko-vdr-public/8679.png + - data/images/ko/ko-vdr-public/5738.png + - data/images/ko/ko-vdr-public/8622.png + - data/images/ko/ko-vdr-public/6057.png + - data/images/ko/ko-vdr-public/8819.png + - data/images/ko/ko-vdr-public/8640.png + - data/images/ko/ko-vdr-public/8750.png +- source_sentence: 인천 연안의 5월 수질 4등급 평가와 특별관리해역 민관산학협의회가 추진하는 수질 개선 대책은 어떻게 연계되는가? + sentences: + - data/images/ko/ko-vdr-public/903.png + - data/images/ko/ko-vdr-public/579.png + - data/images/ko/ko-vdr-public/8442.png + - data/images/ko/ko-vdr-public/574.png + - data/images/ko/ko-vdr-public/619.png + - data/images/ko/ko-vdr-public/603.png + - data/images/ko/ko-vdr-public/8485.png + - data/images/ko/ko-vdr-public/568.png + - data/images/ko/ko-vdr-public/8463.png +- source_sentence: 합성데이터 검증 단계에서 병행 수행 가능한 측정지표와 임계값 산출 방법은 무엇이며, 문서 하단 푸터에 표시된 페이지 + 번호가 해당 검증 단계의 위치를 파악하는 데 어떤 역할을 하는가? + sentences: + - data/images/ko/ko-vdr-public/3982.png + - data/images/ko/ko-vdr-public/3939.png + - data/images/ko/ko-vdr-public/3930.png + - data/images/ko/ko-vdr-public/3964.png + - data/images/ko/ko-vdr-public/3960.png + - data/images/ko/ko-vdr-public/3911.png + - data/images/ko/ko-vdr-public/3927.png + - data/images/ko/ko-vdr-public/3963.png + - data/images/ko/ko-vdr-public/3951.png +- source_sentence: '''이대남'' 담론의 부정적 인식이 세대론의 정치적 악용 가능성 및 특성 오류 문제와 연관되어 있는가?' + sentences: + - data/images/ko/ko-vdr-public/5026.png + - data/images/ko/ko-vdr-public/5027.png + - data/images/ko/ko-vdr-public/5025.png + - data/images/ko/ko-vdr-public/5019.png + - data/images/ko/ko-vdr-public/5029.png + - data/images/ko/ko-vdr-public/5031.png + - data/images/ko/ko-vdr-public/5032.png + - data/images/ko/ko-vdr-public/5020.png + - data/images/ko/ko-vdr-public/5021.png +- source_sentence: 상봉 지점 월성원전 인근 방사능 데이터 양지 지역 대기확산인자 2024년 원자력사업자 평가 정확도 연관성 + sentences: + - data/images/ko/ko-vdr-public/5420.png + - data/images/ko/ko-vdr-public/5464.png + - data/images/ko/ko-vdr-public/5547.png + - data/images/ko/ko-vdr-public/5442.png + - data/images/ko/ko-vdr-public/5443.png + - data/images/ko/ko-vdr-public/5452.png + - data/images/ko/ko-vdr-public/5459.png + - data/images/ko/ko-vdr-public/5465.png + - data/images/ko/ko-vdr-public/5435.png +datasets: +- whybe-choi/ko-vdr-hn +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@5 +- cosine_ndcg@10 +- cosine_mrr@10 +- cosine_map@100 +model-index: +- name: Qwen3-VL-Embedding-2B model trained on Korean Visual Document Retrieval query-document + screenshot pairs + results: + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: kovidore v2 cybersecurity beir eval + type: kovidore-v2-cybersecurity-beir-eval + metrics: + - type: cosine_accuracy@1 + value: 0.7315436241610739 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9261744966442953 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9463087248322147 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9798657718120806 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7315436241610739 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.48098434004474266 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.348993288590604 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.20872483221476512 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.3631991051454139 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6169542984979226 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.7138382869926494 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8173058485139023 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.6936532707888095 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.7410309053140834 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8290161926067966 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.6625934366091845 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.7315436241610739 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9261744966442953 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9463087248322147 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9731543624161074 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7315436241610739 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.4742729306487695 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.34765100671140936 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.2080536912751678 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.3631991051454139 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6096836049856184 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.7128795142217961 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8150687120485778 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.6923826216028253 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.739748418907986 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8282518376478109 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.6619063742148552 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.738255033557047 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9194630872483222 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9463087248322147 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9664429530201343 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.738255033557047 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.47651006711409394 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.35033557046979863 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.20671140939597313 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.36991051454138707 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6130393096836051 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.7171939916906359 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8089165867689357 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.6987399650596461 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.7416619348520129 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8315143283264089 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.6688434168007569 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.7315436241610739 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9261744966442953 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9463087248322147 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9664429530201343 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7315436241610739 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.47651006711409394 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.34765100671140936 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.20872483221476512 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.3665548098434005 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6119207414509428 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.713614573346117 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8133908596995846 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.6938163828188982 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.741385741028066 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8265473527218494 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.6665971203101853 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.7583892617449665 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9261744966442953 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9463087248322147 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9664429530201343 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7583892617449665 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.4787472035794184 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.35167785234899335 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.20939597315436242 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.3731064237775647 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6149408756791308 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.7210450623202301 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8173058485139021 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.7013571322957366 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.7471733977121868 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8388516032811335 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.6722116398899752 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.7651006711409396 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9261744966442953 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9463087248322147 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9664429530201343 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7651006711409396 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.48545861297539156 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.3516778523489933 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.21140939597315433 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.37534356024288906 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.620198146372643 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.7177532758069671 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8234579737935442 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.7019182786587311 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.7514177915116257 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8423005219985085 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.6747536982838555 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.7785234899328859 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9194630872483222 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9463087248322147 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9664429530201343 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7785234899328859 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.47651006711409394 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.35570469798657717 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.21073825503355703 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.3842921061041866 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6115851709811442 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.7199904122722915 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8206615532118887 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.7077557941882081 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.7540891670555786 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8483594332587621 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.6799104114771867 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.7651006711409396 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9261744966442953 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9395973154362416 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9731543624161074 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7651006711409396 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.4787472035794184 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.35167785234899335 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.21208053691275167 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.3786992649408757 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6138223074464685 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.7123841482901886 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8260945989133909 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.7007796833490093 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.7524870059834308 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8426547352721849 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.6753261600591506 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.7718120805369127 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9261744966442953 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9395973154362416 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9664429530201343 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7718120805369127 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.4787472035794184 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.3516778523489933 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.210738255033557 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.38205496963886226 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6138223074464685 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.7134387983381272 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8210610418664109 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.7029767697161534 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.7528730911069387 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.844220730797912 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.6790280272917942 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.7718120805369127 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9261744966442953 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9463087248322147 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9664429530201343 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.7718120805369127 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.4787472035794184 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.3503355704697987 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.210738255033557 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.38093640140620005 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6138223074464685 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.7127197187599872 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8210610418664109 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.7019784344390901 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.7521455342884376 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8458985831469054 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.676805434871088 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.785234899328859 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.9261744966442953 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.9463087248322147 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.9664429530201343 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.785234899328859 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.4787472035794184 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.35302013422818795 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.2114093959731543 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.3876478108021732 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.6138223074464685 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.7123841482901886 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.8227388942154042 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.7057676479852213 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.75593898626686 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.8526099925428784 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.6805033568985487 + name: Cosine Map@100 + - task: + type: information-retrieval + name: Information Retrieval + dataset: + name: kovidore v2 hr beir eval + type: kovidore-v2-hr-beir-eval + metrics: + - type: cosine_accuracy@1 + value: 0.4343891402714932 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.6832579185520362 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.7692307692307693 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.8914027149321267 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.4343891402714932 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.34841628959276016 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.2642533936651584 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.183710407239819 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.15182072829131651 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.33859082094376214 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.4224305106658048 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.5823529411764706 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.4122911977338132 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.4813934366008242 + name: Cosine Ndcg@10 + - 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type: cosine_precision@1 + value: 0.26993865030674846 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.18813905930470345 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.14355828220858896 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.09079754601226994 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.11472392638036809 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.24969325153374236 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.318200408997955 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.39284253578732103 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.27880879981577666 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.30991188938028436 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.401010322329341 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.247759826546771 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.27607361963190186 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.49693251533742333 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.5828220858895705 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.7055214723926381 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.27607361963190186 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.18404907975460122 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.1411042944785276 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.09202453987730061 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.11779141104294479 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.24458077709611448 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.3130879345603272 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.40051124744376276 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.27827202454805383 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.3144474034604064 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.4069675723049958 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.2500262757070231 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.27607361963190186 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.49079754601226994 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.5766871165644172 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.6993865030674846 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.27607361963190186 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.18609406952965235 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.1398773006134969 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.09202453987730061 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.11779141104294476 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.2466257668711656 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.308997955010225 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.39948875255623717 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.27385741831910043 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.31118604222357515 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.4006232349790633 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.24670032593748037 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.25766871165644173 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.4785276073619632 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.5705521472392638 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.6871165644171779 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.25766871165644173 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.17995910020449898 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.13742331288343557 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.0901840490797546 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.11012269938650306 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.23742331288343554 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.3038854805725971 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.39284253578732103 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.2690462127630967 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.3061832068339083 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.3935436751387673 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.24383921471159192 + name: Cosine Map@100 + - type: cosine_accuracy@1 + value: 0.26993865030674846 + name: Cosine Accuracy@1 + - type: cosine_accuracy@3 + value: 0.4785276073619632 + name: Cosine Accuracy@3 + - type: cosine_accuracy@5 + value: 0.5950920245398773 + name: Cosine Accuracy@5 + - type: cosine_accuracy@10 + value: 0.6993865030674846 + name: Cosine Accuracy@10 + - type: cosine_precision@1 + value: 0.26993865030674846 + name: Cosine Precision@1 + - type: cosine_precision@3 + value: 0.17791411042944785 + name: Cosine Precision@3 + - type: cosine_precision@5 + value: 0.14355828220858896 + name: Cosine Precision@5 + - type: cosine_precision@10 + value: 0.09202453987730061 + name: Cosine Precision@10 + - type: cosine_recall@1 + value: 0.11625766871165644 + name: Cosine Recall@1 + - type: cosine_recall@3 + value: 0.23640081799591 + name: Cosine Recall@3 + - type: cosine_recall@5 + value: 0.318200408997955 + name: Cosine Recall@5 + - type: cosine_recall@10 + value: 0.4020449897750511 + name: Cosine Recall@10 + - type: cosine_ndcg@5 + value: 0.27875914489130793 + name: Cosine Ndcg@5 + - type: cosine_ndcg@10 + value: 0.31319681048962333 + name: Cosine Ndcg@10 + - type: cosine_mrr@10 + value: 0.4009859772129713 + name: Cosine Mrr@10 + - type: cosine_map@100 + value: 0.24883187796079392 + name: Cosine Map@100 +--- + +# Qwen3-VL-Embedding-2B model trained on Korean Visual Document Retrieval query-document screenshot pairs + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [whybe-choi/Qwen3-VL-Embedding-2B-ko-vdr-preview-v0.7](https://huggingface.co/whybe-choi/Qwen3-VL-Embedding-2B-ko-vdr-preview-v0.7) on the [whybe-choi/ko-vdr-hn](https://huggingface.co/datasets/whybe-choi/ko-vdr-hn) dataset. It maps sentences & paragraphs to a 2048-dimensional dense vector space and can be used for retrieval. + +## Model Details + +### Model Description +- **Model Type:** Sentence Transformer +- **Base model:** [whybe-choi/Qwen3-VL-Embedding-2B-ko-vdr-preview-v0.7](https://huggingface.co/whybe-choi/Qwen3-VL-Embedding-2B-ko-vdr-preview-v0.7) +- **Maximum Sequence Length:** 262144 tokens +- **Output Dimensionality:** 2048 dimensions +- **Similarity Function:** Cosine Similarity +- **Supported Modalities:** Text, Image, Video, Message +- **Training Dataset:** + - [whybe-choi/ko-vdr-hn](https://huggingface.co/datasets/whybe-choi/ko-vdr-hn) +- **Language:** ko +- **License:** apache-2.0 + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'image': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'video': {'method': 'forward', 'method_output_name': 'last_hidden_state'}, 'message': {'method': 'forward', 'method_output_name': 'last_hidden_state', 'format': 'structured'}}, 'module_output_name': 'token_embeddings', 'processing_kwargs': {'chat_template': {'add_generation_prompt': True}}, 'unpad_inputs': False, 'architecture': 'Qwen3VLModel'}) + (1): Pooling({'embedding_dimension': 2048, 'pooling_mode': 'lasttoken', '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("whybe-choi/kovre") +# Run inference +queries = [ + '상봉 지점 월성원전 인근 방사능 데이터 양지 지역 대기확산인자 2024년 원자력사업자 평가 정확도 연관성', +] +documents = [ + 'data/images/ko/ko-vdr-public/5547.png', + 'data/images/ko/ko-vdr-public/5442.png', + 'data/images/ko/ko-vdr-public/5443.png', +] +query_embeddings = model.encode_query(queries) +document_embeddings = model.encode_document(documents) +print(query_embeddings.shape, document_embeddings.shape) +# [1, 2048] [3, 2048] + +# Get the similarity scores for the embeddings +similarities = model.similarity(query_embeddings, document_embeddings) +print(similarities) +# tensor([[0.4744, 0.2573, 0.3265]]) +``` + + + + + + +## Evaluation + +### Metrics + +#### Information Retrieval + +* Datasets: `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval`, `kovidore-v2-economic-beir-eval`, `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval`, `kovidore-v2-economic-beir-eval`, `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval`, `kovidore-v2-economic-beir-eval`, `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval`, `kovidore-v2-economic-beir-eval`, `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval`, `kovidore-v2-economic-beir-eval`, `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval`, `kovidore-v2-economic-beir-eval`, `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval`, `kovidore-v2-economic-beir-eval`, `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval`, `kovidore-v2-economic-beir-eval`, `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval`, `kovidore-v2-economic-beir-eval`, `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval`, `kovidore-v2-economic-beir-eval`, `kovidore-v2-cybersecurity-beir-eval`, `kovidore-v2-hr-beir-eval`, `kovidore-v2-energy-beir-eval` and `kovidore-v2-economic-beir-eval` +* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.sentence_transformer.evaluation.InformationRetrievalEvaluator) with these parameters: + ```json + { + "query_prompt": "Find a document image that matches the given query.", + "corpus_prompt": "Represent the user's input." + } + ``` + +| Metric | kovidore-v2-cybersecurity-beir-eval | kovidore-v2-hr-beir-eval | kovidore-v2-energy-beir-eval | kovidore-v2-economic-beir-eval | +|:--------------------|:------------------------------------|:-------------------------|:-----------------------------|:-------------------------------| +| cosine_accuracy@1 | 0.7852 | 0.4434 | 0.6763 | 0.2699 | +| cosine_accuracy@3 | 0.9262 | 0.6878 | 0.8671 | 0.4785 | +| cosine_accuracy@5 | 0.9463 | 0.7783 | 0.896 | 0.5951 | +| cosine_accuracy@10 | 0.9664 | 0.8869 | 0.9422 | 0.6994 | +| cosine_precision@1 | 0.7852 | 0.4434 | 0.6763 | 0.2699 | +| cosine_precision@3 | 0.4787 | 0.3409 | 0.4547 | 0.1779 | +| cosine_precision@5 | 0.353 | 0.2697 | 0.3503 | 0.1436 | +| cosine_precision@10 | 0.2114 | 0.186 | 0.2127 | 0.092 | +| cosine_recall@1 | 0.3876 | 0.1569 | 0.2649 | 0.1163 | +| cosine_recall@3 | 0.6138 | 0.3349 | 0.5047 | 0.2364 | +| cosine_recall@5 | 0.7124 | 0.4308 | 0.6193 | 0.3182 | +| cosine_recall@10 | 0.8227 | 0.5866 | 0.7361 | 0.402 | +| cosine_ndcg@5 | 0.7058 | 0.4208 | 0.6115 | 0.2788 | +| **cosine_ndcg@10** | **0.7559** | **0.4891** | **0.6636** | **0.3132** | +| cosine_mrr@10 | 0.8526 | 0.5919 | 0.7763 | 0.401 | +| cosine_map@100 | 0.6805 | 0.4058 | 0.5822 | 0.2488 | + + + + + +## Training Details + +### Training Dataset + +#### whybe-choi/ko-vdr-hn + +* Dataset: [whybe-choi/ko-vdr-hn](https://huggingface.co/datasets/whybe-choi/ko-vdr-hn) at [b58db4a](https://huggingface.co/datasets/whybe-choi/ko-vdr-hn/tree/b58db4ab10d086eb13dfd067d270aa7648667d8f) +* Size: 265,311 training samples +* Columns: anchor, positive, negative_1, negative_2, negative_3, negative_4, negative_5, negative_6, negative_7, negative_8, and label +* Approximate statistics based on the first 100 samples: + | | anchor | positive | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | label | + |:---------|:-----------------------------------------------------------------------------------|:---------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------|:-----------------------------------| + | type | string | string | string | string | string | string | string | string | string | string | list | + | modality | text | image | image | image | image | image | image | image | image | image | | + | details | | | | | | | | | | | | +* Samples: + | anchor | positive | negative_1 | negative_2 | negative_3 | negative_4 | negative_5 | negative_6 | negative_7 | negative_8 | label | + |:----------------------------------------------------------------------------------|:---------------------------------------------------|:----------------------------------------------------------|:----------------------------------------------------------|:---------------------------------------------------|:----------------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------|:----------------------------------------------------------|:---------------------------------------------------|:---------------------------------------------------------------| + | 100년 재현빈도 설계기준 강화와 강구항 방파제 건설 투자 계획은 글로벌 초대형 컨테이너선 증가 추세에 어떻게 대응하나요? | data/images/ko/ko-vdr-public/7130.png | data/images/ko/ko-vdr-public/791.png | data/images/ko/ko-vdr-public/749.png | data/images/ko/ko-vdr-public/7132.png | data/images/ko/ko-vdr-private/579ce234c8.png | data/images/ko/ko-vdr-public/7122.png | data/images/ko/ko-vdr-public/881.png | data/images/ko/ko-vdr-public/7120.png | data/images/ko/ko-vdr-public/7152.png | [-1.6875, -2.125, -2.8125, -3.1875, -3.1875, ...] | + | 엔터테인먼트 센터 비게임 수익 비중과 Commission Model 카지노 배분 비율 차이 | data/images/ko/ko-vdr-public/8398.png | data/images/ko/ko-vdr-public/8416.png | data/images/ko/ko-vdr-private/0f1be1a32f.png | data/images/ko/ko-vdr-public/8418.png | data/images/ko/ko-vdr-public/8417.png | data/images/ko/ko-vdr-public/8419.png | data/images/ko/ko-vdr-public/8412.png | data/images/ko/ko-vdr-private/3fbb042d71.png | data/images/ko/ko-vdr-public/8411.png | [-0.75, -0.875, -2.1875, -2.5625, -2.875, ...] | + | 외국인 근로자 분류 기준과 비제조업 교대근무 현황의 데이터 수집 방법 및 중복 계산 가능성 차이는 무엇인가? | data/images/ko/ko-vdr-public/7703.png | data/images/ko/ko-vdr-private/7c4515ed2d.png | data/images/ko/ko-vdr-public/7811.png | data/images/ko/ko-vdr-public/7796.png | data/images/ko/ko-vdr-private/ba3068cebd.png | data/images/ko/ko-vdr-public/7803.png | data/images/ko/ko-vdr-public/7944.png | data/images/ko/ko-vdr-private/231af92145.png | data/images/ko/ko-vdr-public/7637.png | [-2.25, -2.25, -2.3125, -2.625, -2.625, ...] | +* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters: + ```json + { + "loss": "MinMaxDistillKLDivLoss", + "matryoshka_dims": [ + 2048, + 1024, + 768, + 512, + 256, + 128 + ], + "matryoshka_weights": [ + 1, + 1, + 1, + 1, + 1, + 1 + ], + "n_dims_per_step": -1 + } + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `num_train_epochs`: 1.0 +- `learning_rate`: 1e-06 +- `lr_scheduler_type`: cosine +- `warmup_steps`: 0.1 +- `gradient_accumulation_steps`: 8 +- `bf16`: True +- `gradient_checkpointing`: True +- `per_device_eval_batch_size`: 128 +- `eval_on_start`: True +- `ddp_find_unused_parameters`: True +- `ddp_timeout`: 3600 + +#### All Hyperparameters +
Click to expand + +- `per_device_train_batch_size`: 8 +- `num_train_epochs`: 1.0 +- `max_steps`: -1 +- `learning_rate`: 1e-06 +- `lr_scheduler_type`: cosine +- `lr_scheduler_kwargs`: None +- `warmup_steps`: 0.1 +- `optim`: adamw_torch_fused +- `optim_args`: None +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `optim_target_modules`: None +- `gradient_accumulation_steps`: 8 +- `average_tokens_across_devices`: True +- `max_grad_norm`: 1.0 +- `label_smoothing_factor`: 0.0 +- `bf16`: True +- `fp16`: False +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: None +- `gradient_checkpointing`: True +- `gradient_checkpointing_kwargs`: None +- `torch_compile`: False +- `torch_compile_backend`: None +- `torch_compile_mode`: None +- `use_liger_kernel`: False +- `liger_kernel_config`: None +- `use_cache`: False +- `neftune_noise_alpha`: None +- `torch_empty_cache_steps`: None +- `auto_find_batch_size`: False +- `log_on_each_node`: True +- `logging_nan_inf_filter`: True +- `include_num_input_tokens_seen`: no +- `log_level`: passive +- `log_level_replica`: warning +- `disable_tqdm`: False +- `project`: huggingface +- `trackio_space_id`: trackio +- `per_device_eval_batch_size`: 128 +- `prediction_loss_only`: True +- `eval_on_start`: True +- `eval_do_concat_batches`: True +- `eval_use_gather_object`: False +- `eval_accumulation_steps`: None +- `include_for_metrics`: [] +- `batch_eval_metrics`: False +- `save_only_model`: False +- `save_on_each_node`: False +- `enable_jit_checkpoint`: False +- `push_to_hub`: False +- `hub_private_repo`: None +- `hub_model_id`: None +- `hub_strategy`: every_save +- `hub_always_push`: False +- `hub_revision`: None +- `load_best_model_at_end`: False +- `ignore_data_skip`: False +- `restore_callback_states_from_checkpoint`: False +- `full_determinism`: False +- `seed`: 42 +- `data_seed`: None +- `use_cpu`: False +- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} +- `parallelism_config`: None +- `dataloader_drop_last`: True +- `dataloader_num_workers`: 0 +- `dataloader_pin_memory`: True +- `dataloader_persistent_workers`: False +- `dataloader_prefetch_factor`: None +- `remove_unused_columns`: True +- `label_names`: None +- `train_sampling_strategy`: random +- `length_column_name`: length +- `ddp_find_unused_parameters`: True +- `ddp_bucket_cap_mb`: None +- `ddp_broadcast_buffers`: False +- `ddp_backend`: None +- `ddp_timeout`: 3600 +- `fsdp`: [] +- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} +- `deepspeed`: None +- `debug`: [] +- `skip_memory_metrics`: True +- `do_predict`: False +- `resume_from_checkpoint`: None +- `warmup_ratio`: None +- `local_rank`: -1 +- `prompts`: None +- `batch_sampler`: batch_sampler +- `multi_dataset_batch_sampler`: proportional +- `router_mapping`: {} +- `learning_rate_mapping`: {} +- `mix_languages`: False +- `query_prompt`: Find a document image that matches the given query. +- `document_prompt`: Represent the user's input. +- `mini_batch_size`: 1 +- `matryoshka_dims`: [2048, 1024, 768, 512, 256, 128] +- `use_lora`: False +- `normalize_scores`: False +- `lora_r`: 32 +- `lora_alpha`: 32 +- `lora_dropout`: 0.05 +- `lora_target_modules`: ['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'] +- `use_self_guide`: True +- `self_guide_margin`: -0.1 +- `hardness_strength`: 0.0 +- `hardness_mode`: None +- `distill_loss`: kldiv_minmax +- `kldiv_temperature`: 1.0 + +
+ +### Training Logs +
Click to expand + +| Epoch | Step | Training Loss | kovidore-v2-cybersecurity-beir-eval_cosine_ndcg@10 | kovidore-v2-hr-beir-eval_cosine_ndcg@10 | kovidore-v2-energy-beir-eval_cosine_ndcg@10 | kovidore-v2-economic-beir-eval_cosine_ndcg@10 | +|:------:|:----:|:-------------:|:--------------------------------------------------:|:---------------------------------------:|:-------------------------------------------:|:---------------------------------------------:| +| 0 | 0 | - | 0.7410 | 0.4814 | 0.6453 | 0.2937 | +| 0.0005 | 1 | 0.2449 | - | - | - | - | +| 0.0010 | 2 | 0.2825 | - | - | - | - | +| 0.0014 | 3 | 0.2627 | - | - | - | - | +| 0.0019 | 4 | 0.2249 | - | - | - | - | +| 0.0024 | 5 | 0.2527 | - | - | - | - | +| 0.0029 | 6 | 0.2571 | - | - | - | - | +| 0.0034 | 7 | 0.2305 | - | - | - | - | +| 0.0039 | 8 | 0.2365 | - | - | - | - | +| 0.0043 | 9 | 0.2366 | - | - | - | - | +| 0.0048 | 10 | 0.2658 | - | - | - | - | +| 0.0053 | 11 | 0.2193 | - | - | - | - | +| 0.0058 | 12 | 0.2151 | - | - | - | - | +| 0.0063 | 13 | 0.2461 | - | - | - | - | +| 0.0068 | 14 | 0.2405 | - | - | - | - | +| 0.0072 | 15 | 0.2294 | - | - | - | - | +| 0.0077 | 16 | 0.2131 | - | - | - | - | +| 0.0082 | 17 | 0.2584 | - | - | - | - | +| 0.0087 | 18 | 0.2793 | - | - | - | - | +| 0.0092 | 19 | 0.2637 | - | - | - | - | +| 0.0096 | 20 | 0.2438 | - | - | - | - | +| 0.0101 | 21 | 0.2481 | - | - | - | - | +| 0.0106 | 22 | 0.2477 | - | - | - | - | +| 0.0111 | 23 | 0.2512 | - | - | - | - | +| 0.0116 | 24 | 0.2692 | - | - | - | - | +| 0.0121 | 25 | 0.2539 | - | - | - | - | +| 0.0125 | 26 | 0.2611 | - | - | - | - | +| 0.0130 | 27 | 0.2598 | - | - | - | - | +| 0.0135 | 28 | 0.2488 | - | - | - | - | +| 0.0140 | 29 | 0.2641 | - | - | - | - | +| 0.0145 | 30 | 0.2654 | - | - | - | - | +| 0.0150 | 31 | 0.2582 | - | - | - | - | +| 0.0154 | 32 | 0.2805 | - | - | - | - | +| 0.0159 | 33 | 0.2494 | - | - | - | - | +| 0.0164 | 34 | 0.2608 | - | - | - | - | +| 0.0169 | 35 | 0.2563 | - | - | - | - | +| 0.0174 | 36 | 0.2634 | - | - | - | - | +| 0.0179 | 37 | 0.2713 | - | - | - | - | +| 0.0183 | 38 | 0.2232 | - | - | - | - | +| 0.0188 | 39 | 0.2582 | - | - | - | - | +| 0.0193 | 40 | 0.2963 | - | - | - | - | +| 0.0198 | 41 | 0.2356 | - | - | - | - | +| 0.0203 | 42 | 0.2339 | - | - | - | - | +| 0.0207 | 43 | 0.2565 | - | - | - | - | +| 0.0212 | 44 | 0.2892 | - | - | - | - | +| 0.0217 | 45 | 0.2464 | - | - | - | - | +| 0.0222 | 46 | 0.2892 | - | - | - | - | +| 0.0227 | 47 | 0.2873 | - | - | - | - | +| 0.0232 | 48 | 0.2819 | - | - | - | - | +| 0.0236 | 49 | 0.2326 | - | - | - | - | +| 0.0241 | 50 | 0.2356 | - | - | - | - | +| 0.0246 | 51 | 0.2410 | - | - | - | - | +| 0.0251 | 52 | 0.2395 | - | - | - | - | +| 0.0256 | 53 | 0.2262 | - | - | - | - | +| 0.0261 | 54 | 0.2552 | - | - | - | - | +| 0.0265 | 55 | 0.2573 | - | - | - | - | +| 0.0270 | 56 | 0.2338 | - | - | - | - | +| 0.0275 | 57 | 0.2572 | - | - | - | - | +| 0.0280 | 58 | 0.2676 | - | - | - | - | +| 0.0285 | 59 | 0.2589 | - | - | - | - | +| 0.0289 | 60 | 0.2477 | - | - | - | - | +| 0.0294 | 61 | 0.2648 | - | - | - | - | +| 0.0299 | 62 | 0.2820 | - | - | - | - | +| 0.0304 | 63 | 0.2554 | - | - | - | - | +| 0.0309 | 64 | 0.2191 | - | - | - | - | +| 0.0314 | 65 | 0.2830 | - | - | - | - | +| 0.0318 | 66 | 0.2255 | - | - | - | - | +| 0.0323 | 67 | 0.2517 | - | - | - | - | +| 0.0328 | 68 | 0.2261 | - | - | - | - | +| 0.0333 | 69 | 0.2453 | - | - | - | - | +| 0.0338 | 70 | 0.2512 | - | - | - | - | +| 0.0343 | 71 | 0.2432 | - | - | - | - | +| 0.0347 | 72 | 0.2361 | - | - | - | - | +| 0.0352 | 73 | 0.2694 | - | - | - | - | +| 0.0357 | 74 | 0.2591 | - | - | - | - | +| 0.0362 | 75 | 0.2635 | - | - | - | - | +| 0.0367 | 76 | 0.2695 | - | - | - | - | +| 0.0372 | 77 | 0.2461 | - | - | - | - | +| 0.0376 | 78 | 0.2404 | - | - | - | - | +| 0.0381 | 79 | 0.2422 | - | - | - | - | +| 0.0386 | 80 | 0.2443 | - | - | - | - | +| 0.0391 | 81 | 0.2444 | - | - | - | - | +| 0.0396 | 82 | 0.2474 | - | - | - | - | +| 0.0400 | 83 | 0.2074 | - | - | - | - | +| 0.0405 | 84 | 0.2445 | - | - | - | - | +| 0.0410 | 85 | 0.2257 | - | - | - | - | +| 0.0415 | 86 | 0.2265 | - | - | - | - | +| 0.0420 | 87 | 0.2610 | - | - | - | - | +| 0.0425 | 88 | 0.2881 | - | - | - | - | +| 0.0429 | 89 | 0.2469 | - | - | - | - | +| 0.0434 | 90 | 0.2416 | - | - | - | - | +| 0.0439 | 91 | 0.2516 | - | - | - | - | +| 0.0444 | 92 | 0.2522 | - | - | - | - | +| 0.0449 | 93 | 0.2492 | - | - | - | - | +| 0.0454 | 94 | 0.2534 | - | - | - | - | +| 0.0458 | 95 | 0.2806 | - | - | - | - | +| 0.0463 | 96 | 0.2311 | - | - | - | - | +| 0.0468 | 97 | 0.2537 | - | - | - | - | +| 0.0473 | 98 | 0.2600 | - | - | - | - | +| 0.0478 | 99 | 0.2487 | - | - | - | - | +| 0.0482 | 100 | 0.2221 | 0.7397 | 0.4812 | 0.6467 | 0.2911 | +| 0.0487 | 101 | 0.2678 | - | - | - | - | +| 0.0492 | 102 | 0.2065 | - | - | - | - | +| 0.0497 | 103 | 0.2242 | - | - | - | - | +| 0.0502 | 104 | 0.2693 | - | - | - | - | +| 0.0507 | 105 | 0.2317 | - | - | - | - | +| 0.0511 | 106 | 0.2472 | - | - | - | - | +| 0.0516 | 107 | 0.2621 | - | - | - | - | +| 0.0521 | 108 | 0.2493 | - | - | - | - | +| 0.0526 | 109 | 0.2426 | - | - | - | - | +| 0.0531 | 110 | 0.2419 | - | - | - | - | +| 0.0536 | 111 | 0.2314 | - | - | - | - | +| 0.0540 | 112 | 0.2491 | - | - | - | - | +| 0.0545 | 113 | 0.2695 | - | - | - | - | +| 0.0550 | 114 | 0.2577 | - | - | - | - | +| 0.0555 | 115 | 0.2613 | - | - | - | - | +| 0.0560 | 116 | 0.2499 | - | - | - | - | +| 0.0565 | 117 | 0.2605 | - | - | - | - | +| 0.0569 | 118 | 0.2381 | - | - | - | - | +| 0.0574 | 119 | 0.2367 | - | - | - | - | +| 0.0579 | 120 | 0.2411 | - | - | - | - | +| 0.0584 | 121 | 0.2487 | - | - | - | - | +| 0.0589 | 122 | 0.2910 | - | - | - | - | +| 0.0593 | 123 | 0.2085 | - | - | - | - | +| 0.0598 | 124 | 0.2339 | - | - | - | - | +| 0.0603 | 125 | 0.2675 | - | - | - | - | +| 0.0608 | 126 | 0.2201 | - | - | - | - | +| 0.0613 | 127 | 0.2438 | - | - | - | - | +| 0.0618 | 128 | 0.2413 | - | - | - | - | +| 0.0622 | 129 | 0.2629 | - | - | - | - | +| 0.0627 | 130 | 0.2469 | - | - | - | - | +| 0.0632 | 131 | 0.2331 | - | - | - | - | +| 0.0637 | 132 | 0.2698 | - | - | - | - | +| 0.0642 | 133 | 0.2234 | - | - | - | - | +| 0.0647 | 134 | 0.2726 | - | - | - | - | +| 0.0651 | 135 | 0.2723 | - | - | - | - | +| 0.0656 | 136 | 0.2332 | - | - | - | - | +| 0.0661 | 137 | 0.2488 | - | - | - | - | +| 0.0666 | 138 | 0.2442 | - | - | - | - | +| 0.0671 | 139 | 0.2376 | - | - | - | - | +| 0.0675 | 140 | 0.2428 | - | - | - | - | +| 0.0680 | 141 | 0.2723 | - | - | - | - | +| 0.0685 | 142 | 0.2557 | - | - | - | - | +| 0.0690 | 143 | 0.2748 | - | - | - | - | +| 0.0695 | 144 | 0.2521 | - | - | - | - | +| 0.0700 | 145 | 0.2556 | - | - | - | - | +| 0.0704 | 146 | 0.2328 | - | - | - | - | +| 0.0709 | 147 | 0.2469 | - | - | - | - | +| 0.0714 | 148 | 0.2351 | - | - | - | - | +| 0.0719 | 149 | 0.2147 | - | - | - | - | +| 0.0724 | 150 | 0.2351 | - | - | - | - | +| 0.0729 | 151 | 0.2165 | - | - | - | - | +| 0.0733 | 152 | 0.2736 | - | - | - | - | +| 0.0738 | 153 | 0.2625 | - | - | - | - | +| 0.0743 | 154 | 0.2297 | - | - | - | - | +| 0.0748 | 155 | 0.2538 | - | - | - | - | +| 0.0753 | 156 | 0.2476 | - | - | - | - | +| 0.0757 | 157 | 0.2318 | - | - | - | - | +| 0.0762 | 158 | 0.2080 | - | - | - | - | +| 0.0767 | 159 | 0.2157 | - | - | - | - | +| 0.0772 | 160 | 0.2255 | - | - | - | - | +| 0.0777 | 161 | 0.2351 | - | - | - | - | +| 0.0782 | 162 | 0.2646 | - | - | - | - | +| 0.0786 | 163 | 0.2414 | - | - | - | - | +| 0.0791 | 164 | 0.2479 | - | - | - | - | +| 0.0796 | 165 | 0.2665 | - | - | - | - | +| 0.0801 | 166 | 0.2573 | - | - | - | - | +| 0.0806 | 167 | 0.2592 | - | - | - | - | +| 0.0811 | 168 | 0.2205 | - | - | - | - | +| 0.0815 | 169 | 0.2680 | - | - | - | - | +| 0.0820 | 170 | 0.2401 | - | - | - | - | +| 0.0825 | 171 | 0.2661 | - | - | - | - | +| 0.0830 | 172 | 0.2478 | - | - | - | - | +| 0.0835 | 173 | 0.2433 | - | - | - | - | +| 0.0840 | 174 | 0.2378 | - | - | - | - | +| 0.0844 | 175 | 0.2520 | - | - | - | - | +| 0.0849 | 176 | 0.2364 | - | - | - | - | +| 0.0854 | 177 | 0.2329 | - | - | - | - | +| 0.0859 | 178 | 0.2466 | - | - | - | - | +| 0.0864 | 179 | 0.2113 | - | - | - | - | +| 0.0868 | 180 | 0.2498 | - | - | - | - | +| 0.0873 | 181 | 0.2235 | - | - | - | - | +| 0.0878 | 182 | 0.2501 | - | - | - | - | +| 0.0883 | 183 | 0.2454 | - | - | - | - | +| 0.0888 | 184 | 0.2389 | - | - | - | - | +| 0.0893 | 185 | 0.2605 | - | - | - | - | +| 0.0897 | 186 | 0.2440 | - | - | - | - | +| 0.0902 | 187 | 0.2547 | - | - | - | - | +| 0.0907 | 188 | 0.2450 | - | - | - | - | +| 0.0912 | 189 | 0.2275 | - | - | - | - | +| 0.0917 | 190 | 0.2400 | - | - | - | - | +| 0.0922 | 191 | 0.2757 | - | - | - | - | +| 0.0926 | 192 | 0.2431 | - | - | - | - | +| 0.0931 | 193 | 0.2433 | - | - | - | - | +| 0.0936 | 194 | 0.2688 | - | - | - | - | +| 0.0941 | 195 | 0.2551 | - | - | - | - | +| 0.0946 | 196 | 0.2350 | - | - | - | - | +| 0.0950 | 197 | 0.2437 | - | - | - | - | +| 0.0955 | 198 | 0.2480 | - | - | - | - | +| 0.0960 | 199 | 0.2132 | - | - | - | - | +| 0.0965 | 200 | 0.2735 | 0.7417 | 0.4864 | 0.6559 | 0.3005 | +| 0.0970 | 201 | 0.2133 | - | - | - | - | +| 0.0975 | 202 | 0.2567 | - | - | - | - | +| 0.0979 | 203 | 0.2504 | - | - | - | - | +| 0.0984 | 204 | 0.2337 | - | - | - | - | +| 0.0989 | 205 | 0.2562 | - | - | - | - | +| 0.0994 | 206 | 0.2574 | - | - | - | - | +| 0.0999 | 207 | 0.2504 | - | - | - | - | +| 0.1004 | 208 | 0.2687 | - | - | - | - | +| 0.1008 | 209 | 0.2632 | - | - | - | - | +| 0.1013 | 210 | 0.2322 | - | - | - | - | +| 0.1018 | 211 | 0.2234 | - | - | - | - | +| 0.1023 | 212 | 0.2698 | - | - | - | - | +| 0.1028 | 213 | 0.2467 | - | - | - | - | +| 0.1033 | 214 | 0.2581 | - | - | - | - | +| 0.1037 | 215 | 0.3068 | - | - | - | - | +| 0.1042 | 216 | 0.2684 | - | - | - | - | +| 0.1047 | 217 | 0.2206 | - | - | - | - | +| 0.1052 | 218 | 0.2487 | - | - | - | - | +| 0.1057 | 219 | 0.2815 | - | - | - | - | +| 0.1061 | 220 | 0.2244 | - | - | - | - | +| 0.1066 | 221 | 0.2387 | - | - | - | - | +| 0.1071 | 222 | 0.2407 | - | - | - | - | +| 0.1076 | 223 | 0.2276 | - | - | - | - | +| 0.1081 | 224 | 0.2506 | - | - | - | - | +| 0.1086 | 225 | 0.2292 | - | - | - | - | +| 0.1090 | 226 | 0.2309 | - | - | - | - | +| 0.1095 | 227 | 0.2284 | - | - | - | - | +| 0.1100 | 228 | 0.2408 | - | - | - | - | +| 0.1105 | 229 | 0.2258 | - | - | - | - | +| 0.1110 | 230 | 0.2557 | - | - | - | - | +| 0.1115 | 231 | 0.2357 | - | - | - | - | +| 0.1119 | 232 | 0.2619 | - | - | - | - | +| 0.1124 | 233 | 0.2534 | - | - | - | - | +| 0.1129 | 234 | 0.2685 | - | - | - | - | +| 0.1134 | 235 | 0.2335 | - | - | - | - | +| 0.1139 | 236 | 0.2766 | - | - | - | - | +| 0.1143 | 237 | 0.2699 | - | - | - | - | +| 0.1148 | 238 | 0.2405 | - | - | - | - | +| 0.1153 | 239 | 0.2676 | - | - | - | - | +| 0.1158 | 240 | 0.2464 | - | - | - | - | +| 0.1163 | 241 | 0.2670 | - | - | - | - | +| 0.1168 | 242 | 0.2518 | - | - | - | - | +| 0.1172 | 243 | 0.2529 | - | - | - | - | +| 0.1177 | 244 | 0.2484 | - | - | - | - | +| 0.1182 | 245 | 0.2344 | - | - | - | - | +| 0.1187 | 246 | 0.2523 | - | - | - | - | +| 0.1192 | 247 | 0.2423 | - | - | - | - | +| 0.1197 | 248 | 0.2311 | - | - | - | - | +| 0.1201 | 249 | 0.2314 | - | - | - | - | +| 0.1206 | 250 | 0.2434 | - | - | - | - | +| 0.1211 | 251 | 0.2370 | - | - | - | - | +| 0.1216 | 252 | 0.2249 | - | - | - | - | +| 0.1221 | 253 | 0.2417 | - | - | - | - | +| 0.1225 | 254 | 0.2356 | - | - | - | - | +| 0.1230 | 255 | 0.2570 | - | - | - | - | +| 0.1235 | 256 | 0.2465 | - | - | - | - | +| 0.1240 | 257 | 0.2661 | - | - | - | - | +| 0.1245 | 258 | 0.2475 | - | - | - | - | +| 0.1250 | 259 | 0.2572 | - | - | - | - | +| 0.1254 | 260 | 0.2330 | - | - | - | - | +| 0.1259 | 261 | 0.2189 | - | - | - | - | +| 0.1264 | 262 | 0.2407 | - | - | - | - | +| 0.1269 | 263 | 0.2373 | - | - | - | - | +| 0.1274 | 264 | 0.2360 | - | - | - | - | +| 0.1279 | 265 | 0.2670 | - | - | - | - | +| 0.1283 | 266 | 0.2665 | - | - | - | - | +| 0.1288 | 267 | 0.2287 | - | - | - | - | +| 0.1293 | 268 | 0.2302 | - | - | - | - | +| 0.1298 | 269 | 0.2456 | - | - | - | - | +| 0.1303 | 270 | 0.2429 | - | - | - | - | +| 0.1308 | 271 | 0.2328 | - | - | - | - | +| 0.1312 | 272 | 0.2640 | - | - | - | - | +| 0.1317 | 273 | 0.2416 | - | - | - | - | +| 0.1322 | 274 | 0.2454 | - | - | - | - | +| 0.1327 | 275 | 0.2381 | - | - | - | - | +| 0.1332 | 276 | 0.2795 | - | - | - | - | +| 0.1336 | 277 | 0.2473 | - | - | - | - | +| 0.1341 | 278 | 0.2526 | - | - | - | - | +| 0.1346 | 279 | 0.2262 | - | - | - | - | +| 0.1351 | 280 | 0.2604 | - | - | - | - | +| 0.1356 | 281 | 0.2355 | - | - | - | - | +| 0.1361 | 282 | 0.2301 | - | - | - | - | +| 0.1365 | 283 | 0.2502 | - | - | - | - | +| 0.1370 | 284 | 0.2518 | - | - | - | - | +| 0.1375 | 285 | 0.2366 | - | - | - | - | +| 0.1380 | 286 | 0.2293 | - | - | - | - | +| 0.1385 | 287 | 0.2409 | - | - | - | - | +| 0.1390 | 288 | 0.2376 | - | - | - | - | +| 0.1394 | 289 | 0.2305 | - | - | - | - | +| 0.1399 | 290 | 0.2367 | - | - | - | - | +| 0.1404 | 291 | 0.2122 | - | - | - | - | +| 0.1409 | 292 | 0.2531 | - | - | - | - | +| 0.1414 | 293 | 0.2434 | - | - | - | - | +| 0.1418 | 294 | 0.2504 | - | - | - | - | +| 0.1423 | 295 | 0.2269 | - | - | - | - | +| 0.1428 | 296 | 0.2538 | - | - | - | - | +| 0.1433 | 297 | 0.2648 | - | - | - | - | +| 0.1438 | 298 | 0.2479 | - | - | - | - | +| 0.1443 | 299 | 0.2345 | - | - | - | - | +| 0.1447 | 300 | 0.2421 | 0.7414 | 0.4863 | 0.6626 | 0.3061 | +| 0.1452 | 301 | 0.2489 | - | - | - | - | +| 0.1457 | 302 | 0.2514 | - | - | - | - | +| 0.1462 | 303 | 0.2103 | - | - | - | - | +| 0.1467 | 304 | 0.2294 | - | - | - | - | +| 0.1472 | 305 | 0.2233 | - | - | - | - | +| 0.1476 | 306 | 0.2710 | - | - | - | - | +| 0.1481 | 307 | 0.2259 | - | - | - | - | +| 0.1486 | 308 | 0.2643 | - | - | - | - | +| 0.1491 | 309 | 0.2353 | - | - | - | - | +| 0.1496 | 310 | 0.2176 | - | - | - | - | +| 0.1501 | 311 | 0.2450 | - | - | - | - | +| 0.1505 | 312 | 0.2437 | - | - | - | - | +| 0.1510 | 313 | 0.2276 | - | - | - | - | +| 0.1515 | 314 | 0.2156 | - | - | - | - | +| 0.1520 | 315 | 0.2460 | - | - | - | - | +| 0.1525 | 316 | 0.2481 | - | - | - | - | +| 0.1529 | 317 | 0.2589 | - | - | - | - | +| 0.1534 | 318 | 0.2706 | - | - | - | - | +| 0.1539 | 319 | 0.2442 | - | - | - | - | +| 0.1544 | 320 | 0.2452 | - | - | - | - | +| 0.1549 | 321 | 0.2522 | - | - | - | - | +| 0.1554 | 322 | 0.2255 | - | - | - | - | +| 0.1558 | 323 | 0.2362 | - | - | - | - | +| 0.1563 | 324 | 0.2328 | - | - | - | - | +| 0.1568 | 325 | 0.2611 | - | - | - | - | +| 0.1573 | 326 | 0.2434 | - | - | - | - | +| 0.1578 | 327 | 0.2456 | - | - | - | - | +| 0.1583 | 328 | 0.2149 | - | - | - | - | +| 0.1587 | 329 | 0.2341 | - | - | - | - | +| 0.1592 | 330 | 0.2540 | - | - | - | - | +| 0.1597 | 331 | 0.2508 | - | - | - | - | +| 0.1602 | 332 | 0.2610 | - | - | - | - | +| 0.1607 | 333 | 0.2377 | - | - | - | - | +| 0.1611 | 334 | 0.2280 | - | - | - | - | +| 0.1616 | 335 | 0.2553 | - | - | - | - | +| 0.1621 | 336 | 0.2671 | - | - | - | - | +| 0.1626 | 337 | 0.2317 | - | - | - | - | +| 0.1631 | 338 | 0.2669 | - | - | - | - | +| 0.1636 | 339 | 0.2404 | - | - | - | - | +| 0.1640 | 340 | 0.2463 | - | - | - | - | +| 0.1645 | 341 | 0.2371 | - | - | - | - | +| 0.1650 | 342 | 0.2196 | - | - | - | - | +| 0.1655 | 343 | 0.2223 | - | - | - | - | +| 0.1660 | 344 | 0.2339 | - | - | - | - | +| 0.1665 | 345 | 0.2849 | - | - | - | - | +| 0.1669 | 346 | 0.2405 | - | - | - | - | +| 0.1674 | 347 | 0.2538 | - | - | - | - | +| 0.1679 | 348 | 0.2522 | - | - | - | - | +| 0.1684 | 349 | 0.2341 | - | - | - | - | +| 0.1689 | 350 | 0.2271 | - | - | - | - | +| 0.1694 | 351 | 0.2078 | - | - | - | - | +| 0.1698 | 352 | 0.2450 | - | - | - | - | +| 0.1703 | 353 | 0.2331 | - | - | - | - | +| 0.1708 | 354 | 0.2466 | - | - | - | - | +| 0.1713 | 355 | 0.2634 | - | - | - | - | +| 0.1718 | 356 | 0.2023 | - | - | - | - | +| 0.1722 | 357 | 0.2315 | - | - | - | - | +| 0.1727 | 358 | 0.2176 | - | - | - | - | +| 0.1732 | 359 | 0.2552 | - | - | - | - | +| 0.1737 | 360 | 0.2547 | - | - | - | - | +| 0.1742 | 361 | 0.2555 | - | - | - | - | +| 0.1747 | 362 | 0.2672 | - | - | - | - | +| 0.1751 | 363 | 0.2487 | - | - | - | - | +| 0.1756 | 364 | 0.2176 | - | - | - | - | +| 0.1761 | 365 | 0.2317 | - | - | - | - | +| 0.1766 | 366 | 0.2403 | - | - | - | - | +| 0.1771 | 367 | 0.2255 | - | - | - | - | +| 0.1776 | 368 | 0.2553 | - | - | - | - | +| 0.1780 | 369 | 0.2532 | - | - | - | - | +| 0.1785 | 370 | 0.2540 | - | - | - | - | +| 0.1790 | 371 | 0.2388 | - | - | - | - | +| 0.1795 | 372 | 0.2764 | - | - | - | - | +| 0.1800 | 373 | 0.2402 | - | - | - | - | +| 0.1804 | 374 | 0.2478 | - | - | - | - | +| 0.1809 | 375 | 0.2494 | - | - | - | - | +| 0.1814 | 376 | 0.2233 | - | - | - | - | +| 0.1819 | 377 | 0.2500 | - | - | - | - | +| 0.1824 | 378 | 0.2391 | - | - | - | - | +| 0.1829 | 379 | 0.2086 | - | - | - | - | +| 0.1833 | 380 | 0.2400 | - | - | - | - | +| 0.1838 | 381 | 0.2449 | - | - | - | - | +| 0.1843 | 382 | 0.2343 | - | - | - | - | +| 0.1848 | 383 | 0.2483 | - | - | - | - | +| 0.1853 | 384 | 0.2193 | - | - | - | - | +| 0.1858 | 385 | 0.2390 | - | - | - | - | +| 0.1862 | 386 | 0.2297 | - | - | - | - | +| 0.1867 | 387 | 0.1871 | - | - | - | - | +| 0.1872 | 388 | 0.2251 | - | - | - | - | +| 0.1877 | 389 | 0.2765 | - | - | - | - | +| 0.1882 | 390 | 0.2167 | - | - | - | - | +| 0.1886 | 391 | 0.2472 | - | - | - | - | +| 0.1891 | 392 | 0.2635 | - | - | - | - | +| 0.1896 | 393 | 0.2649 | - | - | - | - | +| 0.1901 | 394 | 0.2294 | - | - | - | - | +| 0.1906 | 395 | 0.2493 | - | - | - | - | +| 0.1911 | 396 | 0.2717 | - | - | - | - | +| 0.1915 | 397 | 0.2062 | - | - | - | - | +| 0.1920 | 398 | 0.2252 | - | - | - | - | +| 0.1925 | 399 | 0.2455 | - | - | - | - | +| 0.1930 | 400 | 0.2358 | 0.7472 | 0.4872 | 0.6599 | 0.3046 | +| 0.1935 | 401 | 0.2542 | - | - | - | - | +| 0.1940 | 402 | 0.2568 | - | - | - | - | +| 0.1944 | 403 | 0.2545 | - | - | - | - | +| 0.1949 | 404 | 0.2506 | - | - | - | - | +| 0.1954 | 405 | 0.2318 | - | - | - | - | +| 0.1959 | 406 | 0.2238 | - | - | - | - | +| 0.1964 | 407 | 0.2605 | - | - | - | - | +| 0.1969 | 408 | 0.2387 | - | - | - | - | +| 0.1973 | 409 | 0.2281 | - | - | - | - | +| 0.1978 | 410 | 0.2172 | - | - | - | - | +| 0.1983 | 411 | 0.2677 | - | - | - | - | +| 0.1988 | 412 | 0.2263 | - | - | - | - | +| 0.1993 | 413 | 0.2409 | - | - | - | - | +| 0.1997 | 414 | 0.2397 | - | - | - | - | +| 0.2002 | 415 | 0.1947 | - | - | - | - | +| 0.2007 | 416 | 0.2252 | - | - | - | - | +| 0.2012 | 417 | 0.2508 | - | - | - | - | +| 0.2017 | 418 | 0.2376 | - | - | - | - | +| 0.2022 | 419 | 0.2102 | - | - | - | - | +| 0.2026 | 420 | 0.2384 | - | - | - | - | +| 0.2031 | 421 | 0.2234 | - | - | - | - | +| 0.2036 | 422 | 0.2500 | - | - | - | - | +| 0.2041 | 423 | 0.2523 | - | - | - | - | +| 0.2046 | 424 | 0.2218 | - | - | - | - | +| 0.2051 | 425 | 0.2528 | - | - | - | - | +| 0.2055 | 426 | 0.2483 | - | - | - | - | +| 0.2060 | 427 | 0.2640 | - | - | - | - | +| 0.2065 | 428 | 0.2134 | - | - | - | - | +| 0.2070 | 429 | 0.2427 | - | - | - | - | +| 0.2075 | 430 | 0.2134 | - | - | - | - | +| 0.2079 | 431 | 0.2416 | - | - | - | - | +| 0.2084 | 432 | 0.2526 | - | - | - | - | +| 0.2089 | 433 | 0.2095 | - | - | - | - | +| 0.2094 | 434 | 0.2307 | - | - | - | - | +| 0.2099 | 435 | 0.2548 | - | - | - | - | +| 0.2104 | 436 | 0.2688 | - | - | - | - | +| 0.2108 | 437 | 0.2642 | - | - | - | - | +| 0.2113 | 438 | 0.2608 | - | - | - | - | +| 0.2118 | 439 | 0.2345 | - | - | - | - | +| 0.2123 | 440 | 0.2544 | - | - | - | - | +| 0.2128 | 441 | 0.2104 | - | - | - | - | +| 0.2133 | 442 | 0.2717 | - | - | - | - | +| 0.2137 | 443 | 0.2410 | - | - | - | - | +| 0.2142 | 444 | 0.2208 | - | - | - | - | +| 0.2147 | 445 | 0.2335 | - | - | - | - | +| 0.2152 | 446 | 0.2425 | - | - | - | - | +| 0.2157 | 447 | 0.2486 | - | - | - | - | +| 0.2162 | 448 | 0.2475 | - | - | - | - | +| 0.2166 | 449 | 0.2102 | - | - | - | - | +| 0.2171 | 450 | 0.2624 | - | - | - | - | +| 0.2176 | 451 | 0.2285 | - | - | - | - | +| 0.2181 | 452 | 0.2227 | - | - | - | - | +| 0.2186 | 453 | 0.2213 | - | - | - | - | +| 0.2190 | 454 | 0.2487 | - | - | - | - | +| 0.2195 | 455 | 0.2457 | - | - | - | - | +| 0.2200 | 456 | 0.2318 | - | - | - | - | +| 0.2205 | 457 | 0.2040 | - | - | - | - | +| 0.2210 | 458 | 0.2360 | - | - | - | - | +| 0.2215 | 459 | 0.2573 | - | - | - | - | +| 0.2219 | 460 | 0.2223 | - | - | - | - | +| 0.2224 | 461 | 0.2295 | - | - | - | - | +| 0.2229 | 462 | 0.2662 | - | - | - | - | +| 0.2234 | 463 | 0.2400 | - | - | - | - | +| 0.2239 | 464 | 0.2458 | - | - | - | - | +| 0.2244 | 465 | 0.2283 | - | - | - | - | +| 0.2248 | 466 | 0.2412 | - | - | - | - | +| 0.2253 | 467 | 0.2615 | - | - | - | - | +| 0.2258 | 468 | 0.2309 | - | - | - | - | +| 0.2263 | 469 | 0.2244 | - | - | - | - | +| 0.2268 | 470 | 0.2212 | - | - | - | - | +| 0.2272 | 471 | 0.2433 | - | - | - | - | +| 0.2277 | 472 | 0.2131 | - | - | - | - | +| 0.2282 | 473 | 0.2349 | - | - | - | - | +| 0.2287 | 474 | 0.2450 | - | - | - | - | +| 0.2292 | 475 | 0.2085 | - | - | - | - | +| 0.2297 | 476 | 0.2466 | - | - | - | - | +| 0.2301 | 477 | 0.2324 | - | - | - | - | +| 0.2306 | 478 | 0.2445 | - | - | - | - | +| 0.2311 | 479 | 0.2503 | - | - | - | - | +| 0.2316 | 480 | 0.2606 | - | - | - | - | +| 0.2321 | 481 | 0.2592 | - | - | - | - | +| 0.2326 | 482 | 0.2436 | - | - | - | - | +| 0.2330 | 483 | 0.2461 | - | - | - | - | +| 0.2335 | 484 | 0.2134 | - | - | - | - | +| 0.2340 | 485 | 0.2350 | - | - | - | - | +| 0.2345 | 486 | 0.2495 | - | - | - | - | +| 0.2350 | 487 | 0.2379 | - | - | - | - | +| 0.2355 | 488 | 0.2570 | - | - | - | - | +| 0.2359 | 489 | 0.2201 | - | - | - | - | +| 0.2364 | 490 | 0.2827 | - | - | - | - | +| 0.2369 | 491 | 0.2358 | - | - | - | - | +| 0.2374 | 492 | 0.2368 | - | - | - | - | +| 0.2379 | 493 | 0.2361 | - | - | - | - | +| 0.2383 | 494 | 0.2448 | - | - | - | - | +| 0.2388 | 495 | 0.2360 | - | - | - | - | +| 0.2393 | 496 | 0.2446 | - | - | - | - | +| 0.2398 | 497 | 0.2572 | - | - | - | - | +| 0.2403 | 498 | 0.2530 | - | - | - | - | +| 0.2408 | 499 | 0.2588 | - | - | - | - | +| 0.2412 | 500 | 0.2192 | 0.7514 | 0.4902 | 0.6614 | 0.3044 | +| 0.2417 | 501 | 0.2508 | - | - | - | - | +| 0.2422 | 502 | 0.2373 | - | - | - | - | +| 0.2427 | 503 | 0.2247 | - | - | - | - | +| 0.2432 | 504 | 0.2527 | - | - | - | - | +| 0.2437 | 505 | 0.2415 | - | - | - | - | +| 0.2441 | 506 | 0.2240 | - | - | - | - | +| 0.2446 | 507 | 0.2105 | - | - | - | - | +| 0.2451 | 508 | 0.2330 | - | - | - | - | +| 0.2456 | 509 | 0.2612 | - | - | - | - | +| 0.2461 | 510 | 0.2406 | - | - | - | - | +| 0.2465 | 511 | 0.2304 | - | - | - | - | +| 0.2470 | 512 | 0.2204 | - | - | - | - | +| 0.2475 | 513 | 0.2349 | - | - | - | - | +| 0.2480 | 514 | 0.2501 | - | - | - | - | +| 0.2485 | 515 | 0.2447 | - | - | - | - | +| 0.2490 | 516 | 0.2342 | - | - | - | - | +| 0.2494 | 517 | 0.2353 | - | - | - | - | +| 0.2499 | 518 | 0.2312 | - | - | - | - | +| 0.2504 | 519 | 0.2460 | - | - | - | - | +| 0.2509 | 520 | 0.2282 | - | - | - | - | +| 0.2514 | 521 | 0.2354 | - | - | - | - | +| 0.2519 | 522 | 0.2278 | - | - | - | - | +| 0.2523 | 523 | 0.2437 | - | - | - | - | +| 0.2528 | 524 | 0.2375 | - | - | - | - | +| 0.2533 | 525 | 0.2616 | - | - | - | - | +| 0.2538 | 526 | 0.2212 | - | - | - | - | +| 0.2543 | 527 | 0.2787 | - | - | - | - | +| 0.2547 | 528 | 0.2063 | - | - | - | - | +| 0.2552 | 529 | 0.2306 | - | - | - | - | +| 0.2557 | 530 | 0.2436 | - | - | - | - | +| 0.2562 | 531 | 0.2354 | - | - | - | - | +| 0.2567 | 532 | 0.2642 | - | - | - | - | +| 0.2572 | 533 | 0.2133 | - | - | - | - | +| 0.2576 | 534 | 0.2316 | - | - | - | - | +| 0.2581 | 535 | 0.2365 | - | - | - | - | +| 0.2586 | 536 | 0.2358 | - | - | - | - | +| 0.2591 | 537 | 0.2315 | - | - | - | - | +| 0.2596 | 538 | 0.2237 | - | - | - | - | +| 0.2601 | 539 | 0.2650 | - | - | - | - | +| 0.2605 | 540 | 0.2075 | - | - | - | - | +| 0.2610 | 541 | 0.2101 | - | - | - | - | +| 0.2615 | 542 | 0.2413 | - | - | - | - | +| 0.2620 | 543 | 0.2447 | - | - | - | - | +| 0.2625 | 544 | 0.2316 | - | - | - | - | +| 0.2630 | 545 | 0.2674 | - | - | - | - | +| 0.2634 | 546 | 0.2375 | - | - | - | - | +| 0.2639 | 547 | 0.2208 | - | - | - | - | +| 0.2644 | 548 | 0.2531 | - | - | - | - | +| 0.2649 | 549 | 0.2801 | - | - | - | - | +| 0.2654 | 550 | 0.2416 | - | - | - | - | +| 0.2658 | 551 | 0.2483 | - | - | - | - | +| 0.2663 | 552 | 0.2546 | - 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| - | - | - | +| 0.3908 | 810 | 0.2200 | - | - | - | - | +| 0.3913 | 811 | 0.2505 | - | - | - | - | +| 0.3918 | 812 | 0.2284 | - | - | - | - | +| 0.3923 | 813 | 0.2152 | - | - | - | - | +| 0.3927 | 814 | 0.2309 | - | - | - | - | +| 0.3932 | 815 | 0.2324 | - | - | - | - | +| 0.3937 | 816 | 0.2254 | - | - | - | - | +| 0.3942 | 817 | 0.2516 | - | - | - | - | +| 0.3947 | 818 | 0.2173 | - | - | - | - | +| 0.3952 | 819 | 0.2301 | - | - | - | - | +| 0.3956 | 820 | 0.2485 | - | - | - | - | +| 0.3961 | 821 | 0.2718 | - | - | - | - | +| 0.3966 | 822 | 0.2247 | - | - | - | - | +| 0.3971 | 823 | 0.2236 | - | - | - | - | +| 0.3976 | 824 | 0.2281 | - | - | - | - | +| 0.3980 | 825 | 0.2381 | - | - | - | - | +| 0.3985 | 826 | 0.2415 | - | - | - | - | +| 0.3990 | 827 | 0.2378 | - | - | - | - | +| 0.3995 | 828 | 0.2309 | - | - | - | - | +| 0.4000 | 829 | 0.2237 | - | - | - | - | +| 0.4005 | 830 | 0.2430 | - | - | - | - | +| 0.4009 | 831 | 0.2622 | - | - | - | - | +| 0.4014 | 832 | 0.2294 | - | - | - | - | +| 0.4019 | 833 | 0.2102 | - | - | - | - | +| 0.4024 | 834 | 0.2231 | - | - | - | - | +| 0.4029 | 835 | 0.2424 | - | - | - | - | +| 0.4034 | 836 | 0.2534 | - | - | - | - | +| 0.4038 | 837 | 0.2365 | - | - | - | - | +| 0.4043 | 838 | 0.2039 | - | - | - | - | +| 0.4048 | 839 | 0.2364 | - | - | - | - | +| 0.4053 | 840 | 0.2306 | - | - | - | - | +| 0.4058 | 841 | 0.2514 | - | - | - | - | +| 0.4062 | 842 | 0.2437 | - | - | - | - | +| 0.4067 | 843 | 0.2097 | - | - | - | - | +| 0.4072 | 844 | 0.2645 | - | - | - | - | +| 0.4077 | 845 | 0.2173 | - | - | - | - | +| 0.4082 | 846 | 0.2259 | - | - | - | - | +| 0.4087 | 847 | 0.2514 | - | - | - | - | +| 0.4091 | 848 | 0.2234 | - | - | - | - | +| 0.4096 | 849 | 0.2155 | - | - | - | - | +| 0.4101 | 850 | 0.2541 | - | - | - | - | +| 0.4106 | 851 | 0.2175 | - | - | - | - | +| 0.4111 | 852 | 0.2876 | - | - | - | - | +| 0.4116 | 853 | 0.2355 | - | - | - | - | +| 0.4120 | 854 | 0.2611 | - | - | - | - | +| 0.4125 | 855 | 0.2132 | - | - | - | - | +| 0.4130 | 856 | 0.2392 | - | - | - | - | +| 0.4135 | 857 | 0.2523 | - | - | - | - | +| 0.4140 | 858 | 0.2433 | - | - | - | - | +| 0.4145 | 859 | 0.2444 | - | - | - | - | +| 0.4149 | 860 | 0.1955 | - | - | - | - | +| 0.4154 | 861 | 0.2114 | - | - | - | - | +| 0.4159 | 862 | 0.2245 | - | - | - | - | +| 0.4164 | 863 | 0.2461 | - | - | - | - | +| 0.4169 | 864 | 0.2541 | - | - | - | - | +| 0.4173 | 865 | 0.2691 | - | - | - | - | +| 0.4178 | 866 | 0.2519 | - | - | - | - | +| 0.4183 | 867 | 0.2585 | - | - | - | - | +| 0.4188 | 868 | 0.2205 | - | - | - | - | +| 0.4193 | 869 | 0.2003 | - | - | - | - | +| 0.4198 | 870 | 0.2473 | - | - | - | - | +| 0.4202 | 871 | 0.2131 | - | - | - | - | +| 0.4207 | 872 | 0.2516 | - | - | - | - | +| 0.4212 | 873 | 0.2266 | - | - | - | - | +| 0.4217 | 874 | 0.2126 | - | - | - | - | +| 0.4222 | 875 | 0.2439 | - | - | - | - | +| 0.4227 | 876 | 0.2154 | - | - | - | - | +| 0.4231 | 877 | 0.2445 | - | - | - | - | +| 0.4236 | 878 | 0.2398 | - | - | - | - | +| 0.4241 | 879 | 0.2430 | - | - | - | - | +| 0.4246 | 880 | 0.2273 | - | - | - | - | +| 0.4251 | 881 | 0.2390 | - | - | - | - | +| 0.4255 | 882 | 0.2021 | - | - | - | - | +| 0.4260 | 883 | 0.2370 | - | - | - | - | +| 0.4265 | 884 | 0.2532 | - | - | - | - | +| 0.4270 | 885 | 0.2446 | - | - | - | - | +| 0.4275 | 886 | 0.2483 | - | - | - | - | +| 0.4280 | 887 | 0.2266 | - | - | - | - | +| 0.4284 | 888 | 0.2584 | - | - | - | - | +| 0.4289 | 889 | 0.2497 | - | - | - | - | +| 0.4294 | 890 | 0.2441 | - | - | - | - | +| 0.4299 | 891 | 0.2202 | - | - | - | - | +| 0.4304 | 892 | 0.2341 | - | - | - | - | +| 0.4309 | 893 | 0.2396 | - | - | - | - | +| 0.4313 | 894 | 0.2438 | - | - | - | - | +| 0.4318 | 895 | 0.2261 | - | - | - | - | +| 0.4323 | 896 | 0.2793 | - | - | - | - | +| 0.4328 | 897 | 0.2401 | - | - | - | - | +| 0.4333 | 898 | 0.2290 | - | - | - | - | +| 0.4337 | 899 | 0.2575 | - | - | - | - | +| 0.4342 | 900 | 0.2488 | 0.7521 | 0.4891 | 0.6652 | 0.3062 | +| 0.4347 | 901 | 0.2776 | - | - | - | - | +| 0.4352 | 902 | 0.2201 | - | - | - | - | +| 0.4357 | 903 | 0.2184 | - | - | - | - | +| 0.4362 | 904 | 0.2213 | - | - | - | - | +| 0.4366 | 905 | 0.2259 | - | - | - | - | +| 0.4371 | 906 | 0.2367 | - | - | - | - | +| 0.4376 | 907 | 0.2215 | - | - | - | - | +| 0.4381 | 908 | 0.1821 | - | - | - | - | +| 0.4386 | 909 | 0.2582 | - | - | - | - | +| 0.4391 | 910 | 0.2172 | - | - | - | - | +| 0.4395 | 911 | 0.2449 | - | - | - | - | +| 0.4400 | 912 | 0.2250 | - | - | - | - | +| 0.4405 | 913 | 0.2228 | - | - | - | - | +| 0.4410 | 914 | 0.2616 | - | - | - | - | +| 0.4415 | 915 | 0.2341 | - | - | - | - | +| 0.4420 | 916 | 0.2209 | - | - | - | - | +| 0.4424 | 917 | 0.2390 | - | - | - | - | +| 0.4429 | 918 | 0.2363 | - | - | - | - | +| 0.4434 | 919 | 0.2487 | - | - | - | - | +| 0.4439 | 920 | 0.2515 | - | - | - | - | +| 0.4444 | 921 | 0.2148 | - | - | - | - | +| 0.4448 | 922 | 0.2263 | - | - | - | - | +| 0.4453 | 923 | 0.2573 | - | - | - | - | +| 0.4458 | 924 | 0.2460 | - | - | - | - | +| 0.4463 | 925 | 0.2267 | - | - | - | - | +| 0.4468 | 926 | 0.2634 | - | - | - | - | +| 0.4473 | 927 | 0.1979 | - | - | - | - | +| 0.4477 | 928 | 0.2026 | - | - | - | - | +| 0.4482 | 929 | 0.2278 | - | - | - | - | +| 0.4487 | 930 | 0.2566 | - | - | - | - | +| 0.4492 | 931 | 0.2670 | - | - | - | - | +| 0.4497 | 932 | 0.2607 | - | - | - | - | +| 0.4502 | 933 | 0.2235 | - | - | - | - | +| 0.4506 | 934 | 0.2322 | - | - | - | - | +| 0.4511 | 935 | 0.2176 | - | - | - | - | +| 0.4516 | 936 | 0.2387 | - | - | - | - | +| 0.4521 | 937 | 0.2411 | - | - | - | - | +| 0.4526 | 938 | 0.2374 | - | - | - | - | +| 0.4530 | 939 | 0.2131 | - | - | - | - | +| 0.4535 | 940 | 0.2092 | - | - | - | - | +| 0.4540 | 941 | 0.2245 | - | - | - | - | +| 0.4545 | 942 | 0.2502 | - | - | - | - | +| 0.4550 | 943 | 0.2308 | - | - | - | - | +| 0.4555 | 944 | 0.2294 | - | - | - | - | +| 0.4559 | 945 | 0.2168 | - | - | - | - | +| 0.4564 | 946 | 0.2500 | - | - | - | - | +| 0.4569 | 947 | 0.1958 | - | - | - | - | +| 0.4574 | 948 | 0.2479 | - | - | - | - | +| 0.4579 | 949 | 0.2512 | - | - | - | - | +| 0.4584 | 950 | 0.2634 | - | - | - | - | +| 0.4588 | 951 | 0.2149 | - | - | - | - | +| 0.4593 | 952 | 0.2528 | - | - | - | - | +| 0.4598 | 953 | 0.2436 | - | - | - | - | +| 0.4603 | 954 | 0.2445 | - | - | - | - | +| 0.4608 | 955 | 0.2555 | - | - | - | - | +| 0.4613 | 956 | 0.2315 | - | - | - | - | +| 0.4617 | 957 | 0.2336 | - | - | - | - | +| 0.4622 | 958 | 0.2455 | - | - | - | - | +| 0.4627 | 959 | 0.2051 | - | - | - | - | +| 0.4632 | 960 | 0.2739 | - | - | - | - | +| 0.4637 | 961 | 0.2378 | - | - | - | - | +| 0.4641 | 962 | 0.2535 | - | - | - | - | +| 0.4646 | 963 | 0.2254 | - | - | - | - | +| 0.4651 | 964 | 0.2358 | - | - | - | - | +| 0.4656 | 965 | 0.2296 | - | - | - | - | +| 0.4661 | 966 | 0.2711 | - | - | - | - | +| 0.4666 | 967 | 0.2399 | - | - | - | - | +| 0.4670 | 968 | 0.2165 | - | - | - | - | +| 0.4675 | 969 | 0.2573 | - | - | - | - | +| 0.4680 | 970 | 0.2327 | - | - | - | - | +| 0.4685 | 971 | 0.2294 | - | - | - | - | +| 0.4690 | 972 | 0.2448 | - | - | - | - | +| 0.4695 | 973 | 0.2186 | - | - | - | - | +| 0.4699 | 974 | 0.2612 | - | - | - | - | +| 0.4704 | 975 | 0.2229 | - | - | - | - | +| 0.4709 | 976 | 0.2226 | - | - | - | - | +| 0.4714 | 977 | 0.2237 | - | - | - | - | +| 0.4719 | 978 | 0.2431 | - | - | - | - | +| 0.4723 | 979 | 0.2208 | - | - | - | - | +| 0.4728 | 980 | 0.2555 | - | - | - | - | +| 0.4733 | 981 | 0.2688 | - | - | - | - | +| 0.4738 | 982 | 0.2363 | - | - | - | - | +| 0.4743 | 983 | 0.2451 | - | - | - | - | +| 0.4748 | 984 | 0.2664 | - | - | - | - | +| 0.4752 | 985 | 0.2352 | - | - | - | - | +| 0.4757 | 986 | 0.2436 | - | - | - | - | +| 0.4762 | 987 | 0.2515 | - | - | - | - | +| 0.4767 | 988 | 0.2205 | - | - | - | - | +| 0.4772 | 989 | 0.2187 | - | - | - | - | +| 0.4777 | 990 | 0.2214 | - | - | - | - | +| 0.4781 | 991 | 0.2122 | - | - | - | - | +| 0.4786 | 992 | 0.2562 | - | - | - | - | +| 0.4791 | 993 | 0.2531 | - | - | - | - | +| 0.4796 | 994 | 0.2311 | - | - | - | - | +| 0.4801 | 995 | 0.2564 | - | - | - | - | +| 0.4806 | 996 | 0.2559 | - | - | - | - | +| 0.4810 | 997 | 0.2666 | - | - | - | - | +| 0.4815 | 998 | 0.2428 | - | - | - | - | +| 0.4820 | 999 | 0.2234 | - | - | - | - | +| 0.4825 | 1000 | 0.2284 | 0.7559 | 0.4891 | 0.6636 | 0.3132 | + +
+ +### Training Time +- **Training**: 1.5 days +- **Evaluation**: 3.9 hours +- **Total**: 1.7 days + +### Framework Versions +- Python: 3.10.12 +- Sentence Transformers: 5.5.1 +- Transformers: 5.5.4 +- PyTorch: 2.8.0+cu128 +- Accelerate: 1.13.0 +- Datasets: 4.8.4 +- Tokenizers: 0.22.2 + +## 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} +} +``` + +#### MinMaxDistillKLDivLoss +```bibtex +@misc{lin2020distillingdenserepresentationsranking, + title={Distilling Dense Representations for Ranking using Tightly-Coupled Teachers}, + author={Sheng-Chieh Lin and Jheng-Hong Yang and Jimmy Lin}, + year={2020}, + eprint={2010.11386}, + archivePrefix={arXiv}, + primaryClass={cs.IR}, + url={https://arxiv.org/abs/2010.11386}, +} +``` + + + + + + \ No newline at end of file