sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:135 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
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:135
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: "Worldwide Sales Change \nBy Business Segment\nOrganic sales\n\
    Acquisitions\nDivestitures\nTranslation\nTotal sales change\nSafety and Industrial\n\
    1.0 %\n \n %\n %\n(4.2) %\n(3.2) %\nTransportation and Electronics\n1.2 \n \n\
    (0.5)\n(4.6)\n(3.9)\nHealth Care\n3.2 \n \n(1.4)\n(3.8)\n(2.0)\nConsumer\n(0.9)\n\
    \ \n(0.4)\n(2.6)\n(3.9)\nTotal Company\n1.2 \n \n(0.5)\n(3.9)\n(3.2)\nTable of\
    \ Contents\nSales and operating income (loss) by business segment:\nThe following\
    \ tables contain sales and operating income (loss) results by business segment\
    \ for the years ended December 31, 2022 and 2021. Refer to the section entitled\n\
    Performance by Business Segment later in MD&A for additional discussion concerning\
    \ 2022 versus 2021 results, including Corporate and Unallocated. Refer to Note\
    \ 19 for\nadditional information on business segments.\n2022\n2021\n% change\n\
    (Dollars in millions)\nNet Sales\n% of Total\nOperating\nIncome (Loss)\nNet Sales\n\
    % of Total\nOperating\nIncome (Loss)\nNet Sales\nOperating Income\n(Loss)\nBusiness\
    \ Segments\nSafety and Industrial\n$\n11,604\n33.9 % $\n1,199 $\n11,981\n33.9\
    \ % $\n2,466\n(3.2)%\n(51.4)%\nTransportation and Electronics\n8,902\n26.0 \n\
    1,012\n9,262\n26.2 \n1,880\n(3.9)\n(46.2)\nHealth Care\n8,421\n24.6 \n1,815\n\
    8,597\n24.3 \n2,037\n(2.0)\n(10.9)\nConsumer\n5,298\n15.5 \n994\n5,513\n15.6 \n\
    1,162\n(3.9)\n(14.4)\nCorporate and Unallocated\n4\n \n1,519\n2\n \n(176)\nTotal\
    \ Company\n$\n34,229 \n100.0 % $\n6,539 $\n35,355 \n100.0 % $\n7,369 \n(3.2)%\n\
    (11.3)%\nYear ended December 31, 2022\nWorldwide Sales Change \nBy Business Segment\n\
    Organic sales\nAcquisitions\nDivestitures\nTranslation\nTotal sales change\nSafety\
    \ and Industrial\n1.0 %\n \n %\n %\n(4.2) %\n(3.2) %\nTransportation and Electronics\n\
    1.2 \n \n(0.5)\n(4.6)\n(3.9)\nHealth Care\n3.2 \n \n(1.4)\n(3.8)\n(2.0)\nConsumer\n\
    (0.9)\n \n(0.4)\n(2.6)\n(3.9)\nTotal Company\n1.2 \n \n(0.5)\n(3.9)\n(3.2)\nSales\
    \ by geographic area:\nPercent change information compares the years ended December\
    \ 31, 2022 and 2021 with the same prior year period, unless otherwise indicated.\
    \ Additional discussion of\nbusiness segment results is provided in the Performance\
    \ by Business Segment section.\nYear ended December 31, 2022\nAmericas\nAsia Pacific\n\
    Europe, Middle East\n& Africa\nOther Unallocated\nWorldwide\nNet sales (millions)\n\
    $\n18,400 \n$\n9,901 \n$\n5,928 \n$\n \n$\n34,229 \n% of worldwide sales\n53.8\
    \ %\n28.9 %\n17.3 %\n100.0 %\nComponents of net sales change:\nOrganic sales\n\
    2.6 \n0.3 \n(0.6)\n1.2 \nDivestitures\n(0.6)\n(0.4)\n(0.6)\n(0.5)\nTranslation\n\
    (0.3)\n(6.5)\n(9.8)\n(3.9)\nTotal sales change\n1.7 %\n(6.6) %\n(11.0) %\n(3.2)\
    \ %\nYear ended December 31, 2021\nAmericas\nAsia Pacific\nEurope, Middle East\n\
    & Africa\nOther Unallocated\nWorldwide\nNet sales (millions)\n$\n18,097 \n$\n\
    10,600 \n$\n6,660 \n$\n(2)\n$\n35,355 \n% of worldwide sales\n51.2 %\n30.0 %\n\
    18.8 %\n100.0 %\nComponents of net sales change:\nOrganic sales\n9.8 \n8.5 \n\
    6.3 \n8.8 \nDivestitures\n(0.6)\n \n(1.1)\n(0.5)\nTranslation\n0.3 \n2.3 \n3.8\
    \ \n1.6 \nTotal sales change\n9.5 %\n10.8 %\n9.0 %\n9.9 %\n25"
  sentences:
  - Has Pepsico reported any materially important ongoing legal battles from FY2022
    and FY2021?
  - 'We need to calculate a financial metric by using information only provided within
    the balance sheet. Please answer the following question: what is Boeing''s year
    end FY2018 net property, plant, and equipment (in USD millions)?'
  - If we exclude the impact of M&A, which segment has dragged down 3M's overall growth
    in 2022?
- source_sentence: "dited) \n \nThree months ended \n \nTwelve months ended \n \n\
    December 31, \n2022 \n \nDecember 31, \n2021 \n \nDecember 31, \n2022 \n \nDecember\
    \ 31, \n2021 \nLas Vegas Strip Resorts \n$ \n877,052 $ \n698,739 $ \n3,142,308\
    \ $ \n1,738,211 \nRegional Operations \n \n319,517 \n309,250 \n1,294,630 \n1,217,814\
    \ \nMGM China \n \n(54,979) \n5,015 \n(203,136) \n25,367 \nUnconsolidated affiliates(1)\
    \ \n \n(43,029) \n(49,698) \n(222,079) \n(131,590) \nManagement and other operations\
    \ \n \n(3,037) \n2,087 \n \n(11,934) \n15,766 \nStock compensation \n \n(25,159)\
    \ \n(26,494) \n(71,297) \n(63,984) \nCorporate(2) \n \n(113,058) \n(117,491) \n\
    (431,238) \n(380,501) \n \n$ \n957,307 \n $ \n3,497,254\nPage 13 of 15 \n \n \n\
    MGM RESORTS INTERNATIONAL AND SUBSIDIARIES \nSUPPLEMENTAL DATA NET REVENUES \n\
    (In thousands) \n(Unaudited) \n \nThree months ended \n \nTwelve months ended\
    \ \n \nDecember 31, \n2022 \n \nDecember 31, \n2021 \n \nDecember 31, \n2022 \n\
    \ \nDecember 31, \n2021 \nLas Vegas Strip Resorts \n$ \n2,297,282 $ \n1,806,686\
    \ $ \n8,398,372 $ \n4,737,185 \nRegional Operations \n \n991,452 \n899,607 \n\
    3,815,885 \n3,392,363 \nMGM China \n \n174,720 \n314,717 \n673,593 \n1,210,761\
    \ \nManagement and other operations \n \n128,762 \n35,882 \n239,635 \n339,831\
    \ \n \n$ \n3,592,216 $ \n3,056,892 $ \n13,127,485 $ \n9,680,140 \n \n \n \n \n\
    \ \nMGM RESORTS INTERNATIONAL AND SUBSIDIARIES \nSUPPLEMENTAL DATA ADJUSTED PROPERTY\
    \ EBITDAR AND ADJUSTED EBITDAR \n(In thousands) \n(Unaudited) \n \nThree months\
    \ ended \n \nTwelve months ended \n \nDecember 31, \n2022 \n \nDecember 31, \n\
    2021 \n \nDecember 31, \n2022 \n \nDecember 31, \n2021 \nLas Vegas Strip Resorts\
    \ \n$ \n877,052 $ \n698,739 $ \n3,142,308 $ \n1,738,211 \nRegional Operations\
    \ \n \n319,517 \n309,250 \n1,294,630 \n1,217,814 \nMGM China \n \n(54,979) \n\
    5,015 \n(203,136) \n25,367 \nUnconsolidated affiliates(1) \n \n(43,029) \n(49,698)\
    \ \n(222,079) \n(131,590) \nManagement and other operations \n \n(3,037) \n2,087\
    \ \n \n(11,934) \n15,766 \nStock compensation \n \n(25,159) \n(26,494) \n(71,297)\
    \ \n(63,984) \nCorporate(2) \n \n(113,058) \n(117,491) \n(431,238) \n(380,501)\
    \ \n \n$ \n957,307 \n $ \n3,497,254 \n \n(1) Represents the Company's share of\
    \ operating income (loss) excluding investments in real estate ventures, adjusted\
    \ for the effect of certain basis \ndifferences. Includes the Company's share\
    \ of operating results of CityCenter through September 26, 2021 during the twelve\
    \ months ended December 31, \n2021. \n(2) Three months ended December 31, 2022\
    \ includes amounts related to MGM China of $5 million, global development of $6\
    \ million, and transaction costs of \n$2 million. Twelve months ended December\
    \ 31, 2022 includes amounts related to MGM China of $18 million, global development\
    \ of $20 million, and \ntransaction costs of $42 million. Three months ended December\
    \ 31, 2021 includes amounts related to MGM China of $4 million, global development\
    \ of $10 \nmillion, and transaction costs of $8 million. Twelve months ended December\
    \ 31, 2021 includes amounts related to MGM China of $16 million, global \ndevelopment\
    \ of $23 million, and transaction costs of $34 million."
  sentences:
  - Which region had the Highest EBITDAR Contribution for MGM during FY2022?
  - How much has the effective tax rate of American Express changed between FY2021
    and FY2022?
  - Which region had the worst topline performance for MGM during FY2022?
- source_sentence: "Amcor plc and Subsidiaries\nConsolidated Balance Sheet\n(in millions)\n\
    As of June 30,\n2020\n2019\nAssets\nCurrent assets:\nCash and cash equivalents\n\
    $\n742.6 \n$\n601.6 \nTrade receivables, net\n1,615.9 \n1,864.3 \nInventories,\
    \ net\n1,831.9 \n1,953.8 \nPrepaid expenses and other current assets\n344.3 \n\
    374.3 \nAssets held for sale\n \n416.1 \nTotal current assets\n4,534.7 \n5,210.1\
    \ \nNon-current assets:\nInvestments in affiliated companies\n77.7 \n98.9 \nProperty,\
    \ plant and equipment, net\n3,614.8 \n3,975.0 \nOperating lease assets\n525.3\
    \ \n \nDeferred tax assets\n135.4 \n190.9 \nOther intangible assets, net\n1,994.3\
    \ \n2,306.8 \nGoodwill\n5,339.3 \n5,156.0 \nEmployee benefit assets\n43.4 \n40.2\
    \ \nOther non-current assets\n177.2 \n187.1 \nTotal non-current assets\n11,907.4\
    \ \n11,954.9 \nTotal assets\n$\n16,442.1 \n$\n17,165.0 \nLiabilities\nCurrent\
    \ liabilities:\nCurrent portion of long-term debt\n$\n11.1 \n$\n5.4 \nShort-term\
    \ debt\n195.2 \n788.8 \nTrade payables\n2,170.8 \n2,303.4 \nAccrued employee costs\n\
    476.5 \n378.4 \nOther current liabilities\n1,120.0 \n1,044.9 \nLiabilities held\
    \ for sale\n \n20.9 \nTotal current liabilities\n3,973.6 \n4,541.8 \nNon-current\
    \ liabilities:\nLong-term debt, less current portion\n6,028.4 \n5,309.0 \nOperating\
    \ lease liabilities\n465.7 \n \nDeferred tax liabilities\n672.4 \n1,011.7 \nEmployee\
    \ benefit obligations\n391.7 \n386.8 \nOther non-current liabilities\n223.2 \n\
    241.0 \nTotal non-current liabilities\n7,781.4 \n6,948.5 \nTotal liabilities\n\
    11,755.0 \n11,490.3 \nCommitments and contingencies (See Note 19)\nShareholders'\
    \ Equity\nAmcor plc shareholders equity:\nOrdinary shares ($0.01 par value):\n\
    Authorized (9,000.0 shares)\nIssued (1,568.5 and 1,625.9 shares, respectively)\n\
    15.7 \n16.3 \nAdditional paid-in capital\n5,480.0 \n6,007.5 \nRetained earnings\n\
    246.5 \n323.7 \nAccumulated other comprehensive income (loss)\n(1,049.3)\n(722.4)\n\
    Treasury shares (6.7 and 1.4 shares, respectively)\n(67.0)\n(16.1)\nTotal Amcor\
    \ plc shareholders' equity\n4,625.9 \n5,609.0 \nNon-controlling interest\n61.2\
    \ \n65.7 \nTotal shareholders' equity\n4,687.1 \n5,674.7 \nTotal liabilities and\
    \ shareholders' equity\n$\n16,442.1 \n$\n17,165.0 \nSee accompanying notes to\
    \ consolidated financial statements.\n50\nAmcor plc and Subsidiaries\nConsolidated\
    \ Balance Sheet\n(in millions)\nAs of June 30,\n2020\n2019\nAssets\nCurrent assets:\n\
    Cash and cash equivalents\n$\n742.6 \n$\n601.6 \nTrade receivables, net\n1,615.9\
    \ \n1,864.3 \nInventories, net\n1,831.9 \n1,953.8 \nPrepaid expenses and other\
    \ current assets\n344.3 \n374.3 \nAssets held for sale\n \n416.1 \nTotal current\
    \ assets\n4,534.7 \n5,210.1 \nNon-current assets:\nInvestments in affiliated companies\n\
    77.7 \n98.9 \nProperty, plant and equipment, net\n3,614.8 \n3,975.0 \nOperating\
    \ lease assets\n525.3 \n \nDeferred tax assets\n135.4 \n190.9 \nOther intangible\
    \ assets, net\n1,994.3 \n2,306.8 \nGoodwill\n5,339.3 \n5,156.0 \nEmployee benefit\
    \ assets\n43.4 \n40.2 \nOther non-current assets\n177.2 \n187.1 \nTotal non-current\
    \ assets\n11,907.4 \n11,954.9 \nTotal assets\n$\n16,442.1 \n$\n17,165.0 \nLiabilities\n\
    Current liabilities:\nCurrent portion of long-term debt\n$\n11.1 \n$\n5.4 \nShort-term\
    \ debt\n195.2 \n788.8 \nTrade payables\n2,170.8 \n2,303.4 \nAccrued employee costs\n\
    476.5 \n378.4 \nOther current liabilities\n1,120.0 \n1,044.9 \nLiabilities held\
    \ for sale\n \n20.9 \nTotal current liabilities\n3,973.6 \n4,541.8 \nNon-current\
    \ liabilities:\nLong-term debt, less current portion\n6,028.4 \n5,309.0 \nOperating\
    \ lease liabilities\n465.7 \n \nDeferred tax liabilities\n672.4 \n1,011.7 \nEmployee\
    \ benefit obligations\n391.7 \n386.8 \nOther non-current liabilities\n223.2 \n\
    241.0 \nTotal non-current liabilities\n7,781.4 \n6,948.5 \nTotal liabilities\n\
    11,755.0 \n11,490.3 \nCommitments and contingencies (See Note 19)\nShareholders'\
    \ Equity\nAmcor plc shareholders equity:\nOrdinary shares ($0.01 par value):\n\
    Authorized (9,000.0 shares)\nIssued (1,568.5 and 1,625.9 shares, respectively)\n\
    15.7 \n16.3 \nAdditional paid-in capital\n5,480.0 \n6,007.5 \nRetained earnings\n\
    246.5 \n323.7 \nAccumulated other comprehensive income (loss)\n(1,049.3)\n(722.4)\n\
    Treasury shares (6.7 and 1.4 shares, respectively)\n(67.0)\n(16.1)\nTotal Amcor\
    \ plc shareholders' equity\n4,625.9 \n5,609.0 \nNon-controlling interest\n61.2\
    \ \n65.7 \nTotal shareholders' equity\n4,687.1 \n5,674.7 \nTotal liabilities and\
    \ shareholders' equity\n$\n16,442.1 \n$\n17,165.0 \nSee accompanying notes to\
    \ consolidated financial statements.\n50"
  sentences:
  - What is Amcor's year end FY2020 net AR (in USD millions)? Address the question
    by adopting the perspective of a financial analyst who can only use the details
    shown within the balance sheet.
  - Is Boeing's business subject to cyclicality?
  - Does AMD have a reasonably healthy liquidity profile based on its quick ratio
    for FY22? If the quick ratio is not relevant to measure liquidity, please state
    that and explain why.
- source_sentence: 'Exhibit 99.1

    Johnson & Johnson Announces Updated Financials and 2023 Guidance Following Completion
    of the Kenvue

    Separation


    Company expects increased 2023 Reported Sales Growth of 7.0% - 8.0%, Operational
    Sales Growth of 7.5% - 8.5%, and

    Adjusted Operational Sales Growth of 6.2% - 7.2%; Figures exclude the COVID-19
    Vaccine


    Company expects 2023 Adjusted Reported Earnings Per Share (EPS) of $10.00 - $10.10,
    reflecting increased growth of

    12.5% at the mid-point and Adjusted Operational EPS of $9.90 - $10.00, reflecting
    increased growth of 11.5% at the mid-

    point


    Company reduced outstanding share count by approximately 191 million; 2023 guidance
    reflects only a partial-year benefit

    of approximately 73.5 million shares or $0.28 benefit to EPS


    Company secured $13.2 billion in cash proceeds from the Kenvue debt offering and
    initial public offering and maintains 9.5%

    of equity stake in Kenvue


    Company maintains its quarterly dividend of $1.19 per share

    New Brunswick, N.J. (August 30, 2023) Johnson & Johnson (NYSE: JNJ) (the Company)
    today announced updates to its financials and

    2023 guidance which reflect its operations as a company focused on transformational
    innovation in Pharmaceutical and MedTech. The

    Company has published a recorded webinar for investors to provide additional context
    behind the updated financials and 2023 guidance

    found in this release, which may be accessed by visiting the Investors section
    of the Company''s website at webcasts & presentations.

    The completion of this transaction uniquely positions Johnson & Johnson as a Pharmaceutical
    and MedTech company focused on delivering

    transformative healthcare solutions to patients, said Joaquin Duato, Chairman
    of the Board and Chief Executive Officer. We are incredibly

    proud of the focus and dedication of our employees worldwide to achieve this milestone,
    which we are confident will unlock near- and long-

    term value for all of our stakeholders.

    As previously announced, the Company recently completed an exchange offer to finalize
    the separation of Kenvue Inc., formerly Johnson &

    Johnsons Consumer Health business. As a result of the completion of the exchange
    offer, Johnson & Johnson will now present its

    Consumer Health business financial results as discontinued operations, including
    a gain of approximately $20 billion in the third quarter of

    2023

    Exhibit 99.1

    Johnson & Johnson Announces Updated Financials and 2023 Guidance Following Completion
    of the Kenvue

    Separation


    Company expects increased 2023 Reported Sales Growth of 7.0% - 8.0%, Operational
    Sales Growth of 7.5% - 8.5%, and

    Adjusted Operational Sales Growth of 6.2% - 7.2%; Figures exclude the COVID-19
    Vaccine


    Company expects 2023 Adjusted Reported Earnings Per Share (EPS) of $10.00 - $10.10,
    reflecting increased growth of

    12.5% at the mid-point and Adjusted Operational EPS of $9.90 - $10.00, reflecting
    increased growth of 11.5% at the mid-

    point


    Company reduced outstanding share count by approximately 191 million; 2023 guidance
    reflects only a partial-year benefit

    of approximately 73.5 million shares or $0.28 benefit to EPS


    Company secured $13.2 billion in cash proceeds from the Kenvue debt offering and
    initial public offering and maintains 9.5%

    of equity stake in Kenvue


    Company maintains its quarterly dividend of $1.19 per share

    New Brunswick, N.J. (August 30, 2023) Johnson & Johnson (NYSE: JNJ) (the Company)
    today announced updates to its financials and

    2023 guidance which reflect its operations as a company focused on transformational
    innovation in Pharmaceutical and MedTech. The

    Company has published a recorded webinar for investors to provide additional context
    behind the updated financials and 2023 guidance

    found in this release, which may be accessed by visiting the Investors section
    of the Company''s website at webcasts & presentations.

    The completion of this transaction uniquely positions Johnson & Johnson as a Pharmaceutical
    and MedTech company focused on delivering

    transformative healthcare solutions to patients, said Joaquin Duato, Chairman
    of the Board and Chief Executive Officer. We are incredibly

    proud of the focus and dedication of our employees worldwide to achieve this milestone,
    which we are confident will unlock near- and long-

    term value for all of our stakeholders.

    As previously announced, the Company recently completed an exchange offer to finalize
    the separation of Kenvue Inc., formerly Johnson &

    Johnsons Consumer Health business. As a result of the completion of the exchange
    offer, Johnson & Johnson will now present its

    Consumer Health business financial results as discontinued operations, including
    a gain of approximately $20 billion in the third quarter of

    2023.'
  sentences:
  - What is the quantity of restructuring costs directly outlined in Pepsico's income
    statements for FY2022? If restructuring costs are not explicitly outlined then
    state 0.
  - Were there any board member nominees who had substantially more votes against
    joining than the other nominees?
  - Which business segment of JnJ will be treated as a discontinued operation from
    August 30, 2023 onward?
- source_sentence: "Table of Contents\nAMAZON.COM, INC.\nCONSOLIDATED STATEMENTS OF\
    \ OPERATIONS\n(in millions, except per share data)\n \n \nYear Ended December\
    \ 31,\n \n2015\n \n2016\n \n2017\nNet product sales\n$\n79,268 $\n94,665 $\n118,573\n\
    Net service sales\n27,738 \n41,322 \n59,293\nTotal net sales\n107,006 \n135,987\
    \ \n177,866\nOperating expenses:\n \n \n \nCost of sales\n71,651 \n88,265 \n111,934\n\
    Fulfillment\n13,410 \n17,619 \n25,249\nMarketing\n5,254 \n7,233 \n10,069\nTechnology\
    \ and content\n12,540 \n16,085 \n22,620\nGeneral and administrative\n1,747 \n\
    2,432 \n3,674\nOther operating expense, net\n171 \n167 \n214\nTotal operating\
    \ expenses\n104,773 \n131,801 \n173,760\nOperating income\n2,233 \n4,186 \n4,106\n\
    Interest income\n50 \n100 \n202\nInterest expense\n(459) \n(484) \n(848)\nOther\
    \ income (expense), net\n(256) \n90 \n346\nTotal non-operating income (expense)\n\
    (665) \n(294) \n(300)\nIncome before income taxes\n1,568 \n3,892 \n3,806\nProvision\
    \ for income taxes\n(950) \n(1,425) \n(769)\nEquity-method investment activity,\
    \ net of tax\n(22) \n(96) \n(4)\nNet income\n$\n596 $\n2,371 $\n3,033\nBasic earnings\
    \ per share\n$\n1.28 $\n5.01 $\n6.32\nDiluted earnings per share\n$\n1.25 $\n\
    4.90 $\n6.15\nWeighted-average shares used in computation of earnings per share:\n\
    \ \n \n \nBasic\n467 \n474 \n480\nDiluted\n477 \n484 \n493\nSee accompanying notes\
    \ to consolidated financial statements.\n38\nTable of Contents\nAMAZON.COM, INC.\n\
    CONSOLIDATED STATEMENTS OF OPERATIONS\n(in millions, except per share data)\n\
    \ \n \nYear Ended December 31,\n \n2015\n \n2016\n \n2017\nNet product sales\n\
    $\n79,268 $\n94,665 $\n118,573\nNet service sales\n27,738 \n41,322 \n59,293\n\
    Total net sales\n107,006 \n135,987 \n177,866\nOperating expenses:\n \n \n \nCost\
    \ of sales\n71,651 \n88,265 \n111,934\nFulfillment\n13,410 \n17,619 \n25,249\n\
    Marketing\n5,254 \n7,233 \n10,069\nTechnology and content\n12,540 \n16,085 \n\
    22,620\nGeneral and administrative\n1,747 \n2,432 \n3,674\nOther operating expense,\
    \ net\n171 \n167 \n214\nTotal operating expenses\n104,773 \n131,801 \n173,760\n\
    Operating income\n2,233 \n4,186 \n4,106\nInterest income\n50 \n100 \n202\nInterest\
    \ expense\n(459) \n(484) \n(848)\nOther income (expense), net\n(256) \n90 \n346\n\
    Total non-operating income (expense)\n(665) \n(294) \n(300)\nIncome before income\
    \ taxes\n1,568 \n3,892 \n3,806\nProvision for income taxes\n(950) \n(1,425) \n\
    (769)\nEquity-method investment activity, net of tax\n(22) \n(96) \n(4)\nNet income\n\
    $\n596 $\n2,371 $\n3,033\nBasic earnings per share\n$\n1.28 $\n5.01 $\n6.32\n\
    Diluted earnings per share\n$\n1.25 $\n4.90 $\n6.15\nWeighted-average shares used\
    \ in computation of earnings per share:\n \n \n \nBasic\n467 \n474 \n480\nDiluted\n\
    477 \n484 \n493\nSee accompanying notes to consolidated financial statements.\n\
    38"
  sentences:
  - 'We want to calculate a financial metric. Please help us compute it by basing
    your answers off of the statement of income and the statement of cash flows. Here''s
    the question: what is the FY2015 unadjusted EBITDA % margin for Netflix? Calculate
    unadjusted EBITDA using unadjusted operating income and D&A (from cash flow statement).'
  - What is Amazon's year-over-year change in revenue from FY2016 to FY2017 (in units
    of percents and round to one decimal place)? Calculate what was asked by utilizing
    the line items clearly shown in the statement of income.
  - What is the FY2018 - FY2020 3 year average of capex as a % of revenue for MGM
    Resorts? Answer in units of percents and round to one decimal place. Please utilize
    information provided primarily within the statement of cash flows and the statement
    of income.
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: 0.4
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4666666666666667
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5333333333333333
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6666666666666666
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.4
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15555555555555553
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10666666666666667
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06666666666666667
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.4
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4666666666666667
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5333333333333333
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6666666666666666
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.502939332667103
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.4540740740740741
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.46697870822531695
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 512
      type: dim_512
    metrics:
    - type: cosine_accuracy@1
      value: 0.26666666666666666
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4666666666666667
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.5333333333333333
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6666666666666666
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.26666666666666666
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.15555555555555553
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.10666666666666667
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.06666666666666667
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.26666666666666666
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4666666666666667
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.5333333333333333
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6666666666666666
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.45372996647663066
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.38740740740740737
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.40238523659576286
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 256
      type: dim_256
    metrics:
    - type: cosine_accuracy@1
      value: 0.2
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.4
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.6
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.13333333333333333
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.060000000000000005
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.4
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.6
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.37396900668871336
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.3050793650793651
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3252781685635812
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 128
      type: dim_128
    metrics:
    - type: cosine_accuracy@1
      value: 0.2
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.3333333333333333
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.4
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.5333333333333333
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.2
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.1111111111111111
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.08
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.05333333333333333
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.2
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.3333333333333333
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.4
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.5333333333333333
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.34596364164665
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.28833333333333333
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3060450128033935
      name: Cosine Map@100
  - task:
      type: information-retrieval
      name: Information Retrieval
    dataset:
      name: dim 64
      type: dim_64
    metrics:
    - type: cosine_accuracy@1
      value: 0.26666666666666666
      name: Cosine Accuracy@1
    - type: cosine_accuracy@3
      value: 0.26666666666666666
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 0.3333333333333333
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 0.4666666666666667
      name: Cosine Accuracy@10
    - type: cosine_precision@1
      value: 0.26666666666666666
      name: Cosine Precision@1
    - type: cosine_precision@3
      value: 0.08888888888888888
      name: Cosine Precision@3
    - type: cosine_precision@5
      value: 0.06666666666666667
      name: Cosine Precision@5
    - type: cosine_precision@10
      value: 0.04666666666666666
      name: Cosine Precision@10
    - type: cosine_recall@1
      value: 0.26666666666666666
      name: Cosine Recall@1
    - type: cosine_recall@3
      value: 0.26666666666666666
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 0.3333333333333333
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 0.4666666666666667
      name: Cosine Recall@10
    - type: cosine_ndcg@10
      value: 0.3413478052343165
      name: Cosine Ndcg@10
    - type: cosine_mrr@10
      value: 0.30396825396825394
      name: Cosine Mrr@10
    - type: cosine_map@100
      value: 0.3220458144190396
      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 = [
    'Table of Contents\nAMAZON.COM, INC.\nCONSOLIDATED STATEMENTS OF OPERATIONS\n(in millions, except per share data)\n \n \nYear Ended December 31,\n \n2015\n \n2016\n \n2017\nNet product sales\n$\n79,268 $\n94,665 $\n118,573\nNet service sales\n27,738 \n41,322 \n59,293\nTotal net sales\n107,006 \n135,987 \n177,866\nOperating expenses:\n \n \n \nCost of sales\n71,651 \n88,265 \n111,934\nFulfillment\n13,410 \n17,619 \n25,249\nMarketing\n5,254 \n7,233 \n10,069\nTechnology and content\n12,540 \n16,085 \n22,620\nGeneral and administrative\n1,747 \n2,432 \n3,674\nOther operating expense, net\n171 \n167 \n214\nTotal operating expenses\n104,773 \n131,801 \n173,760\nOperating income\n2,233 \n4,186 \n4,106\nInterest income\n50 \n100 \n202\nInterest expense\n(459) \n(484) \n(848)\nOther income (expense), net\n(256) \n90 \n346\nTotal non-operating income (expense)\n(665) \n(294) \n(300)\nIncome before income taxes\n1,568 \n3,892 \n3,806\nProvision for income taxes\n(950) \n(1,425) \n(769)\nEquity-method investment activity, net of tax\n(22) \n(96) \n(4)\nNet income\n$\n596 $\n2,371 $\n3,033\nBasic earnings per share\n$\n1.28 $\n5.01 $\n6.32\nDiluted earnings per share\n$\n1.25 $\n4.90 $\n6.15\nWeighted-average shares used in computation of earnings per share:\n \n \n \nBasic\n467 \n474 \n480\nDiluted\n477 \n484 \n493\nSee accompanying notes to consolidated financial statements.\n38\nTable of Contents\nAMAZON.COM, INC.\nCONSOLIDATED STATEMENTS OF OPERATIONS\n(in millions, except per share data)\n \n \nYear Ended December 31,\n \n2015\n \n2016\n \n2017\nNet product sales\n$\n79,268 $\n94,665 $\n118,573\nNet service sales\n27,738 \n41,322 \n59,293\nTotal net sales\n107,006 \n135,987 \n177,866\nOperating expenses:\n \n \n \nCost of sales\n71,651 \n88,265 \n111,934\nFulfillment\n13,410 \n17,619 \n25,249\nMarketing\n5,254 \n7,233 \n10,069\nTechnology and content\n12,540 \n16,085 \n22,620\nGeneral and administrative\n1,747 \n2,432 \n3,674\nOther operating expense, net\n171 \n167 \n214\nTotal operating expenses\n104,773 \n131,801 \n173,760\nOperating income\n2,233 \n4,186 \n4,106\nInterest income\n50 \n100 \n202\nInterest expense\n(459) \n(484) \n(848)\nOther income (expense), net\n(256) \n90 \n346\nTotal non-operating income (expense)\n(665) \n(294) \n(300)\nIncome before income taxes\n1,568 \n3,892 \n3,806\nProvision for income taxes\n(950) \n(1,425) \n(769)\nEquity-method investment activity, net of tax\n(22) \n(96) \n(4)\nNet income\n$\n596 $\n2,371 $\n3,033\nBasic earnings per share\n$\n1.28 $\n5.01 $\n6.32\nDiluted earnings per share\n$\n1.25 $\n4.90 $\n6.15\nWeighted-average shares used in computation of earnings per share:\n \n \n \nBasic\n467 \n474 \n480\nDiluted\n477 \n484 \n493\nSee accompanying notes to consolidated financial statements.\n38',
    "What is Amazon's year-over-year change in revenue from FY2016 to FY2017 (in units of percents and round to one decimal place)? Calculate what was asked by utilizing the line items clearly shown in the statement of income.",
    'What is the FY2018 - FY2020 3 year average of capex as a % of revenue for MGM Resorts? Answer in units of percents and round to one decimal place. Please utilize information provided primarily within the statement of cash flows and the statement of income.',
]
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   | 0.4        | 0.2667     | 0.2       | 0.2       | 0.2667     |
| cosine_accuracy@3   | 0.4667     | 0.4667     | 0.4       | 0.3333    | 0.2667     |
| cosine_accuracy@5   | 0.5333     | 0.5333     | 0.4       | 0.4       | 0.3333     |
| cosine_accuracy@10  | 0.6667     | 0.6667     | 0.6       | 0.5333    | 0.4667     |
| cosine_precision@1  | 0.4        | 0.2667     | 0.2       | 0.2       | 0.2667     |
| cosine_precision@3  | 0.1556     | 0.1556     | 0.1333    | 0.1111    | 0.0889     |
| cosine_precision@5  | 0.1067     | 0.1067     | 0.08      | 0.08      | 0.0667     |
| cosine_precision@10 | 0.0667     | 0.0667     | 0.06      | 0.0533    | 0.0467     |
| cosine_recall@1     | 0.4        | 0.2667     | 0.2       | 0.2       | 0.2667     |
| cosine_recall@3     | 0.4667     | 0.4667     | 0.4       | 0.3333    | 0.2667     |
| cosine_recall@5     | 0.5333     | 0.5333     | 0.4       | 0.4       | 0.3333     |
| cosine_recall@10    | 0.6667     | 0.6667     | 0.6       | 0.5333    | 0.4667     |
| **cosine_ndcg@10**  | **0.5029** | **0.4537** | **0.374** | **0.346** | **0.3413** |
| cosine_mrr@10       | 0.4541     | 0.3874     | 0.3051    | 0.2883    | 0.304      |
| cosine_map@100      | 0.467      | 0.4024     | 0.3253    | 0.306     | 0.322      |

<!--
## 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: 508.73 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 39.7 tokens</li><li>max: 175 tokens</li></ul> |
* Samples:
  | positive                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | anchor                                                                                                                                                                                                                                                               |
  |:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>Twelve Months Ended June 30, 2022<br>Twelve Months Ended June 30, 2023<br>($ million)<br>EBITDA<br>EBIT<br>Net <br>Income<br>EPS <br>(Diluted<br>US <br>cents)(1)<br>EBITDA<br>EBIT<br>Net <br>Income<br>EPS <br>(Diluted <br>US <br>cents)(1)<br>Net income attributable to Amcor<br> <br>805 <br> <br>805 <br> <br>805 <br> <br>52.9 <br> <br>1,048 <br> <br>1,048 <br> <br>1,048 <br> <br>70.5 <br>Net income attributable to non-controlling <br>interests<br> <br>10 <br> <br>10 <br> <br>10 <br> <br>10 <br>Tax expense<br> <br>300 <br> <br>300 <br> <br>193 <br> <br>193 <br>Interest expense, net<br> <br>135 <br> <br>135 <br> <br>259 <br> <br>259 <br>Depreciation and amortization<br> <br>579 <br> <br>569 <br>EBITDA, EBIT, Net income and EPS<br> <br>1,829 <br> <br>1,250 <br> <br>805 <br> <br>52.9 <br> <br>2,080 <br> <br>1,510 <br> <br>1,048 <br> <br>70.5 <br>2019 Bemis Integration Plan<br> <br>37 <br> <br>37 <br> <br>37 <br> <br>2.5 <br> <br> <br> <br> <br> <br> <br> <br> <br>Net loss on disposals(2)<br> <br>10 <br> <br>10 <br> <br>10 <br> <br>0.7 <br> <br> <br> <br> <br> <br> <br> <br> <br>Impact of hyperinflation<br> <br>16 <br> <br>16 <br> <br>16 <br> <br>1.0 <br> <br>24 <br> <br>24 <br> <br>24 <br> <br>1.9 <br>Property and other losses, net(3)<br> <br>13 <br> <br>13 <br> <br>13 <br> <br>0.8 <br> <br>2 <br> <br>2 <br> <br>2 <br> <br>0.1 <br>Russia-Ukraine conflict impacts(4)<br> <br>200 <br> <br>200 <br> <br>200 <br> <br>13.2 <br> <br>(90) <br>(90) <br>(90) <br>(6.0) <br>Pension settlements<br> <br>8...</code> | <code>What Was AMCOR's Adjusted Non GAAP EBITDA for FY 2023</code>                                                                                                                                                                                                   |
  | <code>SQUARE,INC.<br>CONSOLIDATEDBALANCESHEETS<br>(In thousands, except share and per share data)<br><br>December31,<br><br>2016<br><br>2015<br>Assets<br><br> <br>Currentassets:<br><br> <br>Cashandcashequivalents<br>$<br>452,030 $<br>461,329<br>Short-terminvestments<br>59,901 <br><br>Restrictedcash<br>22,131 <br>13,537<br>Settlementsreceivable<br>321,102 <br>142,727<br>Customerfundsheld<br>43,574 <br>9,446<br>Loansheldforsale<br>42,144 <br>604<br>Merchantcashadvancereceivable,net<br>4,212 <br>36,473<br>Othercurrentassets<br>56,331 <br>41,447<br>Totalcurrentassets<br>1,001,425 <br>705,563<br>Propertyandequipment,net<br>88,328 <br>87,222<br>Goodwill<br>57,173 <br>56,699<br>Acquiredintangibleassets,net<br>19,292 <br>26,776<br>Long-terminvestments<br>27,366 <br><br>Restrictedcash<br>14,584 <br>14,686<br>Otherassets<br>3,194 <br>3,826<br>Totalassets<br>$<br>1,211,362 $<br>894,772<br>LiabilitiesandStockholdersEquity<br><br> <br>Currentliabilities:<br><br> <br>Accountspayable<br>$<br>12,602 $<br>18,869<br>Customerspayable<br>388,058 <br>215,365<br>Customerfundsobligation<br>43,574 <br>9,446<br>Accruedtransactionlosses<br>20,064 <br>17,176<br>Accruedexpenses<br>39,543 <br>44,401<br>Othercurrentliabilities<br>73,623 <br>28,945<br>Totalcurrentliabilities<br>577,464 <br>33...</code>                                                                                                                                                                                                                                           | <code>Considering the data in the balance sheet, what is Block's (formerly known as Square) FY2016 working capital ratio? Define working capital ratio as total current assets divided by total current liabilities. Round your answer to two decimal places.</code> |
  | <code>Consolidated Balance Sheets <br>Verizon Communications Inc. and Subsidiaries <br>(dollars in millions, except per share amounts) <br>At December 31,<br>2022<br>2021 <br>Assets <br>Current assets <br>Cash and cash equivalents<br>$ <br>2,605 <br>$ <br>2,921 <br>Accounts receivable<br> <br>25,332 <br> <br>24,742 <br>Less Allowance for credit losses<br> <br>826 <br> <br>896 <br>Accounts receivable, net <br> <br>24,506 <br> <br>23,846 <br>Inventories<br> <br>2,388 <br> <br>3,055 <br>Prepaid expenses and other<br> <br>8,358 <br> <br>6,906 <br>Total current assets<br> <br>37,857 <br> <br>36,728 <br>Property, plant and equipment<br> <br>307,689 <br> <br>289,897 <br>Less Accumulated depreciation<br> <br>200,255 <br> <br>190,201 <br>Property, plant and equipment, net<br> <br>107,434 <br> <br>99,696 <br>Investments in unconsolidated businesses<br> <br>1,071 <br> <br>1,061 <br>Wireless licenses<br> <br>149,796 <br> <br>147,619 <br>Goodwill<br> <br>28,671 <br> <br>28,603 <br>Other intangible assets, net<br> <br>11,461 <br> <br>11,677 <br>Operating lease right-of-use assets<br> <br>26,130 <br> <br>27,883 <br>Other assets<br> <br>17,260 <br> <br>13,329 <br>Total assets<br>$ <br>379,680 <br>$ <br>366,596 <br>Liabilities and Equity <br>Current liabilities <br>Debt maturing within o...</code>                                                                                                                                                                                                                                  | <code>Does Verizon have a reasonably healthy liquidity profile based on its quick ratio for FY 2022? If the quick ratio is not relevant to measure liquidity, please state that and explain why.</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
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `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`: 16
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 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  | 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** | **0.5029**             | **0.4537**             | **0.374**              | **0.346**              | **0.3413**            |

* 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}
}
```

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