README.md

metadata

base_model: Qwen/Qwen2.5-0.5B-Instruct
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
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:1077240
  - loss:MultipleNegativesRankingLoss
widget:
  - source_sentence: When was the frigate the USS Alliance built?
    sentences:
      - >-
        Middle Stone Age

        The Middle Stone Age (or MSA) was a period of African prehistory between
        the Early Stone Age and the Later Stone Age. It is generally considered
        to have begun around 280,000 years ago and ended around 50–25,000 years
        ago.[1] The beginnings of particular MSA stone tools have their origins
        as far back as 550–500,000 years ago and as such some researchers
        consider this to be the beginnings of the MSA.[2] The MSA is often
        mistakenly understood to be synonymous with the Middle Paleolithic of
        Europe, especially due to their roughly contemporaneous time span,
        however, the Middle Paleolithic of Europe represents an entirely
        different hominin population, Homo neanderthalensis, than the MSA of
        Africa, which did not have Neanderthal populations. Additionally,
        current archaeological research in Africa has yielded much evidence to
        suggest that modern human behavior and cognition was beginning to
        develop much earlier in Africa during the MSA than it was in Europe
        during the Middle Paleolithic.[3] The MSA is associated with both
        anatomically modern humans (Homo sapiens) as well as archaic Homo
        sapiens, sometimes referred to as Homo helmei. Early physical evidence
        comes from the Gademotta Formation in Ethiopia, the Kapthurin Formation
        in Kenya and Kathu Pan in South Africa.[2]
      - >-
        USS Alliance (1778)

        Originally named Hancock, she was laid down in 1777 on the Merrimack
        River at Amesbury, Massachusetts, by the partners and cousins, William
        and James K. Hackett, launched on 28 April 1778, and renamed Alliance on
        29 May 1778 by resolution of the Continental Congress. Her first
        commanding officer was Capt. Pierre Landais, a former officer of the
        French Navy who had come to the New World hoping to become a naval
        counterpart of Lafayette. The frigate's first captain was widely
        accepted as such in America. Massachusetts made him an honorary citizen
        and the Continental Congress gave him command of Alliance, thought to be
        the finest warship built to that date on the western side of the
        Atlantic.
      - >-
        USS Frigate Bird (AMS-191)

        The second ship in the Navy to be named "Frigate Bird", she was laid
        down 20 July 1953, as AMS-191; launched 24 October 1953, by Quincy Adams
        Yacht Yard, Inc., Quincy, Massachusetts; sponsored by Mrs. Matthew
        Gushing; and commissioned 13 January 1955, Lieutenant (jg) George B.
        Shick, Jr., in command. She was reclassified MSC-191 on 7 February 1955.
  - source_sentence: How many bananas do Americans consume each year?
    sentences:
      - >-
        Dust Bowl

        The Dust Bowl was a period of severe dust storms that greatly damaged
        the ecology and agriculture of the American and Canadian prairies during
        the 1930s; severe drought and a failure to apply dryland farming methods
        to prevent the aeolian processes (wind erosion) caused the
        phenomenon.[1][2] The drought came in three waves, 1934, 1936, and
        1939–1940, but some regions of the high plains experienced drought
        conditions for as many as eight years.[3] With insufficient
        understanding of the ecology of the plains, farmers had conducted
        extensive deep plowing of the virgin topsoil of the Great Plains during
        the previous decade; this had displaced the native, deep-rooted grasses
        that normally trapped soil and moisture even during periods of drought
        and high winds. The rapid mechanization of farm equipment, especially
        small gasoline tractors, and widespread use of the combine harvester
        contributed to farmers' decisions to convert arid grassland (much of
        which received no more than 10inches (~250mm) of precipitation per year)
        to cultivated cropland.[4]
      - >-
        Banana production in the United States

        Commercial banana production in the United States is relatively limited
        in scale and economic impact. While Americans eat 26 pounds (12kg) of
        bananas per person per year, the vast majority of the fruit is imported
        from other countries, chiefly Central and South America, where the US
        has previously occupied areas containing banana plantations, and
        controlled the importation of bananas via various fruit companies, such
        as Dole and Chiquita. [1]
      - >-
        Spinach in the United States

        Per capita spinach consumption is greatest in the Northeast and Western
        US. About 80% of fresh-market spinach is purchased at retail and
        consumed at home, while 91% of processed spinach is consumed at home.
        Per capita spinach use is strongest among Asians, highest among women 40
        and older, and weakest among teenage girls.
  - source_sentence: Which rapper has the most Grammy wins?
    sentences:
      - >-
        Rhineland

        The Rhineland (German: Rheinland, French: Rhénanie, Latinised name:
        Rhenania) is the name used for a loosely defined area of Western Germany
        along the Rhine, chiefly its middle section.
      - |-
        Grammy Award for Best Rap Album
        6 wins Eminem 4 wins Kanye West 2 wins Outkast Kendrick Lamar
      - >-
        Latin Grammy Award records

        René Pérez Joglar "Residente" and Eduardo Cabra "Visitante" with 24
        awards, have won more than any other male artist.
  - source_sentence: Is Iodine radioactive?
    sentences:
      - >-
        Double-decker bus

        With the exception of coaches, double-decker buses are uncommon in the
        United States. Many private operators, such as Megabus, run by Coach
        USA, employs double-decker buses on its busier intercity routes.
      - >-
        Iodine-123

        I is the most suitable isotope of iodine for the diagnostic study of
        thyroid diseases. The half-life of approximately 13.13 hours is ideal
        for the 24-hour iodine uptake test and I has other advantages for
        diagnostic imaging thyroid tissue and thyroid cancer metastasis. The
        energy of the photon, 159 keV, is ideal for the NaI (sodium iodide)
        crystal detector of current gamma cameras and also for the pinhole
        collimators. It has much greater photon flux than I. It gives
        approximately 20 times the counting rate of I for the same administered
        dose. The radiation burden to the thyroid is far less (1%) than that of
        I. Moreover, scanning a thyroid remnant or metastasis with I does not
        cause "stunning" of the tissue (with loss of uptake), because of the low
        radiation burden of this isotope. (For the same reasons, I is never used
        for thyroid cancer or Graves disease "treatment", and this role is
        reserved for I.)
      - >-
        Iodine-131

        Iodine-131 (131I) is an important radioisotope of iodine discovered by
        Glenn Seaborg and John Livingood in 1938 at the University of
        California, Berkeley.[1] It has a radioactive decay half-life of about
        eight days. It is associated with nuclear energy, medical diagnostic and
        treatment procedures, and natural gas production. It also plays a major
        role as a radioactive isotope present in nuclear fission products, and
        was a significant contributor to the health hazards from open-air atomic
        bomb testing in the 1950s, and from the Chernobyl disaster, as well as
        being a large fraction of the contamination hazard in the first weeks in
        the Fukushima nuclear crisis. This is because I-131 is a major fission
        product of uranium and plutonium, comprising nearly 3% of the total
        products of fission (by weight). See fission product yield for a
        comparison with other radioactive fission products. I-131 is also a
        major fission product of uranium-233, produced from thorium.
  - source_sentence: Is a birth certificate a form of ID?
    sentences:
      - >-
        Identity documents in the United States

        The birth certificate is the initial identification document issued to
        parents shortly after the birth of their child. The birth certificate is
        typically issued by local governments, usually the city or county where
        a child is born. It is an important record, often called a "feeder
        document," because it establishes U.S. citizenship through birthright
        citizenship, which is then used to obtain, or is the basis for, all
        other identity documents.[2] By itself, the birth certificate is usually
        only considered proof of citizenship but not proof of identity, since it
        is issued without a photograph at birth, containing no identifying
        features. A birth certificate is normally produced along with proof of
        identity, such as a driver's license or the testimony of a third party
        (such as a parent), to establish identity or entitlement to a service.
      - >-
        Identity document

        In Canada, different forms of identification documentation are used, but
        there is no de jure national identity card. The Canadian passport is
        issued by the federal (national) government, and the provinces and
        territories issue various documents which can be used for identification
        purposes. The most commonly used forms of identification within Canada
        are the health card and driver's licence issued by provincial and
        territorial governments. The widespread usage of these two documents for
        identification purposes has made them de facto identity cards.
      - >-
        International Ladies' Garment Workers' Union

        The ILGWU was founded on June 3, 1900[2] in New York City by seven local
        unions, with a few thousand members between them. The union grew rapidly
        in the next few years but began to stagnate as the conservative
        leadership favored the interests of skilled workers, such as cutters.
        This did not sit well with the majority of immigrant workers,
        particularly Jewish workers with a background in Bundist activities in
        Tsarist Russia, or with Polish and Italian workers, many of whom had
        strong socialist and anarchist leanings.
model-index:
  - name: SentenceTransformer based on Qwen/Qwen2.5-0.5B-Instruct
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoClimateFEVER
          type: NanoClimateFEVER
        metrics:
          - type: cosine_accuracy@1
            value: 0.18
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.28
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.32
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.4
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.18
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.07200000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.052000000000000005
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.07
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.12833333333333333
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.145
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.19333333333333333
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.16942887258019743
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.24757142857142853
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.1355707100998398
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoDBPedia
          type: NanoDBPedia
        metrics:
          - type: cosine_accuracy@1
            value: 0.6
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.72
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.82
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.88
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.6
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.4333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.4159999999999999
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.34
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.05876239296513622
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.12033972099857915
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.1722048755781874
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.23956661282775613
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.43285369033010385
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6865000000000001
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.26928358503732847
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFEVER
          type: NanoFEVER
        metrics:
          - type: cosine_accuracy@1
            value: 0.28
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.56
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.66
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.82
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.28
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.18666666666666668
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.136
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08599999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.26
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.52
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.63
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.79
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.5142272491063538
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.4404126984126984
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.4252817429020436
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoFiQA2018
          type: NanoFiQA2018
        metrics:
          - type: cosine_accuracy@1
            value: 0.18
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.28
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.4
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.5
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.18
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.096
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.062
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.0968888888888889
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.1971111111111111
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.274968253968254
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.3351904761904762
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.23653831229880148
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2642460317460318
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.19230802555548754
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoHotpotQA
          type: NanoHotpotQA
        metrics:
          - type: cosine_accuracy@1
            value: 0.46
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.56
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.6
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.68
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.46
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.23333333333333336
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.14800000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.08999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.23
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.35
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.37
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.45
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.4053306177663136
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.512126984126984
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.34910258985509013
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoMSMARCO
          type: NanoMSMARCO
        metrics:
          - type: cosine_accuracy@1
            value: 0.22
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.34
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.34
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.44
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.22
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.11333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.068
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.044000000000000004
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.22
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.34
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.34
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.44
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3192297885891097
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2824126984126984
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.2997920687164519
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNFCorpus
          type: NanoNFCorpus
        metrics:
          - type: cosine_accuracy@1
            value: 0.32
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.42
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.46
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.52
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.32
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.28
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.24
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.18
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.012493999137489852
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.03666681098754983
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.05620504095265686
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.06687360120479266
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.22113733731375582
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3847142857142857
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.08151129221772184
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoNQ
          type: NanoNQ
        metrics:
          - type: cosine_accuracy@1
            value: 0.28
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.4
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.58
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.28
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.13333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10000000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.05800000000000001
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.27
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.37
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.47
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.53
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.397781398000215
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.3704682539682539
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.36349470659883915
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoQuoraRetrieval
          type: NanoQuoraRetrieval
        metrics:
          - type: cosine_accuracy@1
            value: 0.84
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.92
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.92
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.98
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.84
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3666666666666666
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.236
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.128
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.7406666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.8686666666666667
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.8859999999999999
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.956
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.8890148621063009
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.8828571428571428
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.8629278499278499
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSCIDOCS
          type: NanoSCIDOCS
        metrics:
          - type: cosine_accuracy@1
            value: 0.26
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.38
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.48
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.62
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.26
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.15333333333333332
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.14800000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.118
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.053000000000000005
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.09300000000000001
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.15
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.24066666666666667
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.21606068741193518
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.35269047619047617
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.15911734324501478
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoArguAna
          type: NanoArguAna
        metrics:
          - type: cosine_accuracy@1
            value: 0.06
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.42
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.52
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.68
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.06
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.13999999999999999
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10400000000000002
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.068
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.06
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.42
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.52
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.68
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.37083936956670244
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.2719126984126984
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.28287447040897273
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoSciFact
          type: NanoSciFact
        metrics:
          - type: cosine_accuracy@1
            value: 0.26
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.38
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.26
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.1333333333333333
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.10400000000000001
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.066
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.235
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.355
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.465
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.58
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.39672106927390305
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.35040476190476183
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3458399624509464
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: NanoTouche2020
          type: NanoTouche2020
        metrics:
          - type: cosine_accuracy@1
            value: 0.4489795918367347
            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: 0.9795918367346939
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.4489795918367347
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.3945578231292517
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.3673469387755102
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.3346938775510204
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.0346506610646935
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.09241187721057745
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.13780926782570618
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.23921204048524203
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.37383535385863614
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.6103984450923227
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3108247074769811
            name: Cosine Map@100
      - task:
          type: nano-beir
          name: Nano BEIR
        dataset:
          name: NanoBEIR mean
          type: NanoBEIR_mean
        metrics:
          - type: cosine_accuracy@1
            value: 0.33761381475667196
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 0.49032967032967034
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 0.5627629513343799
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 0.6676609105180534
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.33761381475667196
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.2139403453689168
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.17194976452119312
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.12513029827315542
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.18011250836329812
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 0.29934842463906286
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 0.3551682644865234
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 0.441603286977559
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.3802306621694099
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.43513199272382946
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.3136868503455821
            name: Cosine Map@100

SentenceTransformer based on Qwen/Qwen2.5-0.5B-Instruct

This is a sentence-transformers model finetuned from Qwen/Qwen2.5-0.5B-Instruct. It maps sentences & paragraphs to a 896-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: Qwen/Qwen2.5-0.5B-Instruct
• Maximum Sequence Length: 1024 tokens
• Output Dimensionality: 896 dimensions
• Similarity Function: Cosine Similarity

Model Sources

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

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 1024, 'do_lower_case': False}) with Transformer model: Qwen2Model 
  (1): Pooling({'word_embedding_dimension': 896, '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("AlexWortega/qwen_emb_600_best_21.11")
# Run inference
sentences = [
    'Is a birth certificate a form of ID?',
    'Identity documents in the United States\nThe birth certificate is the initial identification document issued to parents shortly after the birth of their child. The birth certificate is typically issued by local governments, usually the city or county where a child is born. It is an important record, often called a "feeder document," because it establishes U.S. citizenship through birthright citizenship, which is then used to obtain, or is the basis for, all other identity documents.[2] By itself, the birth certificate is usually only considered proof of citizenship but not proof of identity, since it is issued without a photograph at birth, containing no identifying features. A birth certificate is normally produced along with proof of identity, such as a driver\'s license or the testimony of a third party (such as a parent), to establish identity or entitlement to a service.',
    "Identity document\nIn Canada, different forms of identification documentation are used, but there is no de jure national identity card. The Canadian passport is issued by the federal (national) government, and the provinces and territories issue various documents which can be used for identification purposes. The most commonly used forms of identification within Canada are the health card and driver's licence issued by provincial and territorial governments. The widespread usage of these two documents for identification purposes has made them de facto identity cards.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 896]

# 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	0.18	0.6	0.28	0.18	0.46	0.22	0.32	0.28	0.84	0.26	0.06	0.26	0.449
cosine_accuracy@3	0.28	0.72	0.56	0.28	0.56	0.34	0.42	0.4	0.92	0.38	0.42	0.38	0.7143
cosine_accuracy@5	0.32	0.82	0.66	0.4	0.6	0.34	0.46	0.5	0.92	0.48	0.52	0.5	0.7959
cosine_accuracy@10	0.4	0.88	0.82	0.5	0.68	0.44	0.52	0.58	0.98	0.62	0.68	0.6	0.9796
cosine_precision@1	0.18	0.6	0.28	0.18	0.46	0.22	0.32	0.28	0.84	0.26	0.06	0.26	0.449
cosine_precision@3	0.1	0.4333	0.1867	0.1133	0.2333	0.1133	0.28	0.1333	0.3667	0.1533	0.14	0.1333	0.3946
cosine_precision@5	0.072	0.416	0.136	0.096	0.148	0.068	0.24	0.1	0.236	0.148	0.104	0.104	0.3673
cosine_precision@10	0.052	0.34	0.086	0.062	0.09	0.044	0.18	0.058	0.128	0.118	0.068	0.066	0.3347
cosine_recall@1	0.07	0.0588	0.26	0.0969	0.23	0.22	0.0125	0.27	0.7407	0.053	0.06	0.235	0.0347
cosine_recall@3	0.1283	0.1203	0.52	0.1971	0.35	0.34	0.0367	0.37	0.8687	0.093	0.42	0.355	0.0924
cosine_recall@5	0.145	0.1722	0.63	0.275	0.37	0.34	0.0562	0.47	0.886	0.15	0.52	0.465	0.1378
cosine_recall@10	0.1933	0.2396	0.79	0.3352	0.45	0.44	0.0669	0.53	0.956	0.2407	0.68	0.58	0.2392
cosine_ndcg@10	0.1694	0.4329	0.5142	0.2365	0.4053	0.3192	0.2211	0.3978	0.889	0.2161	0.3708	0.3967	0.3738
cosine_mrr@10	0.2476	0.6865	0.4404	0.2642	0.5121	0.2824	0.3847	0.3705	0.8829	0.3527	0.2719	0.3504	0.6104
cosine_map@100	0.1356	0.2693	0.4253	0.1923	0.3491	0.2998	0.0815	0.3635	0.8629	0.1591	0.2829	0.3458	0.3108

Nano BEIR

• Dataset: NanoBEIR_mean
• Evaluated with NanoBEIREvaluator

Metric	Value
cosine_accuracy@1	0.3376
cosine_accuracy@3	0.4903
cosine_accuracy@5	0.5628
cosine_accuracy@10	0.6677
cosine_precision@1	0.3376
cosine_precision@3	0.2139
cosine_precision@5	0.1719
cosine_precision@10	0.1251
cosine_recall@1	0.1801
cosine_recall@3	0.2993
cosine_recall@5	0.3552
cosine_recall@10	0.4416
cosine_ndcg@10	0.3802
cosine_mrr@10	0.4351
cosine_map@100	0.3137

Training Details

Training Dataset

Unnamed Dataset

• Size: 1,077,240 training samples
• Columns: query, response, and negative
• Approximate statistics based on the first 1000 samples:

  	query	response	negative
  type	string	string	string
  details	min: 4 tokens mean: 8.76 tokens max: 26 tokens	min: 23 tokens mean: 141.88 tokens max: 532 tokens	min: 4 tokens mean: 134.02 tokens max: 472 tokens

• Samples:

  query	response	negative
  Was there a year 0?	Year zero Year zero does not exist in the anno Domini system usually used to number years in the Gregorian calendar and in its predecessor, the Julian calendar. In this system, the year 1 BC is followed by AD 1. However, there is a year zero in astronomical year numbering (where it coincides with the Julian year 1 BC) and in ISO 8601:2004 (where it coincides with the Gregorian year 1 BC) as well as in all Buddhist and Hindu calendars.	504 Year 504 (DIV) was a leap year starting on Thursday (link will display the full calendar) of the Julian calendar. At the time, it was known as the Year of the Consulship of Nicomachus without colleague (or, less frequently, year 1257 "Ab urbe condita"). The denomination 504 for this year has been used since the early medieval period, when the Anno Domini calendar era became the prevalent method in Europe for naming years.
  When is the dialectical method used?	Dialectic Dialectic or dialectics (Greek: διαλεκτική, dialektikḗ; related to dialogue), also known as the dialectical method, is at base a discourse between two or more people holding different points of view about a subject but wishing to establish the truth through reasoned arguments. Dialectic resembles debate, but the concept excludes subjective elements such as emotional appeal and the modern pejorative sense of rhetoric.[1][2] Dialectic may be contrasted with the didactic method, wherein one side of the conversation teaches the other. Dialectic is alternatively known as minor logic, as opposed to major logic or critique.	Derek Bentley case Another factor in the posthumous defence was that a "confession" recorded by Bentley, which was claimed by the prosecution to be a "verbatim record of dictated monologue", was shown by forensic linguistics methods to have been largely edited by policemen. Linguist Malcolm Coulthard showed that certain patterns, such as the frequency of the word "then" and the grammatical use of "then" after the grammatical subject ("I then" rather than "then I"), were not consistent with Bentley's use of language (his idiolect), as evidenced in court testimony. These patterns fit better the recorded testimony of the policemen involved. This is one of the earliest uses of forensic linguistics on record.
  What do Grasshoppers eat?	Grasshopper Grasshoppers are plant-eaters, with a few species at times becoming serious pests of cereals, vegetables and pasture, especially when they swarm in their millions as locusts and destroy crops over wide areas. They protect themselves from predators by camouflage; when detected, many species attempt to startle the predator with a brilliantly-coloured wing-flash while jumping and (if adult) launching themselves into the air, usually flying for only a short distance. Other species such as the rainbow grasshopper have warning coloration which deters predators. Grasshoppers are affected by parasites and various diseases, and many predatory creatures feed on both nymphs and adults. The eggs are the subject of attack by parasitoids and predators.	Groundhog Very often the dens of groundhogs provide homes for other animals including skunks, red foxes, and cottontail rabbits. The fox and skunk feed upon field mice, grasshoppers, beetles and other creatures that destroy farm crops. In aiding these animals, the groundhog indirectly helps the farmer. In addition to providing homes for itself and other animals, the groundhog aids in soil improvement by bringing subsoil to the surface. The groundhog is also a valuable game animal and is considered a difficult sport when hunted in a fair manner. In some parts of Appalachia, they are eaten.

• Loss: MultipleNegativesRankingLoss with these parameters:

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

Training Hyperparameters

Non-Default Hyperparameters

• eval_strategy: steps
• gradient_accumulation_steps: 8
• learning_rate: 0.0001
• max_grad_norm: 0.01
• num_train_epochs: 2
• warmup_ratio: 0.4
• bf16: True
• dataloader_num_workers: 8
• 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: 8
• per_device_eval_batch_size: 8
• per_gpu_train_batch_size: None
• per_gpu_eval_batch_size: None
• gradient_accumulation_steps: 8
• 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-08
• max_grad_norm: 0.01
• num_train_epochs: 2
• max_steps: -1
• lr_scheduler_type: linear
• lr_scheduler_kwargs: {}
• warmup_ratio: 0.4
• 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: 8
• 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
• 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: None
• batch_sampler: no_duplicates
• multi_dataset_batch_sampler: proportional

Training Logs

Click to expand

Epoch	Step	Training 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
0.0002	2	1.8808	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0005	4	1.9239	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0007	6	2.0324	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0010	8	2.0387	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0012	10	2.0336	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0014	12	1.9943	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0017	14	1.971	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0019	16	1.9206	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0021	18	1.8157	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0024	20	1.8605	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0026	22	1.862	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0029	24	1.9313	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0031	26	1.8326	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0033	28	1.9208	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0036	30	2.4718	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0038	32	2.4819	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0040	34	2.4956	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0043	36	2.4335	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0045	38	2.4694	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0048	40	2.5719	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0050	42	2.4666	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0052	44	2.4919	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0055	46	2.4179	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0057	48	2.4022	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0059	50	2.39	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0062	52	2.4682	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0064	54	2.3442	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0067	56	2.3157	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0069	58	2.2665	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0071	60	2.2969	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0074	62	2.1652	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0076	64	2.1243	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0078	66	2.0499	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0081	68	2.0115	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0083	70	1.8372	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0086	72	1.6257	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0088	74	1.6398	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0090	76	1.4927	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0093	78	1.3491	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0095	80	1.3303	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0097	82	1.3846	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0100	84	1.2647	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0102	86	1.1579	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0105	88	1.0146	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0107	90	0.9201	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0109	92	0.8631	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0112	94	0.7801	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0114	96	0.7813	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0116	98	0.7898	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0119	100	0.722	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0121	102	0.7595	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0124	104	0.6245	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0126	106	0.6036	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0128	108	0.7248	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0131	110	0.637	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0133	112	0.6205	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0135	114	0.5956	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0138	116	0.6126	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0140	118	0.547	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0143	120	0.5414	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0145	122	0.4896	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0147	124	0.5351	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0150	126	0.5404	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0152	128	0.4479	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0154	130	1.1779	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0157	132	1.4533	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0159	134	1.5042	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0162	136	1.2167	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0164	138	1.2484	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0166	140	1.1236	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0169	142	1.1729	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0171	144	1.0076	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0173	146	1.0314	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0176	148	0.9106	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0178	150	0.8994	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0181	152	0.9679	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0183	154	0.8576	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0185	156	0.777	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0188	158	0.8527	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0190	160	0.864	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0192	162	0.807	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0195	164	0.9083	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0197	166	0.7705	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0200	168	0.7179	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0202	170	0.7485	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0204	172	0.7198	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0207	174	0.7712	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0209	176	0.82	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0212	178	0.7744	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0214	180	0.7668	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0216	182	0.6501	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0219	184	0.6327	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0221	186	0.752	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0223	188	0.6204	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0226	190	0.6258	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0228	192	0.607	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0231	194	0.5688	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0233	196	0.6831	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0235	198	0.5653	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0238	200	0.5966	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0240	202	0.5798	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0242	204	0.5991	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0245	206	0.5856	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0247	208	0.5935	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0250	210	0.5624	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0252	212	0.6188	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0254	214	0.5497	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0257	216	0.582	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0259	218	0.5912	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0261	220	0.4818	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0264	222	0.5686	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0266	224	0.5174	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0269	226	0.523	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0271	228	0.5337	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0273	230	0.5253	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0276	232	0.5434	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0278	234	0.5351	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0280	236	0.5202	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0283	238	0.4611	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0285	240	0.4509	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0288	242	0.5217	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0290	244	0.5256	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0292	246	0.5529	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0295	248	0.4944	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0297	250	0.568	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0299	252	0.5024	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0302	254	0.5094	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0304	256	0.5057	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0307	258	0.5424	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0309	260	0.6485	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0311	262	0.4823	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0314	264	0.475	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0316	266	0.3753	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0318	268	0.5117	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0321	270	0.4067	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0323	272	0.4706	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0326	274	0.4099	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0328	276	0.4251	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0330	278	0.4392	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0333	280	0.5373	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0335	282	0.4259	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0337	284	0.4227	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0340	286	0.4774	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0342	288	0.4878	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0345	290	0.5619	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0347	292	0.5061	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0349	294	0.5434	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0352	296	0.5115	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0354	298	0.4281	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0356	300	0.4287	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0359	302	0.4864	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0361	304	0.4724	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0364	306	0.4607	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0366	308	0.3978	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0368	310	0.4851	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0371	312	0.3466	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0373	314	0.565	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0375	316	0.4122	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0378	318	0.3757	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0380	320	0.4673	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0383	322	0.4358	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0385	324	0.4423	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0387	326	0.3754	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0390	328	0.4358	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0392	330	0.408	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0394	332	0.3901	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0397	334	0.4155	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0399	336	0.379	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0402	338	0.373	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0404	340	0.2917	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0406	342	0.3755	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0409	344	0.3262	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0411	346	0.4975	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0414	348	0.3469	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0416	350	0.3895	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0418	352	0.4424	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0421	354	0.3609	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0423	356	0.434	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0425	358	0.4474	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0428	360	0.3514	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0430	362	0.4029	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0433	364	0.4438	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0435	366	0.4271	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0437	368	0.3825	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0440	370	0.3848	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0442	372	0.4088	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0444	374	0.4188	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0447	376	0.4333	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0449	378	0.3784	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0452	380	0.4509	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0454	382	0.4084	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0456	384	0.371	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0459	386	0.3965	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0461	388	0.375	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0463	390	0.4098	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0466	392	0.4198	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0468	394	0.3854	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0471	396	0.3146	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0473	398	0.3892	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0475	400	0.3295	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0478	402	0.4124	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0480	404	0.3039	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0482	406	0.3353	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0485	408	0.4382	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0487	410	0.4013	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0490	412	0.3283	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0492	414	0.4264	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0494	416	0.4295	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0497	418	0.3451	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0499	420	0.2973	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0501	422	0.3734	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0504	424	0.3992	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0506	426	0.3234	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0509	428	0.4007	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0511	430	0.4446	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0513	432	0.282	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0516	434	0.3922	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0518	436	0.4224	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0520	438	0.3362	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0523	440	0.3461	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0525	442	0.344	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0528	444	0.4355	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0530	446	0.3443	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0532	448	0.4363	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0535	450	0.3282	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0537	452	0.3761	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0539	454	0.3279	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0542	456	0.3774	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0544	458	0.3888	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0547	460	0.5149	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0549	462	0.343	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0551	464	0.3943	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0554	466	0.366	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0556	468	0.344	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0558	470	0.3681	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0561	472	0.3041	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0563	474	0.3857	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0566	476	0.3665	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0568	478	0.3871	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0570	480	0.4707	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0573	482	0.4031	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0575	484	0.385	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0577	486	0.2868	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0580	488	0.3637	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0582	490	0.4484	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0585	492	0.4984	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0587	494	0.3725	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0589	496	0.3102	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0592	498	0.3529	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0594	500	0.3929	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0596	502	0.3012	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0599	504	0.4137	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0601	506	0.3987	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0604	508	0.3724	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0606	510	0.3761	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0608	512	0.389	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0611	514	0.3775	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0613	516	0.3429	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0616	518	0.348	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0618	520	0.3706	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0620	522	0.3563	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0623	524	0.3029	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0625	526	0.4227	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0627	528	0.3457	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0630	530	0.3666	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0632	532	0.3331	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0635	534	0.3362	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0637	536	0.3974	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0639	538	0.3841	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0642	540	0.3318	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0644	542	0.3349	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0646	544	0.461	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0649	546	0.3271	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0651	548	0.3901	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0654	550	0.3292	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0656	552	0.3291	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0658	554	0.374	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0661	556	0.3432	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0663	558	0.2994	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0665	560	0.3391	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0668	562	0.3764	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0670	564	0.2555	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0673	566	0.3553	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0675	568	0.3436	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0677	570	0.4347	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0680	572	0.3271	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0682	574	0.2988	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0684	576	0.3698	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0687	578	0.3309	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0689	580	0.3529	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0692	582	0.3685	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0694	584	0.333	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0696	586	0.3344	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0699	588	0.3496	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0701	590	0.3616	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0703	592	0.3637	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0706	594	0.3745	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0708	596	0.3465	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0711	598	0.4128	-	-	-	-	-	-	-	-	-	-	-	-	-	-
0.0713	600	0.3674	0.1694	0.4329	0.5142	0.2365	0.4053	0.3192	0.2211	0.3978	0.8890	0.2161	0.3708	0.3967	0.3738	0.3802

Framework Versions

• Python: 3.10.12
• Sentence Transformers: 3.3.0
• Transformers: 4.46.2
• PyTorch: 2.1.1+cu121
• Accelerate: 0.34.2
• Datasets: 3.1.0
• Tokenizers: 0.20.3

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}
}