Add new SentenceTransformer model

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 768,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,1131 @@
+---
+language:
+- en
+license: apache-2.0
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:135
+- loss:MatryoshkaLoss
+- loss:MultipleNegativesRankingLoss
+base_model: BAAI/bge-base-en-v1.5
+widget:
+- source_sentence: "Table of Contents\nConsolidated Statements of Earnings\n$ and\
+    \ shares in millions, except per share amounts\nFiscal Years Ended\n\nJanuary\
+    \ 28, 2017 \nJanuary 30, 2016 \nJanuary 31, 2015\nRevenue\n $\n39,403\n $\n39,528\n\
+    \ $\n40,339\nCostofgoodssold\n\n29,963\n\n30,334\n\n31,292\nRestructuringchargescostofgoodssold\n\
+    \n\n\n3\n\n\nGrossprofit\n\n9,440\n\n9,191\n\n9,047\nSelling,generalandadministrativeexpenses\n\
+    \n7,547\n\n7,618\n\n7,592\nRestructuringcharges\n\n39\n\n198\n\n5\nOperatingincome\n\
+    \n1,854\n\n1,375\n\n1,450\nOtherincome(expense)\n \n \n \nGainonsaleofinvestments\n\
+    \n3\n\n2\n\n13\nInvestmentincomeandother\n\n31\n\n13\n\n14\nInterestexpense\n\n\
+    (72) \n(80) \n(90)\nEarningsfromcontinuingoperationsbeforeincometaxexpense\n\n\
+    1,816\n\n1,310\n\n1,387\nIncometaxexpense\n\n609\n\n503\n\n141\nNetearningsfromcontinuingoperations\n\
+    \n1,207\n\n807\n\n1,246\nGain(loss)fromdiscontinuedoperations(Note2),netoftaxexpenseof$7,$1and$0\n\
+    \n21\n\n90\n\n(11)\nNetearningsincludingnoncontrollinginterests\n\n1,228\n\n897\n\
+    \n1,235\nNetearningsfromdiscontinuedoperationsattributabletononcontrollinginterests\n\
+    \n\n\n\n\n(2)\nNetearningsattributabletoBestBuyCo.,Inc.shareholders\n $\n1,228\n\
+    \ $\n897\n $\n1,233\n\n \n \n \nBasicearnings(loss)pershareattributabletoBestBuyCo.,Inc.shareholders\n\
+    \ \n \n \nContinuingoperations\n $\n3.79\n $\n2.33\n $\n3.57\nDiscontinuedoperations\n\
+    \n0.07\n\n0.26\n\n(0.04)\nBasicearningspershare\n $\n3.86\n $\n2.59\n $\n3.53\n\
+    \n \n \n \nDilutedearnings(loss)pershareattributabletoBestBuyCo.,Inc.shareholders\n\
+    \ \n \n \nContinuingoperations\n $\n3.74\n $\n2.30\n $\n3.53\nDiscontinuedoperations\n\
+    \n0.07\n\n0.26\n\n(0.04)\nDilutedearningspershare\n $\n3.81\n $\n2.56\n $\n3.49\n\
+    \n \n \n \nWeighted-averagecommonsharesoutstanding\n \n \n \nBasic\n\n318.5\n\n\
+    346.5\n\n349.5\nDiluted\n\n322.6\n\n350.7\n\n353.6\nSeeNotestoConsolidatedFinancialStatements.\n\
+    54\nTable of Contents\nConsolidated Statements of Earnings\n$ and shares in millions,\
+    \ except per share amounts\nFiscal Years Ended\n\nJanuary 28, 2017 \nJanuary 30,\
+    \ 2016 \nJanuary 31, 2015\nRevenue\n $\n39,403\n $\n39,528\n $\n40,339\nCostofgoodssold\n\
+    \n29,963\n\n30,334\n\n31,292\nRestructuringchargescostofgoodssold\n\n\n\n3\n\n\
+    \nGrossprofit\n\n9,440\n\n9,191\n\n9,047\nSelling,generalandadministrativeexpenses\n\
+    \n7,547\n\n7,618\n\n7,592\nRestructuringcharges\n\n39\n\n198\n\n5\nOperatingincome\n\
+    \n1,854\n\n1,375\n\n1,450\nOtherincome(expense)\n \n \n \nGainonsaleofinvestments\n\
+    \n3\n\n2\n\n13\nInvestmentincomeandother\n\n31\n\n13\n\n14\nInterestexpense\n\n\
+    (72) \n(80) \n(90)\nEarningsfromcontinuingoperationsbeforeincometaxexpense\n\n\
+    1,816\n\n1,310\n\n1,387\nIncometaxexpense\n\n609\n\n503\n\n141\nNetearningsfromcontinuingoperations\n\
+    \n1,207\n\n807\n\n1,246\nGain(loss)fromdiscontinuedoperations(Note2),netoftaxexpenseof$7,$1and$0\n\
+    \n21\n\n90\n\n(11)\nNetearningsincludingnoncontrollinginterests\n\n1,228\n\n897\n\
+    \n1,235\nNetearningsfromdiscontinuedoperationsattributabletononcontrollinginterests\n\
+    \n\n\n\n\n(2)\nNetearningsattributabletoBestBuyCo.,Inc.shareholders\n $\n1,228\n\
+    \ $\n897\n $\n1,233\n\n \n \n \nBasicearnings(loss)pershareattributabletoBestBuyCo.,Inc.shareholders\n\
+    \ \n \n \nContinuingoperations\n $\n3.79\n $\n2.33\n $\n3.57\nDiscontinuedoperations\n\
+    \n0.07\n\n0.26\n\n(0.04)\nBasicearningspershare\n $\n3.86\n $\n2.59\n $\n3.53\n\
+    \n \n \n \nDilutedearnings(loss)pershareattributabletoBestBuyCo.,Inc.shareholders\n\
+    \ \n \n \nContinuingoperations\n $\n3.74\n $\n2.30\n $\n3.53\nDiscontinuedoperations\n\
+    \n0.07\n\n0.26\n\n(0.04)\nDilutedearningspershare\n $\n3.81\n $\n2.56\n $\n3.49\n\
+    \n \n \n \nWeighted-averagecommonsharesoutstanding\n \n \n \nBasic\n\n318.5\n\n\
+    346.5\n\n349.5\nDiluted\n\n322.6\n\n350.7\n\n353.6\nSeeNotestoConsolidatedFinancialStatements.\n\
+    54"
+  sentences:
+  - As of May 26, 2023, what is the total amount Pepsico may borrow under its unsecured
+    revolving credit agreements?
+  - In 2022 Q2, which of JPM's business segments had the highest net income?
+  - In agreement with the information outlined in the income statement, what is the
+    FY2015 - FY2017 3 year average net profit margin (as a %) for Best Buy? Answer
+    in units of percents and round to one decimal place.
+- source_sentence: "Lockheed Martin Corporation\nConsolidated Statements of Earnings\n\
+    (in millions, except per share data)\n \n \nYears Ended December 31,\n2022\n2021\n\
+    2020\nNet sales\nProducts\n$ \n55,466 $ \n56,435 $ \n54,928 \nServices\n \n10,518\
+    \ \n10,609 \n10,470 \nTotal net sales\n \n65,984 \n67,044 \n65,398 \nCost of sales\n\
+    Products\n \n(49,577) \n(50,273) \n(48,996) \nServices\n \n(9,280) \n(9,463) \n\
+    (9,371) \nSeverance and other charges\n \n(100) \n(36) \n(27) \nOther unallocated,\
+    \ net\n \n1,260 \n1,789 \n1,650 \nTotal cost of sales\n \n(57,697) \n(57,983)\
+    \ \n(56,744) \nGross profit\n \n8,287 \n9,061 \n8,654 \nOther income (expense),\
+    \ net\n \n61 \n62 \n(10) \nOperating profit\n \n8,348 \n9,123 \n8,644 \nInterest\
+    \ expense\n \n(623) \n(569) \n(591) \nNon-service FAS pension (expense) income\n\
+    \ \n(971) \n(1,292) \n219 \nOther non-operating (expense) income, net\n \n(74)\
+    \ \n288 \n(37) \nEarnings from continuing operations before income taxes\n \n\
+    6,680 \n7,550 \n8,235 \nIncome tax expense\n \n(948) \n(1,235) \n(1,347) \nNet\
+    \ earnings from continuing operations\n \n5,732 \n6,315 \n6,888 \nNet loss from\
+    \ discontinued operations\n \n \n \n(55) \nNet earnings\n$ \n5,732 $ \n6,315 $\
+    \ \n6,833 \n \nEarnings (loss) per common share\nBasic\nContinuing operations\n\
+    $ \n21.74 $ \n22.85 $ \n24.60 \nDiscontinued operations\n \n \n \n(0.20) \nBasic\
+    \ earnings per common share\n$ \n21.74 $ \n22.85 $ \n24.40 \nDiluted\nContinuing\
+    \ operations\n$ \n21.66 $ \n22.76 $ \n24.50 \nDiscontinued operations\n \n \n\
+    \ \n(0.20) \nDiluted earnings per common share\n$ \n21.66 $ \n22.76 $ \n24.30\
+    \ \nThe accompanying notes are an integral part of these consolidated financial\
+    \ statements.\nTable of Contents \n63\nLockheed Martin Corporation\nConsolidated\
+    \ Statements of Earnings\n(in millions, except per share data)\n \n \nYears Ended\
+    \ December 31,\n2022\n2021\n2020\nNet sales\nProducts\n$ \n55,466 $ \n56,435 $\
+    \ \n54,928 \nServices\n \n10,518 \n10,609 \n10,470 \nTotal net sales\n \n65,984\
+    \ \n67,044 \n65,398 \nCost of sales\nProducts\n \n(49,577) \n(50,273) \n(48,996)\
+    \ \nServices\n \n(9,280) \n(9,463) \n(9,371) \nSeverance and other charges\n \n\
+    (100) \n(36) \n(27) \nOther unallocated, net\n \n1,260 \n1,789 \n1,650 \nTotal\
+    \ cost of sales\n \n(57,697) \n(57,983) \n(56,744) \nGross profit\n \n8,287 \n\
+    9,061 \n8,654 \nOther income (expense), net\n \n61 \n62 \n(10) \nOperating profit\n\
+    \ \n8,348 \n9,123 \n8,644 \nInterest expense\n \n(623) \n(569) \n(591) \nNon-service\
+    \ FAS pension (expense) income\n \n(971) \n(1,292) \n219 \nOther non-operating\
+    \ (expense) income, net\n \n(74) \n288 \n(37) \nEarnings from continuing operations\
+    \ before income taxes\n \n6,680 \n7,550 \n8,235 \nIncome tax expense\n \n(948)\
+    \ \n(1,235) \n(1,347) \nNet earnings from continuing operations\n \n5,732 \n6,315\
+    \ \n6,888 \nNet loss from discontinued operations\n \n \n \n(55) \nNet earnings\n\
+    $ \n5,732 $ \n6,315 $ \n6,833 \n \nEarnings (loss) per common share\nBasic\nContinuing\
+    \ operations\n$ \n21.74 $ \n22.85 $ \n24.60 \nDiscontinued operations\n \n \n\
+    \ \n(0.20) \nBasic earnings per common share\n$ \n21.74 $ \n22.85 $ \n24.40 \n\
+    Diluted\nContinuing operations\n$ \n21.66 $ \n22.76 $ \n24.50 \nDiscontinued operations\n\
+    \ \n \n \n(0.20) \nDiluted earnings per common share\n$ \n21.66 $ \n22.76 $ \n\
+    24.30 \nThe accompanying notes are an integral part of these consolidated financial\
+    \ statements.\nTable of Contents \n63"
+  sentences:
+  - What is Lockheed Martin's 2 year total revenue CAGR from FY2020 to FY2022 (in
+    units of percents and round to one decimal place)? Provide a response to the question
+    by primarily using the statement of income.
+  - 'When primarily referencing the income statement and the statement of financial
+    position, what is the FY2021 inventory turnover ratio for Nike? Inventory turnover
+    ratio is defined as: (FY2021 COGS) / (average inventory between FY2020 and FY2021).
+    Round your answer to two decimal places.'
+  - Does 3M have a reasonably healthy liquidity profile based on its quick ratio for
+    Q2 of FY2023? If the quick ratio is not relevant to measure liquidity, please
+    state that and explain why.
+- source_sentence: "TableofContents\n\n\nConsolidated Statements of Income \nCorning\
+    \ Incorporated and Subsidiary Companies\n\n\n\n\nYearendedDecember31,\n\n(Inmillions,exceptpershareamounts)\n\
+    \n2021\n \n2020\n \n2019\n\nNetsales\n $\n14,082 $\n11,303 $\n11,503\nCostofsales\n\
+    \ \n9,019 \n7,772 \n7,468\n\n \n \n \n\nGrossmargin\n \n5,063 \n3,531 \n4,035\n\
+    \n \n \n \n\nOperatingexpenses:\n \n \n \n\nSelling,generalandadministrativeexpenses\n\
+    \ \n1,827 \n1,747 \n1,585\nResearch,developmentandengineeringexpenses\n \n995\
+    \ \n1,154 \n1,031\nAmortizationofpurchasedintangibles\n \n129 \n121 \n113\n\n\
+    \ \n \n \n\nOperatingincome\n \n2,112 \n509 \n1,306\n\n \n \n \n\nEquityinearnings(losses)ofaffiliatedcompanies(Note3)\n\
+    \ \n35 \n(25) \n17\nInterestincome\n \n11 \n15 \n21\nInterestexpense\n \n(300)\
+    \ \n(276) \n(221)\nTranslatedearningscontractgain(loss),net(Note15)\n \n354 \n\
+    (38) \n248\nTransaction-relatedgain,net(Note4)\n \n \n498 \n\nOtherincome(expense),net\n\
+    \ \n185 \n(60) \n(155)\n\n \n \n \n\nIncomebeforeincometaxes\n \n2,397 \n623 \n\
+    1,216\nProvisionforincometaxes(Note8)\n \n(491) \n(111) \n(256)\n\n \n \n \n\n\
+    NetincomeattributabletoCorningIncorporated\n $\n1,906 $\n512 $\n960\n\n \n \n\
+    \ \n\nEarningspercommonshareattributabletoCorningIncorporated:\n \n \n \n\nBasic(Note18)\n\
+    \ $\n1.30 $\n0.54 $\n1.11\nDiluted(Note18)\n $\n1.28 $\n0.54 $\n1.07\n\n \n \n\
+    \ \n\nReconciliationofnetincomeattributabletoCorningIncorporatedversusnetincomeavailabletocommon\n\
+    shareholders:\n \n \n \n\n\n \n \n \n\nNetincomeattributabletoCorningIncorporated\n\
+    \ $\n1,906 $\n512 $\n960\n\n \n \n \n\nSeriesAconvertiblepreferredstockdividend\n\
+    \ \n(24) \n(98) \n(98)\nExcessconsiderationpaidforredemptionofpreferredstock(1)\n\
+    \ \n(803) \n \n \n\n \n \n \n\nNetincomeavailabletocommonshareholders\n $\n1,079\
+    \ $\n414 $\n862\n\n\n(1)\nRefertoNote17(Shareholders'Equity)andNote18(EarningsperCommonShare)totheconsolidatedfinancialstatementsforadditionalinformation.\n\
+    \nTheaccompanyingnotesareanintegralpartoftheseconsolidatedfinancialstatements.\n\
+    \n65\nTableofContents\n\n\nConsolidated Statements of Income \nCorning Incorporated\
+    \ and Subsidiary Companies\n\n\n\n\nYearendedDecember31,\n\n(Inmillions,exceptpershareamounts)\n\
+    \n2021\n \n2020\n \n2019\n\nNetsales\n $\n14,082 $\n11,303 $\n11,503\nCostofsales\n\
+    \ \n9,019 \n7,772 \n7,468\n\n \n \n \n\nGrossmargin\n \n5,063 \n3,531 \n4,035\n\
+    \n \n \n \n\nOperatingexpenses:\n \n \n \n\nSelling,generalandadministrativeexpenses\n\
+    \ \n1,827 \n1,747 \n1,585\nResearch,developmentandengineeringexpenses\n \n995\
+    \ \n1,154 \n1,031\nAmortizationofpurchasedintangibles\n \n129 \n121 \n113\n\n\
+    \ \n \n \n\nOperatingincome\n \n2,112 \n509 \n1,306\n\n \n \n \n\nEquityinearnings(losses)ofaffiliatedcompanies(Note3)\n\
+    \ \n35 \n(25) \n17\nInterestincome\n \n11 \n15 \n21\nInterestexpense\n \n(300)\
+    \ \n(276) \n(221)\nTranslatedearningscontractgain(loss),net(Note15)\n \n354 \n\
+    (38) \n248\nTransaction-relatedgain,net(Note4)\n \n \n498 \n\nOtherincome(expense),net\n\
+    \ \n185 \n(60) \n(155)\n\n \n \n \n\nIncomebeforeincometaxes\n \n2,397 \n623 \n\
+    1,216\nProvisionforincometaxes(Note8)\n \n(491) \n(111) \n(256)\n\n \n \n \n\n\
+    NetincomeattributabletoCorningIncorporated\n $\n1,906 $\n512 $\n960\n\n \n \n\
+    \ \n\nEarningspercommonshareattributabletoCorningIncorporated:\n \n \n \n\nBasic(Note18)\n\
+    \ $\n1.30 $\n0.54 $\n1.11\nDiluted(Note18)\n $\n1.28 $\n0.54 $\n1.07\n\n \n \n\
+    \ \n\nReconciliationofnetincomeattributabletoCorningIncorporatedversusnetincomeavailabletocommon\n\
+    shareholders:\n \n \n \n\n\n \n \n \n\nNetincomeattributabletoCorningIncorporated\n\
+    \ $\n1,906 $\n512 $\n960\n\n \n \n \n\nSeriesAconvertiblepreferredstockdividend\n\
+    \ \n(24) \n(98) \n(98)\nExcessconsiderationpaidforredemptionofpreferredstock(1)\n\
+    \ \n(803) \n \n \n\n \n \n \n\nNetincomeavailabletocommonshareholders\n $\n1,079\
+    \ $\n414 $\n862\n\n\n(1)\nRefertoNote17(Shareholders'Equity)andNote18(EarningsperCommonShare)totheconsolidatedfinancialstatementsforadditionalinformation.\n\
+    \nTheaccompanyingnotesareanintegralpartoftheseconsolidatedfinancialstatements.\n\
+    \n65"
+  sentences:
+  - Taking into account the information outlined in the income statement, what is
+    the FY2019 - FY2021 3 year average unadjusted operating income % margin for Corning?
+    Answer in units of percents and round to one decimal place.
+  - 'We want to calculate a financial metric. Please help us compute it by basing
+    your answers off of the cash flow statement and the income statement. Here''s
+    the question: what is the FY2022 retention ratio (using total cash dividends paid
+    and net income attributable to shareholders) for General Mills? Round answer to
+    two decimal places.'
+  - What is Netflix's year end FY2017 total current liabilities (in USD millions)?
+    Base your judgments on the information provided primarily in the balance sheet.
+- source_sentence: "Forward-Looking Statements\nThis Annual Report on Form 10-K contains\
+    \ statements reflecting our views about our future performance that constitute\n\
+    forward-looking statements within the meaning of the Private Securities Litigation\
+    \ Reform Act of 1995 (Reform Act).\nStatements that constitute forward-looking\
+    \ statements within the meaning of the Reform Act are generally identified through\
+    \ the\ninclusion of words such as aim, anticipate, believe, drive, estimate, expect,\
+    \ expressed confidence, forecast,\nfuture, goal, guidance, intend, may, objective,\
+    \ outlook, plan, position, potential, project, seek,\nshould, strategy, target,\
+    \ will or similar statements or variations of such words and other similar expressions.\
+    \ All\nstatements addressing our future operating performance, and statements\
+    \ addressing events and developments that we expect or\nanticipate will occur\
+    \ in the future, are forward-looking statements within the meaning of the Reform\
+    \ Act. These forward-looking\nstatements are based on currently available information,\
+    \ operating plans and projections about future events and trends. They\ninherently\
+    \ involve risks and uncertainties that could cause actual results to differ materially\
+    \ from those predicted in any such\nforward-looking statement. These risks and\
+    \ uncertainties include, but are not limited to, those described in Item 1A. Risk\n\
+    Factors and Item 7. Managements Discussion and Analysis of Financial Condition\
+    \ and Results of Operations Our Business\n Our Business Risks. Investors are cautioned\
+    \ not to place undue reliance on any such forward-looking statements, which speak\n\
+    only as of the date they are made. We undertake no obligation to update any forward-looking\
+    \ statement, whether as a result of\nnew information, future events or otherwise.\
+    \ The discussion of risks in this report is by no means all-inclusive but is designed\
+    \ to\nhighlight what we believe are important factors to consider when evaluating\
+    \ our future performance.\nPART I\nItem 1. Business.\nWhen used in this report,\
+    \ the terms we, us, our, PepsiCo and the Company mean PepsiCo, Inc. and its consolidated\n\
+    subsidiaries, collectively. Certain terms used in this Annual Report on Form 10-K\
+    \ are defined in the Glossary included in Item 7.\nof this report.\nCompany Overview\n\
+    We were incorporated in Delaware in 1919 and reincorporated in North Carolina\
+    \ in 1986. We are a leading global beverage and\nconvenient food company with\
+    \ a complementary portfolio of brands, including Lays, Doritos, Cheetos, Gatorade,\
+    \ Pepsi-Cola,\nMountain Dew, Quaker and SodaStream. Through our operations, authorized\
+    \ bottlers, contract manufacturers and other third\nparties, we make, market,\
+    \ distribute and sell a wide variety of beverages and convenient foods, serving\
+    \ customers and consumers\nin more than 200 countries and territories.\nOur Operations\n\
+    We are organized into seven reportable segments (also referred to as divisions),\
+    \ as follows:\n1) Frito-Lay North America (FLNA), which includes our branded convenient\
+    \ food businesses in the United States and\nCanada;\n2) Quaker Foods North America\
+    \ (QFNA), which includes our branded convenient food businesses, such as cereal,\
+    \ rice, pasta\nand other branded food, in the United States and Canada;\n3) PepsiCo\
+    \ Beverages North America (PBNA), which includes our beverage businesses in the\
+    \ United States and Canada;\n4) Latin America (LatAm), which includes all of our\
+    \ beverage and convenient food businesses in Latin America;\n5) Europe, which\
+    \ includes all of our beverage and convenient food businesses in Europe;\nTable\
+    \ of Contents\nForward-Looking Statements\nThis Annual Report on Form 10-K contains\
+    \ statements reflecting our views about our future performance that constitute\n\
+    forward-looking statements within the meaning of the Private Securities Litigation\
+    \ Reform Act of 1995 (Reform Act).\nStatements that constitute forward-looking\
+    \ statements within the meaning of the Reform Act are generally identified through\
+    \ the\ninclusion of words such as aim, anticipate, believe, drive, estimate, expect,\
+    \ expressed confidence, forecast,\nfuture, goal, guidance, intend, may, objective,\
+    \ outlook, plan, position, potential, project, seek,\nshould, strategy, target,\
+    \ will or similar statements or variations of such words and other similar expressions.\
+    \ All\nstatements addressing our future operating performance, and statements\
+    \ addressing events and developments that we expect or\nanticipate will occur\
+    \ in the future, are forward-looking statements within the meaning of the Reform\
+    \ Act. These forward-looking\nstatements are based on currently available information,\
+    \ operating plans and projections about future events and trends. They\ninherently\
+    \ involve risks and uncertainties that could cause actual results to differ materially\
+    \ from those predicted in any such\nforward-looking statement. These risks and\
+    \ uncertainties include, but are not limited to, those described in Item 1A. Risk\n\
+    Factors and Item 7. Managements Discussion and Analysis of Financial Condition\
+    \ and Results of Operations Our Business\n Our Business Risks. Investors are cautioned\
+    \ not to place undue reliance on any such forward-looking statements, which speak\n\
+    only as of the date they are made. We undertake no obligation to update any forward-looking\
+    \ statement, whether as a result of\nnew information, future events or otherwise.\
+    \ The discussion of risks in this report is by no means all-inclusive but is designed\
+    \ to\nhighlight what we believe are important factors to consider when evaluating\
+    \ our future performance.\nPART I\nItem 1. Business.\nWhen used in this report,\
+    \ the terms we, us, our, PepsiCo and the Company mean PepsiCo, Inc. and its consolidated\n\
+    subsidiaries, collectively. Certain terms used in this Annual Report on Form 10-K\
+    \ are defined in the Glossary included in Item 7.\nof this report.\nCompany Overview\n\
+    We were incorporated in Delaware in 1919 and reincorporated in North Carolina\
+    \ in 1986. We are a leading global beverage and\nconvenient food company with\
+    \ a complementary portfolio of brands, including Lays, Doritos, Cheetos, Gatorade,\
+    \ Pepsi-Cola,\nMountain Dew, Quaker and SodaStream. Through our operations, authorized\
+    \ bottlers, contract manufacturers and other third\nparties, we make, market,\
+    \ distribute and sell a wide variety of beverages and convenient foods, serving\
+    \ customers and consumers\nin more than 200 countries and territories.\nOur Operations\n\
+    We are organized into seven reportable segments (also referred to as divisions),\
+    \ as follows:\n1) Frito-Lay North America (FLNA), which includes our branded convenient\
+    \ food businesses in the United States and\nCanada;\n2) Quaker Foods North America\
+    \ (QFNA), which includes our branded convenient food businesses, such as cereal,\
+    \ rice, pasta\nand other branded food, in the United States and Canada;\n3) PepsiCo\
+    \ Beverages North America (PBNA), which includes our beverage businesses in the\
+    \ United States and Canada;\n4) Latin America (LatAm), which includes all of our\
+    \ beverage and convenient food businesses in Latin America;\n5) Europe, which\
+    \ includes all of our beverage and convenient food businesses in Europe;\n2\n\n\
+    6) Africa, Middle East and South Asia (AMESA), which includes all of our beverage\
+    \ and convenient food businesses in\nAfrica, the Middle East and South Asia; and\n\
+    7) Asia Pacific, Australia and New Zealand and China Region (APAC), which includes\
+    \ all of our beverage and convenient\nfood businesses in Asia Pacific, Australia\
+    \ and New Zealand, and China region.\nTable of Contents\n6) Africa, Middle East\
+    \ and South Asia (AMESA), which includes all of our beverage and convenient food\
+    \ businesses in\nAfrica, the Middle East and South Asia; and\n7) Asia Pacific,\
+    \ Australia and New Zealand and China Region (APAC), which includes all of our\
+    \ beverage and convenient\nfood businesses in Asia Pacific, Australia and New\
+    \ Zealand, and China region.\nFrito-Lay North America\nEither independently or\
+    \ in conjunction with third parties, FLNA makes, markets, distributes and sells\
+    \ branded convenient\nfoods. These foods include branded dips, Cheetos cheese-flavored\
+    \ snacks, Doritos tortilla chips, Fritos corn chips, Lays potato\nchips, Ruffles\
+    \ potato chips and Tostitos tortilla chips. FLNAs branded products are sold to\
+    \ independent distributors and retailers.\nIn addition, FLNAs joint venture with\
+    \ Strauss Group makes, markets, distributes and sells Sabra refrigerated dips\
+    \ and spreads.\nQuaker Foods North America\nEither independently or in conjunction\
+    \ with third parties, QFNA makes, markets, distributes and sells branded convenient\
+    \ foods,\nwhich include cereals, rice, pasta and other branded products. QFNAs\
+    \ products include Capn Crunch cereal, Life cereal, Pearl\nMilling Company syrups\
+    \ and mixes, Quaker Chewy granola bars, Quaker grits, Quaker oatmeal, Quaker rice\
+    \ cakes, Quaker\nSimply Granola and Rice-A-Roni side dishes. QFNAs branded products\
+    \ are sold to independent distributors and retailers.\nPepsiCo Beverages North\
+    \ America\nEither independently or in conjunction with third parties, PBNA makes,\
+    \ markets and sells beverage concentrates, fountain syrups\nand finished goods\
+    \ under various beverage brands including Aquafina, Diet Mountain Dew, Diet Pepsi,\
+    \ Gatorade, Gatorade Zero,\nMountain Dew, Pepsi and Propel. PBNA operates its\
+    \ own bottling plants and distribution facilities and sells branded finished\n\
+    goods directly to independent distributors and retailers. PBNA also sells concentrate\
+    \ and finished goods for our brands to\nauthorized and independent bottlers, who\
+    \ in turn sell our branded finished goods to independent distributors and retailers\
+    \ in\ncertain markets. PBNA also, either independently or in conjunction with\
+    \ third parties, makes, markets, distributes and sells ready-\nto-drink tea and\
+    \ coffee products through joint ventures with Unilever (under the Lipton brand\
+    \ name) and Starbucks, respectively.\nFurther, PBNA manufactures and distributes\
+    \ certain brands licensed from Keurig Dr Pepper Inc., including Crush, Dr Pepper\
+    \ and\nSchweppes, and certain juice brands licensed from Dole Food Company, Inc.\
+    \ and Ocean Spray Cranberries, Inc. In 2022, PBNA\nbegan to distribute Hard MTN\
+    \ Dew, an alcoholic beverage manufactured and owned by the Boston Beer Company.\
+    \ In the first\nquarter of 2022, we sold our Tropicana, Naked and other select\
+    \ juice brands to PAI Partners, while retaining a 39%\nnoncontrolling interest\
+    \ in a newly formed joint venture, Tropicana Brands Group (TBG), operating across\
+    \ North America and\nEurope (Juice Transaction). In the United States, PepsiCo\
+    \ acts as the exclusive distributor for TBGs portfolio of brands for\nsmall-format\
+    \ and foodservice customers with chilled direct-store-delivery (DSD). See Note\
+    \ 13 to our consolidated financial\nstatements for further information.\nLatin\
+    \ America\nEither independently or in conjunction with third parties, LatAm makes,\
+    \ markets, distributes and sells a number of convenient\nfood brands including\
+    \ Cheetos, Doritos, Emperador, Lays, Marias Gamesa, Ruffles, Sabritas, Saladitas\
+    \ and Tostitos, as well as\nmany Quaker-branded convenient foods. LatAm also,\
+    \ either independently or in conjunction with third parties, makes, markets,\n\
+    distributes and sells beverage concentrates, fountain syrups and finished goods\
+    \ under various beverage brands including 7UP,\nDiet 7UP, Gatorade, H2oh!, Manzanita\
+    \ Sol, Mirinda, Pepsi, Pepsi Black, San Carlos and Toddy. These branded products\
+    \ are sold\nto authorized and independent bottlers, independent distributors and\
+    \ retailers. LatAm\n3"
+  sentences:
+  - Among operations, investing, and financing activities, which brought in the most
+    (or lost the least) cash flow for AMD in FY22?
+  - Which type of debt received the largest investment among the short term investments
+    for MGM in H1 FY2023?
+  - What are the geographies that Pepsico primarily operates in as of FY2022?
+- source_sentence: "Pension and postretirement health care and life insurance benefits\
+    \ earned during the year, as well as interest on projected benefit obligations,\
+    \ \nare accrued.\nfor assets and liabilities. We record these translation adjustments\
+    \ in Accumulated other comprehensive loss, a separate component of Equity, \n\
+    in our consolidated balance sheets. We record exchange gains and losses resulting\
+    \ from the conversion of transaction currency to functional \ncurrency as a component\
+    \ of Other income (expense), net. \nEmployee Benefit Plans \nPension and postretirement\
+    \ health care and life insurance benefits earned during the year, as well as interest\
+    \ on projected benefit obligations, \nare accrued. Prior service costs and credits\
+    \ resulting from changes in plan benefits are generally amortized over the average\
+    \ remaining service \nperiod of the employees expected to receive benefits. Expected\
+    \ return on plan assets is determined by applying the return on assets \nassumption\
+    \ to the actual fair value of plan assets. Actuarial gains and losses are recognized\
+    \ in Other income (expense), net in the year in \nwhich they occur. These gains\
+    \ and losses are measured annually as of December 31 or upon a remeasurement event.\
+    \ Verizon management \nemployees no longer earn pension benefits or earn service\
+    \ towards the Company retiree medical subsidy. See Note 11 for additional \ninformation.\
+    \ \nWe recognize a pension or a postretirement plans funded status as either an\
+    \ asset or liability in the consolidated balance sheets. Also, we \nmeasure any\
+    \ unrecognized prior service costs and credits that arise during the period as\
+    \ a component of Accumulated other comprehensive \nincome, net of applicable income\
+    \ tax. \nDerivative Instruments \nWe enter into derivative transactions primarily\
+    \ to manage our exposure to fluctuations in foreign currency exchange rates and\
+    \ interest rates. \nWe employ risk management strategies, which may include the\
+    \ use of a variety of derivatives including cross currency swaps, forward \nstarting\
+    \ interest rate swaps, interest rate swaps, treasury rate locks, interest rate\
+    \ caps and foreign exchange forwards. We do not hold \nderivatives for trading\
+    \ purposes. \nWe measure all derivatives at fair value and recognize them as either\
+    \ assets or liabilities in our consolidated balance sheets. Our derivative \n\
+    instruments are valued primarily using models based on readily observable market\
+    \ parameters for all substantial terms of our derivative \ncontracts and thus\
+    \ are classified as Level 2. Changes in the fair values of derivative instruments\
+    \ applied as economic hedges are recognized in \nearnings in the current period.\
+    \ For fair value hedges, the change in the fair value of the derivative instruments\
+    \ is recognized in earnings, along \nwith the change in the fair value of the\
+    \ hedged item. For cash flow hedges, the change in the fair value of the derivative\
+    \ instruments is \nreported in Other comprehensive income (loss) and recognized\
+    \ in earnings when the hedged item is recognized in earnings. For net \ninvestment\
+    \ hedges of certain of our foreign operations, the change in the fair value of\
+    \ the hedging instruments is reported in Other \ncomprehensive income (loss) as\
+    \ part of the cumulative translation adjustment and partially offsets the impact\
+    \ of foreign currency changes on \nthe value of our net investment. \nCash flows\
+    \ from derivatives, which are designated as accounting hedges or applied as economic\
+    \ hedges, are presented consistently with the \ncash flow classification of the\
+    \ related hedged items. See Note 9 for additional information. \nVariable Interest\
+    \ Entities \nVIEs are entities that lack sufficient equity to permit the entity\
+    \ to finance its activities without additional subordinated financial support\
+    \ from \nother parties, have equity investors that do not have the ability to\
+    \ make significant decisions relating to the entitys operations through voting\
+    \ \nrights, do not have the obligation to absorb the expected losses, or do not\
+    \ have the right to receive the residual returns of the entity. We \nconsolidate\
+    \ the assets and liabilities of VIEs when we are deemed to be the primary beneficiary.\
+    \ The primary beneficiary is the party that has \nthe power to make the decisions\
+    \ that most significantly affect the economic performance of the VIE and has the\
+    \ obligation to absorb losses or \nthe right to receive benefits that could potentially\
+    \ be significant to the VIE.\n63\nVerizon 2021 Annual Report on Form 10-K\n\n\
+    Estimated Future Benefit Payments \nThe benefit payments to retirees are expected\
+    \ to be paid as follows: \n(dollars in millions) \nYear\nPension Benefits \nHealth\
+    \ Care and Life \n2022\n$ \n2,049 \n$ \n906 \n2023\n1,648 \n883 \n2024\n1,097\
+    \ \n862 \n2025\n1,066 \n850 \n2026\n1,034 \n840 \n2027 to 2031\n5,097 \n4,139\n\
+    fair value is measured using the NAV per share as a practical expedient are not\
+    \ leveled within the fair value hierarchy but are included in total \ninvestments.\
+    \ \nEmployer Contributions \nIn 2021, we made no discretionary contribution to\
+    \ our qualified pension plans, $58 million of contributions to our nonqualified\
+    \ pension plans \nand $885 million of contributions to our other postretirement\
+    \ benefit plans. No qualified pension plans contributions are expected to be made\
+    \ \nin 2022. Nonqualified pension plans contributions are estimated to be approximately\
+    \ $60 million and contributions to our other postretirement \nbenefit plans are\
+    \ estimated to be approximately $860 million in 2022. \nEstimated Future Benefit\
+    \ Payments \nThe benefit payments to retirees are expected to be paid as follows:\
+    \ \n(dollars in millions) \nYear\nPension Benefits \nHealth Care and Life \n2022\n\
+    $ \n2,049 \n$ \n906 \n2023\n1,648 \n883 \n2024\n1,097 \n862 \n2025\n1,066 \n850\
+    \ \n2026\n1,034 \n840 \n2027 to 2031\n5,097 \n4,139 \nSavings Plan and Employee\
+    \ Stock Ownership Plans \nWe maintain four leveraged employee stock ownership\
+    \ plans (ESOP). We match a certain percentage of eligible employee contributions\
+    \ to \ncertain savings plans with shares of our common stock from this ESOP. At\
+    \ December 31, 2021, the number of allocated shares of common \nstock in this\
+    \ ESOP was 44 million. There were no unallocated shares of common stock in this\
+    \ ESOP at December 31, 2021. All leveraged \nESOP shares are included in earnings\
+    \ per share computations. \nTotal savings plan costs were $690 million in 2021,\
+    \ $730 million in 2020 and $897 million in 2019. \nSeverance Benefits \nThe following\
+    \ table provides an analysis of our severance liability: \n(dollars in millions)\
+    \ \nYear \nBeginning of \nYear \nCharged to \nExpense\nPayments\nOther\nEnd of\
+    \ Year \n2019\n$ \n2,156 \n$ \n260 \n$ \n(1,847) $ \n(4) $\n565 \n2020\n565 \n\
+    309 \n(248)\n(24)\n602 \n2021\n602 \n233 \n(258)\n(29)\n548 \nSeverance, Pension\
+    \ and Benefits (Credits) Charges \nDuring 2021, in accordance with our accounting\
+    \ policy to recognize actuarial gains and losses in the period in which they occur,\
+    \ we recorded \nnet pre-tax pension and benefits credits of $2.4 billion in our\
+    \ pension and postretirement benefit plans. The credits were recorded in Other\
+    \ \nincome (expense), net in our consolidated statement of income and were primarily\
+    \ driven by a credit of $1.1 billion due to an increase in our \ndiscount rate\
+    \ assumption used to determine the current year liabilities of our pension plans\
+    \ and postretirement benefit plans from a weighted-\naverage of 2.6% at December\
+    \ 31, 2020 to a weighted-average of 2.9% at December 31, 2021, a credit of $847\
+    \ million due to the difference \nbetween our estimated and our actual return\
+    \ on assets and a credit of $453 million due to other actuarial assumption adjustments.\
+    \ During \n2021, we also recorded net pre-tax severance charges of $233 million\
+    \ in Selling, general and administrative expense in our consolidated \nstatements\
+    \ of income. \nDuring 2020, we recorded net pre-tax pension and benefits charges\
+    \ of $1.6 billion in our pension and postretirement benefit plans. The \ncharges\
+    \ were recorded in Other income (expense), net in our consolidated statement of\
+    \ income and were primarily driven by a charge of \n$3.2 billion due to a decrease\
+    \ in our discount rate assumption used to determine the current year liabilities\
+    \ of our pension plans and \npostretirement benefit plans from a weighted-average\
+    \ of 3.3% at December 31, 2019 to a weighted-average of 2.6% at December 31, 2020,\
+    \ \npartially offset by a credit of $1.6 billion due to the difference between\
+    \ our estimated and our actual return on assets. During 2020, we also \nrecorded\
+    \ net pre-tax severance charges of $309 million in Selling, general and administrative\
+    \ expense in our consolidated statements of \nincome. \nDuring 2019, we recorded\
+    \ net pre-tax pension and benefits charges of $126 million in our pension and\
+    \ postretirement benefit plans. The \ncharges were recorded in Other income (expense),\
+    \ net in our consolidated statement of income and were primarily driven by a charge\
+    \ of \n$4.3 billion due to a decrease in our discount rate assumption used to\
+    \ determine the current year liabilities of our pension plans and \npostretirement\
+    \ benefits plans from a weighted-average of 4.4% at December 31, 2018 to a weighted-average\
+    \ of 3.3% at December 31, 2019, \npartially offset by a credit of $2.3 billion\
+    \ due to the difference between our estimated return on assets and our actual\
+    \ return on assets and a \n94\nVerizon 2021 Annual Report on Form 10-K"
+  sentences:
+  - 'Based on the information provided primarily in the balance sheet and the statement
+    of income, what is FY2020 days payable outstanding (DPO) for Corning? DPO is defined
+    as: 365 * (average accounts payable between FY2019 and FY2020) / (FY2020 COGS
+    + change in inventory between FY2019 and FY2020). Round your answer to two decimal
+    places.'
+  - As of FY 2021, how much did Verizon expect to pay for its retirees in 2024?
+  - What was the largest liability in American Express's Balance Sheet in 2022?
+pipeline_tag: sentence-similarity
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+model-index:
+- name: BGE base Financial Matryoshka
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 768
+      type: dim_768
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.4666666666666667
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7333333333333333
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.7333333333333333
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8666666666666667
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.4666666666666667
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24444444444444446
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.14666666666666664
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08666666666666668
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.4666666666666667
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7333333333333333
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.7333333333333333
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8666666666666667
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6567969552749856
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5918518518518517
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5968994708994709
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 512
+      type: dim_512
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.4666666666666667
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.7333333333333333
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8666666666666667
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.4666666666666667
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.24444444444444446
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08666666666666668
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.4666666666666667
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.7333333333333333
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8666666666666667
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.665440059435613
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.601111111111111
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6069135802469137
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 256
+      type: dim_256
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.4666666666666667
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8666666666666667
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.4666666666666667
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.26666666666666666
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08666666666666668
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.4666666666666667
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8666666666666667
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6875189227069145
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.6288888888888889
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6366666666666667
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 128
+      type: dim_128
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.4666666666666667
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.9333333333333333
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.4666666666666667
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.26666666666666666
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.16
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.09333333333333335
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.4666666666666667
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.9333333333333333
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.711266068514116
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.64
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6423809523809524
+      name: Cosine Map@100
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: dim 64
+      type: dim_64
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.4666666666666667
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.8666666666666667
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.8666666666666667
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8666666666666667
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.4666666666666667
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.2888888888888889
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.17333333333333337
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08666666666666668
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.4666666666666667
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.8666666666666667
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.8666666666666667
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8666666666666667
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6753953169047638
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.611111111111111
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.6185714285714285
+      name: Cosine Map@100
+---
+
+# BGE base Financial Matryoshka
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
+
+## Model Details
+
+### Model Description
+- **Model Type:** Sentence Transformer
+- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 768 dimensions
+- **Similarity Function:** Cosine Similarity
+- **Training Dataset:**
+    - json
+- **Language:** en
+- **License:** apache-2.0
+
+### 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': 512, 'do_lower_case': True}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## 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("shivamsharma1967/_bge-base-financial-matryoshka_")
+# Run inference
+sentences = [
+    'Pension and postretirement health care and life insurance benefits earned during the year, as well as interest on projected benefit obligations, \nare accrued.\nfor assets and liabilities. We record these translation adjustments in Accumulated other comprehensive loss, a separate component of Equity, \nin our consolidated balance sheets. We record exchange gains and losses resulting from the conversion of transaction currency to functional \ncurrency as a component of Other income (expense), net. \nEmployee Benefit Plans \nPension and postretirement health care and life insurance benefits earned during the year, as well as interest on projected benefit obligations, \nare accrued. Prior service costs and credits resulting from changes in plan benefits are generally amortized over the average remaining service \nperiod of the employees expected to receive benefits. Expected return on plan assets is determined by applying the return on assets \nassumption to the actual fair value of plan assets. Actuarial gains and losses are recognized in Other income (expense), net in the year in \nwhich they occur. These gains and losses are measured annually as of December 31 or upon a remeasurement event. Verizon management \nemployees no longer earn pension benefits or earn service towards the Company retiree medical subsidy. See Note 11 for additional \ninformation. \nWe recognize a pension or a postretirement plans funded status as either an asset or liability in the consolidated balance sheets. Also, we \nmeasure any unrecognized prior service costs and credits that arise during the period as a component of Accumulated other comprehensive \nincome, net of applicable income tax. \nDerivative Instruments \nWe enter into derivative transactions primarily to manage our exposure to fluctuations in foreign currency exchange rates and interest rates. \nWe employ risk management strategies, which may include the use of a variety of derivatives including cross currency swaps, forward \nstarting interest rate swaps, interest rate swaps, treasury rate locks, interest rate caps and foreign exchange forwards. We do not hold \nderivatives for trading purposes. \nWe measure all derivatives at fair value and recognize them as either assets or liabilities in our consolidated balance sheets. Our derivative \ninstruments are valued primarily using models based on readily observable market parameters for all substantial terms of our derivative \ncontracts and thus are classified as Level 2. Changes in the fair values of derivative instruments applied as economic hedges are recognized in \nearnings in the current period. For fair value hedges, the change in the fair value of the derivative instruments is recognized in earnings, along \nwith the change in the fair value of the hedged item. For cash flow hedges, the change in the fair value of the derivative instruments is \nreported in Other comprehensive income (loss) and recognized in earnings when the hedged item is recognized in earnings. For net \ninvestment hedges of certain of our foreign operations, the change in the fair value of the hedging instruments is reported in Other \ncomprehensive income (loss) as part of the cumulative translation adjustment and partially offsets the impact of foreign currency changes on \nthe value of our net investment. \nCash flows from derivatives, which are designated as accounting hedges or applied as economic hedges, are presented consistently with the \ncash flow classification of the related hedged items. See Note 9 for additional information. \nVariable Interest Entities \nVIEs are entities that lack sufficient equity to permit the entity to finance its activities without additional subordinated financial support from \nother parties, have equity investors that do not have the ability to make significant decisions relating to the entitys operations through voting \nrights, do not have the obligation to absorb the expected losses, or do not have the right to receive the residual returns of the entity. We \nconsolidate the assets and liabilities of VIEs when we are deemed to be the primary beneficiary. The primary beneficiary is the party that has \nthe power to make the decisions that most significantly affect the economic performance of the VIE and has the obligation to absorb losses or \nthe right to receive benefits that could potentially be significant to the VIE.\n63\nVerizon 2021 Annual Report on Form 10-K\n\nEstimated Future Benefit Payments \nThe benefit payments to retirees are expected to be paid as follows: \n(dollars in millions) \nYear\nPension Benefits \nHealth Care and Life \n2022\n$ \n2,049 \n$ \n906 \n2023\n1,648 \n883 \n2024\n1,097 \n862 \n2025\n1,066 \n850 \n2026\n1,034 \n840 \n2027 to 2031\n5,097 \n4,139\nfair value is measured using the NAV per share as a practical expedient are not leveled within the fair value hierarchy but are included in total \ninvestments. \nEmployer Contributions \nIn 2021, we made no discretionary contribution to our qualified pension plans, $58 million of contributions to our nonqualified pension plans \nand $885 million of contributions to our other postretirement benefit plans. No qualified pension plans contributions are expected to be made \nin 2022. Nonqualified pension plans contributions are estimated to be approximately $60 million and contributions to our other postretirement \nbenefit plans are estimated to be approximately $860 million in 2022. \nEstimated Future Benefit Payments \nThe benefit payments to retirees are expected to be paid as follows: \n(dollars in millions) \nYear\nPension Benefits \nHealth Care and Life \n2022\n$ \n2,049 \n$ \n906 \n2023\n1,648 \n883 \n2024\n1,097 \n862 \n2025\n1,066 \n850 \n2026\n1,034 \n840 \n2027 to 2031\n5,097 \n4,139 \nSavings Plan and Employee Stock Ownership Plans \nWe maintain four leveraged employee stock ownership plans (ESOP). We match a certain percentage of eligible employee contributions to \ncertain savings plans with shares of our common stock from this ESOP. At December 31, 2021, the number of allocated shares of common \nstock in this ESOP was 44 million. There were no unallocated shares of common stock in this ESOP at December 31, 2021. All leveraged \nESOP shares are included in earnings per share computations. \nTotal savings plan costs were $690 million in 2021, $730 million in 2020 and $897 million in 2019. \nSeverance Benefits \nThe following table provides an analysis of our severance liability: \n(dollars in millions) \nYear \nBeginning of \nYear \nCharged to \nExpense\nPayments\nOther\nEnd of Year \n2019\n$ \n2,156 \n$ \n260 \n$ \n(1,847) $ \n(4) $\n565 \n2020\n565 \n309 \n(248)\n(24)\n602 \n2021\n602 \n233 \n(258)\n(29)\n548 \nSeverance, Pension and Benefits (Credits) Charges \nDuring 2021, in accordance with our accounting policy to recognize actuarial gains and losses in the period in which they occur, we recorded \nnet pre-tax pension and benefits credits of $2.4 billion in our pension and postretirement benefit plans. The credits were recorded in Other \nincome (expense), net in our consolidated statement of income and were primarily driven by a credit of $1.1 billion due to an increase in our \ndiscount rate assumption used to determine the current year liabilities of our pension plans and postretirement benefit plans from a weighted-\naverage of 2.6% at December 31, 2020 to a weighted-average of 2.9% at December 31, 2021, a credit of $847 million due to the difference \nbetween our estimated and our actual return on assets and a credit of $453 million due to other actuarial assumption adjustments. During \n2021, we also recorded net pre-tax severance charges of $233 million in Selling, general and administrative expense in our consolidated \nstatements of income. \nDuring 2020, we recorded net pre-tax pension and benefits charges of $1.6 billion in our pension and postretirement benefit plans. The \ncharges were recorded in Other income (expense), net in our consolidated statement of income and were primarily driven by a charge of \n$3.2 billion due to a decrease in our discount rate assumption used to determine the current year liabilities of our pension plans and \npostretirement benefit plans from a weighted-average of 3.3% at December 31, 2019 to a weighted-average of 2.6% at December 31, 2020, \npartially offset by a credit of $1.6 billion due to the difference between our estimated and our actual return on assets. During 2020, we also \nrecorded net pre-tax severance charges of $309 million in Selling, general and administrative expense in our consolidated statements of \nincome. \nDuring 2019, we recorded net pre-tax pension and benefits charges of $126 million in our pension and postretirement benefit plans. The \ncharges were recorded in Other income (expense), net in our consolidated statement of income and were primarily driven by a charge of \n$4.3 billion due to a decrease in our discount rate assumption used to determine the current year liabilities of our pension plans and \npostretirement benefits plans from a weighted-average of 4.4% at December 31, 2018 to a weighted-average of 3.3% at December 31, 2019, \npartially offset by a credit of $2.3 billion due to the difference between our estimated return on assets and our actual return on assets and a \n94\nVerizon 2021 Annual Report on Form 10-K',
+    'As of FY 2021, how much did Verizon expect to pay for its retirees in 2024?',
+    "What was the largest liability in American Express's Balance Sheet in 2022?",
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 768]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | dim_768    | dim_512    | dim_256    | dim_128    | dim_64     |
+|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
+| cosine_accuracy@1   | 0.4667     | 0.4667     | 0.4667     | 0.4667     | 0.4667     |
+| cosine_accuracy@3   | 0.7333     | 0.7333     | 0.8        | 0.8        | 0.8667     |
+| cosine_accuracy@5   | 0.7333     | 0.8        | 0.8        | 0.8        | 0.8667     |
+| cosine_accuracy@10  | 0.8667     | 0.8667     | 0.8667     | 0.9333     | 0.8667     |
+| cosine_precision@1  | 0.4667     | 0.4667     | 0.4667     | 0.4667     | 0.4667     |
+| cosine_precision@3  | 0.2444     | 0.2444     | 0.2667     | 0.2667     | 0.2889     |
+| cosine_precision@5  | 0.1467     | 0.16       | 0.16       | 0.16       | 0.1733     |
+| cosine_precision@10 | 0.0867     | 0.0867     | 0.0867     | 0.0933     | 0.0867     |
+| cosine_recall@1     | 0.4667     | 0.4667     | 0.4667     | 0.4667     | 0.4667     |
+| cosine_recall@3     | 0.7333     | 0.7333     | 0.8        | 0.8        | 0.8667     |
+| cosine_recall@5     | 0.7333     | 0.8        | 0.8        | 0.8        | 0.8667     |
+| cosine_recall@10    | 0.8667     | 0.8667     | 0.8667     | 0.9333     | 0.8667     |
+| **cosine_ndcg@10**  | **0.6568** | **0.6654** | **0.6875** | **0.7113** | **0.6754** |
+| cosine_mrr@10       | 0.5919     | 0.6011     | 0.6289     | 0.64       | 0.6111     |
+| cosine_map@100      | 0.5969     | 0.6069     | 0.6367     | 0.6424     | 0.6186     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### json
+
+* Dataset: json
+* Size: 135 training samples
+* Columns: <code>positive</code> and <code>anchor</code>
+* Approximate statistics based on the first 135 samples:
+  |         | positive                                                                              | anchor                                                                              |
+  |:--------|:--------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
+  | type    | string                                                                                | string                                                                              |
+  | details | <ul><li>min: 359 tokens</li><li>mean: 507.28 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 39.07 tokens</li><li>max: 175 tokens</li></ul> |
+* Samples:
+  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          | anchor                                                                                                                                                                                                                                                                                                                                                                                                 |
+  |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Walmart Inc.<br>Consolidated Statements of Income<br> <br> <br>Fiscal Years Ended January 31,<br>(Amounts in millions, except per share data)<br> <br>2020<br> <br>2019<br> <br>2018<br>Revenues:<br> <br> <br> <br>Net sales<br> $<br>519,926<br> $<br>510,329 $<br>495,761<br>Membership and other income<br> <br>4,038<br> <br>4,076 <br>4,582<br>Total revenues<br> <br>523,964<br> <br>514,405 <br>500,343<br>Costs and expenses:<br> <br> <br> <br>Cost of sales<br> <br>394,605<br> <br>385,301 <br>373,396<br>Operating, selling, general and administrative expenses<br> <br>108,791<br> <br>107,147 <br>106,510<br>Operating income<br> <br>20,568<br> <br>21,957 <br>20,437<br>Interest:<br> <br> <br> <br>Debt<br> <br>2,262<br> <br>1,975 <br>1,978<br>Finance, capital lease and financing obligations<br> <br>337<br> <br>371 <br>352<br>Interest income<br> <br>(189) <br>(217) <br>(152)<br>Interest, net<br> <br>2,410<br> <br>2,129 <br>2,178<br>Loss on extinguishment of debt<br> <br><br> <br> <br>3,136<br>Other (gains) and losses<br> <br>(1,958) <br>8,368 <br><br>Income before income taxes<br> <br>20,116<br> <br>11,460 <br>15,123<br>Provision for income taxes<br> <br>4,915<br> <br>4,281 <br>4,600<br>Consolidated net income<br> <br>15,201<br> <br>7,179 <br>10,523<br>Consolidated net income attributable to noncontrolling interest<br> <br>(320) <br>(509...</code> | <code>What is the FY2018 - FY2020 3 year average unadjusted EBITDA % margin for Walmart? Define unadjusted EBITDA as unadjusted operating income + depreciation and amortization from the cash flow statement. Answer in units of percents and round to one decimal place. Calculate what was asked by utilizing the line items clearly shown in the P&L statement and the cash flow statement.</code> |
+  | <code>Analysis of Consolidated Earnings Before Provision for Taxes on Income<br>Consolidated earnings before provision for taxes on income was $21.7 billion and $22.8 billion for the years 2022 and 2021, respectively. As a percent to<br>sales, consolidated earnings before provision for taxes on income was 22.9% and 24.3%, in 2022 and 2021, respectively.<br>(Dollars in billions. Percentages in chart are as a percent to total sales)<br>Cost of Products Sold and Selling, Marketing and Administrative Expenses:<br>(Dollars in billions. Percentages in chart are as a percent to total sales)<br>Cost of products sold increased as a percent to sales driven by:<br><br>One-time COVID-19 vaccine manufacturing exit related costs<br><br>Currency impacts in the Pharmaceutical segment<br><br>Commodity inflation in the MedTech and Consumer Health segments<br>partially offset by<br><br>Supply chain benefits in the Consumer Health segment<br>The intangible asset amortization expense included in cost of products sold was $4.3 billion and $4.7 billion for the ...</code>                                                                                                                                                                                                                                                                                                          | <code>What drove gross margin change as of FY2022 for JnJ? If gross margin is not a useful metric for a company like this, then please state that and explain why.</code>                                                                                                                                                                                                                              |
+  | <code>(Millions)<br>United States<br>EMEA<br>APAC<br>LACC<br>Other Unallocated<br>Consolidated<br>2022<br>Total revenues net of interest expense<br>$<br>41,396 <br>$<br>4,871 <br>$<br>3,835 <br>$<br>2,917 <br>$<br>(157)<br>$<br>52,862 <br>Pretax income (loss) from continuing operations<br>10,383 <br>550 <br>376 <br>500 <br>(2,224)<br>9,585 <br>2021<br>Total revenues net of interest expense<br>$<br>33,103 <br>$<br>3,643 <br>$<br>3,418 <br>$<br>2,238 <br>$<br>(22)<br>$<br>42,380 <br>Pretax income (loss) from continuing operations<br>10,325 <br>460 <br>420 <br>494 <br>(1,010)<br>10,689 <br>2020<br>Total revenues net of interest expense<br>$<br>28,263 <br>$<br>3,087 <br>$<br>3,271 <br>$<br>2,019 <br>$<br>(553)<br>$<br>36,087 <br>Pretax income (loss) from continuing operations<br>5,422 <br>187 <br>328 <br>273 <br>(1,914)<br>4,296<br>Table of Contents<br>GEOGRAPHIC OPERATIONS<br>The following table presents our total revenues net of interest expense and pretax income (loss) from continuing operations in different geographic regions<br>based, in part, upon internal allocations, which necessarily involve managements judgment.<br>Effective for the first quarter of 2022, we changed the way in which we allocate certain ...</code>                                                                                                                            | <code>What are the geographies that American Express primarily operates in as of 2022?</code>                                                                                                                                                                                                                                                                                                          |
+* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
+  ```json
+  {
+      "loss": "MultipleNegativesRankingLoss",
+      "matryoshka_dims": [
+          768,
+          512,
+          256,
+          128,
+          64
+      ],
+      "matryoshka_weights": [
+          1,
+          1,
+          1,
+          1,
+          1
+      ],
+      "n_dims_per_step": -1
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: epoch
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `num_train_epochs`: 4
+- `lr_scheduler_type`: cosine
+- `warmup_ratio`: 0.1
+- `bf16`: True
+- `tf32`: False
+- `load_best_model_at_end`: True
+- `optim`: adamw_torch_fused
+- `batch_sampler`: no_duplicates
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: epoch
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 16
+- `per_device_eval_batch_size`: 16
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `torch_empty_cache_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1.0
+- `num_train_epochs`: 4
+- `max_steps`: -1
+- `lr_scheduler_type`: cosine
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.1
+- `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`: False
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: True
+- `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_fused
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: None
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `include_for_metrics`: []
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `use_liger_kernel`: False
+- `eval_use_gather_object`: False
+- `average_tokens_across_devices`: False
+- `prompts`: None
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: proportional
+
+</details>
+
+### Training Logs
+| Epoch   | Step   | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
+|:-------:|:------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
+| 0       | 0      | -             | 0.6632                 | 0.6120                 | 0.5673                 | 0.5358                 | 0.4391                |
+| 1.0     | 9      | -             | 0.6499                 | 0.6759                 | 0.6894                 | 0.6436                 | 0.5923                |
+| 1.1111  | 10     | 5.3139        | -                      | -                      | -                      | -                      | -                     |
+| 2.0     | 18     | -             | 0.6462                 | 0.6730                 | 0.7133                 | 0.6561                 | 0.6601                |
+| 2.2222  | 20     | 1.6581        | -                      | -                      | -                      | -                      | -                     |
+| **3.0** | **27** | **-**         | **0.6612**             | **0.693**              | **0.7113**             | **0.7162**             | **0.7075**            |
+| 3.3333  | 30     | 1.1123        | -                      | -                      | -                      | -                      | -                     |
+| 4.0     | 36     | -             | 0.6658                 | 0.6930                 | 0.7133                 | 0.7162                 | 0.7075                |
+| 1.0     | 9      | -             | 0.6814                 | 0.6590                 | 0.7121                 | 0.7068                 | 0.6836                |
+| 1.1111  | 10     | 0.577         | -                      | -                      | -                      | -                      | -                     |
+| 2.0     | 18     | -             | 0.6322                 | 0.6625                 | 0.7068                 | 0.6788                 | 0.6749                |
+| 2.2222  | 20     | 0.3614        | -                      | -                      | -                      | -                      | -                     |
+| **3.0** | **27** | **-**         | **0.6322**             | **0.6654**             | **0.6875**             | **0.7113**             | **0.6708**            |
+| 3.3333  | 30     | 0.395         | -                      | -                      | -                      | -                      | -                     |
+| 4.0     | 36     | -             | 0.6568                 | 0.6654                 | 0.6875                 | 0.7113                 | 0.6754                |
+
+* The bold row denotes the saved checkpoint.
+
+### Framework Versions
+- Python: 3.11.11
+- Sentence Transformers: 3.4.1
+- Transformers: 4.48.3
+- PyTorch: 2.5.1+cu124
+- Accelerate: 1.3.0
+- Datasets: 3.3.2
+- Tokenizers: 0.21.0
+
+## 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",
+}
+```
+
+#### MatryoshkaLoss
+```bibtex
+@misc{kusupati2024matryoshka,
+    title={Matryoshka Representation Learning},
+    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
+    year={2024},
+    eprint={2205.13147},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+#### 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}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "BAAI/bge-base-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.48.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.4.1",
+    "transformers": "4.48.3",
+    "pytorch": "2.5.1+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": "cosine"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa858f2f5a7c27d703ca38561e169aa8c669c0a4d7b67cc2474bba8d752ef247
+size 437951328

modules.json

@@ -0,0 +1,20 @@
+[
+  {
+    "idx": 0,
+    "name": "0",
+    "path": "",
+    "type": "sentence_transformers.models.Transformer"
+  },
+  {
+    "idx": 1,
+    "name": "1",
+    "path": "1_Pooling",
+    "type": "sentence_transformers.models.Pooling"
+  },
+  {
+    "idx": 2,
+    "name": "2",
+    "path": "2_Normalize",
+    "type": "sentence_transformers.models.Normalize"
+  }
+]

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+  "max_seq_length": 512,
+  "do_lower_case": true
+}

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
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+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}