base_model	datasets	language	library_name	metrics	pipeline_tag	tags	widget	model-index
embaas/sentence-transformers-multilingual-e5-large			sentence-transformers	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 dot_accuracy@1 dot_accuracy@3 dot_accuracy@5 dot_accuracy@10 dot_precision@1 dot_precision@3 dot_precision@5 dot_precision@10 dot_recall@1 dot_recall@3 dot_recall@5 dot_recall@10 dot_ndcg@10 dot_mrr@10 dot_map@100	sentence-similarity	sentence-transformers sentence-similarity feature-extraction generated_from_trainer dataset_size:41881 loss:MultipleNegativesRankingLoss	source_sentence sentences 얕은 기초의 허용 수평 지지력을 결정할 때 고려해야 하는 주요 요소는 무엇인가? ④계측기간과빈도는측정하고자하는계측값의변화정도및변화의지속시간과관련되며비탈면의파괴속도가빠른경우또는변화가있는경우에는측정빈도를높여측정하여야하고변화가장기간지속되는경우에는측정기간도이에맞춰측정하여야한다 .⑤깎기비탈면의계측기설치위치는비탈면자체및불연속면등에의해이완된암반의거동을충분히고려하고유사한조건하에서계측예를참고로하여선정하여 건설기준제정또는개정에따른경과조치이기준은발간시점부터사용하며,이미시행중에있는설계용역이나건설공사는발주기관의장이필요하다고인정하는경우종전에적용하고있는기준을그대로사용할수있습니다. (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 sentences 33.3품질및성능시험 섹션에서는 어떤 종류의 시험이 포함되어 있습니까? 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. 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 sentences 비탈면 녹화의 목적은 무엇인가요? 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, 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 sentences 비탈면이나 옹벽 구조물의 설계 시 허용되는 변형은 무엇입니까? 4.1.5돌(블록 )붙이기공법의배수시설(1)돌(블록 )붙이기를한경우배면의지하수배수를위해일정두께를자갈로뒤채움하며,세립분의유출위험이있는경우에는필터재료를설치한다 .(2)비탈면배면으로부터유입되는지하수또는표면에서유입되는물을배수시키기위해설치하는배수시설은다음과같다 . 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 . 4.2기초구조물의설계거동한계4.2.1기능수행수준에 따른설계거동한계(1)비탈면이나옹벽구조물은부분적인항복과소성변형을허용할수있으나 ,주변구조물및부속시설들은탄성또는탄성에준하는거동을허용할수있으므로기초내진설계시유의하여야한다 .(2)얕은기초및깊은기초는지진시그주변지반의소성거동은허용할수있으나 ,기초구조물자체와모든상부구조물및부속시설이탄성또는탄성에준하는거동을허용한다 . source_sentence sentences 억지말뚝으로 보강된 비탈면의 내진설계 결정 기준은 무엇인가요? 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. (9)매립지반에설치하는기초는특별히주의하여야하는데 ,기초하부의접지압력은가급적균등하게분포하도록설계하고 ,접지압력의분포는흙또는암,그리고구조물의특성에따라달라지며 ,토질역학및암석역학적원리들과일치하여야한다 .2.조사및계획∙내용없음 .3.재료∙내용없음 .4.설계4.1지지력산정4.1.1지지력산정을위한고려사항(1)기초설계시시추조사 ,현장및실내시험을통하여지반특성을파악한후지지력을산정한다 .그러나상재하중이작은구조물또는가설구조물의기초는인근구조물의경험값,기초설계및시공성과 ,현장시험자료를통하여지지력을추정할수있다 . 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 .	name results SentenceTransformer based on embaas/sentence-transformers-multilingual-e5-large task dataset metrics type name information-retrieval Information Retrieval name type Unknown unknown type value name cosine_accuracy@1 0.5449090205187767 Cosine Accuracy@1 type value name cosine_accuracy@3 0.721835075493612 Cosine Accuracy@3 type value name cosine_accuracy@5 0.7779713511420828 Cosine Accuracy@5 type value name cosine_accuracy@10 0.8453348819202477 Cosine Accuracy@10 type value name cosine_precision@1 0.5449090205187767 Cosine Precision@1 type value name cosine_precision@3 0.24061169183120404 Cosine Precision@3 type value name cosine_precision@5 0.15559427022841657 Cosine Precision@5 type value name cosine_precision@10 0.08453348819202476 Cosine Precision@10 type value name cosine_recall@1 0.5449090205187767 Cosine Recall@1 type value name cosine_recall@3 0.721835075493612 Cosine Recall@3 type value name cosine_recall@5 0.7779713511420828 Cosine Recall@5 type value name cosine_recall@10 0.8453348819202477 Cosine Recall@10 type value name cosine_ndcg@10 0.6940049751432595 Cosine Ndcg@10 type value name cosine_mrr@10 0.6456807790867042 Cosine Mrr@10 type value name cosine_map@100 0.6520389829933901 Cosine Map@100 type value name dot_accuracy@1 0.5449090205187767 Dot Accuracy@1 type value name dot_accuracy@3 0.721835075493612 Dot Accuracy@3 type value name dot_accuracy@5 0.7779713511420828 Dot Accuracy@5 type value name dot_accuracy@10 0.8453348819202477 Dot Accuracy@10 type value name dot_precision@1 0.5449090205187767 Dot Precision@1 type value name dot_precision@3 0.24061169183120404 Dot Precision@3 type value name dot_precision@5 0.15559427022841657 Dot Precision@5 type value name dot_precision@10 0.08453348819202476 Dot Precision@10 type value name dot_recall@1 0.5449090205187767 Dot Recall@1 type value name dot_recall@3 0.721835075493612 Dot Recall@3 type value name dot_recall@5 0.7779713511420828 Dot Recall@5 type value name dot_recall@10 0.8453348819202477 Dot Recall@10 type value name dot_ndcg@10 0.6940049751432595 Dot Ndcg@10 type value name dot_mrr@10 0.6456807790867042 Dot Mrr@10 type value name dot_map@100 0.652039280798566 Dot Map@100

SentenceTransformer based on embaas/sentence-transformers-multilingual-e5-large

This is a sentence-transformers model finetuned from embaas/sentence-transformers-multilingual-e5-large. 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: embaas/sentence-transformers-multilingual-e5-large
• Maximum Sequence Length: 512 tokens
• Output Dimensionality: 1024 tokens
• 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': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel 
  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

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("jerryyun/kicon_e5large_15_v1")
# 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	0.5449
cosine_accuracy@3	0.7218
cosine_accuracy@5	0.778
cosine_accuracy@10	0.8453
cosine_precision@1	0.5449
cosine_precision@3	0.2406
cosine_precision@5	0.1556
cosine_precision@10	0.0845
cosine_recall@1	0.5449
cosine_recall@3	0.7218
cosine_recall@5	0.778
cosine_recall@10	0.8453
cosine_ndcg@10	0.694
cosine_mrr@10	0.6457
cosine_map@100	0.652
dot_accuracy@1	0.5449
dot_accuracy@3	0.7218
dot_accuracy@5	0.778
dot_accuracy@10	0.8453
dot_precision@1	0.5449
dot_precision@3	0.2406
dot_precision@5	0.1556
dot_precision@10	0.0845
dot_recall@1	0.5449
dot_recall@3	0.7218
dot_recall@5	0.778
dot_recall@10	0.8453
dot_ndcg@10	0.694
dot_mrr@10	0.6457
dot_map@100	0.652

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

• 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
• 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}
• 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: False
• hub_always_push: False
• gradient_checkpointing: False
• gradient_checkpointing_kwargs: None
• include_inputs_for_metrics: False
• eval_do_concat_batches: True
• fp16_backend: auto
• push_to_hub_model_id: None
• push_to_hub_organization: None
• mp_parameters:
• auto_find_batch_size: False
• full_determinism: False
• torchdynamo: None
• ray_scope: last
• ddp_timeout: 1800
• torch_compile: False
• torch_compile_backend: None
• torch_compile_mode: None
• dispatch_batches: None
• split_batches: None
• include_tokens_per_second: False
• include_num_input_tokens_seen: False
• neftune_noise_alpha: None
• optim_target_modules: None
• batch_eval_metrics: False
• batch_sampler: batch_sampler
• multi_dataset_batch_sampler: round_robin

Training Logs

Click to expand

Epoch	Step	Training Loss	cosine_map@100
0.0115	15	-	0.4930
0.0229	30	-	0.5107
0.0344	45	-	0.5290
0.0458	60	-	0.5474
0.0573	75	-	0.5568
0.0688	90	-	0.5569
0.0802	105	-	0.5483
0.0917	120	-	0.5353
0.1031	135	-	0.5278
0.1146	150	-	0.5300
0.1261	165	-	0.5439
0.1375	180	-	0.5566
0.1490	195	-	0.5671
0.1604	210	-	0.5747
0.1719	225	-	0.5794
0.1833	240	-	0.5870
0.1948	255	-	0.5919
0.2063	270	-	0.5956
0.2177	285	-	0.6004
0.2292	300	-	0.6009
0.2406	315	-	0.6017
0.2521	330	-	0.6084
0.2636	345	-	0.6156
0.2750	360	-	0.6073
0.2865	375	-	0.6096
0.2979	390	-	0.6168
0.3094	405	-	0.6219
0.3209	420	-	0.6208
0.3323	435	-	0.6180
0.3438	450	-	0.6253
0.3552	465	-	0.6225
0.3667	480	-	0.6297
0.3782	495	-	0.6315
0.3820	500	0.5905	-
0.3896	510	-	0.6347
0.4011	525	-	0.6398
0.4125	540	-	0.6462
0.4240	555	-	0.6459
0.4354	570	-	0.6441
0.4469	585	-	0.6359
0.4584	600	-	0.6424
0.4698	615	-	0.6405
0.4813	630	-	0.6340
0.4927	645	-	0.6416
0.5042	660	-	0.6444
0.5157	675	-	0.6381
0.5271	690	-	0.6363
0.5386	705	-	0.6408
0.5500	720	-	0.6414
0.5615	735	-	0.6509
0.5730	750	-	0.6509
0.5844	765	-	0.6516
0.5959	780	-	0.6505
0.6073	795	-	0.6493
0.6188	810	-	0.6476
0.6303	825	-	0.6458
0.6417	840	-	0.6461
0.6532	855	-	0.6538
0.6646	870	-	0.6439
0.6761	885	-	0.6530
0.6875	900	-	0.6432
0.6990	915	-	0.6460
0.7105	930	-	0.6594
0.7219	945	-	0.6650
0.7334	960	-	0.6536
0.7448	975	-	0.6484
0.7563	990	-	0.6426
0.7639	1000	0.086	-
0.7678	1005	-	0.6509
0.7792	1020	-	0.6485
0.7907	1035	-	0.6464
0.8021	1050	-	0.6595
0.8136	1065	-	0.6538
0.8251	1080	-	0.6517
0.8365	1095	-	0.6638
0.8480	1110	-	0.6624
0.8594	1125	-	0.6582
0.8709	1140	-	0.6517
0.8824	1155	-	0.6504
0.8938	1170	-	0.6545
0.9053	1185	-	0.6588
0.9167	1200	-	0.6558
0.9282	1215	-	0.6615
0.9396	1230	-	0.6640
0.9511	1245	-	0.6612
0.9626	1260	-	0.6626
0.9740	1275	-	0.6519
0.9855	1290	-	0.6489
0.9969	1305	-	0.6542
1.0	1309	-	0.6542
1.0084	1320	-	0.6554
1.0199	1335	-	0.6596
1.0313	1350	-	0.6671
1.0428	1365	-	0.6636
1.0542	1380	-	0.6729
1.0657	1395	-	0.6608
1.0772	1410	-	0.6718
1.0886	1425	-	0.6614
1.1001	1440	-	0.6639
1.1115	1455	-	0.6649
1.1230	1470	-	0.6680
1.1345	1485	-	0.6516
1.1459	1500	0.0704	0.6764
1.1574	1515	-	0.6713
1.1688	1530	-	0.6690
1.1803	1545	-	0.6750
1.1917	1560	-	0.6730
1.2032	1575	-	0.6690
1.2147	1590	-	0.6590
1.2261	1605	-	0.6699
1.2376	1620	-	0.6627
1.2490	1635	-	0.6747
1.2605	1650	-	0.6726
1.2720	1665	-	0.6548
1.2834	1680	-	0.6607
1.2949	1695	-	0.6678
1.3063	1710	-	0.6632
1.3178	1725	-	0.6505
1.3293	1740	-	0.6583
1.3407	1755	-	0.6713
1.3522	1770	-	0.6434
1.3636	1785	-	0.6715
1.3751	1800	-	0.6699
1.3866	1815	-	0.6663
1.3980	1830	-	0.6646
1.4095	1845	-	0.6547
1.4209	1860	-	0.6650
1.4324	1875	-	0.6569
1.4439	1890	-	0.6496
1.4553	1905	-	0.6638
1.4668	1920	-	0.6714
1.4782	1935	-	0.6474
1.4897	1950	-	0.6741
1.5011	1965	-	0.6626
1.5126	1980	-	0.6562
1.5241	1995	-	0.6525
1.5279	2000	0.0563	-
1.5355	2010	-	0.6672
1.5470	2025	-	0.6577
1.5584	2040	-	0.6609
1.5699	2055	-	0.6583
1.5814	2070	-	0.6734
1.5928	2085	-	0.6592
1.6043	2100	-	0.6553
1.6157	2115	-	0.6584
1.6272	2130	-	0.6515
1.6387	2145	-	0.6541
1.6501	2160	-	0.6601
1.6616	2175	-	0.6641
1.6730	2190	-	0.6605
1.6845	2205	-	0.6618
1.6960	2220	-	0.6637
1.7074	2235	-	0.6614
1.7189	2250	-	0.6577
1.7303	2265	-	0.6587
1.7418	2280	-	0.6556
1.7532	2295	-	0.6464
1.7647	2310	-	0.6655
1.7762	2325	-	0.6641
1.7876	2340	-	0.6518
1.7991	2355	-	0.6767
1.8105	2370	-	0.6693
1.8220	2385	-	0.6599
1.8335	2400	-	0.6651
1.8449	2415	-	0.6736
1.8564	2430	-	0.6568
1.8678	2445	-	0.6551
1.8793	2460	-	0.6615
1.8908	2475	-	0.6634
1.9022	2490	-	0.6547
1.9099	2500	0.0407	-
1.9137	2505	-	0.6636
1.9251	2520	-	0.6721
1.9366	2535	-	0.6656
1.9481	2550	-	0.6667
1.9595	2565	-	0.6624
1.9710	2580	-	0.6605
1.9824	2595	-	0.6463
1.9939	2610	-	0.6723
2.0	2618	-	0.6774
2.0053	2625	-	0.6737
2.0168	2640	-	0.6717
2.0283	2655	-	0.6728
2.0397	2670	-	0.6665
2.0512	2685	-	0.6725
2.0626	2700	-	0.6606
2.0741	2715	-	0.6644
2.0856	2730	-	0.6708
2.0970	2745	-	0.6606
2.1085	2760	-	0.6699
2.1199	2775	-	0.6730
2.1314	2790	-	0.6671
2.1429	2805	-	0.6669
2.1543	2820	-	0.6760
2.1658	2835	-	0.6710
2.1772	2850	-	0.6584
2.1887	2865	-	0.6749
2.2002	2880	-	0.6680
2.2116	2895	-	0.6741
2.2231	2910	-	0.6693
2.2345	2925	-	0.6634
2.2460	2940	-	0.6585
2.2574	2955	-	0.6669
2.2689	2970	-	0.6737
2.2804	2985	-	0.6592
2.2918	3000	0.0345	0.6662
2.3033	3015	-	0.6731
2.3147	3030	-	0.6670
2.3262	3045	-	0.6604
2.3377	3060	-	0.6682
2.3491	3075	-	0.6610
2.3606	3090	-	0.6683
2.3720	3105	-	0.6785
2.3835	3120	-	0.6662
2.3950	3135	-	0.6603
2.4064	3150	-	0.6652
2.4179	3165	-	0.6604
2.4293	3180	-	0.6665
2.4408	3195	-	0.6630
2.4523	3210	-	0.6512
2.4637	3225	-	0.6625
2.4752	3240	-	0.6559
2.4866	3255	-	0.6612
2.4981	3270	-	0.6696
2.5095	3285	-	0.6558
2.5210	3300	-	0.6530
2.5325	3315	-	0.6571
2.5439	3330	-	0.6609
2.5554	3345	-	0.6587
2.5668	3360	-	0.6687
2.5783	3375	-	0.6630
2.5898	3390	-	0.6706
2.6012	3405	-	0.6504
2.6127	3420	-	0.6652
2.6241	3435	-	0.6718
2.6356	3450	-	0.6717
2.6471	3465	-	0.6604
2.6585	3480	-	0.6554
2.6700	3495	-	0.6566
2.6738	3500	0.0254	-
2.6814	3510	-	0.6603
2.6929	3525	-	0.6765
2.7044	3540	-	0.6709
2.7158	3555	-	0.6663
2.7273	3570	-	0.6617
2.7387	3585	-	0.6595
2.7502	3600	-	0.6613
2.7617	3615	-	0.6581
2.7731	3630	-	0.6727
2.7846	3645	-	0.6603
2.7960	3660	-	0.6587
2.8075	3675	-	0.6703
2.8189	3690	-	0.6708
2.8304	3705	-	0.6673
2.8419	3720	-	0.6673
2.8533	3735	-	0.6766
2.8648	3750	-	0.6654
2.8762	3765	-	0.6568
2.8877	3780	-	0.6606
2.8992	3795	-	0.6522
2.9106	3810	-	0.6550
2.9221	3825	-	0.6773
2.9335	3840	-	0.6714
2.9450	3855	-	0.6721
2.9565	3870	-	0.6667
2.9679	3885	-	0.6639
2.9794	3900	-	0.6674
2.9908	3915	-	0.6626
3.0	3927	-	0.6654
3.0023	3930	-	0.6667
3.0138	3945	-	0.6707
3.0252	3960	-	0.6687
3.0367	3975	-	0.6731
3.0481	3990	-	0.6771
3.0558	4000	0.0198	-
3.0596	4005	-	0.6792
3.0710	4020	-	0.6672
3.0825	4035	-	0.6714
3.0940	4050	-	0.6707
3.1054	4065	-	0.6688
3.1169	4080	-	0.6838
3.1283	4095	-	0.6718
3.1398	4110	-	0.6595
3.1513	4125	-	0.6741
3.1627	4140	-	0.6737
3.1742	4155	-	0.6731
3.1856	4170	-	0.6700
3.1971	4185	-	0.6748
3.2086	4200	-	0.6746
3.2200	4215	-	0.6716
3.2315	4230	-	0.6755
3.2429	4245	-	0.6700
3.2544	4260	-	0.6652
3.2659	4275	-	0.6700
3.2773	4290	-	0.6724
3.2888	4305	-	0.6643
3.3002	4320	-	0.6737
3.3117	4335	-	0.6756
3.3231	4350	-	0.6708
3.3346	4365	-	0.6682
3.3461	4380	-	0.6788
3.3575	4395	-	0.6732
3.3690	4410	-	0.6753
3.3804	4425	-	0.6803
3.3919	4440	-	0.6738
3.4034	4455	-	0.6732
3.4148	4470	-	0.6701
3.4263	4485	-	0.6668
3.4377	4500	0.0185	0.6700
3.4492	4515	-	0.6633
3.4607	4530	-	0.6696
3.4721	4545	-	0.6822
3.4836	4560	-	0.6815
3.4950	4575	-	0.6717
3.5065	4590	-	0.6590
3.5180	4605	-	0.6560
3.5294	4620	-	0.6660
3.5409	4635	-	0.6698
3.5523	4650	-	0.6660
3.5638	4665	-	0.6638
3.5752	4680	-	0.6696
3.5867	4695	-	0.6639
3.5982	4710	-	0.6666
3.6096	4725	-	0.6626
3.6211	4740	-	0.6691
3.6325	4755	-	0.6648
3.6440	4770	-	0.6619
3.6555	4785	-	0.6640
3.6669	4800	-	0.6667
3.6784	4815	-	0.6670
3.6898	4830	-	0.6704
3.7013	4845	-	0.6591
3.7128	4860	-	0.6658
3.7242	4875	-	0.6597
3.7357	4890	-	0.6629
3.7471	4905	-	0.6615
3.7586	4920	-	0.6519
3.7701	4935	-	0.6557
3.7815	4950	-	0.6668
3.7930	4965	-	0.6587
3.8044	4980	-	0.6653
3.8159	4995	-	0.6683
3.8197	5000	0.0165	-
3.8273	5010	-	0.6671
3.8388	5025	-	0.6671
3.8503	5040	-	0.6709
3.8617	5055	-	0.6708
3.8732	5070	-	0.6633
3.8846	5085	-	0.6597
3.8961	5100	-	0.6590
3.9076	5115	-	0.6577
3.9190	5130	-	0.6607
3.9305	5145	-	0.6674
3.9419	5160	-	0.6647
3.9534	5175	-	0.6609
3.9649	5190	-	0.6657
3.9763	5205	-	0.6576
3.9878	5220	-	0.6624
3.9992	5235	-	0.6612
4.0	5236	-	0.6611
4.0107	5250	-	0.6639
4.0222	5265	-	0.6735
4.0336	5280	-	0.6779
4.0451	5295	-	0.6802
4.0565	5310	-	0.6761
4.0680	5325	-	0.6718
4.0794	5340	-	0.6678
4.0909	5355	-	0.6726
4.1024	5370	-	0.6733
4.1138	5385	-	0.6756
4.1253	5400	-	0.6787
4.1367	5415	-	0.6676
4.1482	5430	-	0.6689
4.1597	5445	-	0.6674
4.1711	5460	-	0.6667
4.1826	5475	-	0.6773
4.1940	5490	-	0.6780
4.2017	5500	0.013	-
4.2055	5505	-	0.6732
4.2170	5520	-	0.6697
4.2284	5535	-	0.6683
4.2399	5550	-	0.6717
4.2513	5565	-	0.6692
4.2628	5580	-	0.6606
4.2743	5595	-	0.6621
4.2857	5610	-	0.6679
4.2972	5625	-	0.6691
4.3086	5640	-	0.6798
4.3201	5655	-	0.6765
4.3316	5670	-	0.6658
4.3430	5685	-	0.6756
4.3545	5700	-	0.6713
4.3659	5715	-	0.6721
4.3774	5730	-	0.6776
4.3888	5745	-	0.6747
4.4003	5760	-	0.6639
4.4118	5775	-	0.6755
4.4232	5790	-	0.6719
4.4347	5805	-	0.6670
4.4461	5820	-	0.6615
4.4576	5835	-	0.6556
4.4691	5850	-	0.6584
4.4805	5865	-	0.6575
4.4920	5880	-	0.6659
4.5034	5895	-	0.6642
4.5149	5910	-	0.6670
4.5264	5925	-	0.6619
4.5378	5940	-	0.6619
4.5493	5955	-	0.6687
4.5607	5970	-	0.6656
4.5722	5985	-	0.6661
4.5837	6000	0.015	0.6657
4.5951	6015	-	0.6635
4.6066	6030	-	0.6608
4.6180	6045	-	0.6634
4.6295	6060	-	0.6713
4.6409	6075	-	0.6649
4.6524	6090	-	0.6555
4.6639	6105	-	0.6576
4.6753	6120	-	0.6627
4.6868	6135	-	0.6669
4.6982	6150	-	0.6675
4.7097	6165	-	0.6561
4.7212	6180	-	0.6535
4.7326	6195	-	0.6604
4.7441	6210	-	0.6638
4.7555	6225	-	0.6621
4.7670	6240	-	0.6548
4.7785	6255	-	0.6577
4.7899	6270	-	0.6478
4.8014	6285	-	0.6571
4.8128	6300	-	0.6634
4.8243	6315	-	0.6637
4.8358	6330	-	0.6606
4.8472	6345	-	0.6706
4.8587	6360	-	0.6695
4.8701	6375	-	0.6690
4.8816	6390	-	0.6654
4.8930	6405	-	0.6551
4.9045	6420	-	0.6537
4.9160	6435	-	0.6605
4.9274	6450	-	0.6526
4.9389	6465	-	0.6743
4.9503	6480	-	0.6738
4.9618	6495	-	0.6576
4.9656	6500	0.0115	-
4.9733	6510	-	0.6542
4.9847	6525	-	0.6545
4.9962	6540	-	0.6605
5.0	6545	-	0.6599
5.0076	6555	-	0.6616
5.0191	6570	-	0.6657
5.0306	6585	-	0.6644
5.0420	6600	-	0.6680
5.0535	6615	-	0.6716
5.0649	6630	-	0.6737
5.0764	6645	-	0.6703
5.0879	6660	-	0.6693
5.0993	6675	-	0.6658
5.1108	6690	-	0.6630
5.1222	6705	-	0.6652
5.1337	6720	-	0.6672
5.1451	6735	-	0.6647
5.1566	6750	-	0.6707
5.1681	6765	-	0.6672
5.1795	6780	-	0.6588
5.1910	6795	-	0.6606
5.2024	6810	-	0.6689
5.2139	6825	-	0.6695
5.2254	6840	-	0.6693
5.2368	6855	-	0.6729
5.2483	6870	-	0.6711
5.2597	6885	-	0.6656
5.2712	6900	-	0.6556
5.2827	6915	-	0.6541
5.2941	6930	-	0.6702
5.3056	6945	-	0.6734
5.3170	6960	-	0.6704
5.3285	6975	-	0.6671
5.3400	6990	-	0.6645
5.3476	7000	0.0119	-
5.3514	7005	-	0.6719
5.3629	7020	-	0.6710
5.3743	7035	-	0.6666
5.3858	7050	-	0.6699
5.3972	7065	-	0.6728
5.4087	7080	-	0.6735
5.4202	7095	-	0.6700
5.4316	7110	-	0.6666
5.4431	7125	-	0.6650
5.4545	7140	-	0.6603
5.4660	7155	-	0.6573
5.4775	7170	-	0.6656
5.4889	7185	-	0.6672
5.5004	7200	-	0.6606
5.5118	7215	-	0.6623
5.5233	7230	-	0.6628
5.5348	7245	-	0.6602
5.5462	7260	-	0.6628
5.5577	7275	-	0.6642
5.5691	7290	-	0.6675
5.5806	7305	-	0.6618
5.5921	7320	-	0.6619
5.6035	7335	-	0.6603
5.6150	7350	-	0.6657
5.6264	7365	-	0.6709
5.6379	7380	-	0.6718
5.6494	7395	-	0.6640
5.6608	7410	-	0.6588
5.6723	7425	-	0.6664
5.6837	7440	-	0.6685
5.6952	7455	-	0.6651
5.7066	7470	-	0.6692
5.7181	7485	-	0.6687
5.7296	7500	0.0114	0.6551
5.7410	7515	-	0.6559
5.7525	7530	-	0.6590
5.7639	7545	-	0.6576
5.7754	7560	-	0.6545
5.7869	7575	-	0.6555
5.7983	7590	-	0.6600
5.8098	7605	-	0.6611
5.8212	7620	-	0.6641
5.8327	7635	-	0.6579
5.8442	7650	-	0.6669
5.8556	7665	-	0.6711
5.8671	7680	-	0.6661
5.8785	7695	-	0.6579
5.8900	7710	-	0.6527
5.9015	7725	-	0.6539
5.9129	7740	-	0.6476
5.9244	7755	-	0.6663
5.9358	7770	-	0.6705
5.9473	7785	-	0.6634
5.9587	7800	-	0.6641
5.9702	7815	-	0.6565
5.9817	7830	-	0.6552
5.9931	7845	-	0.6540
6.0	7854	-	0.6546
6.0046	7860	-	0.6577
6.0160	7875	-	0.6640
6.0275	7890	-	0.6682
6.0390	7905	-	0.6678
6.0504	7920	-	0.6692
6.0619	7935	-	0.6641
6.0733	7950	-	0.6589
6.0848	7965	-	0.6571
6.0963	7980	-	0.6618
6.1077	7995	-	0.6672
6.1115	8000	0.009	-
6.1192	8010	-	0.6695
6.1306	8025	-	0.6673
6.1421	8040	-	0.6651
6.1536	8055	-	0.6671
6.1650	8070	-	0.6693
6.1765	8085	-	0.6629
6.1879	8100	-	0.6642
6.1994	8115	-	0.6708
6.2108	8130	-	0.6709
6.2223	8145	-	0.6714
6.2338	8160	-	0.6670
6.2452	8175	-	0.6681
6.2567	8190	-	0.6603
6.2681	8205	-	0.6596
6.2796	8220	-	0.6623
6.2911	8235	-	0.6643
6.3025	8250	-	0.6669
6.3140	8265	-	0.6750
6.3254	8280	-	0.6714
6.3369	8295	-	0.6529
6.3484	8310	-	0.6559
6.3598	8325	-	0.6644
6.3713	8340	-	0.6665
6.3827	8355	-	0.6677
6.3942	8370	-	0.6733
6.4057	8385	-	0.6766
6.4171	8400	-	0.6740
6.4286	8415	-	0.6690
6.4400	8430	-	0.6586
6.4515	8445	-	0.6544
6.4629	8460	-	0.6566
6.4744	8475	-	0.6576
6.4859	8490	-	0.6555
6.4935	8500	0.0103	-
6.4973	8505	-	0.6641
6.5088	8520	-	0.6660
6.5202	8535	-	0.6628
6.5317	8550	-	0.6612
6.5432	8565	-	0.6555
6.5546	8580	-	0.6609
6.5661	8595	-	0.6665
6.5775	8610	-	0.6616
6.5890	8625	-	0.6589
6.6005	8640	-	0.6581
6.6119	8655	-	0.6566
6.6234	8670	-	0.6663
6.6348	8685	-	0.6696
6.6463	8700	-	0.6675
6.6578	8715	-	0.6625
6.6692	8730	-	0.6607
6.6807	8745	-	0.6611
6.6921	8760	-	0.6667
6.7036	8775	-	0.6688
6.7150	8790	-	0.6689
6.7265	8805	-	0.6655
6.7380	8820	-	0.6614
6.7494	8835	-	0.6600
6.7609	8850	-	0.6556
6.7723	8865	-	0.6555
6.7838	8880	-	0.6646
6.7953	8895	-	0.6544
6.8067	8910	-	0.6545
6.8182	8925	-	0.6619
6.8296	8940	-	0.6657
6.8411	8955	-	0.6665
6.8526	8970	-	0.6729
6.8640	8985	-	0.6692
6.8755	9000	0.0108	0.6666
6.8869	9015	-	0.6601
6.8984	9030	-	0.6598
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12.5477	16425	-	0.6548
12.5592	16440	-	0.6563
12.5707	16455	-	0.6552
12.5821	16470	-	0.6561
12.5936	16485	-	0.6567
12.6050	16500	0.0058	0.6550
12.6165	16515	-	0.6551
12.6280	16530	-	0.6560
12.6394	16545	-	0.6573
12.6509	16560	-	0.6578
12.6623	16575	-	0.6568
12.6738	16590	-	0.6551
12.6853	16605	-	0.6539
12.6967	16620	-	0.6532
12.7082	16635	-	0.6531
12.7196	16650	-	0.6526
12.7311	16665	-	0.6500
12.7426	16680	-	0.6503
12.7540	16695	-	0.6524
12.7655	16710	-	0.6539
12.7769	16725	-	0.6535
12.7884	16740	-	0.6539
12.7998	16755	-	0.6534
12.8113	16770	-	0.6526
12.8228	16785	-	0.6540
12.8342	16800	-	0.6537
12.8457	16815	-	0.6549
12.8571	16830	-	0.6562
12.8686	16845	-	0.6560
12.8801	16860	-	0.6575
12.8915	16875	-	0.6581
12.9030	16890	-	0.6564
12.9144	16905	-	0.6547
12.9259	16920	-	0.6553
12.9374	16935	-	0.6550
12.9488	16950	-	0.6545
12.9603	16965	-	0.6530
12.9717	16980	-	0.6535
12.9832	16995	-	0.6527
12.9870	17000	0.0043	-
12.9947	17010	-	0.6529
13.0	17017	-	0.6531
13.0061	17025	-	0.6536
13.0176	17040	-	0.6536
13.0290	17055	-	0.6553
13.0405	17070	-	0.6565
13.0519	17085	-	0.6558
13.0634	17100	-	0.6563
13.0749	17115	-	0.6550
13.0863	17130	-	0.6544
13.0978	17145	-	0.6530
13.1092	17160	-	0.6527
13.1207	17175	-	0.6563
13.1322	17190	-	0.6571
13.1436	17205	-	0.6573
13.1551	17220	-	0.6556
13.1665	17235	-	0.6541
13.1780	17250	-	0.6536
13.1895	17265	-	0.6533
13.2009	17280	-	0.6533
13.2124	17295	-	0.6531
13.2238	17310	-	0.6542
13.2353	17325	-	0.6539
13.2468	17340	-	0.6543
13.2582	17355	-	0.6547
13.2697	17370	-	0.6563
13.2811	17385	-	0.6566
13.2926	17400	-	0.6553
13.3040	17415	-	0.6541
13.3155	17430	-	0.6542
13.3270	17445	-	0.6558
13.3384	17460	-	0.6552
13.3499	17475	-	0.6554
13.3613	17490	-	0.6548
13.3690	17500	0.0058	-
13.3728	17505	-	0.6543
13.3843	17520	-	0.6551
13.3957	17535	-	0.6549
13.4072	17550	-	0.6550
13.4186	17565	-	0.6550
13.4301	17580	-	0.6567
13.4416	17595	-	0.6583
13.4530	17610	-	0.6577
13.4645	17625	-	0.6577
13.4759	17640	-	0.6577
13.4874	17655	-	0.6585
13.4989	17670	-	0.6570
13.5103	17685	-	0.6546
13.5218	17700	-	0.6541
13.5332	17715	-	0.6538
13.5447	17730	-	0.6553
13.5561	17745	-	0.6558
13.5676	17760	-	0.6555
13.5791	17775	-	0.6548
13.5905	17790	-	0.6547
13.6020	17805	-	0.6539
13.6134	17820	-	0.6539
13.6249	17835	-	0.6545
13.6364	17850	-	0.6544
13.6478	17865	-	0.6547
13.6593	17880	-	0.6534
13.6707	17895	-	0.6534
13.6822	17910	-	0.6527
13.6937	17925	-	0.6532
13.7051	17940	-	0.6544
13.7166	17955	-	0.6531
13.7280	17970	-	0.6531
13.7395	17985	-	0.6519
13.7510	18000	0.0048	0.6521
13.7624	18015	-	0.6493
13.7739	18030	-	0.6472
13.7853	18045	-	0.6481
13.7968	18060	-	0.6485
13.8083	18075	-	0.6476
13.8197	18090	-	0.6480
13.8312	18105	-	0.6492
13.8426	18120	-	0.6510
13.8541	18135	-	0.6514
13.8655	18150	-	0.6518
13.8770	18165	-	0.6520
13.8885	18180	-	0.6507
13.8999	18195	-	0.6506
13.9114	18210	-	0.6504
13.9228	18225	-	0.6500
13.9343	18240	-	0.6503
13.9458	18255	-	0.6496
13.9572	18270	-	0.6503
13.9687	18285	-	0.6492
13.9801	18300	-	0.6495
13.9916	18315	-	0.6504
14.0	18326	-	0.6503
14.0031	18330	-	0.6498
14.0145	18345	-	0.6499
14.0260	18360	-	0.6500
14.0374	18375	-	0.6507
14.0489	18390	-	0.6513
14.0604	18405	-	0.6511
14.0718	18420	-	0.6514
14.0833	18435	-	0.6519
14.0947	18450	-	0.6516
14.1062	18465	-	0.6522
14.1176	18480	-	0.6527
14.1291	18495	-	0.6524
14.1329	18500	0.0045	-
14.1406	18510	-	0.6527
14.1520	18525	-	0.6532
14.1635	18540	-	0.6530
14.1749	18555	-	0.6531
14.1864	18570	-	0.6531
14.1979	18585	-	0.6527
14.2093	18600	-	0.6535
14.2208	18615	-	0.6536
14.2322	18630	-	0.6531
14.2437	18645	-	0.6527
14.2552	18660	-	0.6532
14.2666	18675	-	0.6535
14.2781	18690	-	0.6535
14.2895	18705	-	0.6535
14.3010	18720	-	0.6535
14.3125	18735	-	0.6537
14.3239	18750	-	0.6536
14.3354	18765	-	0.6534
14.3468	18780	-	0.6547
14.3583	18795	-	0.6541
14.3697	18810	-	0.6539
14.3812	18825	-	0.6548
14.3927	18840	-	0.6547
14.4041	18855	-	0.6548
14.4156	18870	-	0.6546
14.4270	18885	-	0.6544
14.4385	18900	-	0.6543
14.4500	18915	-	0.6541
14.4614	18930	-	0.6537
14.4729	18945	-	0.6536
14.4843	18960	-	0.6533
14.4958	18975	-	0.6531
14.5073	18990	-	0.6517
14.5149	19000	0.0055	-
14.5187	19005	-	0.6517
14.5302	19020	-	0.6521
14.5416	19035	-	0.6522
14.5531	19050	-	0.6528
14.5646	19065	-	0.6524
14.5760	19080	-	0.6529
14.5875	19095	-	0.6529
14.5989	19110	-	0.6525
14.6104	19125	-	0.6523
14.6218	19140	-	0.6524
14.6333	19155	-	0.6528
14.6448	19170	-	0.6526
14.6562	19185	-	0.6530
14.6677	19200	-	0.6532
14.6791	19215	-	0.6532
14.6906	19230	-	0.6532
14.7021	19245	-	0.6533
14.7135	19260	-	0.6526
14.7250	19275	-	0.6522
14.7364	19290	-	0.6520
14.7479	19305	-	0.6522
14.7594	19320	-	0.6522
14.7708	19335	-	0.6522
14.7823	19350	-	0.6521
14.7937	19365	-	0.6521
14.8052	19380	-	0.6521
14.8167	19395	-	0.6521
14.8281	19410	-	0.6522
14.8396	19425	-	0.6523
14.8510	19440	-	0.6523
14.8625	19455	-	0.6523
14.8739	19470	-	0.6521
14.8854	19485	-	0.6522
14.8969	19500	0.0039	0.6522
14.9083	19515	-	0.6521
14.9198	19530	-	0.6523
14.9312	19545	-	0.6522
14.9427	19560	-	0.6521
14.9542	19575	-	0.6520
14.9656	19590	-	0.6521
14.9771	19605	-	0.6520
14.9885	19620	-	0.6520
15.0	19635	-	0.6520

Framework Versions

• Python: 3.11.0rc1
• Sentence Transformers: 3.0.1
• Transformers: 4.41.1
• PyTorch: 2.2.2+cu121
• Accelerate: 0.30.1
• Datasets: 2.20.0
• Tokenizers: 0.19.1

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}
}