--- 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](https://www.SBERT.net) model finetuned from [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/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](https://huggingface.co/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](https://sbert.net) - **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers) - **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) ### 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: ```bash pip install -U sentence-transformers ``` Then you can load this model and run inference. ```python 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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.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 | | | | * 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](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: ```json { "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 ```bibtex @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 ```bibtex @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} } ```