metadata

tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:99239
  - loss:MultipleNegativesRankingLoss
base_model: Alibaba-NLP/gte-multilingual-mlm-base
widget:
  - source_sentence: '''백발 급구''의 주인공 구신의 직업은 무엇인가요?'
    sentences:
      - >-
        그레고리아 데 헤수스(Gregoria de Jesús, 1875년 5월 9일 - 1943년 3월 15일)는 필리핀의 독립운동가이자
        카티푸난의 여성 지도자였다. 필리핀 임시 혁명정부의 제1대 부통령이자 첫 번째 퍼스트 레이디였다.
      - >-
        ＜백발 급구＞는 모스크바와 페테르부르크의 건축물들에 대한 작가의 폭넓은 이해를 엿볼 수 있는 작품이다. 건축물 사진첩들을 뒤적이며
        소일하는 것을 위로 삼아 부정한 아내와의 원만치 못한 결혼 생활을 견뎌가고 있는 주인공 구신은 페테르부르크 출장 중에 렌필름
        영화사 앞에서 이류 배우 나타샤와 만나게 된다. 사출 전문가라는 특이한 직업과는 어울리지 않게 페테르부르크의 유명 건축물들을
        건설한 예술가들에 대한 이해와 삶의 깊이를 가진 구신의 매력에 빠져든 나타샤는 구신과 보낸 이틀을 잊지 못하게 된다. 모스크바로
        돌아온 구신도 나타샤와의 사랑을 통해 삶의 의미를 다시 찾게 되고 모스크바의 생활을 정리하고 페테르부르크의 나타샤에게로 되돌아가게
        되면서 작품은 끝을 맺는다.
      - >-
        1991년 의족을 하고 눈을 가린 초인은 절대 붕대를 풀어서는 안된다고 어머니는 말한다. 그때 어머니가 아버지에게 가정폭력을
        당하자 붕대를 풀고 아버지를 살해한다.

        결국 모자는 동반자살을 위해 초인의 목을 조르려고 하자 초인은 붕대를 벗고 달아난다.19년후인 2010년 자동차 폐기장에서 일하는
        임규남(고수 분)은 교통 사고로 인해 병원에 입원하게 되고, 그 때문에 일하던 곳에서 해직당한다. 그 이후 일자리를 알아보던
        규남은 전당포 일을 맡게 된다. 사장은 자꾸 장부와 금고의 돈이 맞지 않는다며 의문을 제기한다. 어느 날 자동차 폐기장에서 일하던
        규남의 친구들이 전당포로 놀러온다. 다 함께 점심을 먹던 중 갑자기 몸이 움직이지 않고, 이상한 기운이 감돈다. 규남은 살짝
        정신을 차렸지만 친구들과 사장은 움직이지 않는다. 잠시 후 사람이 들어오고 사장은 초점이 없는 눈으로 금고의 돈을 꺼내 그
        사람에게 건넨다. 뭔가 일이 이상하게 흘러간다고 생각한 규남은 정신을 바짝 차려 움직이는데 성공한다. 초인은 규남만 움직이는 것을
        보며 놀라움을 감추지 못한다. 규남은 초인을 물리치려 하지만 규남은 조종당하는 자신의 친구들과 사장의 공격을 당하고 결국 사장이
        죽게된다. 초인이 물러난 후 정신이 깨어난 규남의 친구들은 규남과 함께 녹화된 CCTV 테잎을 보게 되고 초인에 의해 조종받았다는
        사실을 알게 되고 싸우기로 결심한다. 그리고 자신을 제외한 모든 사람들을 눈으로 조종한다는 사실을 깨달은 규남은 전투 끝에 규남을
        제압하고 얼굴에 봉지를 씌워 경찰서에 데려간다. 경찰에게 자초지종을 이야기 하고 CCTV 비디오 테이프를 건네주지만 경찰은 규남을
        믿지 않는다. 결국 경찰은 봉투를 벗기게 되고 전부 조종당한다. 총격전 끝에 가까스로 살아남은 규남은 경찰의 총을 뺏어가면서
        총기탈취 용의자로 몰리게 된다. 한편 규남의 친구들은 눈으로 조종한다는 사실을 깨닫고 각종 무기와 방어구를 갖고 초인을 대하지만
        전혀 통하지 않고, 초인에게 끌려간다. 규남은 전당포에서 인질로 붙잡힌 친구들을 찾지만 친구들은 초인에 의해 살해된다. 초인을
        쫒던 규남은 옥상에서 초인을 만나 죽이려 하지만 반대편에 사장의 딸 영숙(정은채 분)이 난간위에 올라가 떨어질 위기에 처했다는
        것을 보고 잠시 움찔하지만 결국 초인과 같이 뛰어내려 초인은 죽고 규남은 살아남는다. 영숙은 다행히 옥상쪽으로 떨어져 살아남는다.
        이 사고로 규남은 전신마비가 되고 스튜어디스가 된 영숙은 그를 챙긴다. 몇 개월후 지하철 선로에 떨어진 유치원생이 위기에 처한다.
        유치원생을 넘어 전차가 지나가고, 규남에 의해 구조된다.
  - source_sentence: 산화적 인산화와 ATP 합성의 차이는 무엇인가요?
    sentences:
      - >-
        ATP를 만드는데 사용되는 또 다른 방법은 산화적 인산화를 통해서이며, 이는 세포 호흡 중에 일어난다. 산화적 인산화는 NADH를
        NAD로 산화시키는 과정에서 2.5개의 ATP를 생성하고, FADH를 FAD로 산화시키는 과정에서 1.5개의 ATP를 생성한다.
        미토콘드리아 내막을 가로질러 양성자의 전기화학적 구배로 저장된 위치 에너지는 ADP와 P(무기인산)로부터 ATP를 생성하는데
        필요하고, 이 부분에서 기질수준 인산화와 큰 차이가 난다. 이러한 H의 농도 기울기는 ATP 생성효소에 의해 이용되어, H가
        전기화학적인 구배에 의해 ATP 생성효소를 통해서 미토콘드리아의 막 사이 공간에서 미토콘드리아 기질로 확산될 때(화학삼투)
        방출되는 자유 에너지를 ATP 생성과 짝짓는다. 역으로, 전자전달은 H를 미토콘드리아 기질에서 막 사이 공간으로 능동수송하는데
        필요한 에너지를 제공한다.
      - >-
        역사적으로 아디프산은 산화에 쓰이는 여러 지방(FAT)에서 만들어졌다. 현재 아디프산은 사이클로헥세인올과 "KA
        oil"(케톤-알코올 오일의 약자(Ketone-Alcohol oil), 이하 케알오일)이라 불리는 사이클로헥세온의 혼합물로부터
        만들어진다. 이 케알오일은

        여러가지를 거쳐 질산과 산화된다. 사이클로헥세인올이 아질산을 내놓으면서 케톤으로 바뀐다:
      - >-
        산화적 인산화는 에너지 방출 반응을 사용하여 에너지 흡수 반응을 진행시킨다. 두 세트의 반응이 짝지어져 있다고 하는데, 이는 두
        반응 중 하나가 작동하지 않으면 다른 하나도 작동할 수 없다는 것을 의미한다. NADH와 같은 전자공여체에서부터 산소(O)와 같은
        전자수용체에 이르기까지 전자전달계를 통한 전자의 흐름은 에너지를 방출하는 과정이다. 반면에 ATP 합성은 에너지를 흡수하는
        과정이다. 전자전달계와 ATP 생성효소는 모두 미토콘드리아 내막에 존재하고, 미토콘드리아 내막을 통과하는 H의 움직임에 의해
        전자전달계에서 ATP 생성효소로 에너지가 전달되는데, 이러한 과정을 화학삼투라고 한다. 실제로 이것은 단순한 전기 회로 같으며,
        H는 전자전달계의 H 펌핑 효소들에 의해 미토콘드리아 기질(N쪽, 상대적으로 H 농도가 낮은 쪽)에서 막 사이 공간(P쪽,
        상대적으로 H 농도가 높은 쪽)으로 능동수송된다. 이러한 효소들은 배터리와 같으며, 회로를 통해 전류를 흐르게 하는 일을
        수행한다. 양성자(H)의 움직임은 막을 가로질러 전기화학적인 기울기를 만들어 내는데, 이것은 종종 양성자
        구동력(proton-motive force)라고 불린다. 양성자 구동력은 H의 농도 차이(양성자 기울기, ΔpH)로 인한 화학적인
        위치 에너지와 H가 상대 이온 없이 막을 지나 이동할 때, 전하의 분리 차이로 인한 전기적인 위치 에너지의 2가지 구성 요소를
        가진다.
  - source_sentence: 이승만의 독립운동 단체 활동에 대해 자세히 알고 싶습니까?
    sentences:
      - >-
        한편, 북미정상회담을 준비하던 와중에 틸러슨 국무장관이 경질되는 사건이 일어났다. 더욱이 트위터를 통해 소식을 알렸는데 트럼프와
        틸러슨이 자주 이견을 보였고 북한에 상대적으로 유화적인 태도를 보였기 때문인 것으로 알려졌다. 후임에는 마이크 폼페이오
        CIA국장이 내정되어 4월 27일 임명되었는데 미국의 대외정책이 더욱 강경해질 것이라는 분석이 나왔다. 또한 북미정상회담 발표
        이후 북한은 자제하던 대미 공세를 15일부터 재개했는데 미국이 인권문제를 언급할 것을 사전에 예방하면서 협상력을 높이기 위한 명분
        쌓기라는 관측이다.
      - >-
        독립협회와 청년이승만(獨立協會와 靑年 李承晩 Independence Association and young Rhee
        Syng-Man)은 1959년 서울에서 상영된 대한민국의 영화로, 대통령 이승만의 청년기 개화, 계몽운동사를 다루었다. 특히
        기울어져 가는 이조 말엽의 국운을 바로잡기 위한 그의 투쟁사를 중심으로 한 전기물이었다.경기도 안양군의 스튜디오에서 제작되었으며
        서울 시공관에서 1959년 11월 20일 개봉되었다.1960년 1월 1일자 동아일보의 영화평 보도에 의하면 연기자 5백여 명과
        엑스트라 6만 명이 출연하였다 한다. 그밖에 특별히 외국인 연기자들이 초빙되기도 했다.
      - >-
        이후 이승만이 영향을 발휘하던 독립운동단체 흥업구락부에서도 가입하여 활동하였는데 1925년 11월 신흥우,송진우 등과 함께 미국의
        이승만과 사전 협의를 거친 끝에 태평양문제연구회 조선지회를 조직하고 위원을 맡았다. 1927년 1월에는 비타협적 민족주의자와
        사회주의자들이 결합한 좌우연합 독립운동 단체인 신간회 발기인으로 참가했다. 1934년 2월 연희전문학교 부교장을 맡았다.
        연희전문학교 부교장 시절 특히 체육에 관심이 깊어, 그의 주선으로 연희전문학교 주최 전국중학교체육대회를 매년 개최하기도 하였다.
        1937년 7월 조선체육회 회장을 맡았다.
  - source_sentence: 아키야마가 나오를 도와주기로 결심한 이유는 무엇인가요?
    sentences:
      - >-
        장위(, 1976년 11월 10일 ~ )는 중화인민공화국의 배우이다. 후베이 성 스옌 시 윈 현 출생이며, 본명은 장밍룽()이다.
        2006년 11월 중화인민공화국의 유명 감독과 프로듀서 등과 성관계를 가진 것을 찍은 녹음 테이프를 공개하여 논란을 불러일으켰다.
      - >-
        출소한 아키야마를 만난 나오는 다짜고짜 도움을 청해보지만, 대답은 당연히 ‘No’. 하지만 절박한 심정의 그녀는 끈질기게
        아키야마의 뒤를 쫓는다. 나오가 귀찮아진 아키야마는 “도와 줄 테니 잠시 기다리라”는 거짓말로 그녀를 떼어놓지만, 순진한 나오는
        정말로 밤을 세워가며 아키야마를 기다린다. 그런 모습을 멀리서 지켜본 아키야마는 마음을 고쳐먹고 나오를 도와주기로 결심한다.
      - >-
        코코야시 마을의 여자 해병 벨메일에 의해 구해졌고, 비가 오고 폭풍이 오는 위급한 상황임에도 불구하고 해맑게 웃고 있었다. 자신과
        같이 구조된 여자아이는 바로 '노지코', 자신의 양언니이다.(KBS 더빙판에는 '사비나'로 번역.) 나미는 벨메일의 손에서
        코코야시 마을에서 키워졌으며 여전히 행복한 나날을 지내고 있었다. 나미는 바다에 관해 관심이 많았으며 특히 지도를 그리는 실력은
        거의 신동에 가까웠다. 그러나 벨메일이 자신의 친엄마가 아니라는 사실을 마음 속에 새겨두고 있었으며, 그것 때문에 벨메일은
        순간적인 홧김에 나미를 집 밖으로 쫓아버린다. 벨메일은 나미가 좋아하는 귤 음식을 한다며 노지코에게 나미를 다시 불러오라고
        하였다. 노지코는 나미에게 가서 벨메일이 미안하다고 하니 같이 집으로 돌아가자고 하였고 나미는 승낙하였다. 그때 갑자기 코코야시
        마을에 아론 일당이 들어왔다. 아론 일당의 선장 아론은 '지금부터 이 마을을 지배할테니, 어른당 10만 베리, 어린이당 5만
        베리를 납부하라'고 명령하였다. 마을 사람들은 어쩔 수 없이 모두 납부하였고 겐조를 비롯한 마을 사람들은 벨메일의 집을 건드리지
        않아서 다행이라 생각했지만 불운하게도 벨메일이 요리를 하고 있어 집 밖으로 피어오르는 연기때문에 들키고 말았다.

        벨메일은 아론 일당이 자신의 집 앞에 와있다는 것을 짐작하고 아론에게 총을 겨누었으나 어인의 엄청난 힘으로 벨메일은 고폭행
        당했다때 나미와 노지코, 겐조가 달려왔고 벨메일은 전재산이 겨우 10만베리라 모두 내주었다. 아론 일당은 나미와 노지코가 벨메일의
        자식이 아니라면 그 돈을 겐조가 내고 벨메일을 살려 주기로 하였다. 이때 벨메일은 결국 나미와 노지코를 자기 자식이라
        발언해버리고, 돈이 모자란 조건에 아론은 벨메일을 죽이기로 한다. 벨메일은 나미와 노지코에게 '생존하면 반드시 행복한 일이 많이
        있을것이다. 나미, 노지코.

        사랑한다."라는 유언을 남기며 아론의 총탄에 목숨을 잃고 겐조 또한 간부에게 베여버려 크게 다치고 만다. 이때 나미가 해도를
        그리는 데 엄청난 실력을 갖추고 있는 것을 알아버린 아론은 나미를 강제로 끌고 가서 자신의 측량실에 나미에게 해도를 그리라고
        한다. 나미는 마을 사람들이 자신 때문에 죽는 것을 꺼려하여 결국 아론 파크의 문신을 어깨에 새기고 만다. 아론은 '내 눈앞에
        1억 베리를 바치면 너를 포함한 코코야시 마을을 해방시켜주겠다'고 제안했고 나미는 그 제안을 받아들였다. 그 때부터 나미는
        코코야시 마을을 사기 위해 해적 전문 도둑이 되어 바다로 떠나서 해적들한테서 돈을 뺐기 시작했다.
  - source_sentence: 안압지의 역사적 이름은 무엇인가요?
    sentences:
      - >-
        1980년, 안압지에서 발굴된 토기 파편 등으로 신라시대에 이 곳이 월지(月池)라고 불렸다는 사실이 확인되었다. 이는 신라 왕궁인
        반월성(半月城)과 가까이 있었기 때문이며, 임해전의 이름도 본디 월지궁이었다고 한다. 조선시대에는 폐허가 된 이곳에 기러기와
        오리들이 날아들자 조선의 묵객들이 안압지(雁鴨池)라는 이름을 붙였다. 《삼국사기》에 동궁을 임해전(臨海殿), 즉 바다에 면한
        건물이라고 불렀다는 기록이 있으며, 여기에서 안압지는 바다를 상징한다.
      - >-
        성은 왕, 본관은 개성이며, 이름은 기록이 남아있지 않아 알 수 없다. 충혜왕, 공민왕 등과 이복 형제간이다. 용산원자의 어머니
        조국장공주는 원나라 황족 출신으로, 원 순종의 아들인 위왕 에무게의 딸이다. 1324년(충숙왕 11년)에 충숙왕이 원나라에 있을
        때 그와 혼인하였다.
      - 안압지라는 명칭은 조선 초기에 간행된 《동국여지승람》과 《동경잡기》등에 나타나고 있다.
pipeline_tag: sentence-similarity
library_name: sentence-transformers

SentenceTransformer based on Alibaba-NLP/gte-multilingual-mlm-base

This is a sentence-transformers model finetuned from Alibaba-NLP/gte-multilingual-mlm-base. It maps sentences & paragraphs to a 768-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: Alibaba-NLP/gte-multilingual-mlm-base
Maximum Sequence Length: 512 tokens
Output Dimensionality: 768 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: NewModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)

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("seongil-dn/gte-base-filtered-neg4-bs96")
# Run inference
sentences = [
    '안압지의 역사적 이름은 무엇인가요?',
    '1980년, 안압지에서 발굴된 토기 파편 등으로 신라시대에 이 곳이 월지(月池)라고 불렸다는 사실이 확인되었다. 이는 신라 왕궁인 반월성(半月城)과 가까이 있었기 때문이며, 임해전의 이름도 본디 월지궁이었다고 한다. 조선시대에는 폐허가 된 이곳에 기러기와 오리들이 날아들자 조선의 묵객들이 안압지(雁鴨池)라는 이름을 붙였다. 《삼국사기》에 동궁을 임해전(臨海殿), 즉 바다에 면한 건물이라고 불렀다는 기록이 있으며, 여기에서 안압지는 바다를 상징한다.',
    '안압지라는 명칭은 조선 초기에 간행된 《동국여지승람》과 《동경잡기》등에 나타나고 있다.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

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

Training Details

Training Dataset

Unnamed Dataset

Size: 99,239 training samples
Columns: anchor, positive, and negative

Approximate statistics based on the first 1000 samples:

	anchor	positive	negative
type	string	string	string
details	min: 8 tokens mean: 18.0 tokens max: 32 tokens	min: 16 tokens mean: 155.17 tokens max: 512 tokens	min: 2 tokens mean: 132.62 tokens max: 512 tokens

Samples:

anchor	positive	negative
`야노 타쿠지의 동생은 어떤 성격을 가지고 있나요?`	중반부터 추가된 새로운 전사. 예전 유스케들을 감싸 볼트에게 살해당한 야노 타쿠지의 동생. 강한 파워 파이터 이기도 하며 형의 사후 도로테 박사들에 의해 라이브맨을 지원하는 훈련을 받고 있었다. 타케시라는 동생이 있으나 극중에서 이들 사이의 관계는 뚜렷하지 않은 편이다. 권투가 뛰어나고 조금 무모하고 말투도 난폭하다. 특히 형들의 적인 볼트가 관련되면 냉정할 수 없게 되지만 실력이 따라주지 않는 경우가 많아서 초기에는 멤버 3명의 발목을 잡기도 했다. 하지만 곤란한 사람은 내버려 둘수없는 상냥한 성격이다.	`노노의 남동생이며 게임을 좋아한다.`
`야노 타쿠지의 동생은 어떤 성격을 가지고 있나요?`	중반부터 추가된 새로운 전사. 예전 유스케들을 감싸 볼트에게 살해당한 야노 타쿠지의 동생. 강한 파워 파이터 이기도 하며 형의 사후 도로테 박사들에 의해 라이브맨을 지원하는 훈련을 받고 있었다. 타케시라는 동생이 있으나 극중에서 이들 사이의 관계는 뚜렷하지 않은 편이다. 권투가 뛰어나고 조금 무모하고 말투도 난폭하다. 특히 형들의 적인 볼트가 관련되면 냉정할 수 없게 되지만 실력이 따라주지 않는 경우가 많아서 초기에는 멤버 3명의 발목을 잡기도 했다. 하지만 곤란한 사람은 내버려 둘수없는 상냥한 성격이다.	`야스히코의 여동생.`
`야노 타쿠지의 동생은 어떤 성격을 가지고 있나요?`	중반부터 추가된 새로운 전사. 예전 유스케들을 감싸 볼트에게 살해당한 야노 타쿠지의 동생. 강한 파워 파이터 이기도 하며 형의 사후 도로테 박사들에 의해 라이브맨을 지원하는 훈련을 받고 있었다. 타케시라는 동생이 있으나 극중에서 이들 사이의 관계는 뚜렷하지 않은 편이다. 권투가 뛰어나고 조금 무모하고 말투도 난폭하다. 특히 형들의 적인 볼트가 관련되면 냉정할 수 없게 되지만 실력이 따라주지 않는 경우가 많아서 초기에는 멤버 3명의 발목을 잡기도 했다. 하지만 곤란한 사람은 내버려 둘수없는 상냥한 성격이다.	`그의 동생인 고기석 또한 만화가이다.`

Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

per_device_train_batch_size: 96
learning_rate: 0.0001
adam_epsilon: 1e-07
num_train_epochs: 5
warmup_ratio: 0.05
bf16: True
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: no
prediction_loss_only: True
per_device_train_batch_size: 96
per_device_eval_batch_size: 8
per_gpu_train_batch_size: None
per_gpu_eval_batch_size: None
gradient_accumulation_steps: 1
eval_accumulation_steps: None
torch_empty_cache_steps: None
learning_rate: 0.0001
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-07
max_grad_norm: 1.0
num_train_epochs: 5
max_steps: -1
lr_scheduler_type: linear
lr_scheduler_kwargs: {}
warmup_ratio: 0.05
warmup_steps: 0
log_level: passive
log_level_replica: warning
log_on_each_node: True
logging_nan_inf_filter: True
save_safetensors: True
save_on_each_node: False
save_only_model: False
restore_callback_states_from_checkpoint: False
no_cuda: False
use_cpu: False
use_mps_device: False
seed: 42
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: True
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
eval_on_start: False
eval_use_gather_object: False
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss
0.0010	1	6.0761
0.0019	2	5.4696
0.0029	3	6.0283
0.0039	4	5.725
0.0048	5	5.6116
0.0058	6	5.447
0.0068	7	5.4655
0.0077	8	5.6126
0.0087	9	5.9597
0.0097	10	5.6583
0.0106	11	5.6846
0.0116	12	5.3796
0.0126	13	5.9556
0.0136	14	5.9142
0.0145	15	5.8192
0.0155	16	5.4182
0.0165	17	5.3768
0.0174	18	5.7815
0.0184	19	5.5915
0.0194	20	5.5445
0.0203	21	5.4324
0.0213	22	5.365
0.0223	23	5.4745
0.0232	24	5.4416
0.0242	25	5.3282
0.0252	26	5.6499
0.0261	27	5.537
0.0271	28	5.3231
0.0281	29	5.5125
0.0290	30	5.4939
0.0300	31	5.7354
0.0310	32	5.2808
0.0319	33	5.6719
0.0329	34	5.4923
0.0339	35	5.2214
0.0348	36	5.2656
0.0358	37	5.3045
0.0368	38	5.6441
0.0378	39	5.2787
0.0387	40	5.0395
0.0397	41	5.398
0.0407	42	4.9811
0.0416	43	5.6311
0.0426	44	5.1735
0.0436	45	4.8979
0.0445	46	5.0585
0.0455	47	4.7773
0.0465	48	4.6178
0.0474	49	4.896
0.0484	50	4.7486
0.0494	51	4.6619
0.0503	52	4.632
0.0513	53	4.7354
0.0523	54	4.838
0.0532	55	4.694
0.0542	56	4.6128
0.0552	57	4.4835
0.0561	58	4.4826
0.0571	59	4.5447
0.0581	60	4.3147
0.0591	61	4.4533
0.0600	62	4.2798
0.0610	63	4.1942
0.0620	64	4.057
0.0629	65	4.2321
0.0639	66	3.99
0.0649	67	3.8962
0.0658	68	3.6657
0.0668	69	3.5564
0.0678	70	3.5317
0.0687	71	3.2773
0.0697	72	2.9232
0.0707	73	3.2449
0.0716	74	3.4219
0.0726	75	2.9998
0.0736	76	3.0874
0.0745	77	3.1482
0.0755	78	3.1247
0.0765	79	2.8267
0.0774	80	2.8905
0.0784	81	2.6198
0.0794	82	2.5581
0.0803	83	2.4113
0.0813	84	2.4397
0.0823	85	2.1872
0.0833	86	2.3872
0.0842	87	2.3307
0.0852	88	2.0877
0.0862	89	2.263
0.0871	90	2.3048
0.0881	91	2.2513
0.0891	92	1.9171
0.0900	93	1.8847
0.0910	94	1.7235
0.0920	95	1.7936
0.0929	96	1.5383
0.0939	97	1.7893
0.0949	98	1.7196
0.0958	99	1.3623
0.0968	100	1.4125
0.0978	101	1.4238
0.0987	102	1.3337
0.0997	103	1.1182
0.1007	104	1.2398
0.1016	105	1.2641
0.1026	106	1.127
0.1036	107	1.0222
0.1045	108	0.9893
0.1055	109	1.0013
0.1065	110	1.0016
0.1075	111	0.8696
0.1084	112	0.7812
0.1094	113	0.8705
0.1104	114	0.7513
0.1113	115	0.7766
0.1123	116	0.7832
0.1133	117	0.8242
0.1142	118	0.7847
0.1152	119	0.6173
0.1162	120	0.6534
0.1171	121	0.8419
0.1181	122	0.643
0.1191	123	0.6175
0.1200	124	0.6818
0.1210	125	0.7784
0.1220	126	0.6841
0.1229	127	0.5816
0.1239	128	0.6703
0.1249	129	0.791
0.1258	130	0.702
0.1268	131	0.7459
0.1278	132	0.5366
0.1288	133	0.6364
0.1297	134	0.5861
0.1307	135	0.6055
0.1317	136	0.4942
0.1326	137	0.7339
0.1336	138	0.7158
0.1346	139	0.5765
0.1355	140	0.5728
0.1365	141	0.8744
0.1375	142	0.8383
0.1384	143	0.6794
0.1394	144	0.5059
0.1404	145	0.5983
0.1413	146	0.4877
0.1423	147	0.5052
0.1433	148	0.4639
0.1442	149	0.8201
0.1452	150	0.7705
0.1462	151	0.5388
0.1471	152	0.4903
0.1481	153	0.6167
0.1491	154	0.5446
0.1500	155	0.4804
0.1510	156	0.4164
0.1520	157	0.6186
0.1530	158	0.626
0.1539	159	0.4926
0.1549	160	0.3961
0.1559	161	0.519
0.1568	162	0.5028
0.1578	163	0.3303
0.1588	164	0.3655
0.1597	165	0.5287
0.1607	166	0.4638
0.1617	167	0.3889
0.1626	168	0.2826
0.1636	169	0.4772
0.1646	170	0.4887
0.1655	171	0.3495
0.1665	172	0.3662
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0.7870	813	0.281
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0.7967	823	0.2102
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0.9380	969	0.2253
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0.9439	975	0.2114
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1.0010	1033	0.2929
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1.3591	1403	0.0408
1.3601	1404	0.0269
1.3611	1405	0.1212
1.3621	1406	0.0568
1.3630	1407	0.0338
1.3640	1408	0.0378
1.3650	1409	0.0868
1.3659	1410	0.0545
1.3669	1411	0.0284
1.3679	1412	0.0559
1.3688	1413	0.0659
1.3698	1414	0.0518
1.3708	1415	0.049
1.3717	1416	0.0413
1.3727	1417	0.0651
1.3737	1418	0.044
1.3746	1419	0.0448
1.3756	1420	0.0675
1.3766	1421	0.0658
1.3775	1422	0.0765
1.3785	1423	0.0284
1.3795	1424	0.0234
1.3804	1425	0.088
1.3814	1426	0.0707
1.3824	1427	0.0494
1.3833	1428	0.0322
1.3843	1429	0.0496
1.3853	1430	0.0611
1.3863	1431	0.0479
1.3872	1432	0.0387
1.3882	1433	0.0742
1.3892	1434	0.0475
1.3901	1435	0.0299
1.3911	1436	0.0857
1.3921	1437	0.143
1.3930	1438	0.1043
1.3940	1439	0.1216
1.3950	1440	0.0377
1.3959	1441	0.147
1.3969	1442	0.0925
1.3979	1443	0.0556
1.3988	1444	0.0322
1.3998	1445	0.1185
1.4008	1446	0.0673
1.4017	1447	0.049
1.4027	1448	0.0342
1.4037	1449	0.0501
1.4046	1450	0.0606
1.4056	1451	0.0406
1.4066	1452	0.0498
1.4076	1453	0.0598
1.4085	1454	0.0416
1.4095	1455	0.0258
1.4105	1456	0.032
1.4114	1457	0.168
1.4124	1458	0.0943
1.4134	1459	0.0501
1.4143	1460	0.0521
1.4153	1461	0.0804
1.4163	1462	0.0372
1.4172	1463	0.0323
1.4182	1464	0.0308
1.4192	1465	0.0867
1.4201	1466	0.0868
1.4211	1467	0.0383
1.4221	1468	0.0482
1.4230	1469	0.0741
1.4240	1470	0.0695
1.4250	1471	0.0493
1.4259	1472	0.0489
1.4269	1473	0.0826
1.4279	1474	0.0544
1.4288	1475	0.0564
1.4298	1476	0.046
1.4308	1477	0.0672
1.4318	1478	0.0395
1.4327	1479	0.0499
1.4337	1480	0.0369
1.4347	1481	0.0839
1.4356	1482	0.0597
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1.4376	1484	0.0557
1.4385	1485	0.07
1.4395	1486	0.098
1.4405	1487	0.0649
1.4414	1488	0.0346
1.4424	1489	0.0739
1.4434	1490	0.0452
1.4443	1491	0.0451
1.4453	1492	0.035
1.4463	1493	0.0749
1.4472	1494	0.0704
1.4482	1495	0.0486
1.4492	1496	0.0474
1.4501	1497	0.1025
1.4511	1498	0.0402
1.4521	1499	0.0479
1.4530	1500	0.0263
1.4540	1501	0.0555
1.4550	1502	0.0655
1.4560	1503	0.0779
1.4569	1504	0.0453
1.4579	1505	0.0816
1.4589	1506	0.0539
1.4598	1507	0.0511
1.4608	1508	0.0645
1.4618	1509	0.0602
1.4627	1510	0.0507
1.4637	1511	0.0478
1.4647	1512	0.0456
1.4656	1513	0.0494
1.4666	1514	0.0764
1.4676	1515	0.0231
1.4685	1516	0.0312
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1.4705	1518	0.0561
1.4714	1519	0.0426
1.4724	1520	0.0407
1.4734	1521	0.0642
1.4743	1522	0.0263
1.4753	1523	0.0284
1.4763	1524	0.028
1.4773	1525	0.0652
1.4782	1526	0.0476
1.4792	1527	0.0339
1.4802	1528	0.0359
1.4811	1529	0.0639
1.4821	1530	0.0384
1.4831	1531	0.0349
1.4840	1532	0.0451
1.4850	1533	0.0769
1.4860	1534	0.0534
1.4869	1535	0.0312
1.4879	1536	0.0287
1.4889	1537	0.0807
1.4898	1538	0.0746
1.4908	1539	0.0732
1.4918	1540	0.0579
1.4927	1541	0.082
1.4937	1542	0.0476
1.4947	1543	0.0664
1.4956	1544	0.0418
1.4966	1545	0.0601
1.4976	1546	0.0297
1.4985	1547	0.025
1.4995	1548	0.0364
1.5005	1549	0.0697
1.5015	1550	0.0482
1.5024	1551	0.0578
1.5034	1552	0.0229
1.5044	1553	0.0445
1.5053	1554	0.0419
1.5063	1555	0.029
1.5073	1556	0.0283
1.5082	1557	0.0527
1.5092	1558	0.0352
1.5102	1559	0.0279
1.5111	1560	0.0526
1.5121	1561	0.0852
1.5131	1562	0.0304
1.5140	1563	0.0458
1.5150	1564	0.075
1.5160	1565	0.0927
1.5169	1566	0.0285
1.5179	1567	0.0373
1.5189	1568	0.037
1.5198	1569	0.0571
1.5208	1570	0.0577
1.5218	1571	0.0316
1.5227	1572	0.0316
1.5237	1573	0.0609
1.5247	1574	0.0495
1.5257	1575	0.037
1.5266	1576	0.0218
1.5276	1577	0.0582
1.5286	1578	0.0535
1.5295	1579	0.0457
1.5305	1580	0.0259
1.5315	1581	0.0974
1.5324	1582	0.0609
1.5334	1583	0.0336
1.5344	1584	0.0319
1.5353	1585	0.0409
1.5363	1586	0.0442
1.5373	1587	0.0385
1.5382	1588	0.0486
1.5392	1589	0.069
1.5402	1590	0.0527
1.5411	1591	0.029
1.5421	1592	0.0222
1.5431	1593	0.0808
1.5440	1594	0.0558
1.5450	1595	0.0459
1.5460	1596	0.0383
1.5470	1597	0.0581
1.5479	1598	0.045
1.5489	1599	0.0831
1.5499	1600	0.0514
1.5508	1601	0.0731
1.5518	1602	0.0503
1.5528	1603	0.0498
1.5537	1604	0.0641
1.5547	1605	0.0652
1.5557	1606	0.0871
1.5566	1607	0.0448
1.5576	1608	0.0325
1.5586	1609	0.0578
1.5595	1610	0.0573
1.5605	1611	0.068
1.5615	1612	0.0538
1.5624	1613	0.0416
1.5634	1614	0.0545
1.5644	1615	0.0316
1.5653	1616	0.0495
1.5663	1617	0.1282
1.5673	1618	0.0566
1.5682	1619	0.0567
1.5692	1620	0.0697
1.5702	1621	0.1178
1.5712	1622	0.0681
1.5721	1623	0.0402
1.5731	1624	0.0338
1.5741	1625	0.0636
1.5750	1626	0.0334
1.5760	1627	0.0286
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1.5779	1629	0.0553
1.5789	1630	0.0879
1.5799	1631	0.0423
1.5808	1632	0.0362
1.5818	1633	0.0477
1.5828	1634	0.0911
1.5837	1635	0.0235
1.5847	1636	0.0599
1.5857	1637	0.0705
1.5866	1638	0.0912
1.5876	1639	0.0494
1.5886	1640	0.0255
1.5895	1641	0.0518
1.5905	1642	0.0261
1.5915	1643	0.0266
1.5924	1644	0.0409
1.5934	1645	0.0797
1.5944	1646	0.0591
1.5954	1647	0.0362
1.5963	1648	0.0594
1.5973	1649	0.0736
1.5983	1650	0.0486
1.5992	1651	0.0432
1.6002	1652	0.0428
1.6012	1653	0.0625
1.6021	1654	0.1024
1.6031	1655	0.068
1.6041	1656	0.0764
1.6050	1657	0.071
1.6060	1658	0.0554
1.6070	1659	0.0328
1.6079	1660	0.0511
1.6089	1661	0.0467
1.6099	1662	0.0461
1.6108	1663	0.0365
1.6118	1664	0.0462
1.6128	1665	0.0884
1.6137	1666	0.1012
1.6147	1667	0.0728
1.6157	1668	0.0493
1.6167	1669	0.0603
1.6176	1670	0.0545
1.6186	1671	0.0418
1.6196	1672	0.0305
1.6205	1673	0.0746
1.6215	1674	0.0579
1.6225	1675	0.0303
1.6234	1676	0.0379
1.6244	1677	0.0593
1.6254	1678	0.0869
1.6263	1679	0.0535
1.6273	1680	0.0558
1.6283	1681	0.0573
1.6292	1682	0.0336
1.6302	1683	0.0402
1.6312	1684	0.0438
1.6321	1685	0.0535
1.6331	1686	0.0506
1.6341	1687	0.0477
1.6350	1688	0.0315
1.6360	1689	0.0648
1.6370	1690	0.0313
1.6379	1691	0.0365
1.6389	1692	0.0358
1.6399	1693	0.0601
1.6409	1694	0.0638
1.6418	1695	0.0643
1.6428	1696	0.0376
1.6438	1697	0.0585
1.6447	1698	0.0479
1.6457	1699	0.0507
1.6467	1700	0.0663
1.6476	1701	0.068
1.6486	1702	0.0695
1.6496	1703	0.0297
1.6505	1704	0.0378
1.6515	1705	0.0662
1.6525	1706	0.0468
1.6534	1707	0.0309
1.6544	1708	0.024
1.6554	1709	0.0728
1.6563	1710	0.0337
1.6573	1711	0.0492
1.6583	1712	0.0419
1.6592	1713	0.074
1.6602	1714	0.051
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1.6621	1716	0.0379
1.6631	1717	0.0742
1.6641	1718	0.0589
1.6651	1719	0.0449
1.6660	1720	0.047
1.6670	1721	0.0575
1.6680	1722	0.0971
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1.6699	1724	0.0633
1.6709	1725	0.0392
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1.6728	1727	0.0428
1.6738	1728	0.0582
1.6747	1729	0.0261
1.6757	1730	0.037
1.6767	1731	0.0272
1.6776	1732	0.0377
1.6786	1733	0.0443
1.6796	1734	0.0448
1.6805	1735	0.067
1.6815	1736	0.0556
1.6825	1737	0.0596
1.6834	1738	0.0638
1.6844	1739	0.0383
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1.6864	1741	0.0514
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1.6883	1743	0.0476
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1.6912	1746	0.0579
1.6922	1747	0.0468
1.6931	1748	0.0302
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1.6951	1750	0.0551
1.6960	1751	0.0294
1.6970	1752	0.0533
1.6980	1753	0.0355
1.6989	1754	0.0192
1.6999	1755	0.0191
1.7009	1756	0.0494
1.7018	1757	0.0655
1.7028	1758	0.0363
1.7038	1759	0.0312
1.7047	1760	0.0392
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1.7067	1762	0.0601
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1.7086	1764	0.0481
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1.7106	1766	0.084
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1.7125	1768	0.0708
1.7135	1769	0.0755
1.7144	1770	0.0587
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1.7173	1773	0.1474
1.7183	1774	0.0643
1.7193	1775	0.0451
1.7202	1776	0.0527
1.7212	1777	0.0673
1.7222	1778	0.0744
1.7231	1779	0.0311
1.7241	1780	0.0581
1.7251	1781	0.065
1.7260	1782	0.0875
1.7270	1783	0.0341
1.7280	1784	0.0402
1.7289	1785	0.0342
1.7299	1786	0.0268
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1.7318	1788	0.0332
1.7328	1789	0.0527
1.7338	1790	0.0382
1.7348	1791	0.0422
1.7357	1792	0.0416
1.7367	1793	0.0515
1.7377	1794	0.0327
1.7386	1795	0.0348
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1.7406	1797	0.0523
1.7415	1798	0.0576
1.7425	1799	0.0494
1.7435	1800	0.0309
1.7444	1801	0.0371
1.7454	1802	0.0487
1.7464	1803	0.0487
1.7473	1804	0.0352
1.7483	1805	0.0484
1.7493	1806	0.0603
1.7502	1807	0.0398
1.7512	1808	0.0462
1.7522	1809	0.0372
1.7531	1810	0.043
1.7541	1811	0.0423
1.7551	1812	0.0345
1.7561	1813	0.055
1.7570	1814	0.0384
1.7580	1815	0.0595
1.7590	1816	0.0327
1.7599	1817	0.0398
1.7609	1818	0.0671
1.7619	1819	0.0446
1.7628	1820	0.0445
1.7638	1821	0.0503
1.7648	1822	0.0511
1.7657	1823	0.0352
1.7667	1824	0.02
1.7677	1825	0.0695
1.7686	1826	0.0409
1.7696	1827	0.0323
1.7706	1828	0.0439
1.7715	1829	0.0538
1.7725	1830	0.0556
1.7735	1831	0.03
1.7744	1832	0.0547
1.7754	1833	0.0771
1.7764	1834	0.0271
1.7773	1835	0.0375
1.7783	1836	0.0299
1.7793	1837	0.0481
1.7803	1838	0.029
1.7812	1839	0.0166
1.7822	1840	0.0568
1.7832	1841	0.0895
1.7841	1842	0.057
1.7851	1843	0.0438
1.7861	1844	0.0335
1.7870	1845	0.0912
1.7880	1846	0.0701
1.7890	1847	0.0296
1.7899	1848	0.0372
1.7909	1849	0.0386
1.7919	1850	0.0434
1.7928	1851	0.0279
1.7938	1852	0.0333
1.7948	1853	0.07
1.7957	1854	0.072
1.7967	1855	0.0458
1.7977	1856	0.059
1.7986	1857	0.0375
1.7996	1858	0.0517
1.8006	1859	0.0284
1.8015	1860	0.0401
1.8025	1861	0.0451
1.8035	1862	0.0294
1.8045	1863	0.0486
1.8054	1864	0.0442
1.8064	1865	0.0885
1.8074	1866	0.0481
1.8083	1867	0.046
1.8093	1868	0.031
1.8103	1869	0.0835
1.8112	1870	0.0547
1.8122	1871	0.0438
1.8132	1872	0.0364
1.8141	1873	0.0722
1.8151	1874	0.0559
1.8161	1875	0.0349
1.8170	1876	0.0411
1.8180	1877	0.0598
1.8190	1878	0.0646
1.8199	1879	0.0341
1.8209	1880	0.0258
1.8219	1881	0.051
1.8228	1882	0.0455
1.8238	1883	0.0752
1.8248	1884	0.04
1.8258	1885	0.0323
1.8267	1886	0.0343
1.8277	1887	0.0462
1.8287	1888	0.0635
1.8296	1889	0.0656
1.8306	1890	0.0461
1.8316	1891	0.036
1.8325	1892	0.0281
1.8335	1893	0.0487
1.8345	1894	0.0538
1.8354	1895	0.0405
1.8364	1896	0.0221
1.8374	1897	0.0537
1.8383	1898	0.0323
1.8393	1899	0.043
1.8403	1900	0.0316
1.8412	1901	0.0633
1.8422	1902	0.0627
1.8432	1903	0.0334
1.8441	1904	0.038
1.8451	1905	0.0733
1.8461	1906	0.0575
1.8470	1907	0.0298
1.8480	1908	0.0602
1.8490	1909	0.0762
1.8500	1910	0.0528
1.8509	1911	0.0582
1.8519	1912	0.0384
1.8529	1913	0.0405
1.8538	1914	0.0292
1.8548	1915	0.0337
1.8558	1916	0.0257
1.8567	1917	0.0551
1.8577	1918	0.061
1.8587	1919	0.0636
1.8596	1920	0.0334
1.8606	1921	0.0516
1.8616	1922	0.071
1.8625	1923	0.0344
1.8635	1924	0.0368
1.8645	1925	0.0841
1.8654	1926	0.0388
1.8664	1927	0.0255
1.8674	1928	0.0402
1.8683	1929	0.0377
1.8693	1930	0.0416
1.8703	1931	0.0338
1.8712	1932	0.0407
1.8722	1933	0.0773
1.8732	1934	0.0669
1.8742	1935	0.0409
1.8751	1936	0.0834
1.8761	1937	0.057
1.8771	1938	0.0486
1.8780	1939	0.0472
1.8790	1940	0.0439
1.8800	1941	0.0312
1.8809	1942	0.0304
1.8819	1943	0.0398
1.8829	1944	0.0399
1.8838	1945	0.0736
1.8848	1946	0.0331
1.8858	1947	0.0351
1.8867	1948	0.0333
1.8877	1949	0.073
1.8887	1950	0.0461
1.8896	1951	0.0351
1.8906	1952	0.0442
1.8916	1953	0.0329
1.8925	1954	0.0386
1.8935	1955	0.0337
1.8945	1956	0.0309
1.8955	1957	0.0529
1.8964	1958	0.058
1.8974	1959	0.0778
1.8984	1960	0.0279
1.8993	1961	0.0532
1.9003	1962	0.0496
1.9013	1963	0.0554
1.9022	1964	0.0242
1.9032	1965	0.0589
1.9042	1966	0.0479
1.9051	1967	0.0424
1.9061	1968	0.0342
1.9071	1969	0.0791
1.9080	1970	0.0439
1.9090	1971	0.0533
1.9100	1972	0.0455
1.9109	1973	0.0417
1.9119	1974	0.0735
1.9129	1975	0.0306
1.9138	1976	0.0189
1.9148	1977	0.0305
1.9158	1978	0.0344
1.9167	1979	0.0358
1.9177	1980	0.0196
1.9187	1981	0.0508
1.9197	1982	0.0292
1.9206	1983	0.0216
1.9216	1984	0.0323
1.9226	1985	0.0765
1.9235	1986	0.0774
1.9245	1987	0.0367
1.9255	1988	0.0278
1.9264	1989	0.041
1.9274	1990	0.0481
1.9284	1991	0.032
1.9293	1992	0.0413
1.9303	1993	0.0463
1.9313	1994	0.0316
1.9322	1995	0.0417
1.9332	1996	0.0547
1.9342	1997	0.0738
1.9351	1998	0.0834
1.9361	1999	0.0454
1.9371	2000	0.0341
1.9380	2001	0.0567
1.9390	2002	0.0475
1.9400	2003	0.0473
1.9409	2004	0.047
1.9419	2005	0.0575
1.9429	2006	0.0655
1.9439	2007	0.0558
1.9448	2008	0.031
1.9458	2009	0.046
1.9468	2010	0.057
1.9477	2011	0.0613
1.9487	2012	0.0344
1.9497	2013	0.0572
1.9506	2014	0.03
1.9516	2015	0.0305
1.9526	2016	0.0328
1.9535	2017	0.0599
1.9545	2018	0.0626
1.9555	2019	0.0406
1.9564	2020	0.0366
1.9574	2021	0.0832
1.9584	2022	0.0404
1.9593	2023	0.0362
1.9603	2024	0.0289
1.9613	2025	0.0395
1.9622	2026	0.0278
1.9632	2027	0.0363
1.9642	2028	0.0335
1.9652	2029	0.0394
1.9661	2030	0.0533
1.9671	2031	0.0244
1.9681	2032	0.0226
1.9690	2033	0.0502
1.9700	2034	0.0405
1.9710	2035	0.0211
1.9719	2036	0.0237
1.9729	2037	0.061
1.9739	2038	0.0346
1.9748	2039	0.0327
1.9758	2040	0.0448
1.9768	2041	0.0607
1.9777	2042	0.07
1.9787	2043	0.0422
1.9797	2044	0.0441
1.9806	2045	0.0452
1.9816	2046	0.0282
1.9826	2047	0.0445
1.9835	2048	0.0721
1.9845	2049	0.0442
1.9855	2050	0.056
1.9864	2051	0.0302
1.9874	2052	0.0412
1.9884	2053	0.0468
1.9894	2054	0.0417
1.9903	2055	0.0315
1.9913	2056	0.0492
1.9923	2057	0.0589
1.9932	2058	0.0827
1.9942	2059	0.0659
1.9952	2060	0.045
1.9961	2061	0.0364
1.9971	2062	0.042
1.9981	2063	0.0425
1.9990	2064	0.0818
2.0010	2065	0.0754
2.0019	2066	0.0576
2.0029	2067	0.0407
2.0039	2068	0.0228
2.0048	2069	0.0618
2.0058	2070	0.0549
2.0068	2071	0.0443
2.0077	2072	0.0611
2.0087	2073	0.0602
2.0097	2074	0.0586
2.0106	2075	0.0422
2.0116	2076	0.0535
2.0126	2077	0.0921
2.0136	2078	0.0767
2.0145	2079	0.0302
2.0155	2080	0.0256
2.0165	2081	0.0911
2.0174	2082	0.0464
2.0184	2083	0.0486
2.0194	2084	0.0506
2.0203	2085	0.086
2.0213	2086	0.0733
2.0223	2087	0.0356
2.0232	2088	0.0327
2.0242	2089	0.0501
2.0252	2090	0.0488
2.0261	2091	0.0296
2.0271	2092	0.0271
2.0281	2093	0.0738
2.0290	2094	0.0226
2.0300	2095	0.0357
2.0310	2096	0.021
2.0319	2097	0.0365
2.0329	2098	0.0224
2.0339	2099	0.0176
2.0348	2100	0.0331
2.0358	2101	0.0496
2.0368	2102	0.0559
2.0378	2103	0.0368
2.0387	2104	0.0326
2.0397	2105	0.0602
2.0407	2106	0.0395
2.0416	2107	0.0319
2.0426	2108	0.033
2.0436	2109	0.0726
2.0445	2110	0.0404
2.0455	2111	0.0497
2.0465	2112	0.0478
2.0474	2113	0.0635
2.0484	2114	0.0259
2.0494	2115	0.0159
2.0503	2116	0.0287
2.0513	2117	0.0574
2.0523	2118	0.0345
2.0532	2119	0.0181
2.0542	2120	0.0255
2.0552	2121	0.0551
2.0561	2122	0.0398
2.0571	2123	0.0226
2.0581	2124	0.021
2.0591	2125	0.0827
2.0600	2126	0.0603
2.0610	2127	0.0414
2.0620	2128	0.0595
2.0629	2129	0.0945
2.0639	2130	0.064
2.0649	2131	0.0436
2.0658	2132	0.0254
2.0668	2133	0.0802
2.0678	2134	0.0389
2.0687	2135	0.0377
2.0697	2136	0.0283
2.0707	2137	0.0725
2.0716	2138	0.0494
2.0726	2139	0.0417
2.0736	2140	0.0276
2.0745	2141	0.0638
2.0755	2142	0.0467
2.0765	2143	0.0352
2.0774	2144	0.0347
2.0784	2145	0.0414
2.0794	2146	0.0329
2.0803	2147	0.014
2.0813	2148	0.0141
2.0823	2149	0.0692
2.0833	2150	0.0456
2.0842	2151	0.0362
2.0852	2152	0.0325
2.0862	2153	0.0318
2.0871	2154	0.0169
2.0881	2155	0.0216
2.0891	2156	0.024
2.0900	2157	0.0673
2.0910	2158	0.0435
2.0920	2159	0.0398
2.0929	2160	0.0233
2.0939	2161	0.0642
2.0949	2162	0.0326
2.0958	2163	0.0311
2.0968	2164	0.0226
2.0978	2165	0.0466
2.0987	2166	0.022
2.0997	2167	0.0311
2.1007	2168	0.0205
2.1016	2169	0.0598
2.1026	2170	0.0474
2.1036	2171	0.0513
2.1045	2172	0.0603
2.1055	2173	0.0385
2.1065	2174	0.035
2.1075	2175	0.0282
2.1084	2176	0.0225
2.1094	2177	0.0586
2.1104	2178	0.0613
2.1113	2179	0.0415
2.1123	2180	0.0366
2.1133	2181	0.0716
2.1142	2182	0.0315
2.1152	2183	0.0192
2.1162	2184	0.0294
2.1171	2185	0.0531
2.1181	2186	0.0358
2.1191	2187	0.0246
2.1200	2188	0.0377
2.1210	2189	0.0838
2.1220	2190	0.0751
2.1229	2191	0.0284
2.1239	2192	0.0217
2.1249	2193	0.0726
2.1258	2194	0.0222
2.1268	2195	0.0317
2.1278	2196	0.0236
2.1288	2197	0.0953
2.1297	2198	0.0425
2.1307	2199	0.042
2.1317	2200	0.0288
2.1326	2201	0.0646
2.1336	2202	0.0578
2.1346	2203	0.0245
2.1355	2204	0.0319
2.1365	2205	0.0605
2.1375	2206	0.0488
2.1384	2207	0.0387
2.1394	2208	0.0236
2.1404	2209	0.0538
2.1413	2210	0.0357
2.1423	2211	0.0355
2.1433	2212	0.0279
2.1442	2213	0.0378
2.1452	2214	0.0324
2.1462	2215	0.0238
2.1471	2216	0.0174
2.1481	2217	0.1084
2.1491	2218	0.0533
2.1500	2219	0.0496
2.1510	2220	0.0406
2.1520	2221	0.1079
2.1530	2222	0.0305
2.1539	2223	0.0301
2.1549	2224	0.0277
2.1559	2225	0.1052
2.1568	2226	0.049
2.1578	2227	0.0139
2.1588	2228	0.0223
2.1597	2229	0.034
2.1607	2230	0.0206
2.1617	2231	0.0255
2.1626	2232	0.0228
2.1636	2233	0.0456
2.1646	2234	0.0378
2.1655	2235	0.0199
2.1665	2236	0.02
2.1675	2237	0.0459
2.1684	2238	0.064
2.1694	2239	0.0346
2.1704	2240	0.0271
2.1713	2241	0.0472
2.1723	2242	0.0294
2.1733	2243	0.0278
2.1742	2244	0.0181
2.1752	2245	0.0466
2.1762	2246	0.0363
2.1772	2247	0.0276
2.1781	2248	0.028
2.1791	2249	0.0565
2.1801	2250	0.0399
2.1810	2251	0.0301
2.1820	2252	0.0278
2.1830	2253	0.0562
2.1839	2254	0.0402
2.1849	2255	0.0328
2.1859	2256	0.0228
2.1868	2257	0.0762
2.1878	2258	0.0567
2.1888	2259	0.03
2.1897	2260	0.0525
2.1907	2261	0.063
2.1917	2262	0.0351
2.1926	2263	0.0176
2.1936	2264	0.0156
2.1946	2265	0.0574
2.1955	2266	0.0302
2.1965	2267	0.0205
2.1975	2268	0.021
2.1985	2269	0.0713
2.1994	2270	0.0265
2.2004	2271	0.0218
2.2014	2272	0.0183
2.2023	2273	0.0318
2.2033	2274	0.0325
2.2043	2275	0.0194
2.2052	2276	0.0144
2.2062	2277	0.0331
2.2072	2278	0.0312
2.2081	2279	0.0198
2.2091	2280	0.0163
2.2101	2281	0.0636
2.2110	2282	0.0301
2.2120	2283	0.0282
2.2130	2284	0.027
2.2139	2285	0.0214
2.2149	2286	0.0306
2.2159	2287	0.0179
2.2168	2288	0.0156
2.2178	2289	0.038
2.2188	2290	0.0366
2.2197	2291	0.0233
2.2207	2292	0.0144
2.2217	2293	0.0483
2.2227	2294	0.0295
2.2236	2295	0.0184
2.2246	2296	0.0173
2.2256	2297	0.0535
2.2265	2298	0.0438
2.2275	2299	0.0211
2.2285	2300	0.0201
2.2294	2301	0.0826
2.2304	2302	0.031
2.2314	2303	0.0191
2.2323	2304	0.0128
2.2333	2305	0.0639
2.2343	2306	0.051
2.2352	2307	0.0226
2.2362	2308	0.0195
2.2372	2309	0.0457
2.2381	2310	0.0218
2.2391	2311	0.0251
2.2401	2312	0.0159
2.2410	2313	0.0425
2.2420	2314	0.0358
2.2430	2315	0.021
2.2439	2316	0.0176
2.2449	2317	0.0304
2.2459	2318	0.042
2.2469	2319	0.0221
2.2478	2320	0.014
2.2488	2321	0.055
2.2498	2322	0.0497
2.2507	2323	0.025
2.2517	2324	0.015
2.2527	2325	0.0361
2.2536	2326	0.0275
2.2546	2327	0.0223
2.2556	2328	0.0257
2.2565	2329	0.0339
2.2575	2330	0.0212
2.2585	2331	0.0134
2.2594	2332	0.016
2.2604	2333	0.0281
2.2614	2334	0.018
2.2623	2335	0.0136
2.2633	2336	0.0113
2.2643	2337	0.0189
2.2652	2338	0.0142
2.2662	2339	0.0097
2.2672	2340	0.0069
2.2682	2341	0.0771
2.2691	2342	0.0221
2.2701	2343	0.0174
2.2711	2344	0.0149
2.2720	2345	0.0424
2.2730	2346	0.0258
2.2740	2347	0.0259
2.2749	2348	0.0212
2.2759	2349	0.0376
2.2769	2350	0.0159
2.2778	2351	0.0173
2.2788	2352	0.0133
2.2798	2353	0.0196
2.2807	2354	0.0178
2.2817	2355	0.0164
2.2827	2356	0.0103
2.2836	2357	0.0612
2.2846	2358	0.0159
2.2856	2359	0.0181
2.2865	2360	0.0153
2.2875	2361	0.0298
2.2885	2362	0.0197
2.2894	2363	0.0202
2.2904	2364	0.0192
2.2914	2365	0.0347
2.2924	2366	0.0148
2.2933	2367	0.0122
2.2943	2368	0.0161
2.2953	2369	0.0253
2.2962	2370	0.0206
2.2972	2371	0.0187
2.2982	2372	0.0194
2.2991	2373	0.0372
2.3001	2374	0.0211
2.3011	2375	0.0187
2.3020	2376	0.0123
2.3030	2377	0.0362
2.3040	2378	0.0317
2.3049	2379	0.019
2.3059	2380	0.0336
2.3069	2381	0.0287
2.3078	2382	0.0204
2.3088	2383	0.0138
2.3098	2384	0.0214
2.3107	2385	0.0462
2.3117	2386	0.0259
2.3127	2387	0.0144
2.3136	2388	0.018
2.3146	2389	0.019
2.3156	2390	0.0174
2.3166	2391	0.0173
2.3175	2392	0.009
2.3185	2393	0.0216
2.3195	2394	0.0131
2.3204	2395	0.0153
2.3214	2396	0.0117
2.3224	2397	0.0407
2.3233	2398	0.0264
2.3243	2399	0.0267
2.3253	2400	0.0194
2.3262	2401	0.0459
2.3272	2402	0.0249
2.3282	2403	0.0188
2.3291	2404	0.0148
2.3301	2405	0.0353
2.3311	2406	0.0187
2.3320	2407	0.0178
2.3330	2408	0.0093
2.3340	2409	0.0252
2.3349	2410	0.025
2.3359	2411	0.0156
2.3369	2412	0.0125
2.3379	2413	0.0512
2.3388	2414	0.0252
2.3398	2415	0.0147
2.3408	2416	0.0101
2.3417	2417	0.0295
2.3427	2418	0.0162
2.3437	2419	0.0165
2.3446	2420	0.0148
2.3456	2421	0.0334
2.3466	2422	0.0206
2.3475	2423	0.015
2.3485	2424	0.0101
2.3495	2425	0.0337
2.3504	2426	0.0311
2.3514	2427	0.0147
2.3524	2428	0.0207
2.3533	2429	0.0681
2.3543	2430	0.0341
2.3553	2431	0.0175
2.3562	2432	0.0162
2.3572	2433	0.0213
2.3582	2434	0.0131
2.3591	2435	0.013
2.3601	2436	0.0131
2.3611	2437	0.0368
2.3621	2438	0.0137
2.3630	2439	0.0135
2.3640	2440	0.0174
2.3650	2441	0.0437
2.3659	2442	0.0211
2.3669	2443	0.0075
2.3679	2444	0.0167
2.3688	2445	0.0247
2.3698	2446	0.0228
2.3708	2447	0.0171
2.3717	2448	0.0171
2.3727	2449	0.0278
2.3737	2450	0.0161
2.3746	2451	0.0189
2.3756	2452	0.0205
2.3766	2453	0.0249
2.3775	2454	0.0301
2.3785	2455	0.0131
2.3795	2456	0.0103
2.3804	2457	0.0389
2.3814	2458	0.0259
2.3824	2459	0.0135
2.3833	2460	0.0125
2.3843	2461	0.0159
2.3853	2462	0.0187
2.3863	2463	0.0181
2.3872	2464	0.0118
2.3882	2465	0.0343
2.3892	2466	0.0213
2.3901	2467	0.0133
2.3911	2468	0.0162
2.3921	2469	0.0407
2.3930	2470	0.0308
2.3940	2471	0.0272
2.3950	2472	0.0207
2.3959	2473	0.0528
2.3969	2474	0.0205
2.3979	2475	0.0152
2.3988	2476	0.0107
2.3998	2477	0.0286
2.4008	2478	0.0188
2.4017	2479	0.0136
2.4027	2480	0.013
2.4037	2481	0.0262
2.4046	2482	0.0157
2.4056	2483	0.0154
2.4066	2484	0.0211
2.4076	2485	0.0228
2.4085	2486	0.0242
2.4095	2487	0.0089
2.4105	2488	0.0085
2.4114	2489	0.0541
2.4124	2490	0.0409
2.4134	2491	0.0271
2.4143	2492	0.0232
2.4153	2493	0.0321
2.4163	2494	0.0178
2.4172	2495	0.0254
2.4182	2496	0.0114
2.4192	2497	0.0301
2.4201	2498	0.0531
2.4211	2499	0.0207
2.4221	2500	0.0287
2.4230	2501	0.0555
2.4240	2502	0.0196
2.4250	2503	0.0277
2.4259	2504	0.0157
2.4269	2505	0.0428
2.4279	2506	0.0173
2.4288	2507	0.013
2.4298	2508	0.0131
2.4308	2509	0.0235
2.4318	2510	0.013
2.4327	2511	0.0129
2.4337	2512	0.0385
2.4347	2513	0.0398
2.4356	2514	0.0252
2.4366	2515	0.018
2.4376	2516	0.0165
2.4385	2517	0.0291
2.4395	2518	0.0318
2.4405	2519	0.019
2.4414	2520	0.0133
2.4424	2521	0.0364
2.4434	2522	0.0164
2.4443	2523	0.0129
2.4453	2524	0.011
2.4463	2525	0.0417
2.4472	2526	0.0238
2.4482	2527	0.016
2.4492	2528	0.0154
2.4501	2529	0.042
2.4511	2530	0.0103
2.4521	2531	0.0117
2.4530	2532	0.0136
2.4540	2533	0.03
2.4550	2534	0.0268
2.4560	2535	0.0297
2.4569	2536	0.0165
2.4579	2537	0.0427
2.4589	2538	0.02
2.4598	2539	0.0212
2.4608	2540	0.0275
2.4618	2541	0.0392
2.4627	2542	0.0176
2.4637	2543	0.0145
2.4647	2544	0.027
2.4656	2545	0.0351
2.4666	2546	0.0221
2.4676	2547	0.0106
2.4685	2548	0.0084
2.4695	2549	0.0272
2.4705	2550	0.0168
2.4714	2551	0.0263
2.4724	2552	0.0222
2.4734	2553	0.0222
2.4743	2554	0.0135
2.4753	2555	0.0238
2.4763	2556	0.0268
2.4773	2557	0.0191
2.4782	2558	0.0132
2.4792	2559	0.0101
2.4802	2560	0.0128
2.4811	2561	0.0258
2.4821	2562	0.0143
2.4831	2563	0.0155
2.4840	2564	0.0193
2.4850	2565	0.0338
2.4860	2566	0.0236
2.4869	2567	0.0105
2.4879	2568	0.0079
2.4889	2569	0.0376
2.4898	2570	0.0247
2.4908	2571	0.0121
2.4918	2572	0.0159
2.4927	2573	0.0341
2.4937	2574	0.0223
2.4947	2575	0.0276
2.4956	2576	0.0097
2.4966	2577	0.0265
2.4976	2578	0.0181
2.4985	2579	0.008
2.4995	2580	0.0172
2.5005	2581	0.0336
2.5015	2582	0.0111
2.5024	2583	0.014
2.5034	2584	0.0135
2.5044	2585	0.0188
2.5053	2586	0.0439
2.5063	2587	0.023
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3.0755	3174	0.0261
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3.0997	3199	0.0159
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3.6467	3764	0.0164
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3.6592	3777	0.019
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4.0774	4208	0.0125
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4.1655	4299	0.0098
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4.1907	4325	0.0293
4.1917	4326	0.0095
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4.1936	4328	0.012
4.1946	4329	0.0292
4.1955	4330	0.0149
4.1965	4331	0.0145
4.1975	4332	0.0149
4.1985	4333	0.0157
4.1994	4334	0.0182
4.2004	4335	0.0115
4.2014	4336	0.0091
4.2023	4337	0.0175
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4.2052	4340	0.0113
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4.2110	4346	0.0205
4.2120	4347	0.0145
4.2130	4348	0.0159
4.2139	4349	0.0162
4.2149	4350	0.0149
4.2159	4351	0.0146
4.2168	4352	0.011
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4.7880	4942	0.0315
4.7890	4943	0.0132
4.7899	4944	0.0101
4.7909	4945	0.0107
4.7919	4946	0.0099
4.7928	4947	0.0076
4.7938	4948	0.0107
4.7948	4949	0.0216
4.7957	4950	0.0211
4.7967	4951	0.0127
4.7977	4952	0.0118
4.7986	4953	0.0162
4.7996	4954	0.0113
4.8006	4955	0.0085
4.8015	4956	0.0113
4.8025	4957	0.0217
4.8035	4958	0.012
4.8045	4959	0.0106
4.8054	4960	0.019
4.8064	4961	0.0275
4.8074	4962	0.0142
4.8083	4963	0.0155
4.8093	4964	0.0077
4.8103	4965	0.0245
4.8112	4966	0.0232
4.8122	4967	0.0107
4.8132	4968	0.0144
4.8141	4969	0.0207
4.8151	4970	0.0173
4.8161	4971	0.0145
4.8170	4972	0.0188
4.8180	4973	0.021
4.8190	4974	0.0219
4.8199	4975	0.0098
4.8209	4976	0.0101
4.8219	4977	0.0111
4.8228	4978	0.0091
4.8238	4979	0.0191
4.8248	4980	0.0086
4.8258	4981	0.0136
4.8267	4982	0.0132
4.8277	4983	0.0109
4.8287	4984	0.018
4.8296	4985	0.0222
4.8306	4986	0.0261
4.8316	4987	0.0139
4.8325	4988	0.0152
4.8335	4989	0.0245
4.8345	4990	0.0133
4.8354	4991	0.0137
4.8364	4992	0.0136
4.8374	4993	0.0154
4.8383	4994	0.0121
4.8393	4995	0.011
4.8403	4996	0.0111
4.8412	4997	0.019
4.8422	4998	0.0319
4.8432	4999	0.0128
4.8441	5000	0.0168
4.8451	5001	0.0181
4.8461	5002	0.0199
4.8470	5003	0.0093
4.8480	5004	0.0185
4.8490	5005	0.016
4.8500	5006	0.0204
4.8509	5007	0.0169
4.8519	5008	0.0152
4.8529	5009	0.015
4.8538	5010	0.0116
4.8548	5011	0.0108
4.8558	5012	0.0096
4.8567	5013	0.016
4.8577	5014	0.0172
4.8587	5015	0.0184
4.8596	5016	0.0096
4.8606	5017	0.0133
4.8616	5018	0.014
4.8625	5019	0.0082
4.8635	5020	0.0173
4.8645	5021	0.0201
4.8654	5022	0.0136
4.8664	5023	0.0119
4.8674	5024	0.0146
4.8683	5025	0.0144
4.8693	5026	0.0126
4.8703	5027	0.0149
4.8712	5028	0.0161
4.8722	5029	0.0169
4.8732	5030	0.0225
4.8742	5031	0.013
4.8751	5032	0.0217
4.8761	5033	0.023
4.8771	5034	0.016
4.8780	5035	0.0119
4.8790	5036	0.0093
4.8800	5037	0.0101
4.8809	5038	0.0156
4.8819	5039	0.0133
4.8829	5040	0.0123
4.8838	5041	0.0279
4.8848	5042	0.0113
4.8858	5043	0.0155
4.8867	5044	0.0091
4.8877	5045	0.0274
4.8887	5046	0.0205
4.8896	5047	0.0111
4.8906	5048	0.0138
4.8916	5049	0.0173
4.8925	5050	0.0194
4.8935	5051	0.0133
4.8945	5052	0.0118
4.8955	5053	0.0231
4.8964	5054	0.0167
4.8974	5055	0.0152
4.8984	5056	0.0144
4.8993	5057	0.0159
4.9003	5058	0.0232
4.9013	5059	0.012
4.9022	5060	0.0075
4.9032	5061	0.0192
4.9042	5062	0.0164
4.9051	5063	0.0141
4.9061	5064	0.0099
4.9071	5065	0.0296
4.9080	5066	0.0143
4.9090	5067	0.0222
4.9100	5068	0.017
4.9109	5069	0.0197
4.9119	5070	0.0296
4.9129	5071	0.0121
4.9138	5072	0.008
4.9148	5073	0.0099
4.9158	5074	0.0134
4.9167	5075	0.0098
4.9177	5076	0.0061
4.9187	5077	0.0153
4.9197	5078	0.0091
4.9206	5079	0.0106
4.9216	5080	0.0088
4.9226	5081	0.0232
4.9235	5082	0.0176
4.9245	5083	0.0115
4.9255	5084	0.0142
4.9264	5085	0.0142
4.9274	5086	0.0127
4.9284	5087	0.0102
4.9293	5088	0.0167
4.9303	5089	0.0176
4.9313	5090	0.0118
4.9322	5091	0.016
4.9332	5092	0.0178
4.9342	5093	0.0228
4.9351	5094	0.0378
4.9361	5095	0.012
4.9371	5096	0.0108
4.9380	5097	0.0264
4.9390	5098	0.0152
4.9400	5099	0.0199
4.9409	5100	0.0139
4.9419	5101	0.0184
4.9429	5102	0.0191
4.9439	5103	0.0114
4.9448	5104	0.0085
4.9458	5105	0.0178
4.9468	5106	0.0197
4.9477	5107	0.0194
4.9487	5108	0.0164
4.9497	5109	0.0104
4.9506	5110	0.0072
4.9516	5111	0.0108
4.9526	5112	0.0113
4.9535	5113	0.0209
4.9545	5114	0.0167
4.9555	5115	0.0115
4.9564	5116	0.0132
4.9574	5117	0.0453
4.9584	5118	0.0234
4.9593	5119	0.0195
4.9603	5120	0.0249
4.9613	5121	0.0148
4.9622	5122	0.0082
4.9632	5123	0.0122
4.9642	5124	0.0247
4.9652	5125	0.0273
4.9661	5126	0.0188
4.9671	5127	0.008
4.9681	5128	0.0107
4.9690	5129	0.025
4.9700	5130	0.0183
4.9710	5131	0.0091
4.9719	5132	0.01
4.9729	5133	0.0201
4.9739	5134	0.0197
4.9748	5135	0.0115
4.9758	5136	0.0146
4.9768	5137	0.0197
4.9777	5138	0.0145
4.9787	5139	0.012
4.9797	5140	0.0205
4.9806	5141	0.0142
4.9816	5142	0.0232
4.9826	5143	0.016
4.9835	5144	0.0189
4.9845	5145	0.0131
4.9855	5146	0.0187
4.9864	5147	0.0081
4.9874	5148	0.0132
4.9884	5149	0.0132
4.9894	5150	0.0154
4.9903	5151	0.0096
4.9913	5152	0.0188
4.9923	5153	0.0184
4.9932	5154	0.0222
4.9942	5155	0.0327
4.9952	5156	0.0127
4.9961	5157	0.02
4.9971	5158	0.0126
4.9981	5159	0.0253
4.9990	5160	0.0334

Framework Versions

Python: 3.10.13
Sentence Transformers: 3.2.1
Transformers: 4.44.2
PyTorch: 2.4.0+cu121
Accelerate: 1.1.1
Datasets: 2.21.0
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}
}