metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:41881
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-m3
widget:
  - source_sentence: 얕은 기초의 허용 수평 지지력을 결정할 때 고려해야 하는 주요 요소는 무엇인가?
    sentences:
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        ④계측기간과빈도는측정하고자하는계측값의변화정도및변화의지속시간과관련되며비탈면의파괴속도가빠른경우또는변화가있는경우에는측정빈도를높여측정하여야하고변화가장기간지속되는경우에는측정기간도이에맞춰측정하여야한다
        .⑤깎기비탈면의계측기설치위치는비탈면자체및불연속면등에의해이완된암반의거동을충분히고려하고유사한조건하에서계측예를참고로하여선정하여
      - >-
        건설기준제정또는개정에따른경과조치이기준은발간시점부터사용하며,이미시행중에있는설계용역이나건설공사는발주기관의장이필요하다고인정하는경우종전에적용하고있는기준을그대로사용할수있습니다.
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        (2)강도가크고불연속면의간격이넓으며틈새가작은암반일경우에는양호한암반으로판정하고기초의지지력을산정한다
        .(3)강도가작고불연속면의간격이매우좁으며풍화상태가심하거나세편상태인암반은불량한암반으로판정하고기초의지지력을산정한다
        .(4)암반의상태를정량적으로등급화하고 ,그에따라등급별로암반의극한지지력을정하여기초를설계할수있다 .(5)암반판정이모호한경우
        ,지질학적으로해명이안되는경우 ,암석이심하게교란된경우 ,절리나층리가지표의경사와유사한경우 ,암의표면이
        30°이상경사진경우에는암반의지지력결정에유의하여야한다 .4.1.7수평지지력(1)얕은기초의허용수평지지력은
        기초저면의전단저항력을적정안전율로나눈값으로하나
        ,지표면근처에서안정된지지층을확보할수있는경우에는기초전면에작용하는수동토압을안전율로나눈값을적용하여허용수평지지력으로 고려할수있으며 ,다만
        ,수동토압이발휘되기위해서는주동변위의 2~20배가발생하여야하므로수동토압을전부보는것에주의가요구된다 .
  - source_sentence: 33.3품질및성능시험 섹션에서는 어떤 종류의 시험이 포함되어 있습니까?
    sentences:
      - >-
        제정:2016년6월30일심의:중앙건설기술심의위원회소관부서:국토교통부기술혁신과관련단체:한국지반공학회개정:2021년5월12일자문검토:국가건설기준센터
        건설기준위원회작성기관:한국지반공학회건설기준
        주요내용제정또는개정(년.월)도로교설계기준∙그동안제개정된각종규칙,기준및최근연구성과등을검토반영,심미적디자인추구,철근콘크리트기둥의연성도내진설계법부록도입함.개정(2010)건축구조설계기준
        ∙건축구조설계기준제정제정(2005.4)건축구조설계기준 ∙재검토기한신설등개정개정(2009.8)건축구조기준
        ∙부분개정개정(2009.12)건축구조기준 ∙재검토기한의연도수정등개정개정(2013.12)건축구조기준 ∙특정한지형조건의기본지상적설하중
        등개정개정(2015.10)건축구조기준∙성능설계법도입및돌발상황에의한하중추가등기준전반에대한최근연구결과및개선된공법반영개정(2016.
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        연약지반설계일반 KDS113005:2021KDS110000지반설계기준
        11.일반사항1.1목적(1)이기준은지반에건설되는구조물에대하여안정성을확보하지못하는연약지반의설계기준을제시하는것을목적으로한다
        .1.2적용범위(1)이기준은지반에건설되는구조물에대하여안정성을확보하지못하는연약지반의보강이나대책공법설계에적용된다
        .(2)연약지반은쌓기규모나구조물목적에따라상대적인의미로평가되며
        ,원지반이건설되는구조물에대해안정성을만족하지못할경우연약지반으로취급하여지반보강이나대책을강구하여야한다 .(3)연약지반대책공법으로서
        지반개량을시행할경우기초지반의특성 ,구조물의종류와크기 ,시공기간과난이도 ,경제성 ,환경영향등을고려하여적합한개량공법을선정하여야한다 .
      - 2조사 22.3계획 23.재료 33.1일반사항  33.2재료특성  33.3품질및성능시험  34.설계 34.1일반사항  34.
  - source_sentence: 비탈면 녹화의 목적은 무엇인가요?
    sentences:
      - 재료24.설계24.1격자블록및돌(블록 )붙이기24.2콘크리트뿜어붙이기 (숏크리트 )44.3비탈면녹화5
      - >-
        개정(1986.11)구조물기초설계기준∙그간의지반공학분야의기술발전을반영하고,관련기준의개정에따른내용조정등수정하고국제표준단위인미터법과SI단위로통일개정.개정(2002.12)구조물기초설계기준
        ∙구조물기초설계기준개정개정(2008.11)구조물기초설계기준∙토목,건축공사등의건설구조물기초설계를국가의설계기준형식에부합시키고,신기술,신공법등의시대적변화를적용시키며설계자의창의적설계를유도할수있도록개정.개정(2014.2)구조물기초설계기준∙도심지지반침하현상의지속적발생으로국민불안이증대하고있으나,다소미흡한지반침하와관련된조사및설계관련하여공동및싱크홀을조사하도록철도설계기준개정사항(2015)을반영하여개정.부분개정(2016.5)KDS115030:2016∙건설기준코드체계전환에따라기존의코드에서진동기계기초에해당되는부분을정비함.제정(2016.6)KDS115030:2016∙한국산업표준과건설기준부합화에따라수정함수정(2018.7)KDS115030:2021∙건설기준코드의통일성을위해작성지침과부합화,
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        시공기면까지의높이(H)일반철도 고속철도일반철도 고속철도H<5.0m H<3.0m 1:1.5
        1:1.85.0m≦H<10.0m3.0m≦H<9.0m1:1.8 1:1.810.0m≦H<15.0m9.0m≦H<15.0m1:2.0
        1:2.0H≧15.0mH≧15.0m 1:2.3
        1:2.3표4.1-2쌓기비탈면(철도)의표준경사가.쌓기비탈면의최종기울기는쌓기지지지반의형상및강도등을고려한비탈면안정을해석하여결정해야하며실제시공시변경된사항이있을경우에는반드시재설계를해야한다
        .나.소단은일반철도의경우시공기면에서매5m마다설치하고 ,고속철도는상부노반쌓기와하부노반쌓기의경계에설치하고다음 6.0m높이마다설치한다
        .이때일반철도와고속철도의소단폭은1.5m로하고외측으로향하는 5%의횡단기울기를둔다.소단의위치가쌓기지지지반면에서
        3.0m이하인경우에는그소단을생략한다 .
  - source_sentence: 비탈면이나 옹벽 구조물의 설계 시 허용되는 변형은 무엇입니까?
    sentences:
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        4.1.5돌(블록 )붙이기공법의배수시설(1)돌(블록
        )붙이기를한경우배면의지하수배수를위해일정두께를자갈로뒤채움하며,세립분의유출위험이있는경우에는필터재료를설치한다
        .(2)비탈면배면으로부터유입되는지하수또는표면에서유입되는물을배수시키기위해설치하는배수시설은다음과같다 .
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        낙석 ‧토석대책시설 KDS117020:2020KDS110000지반설계기준
        11.일반사항1.1목적(1)이기준은낙석‧토석대책시설에대한일반적인설계기준과설계방법을제시하여낙석과토석류로인해발생가능한인명과시설물피해를방지또는저감하는것을목적으로한다
        .1.2적용범위(1)이기준은낙석과토석류대책을위한낙석방지망 ,낙석방지울타리 ,낙석방지옹벽 ,피암터널 ,토석류대책시설의설계에적용한다
        .1.3참고기준1.3.1관련법규내용없음1.3.2관련기준내용없음1.4용어의정의내용없음1.5시설물의구성(1)낙석과토석류대책시설은낙석방지망
        ,낙석방지울타리 ,낙석방지옹벽 ,피암터널 ,토석류대책시설등으로구성된다 .(2)낙석과토석류대책시설중토석류대책시설은발생억제시설
        ,흐름완화및제어시설 ,퇴적및유도시설로나눌수있다 .
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        4.2기초구조물의설계거동한계4.2.1기능수행수준에 따른설계거동한계(1)비탈면이나옹벽구조물은부분적인항복과소성변형을허용할수있으나
        ,주변구조물및부속시설들은탄성또는탄성에준하는거동을허용할수있으므로기초내진설계시유의하여야한다
        .(2)얕은기초및깊은기초는지진시그주변지반의소성거동은허용할수있으나
        ,기초구조물자체와모든상부구조물및부속시설이탄성또는탄성에준하는거동을허용한다 .
  - source_sentence: 억지말뚝으로 보강된 비탈면의 내진설계 결정 기준은 무엇인가요?
    sentences:
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        지반계측 KDS111015:2021KDS110000지반설계기준 344.2.2.2 계측기기운용기법(1)
        인력에의한계측기기운용과자동화장비에의한운용기법으로크게구분할수있으며, 붕괴및활동의진행특성, 계측대상시설물의중요도 , 피해발생시영향,
        경제성, 계측빈도등을고려하여운용기법을선택하여야한다.4.2.2.3 일반적인계측관리의자동화(1)
        기록지또는저장장치에계측자료를기록할때까지를자동화하고 , 그후의처리는별도로컴퓨터로실시하는반자동계측관리기법과 ,
        자료수집ㆍ해석ㆍ그래프화까지를유선ㆍ무선으로온라인화된시스템으로일관하여실시하는전자동계측관리기법및상기의두가지방법을병용하는기법으로구분하며
        , 계측대상조건을고려하여운용기법을선정하여야한다.4.2.2.4 피해방지및최소화방법(1) 조기에징후를감지하는것이중요하고 ,
        모니터링과동시에신속하게그정보를전달ㆍ처리하는것이필요하며계측자료의수집ㆍ처리ㆍ해석까지를일괄하여처리하는자동화기술을사용하는것을고려하여야한다.
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        (9)매립지반에설치하는기초는특별히주의하여야하는데 ,기초하부의접지압력은가급적균등하게분포하도록설계하고
        ,접지압력의분포는흙또는암,그리고구조물의특성에따라달라지며 ,토질역학및암석역학적원리들과일치하여야한다 .2.조사및계획∙내용없음
        .3.재료∙내용없음 .4.설계4.1지지력산정4.1.1지지력산정을위한고려사항(1)기초설계시시추조사
        ,현장및실내시험을통하여지반특성을파악한후지지력을산정한다
        .그러나상재하중이작은구조물또는가설구조물의기초는인근구조물의경험값,기초설계및시공성과 ,현장시험자료를통하여지지력을추정할수있다 .
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        비탈면보강공법 KDS117015:2020KDS110000지반설계기준 9입시켜야한다
        .4.3.3내진설계여부(1)억지말뚝으로보강된비탈면의내진설계는보강되지않은비탈면의내진설계여부에따라결정하며 ,KDS
        119000의비탈면내진등급을참고한다 .(2)억지말뚝으로보강된비탈면의지진시안정해석은 4.4및KDS 119000을참조한다
        .4.4지진시안정해석4.4.1네일(1)지진시네일로보강된비탈면의안정해석에서는내적안정과외적안정성을검토한다
        .(2)네일로보강된비탈면의지진시안정해석에서고려하는지진하중은파괴토체의자중과지진계수 (Am)를곱한등가지진력으로 고려하며
        ,파괴토체의중심에횡방향으로작용시킨다 .(3)지진에의한지진계수는 KDS 119000(1.6.5)에서제시하는유효수평지반가
        속도(S)를이용하여산정한다 .4.4.2록볼트(1)지진시록볼트로보강된비탈면의안정해석에서는외적안정성을검토한다
        .(2)록볼트로보강된비탈면의지진시안정해석에서고려하는지진하중은파괴토체의자중과지진계수 (Am)를곱한등가지진력으로 고려하며
        ,파괴토체의중심에횡방향으로작용시킨다 .
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 based on BAAI/bge-m3
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: Unknown
          type: unknown
        metrics:
          - type: cosine_accuracy@1
            value: 0.6014324428958575
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7603561749903214
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.8102981029810298
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8654665118079752
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6014324428958575
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2534520583301071
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16205962059620596
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08654665118079752
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.6014324428958575
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.7603561749903214
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8102981029810298
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.8654665118079752
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.7346720961043999
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6927054335736117
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6985716777061386
            name: Cosine Map@100

SentenceTransformer based on BAAI/bge-m3

This is a sentence-transformers model finetuned from BAAI/bge-m3. It maps sentences & paragraphs to a 1024-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: BAAI/bge-m3
Maximum Sequence Length: 8192 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity

Model Sources

Documentation: Sentence Transformers Documentation
Repository: Sentence Transformers on GitHub
Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 8192, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Day1Kim/bge-m3_kicon_15")
# Run inference
sentences = [
    '억지말뚝으로 보강된 비탈면의 내진설계 결정 기준은 무엇인가요?',
    '비탈면보강공법 KDS117015:2020KDS110000지반설계기준 9입시켜야한다 .4.3.3내진설계여부(1)억지말뚝으로보강된비탈면의내진설계는보강되지않은비탈면의내진설계여부에따라결정하며 ,KDS 119000의비탈면내진등급을참고한다 .(2)억지말뚝으로보강된비탈면의지진시안정해석은 4.4및KDS 119000을참조한다 .4.4지진시안정해석4.4.1네일(1)지진시네일로보강된비탈면의안정해석에서는내적안정과외적안정성을검토한다 .(2)네일로보강된비탈면의지진시안정해석에서고려하는지진하중은파괴토체의자중과지진계수 (Am)를곱한등가지진력으로 고려하며 ,파괴토체의중심에횡방향으로작용시킨다 .(3)지진에의한지진계수는 KDS 119000(1.6.5)에서제시하는유효수평지반가 속도(S)를이용하여산정한다 .4.4.2록볼트(1)지진시록볼트로보강된비탈면의안정해석에서는외적안정성을검토한다 .(2)록볼트로보강된비탈면의지진시안정해석에서고려하는지진하중은파괴토체의자중과지진계수 (Am)를곱한등가지진력으로 고려하며 ,파괴토체의중심에횡방향으로작용시킨다 .',
    '지반계측 KDS111015:2021KDS110000지반설계기준 344.2.2.2 계측기기운용기법(1) 인력에의한계측기기운용과자동화장비에의한운용기법으로크게구분할수있으며, 붕괴및활동의진행특성, 계측대상시설물의중요도 , 피해발생시영향, 경제성, 계측빈도등을고려하여운용기법을선택하여야한다.4.2.2.3 일반적인계측관리의자동화(1) 기록지또는저장장치에계측자료를기록할때까지를자동화하고 , 그후의처리는별도로컴퓨터로실시하는반자동계측관리기법과 , 자료수집ㆍ해석ㆍ그래프화까지를유선ㆍ무선으로온라인화된시스템으로일관하여실시하는전자동계측관리기법및상기의두가지방법을병용하는기법으로구분하며 , 계측대상조건을고려하여운용기법을선정하여야한다.4.2.2.4 피해방지및최소화방법(1) 조기에징후를감지하는것이중요하고 , 모니터링과동시에신속하게그정보를전달ㆍ처리하는것이필요하며계측자료의수집ㆍ처리ㆍ해석까지를일괄하여처리하는자동화기술을사용하는것을고려하여야한다.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Evaluated with InformationRetrievalEvaluator

Metric	Value
cosine_accuracy@1	0.6014
cosine_accuracy@3	0.7604
cosine_accuracy@5	0.8103
cosine_accuracy@10	0.8655
cosine_precision@1	0.6014
cosine_precision@3	0.2535
cosine_precision@5	0.1621
cosine_precision@10	0.0865
cosine_recall@1	0.6014
cosine_recall@3	0.7604
cosine_recall@5	0.8103
cosine_recall@10	0.8655
cosine_ndcg@10	0.7347
cosine_mrr@10	0.6927
cosine_map@100	0.6986

Training Details

Training Dataset

Unnamed Dataset

Size: 41,881 training samples
Columns: sentence_0 and sentence_1
Approximate statistics based on the first 1000 samples:
sentence_0 sentence_1
type string string
details
min: 10 tokens
mean: 24.26 tokens
max: 50 tokens

min: 15 tokens
mean: 237.12 tokens
max: 424 tokens

	sentence_0	sentence_1
type	string	string
details	min: 10 tokens mean: 24.26 tokens max: 50 tokens	min: 15 tokens mean: 237.12 tokens max: 424 tokens

Samples:

sentence_0	sentence_1
`KDS 10 00 00 설계기준은 어느 나라의 표준인가요?`	`KDS 10 00 00설계기준 Korean Design StandardKDS 10 00 00 : 2021공통설계기준.2021년5월12일개정http://www.kcsc.re.kr`
`KDS 10 00 00 설계기준은 최근에 언제 개정되었나요?`	`KDS 10 00 00설계기준 Korean Design StandardKDS 10 00 00 : 2021공통설계기준.2021년5월12일개정http://www.kcsc.re.kr`
`KDS 10 10 00 설계총칙 문서는 어떤 분야의 설계 기준을 다루고 있나요?`	공통설계기준체계KDS 10 10 00 설계총칙 `21.05

Loss: MultipleNegativesRankingLoss with these parameters:

{
    "scale": 20.0,
    "similarity_fct": "cos_sim"
}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
num_train_epochs: 15
multi_dataset_batch_sampler: round_robin

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 32
per_device_eval_batch_size: 32
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: 5e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1
num_train_epochs: 15
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.0
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: False
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}
tp_size: 0
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: 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
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: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss	cosine_ndcg@10
0.0115	15	-	0.5942
0.0229	30	-	0.5969
0.0344	45	-	0.6025
0.0458	60	-	0.6068
0.0573	75	-	0.6108
0.0688	90	-	0.6167
0.0115	15	-	0.6170
0.0229	30	-	0.6176
0.0344	45	-	0.6189
0.0458	60	-	0.6207
0.0573	75	-	0.6225
0.0688	90	-	0.6264
0.0802	105	-	0.6297
0.0917	120	-	0.6315
0.1031	135	-	0.6334
0.1146	150	-	0.6380
0.1261	165	-	0.6426
0.1375	180	-	0.6464
0.1490	195	-	0.6507
0.1604	210	-	0.6531
0.1719	225	-	0.6575
0.1833	240	-	0.6626
0.1948	255	-	0.6646
0.2063	270	-	0.6650
0.2177	285	-	0.6667
0.2292	300	-	0.6672
0.2406	315	-	0.6697
0.2521	330	-	0.6720
0.2636	345	-	0.6745
0.2750	360	-	0.6745
0.2865	375	-	0.6780
0.2979	390	-	0.6796
0.3094	405	-	0.6779
0.3209	420	-	0.6770
0.3323	435	-	0.6803
0.3438	450	-	0.6830
0.3552	465	-	0.6824
0.3667	480	-	0.6846
0.3782	495	-	0.6815
0.3820	500	0.1899	-
0.3896	510	-	0.6831
0.4011	525	-	0.6835
0.4125	540	-	0.6852
0.4240	555	-	0.6878
0.4354	570	-	0.6889
0.4469	585	-	0.6848
0.4584	600	-	0.6852
0.4698	615	-	0.6870
0.4813	630	-	0.6925
0.4927	645	-	0.6954
0.5042	660	-	0.6881
0.5157	675	-	0.6839
0.5271	690	-	0.6849
0.5386	705	-	0.6934
0.5500	720	-	0.6937
0.5615	735	-	0.6937
0.5730	750	-	0.6870
0.5844	765	-	0.6952
0.5959	780	-	0.7008
0.6073	795	-	0.6957
0.6188	810	-	0.6927
0.6303	825	-	0.6899
0.6417	840	-	0.6874
0.6532	855	-	0.6927
0.6646	870	-	0.6991
0.6761	885	-	0.7024
0.6875	900	-	0.7010
0.6990	915	-	0.6943
0.7105	930	-	0.6987
0.7219	945	-	0.7028
0.7334	960	-	0.7004
0.7448	975	-	0.6997
0.7563	990	-	0.6993
0.7639	1000	0.0837	-
0.7678	1005	-	0.6956
0.7792	1020	-	0.6913
0.7907	1035	-	0.6930
0.8021	1050	-	0.6959
0.8136	1065	-	0.6968
0.8251	1080	-	0.7015
0.8365	1095	-	0.6982
0.8480	1110	-	0.7108
0.8594	1125	-	0.7068
0.8709	1140	-	0.7037
0.8824	1155	-	0.7031
0.8938	1170	-	0.6973
0.9053	1185	-	0.6960
0.9167	1200	-	0.6969
0.9282	1215	-	0.6936
0.9396	1230	-	0.6976
0.9511	1245	-	0.7032
0.9626	1260	-	0.7066
0.9740	1275	-	0.7051
0.9855	1290	-	0.7053
0.9969	1305	-	0.7086
1.0	1309	-	0.7075
1.0084	1320	-	0.7063
1.0199	1335	-	0.7079
1.0313	1350	-	0.7112
1.0428	1365	-	0.7075
1.0542	1380	-	0.7008
1.0657	1395	-	0.7020
1.0772	1410	-	0.7047
1.0886	1425	-	0.7051
1.1001	1440	-	0.7084
1.1115	1455	-	0.7112
1.1230	1470	-	0.7109
1.1345	1485	-	0.7037
1.1459	1500	0.0632	0.7033
1.1574	1515	-	0.7158
1.1688	1530	-	0.7076
1.1803	1545	-	0.7115
1.1917	1560	-	0.7109
1.2032	1575	-	0.7062
1.2147	1590	-	0.7047
1.2261	1605	-	0.7033
1.2376	1620	-	0.7055
1.2490	1635	-	0.7149
1.2605	1650	-	0.7155
1.2720	1665	-	0.7042
1.2834	1680	-	0.6972
1.2949	1695	-	0.7071
1.3063	1710	-	0.7107
1.3178	1725	-	0.7113
1.3293	1740	-	0.7115
1.3407	1755	-	0.7022
1.3522	1770	-	0.7073
1.3636	1785	-	0.6989
1.3751	1800	-	0.6995
1.3866	1815	-	0.7021
1.3980	1830	-	0.7092
1.4095	1845	-	0.7099
1.4209	1860	-	0.7040
1.4324	1875	-	0.7041
1.4439	1890	-	0.7111
1.4553	1905	-	0.7103
1.4668	1920	-	0.7030
1.4782	1935	-	0.7058
1.4897	1950	-	0.7045
1.5011	1965	-	0.7026
1.5126	1980	-	0.7142
1.5241	1995	-	0.7087
1.5279	2000	0.0573	-
1.5355	2010	-	0.7085
1.5470	2025	-	0.7108
1.5584	2040	-	0.7049
1.5699	2055	-	0.7013
1.5814	2070	-	0.6987
1.5928	2085	-	0.7116
1.6043	2100	-	0.7049
1.6157	2115	-	0.6990
1.6272	2130	-	0.6954
1.6387	2145	-	0.7007
1.6501	2160	-	0.6951
1.6616	2175	-	0.7014
1.6730	2190	-	0.7083
1.6845	2205	-	0.7091
1.6960	2220	-	0.7105
1.7074	2235	-	0.7054
1.7189	2250	-	0.6979
1.7303	2265	-	0.6966
1.7418	2280	-	0.6981
1.7532	2295	-	0.7050
1.7647	2310	-	0.7051
1.7762	2325	-	0.7086
1.7876	2340	-	0.7045
1.7991	2355	-	0.6971
1.8105	2370	-	0.7021
1.8220	2385	-	0.7037
1.8335	2400	-	0.6947
1.8449	2415	-	0.6948
1.8564	2430	-	0.6956
1.8678	2445	-	0.6959
1.8793	2460	-	0.6884
1.8908	2475	-	0.7084
1.9022	2490	-	0.7088
1.9099	2500	0.0571	-
1.9137	2505	-	0.7027
1.9251	2520	-	0.6953
1.9366	2535	-	0.7057
1.9481	2550	-	0.7064
1.9595	2565	-	0.7097
1.9710	2580	-	0.7077
1.9824	2595	-	0.6990
1.9939	2610	-	0.7011
2.0	2618	-	0.7042
2.0053	2625	-	0.7002
2.0168	2640	-	0.7037
2.0283	2655	-	0.7053
2.0397	2670	-	0.7081
2.0512	2685	-	0.6980
2.0626	2700	-	0.7129
2.0741	2715	-	0.7139
2.0856	2730	-	0.6974
2.0970	2745	-	0.7056
2.1085	2760	-	0.7145
2.1199	2775	-	0.7088
2.1314	2790	-	0.7063
2.1429	2805	-	0.7128
2.1543	2820	-	0.7086
2.1658	2835	-	0.7133
2.1772	2850	-	0.7067
2.1887	2865	-	0.7056
2.2002	2880	-	0.6942
2.2116	2895	-	0.6983
2.2231	2910	-	0.7075
2.2345	2925	-	0.7027
2.2460	2940	-	0.7078
2.2574	2955	-	0.7066
2.2689	2970	-	0.7042
2.2804	2985	-	0.7107
2.2918	3000	0.0421	0.7122
2.3033	3015	-	0.7119
2.3147	3030	-	0.7157
2.3262	3045	-	0.7152
2.3377	3060	-	0.7153
2.3491	3075	-	0.7092
2.3606	3090	-	0.7059
2.3720	3105	-	0.7070
2.3835	3120	-	0.7090
2.3950	3135	-	0.7058
2.4064	3150	-	0.7060
2.4179	3165	-	0.7161
2.4293	3180	-	0.7147
2.4408	3195	-	0.7087
2.4523	3210	-	0.7083
2.4637	3225	-	0.7085
2.4752	3240	-	0.7078
2.4866	3255	-	0.7093
2.4981	3270	-	0.7111
2.5095	3285	-	0.7138
2.5210	3300	-	0.7186
2.5325	3315	-	0.7065
2.5439	3330	-	0.7076
2.5554	3345	-	0.7044
2.5668	3360	-	0.7054
2.5783	3375	-	0.7098
2.5898	3390	-	0.7075
2.6012	3405	-	0.7051
2.6127	3420	-	0.7114
2.6241	3435	-	0.7130
2.6356	3450	-	0.7098
2.6471	3465	-	0.7138
2.6585	3480	-	0.7067
2.6700	3495	-	0.7011
2.6738	3500	0.0387	-
2.6814	3510	-	0.7088
2.6929	3525	-	0.7114
2.7044	3540	-	0.7046
2.7158	3555	-	0.7083
2.7273	3570	-	0.7145
2.7387	3585	-	0.7135
2.7502	3600	-	0.7140
2.7617	3615	-	0.7161
2.7731	3630	-	0.7093
2.7846	3645	-	0.7060
2.7960	3660	-	0.7032
2.8075	3675	-	0.7097
2.8189	3690	-	0.7094
2.8304	3705	-	0.7124
2.8419	3720	-	0.7137
2.8533	3735	-	0.7165
2.8648	3750	-	0.7195
2.8762	3765	-	0.7140
2.8877	3780	-	0.7117
2.8992	3795	-	0.7046
2.9106	3810	-	0.7089
2.9221	3825	-	0.7115
2.9335	3840	-	0.7107
2.9450	3855	-	0.7093
2.9565	3870	-	0.7065
2.9679	3885	-	0.7089
2.9794	3900	-	0.7084
2.9908	3915	-	0.7153
3.0	3927	-	0.7142
3.0023	3930	-	0.7128
3.0138	3945	-	0.7055
3.0252	3960	-	0.7021
3.0367	3975	-	0.7030
3.0481	3990	-	0.7064
3.0558	4000	0.0402	-
3.0596	4005	-	0.7115
3.0710	4020	-	0.7108
3.0825	4035	-	0.7114
3.0940	4050	-	0.7112
3.1054	4065	-	0.7050
3.1169	4080	-	0.7099
3.1283	4095	-	0.7090
3.1398	4110	-	0.7036
3.1513	4125	-	0.7063
3.1627	4140	-	0.7069
3.1742	4155	-	0.7085
3.1856	4170	-	0.7080
3.1971	4185	-	0.7123
3.2086	4200	-	0.7091
3.2200	4215	-	0.7017
3.2315	4230	-	0.7062
3.2429	4245	-	0.7166
3.2544	4260	-	0.7156
3.2659	4275	-	0.7132
3.2773	4290	-	0.7121
3.2888	4305	-	0.7126
3.3002	4320	-	0.7089
3.3117	4335	-	0.7097
3.3231	4350	-	0.7124
3.3346	4365	-	0.7084
3.3461	4380	-	0.7076
3.3575	4395	-	0.7115
3.3690	4410	-	0.7103
3.3804	4425	-	0.7162
3.3919	4440	-	0.7190
3.4034	4455	-	0.7231
3.4148	4470	-	0.7247
3.4263	4485	-	0.7193
3.4377	4500	0.0312	0.7155
3.4492	4515	-	0.7172
3.4607	4530	-	0.7165
3.4721	4545	-	0.7133
3.4836	4560	-	0.7122
3.4950	4575	-	0.7147
3.5065	4590	-	0.7086
3.5180	4605	-	0.7102
3.5294	4620	-	0.7145
3.5409	4635	-	0.7151
3.5523	4650	-	0.7159
3.5638	4665	-	0.7111
3.5752	4680	-	0.7177
3.5867	4695	-	0.7188
3.5982	4710	-	0.7112
3.6096	4725	-	0.7117
3.6211	4740	-	0.7045
3.6325	4755	-	0.7083
3.6440	4770	-	0.7081
3.6555	4785	-	0.7041
3.6669	4800	-	0.7076
3.6784	4815	-	0.7099
3.6898	4830	-	0.7081
3.7013	4845	-	0.7084
3.7128	4860	-	0.7098
3.7242	4875	-	0.7111
3.7357	4890	-	0.7121
3.7471	4905	-	0.7122
3.7586	4920	-	0.7140
3.7701	4935	-	0.7193
3.7815	4950	-	0.7163
3.7930	4965	-	0.7115
3.8044	4980	-	0.7098
3.8159	4995	-	0.7099
3.8197	5000	0.0296	-
3.8273	5010	-	0.7140
3.8388	5025	-	0.7104
3.8503	5040	-	0.7102
3.8617	5055	-	0.7136
3.8732	5070	-	0.7165
3.8846	5085	-	0.7202
3.8961	5100	-	0.7236
3.9076	5115	-	0.7223
3.9190	5130	-	0.7208
3.9305	5145	-	0.7137
3.9419	5160	-	0.7153
3.9534	5175	-	0.7193
3.9649	5190	-	0.7209
3.9763	5205	-	0.7215
3.9878	5220	-	0.7195
3.9992	5235	-	0.7216
4.0	5236	-	0.7211
4.0107	5250	-	0.7189
4.0222	5265	-	0.7158
4.0336	5280	-	0.7164
4.0451	5295	-	0.7145
4.0565	5310	-	0.7132
4.0680	5325	-	0.7167
4.0794	5340	-	0.7122
4.0909	5355	-	0.7161
4.1024	5370	-	0.7155
4.1138	5385	-	0.7080
4.1253	5400	-	0.6994
4.1367	5415	-	0.7065
4.1482	5430	-	0.7096
4.1597	5445	-	0.7142
4.1711	5460	-	0.7203
4.1826	5475	-	0.7210
4.1940	5490	-	0.7127
4.2017	5500	0.0258	-
4.2055	5505	-	0.7059
4.2170	5520	-	0.7090
4.2284	5535	-	0.7094
4.2399	5550	-	0.7126
4.2513	5565	-	0.7100
4.2628	5580	-	0.7181
4.2743	5595	-	0.7236
4.2857	5610	-	0.7214
4.2972	5625	-	0.7133
4.3086	5640	-	0.7182
4.3201	5655	-	0.7175
4.3316	5670	-	0.7167
4.3430	5685	-	0.7156
4.3545	5700	-	0.7159
4.3659	5715	-	0.7191
4.3774	5730	-	0.7121
4.3888	5745	-	0.7075
4.4003	5760	-	0.7054
4.4118	5775	-	0.7122
4.4232	5790	-	0.7159
4.4347	5805	-	0.7132
4.4461	5820	-	0.7145
4.4576	5835	-	0.7158
4.4691	5850	-	0.7162
4.4805	5865	-	0.7189
4.4920	5880	-	0.7171
4.5034	5895	-	0.7170
4.5149	5910	-	0.7151
4.5264	5925	-	0.7155
4.5378	5940	-	0.7163
4.5493	5955	-	0.7182
4.5607	5970	-	0.7240
4.5722	5985	-	0.7203
4.5837	6000	0.0274	0.7194
4.5951	6015	-	0.7241
4.6066	6030	-	0.7220
4.6180	6045	-	0.7220
4.6295	6060	-	0.7168
4.6409	6075	-	0.7168
4.6524	6090	-	0.7161
4.6639	6105	-	0.7182
4.6753	6120	-	0.7144
4.6868	6135	-	0.7143
4.6982	6150	-	0.7151
4.7097	6165	-	0.7179
4.7212	6180	-	0.7196
4.7326	6195	-	0.7174
4.7441	6210	-	0.7143
4.7555	6225	-	0.7151
4.7670	6240	-	0.7156
4.7785	6255	-	0.7185
4.7899	6270	-	0.7231
4.8014	6285	-	0.7238
4.8128	6300	-	0.7260
4.8243	6315	-	0.7239
4.8358	6330	-	0.7233
4.8472	6345	-	0.7207
4.8587	6360	-	0.7230
4.8701	6375	-	0.7234
4.8816	6390	-	0.7237
4.8930	6405	-	0.7231
4.9045	6420	-	0.7226
4.9160	6435	-	0.7196
4.9274	6450	-	0.7181
4.9389	6465	-	0.7179
4.9503	6480	-	0.7218
4.9618	6495	-	0.7253
4.9656	6500	0.0264	-
4.9733	6510	-	0.7213
4.9847	6525	-	0.7162
4.9962	6540	-	0.7166
5.0	6545	-	0.7187
5.0076	6555	-	0.7193
5.0191	6570	-	0.7145
5.0306	6585	-	0.7193
5.0420	6600	-	0.7189
5.0535	6615	-	0.7170
5.0649	6630	-	0.7150
5.0764	6645	-	0.7140
5.0879	6660	-	0.7184
5.0993	6675	-	0.7134
5.1108	6690	-	0.7178
5.1222	6705	-	0.7226
5.1337	6720	-	0.7187
5.1451	6735	-	0.7170
5.1566	6750	-	0.7152
5.1681	6765	-	0.7147
5.1795	6780	-	0.7111
5.1910	6795	-	0.7152
5.2024	6810	-	0.7197
5.2139	6825	-	0.7124
5.2254	6840	-	0.7152
5.2368	6855	-	0.7169
5.2483	6870	-	0.7166
5.2597	6885	-	0.7163
5.2712	6900	-	0.7184
5.2827	6915	-	0.7161
5.2941	6930	-	0.7176
5.3056	6945	-	0.7135
5.3170	6960	-	0.7130
5.3285	6975	-	0.7216
5.3400	6990	-	0.7220
5.3476	7000	0.0206	-
5.3514	7005	-	0.7216
5.3629	7020	-	0.7167
5.3743	7035	-	0.7161
5.3858	7050	-	0.7132
5.3972	7065	-	0.7128
5.4087	7080	-	0.7199
5.4202	7095	-	0.7189
5.4316	7110	-	0.7172
5.4431	7125	-	0.7120
5.4545	7140	-	0.7117
5.4660	7155	-	0.7140
5.4775	7170	-	0.7136
5.4889	7185	-	0.7204
5.5004	7200	-	0.7222
5.5118	7215	-	0.7140
5.5233	7230	-	0.7110
5.5348	7245	-	0.7218
5.5462	7260	-	0.7213
5.5577	7275	-	0.7173
5.5691	7290	-	0.7219
5.5806	7305	-	0.7191
5.5921	7320	-	0.7180
5.6035	7335	-	0.7166
5.6150	7350	-	0.7159
5.6264	7365	-	0.7197
5.6379	7380	-	0.7206
5.6494	7395	-	0.7196
5.6608	7410	-	0.7191
5.6723	7425	-	0.7246
5.6837	7440	-	0.7265
5.6952	7455	-	0.7215
5.7066	7470	-	0.7125
5.7181	7485	-	0.7123
5.7296	7500	0.0218	0.7204
5.7410	7515	-	0.7270
5.7525	7530	-	0.7260
5.7639	7545	-	0.7245
5.7754	7560	-	0.7262
5.7869	7575	-	0.7244
5.7983	7590	-	0.7220
5.8098	7605	-	0.7215
5.8212	7620	-	0.7244
5.8327	7635	-	0.7280
5.8442	7650	-	0.7285
5.8556	7665	-	0.7212
5.8671	7680	-	0.7215
5.8785	7695	-	0.7244
5.8900	7710	-	0.7210
5.9015	7725	-	0.7205
5.9129	7740	-	0.7201
5.9244	7755	-	0.7171
5.9358	7770	-	0.7145
5.9473	7785	-	0.7158
5.9587	7800	-	0.7192
5.9702	7815	-	0.7223
5.9817	7830	-	0.7246
5.9931	7845	-	0.7212
6.0	7854	-	0.7168
6.0046	7860	-	0.7166
6.0160	7875	-	0.7193
6.0275	7890	-	0.7179
6.0390	7905	-	0.7153
6.0504	7920	-	0.7223
6.0619	7935	-	0.7316
6.0733	7950	-	0.7319
6.0848	7965	-	0.7268
6.0963	7980	-	0.7233
6.1077	7995	-	0.7232
6.1115	8000	0.022	-
6.1192	8010	-	0.7223
6.1306	8025	-	0.7201
6.1421	8040	-	0.7238
6.1536	8055	-	0.7221
6.1650	8070	-	0.7215
6.1765	8085	-	0.7196
6.1879	8100	-	0.7271
6.1994	8115	-	0.7270
6.2108	8130	-	0.7287
6.2223	8145	-	0.7279
6.2338	8160	-	0.7253
6.2452	8175	-	0.7271
6.2567	8190	-	0.7290
6.2681	8205	-	0.7304
6.2796	8220	-	0.7271
6.2911	8235	-	0.7245
6.3025	8250	-	0.7269
6.3140	8265	-	0.7266
6.3254	8280	-	0.7248
6.3369	8295	-	0.7240
6.3484	8310	-	0.7252
6.3598	8325	-	0.7264
6.3713	8340	-	0.7253
6.3827	8355	-	0.7259
6.3942	8370	-	0.7252
6.4057	8385	-	0.7200
6.4171	8400	-	0.7228
6.4286	8415	-	0.7221
6.4400	8430	-	0.7213
6.4515	8445	-	0.7185
6.4629	8460	-	0.7204
6.4744	8475	-	0.7235
6.4859	8490	-	0.7268
6.4935	8500	0.0201	-
6.4973	8505	-	0.7282
6.5088	8520	-	0.7249
6.5202	8535	-	0.7192
6.5317	8550	-	0.7146
6.5432	8565	-	0.7171
6.5546	8580	-	0.7232
6.5661	8595	-	0.7281
6.5775	8610	-	0.7302
6.5890	8625	-	0.7237
6.6005	8640	-	0.7232
6.6119	8655	-	0.7275
6.6234	8670	-	0.7281
6.6348	8685	-	0.7226
6.6463	8700	-	0.7244
6.6578	8715	-	0.7275
6.6692	8730	-	0.7300
6.6807	8745	-	0.7242
6.6921	8760	-	0.7270
6.7036	8775	-	0.7286
6.7150	8790	-	0.7280
6.7265	8805	-	0.7231
6.7380	8820	-	0.7171
6.7494	8835	-	0.7189
6.7609	8850	-	0.7262
6.7723	8865	-	0.7261
6.7838	8880	-	0.7233
6.7953	8895	-	0.7297
6.8067	8910	-	0.7329
6.8182	8925	-	0.7291
6.8296	8940	-	0.7287
6.8411	8955	-	0.7300
6.8526	8970	-	0.7276
6.8640	8985	-	0.7261
6.8755	9000	0.0217	0.7256
6.8869	9015	-	0.7236
6.8984	9030	-	0.7222
6.9099	9045	-	0.7215
6.9213	9060	-	0.7229
6.9328	9075	-	0.7269
6.9442	9090	-	0.7286
6.9557	9105	-	0.7206
6.9672	9120	-	0.7209
6.9786	9135	-	0.7220
6.9901	9150	-	0.7243
7.0	9163	-	0.7269
7.0015	9165	-	0.7277
7.0130	9180	-	0.7287
7.0244	9195	-	0.7275
7.0359	9210	-	0.7252
7.0474	9225	-	0.7243
7.0588	9240	-	0.7246
7.0703	9255	-	0.7245
7.0817	9270	-	0.7223
7.0932	9285	-	0.7198
7.1047	9300	-	0.7212
7.1161	9315	-	0.7243
7.1276	9330	-	0.7230
7.1390	9345	-	0.7197
7.1505	9360	-	0.7201
7.1620	9375	-	0.7197
7.1734	9390	-	0.7212
7.1849	9405	-	0.7213
7.1963	9420	-	0.7239
7.2078	9435	-	0.7220
7.2193	9450	-	0.7206
7.2307	9465	-	0.7213
7.2422	9480	-	0.7207
7.2536	9495	-	0.7268
7.2574	9500	0.0186	-
7.2651	9510	-	0.7265
7.2765	9525	-	0.7203
7.2880	9540	-	0.7193
7.2995	9555	-	0.7185
7.3109	9570	-	0.7228
7.3224	9585	-	0.7270
7.3338	9600	-	0.7247
7.3453	9615	-	0.7204
7.3568	9630	-	0.7202
7.3682	9645	-	0.7211
7.3797	9660	-	0.7243
7.3911	9675	-	0.7270
7.4026	9690	-	0.7298
7.4141	9705	-	0.7315
7.4255	9720	-	0.7339
7.4370	9735	-	0.7325
7.4484	9750	-	0.7289
7.4599	9765	-	0.7264
7.4714	9780	-	0.7257
7.4828	9795	-	0.7242
7.4943	9810	-	0.7265
7.5057	9825	-	0.7276
7.5172	9840	-	0.7311
7.5286	9855	-	0.7296
7.5401	9870	-	0.7259
7.5516	9885	-	0.7291
7.5630	9900	-	0.7298
7.5745	9915	-	0.7252
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7.6318	9990	-	0.7230
7.6394	10000	0.0201	-
7.6432	10005	-	0.7212
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8.4034	11000	0.0178	-
8.4110	11010	-	0.7233
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8.7853	11500	0.0161	-
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9.1673	12000	0.0172	0.7288
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9.5493	12500	0.0147	-
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9.5913	12555	-	0.7303
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9.9351	13005	-	0.7273
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10.6799	13980	-	0.7273
10.6914	13995	-	0.7265
10.6952	14000	0.0161	-
10.7028	14010	-	0.7242
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10.9091	14280	-	0.7248
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10.9664	14355	-	0.7272
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11.0581	14475	-	0.7239
11.0695	14490	-	0.7249
11.0772	14500	0.0152	-
11.0810	14505	-	0.7269
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11.1154	14550	-	0.7298
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11.1497	14595	-	0.7291
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11.4706	15015	-	0.7289
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11.7341	15360	-	0.7295
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11.8411	15500	0.0146	-
11.8487	15510	-	0.7273
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12.0894	15825	-	0.7336
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12.1352	15885	-	0.7345
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12.1925	15960	-	0.7305
12.2040	15975	-	0.7305
12.2154	15990	-	0.7303
12.2231	16000	0.0141	-
12.2269	16005	-	0.7315
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12.2613	16050	-	0.7322
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12.3071	16110	-	0.7308
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12.3759	16200	-	0.7329
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12.4561	16305	-	0.7306
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12.5363	16410	-	0.7329
12.5477	16425	-	0.7327
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12.6050	16500	0.0137	0.7344
12.6165	16515	-	0.7346
12.6280	16530	-	0.7337
12.6394	16545	-	0.7327
12.6509	16560	-	0.7337
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12.6738	16590	-	0.7337
12.6853	16605	-	0.7331
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12.7196	16650	-	0.7335
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12.7655	16710	-	0.7329
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12.8457	16815	-	0.7346
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12.8801	16860	-	0.7334
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12.9259	16920	-	0.7315
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12.9488	16950	-	0.7315
12.9603	16965	-	0.7317
12.9717	16980	-	0.7331
12.9832	16995	-	0.7324
12.9870	17000	0.0133	-
12.9947	17010	-	0.7333
13.0	17017	-	0.7323
13.0061	17025	-	0.7310
13.0176	17040	-	0.7301
13.0290	17055	-	0.7301
13.0405	17070	-	0.7298
13.0519	17085	-	0.7312
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13.0749	17115	-	0.7331
13.0863	17130	-	0.7337
13.0978	17145	-	0.7346
13.1092	17160	-	0.7337
13.1207	17175	-	0.7333
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13.1436	17205	-	0.7338
13.1551	17220	-	0.7334
13.1665	17235	-	0.7341
13.1780	17250	-	0.7347
13.1895	17265	-	0.7329
13.2009	17280	-	0.7332
13.2124	17295	-	0.7336
13.2238	17310	-	0.7331
13.2353	17325	-	0.7335
13.2468	17340	-	0.7330
13.2582	17355	-	0.7316
13.2697	17370	-	0.7311
13.2811	17385	-	0.7323
13.2926	17400	-	0.7331
13.3040	17415	-	0.7331
13.3155	17430	-	0.7334
13.3270	17445	-	0.7334
13.3384	17460	-	0.7343
13.3499	17475	-	0.7335
13.3613	17490	-	0.7328
13.3690	17500	0.014	-
13.3728	17505	-	0.7334
13.3843	17520	-	0.7338
13.3957	17535	-	0.7336
13.4072	17550	-	0.7336
13.4186	17565	-	0.7334
13.4301	17580	-	0.7331
13.4416	17595	-	0.7334
13.4530	17610	-	0.7344
13.4645	17625	-	0.7353
13.4759	17640	-	0.7358
13.4874	17655	-	0.7354
13.4989	17670	-	0.7374
13.5103	17685	-	0.7370
13.5218	17700	-	0.7377
13.5332	17715	-	0.7374
13.5447	17730	-	0.7367
13.5561	17745	-	0.7353
13.5676	17760	-	0.7359
13.5791	17775	-	0.7354
13.5905	17790	-	0.7340
13.6020	17805	-	0.7345
13.6134	17820	-	0.7349
13.6249	17835	-	0.7356
13.6364	17850	-	0.7357
13.6478	17865	-	0.7348
13.6593	17880	-	0.7351
13.6707	17895	-	0.7346
13.6822	17910	-	0.7353
13.6937	17925	-	0.7347
13.7051	17940	-	0.7347
13.7166	17955	-	0.7346
13.7280	17970	-	0.7337
13.7395	17985	-	0.7342
13.7510	18000	0.0128	0.7345
13.7624	18015	-	0.7341
13.7739	18030	-	0.7341
13.7853	18045	-	0.7334
13.7968	18060	-	0.7332
13.8083	18075	-	0.7332
13.8197	18090	-	0.7333
13.8312	18105	-	0.7324
13.8426	18120	-	0.7322
13.8541	18135	-	0.7325
13.8655	18150	-	0.7325
13.8770	18165	-	0.7332
13.8885	18180	-	0.7333
13.8999	18195	-	0.7339
13.9114	18210	-	0.7338
13.9228	18225	-	0.7342
13.9343	18240	-	0.7349
13.9458	18255	-	0.7349
13.9572	18270	-	0.7355
13.9687	18285	-	0.7366
13.9801	18300	-	0.7365
13.9916	18315	-	0.7372
14.0	18326	-	0.7370
14.0031	18330	-	0.7372
14.0145	18345	-	0.7372
14.0260	18360	-	0.7380
14.0374	18375	-	0.7379
14.0489	18390	-	0.7366
14.0604	18405	-	0.7362
14.0718	18420	-	0.7363
14.0833	18435	-	0.7361
14.0947	18450	-	0.7360
14.1062	18465	-	0.7360
14.1176	18480	-	0.7365
14.1291	18495	-	0.7361
14.1329	18500	0.0132	-
14.1406	18510	-	0.7360
14.1520	18525	-	0.7360
14.1635	18540	-	0.7360
14.1749	18555	-	0.7352
14.1864	18570	-	0.7347
14.1979	18585	-	0.7349
14.2093	18600	-	0.7345
14.2208	18615	-	0.7347
14.2322	18630	-	0.7345
14.2437	18645	-	0.7348
14.2552	18660	-	0.7345
14.2666	18675	-	0.7342
14.2781	18690	-	0.7342
14.2895	18705	-	0.7339
14.3010	18720	-	0.7342
14.3125	18735	-	0.7350
14.3239	18750	-	0.7351
14.3354	18765	-	0.7348
14.3468	18780	-	0.7349
14.3583	18795	-	0.7347
14.3697	18810	-	0.7343
14.3812	18825	-	0.7344
14.3927	18840	-	0.7348
14.4041	18855	-	0.7348
14.4156	18870	-	0.7342
14.4270	18885	-	0.7343
14.4385	18900	-	0.7345
14.4500	18915	-	0.7347
14.4614	18930	-	0.7348
14.4729	18945	-	0.7351
14.4843	18960	-	0.7348
14.4958	18975	-	0.7350
14.5073	18990	-	0.7350
14.5149	19000	0.0127	-
14.5187	19005	-	0.7351
14.5302	19020	-	0.7351
14.5416	19035	-	0.7351
14.5531	19050	-	0.7348
14.5646	19065	-	0.7346
14.5760	19080	-	0.7344
14.5875	19095	-	0.7341
14.5989	19110	-	0.7338
14.6104	19125	-	0.7338
14.6218	19140	-	0.7335
14.6333	19155	-	0.7339
14.6448	19170	-	0.7341
14.6562	19185	-	0.7342
14.6677	19200	-	0.7342
14.6791	19215	-	0.7344
14.6906	19230	-	0.7345
14.7021	19245	-	0.7345
14.7135	19260	-	0.7343
14.7250	19275	-	0.7346
14.7364	19290	-	0.7345
14.7479	19305	-	0.7347
14.7594	19320	-	0.7349
14.7708	19335	-	0.7349
14.7823	19350	-	0.7352
14.7937	19365	-	0.7351
14.8052	19380	-	0.7349
14.8167	19395	-	0.7351
14.8281	19410	-	0.7349
14.8396	19425	-	0.7347
14.8510	19440	-	0.7347
14.8625	19455	-	0.7346
14.8739	19470	-	0.7345
14.8854	19485	-	0.7346
14.8969	19500	0.0124	0.7345
14.9083	19515	-	0.7346
14.9198	19530	-	0.7346
14.9312	19545	-	0.7347
14.9427	19560	-	0.7347
14.9542	19575	-	0.7347
14.9656	19590	-	0.7346
14.9771	19605	-	0.7347
14.9885	19620	-	0.7347
15.0	19635	-	0.7347

Framework Versions

Python: 3.12.3
Sentence Transformers: 4.1.0
Transformers: 4.51.0
PyTorch: 2.6.0+cu126
Accelerate: 1.6.0
Datasets: 3.6.0
Tokenizers: 0.21.1

Citation

BibTeX

Sentence Transformers

@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",
}

MultipleNegativesRankingLoss

@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}
}