metadata

language:
  - yue
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:129371
  - loss:CachedGISTEmbedLoss
base_model: hon9kon9ize/bert-large-cantonese-sts
widget:
  - source_sentence: 'query: is ampulla of vater part of the pancreas'
    sentences:
      - >-
        document: Ampulla of Vater The ampulla of Vater, also known as the
        hepatopancreatic ampulla or the hepatopancreatic duct, is formed by the
        union of the pancreatic duct and the common bile duct. The ampulla is
        specifically located at the major duodenal papilla.
      - 'document: 抗凝加化疗；化疗'
      - >-
        document: Daylight saving time in Australia Daylight saving was first
        used in Australia during World War I, and was applied in all states. It
        was used again during the Second World War. A drought in Tasmania in
        1967 led to the reintroduction of daylight saving in that state during
        the summer, and this was repeated every summer since then. In 1971, New
        South Wales, Victoria,[16] Queensland, South Australia, and the
        Australian Capital Territory followed Tasmania by observing daylight
        saving. Western Australia and the Northern Territory did not. Queensland
        abandoned daylight saving time in 1972.[17]
  - source_sentence: 'query: henry''s law states that the solubility of a gas in a liquid'
    sentences:
      - >-
        document: Henry's law In chemistry, Henry's law is a gas law that states
        that the amount of dissolved gas is proportional to its partial pressure
        in the gas phase. The proportionality factor is called the Henry's law
        constant. It was formulated by the English chemist William Henry, who
        studied the topic in the early 19th century. In his publication about
        the quantity of gases absorbed by water,[1] he described the results of
        his experiments:
      - >-
        document: Saint Stephen's Day Saint Stephen's Day, or the Feast of Saint
        Stephen, is a Christian saint's day to commemorate Saint Stephen, the
        first Christian martyr or protomartyr, celebrated on 26 December in the
        Latin Church and 27 December in Eastern Christianity. The Eastern
        Orthodox Church adheres to the Julian calendar and mark Saint Stephen's
        Day on 27 December according to that calendar, which places it on 9
        January of the Gregorian calendar used in secular contexts. In Latin
        Christian denominations, Saint Stephen's Day marks the second day of
        Christmastide.[1][2]
      - >-
        document: American Revolutionary War The American Revolutionary War
        (1775â€“1783), also known as the American War of Independence,[40] was a
        global war that began as a conflict between Great Britain and its
        Thirteen Colonies which declared independence as the United States of
        America.[N 1]
  - source_sentence: 'query: what is the plot of american horror story hotel'
    sentences:
      - >-
        document: American Horror Story: Hotel The plot centers around the
        enigmatic Hotel Cortez in Los Angeles, California, that catches the eye
        of an intrepid homicide detective (Bentley). The Cortez is host to the
        strange and bizarre, spearheaded by its owner, The Countess (Gaga), who
        is a bloodsucking fashionista. The hotel is loosely based on an actual
        hotel built in 1893 by H. H. Holmes in Chicago, Il. for the 1893 World's
        Columbian Exposition. It became known as the 'Murder Castle' as it was
        built for Holmes to torture, murder, and dispose of evidence just as is
        the Cortez. This season features two murderous threats in the form of
        the Ten Commandments Killer, a serial offender who selects his victims
        in accordance with biblical teachings, and "the Addiction Demon", who
        roams the hotel armed with a drill bit dildo.
      - >-
        document: Book of Job Rabbinic tradition ascribes the authorship of Job
        to Moses, but scholars generally agree that it was written between the
        7th and 4th centuries BCE, with the 6th century BCE as the most likely
        period for various reasons.[17] The anonymous author was almost
        certainly an Israelite, although he has set his story outside Israel, in
        southern Edom or northern Arabia, and makes allusion to places as far
        apart as Mesopotamia and Egypt.[18] According to the 6th-century BCE
        prophet Ezekiel, Job was a man of antiquity renowned for his
        righteousness,[19] and the book's author has chosen this legendary hero
        for his parable.[20]
      - >-
        document: Galešnjak Galešnjak (also called Island of Love, Lover's
        Island, Otok za zaljubljene) is located in the Pašman channel of the
        Adriatic, between the islands of Pašman and the town of Turanj on
        mainland Croatia. It is one of the world's few naturally occurring
        heart-shaped objects such as the Heart Reef in the Whitsundays.
  - source_sentence: >-
      query: what historical event inspired wollstonecraft's book a vindication
      of the rights of woman
    sentences:
      - >-
        document:
        銀河嘅獨特外形自古以嚟就引起人類嘅幻想。例如中國就有牛郎織女嘅故事，相傳身為人類嘅牛郎同身為仙女嘅織女相遇並且墮入愛河，但因為人仙相戀犯天規而俾天界阻止，王母娘娘變條銀河出嚟分隔佢哋，限佢哋淨係喺每年嘅農曆七月初七先可以喺條鵲橋上面相會－呢個傳說就係傳統節日七姐誕嘅起源。
      - >-
        document: Rock Star (2001 film) The singing voice for Wahlberg's
        character was provided by Steelheart frontman Miljenko Matijevic for the
        Steel Dragon Songs, the final number was dubbed by Brian Vander Ark.
        Jeff Scott Soto (of Talisman, Yngwie Malmsteen, Soul SirkUS, and
        Journey) provided the voice of the singer Wahlberg's character replaces.
        Kennedy is the only actor whose actual voice is used.[citation needed].
        Ralph Saenz (Steel Panther) also appears briefly, as the singer
        auditioning ahead of Chris at the studio.
      - >-
        document: A Vindication of the Rights of Woman Wollstonecraft was
        prompted to write the Rights of Woman after reading Charles Maurice de
        Talleyrand-Périgord's 1791 report to the French National Assembly, which
        stated that women should only receive a domestic education; she used her
        commentary on this specific event to launch a broad attack against
        sexual double standards and to indict men for encouraging women to
        indulge in excessive emotion. Wollstonecraft wrote the Rights of Woman
        hurriedly to respond directly to ongoing events; she intended to write a
        more thoughtful second volume but died before completing it.
  - source_sentence: 'query: when did england change from fahrenheit to celsius'
    sentences:
      - >-
        document: Periodic table Importantly, the organization of the periodic
        table can be utilized to derive relationships between various element
        properties, but also predicted chemical properties and behaviours of
        undiscovered or newly synthesized elements. Russian chemist Dmitri
        Mendeleev was first to publish a recognizable periodic table in 1869,
        developed mainly to illustrate periodic trends of the then-known
        elements. He also predicted some properties of unidentified elements
        that were expected to fill gaps within this table. Most of his forecasts
        proved to be correct. Mendeleev's idea has been slowly expanded and
        refined with the discovery or synthesis of further new elements and by
        developing new theoretical models to explain chemical behaviour. The
        modern periodic table now provides a useful framework for analyzing
        chemical reactions, and continues to be widely adopted in chemistry,
        nuclear physics and other sciences.
      - >-
        document: How to Train Your Dragon (franchise) The How to Train Your
        Dragon franchise from DreamWorks Animation consists of two feature films
        How to Train Your Dragon (2010) and How to Train Your Dragon 2 (2014),
        with a third feature film, How to Train Your Dragon: The Hidden World,
        set for a 2019 release. The franchise is inspired by the British book
        series of the same name by Cressida Cowell. The franchise also consists
        of four short films: Legend of the Boneknapper Dragon (2010), Book of
        Dragons (2011), Gift of the Night Fury (2011) and Dawn of the Dragon
        Racers (2014). A television series following the events of the first
        film, Dragons: Riders of Berk, began airing on Cartoon Network in
        September 2012. Its second season was renamed Dragons: Defenders of
        Berk. Set several years later, and as a more immediate prequel to the
        second film, a new television series, titled Dragons: Race to the Edge,
        aired on Netflix in June 2015.[1] The second season of the show was
        added to Netflix in January 2016 and a third season in June 2016. A
        fourth season aired on Netflix in February 2017, a fifth season in
        August 2017, and a sixth and final season on February 16, 2018.
      - >-
        document: Metrication in the United Kingdom Adopting the metric system
        was discussed in Parliament as early as 1818 and some industries and
        even some government agencies had metricated, or were in the process of
        metricating by the mid 1960s. A formal government policy to support
        metrication was agreed by 1965. This policy, initiated in response to
        requests from industry, was to support voluntary metrication, with costs
        picked up where they fell. In 1969 the government created the
        Metrication Board as a quango to promote and coordinate metrication. In
        1978, after some carpet retailers reverted to pricing by the square yard
        rather than the square metre, government policy shifted, and they
        started issuing orders making metrication mandatory in certain sectors.
        In 1980 government policy shifted again to prefer voluntary metrication,
        and the Metrication Board was abolished. By the time the Metrication
        Board was wound up, all the economic sectors that fell within its remit
        except road signage and parts of the retail trade sector had metricated.
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: Bert base fine-tuned with Cantonese and English mixed STS dataset
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: cosine_accuracy@1
            value: 0.06
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.2
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.22
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.26
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.06
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.06666666666666667
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.05200000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.032
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.035
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.105
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.12666666666666665
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.14400000000000002
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.10738523976006756
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.12305555555555553
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.08386746046821102
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: cosine_accuracy@1
            value: 0.1
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.26
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.44
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.52
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.1
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.12666666666666665
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.15200000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.154
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.005776685612719247
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.025711996601987995
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.04879480020144454
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.08175565470928514
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.1564753058784049
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.22302380952380954
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.08481993410477483
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: cosine_accuracy@1
            value: 0.06
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.12
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.06
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.03333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.02
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.012000000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.05
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.09
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.09
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.11
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.07804424038166692
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.07533333333333334
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.07658274436198606
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        metrics:
          - type: cosine_accuracy@1
            value: 0.12
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.22
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.26
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.36
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.12
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.07999999999999999
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.064
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.046000000000000006
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.07085714285714287
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.13621428571428573
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.14993650793650792
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.21193650793650792
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.15989208858068493
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.18794444444444444
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.1278932041519149
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: cosine_accuracy@1
            value: 0.18
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.38
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.4
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.44
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.18
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.13333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.084
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05200000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.09
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.2
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.21
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.26
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.21524243911000313
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2793333333333333
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.16949818775802034
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.08
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.16
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.2
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.24
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.08
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.05333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.04
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.024000000000000004
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.08
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.16
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.2
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.24
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.155021218726892
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.12816666666666665
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.14387227309213746
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: cosine_accuracy@1
            value: 0.1
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.12
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.18
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.1
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.06
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.05600000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.042
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.0023944899556066555
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.004511202133435534
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.005335271278326478
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.006887081773042016
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.0513758550014842
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.11271428571428571
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.011178329865269043
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.12
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.26
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.38
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.44
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.12
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.08666666666666666
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.08
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.04800000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.11
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.24
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.37
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.43
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.26691470842049086
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.21954761904761902
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.22127704921258506
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoQuoraRetrieval
          type: NanoQuoraRetrieval
        metrics:
          - type: cosine_accuracy@1
            value: 0.56
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.66
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.68
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.8
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.56
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.25333333333333335
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.16
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.092
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.49
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.6073333333333334
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.634
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.7406666666666666
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.6315714749064664
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6265555555555555
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.6007758177607536
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: cosine_accuracy@1
            value: 0.06
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.12
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.14
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.22
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.06
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.05333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.036000000000000004
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.026000000000000002
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.015666666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.03666666666666667
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.04066666666666666
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.05666666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.05444580189319236
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.10085714285714287
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.03825732082321992
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: cosine_accuracy@1
            value: 0.12
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.34
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.52
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.64
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.12
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10400000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.064
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.12
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.34
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.52
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.64
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.36676045848370026
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2815
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.28967419376346565
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: cosine_accuracy@1
            value: 0.18
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.22
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.32
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.36
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.18
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.07999999999999999
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.068
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.04
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.165
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.21
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.345
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.24854556538285397
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.22416666666666665
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.23077037853195492
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: cosine_accuracy@1
            value: 0.3469387755102041
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.7142857142857143
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.7959183673469388
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.9387755102040817
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.3469387755102041
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.32653061224489793
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.30612244897959184
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.2714285714285714
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.01725883684742171
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.06000832753846316
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.10128699807186763
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.17048580946181527
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.29344650277463163
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.5436912860382248
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.18279928418932134
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.1605337519623234
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.2872527472527473
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.35199372056514916
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.42452119309262165
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.1605337519623234
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11281004709576138
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.0940094191522763
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.0694945054945055
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.09630414014919672
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.17041890861447484
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.21512976237088305
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.2644152605549218
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.21424006917696453
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2404530537489721
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.17394355216027804
            name: Cosine Map@100

Bert base fine-tuned with Cantonese and English mixed STS dataset

This is a sentence-transformers model finetuned from hon9kon9ize/bert-large-cantonese-sts. 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: hon9kon9ize/bert-large-cantonese-sts
Maximum Sequence Length: 512 tokens
Output Dimensionality: 1024 dimensions
Similarity Function: Cosine Similarity
Language: yue
License: apache-2.0

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: BertModel 
  (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})
)

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("hon9kon9ize/yue-embed")
# Run inference
sentences = [
    'query: when did england change from fahrenheit to celsius',
    'document: Metrication in the United Kingdom Adopting the metric system was discussed in Parliament as early as 1818 and some industries and even some government agencies had metricated, or were in the process of metricating by the mid 1960s. A formal government policy to support metrication was agreed by 1965. This policy, initiated in response to requests from industry, was to support voluntary metrication, with costs picked up where they fell. In 1969 the government created the Metrication Board as a quango to promote and coordinate metrication. In 1978, after some carpet retailers reverted to pricing by the square yard rather than the square metre, government policy shifted, and they started issuing orders making metrication mandatory in certain sectors. In 1980 government policy shifted again to prefer voluntary metrication, and the Metrication Board was abolished. By the time the Metrication Board was wound up, all the economic sectors that fell within its remit except road signage and parts of the retail trade sector had metricated.',
    "document: Periodic table Importantly, the organization of the periodic table can be utilized to derive relationships between various element properties, but also predicted chemical properties and behaviours of undiscovered or newly synthesized elements. Russian chemist Dmitri Mendeleev was first to publish a recognizable periodic table in 1869, developed mainly to illustrate periodic trends of the then-known elements. He also predicted some properties of unidentified elements that were expected to fill gaps within this table. Most of his forecasts proved to be correct. Mendeleev's idea has been slowly expanded and refined with the discovery or synthesis of further new elements and by developing new theoretical models to explain chemical behaviour. The modern periodic table now provides a useful framework for analyzing chemical reactions, and continues to be widely adopted in chemistry, nuclear physics and other sciences.",
]
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

Datasets: NanoClimateFEVER, NanoDBPedia, NanoFEVER, NanoFiQA2018, NanoHotpotQA, NanoMSMARCO, NanoNFCorpus, NanoNQ, NanoQuoraRetrieval, NanoSCIDOCS, NanoArguAna, NanoSciFact and NanoTouche2020
Evaluated with InformationRetrievalEvaluator

Metric	NanoClimateFEVER	NanoDBPedia	NanoFEVER	NanoFiQA2018	NanoHotpotQA	NanoMSMARCO	NanoNFCorpus	NanoNQ	NanoQuoraRetrieval	NanoSCIDOCS	NanoArguAna	NanoSciFact	NanoTouche2020
cosine_accuracy@1	0.06	0.1	0.06	0.12	0.18	0.08	0.1	0.12	0.56	0.06	0.12	0.18	0.3469
cosine_accuracy@3	0.2	0.26	0.1	0.22	0.38	0.16	0.1	0.26	0.66	0.12	0.34	0.22	0.7143
cosine_accuracy@5	0.22	0.44	0.1	0.26	0.4	0.2	0.12	0.38	0.68	0.14	0.52	0.32	0.7959
cosine_accuracy@10	0.26	0.52	0.12	0.36	0.44	0.24	0.18	0.44	0.8	0.22	0.64	0.36	0.9388
cosine_precision@1	0.06	0.1	0.06	0.12	0.18	0.08	0.1	0.12	0.56	0.06	0.12	0.18	0.3469
cosine_precision@3	0.0667	0.1267	0.0333	0.08	0.1333	0.0533	0.06	0.0867	0.2533	0.0533	0.1133	0.08	0.3265
cosine_precision@5	0.052	0.152	0.02	0.064	0.084	0.04	0.056	0.08	0.16	0.036	0.104	0.068	0.3061
cosine_precision@10	0.032	0.154	0.012	0.046	0.052	0.024	0.042	0.048	0.092	0.026	0.064	0.04	0.2714
cosine_recall@1	0.035	0.0058	0.05	0.0709	0.09	0.08	0.0024	0.11	0.49	0.0157	0.12	0.165	0.0173
cosine_recall@3	0.105	0.0257	0.09	0.1362	0.2	0.16	0.0045	0.24	0.6073	0.0367	0.34	0.21	0.06
cosine_recall@5	0.1267	0.0488	0.09	0.1499	0.21	0.2	0.0053	0.37	0.634	0.0407	0.52	0.3	0.1013
cosine_recall@10	0.144	0.0818	0.11	0.2119	0.26	0.24	0.0069	0.43	0.7407	0.0567	0.64	0.345	0.1705
cosine_ndcg@10	0.1074	0.1565	0.078	0.1599	0.2152	0.155	0.0514	0.2669	0.6316	0.0544	0.3668	0.2485	0.2934
cosine_mrr@10	0.1231	0.223	0.0753	0.1879	0.2793	0.1282	0.1127	0.2195	0.6266	0.1009	0.2815	0.2242	0.5437
cosine_map@100	0.0839	0.0848	0.0766	0.1279	0.1695	0.1439	0.0112	0.2213	0.6008	0.0383	0.2897	0.2308	0.1828

Nano BEIR

Dataset: NanoBEIR_mean
Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.1605
cosine_accuracy@3	0.2873
cosine_accuracy@5	0.352
cosine_accuracy@10	0.4245
cosine_precision@1	0.1605
cosine_precision@3	0.1128
cosine_precision@5	0.094
cosine_precision@10	0.0695
cosine_recall@1	0.0963
cosine_recall@3	0.1704
cosine_recall@5	0.2151
cosine_recall@10	0.2644
cosine_ndcg@10	0.2142
cosine_mrr@10	0.2405
cosine_map@100	0.1739

Training Details

Training Dataset

Unnamed Dataset

Size: 129,371 training samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 13 tokens
mean: 22.58 tokens
max: 134 tokens

min: 7 tokens
mean: 169.6 tokens
max: 512 tokens

	query	answer
type	string	string
details	min: 13 tokens mean: 22.58 tokens max: 134 tokens	min: 7 tokens mean: 169.6 tokens max: 512 tokens

Samples:

query	answer
`query: hotel and restaurant employees and bartenders international union`	document: Hotel Employees and Restaurant Employees Union The Hotel Employees and Restaurant Employees Union (HERE) was a United States labor union representing workers of the hospitality industry, formed in 1891. In 2004, HERE merged with the Union of Needletrades, Industrial, and Textile Employees (UNITE) to form UNITE HERE. HERE notably organized the staff of Yale University in 1984. Other major employers that contracted with this union included several large casinos (Harrah's, Caesars Palace, and Wynn Resorts); hotels (Hilton, Hyatt and Starwood), and Walt Disney World. HERE was affiliated with the AFL-CIO.
`query: 多肢离断伤的并发症是什么？`	`document: 失血性休克；血循环危象；急性肾功能衰竭`
`query: who is the father of kelly taylor's son on 90210`	`document: Kelly Taylor (90210) In 2008, Kelly Taylor returned in the spin-off 90210, now working as a guidance counselor at her alma mater West Beverly Hills High School. It was revealed that in the intervening years, she attained a master's degree and had a son named Sammy with Dylan. She and Dylan ended their relationship soon after. It was also revealed that West Beverly principal Harry Wilson was Kelly's neighbor growing up.[39]`

Loss: CachedGISTEmbedLoss with these parameters:

{'guide': 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})
  (2): Normalize()
), 'temperature': 0.01}

Evaluation Dataset

Unnamed Dataset

Size: 1,000 evaluation samples
Columns: query and answer
Approximate statistics based on the first 1000 samples:
query answer
type string string
details
min: 11 tokens
mean: 22.61 tokens
max: 146 tokens

min: 7 tokens
mean: 164.27 tokens
max: 512 tokens

	query	answer
type	string	string
details	min: 11 tokens mean: 22.61 tokens max: 146 tokens	min: 7 tokens mean: 164.27 tokens max: 512 tokens

Samples:

query	answer
`query: 微创经皮肾镜手术的推荐药有些什么？`	`document: 阿司匹林`
`query: why are the fires in ca called the thomas fires`	document: Thomas Fire On December 4, 2017, the Thomas Fire was reported at 6:26 p.m. PST,[36] to the north of Santa Paula, near Steckel Park and Thomas Aquinas College,[3][24] after which the fire is named.[37] That night, the small brush fire exploded in size and raced through the rugged mountain terrain that lies west of Santa Paula, between Ventura and Ojai.[19][38] Officials blamed strong Santa Ana winds that gusted up to 60 miles per hour (97 km/h) for the sudden expansion.[28][39] Soon after the fire had started, a second blaze was ignited nearly 30 minutes later, about 4 miles (6.4 km) to the north in Upper Ojai at the top of Koenigstein Road.[40] According to eyewitnesses, this second fire was sparked by an explosion in the power line over the area. The second fire was rapidly expanded by the strong Santa Ana winds, and soon merged into the Thomas Fire later that night.[40]
`query: which mountain man rediscovered south pass and brought back important information about this trail`	document: Jedediah Smith Jedediah Strong Smith (January 6, 1799 – May 27, 1831), was a clerk, frontiersman, hunter, trapper, author, cartographer, and explorer of the Rocky Mountains, the North American West, and the Southwest during the early 19th century. After 75 years of obscurity following his death, Smith was rediscovered as the American whose explorations led to the use of the 20-mile (32 km)-wide South Pass as the dominant point of crossing the Continental Divide for pioneers on the Oregon Trail.

Loss: CachedGISTEmbedLoss with these parameters:

{'guide': 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})
  (2): Normalize()
), 'temperature': 0.01}

Training Hyperparameters

Non-Default Hyperparameters

eval_strategy: steps
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
learning_rate: 2e-05
num_train_epochs: 2
warmup_ratio: 0.05
seed: 12
bf16: True
prompts: {'query': 'query: ', 'answer': 'document: '}
batch_sampler: no_duplicates

All Hyperparameters

Click to expand

overwrite_output_dir: False
do_predict: False
eval_strategy: steps
prediction_loss_only: True
per_device_train_batch_size: 128
per_device_eval_batch_size: 128
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: 2e-05
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
max_grad_norm: 1.0
num_train_epochs: 2
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: 12
data_seed: None
jit_mode_eval: False
use_ipex: False
bf16: True
fp16: False
fp16_opt_level: O1
half_precision_backend: auto
bf16_full_eval: False
fp16_full_eval: False
tf32: None
local_rank: 0
ddp_backend: None
tpu_num_cores: None
tpu_metrics_debug: False
debug: []
dataloader_drop_last: False
dataloader_num_workers: 0
dataloader_prefetch_factor: None
past_index: -1
disable_tqdm: False
remove_unused_columns: True
label_names: None
load_best_model_at_end: False
ignore_data_skip: False
fsdp: []
fsdp_min_num_params: 0
fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
fsdp_transformer_layer_cls_to_wrap: None
accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
deepspeed: None
label_smoothing_factor: 0.0
optim: adamw_torch
optim_args: None
adafactor: False
group_by_length: False
length_column_name: length
ddp_find_unused_parameters: None
ddp_bucket_cap_mb: None
ddp_broadcast_buffers: False
dataloader_pin_memory: True
dataloader_persistent_workers: False
skip_memory_metrics: True
use_legacy_prediction_loop: False
push_to_hub: False
resume_from_checkpoint: None
hub_model_id: None
hub_strategy: every_save
hub_private_repo: None
hub_always_push: False
gradient_checkpointing: False
gradient_checkpointing_kwargs: None
include_inputs_for_metrics: False
include_for_metrics: []
eval_do_concat_batches: True
fp16_backend: auto
push_to_hub_model_id: None
push_to_hub_organization: None
mp_parameters:
auto_find_batch_size: False
full_determinism: False
torchdynamo: None
ray_scope: last
ddp_timeout: 1800
torch_compile: False
torch_compile_backend: None
torch_compile_mode: None
dispatch_batches: None
split_batches: None
include_tokens_per_second: False
include_num_input_tokens_seen: False
neftune_noise_alpha: None
optim_target_modules: None
batch_eval_metrics: False
eval_on_start: False
use_liger_kernel: False
eval_use_gather_object: False
average_tokens_across_devices: False
prompts: {'query': 'query: ', 'answer': 'document: '}
batch_sampler: no_duplicates
multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training Loss	Validation Loss	NanoClimateFEVER_cosine_ndcg@10	NanoDBPedia_cosine_ndcg@10	NanoFEVER_cosine_ndcg@10	NanoFiQA2018_cosine_ndcg@10	NanoHotpotQA_cosine_ndcg@10	NanoMSMARCO_cosine_ndcg@10	NanoNFCorpus_cosine_ndcg@10	NanoNQ_cosine_ndcg@10	NanoQuoraRetrieval_cosine_ndcg@10	NanoSCIDOCS_cosine_ndcg@10	NanoArguAna_cosine_ndcg@10	NanoSciFact_cosine_ndcg@10	NanoTouche2020_cosine_ndcg@10	NanoBEIR_mean_cosine_ndcg@10
0.0010	1	31.7042	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0049	5	32.9433	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0099	10	27.0338	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0148	15	18.1598	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0198	20	12.5771	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0247	25	8.6872	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0297	30	6.0455	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0346	35	5.1917	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0396	40	4.8424	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0445	45	4.4785	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0495	50	4.1896	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0544	55	4.2621	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0593	60	3.8401	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0643	65	3.9482	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0692	70	3.7762	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0742	75	3.4895	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0791	80	3.5892	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0841	85	3.5312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0890	90	3.3244	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0940	95	3.4369	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0989	100	3.1867	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1039	105	3.1734	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1088	110	3.2156	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1137	115	2.8888	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1187	120	2.8613	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1236	125	2.8905	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1286	130	2.5984	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1335	135	2.6853	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1385	140	2.7013	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1434	145	2.5577	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1484	150	2.6287	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1533	155	2.6481	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1583	160	2.7741	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1632	165	2.5738	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1682	170	2.5335	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1731	175	2.531	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1780	180	2.437	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1830	185	2.4836	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1879	190	2.4642	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1929	195	2.399	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.1978	200	2.3896	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2028	205	2.3738	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2077	210	2.5518	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2127	215	2.4836	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2176	220	2.2157	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2226	225	2.2986	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2275	230	2.4967	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2324	235	2.121	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2374	240	2.4301	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2423	245	2.5054	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2473	250	2.3213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2522	255	2.1182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2572	260	2.2966	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2621	265	2.2662	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2671	270	2.3188	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2720	275	2.1836	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2770	280	2.2206	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2819	285	2.3144	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2868	290	2.2496	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2918	295	1.9909	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.2967	300	2.1294	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3017	305	2.119	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3066	310	2.0076	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3116	315	2.127	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3165	320	2.1309	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3215	325	2.0868	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3264	330	1.9429	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3314	335	1.9	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3363	340	1.82	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3412	345	1.9731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3462	350	2.0156	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3511	355	2.0106	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3561	360	1.9383	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3610	365	2.0491	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3660	370	1.8893	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3709	375	1.958	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3759	380	1.9821	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3808	385	2.024	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3858	390	2.0182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3907	395	1.9659	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.3956	400	1.8339	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4006	405	1.9081	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4055	410	1.7876	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4105	415	1.8371	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4154	420	1.8274	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4204	425	1.7863	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4253	430	1.9064	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4303	435	1.7721	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4352	440	1.7162	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4402	445	1.9112	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4451	450	1.9384	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4500	455	1.8096	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4550	460	1.7145	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4599	465	1.784	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4649	470	1.9506	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4698	475	1.7243	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4748	480	1.8003	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4797	485	1.7568	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4847	490	1.5696	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4896	495	1.8973	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4946	500	1.6981	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.4995	505	1.7616	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5045	510	1.6573	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5094	515	1.8685	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5143	520	1.8532	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5193	525	1.7603	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5242	530	1.7636	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5292	535	1.4829	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5341	540	1.6959	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5391	545	1.6389	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5440	550	1.6624	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5490	555	1.8193	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5539	560	1.7144	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5589	565	1.4954	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5638	570	1.6659	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5687	575	1.669	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5737	580	1.6931	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5786	585	1.6894	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5836	590	1.6437	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5885	595	1.7259	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5935	600	1.7937	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.5984	605	1.7279	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6034	610	1.6769	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6083	615	1.4731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6133	620	1.6466	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6182	625	1.6954	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6231	630	1.6224	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6281	635	1.62	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6330	640	1.5795	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6380	645	1.5245	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6429	650	1.7629	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6479	655	1.5767	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6528	660	1.6749	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6578	665	1.5602	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6627	670	1.6768	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6677	675	1.8311	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6726	680	1.5973	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6775	685	1.5066	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6825	690	1.6036	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6874	695	1.7857	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6924	700	1.4387	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.6973	705	1.5886	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7023	710	1.551	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7072	715	1.5561	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7122	720	1.4458	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7171	725	1.5703	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7221	730	1.6162	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7270	735	1.5643	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7319	740	1.4894	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7369	745	1.6413	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7418	750	1.5406	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7468	755	1.5185	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7517	760	1.488	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7567	765	1.5041	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7616	770	1.4665	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7666	775	1.5252	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7715	780	1.4925	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7765	785	1.3833	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7814	790	1.3808	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7864	795	1.5468	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7913	800	1.5317	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.7962	805	1.5385	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8012	810	1.4012	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8061	815	1.5531	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8111	820	1.6032	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8160	825	1.4053	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8210	830	1.5082	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8259	835	1.5559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8309	840	1.4286	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8358	845	1.4336	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8408	850	1.3731	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8457	855	1.5706	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8506	860	1.4184	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8556	865	1.4312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8605	870	1.4364	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8655	875	1.5605	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8704	880	1.4219	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8754	885	1.4082	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8803	890	1.3846	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8853	895	1.4292	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8902	900	1.4195	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.8952	905	1.5103	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9001	910	1.5041	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9050	915	1.427	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9100	920	1.4385	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9149	925	1.298	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9199	930	1.4499	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9248	935	1.4752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9298	940	1.4752	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9347	945	1.3705	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9397	950	1.4567	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9446	955	1.3364	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9496	960	1.376	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9545	965	1.35	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9594	970	1.5841	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9644	975	1.3449	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9693	980	1.2132	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9743	985	1.3414	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9792	990	1.5148	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9842	995	1.3866	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9891	1000	1.2051	1.3370	0.0906	0.1578	0.0712	0.1504	0.1887	0.1554	0.0466	0.2528	0.6197	0.0672	0.2857	0.2291	0.2718	0.1990
0.9941	1005	1.3021	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.9990	1010	1.391	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0040	1015	1.1452	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0089	1020	1.3989	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0138	1025	1.2142	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0188	1030	1.2472	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0237	1035	1.3058	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0287	1040	1.2643	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0336	1045	1.2581	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0386	1050	1.2434	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0435	1055	1.1874	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0485	1060	1.0421	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0534	1065	1.3834	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0584	1070	1.3279	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0633	1075	1.3779	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0682	1080	1.3071	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0732	1085	1.1569	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0781	1090	1.2427	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0831	1095	1.1607	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0880	1100	1.2691	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0930	1105	1.2936	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.0979	1110	1.2527	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1029	1115	1.1143	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1078	1120	1.1508	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1128	1125	1.1627	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1177	1130	0.9774	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1227	1135	1.1827	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1276	1140	0.9429	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1325	1145	1.0029	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1375	1150	1.0764	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1424	1155	1.0555	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1474	1160	1.0559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1523	1165	1.0081	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1573	1170	1.1928	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1622	1175	1.0774	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1672	1180	0.9185	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1721	1185	1.0838	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1771	1190	0.9981	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1820	1195	1.0395	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1869	1200	0.9522	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1919	1205	0.9652	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.1968	1210	1.0276	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2018	1215	0.9663	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2067	1220	1.1356	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2117	1225	1.159	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2166	1230	0.8575	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2216	1235	0.9134	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2265	1240	1.1889	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2315	1245	0.935	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2364	1250	0.975	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2413	1255	1.073	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2463	1260	1.0709	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2512	1265	0.9241	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2562	1270	1.0101	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2611	1275	1.1451	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2661	1280	1.0501	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2710	1285	0.9724	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2760	1290	0.9222	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2809	1295	1.086	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2859	1300	0.973	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2908	1305	0.9287	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.2957	1310	0.9051	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3007	1315	0.9531	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3056	1320	0.9605	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3106	1325	0.8778	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3155	1330	0.9399	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3205	1335	0.9185	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3254	1340	0.9078	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3304	1345	0.8266	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3353	1350	0.8186	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3403	1355	0.9394	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3452	1360	1.0972	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3501	1365	0.8895	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3551	1370	0.8678	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3600	1375	0.9493	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3650	1380	0.8449	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3699	1385	0.917	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3749	1390	0.8899	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3798	1395	0.9516	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3848	1400	0.9538	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3897	1405	0.9964	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3947	1410	0.9123	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.3996	1415	0.86	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4045	1420	0.9382	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4095	1425	0.764	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4144	1430	0.9161	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4194	1435	0.937	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4243	1440	0.8487	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4293	1445	0.7928	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4342	1450	0.8586	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4392	1455	0.9355	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4441	1460	0.965	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4491	1465	0.9019	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4540	1470	0.8624	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4590	1475	0.8204	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4639	1480	1.0131	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4688	1485	0.9222	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4738	1490	0.9182	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4787	1495	0.8247	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4837	1500	0.7746	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4886	1505	0.882	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4936	1510	0.8482	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.4985	1515	0.9623	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5035	1520	0.8804	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5084	1525	0.8874	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5134	1530	0.9747	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5183	1535	0.8805	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5232	1540	0.8776	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5282	1545	0.7627	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5331	1550	0.8975	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5381	1555	0.8213	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5430	1560	0.9472	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5480	1565	0.9379	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5529	1570	0.9312	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5579	1575	0.7866	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5628	1580	0.8629	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5678	1585	0.8156	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5727	1590	0.8737	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5776	1595	0.942	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5826	1600	0.8167	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5875	1605	0.9468	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5925	1610	0.9117	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.5974	1615	1.0137	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6024	1620	0.8357	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6073	1625	0.8372	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6123	1630	0.905	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6172	1635	0.9265	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6222	1640	0.846	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6271	1645	0.7729	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6320	1650	0.7885	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6370	1655	0.8717	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6419	1660	0.9845	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6469	1665	0.8286	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6518	1670	0.8979	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6568	1675	0.8502	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6617	1680	0.9423	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6667	1685	1.0128	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6716	1690	0.8535	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6766	1695	0.737	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6815	1700	0.9871	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6864	1705	0.8828	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6914	1710	0.8178	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.6963	1715	0.7703	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7013	1720	0.8739	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7062	1725	0.8582	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7112	1730	0.9181	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7161	1735	0.8801	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7211	1740	0.8009	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7260	1745	0.9779	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7310	1750	0.7777	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7359	1755	0.7864	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7409	1760	1.0066	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7458	1765	0.7776	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7507	1770	0.8122	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7557	1775	0.8025	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7606	1780	0.7559	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7656	1785	0.8819	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7705	1790	0.8901	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7755	1795	0.7598	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7804	1800	0.7542	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7854	1805	0.8178	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7903	1810	0.8374	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.7953	1815	0.8363	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8002	1820	0.8177	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8051	1825	0.9488	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8101	1830	0.9959	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8150	1835	0.7942	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8200	1840	0.8747	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8249	1845	0.9053	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8299	1850	0.7853	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8348	1855	0.838	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8398	1860	0.7732	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8447	1865	0.8613	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8497	1870	0.791	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8546	1875	0.8203	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8595	1880	0.7558	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8645	1885	0.9918	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8694	1890	0.8272	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8744	1895	0.8552	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8793	1900	0.8135	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8843	1905	0.8297	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8892	1910	0.7844	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8942	1915	0.8466	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.8991	1920	0.9099	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9041	1925	0.8139	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9090	1930	0.8628	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9139	1935	0.6778	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9189	1940	0.8251	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9238	1945	0.8915	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9288	1950	0.8136	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9337	1955	0.8879	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9387	1960	0.8758	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9436	1965	0.8153	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9486	1970	0.7253	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9535	1975	0.8493	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9585	1980	1.0186	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9634	1985	0.8412	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9683	1990	0.7027	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9733	1995	0.744	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9782	2000	0.9555	1.1452	0.1064	0.1577	0.0780	0.1597	0.2144	0.1550	0.0513	0.2643	0.6316	0.0525	0.3670	0.2485	0.2937	0.2139
1.9832	2005	0.9095	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9881	2010	0.7378	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9931	2015	0.8024	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
1.9980	2020	0.9107	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
2.0	2022	-	-	0.1074	0.1565	0.0780	0.1599	0.2152	0.1550	0.0514	0.2669	0.6316	0.0544	0.3668	0.2485	0.2934	0.2142

Framework Versions

Python: 3.11.2
Sentence Transformers: 3.3.1
Transformers: 4.47.1
PyTorch: 2.4.0+cu121
Accelerate: 1.0.1
Datasets: 3.1.0
Tokenizers: 0.21.0

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",
}