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--- |
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base_model: BAAI/bge-base-en-v1.5 |
|
datasets: [] |
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language: [] |
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library_name: sentence-transformers |
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metrics: |
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- cosine_accuracy@1 |
|
- cosine_accuracy@3 |
|
- cosine_accuracy@5 |
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- cosine_accuracy@10 |
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- 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 |
|
- dot_accuracy@1 |
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- dot_accuracy@3 |
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- dot_accuracy@5 |
|
- dot_accuracy@10 |
|
- dot_precision@1 |
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- dot_precision@3 |
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- dot_precision@5 |
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- dot_precision@10 |
|
- dot_recall@1 |
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- dot_recall@3 |
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- dot_recall@5 |
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- dot_recall@10 |
|
- dot_ndcg@10 |
|
- dot_mrr@10 |
|
- dot_map@100 |
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pipeline_tag: sentence-similarity |
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tags: |
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- sentence-transformers |
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- sentence-similarity |
|
- feature-extraction |
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- generated_from_trainer |
|
- dataset_size:98400 |
|
- loss:MultipleNegativesRankingLoss |
|
widget: |
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- source_sentence: How did the actions of central banks influence investor sentiment |
|
and market expectations regarding future interest rates? |
|
sentences: |
|
- We may be adversely affected by increased governmental and regulatory scrutiny |
|
or negative publicity. Governmental scrutiny from regulators, legislative bodies |
|
and law enforcement agencies with respect to matters relating to compensation, |
|
our business practices, our past actions and other matters remains at high levels. |
|
Political and public sentiment regarding financial institutions has in the past |
|
resulted and may in the future result in a significant amount of adverse press |
|
coverage, as well as adverse statements or charges by regulators or other government |
|
officials. Press coverage and other public statements that assert some form of |
|
wrongdoing (including, in some cases, press coverage and public statements that |
|
do not directly involve us) often result in some type of investigation by regulators, |
|
legislators and law enforcement officials or in lawsuits. Responding to these |
|
investigations and lawsuits, regardless of the ultimate outcome of the proceeding, |
|
is time-consuming and expensive and can divert the time and effort of our senior |
|
management from our business. Penalties and fines sought by regulatory authorities |
|
have increased substantially and certain regulators have been more likely in recent |
|
years to commence enforcement actions or to support legislation targeted at the |
|
financial services industry. Governmental authorities may also be more likely |
|
to pursue criminal or other actions, including seeking admissions of wrongdoing |
|
or guilty pleas, in connection with the resolution of an inquiry or investigation |
|
to the extent a company is viewed as having previously engaged in criminal, regulatory |
|
or other misconduct. Adverse publicity, governmental scrutiny and legal and enforcement |
|
proceedings can also have a negative impact on our reputation and on the morale |
|
and performance of our employees, which could adversely affect our businesses |
|
and results of operations. Further, we are subject to regulatory settlements, |
|
orders and feedback that require significant remediation activities and enhancements |
|
to existing controls, systems and procedures, which has required and will require |
|
us to commit significant resources, including hiring, as well as testing the operation |
|
and effectiveness of new controls, policies and procedures. The failure to complete |
|
these remediation activities in a timely manner could lead to higher operating |
|
expenses, reputational damage and other negative consequences.The financial services |
|
industry generally and our businesses in particular have been subject to negative |
|
publicity. Our reputation and businesses may be adversely affected by negative |
|
publicity or information regarding our businesses and personnel, whether or not |
|
accurate or true, that may be posted on social media or other internet forums |
|
or published by news organizations. Postings on these types of forums may also |
|
adversely impact risk positions of our clients and other parties that owe us money, |
|
securities or other assets and increase the chance that they will not perform |
|
their obligations to us or reduce the revenues we receive from their use of our |
|
services. The speed and pervasiveness with which information can be disseminated |
|
through these channels, in particular social media, may magnify risks relating |
|
to negative publicity. The rapid dissemination of negative information through |
|
social media, in part, is believed to have led to the collapse of Silicon Valley |
|
Bank (SVB). SVB suffered a level of deposit withdrawals within a time period not |
|
previously experienced by a financial institution. We could also be subject to |
|
rapid deposit withdrawals or other outflows as a result of negative social media |
|
posts or other negative publicity . Substantial civil or criminal liability or |
|
significant regulatory action against us could have material adverse financial |
|
effects or cause us significant reputational harm, which in turn could seriously |
|
harm our business prospects. We face significant legal risks in our businesses, |
|
and the volume of claims and amount of damages and penalties claimed in litigation |
|
and regulatory proceedings against financial institutions remain high. See Notes |
|
18 and 27 to the consolidated financial statements in Part II, Item 8 of this |
|
Form 10-K for information about certain of our legal and regulatory proceedings |
|
and investigations. We have seen legal claims by consumers and clients increase |
|
in a market downturn and employment-related claims increase following periods |
|
in which we have reduced our headcount. Additionally, governmental entities have |
|
been plaintiffs and are parties in certain of our legal proceedings, and we may |
|
face future civil or criminal actions or claims by the same or other governmental |
|
entities, as well as follow-on civil litigation that is often commenced after |
|
regulatory settlements.THE GOLDMAN SACHS GROUP, INC. AND SUBSIDIARIES Goldman |
|
Sachs 2023 Form 10-K 49 |
|
- 'Vanguard U.K. Gilt UCITS ETF Managed by Vanguard Global Advisers, LLC.713 Investment |
|
Objective Vanguard U.K. Gilt UCITS ETF seeks to track the performance of the Bloomberg |
|
Sterling Gilt Float Adjusted Index, a market-weighted index of UK government fixed |
|
income securities denominated in pound sterling. Performance Summary (unaudited) |
|
The Performance Summary does not form part of the Financial Statements. • Inflation |
|
and policymakers’ efforts to rein it in took centre stage for the financial markets |
|
during much of the 12 months ended 30 June 2023. • Early in the period, energy |
|
prices continued to cool amid an outlook for slower economic growth, but price |
|
increases then broadened to other categories, notably the services sector, which |
|
felt the effects of tight labour markets. Central banks including the US Federal |
|
Reserve, the European Central Bank and the Bank of England reacted to the prospect |
|
of inflation remaining stubbornly high by aggressively hiking interest rates even |
|
as their actions fanned fears of a global recession down the road. • Although |
|
progress was slow, signs of inflation moderating later in the period led several |
|
major central banks to slow the pace of their interest rate hikes or even hit |
|
the pause button. • Bonds suffered early in the fiscal year amid aggressive rate |
|
hiking and later when markets began to anticipate that rates would remain higher |
|
for longer. With rising yields pushing prices down, global bonds ended the 12 |
|
months in negative territory. • In this environment, the ETF ’s benchmark index |
|
returned –16.59% for the period. • Returns were negative across all maturities. |
|
Gilts maturing in less than five years posted single-digit declines, while all |
|
others posted declines in the double digits. • Gilts underperformed the broad |
|
UK investment-grade bond market. Benchmark returns in the commentary above are |
|
in British pounds. Benchmark: Bloomberg Sterling Gilt Float Adjusted Index Total |
|
Returns Periods Ended 30 June 2023 (Annualised for periods over one year) One |
|
Year Five YearsTen Years or Since Inception1 EUR-Hedged Accumulating -17.43 % |
|
— -13.62 % Benchmark -17.47 — -13.64 Tracking Difference* 0.04 GBP Accumulating |
|
-16.61 % — -5.94 % Benchmark -16.59 — -5.89 Tracking Difference* -0.02 GBP Distributing |
|
-16.61 % -4.88 % 0.07 % Benchmark2 -16.59 -4.83 0.14 Tracking Difference* -0.02 |
|
Each benchmark is in its respective currency. Sources: Vanguard Global Advisers, |
|
LLC, and Bloomberg. Returns are based on NAV with income reinvested. All of the |
|
returns in this report represent past performance, which is not a guarantee of |
|
future results that may be achieved by the fund. For performance data current |
|
to the most recent month-end, which may be higher or lower than that cited, visit |
|
our website at http://global.vanguard.com. Note, too, that both investment returns |
|
and principal value can fluctuate widely, so an investor ''s shares, when sold, |
|
could be worth more or less than their original cost. * The tracking difference |
|
between the fund return and the index return over a stated period of time can |
|
be attributed to a number of factors, including, without limitation, small differences |
|
in weightings, trading activity, transaction costs and differences in the valuation |
|
and withholding tax treatment between the fund and the index vendor. 1 Since-inception |
|
returns: EUR-Hedged Accumulating, 28 August 2020; GBP Accumulating, 19 February |
|
2019. 2 Bloomberg Barclays Global Aggregate U.K. Government Float Adjusted Index |
|
through 31 March 2016, Bloomberg Sterling Gilt Float Adjusted Index thereafter.' |
|
- 818 Vanguard USD Corporate Bond UCITS ETF Principal US Dollars ($) CouponMaturity |
|
DateFair Value US Dollars ($)% of Total Net Assets Capital One Financial Corp. |
|
235,000 3.27% 1/3/2030 200,081 0.01% Bank of New York Mellon Corp. 210,000 3.85% |
|
28/4/2028 200,072 0.01% T-Mobile USA, Inc. 250,000 2.25% 15/11/2031 200,037 0.01% |
|
Astrazeneca Finance LLC 200,000 4.88% 3/3/2028 199,915 0.01% Aetna, Inc. 233,000 |
|
4.50% 15/5/2042 199,838 0.01% MassMutual Global Funding II 200,000 5.05% 7/12/2027 |
|
199,614 0.01% Dell International LLC/EMC Corp. 200,000 5.25% 1/2/2028 199,599 |
|
0.01% Newmont Corp. 243,000 2.25% 1/10/2030 199,544 0.01% Caterpillar Financial |
|
Services Corp. 200,000 4.80% 6/1/2026 199,539 0.01% Nucor Corp. 210,000 3.95% |
|
1/5/2028 199,494 0.01% Pfizer, Inc. 211,000 2.75% 3/6/2026 199,437 0.01% HSBC |
|
USA, Inc. 200,000 5.63% 17/3/2025 199,421 0.01% Equinix, Inc. 223,000 1.45% 15/5/2026 |
|
199,361 0.01% UnitedHealth Group, Inc. 200,000 5.00% 15/10/2024 199,331 0.01% |
|
Altria Group, Inc. 255,000 4.25% 9/8/2042 199,273 0.01% JPMorgan Chase & Co. 210,000 |
|
4.85% 1/2/2044 199,236 0.01% Fifth Third Bancorp 200,000 6.36% 27/10/2028 199,177 |
|
0.01% Corebridge Financial, Inc. 209,000 3.50% 4/4/2025 199,170 0.01% Energy Transfer |
|
LP 200,000 5.50% 1/6/2027 199,121 0.01% Apple, Inc. 200,000 4.42% 8/5/2026 199,094 |
|
0.01% Walt Disney Co. 220,000 2.20% 13/1/2028 199,079 0.01% Georgia-Pacific LLC |
|
225,000 0.95% 15/5/2026 199,066 0.01% Philip Morris International, Inc. 200,000 |
|
5.00% 17/11/2025 199,057 0.01% Foundry JV Holdco LLC 200,000 5.88% 25/1/2034 198,995 |
|
0.01% Apple, Inc. 200,000 4.30% 10/5/2033 198,990 0.01% John Deere Capital Corp. |
|
200,000 4.70% 10/6/2030 198,715 0.01% Crown Castle, Inc. 205,000 3.20% 1/9/2024 |
|
198,654 0.01% Nestle Holdings, Inc. |
|
- source_sentence: Calculate the total value of bonds issued by companies with a coupon |
|
rate of 1.50% and discuss the potential attractiveness of these bonds to investors. |
|
sentences: |
|
- '774 Vanguard USD Corporate Bond UCITS ETF Principal US Dollars ($) CouponMaturity |
|
DateFair Value US Dollars ($)% of Total Net Assets TotalEnergies Capital International |
|
SA 150,000 2.99% 29/6/2041 114,035 0.01% Banque Federative du Credit Mutuel SA |
|
100,000 4.52% 13/7/2025 97,283 0.01% TotalEnergies Capital International SA 125,000 |
|
3.39% 29/6/2060 91,711 0.01% BPCE SA 135,000 3.58% 19/10/2042 89,713 0.01% Air |
|
Liquide Finance SA 115,000 3.50% 27/9/2046 88,618 0.01% WEA Finance LLC/Westfield |
|
UK & Europe Finance plc 110,000 4.75% 17/9/2044 77,253 0.00% Pernod Ricard SA |
|
75,000 3.25% 8/6/2026 72,024 0.00% Societe Generale SA 75,000 1.38% 8/7/2025 68,268 |
|
0.00% Societe Generale SA 75,000 5.63% 24/11/2045 60,538 0.00% BPCE SA 75,000 |
|
3.65% 14/1/2037 59,575 0.00% Legrand France SA 50,000 8.50% 15/2/2025 52,327 0.00% |
|
Societe Generale SA 50,000 3.65% 8/7/2035 39,945 0.00% Lafarge SA 35,000 7.13% |
|
15/7/2036 38,180 0.00% - - - - 27,649,631 1.69% Germany 1.17% (30 June 2022: 1.46%) |
|
Deutsche Telekom International Finance BV 760,000 8.75% 15/6/2030 910,393 0.06% |
|
Bayer US Finance II LLC 715,000 4.38% 15/12/2028 678,134 0.04% Bayer US Finance |
|
II LLC 550,000 4.25% 15/12/2025 529,590 0.03% Mercedes-Benz Finance North America |
|
LLC 325,000 8.50% 18/1/2031 403,840 0.03% Volkswagen Group of America Finance |
|
LLC 410,000 4.75% 13/11/2028 396,008 0.02% Deutsche Bank AG 450,000 2.31% 16/11/2027 |
|
387,091 0.02% Deutsche Bank AG 401,000 3.96% 26/11/2025 382,263 0.02% Deutsche |
|
Bank AG 430,000 2.13% 24/11/2026 381,773 0.02% Volkswagen Group of America Finance |
|
LLC 410,000 1.25% 24/11/2025 369,307 0.02% EMD Finance LLC 360,000 3.25% 19/3/2025 |
|
345,003 0.02% BMW US Capital LLC 350,000 3.90% 9/4/2025 341,419 0.02% Deutsche |
|
Bank AG 405,000 3.55% 18/9/2031 336,477 0.02% Deutsche Bank AG 335,000 6.72% 18/1/2029 |
|
336,195 0.02% BMW US Capital LLC 350,000 4.15% 9/4/2030 334,517 0.02% Siemens |
|
Financieringsmaatschappij NV 350,000 2.35% 15/10/2026 322,332 0.02% Siemens Financieringsmaatschappij |
|
NV 350,000 4.' |
|
- '300,000 0.50% 15/9/2031 219,934 0.01% Nestle Finance International Ltd. 320,000 |
|
0.88% 14/6/2041 214,208 0.01% Raiffeisen Schweiz Genossenschaft 200,000 4.84% |
|
3/11/2028 200,305 0.01% Holcim Finance Luxembourg SA 250,000 0.63% 6/4/2030 199,592 |
|
0.01% Holcim Finance Luxembourg SA 200,000 1.50% 6/4/2025 190,741 0.01% Givaudan |
|
Finance Europe BV 200,000 1.00% 22/4/2027 180,794 0.01% Argentum Netherlands BV |
|
for Zurich Insurance Co., Ltd. 200,000 2.75% 19/2/2049 174,999 0.01% Sika Capital |
|
BV 200,000 1.50% 29/4/2031 169,245 0.01% Givaudan Finance Europe BV 200,000 1.63% |
|
22/4/2032 167,542 0.01% Tyco Electronics Group SA 200,000 0.00% 16/2/2029 163,007 |
|
0.01% ABB Finance BV 200,000 0.00% 19/1/2030 158,826 0.01% Nestle Finance International |
|
Ltd. 200,000 0.63% 14/2/2034 152,119 0.01% ELM BV for Swiss Life Insurance & Pension |
|
Group 160,000 4.50% Perpetual 152,096 0.01% Novartis Finance SA 100,000 1.63% |
|
9/11/2026 93,717 0.01% Holcim Finance Luxembourg SA 100,000 0.13% 19/7/2027 87,370 |
|
0.01% Adecco International Financial Services BV 100,000 1.00% 21/3/2082 76,895 |
|
0.00% Zurich Finance Ireland Designated Activity Co. 100,000 1.63% 17/6/2039 74,378 |
|
0.00% Nestle Finance International Ltd. 100,000 0.00% 3/3/2033 73,483 0.00% Nestle |
|
Finance International Ltd. 100,000 0.38% 3/12/2040 61,797 0.00% - - - - 54,507,790 |
|
3.47% United Kingdom 8.27% (30 June 2022: 9.16%) BP Capital Markets plc 2,100,000 |
|
3.25% Perpetual 1,935,372 0.12% HSBC Holdings plc 2,060,000 0.31% 13/11/2026 1,863,645 |
|
0.12%' |
|
- 'PART II Item 8 68 (In millions) Fair Value Level Adjusted Cost Basis Unrealized |
|
Gains Unrealized Losses Recorded Basis Cash and Cash Equivalents Short -term Investments |
|
Equity Investments June 30, 2022 Changes in Fair Value Recorded in Other Comprehensive |
|
Income Commercial paper Level 2 $ 2,500 $ 0 $ 0 $ 2,500 $ 2,498 $ 2 $ 0 Certificates |
|
of deposit Level 2 2,071 0 0 2,071 2,032 39 0 U.S. government securities Level |
|
1 79,696 29 (2,178 ) 77,547 9 77,538 0 U.S. agency securities Level 2 419 0 (9 |
|
) 410 0 410 0 Foreign government bonds Level 2 506 0 (24 ) 482 0 482 0 Mortgage |
|
- and asset -backed securities Level 2 727 1 (30 ) 698 0 698 0 Corporate notes |
|
and bonds Level 2 11,661 4 (554 ) 11,111 0 11,111 0 Corporate notes and bonds |
|
Level 3 67 0 0 67 0 67 0 Municipal securities Level 2 368 19 (13 ) 374 0 374 0 |
|
Municipal securities Level 3 103 0 (6 ) 97 0 97 0 Total debt investments $ 98,118 |
|
$ 53 $ (2,814 ) $ 95,357 $ 4,539 $ 90,818 $ 0 Changes in Fair Value Recorded in |
|
Net Income Equity investments Level 1 $ 1,590 $ 1,134 $ 0 $ 456 Equity investments |
|
Other 6,435 0 0 6,435 Total equity investments $ 8,025 $ 1,134 $ 0 $ 6,891 Cash |
|
$ 8,258 $ 8,258 $ 0 $ 0 Derivatives, net (a) 8 0 8 0 Total $ 111,648 $ 13,931 |
|
$ 90,826 $ 6,891 (a) Refer to Note 5 – Derivatives for further information on |
|
the fair value of our derivative instruments. Equity investments presented as |
|
“Other” in the tables above include investments without readily determinable fair |
|
values measured using the equity method or measured at cost with adjustments for |
|
observable changes in price or impairments, and investments m easured at fair |
|
value using net asset value as a practical expedient which are not categorized |
|
in the fair value hierarchy. As of June 30, 2023 and 2022 , equity investments |
|
without readily determinable fair values measured at cost with adjustments for |
|
obse rvable changes in price or impairments were $ 4.2 billion and $3.8 billion |
|
, respectively. Unrealized Losses on Debt Investments Debt investments with continuous |
|
unrealized losses for less than 12 months and 12 months or greater and their related |
|
fair values were as follows: Less than 12 Months 12 Months or Greater Total Unrealized |
|
Losses (In millions) Fair Value Unrealized Losses Fair Value Unrealized Losses |
|
Total Fair Value June 30 , 2023 U.S.' |
|
- source_sentence: Identify the bond with the longest maturity date from the provided |
|
list and state its issuer and interest rate. |
|
sentences: |
|
- Liquidity Risk. Liquidity risk is the risk that sufficient cash cannot be raised |
|
to meet liabilities when due. One of the key liquidity factors influencing the |
|
Company and the Funds is exposure to cash redemptions of redeemable participating |
|
shares. Hence the Company, through the Funds, invests the large majority of its |
|
assets in investments that are traded in active markets and can ordinarily be |
|
readily disposed. However, liquidity risk will occur if an issuer becomes credit-impaired |
|
or if the relevant market becomes illiquid. In such a case, it may not be possible |
|
to initiate or liquidate a position at a price deemed by the Investment Manager |
|
to be demonstrating fair value. Liquidity risk may be temporary or may last for |
|
extended periods. The Company, through the Funds, invests in securities that form |
|
part of the benchmark indices. Benchmark indices are constructed from index rules |
|
requiring securities to have a specified minimum trading volume, which, although |
|
not guaranteeing liquidity, provides indication of the liquid nature of the securities |
|
underlying the Funds. The Funds are exposed to withdrawals and contributions that |
|
are invested to ensure that exposure to the benchmark indices is maintained to |
|
meet the investment objective of the Funds. All the Funds ’ financial liabilities, |
|
based on contractual maturities, fall due within three months. Additionally, the |
|
Funds may use index futures contracts to a limited extent, to maintain full exposure |
|
to the index, maintain liquidity and minimise transaction costs. Funds may purchase |
|
futures contracts to immediately invest incoming cash in the market, or sell futures |
|
in response to cash outflows, thereby simulating a fully invested position in |
|
the underlying index while maintaining a cash balance for liquidity. |
|
- $100,000 2.20% 17/6/2025 94,044 0.00% Wells Fargo & Co. £100,000 2.13% 24/9/2031 |
|
94,000 0.00% New York Life Global Funding $100,000 1.45% 14/1/2025 93,999 0.00% |
|
PACCAR Financial Corp. $100,000 0.90% 8/11/2024 93,978 0.00% Revvity, Inc. $105,000 |
|
3.30% 15/9/2029 93,910 0.00% Freeport-McMoRan, Inc. $100,000 4.13% 1/3/2028 93,897 |
|
0.00% Wells Fargo Commercial Mortgage Trust 2018 $100,000 4.30% 15/1/2052 93,895 |
|
0.00% Altria Group, Inc. €100,000 3.13% 15/6/2031 93,889 0.00% New York Life Global |
|
Funding $100,000 0.90% 29/10/2024 93,860 0.00% JPMorgan Chase & Co. $100,000 2.95% |
|
1/10/2026 93,859 0.00% Exelon Corp. $100,000 4.05% 15/4/2030 93,734 0.00% Wells |
|
Fargo & Co. $100,000 2.19% 30/4/2026 93,726 0.00% Morgan Stanley $100,000 3.13% |
|
27/7/2026 93,704 0.00% Southwest Airlines Co. $110,000 2.63% 10/2/2030 93,681 |
|
0.00% JPMorgan Chase & Co. $100,000 1.56% 10/12/2025 93,650 0.00% Equitable Financial |
|
Life Global Funding $100,000 1.10% 12/11/2024 93,632 0.00% Walt Disney Co. $110,000 |
|
2.00% 1/9/2029 93,622 0.00% Citigroup, Inc. $100,000 3.20% 21/10/2026 93,617 0.00% |
|
PayPal Holdings, Inc. $100,000 1.65% 1/6/2025 93,598 0.00% Brixmor Operating Partnership |
|
LP $100,000 4.13% 15/6/2026 93,553 0.00% Public Service Electric & Gas Co. $100,000 |
|
3.00% 15/5/2027 93,549 0.00% High Street Funding Trust I $100,000 4.11% 15/2/2028 |
|
93,547 0.00% Invesco Finance plc $96,000 5.38% 30/11/2043 93,545 0.00% Philip |
|
Morris International, Inc. $100,000 1.50% 1/5/2025 93,534 0.00% VICI Properties |
|
LP $100,000 5.13% 15/5/2032 93,481 0.00% Citigroup Commercial Mortgage Trust 2018 |
|
$100,000 4.23% 10/6/2051 93,422 0.00% Air Products & Chemicals, Inc. €100,000 |
|
0.50% 5/5/2028 93,418 0.00% Duke Energy Progress LLC $150,000 2.50% 15/8/2050 |
|
93,415 0.00% General Motors Co. $100,000 5.95% 1/4/2049 93,381 0.00% Verizon Communications, |
|
Inc. |
|
- $65,000 5.00% 15/7/2032 64,094 0.00% Fifth Third Bancorp $75,000 1.71% 1/11/2027 |
|
64,024 0.00% Devon Energy Corp. $64,000 5.88% 15/6/2028 63,783 0.00% Pacific Gas |
|
& Electric Co. $100,000 3.50% 1/8/2050 63,779 0.00% Medtronic Global Holdings |
|
SCA $65,000 4.50% 30/3/2033 63,708 0.00% Franklin Resources, Inc. $100,000 2.95% |
|
12/8/2051 63,638 0.00% AT&T, Inc. $75,000 4.55% 9/3/2049 63,608 0.00% Enterprise |
|
Products Operating LLC $75,000 4.25% 15/2/2048 63,582 0.00% Morgan Stanley Bank |
|
of America Merrill Lynch Trust 2016 $67,746 3.27% 15/1/2049 63,553 0.00% Visa, |
|
Inc. $75,000 3.65% 15/9/2047 63,523 0.00% New York City Municipal Water Finance |
|
Authority $60,000 5.44% 15/6/2043 63,518 0.00% Willis-Knighton Medical Center |
|
$100,000 3.07% 1/3/2051 63,500 0.00% Charter Communications Operating LLC/Charter |
|
Communications Operating Capital $100,000 3.70% 1/4/2051 63,461 0.00% Johnson |
|
& Johnson $60,000 4.95% 15/5/2033 63,221 0.00% Federal Home Loan Mortgage Corp. |
|
$75,000 2.07% 25/1/2031 63,215 0.00% CVS Health Corp. $75,000 4.13% 1/4/2040 63,134 |
|
0.00% United States Treasury Note $68,000 1.88% 31/7/2026 62,958 0.00% Enterprise |
|
Products Operating LLC $75,000 4.20% 31/1/2050 62,879 0.00% Commonwealth Edison |
|
Co. $75,000 4.00% 1/3/2048 62,849 0.00% Uniform Mortgage Backed Securities $68,505 |
|
2.50% 1/6/2034 62,837 0.00% Federal Home Loan Mortgage Corp. $75,000 2.02% 25/3/2031 |
|
62,832 0.00% Brighthouse Financial, Inc. $65,000 5.63% 15/5/2030 62,820 0.00% |
|
Eli Lilly & Co. €100,000 1.13% 14/9/2051 62,721 0.00% Stryker Corp. $75,000 1.95% |
|
15/6/2030 62,690 0.00% Cox Communications, Inc. $100,000 2.95% 1/10/2050 62,543 |
|
0.00% |
|
- source_sentence: Which issuer has the highest number of bonds listed in the context |
|
information, and what are the details of those bonds? |
|
sentences: |
|
- 517 Vanguard EUR Corporate Bond UCITS ETF Principal EUR (€) CouponMaturity DateFair |
|
Value EUR (€)% of Total Net Assets Deutsche Telekom AG 350,000 1.75% 25/3/2031 |
|
312,584 0.02% Volkswagen International Finance NV 400,000 1.25% 23/9/2032 309,383 |
|
0.02% Covestro AG 300,000 4.75% 15/11/2028 305,772 0.02% Hamburg Commercial Bank |
|
AG 300,000 4.88% 17/3/2025 298,161 0.02% MTU Aero Engines AG 300,000 3.00% 1/7/2025 |
|
296,850 0.02% Adidas AG 300,000 3.00% 21/11/2025 294,853 0.02% Knorr-Bremse AG |
|
300,000 3.25% 21/9/2027 294,564 0.02% Eurogrid GmbH 300,000 3.28% 5/9/2031 290,932 |
|
0.02% Volkswagen Financial Services AG 300,000 1.50% 1/10/2024 290,663 0.02% Henkel |
|
AG & Co. KGaA 300,000 2.63% 13/9/2027 290,397 0.02% Muenchener Hypothekenbank |
|
eG 300,000 0.88% 11/7/2024 289,863 0.02% Conti-Gummi Finance BV 300,000 1.13% |
|
25/9/2024 289,074 0.02% Volkswagen Leasing GmbH 300,000 0.00% 19/7/2024 287,414 |
|
0.02% HOCHTIEF AG 300,000 1.75% 3/7/2025 286,772 0.02% Santander Consumer Bank |
|
AG 300,000 0.25% 15/10/2024 285,058 0.02% Merck KGaA 300,000 1.63% 25/6/2079 284,407 |
|
0.02% Vonovia Finance BV 400,000 2.75% 22/3/2038 283,910 0.02% E.ON SE 300,000 |
|
1.00% 7/10/2025 282,765 0.02% Heraeus Finance GmbH 300,000 2.63% 9/6/2027 281,328 |
|
0.02% LEG Immobilien SE 400,000 1.00% 19/11/2032 278,523 0.02% Covestro AG 300,000 |
|
0.88% 3/2/2026 278,048 0.02% Deutsche Post AG 279,000 2.88% 11/12/2024 276,566 |
|
0.02% Fresenius Medical Care AG & Co. |
|
- This Annual Report on Form 10-K (“Form 10-K”) contains forward-looking statements, |
|
within the meaning of the Private Securities Litigation Reform Act of 1995, that |
|
involve risks and uncertainties. Many of the forward-looking statements are located |
|
in Part I, Item 1 of this Form 10-K under the heading “Business” and Part II, |
|
Item 7 of this Form 10-K under the heading “Management’s Discussion and Analysis |
|
of Financial Condition and Results of Operations.” Forward-looking statements |
|
provide current expectations of future events based on certain assumptions and |
|
include any statement that does not directly relate to any historical or current |
|
fact. For example, statements in this Form 10-K regarding the potential future |
|
impact of macroeconomic conditions on the Company’s business and results of operations |
|
are forward-looking statements. Forward- looking statements can also be identified |
|
by words such as “future,” “anticipates,” “believes,” “estimates,” “expects,” |
|
“intends,” “plans,” “predicts,” “will,” “would,” “could,” “can,” “may,” and similar |
|
terms. Forward-looking statements are not guarantees of future performance and |
|
the Company’s actual results may differ significantly from the results discussed |
|
in the forward-looking statements. Factors that might cause such differences include, |
|
but are not limited to, those discussed in Part I, Item 1A of this Form 10-K under |
|
the heading “Risk Factors.” The Company assumes no obligation to revise or update |
|
any forward-looking statements for any reason, except as required by law. Unless |
|
otherwise stated, all information presented herein is based on the Company’s fiscal |
|
calendar, and references to particular years, quarters, months or periods refer |
|
to the Company’s fiscal years ended in September and the associated quarters, |
|
months and periods of those fiscal years. Each of the terms the “Company” and |
|
“Apple” as used herein refers collectively to Apple Inc. and its wholly owned |
|
subsidiaries, unless otherwise stated. PART I Item 1. Business Company Background |
|
The Company designs, manufactures and markets smartphones, personal computers, |
|
tablets, wearables and accessories, and sells a variety of related services. The |
|
Company’s fiscal year is the 52- or 53-week period that ends on the last Saturday |
|
of September. Products iPhone iPhone® is the Company’s line of smartphones based |
|
on its iOS operating system. The iPhone line includes iPhone 15 Pro, iPhone 15, |
|
iPhone 14, iPhone 13 and iPhone SE®. Mac Mac® is the Company’s line of personal |
|
computers based on its macOS® operating system. The Mac line includes laptops |
|
MacBook Air® and MacBook Pro®, as well as desktops iMac®, Mac mini®, Mac Studio® |
|
and Mac Pro®. iPad iPad® is the Company’s line of multipurpose tablets based on |
|
its iPadOS® operating system. The iPad line includes iPad Pro®, iPad Air®, iPad |
|
and iPad mini®. Wearables, Home and Accessories Wearables includes smartwatches |
|
and wireless headphones. The Company’s line of smartwatches, based on its watchOS® |
|
operating system, includes Apple Watch Ultra™ 2, Apple Watch® Series 9 and Apple |
|
Watch SE®. The Company’s line of wireless headphones includes AirPods®, AirPods |
|
Pro®, AirPods Max™ and Beats® products. Home includes Apple TV®, the Company’s |
|
media streaming and gaming device based on its tvOS® operating system, and HomePod® |
|
and HomePod mini®, high-fidelity wireless smart speakers. Accessories includes |
|
Apple-branded and third-party accessories. Apple Inc. | 2023 Form 10-K | 1 |
|
- Refer to “Note 2 Accounting for the acquisition of the Credit Suisse Group” in |
|
the “Cons olidated financial statements” section of the UBS Group Annual Report |
|
2023, available under “Annual reporting” at ubs.com/investors, for more information. |
|
2 Refer to the “Share information and earnings per share” section of the UBS Group |
|
first quarter 2024 report, available under “Quarterly reporting” at ubs.com/investors, |
|
for more information. 3 Refer to the “Targets, capital guidance and ambitions” |
|
section of the UBS Group Annual Report 2023, available under “Annual reporting” |
|
at ubs.com/investors, for more information about our performance targets. 4 Refer |
|
to “Alternative performance measures” in the appendix to the UBS Group first quarter |
|
2024 report, available under “Quarterly reporting” at ubs.com/investors, for the |
|
definition and calculation method. 5 Profit or loss information for each of the |
|
first quarter of 2024 and the fourth quarter of 2023 is presented on a consolidated |
|
basis, including for each quarter Credit Suisse data for three months and for |
|
the purpose of the calculatio n of return measures has been annualized multiplying |
|
such by four. Profit or loss information for the first quarter of 2023 includes |
|
pre-acquisition UBS data for three months and for the purpose of the calculation |
|
of return measures has been annualized multiplying such by four. 6 Refer to the |
|
“Group performance” section of the UBS Group first quarter 2024 report, available |
|
under “Quarterly reporting” at ubs.com/investors, for more information about underlying |
|
results. 7 The effective tax rate for the fourth quarter of 2023 is not a meaningful |
|
measure, due to the distortive effect of current unbenefited tax losses at the |
|
form er Credit Suisse entities. 8 Based on the Swiss systemically relevant bank |
|
framework as of 1 January 2020. Refer to the “Capital management” section of the |
|
UBS Group first quarter 2024 report, available under “Quarterly reporting” at |
|
ubs.com/inv estors, for more information. 9 The disclosed ratios represent quarterly |
|
averages for the quarters presented and are calculated based on an average of |
|
61 data points in the first quarter of 2024, 63 data points in the fourth quarter |
|
of 2023 and 64 data points i n the first quarter of 2023. Refer to the “Liquidity |
|
and funding management” section of the UBS Group first quarter 2024 repo rt, available |
|
under “Quarterly reporting” at ubs.com/investors, for more information. 10 Consists |
|
of invested assets for Global Wealth Management, Asset Management and Personal |
|
& Corporate Banking. Refer to “Note 32 Invested assets and net new money” in the |
|
“Consolidated financial statements” section of the UBS Group Annual Report 2023, |
|
available under “Annual reporting” at ubs.co m/investors, for more information. |
|
11 Starting with the second quarter of 2023, invested assets include invested |
|
assets from associates in the Asset Management business division, to better reflect |
|
the business strategy. Comparative figures have been rest ated to reflect this |
|
change. 12 In the second quarter of 2023, the calculation of market capitalization |
|
was amended to reflect total shares issued multiplied by the share price at the |
|
end of the period. The calculation was previously based on total shar es outstanding |
|
multiplied by the share price at the end of the period. Market capitalization |
|
was increased by USD 10.0bn as of 31 March 2023 as a result. |
|
- source_sentence: What factors contributed to the negative performance of global |
|
bonds over the 12-month period? |
|
sentences: |
|
- Vanguard Global Aggregate Bond UCITS ETF Managed by Vanguard Global Advisers, |
|
LLC.561 Investment Objective Vanguard Global Aggregate Bond UCITS ETF seeks to |
|
track the performance of the Bloomberg Global Aggregate Float Adjusted and Scaled |
|
Index, a widely recognised benchmark designed to reflect the characteristics of |
|
the global aggregate bond universe. Performance Summary (unaudited) The Performance |
|
Summary does not form part of the Financial Statements. • Inflation and policymakers’ |
|
efforts to rein it in took centre stage for the financial markets during much |
|
of the 12 months ended 30 June 2023. • Early in the period, energy prices continued |
|
to cool amid an outlook for slower economic growth, but price increases then broadened |
|
to other categories, notably the services sector, which felt the effects of tight |
|
labour markets. Central banks including the US Federal Reserve, the European Central |
|
Bank and the Bank of England reacted to the prospect of inflation remaining stubbornly |
|
high by aggressively hiking interest rates even as their actions fanned fears |
|
of a global recession down the road. • Although progress was slow, signs of inflation |
|
moderating later in the period led several major central banks to slow the pace |
|
of their interest rate hikes or even hit the pause button. • Bonds suffered early |
|
in the fiscal year amid aggressive rate hiking and later when markets began to |
|
anticipate that rates would remain higher for longer. With rising yields pushing |
|
prices down, global bonds ended the 12 months in negative territory. • In this |
|
environment, the ETF ’s benchmark index returned –0.09% for the fiscal year. • |
|
By country, the United States, the largest constituent in the index by far, underperformed |
|
the index, returning –0.94%. Belgium, the UK and the Netherlands were also among |
|
the laggards. Canada, Japan, China and South Korea outperformed and were in positive |
|
territory. • By sector, corporate and non-corporate bonds fared better than government |
|
bonds and US mortgage-backed securities. • By credit quality, bonds on the bottom |
|
rung of the investment-grade ladder tended to perform better than higher-rated |
|
bonds. By maturity, longer-dated bonds lagged. Benchmark returns in the commentary |
|
above are in US dollars. |
|
- $25,000 4.55% 1/3/2029 23,455 0.00% Eaton Corp. $25,000 3.10% 15/9/2027 23,451 |
|
0.00% Penske Truck Leasing Co. LP/PTL Finance Corp. $25,000 4.20% 1/4/2027 23,448 |
|
0.00% Tri-State Generation & Transmission Association, Inc. $25,000 6.00% 15/6/2040 |
|
23,446 0.00% AEP Transmission Co. LLC $25,000 3.10% 1/12/2026 23,440 0.00% Target |
|
Corp. $25,000 3.38% 15/4/2029 23,439 0.00% JM Smucker Co. $25,000 3.38% 15/12/2027 |
|
23,419 0.00% AmerisourceBergen Corp. $25,000 3.45% 15/12/2027 23,403 0.00% Lowe's |
|
Cos, Inc. $25,000 2.50% 15/4/2026 23,399 0.00% McCormick & Co., Inc. $25,000 3.40% |
|
15/8/2027 23,399 0.00% Wells Fargo Commercial Mortgage Trust 2018 $25,000 4.18% |
|
15/6/2051 23,399 0.00% Raytheon Technologies Corp. $25,000 3.13% 4/5/2027 23,398 |
|
0.00% Wells Fargo Commercial Mortgage Trust 2018 $25,000 4.15% 15/8/2051 23,398 |
|
0.00% Masco Corp. $25,000 3.50% 15/11/2027 23,394 0.00% Southern Power Co. $25,000 |
|
5.15% 15/9/2041 23,379 0.00% Kirby Corp. $25,000 4.20% 1/3/2028 23,378 0.00% UBS |
|
Commercial Mortgage Trust 2018 $25,000 4.34% 15/12/2051 23,376 0.00% UDR, Inc. |
|
$25,000 3.50% 1/7/2027 23,375 0.00% Citigroup Commercial Mortgage Trust 2016 $25,000 |
|
3.35% 10/2/2049 23,368 0.00% BANK 2018 $25,000 4.05% 15/3/2061 23,365 0.00% Northwestern |
|
Mutual Life Insurance Co. $30,000 3.85% 30/9/2047 23,364 0.00% Southern California |
|
Edison Co. $25,000 3.65% 1/3/2028 23,357 0.00% Citigroup Commercial Mortgage Trust |
|
2016 $25,000 3.62% 10/2/2049 23,344 0.00% Morgan Stanley Capital I Trust 2016 |
|
$24,970 3.33% 15/3/2049 23,332 0.00% Verizon Communications, Inc. $25,000 4.02% |
|
3/12/2029 23,321 0.00% Raytheon Technologies Corp. $25,000 4.80% 15/12/2043 23,289 |
|
0.00% Georgia Power Co. $25,000 3.25% 30/3/2027 23,286 0.00% Bio-Rad Laboratories, |
|
Inc. $25,000 3.30% 15/3/2027 23,269 0.00% FedEx Corp. |
|
- 'Note 26. Credit Concentrations The firm’s concentrations of credit risk arise |
|
from its market- making, client facilitation, investing, underwriting, lending |
|
and collateralized transactions, and cash management activities, and may be impacted |
|
by changes in economic, industry or political factors. These activities expose |
|
the firm to many different industries and counterparties, and may also subject |
|
the firm to a concentration of credit risk to a particular central bank, counterparty, |
|
borrower or issuer, including sovereign issuers, or to a particular clearinghouse |
|
or exchange. The firm seeks to mitigate credit risk by actively monitoring exposures |
|
and obtaining collateral from counterparties as deemed appropriate. The firm measures |
|
and monitors its credit exposure based on amounts owed to the firm after taking |
|
into account risk mitigants that the firm considers when determining credit risk. |
|
Such risk mitigants include netting and collateral arrangements and economic hedges, |
|
such as credit derivatives, futures and forward contracts. Netting and collateral |
|
agreements permit the firm to offset receivables and payables with such counterparties |
|
and/or enable the firm to obtain collateral on an upfront or contingent basis. |
|
The table below presents the credit concentrations included in trading cash instruments |
|
and investments. As of December $ in millions 2023 2022 U.S. government and agency |
|
obligations $ 260,531 $ 205,935 Percentage of total assets 15.9% 14.3% Non-U.S. |
|
government and agency obligations $ 90,681 $ 40,334 Percentage of total assets |
|
5.5% 2.8% In addition, the firm had $206.07 billion as of December 2023 and $208.53 |
|
billion as of December 2022 of cash deposits held at central banks (included in |
|
cash and cash equivalents), of which $105.66 billion as of December 2023 and $165.77 |
|
billion as of December 2022 was held at the Federal Reserve. As of both December |
|
2023 and December 2022 , the firm did not have credit exposure to any other counterparty |
|
that exceeded 2% of total assets. Collateral obtained by the firm related to derivative |
|
assets is principally cash and is held by the firm or a third-party custodian. |
|
Collateral obtained by the firm related to resale agreements and securities borrowed |
|
transactions is primarily U.S. government and agency obligations, and non-U.S. |
|
government and agency obligations. See Note 11 for further information about collateralized |
|
agreements and financings. The table below presents U.S. government and agency |
|
obligations, and non-U.S. government and agency obligations that collateralize |
|
resale agreements and securities borrowed transactions. As of December $ in millions |
|
2023 2022 U.S. government and agency obligations $ 154,056 $ 164,897 Non-U.S. |
|
government and agency obligations $ 92,833 $ 76,456 In the table above: •Non-U.S. |
|
government and agency obligations primarily consists of securities issued by the |
|
governments of the U.K., Japan, Germany, France and Italy. •Given that the firm’s |
|
primary credit exposure on such transactions is to the counterparty to the transaction, |
|
the firm would be exposed to the collateral issuer only in the event of counterparty |
|
default. Note 27. Legal Proceedings The firm is involved in a number of judicial, |
|
regulatory and arbitration proceedings (including those described below) concerning |
|
matters arising in connection with the conduct of the firm’s businesses. Many |
|
of these proceedings are in early stages, and many of these cases seek an indeterminate |
|
amount of damages. Under ASC 450, an event is “reasonably possible” if “the chance |
|
of the future event or events occurring is more than remote but less than likely” |
|
and an event is “remote” if “the chance of the future event or events occurring |
|
is slight.” Thus, references to the upper end of the range of reasonably possible |
|
loss for cases in which the firm is able to estimate a range of reasonably possible |
|
loss mean the upper end of the range of loss for cases for which the firm believes |
|
the risk of loss is more than slight. THE GOLDMAN SACHS GROUP, INC. AND SUBSIDIARIES |
|
Notes to Consolidated Financial Statements 216 Goldman Sachs 2023 Form 10-K' |
|
model-index: |
|
- name: SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
results: |
|
- task: |
|
type: information-retrieval |
|
name: Information Retrieval |
|
dataset: |
|
name: Unknown |
|
type: unknown |
|
metrics: |
|
- type: cosine_accuracy@1 |
|
value: 0.4418505942275042 |
|
name: Cosine Accuracy@1 |
|
- type: cosine_accuracy@3 |
|
value: 0.6752971137521222 |
|
name: Cosine Accuracy@3 |
|
- type: cosine_accuracy@5 |
|
value: 0.7625919637804188 |
|
name: Cosine Accuracy@5 |
|
- type: cosine_accuracy@10 |
|
value: 0.8507357102433503 |
|
name: Cosine Accuracy@10 |
|
- type: cosine_precision@1 |
|
value: 0.4418505942275042 |
|
name: Cosine Precision@1 |
|
- type: cosine_precision@3 |
|
value: 0.22509903791737407 |
|
name: Cosine Precision@3 |
|
- type: cosine_precision@5 |
|
value: 0.15251839275608375 |
|
name: Cosine Precision@5 |
|
- type: cosine_precision@10 |
|
value: 0.08507357102433502 |
|
name: Cosine Precision@10 |
|
- type: cosine_recall@1 |
|
value: 0.4418505942275042 |
|
name: Cosine Recall@1 |
|
- type: cosine_recall@3 |
|
value: 0.6752971137521222 |
|
name: Cosine Recall@3 |
|
- type: cosine_recall@5 |
|
value: 0.7625919637804188 |
|
name: Cosine Recall@5 |
|
- type: cosine_recall@10 |
|
value: 0.8507357102433503 |
|
name: Cosine Recall@10 |
|
- type: cosine_ndcg@10 |
|
value: 0.6432422811456076 |
|
name: Cosine Ndcg@10 |
|
- type: cosine_mrr@10 |
|
value: 0.5770132656911126 |
|
name: Cosine Mrr@10 |
|
- type: cosine_map@100 |
|
value: 0.5834691562837875 |
|
name: Cosine Map@100 |
|
- type: dot_accuracy@1 |
|
value: 0.4418505942275042 |
|
name: Dot Accuracy@1 |
|
- type: dot_accuracy@3 |
|
value: 0.6752971137521222 |
|
name: Dot Accuracy@3 |
|
- type: dot_accuracy@5 |
|
value: 0.7625919637804188 |
|
name: Dot Accuracy@5 |
|
- type: dot_accuracy@10 |
|
value: 0.8507357102433503 |
|
name: Dot Accuracy@10 |
|
- type: dot_precision@1 |
|
value: 0.4418505942275042 |
|
name: Dot Precision@1 |
|
- type: dot_precision@3 |
|
value: 0.22509903791737407 |
|
name: Dot Precision@3 |
|
- type: dot_precision@5 |
|
value: 0.15251839275608375 |
|
name: Dot Precision@5 |
|
- type: dot_precision@10 |
|
value: 0.08507357102433502 |
|
name: Dot Precision@10 |
|
- type: dot_recall@1 |
|
value: 0.4418505942275042 |
|
name: Dot Recall@1 |
|
- type: dot_recall@3 |
|
value: 0.6752971137521222 |
|
name: Dot Recall@3 |
|
- type: dot_recall@5 |
|
value: 0.7625919637804188 |
|
name: Dot Recall@5 |
|
- type: dot_recall@10 |
|
value: 0.8507357102433503 |
|
name: Dot Recall@10 |
|
- type: dot_ndcg@10 |
|
value: 0.6432422811456076 |
|
name: Dot Ndcg@10 |
|
- type: dot_mrr@10 |
|
value: 0.5770132656911126 |
|
name: Dot Mrr@10 |
|
- type: dot_map@100 |
|
value: 0.5834691562837875 |
|
name: Dot Map@100 |
|
--- |
|
|
|
# SentenceTransformer based on BAAI/bge-base-en-v1.5 |
|
|
|
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). 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 tokens |
|
- **Similarity Function:** Cosine Similarity |
|
<!-- - **Training Dataset:** Unknown --> |
|
<!-- - **Language:** Unknown --> |
|
<!-- - **License:** Unknown --> |
|
|
|
### 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 |
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# Download from the 🤗 Hub |
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model = SentenceTransformer("sujet-ai/Marsilia-Embedding-EN-base") |
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# Run inference |
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sentences = [ |
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'What factors contributed to the negative performance of global bonds over the 12-month period?', |
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'Vanguard Global Aggregate Bond UCITS ETF Managed by Vanguard Global Advisers, LLC.561 Investment Objective Vanguard Global Aggregate Bond UCITS ETF seeks to track the performance of the Bloomberg Global Aggregate Float Adjusted and Scaled Index, a widely recognised benchmark designed to reflect the characteristics of the global aggregate bond universe. Performance Summary (unaudited) The Performance Summary does not form part of the Financial Statements. • Inflation and policymakers’ efforts to rein it in took centre stage for the financial markets during much of the 12 months ended 30 June 2023. • Early in the period, energy prices continued to cool amid an outlook for slower economic growth, but price increases then broadened to other categories, notably the services sector, which felt the effects of tight labour markets. Central banks including the US Federal Reserve, the European Central Bank and the Bank of England reacted to the prospect of inflation remaining stubbornly high by aggressively hiking interest rates even as their actions fanned fears of a global recession down the road. • Although progress was slow, signs of inflation moderating later in the period led several major central banks to slow the pace of their interest rate hikes or even hit the pause button. • Bonds suffered early in the fiscal year amid aggressive rate hiking and later when markets began to anticipate that rates would remain higher for longer. With rising yields pushing prices down, global bonds ended the 12 months in negative territory. • In this environment, the ETF ’s benchmark index returned –0.09% for the fiscal year. • By country, the United States, the largest constituent in the index by far, underperformed the index, returning –0.94%. Belgium, the UK and the Netherlands were also among the laggards. Canada, Japan, China and South Korea outperformed and were in positive territory. • By sector, corporate and non-corporate bonds fared better than government bonds and US mortgage-backed securities. • By credit quality, bonds on the bottom rung of the investment-grade ladder tended to perform better than higher-rated bonds. By maturity, longer-dated bonds lagged. Benchmark returns in the commentary above are in US dollars.', |
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'Note 26. Credit Concentrations The firm’s concentrations of credit risk arise from its market- making, client facilitation, investing, underwriting, lending and collateralized transactions, and cash management activities, and may be impacted by changes in economic, industry or political factors. These activities expose the firm to many different industries and counterparties, and may also subject the firm to a concentration of credit risk to a particular central bank, counterparty, borrower or issuer, including sovereign issuers, or to a particular clearinghouse or exchange. The firm seeks to mitigate credit risk by actively monitoring exposures and obtaining collateral from counterparties as deemed appropriate. The firm measures and monitors its credit exposure based on amounts owed to the firm after taking into account risk mitigants that the firm considers when determining credit risk. Such risk mitigants include netting and collateral arrangements and economic hedges, such as credit derivatives, futures and forward contracts. Netting and collateral agreements permit the firm to offset receivables and payables with such counterparties and/or enable the firm to obtain collateral on an upfront or contingent basis. The table below presents the credit concentrations included in trading cash instruments and investments. As of December $ in millions 2023 2022 U.S. government and agency obligations $ 260,531 $ 205,935 Percentage of total assets 15.9% 14.3% Non-U.S. government and agency obligations $ 90,681 $ 40,334 Percentage of total assets 5.5% 2.8% In addition, the firm had $206.07 billion as of December 2023 and $208.53 billion as of December 2022 of cash deposits held at central banks (included in cash and cash equivalents), of which $105.66 billion as of December 2023 and $165.77 billion as of December 2022 was held at the Federal Reserve. As of both December 2023 and December 2022 , the firm did not have credit exposure to any other counterparty that exceeded 2% of total assets. Collateral obtained by the firm related to derivative assets is principally cash and is held by the firm or a third-party custodian. Collateral obtained by the firm related to resale agreements and securities borrowed transactions is primarily U.S. government and agency obligations, and non-U.S. government and agency obligations. See Note 11 for further information about collateralized agreements and financings. The table below presents U.S. government and agency obligations, and non-U.S. government and agency obligations that collateralize resale agreements and securities borrowed transactions. As of December $ in millions 2023 2022 U.S. government and agency obligations $ 154,056 $ 164,897 Non-U.S. government and agency obligations $ 92,833 $ 76,456 In the table above: •Non-U.S. government and agency obligations primarily consists of securities issued by the governments of the U.K., Japan, Germany, France and Italy. •Given that the firm’s primary credit exposure on such transactions is to the counterparty to the transaction, the firm would be exposed to the collateral issuer only in the event of counterparty default. Note 27. Legal Proceedings The firm is involved in a number of judicial, regulatory and arbitration proceedings (including those described below) concerning matters arising in connection with the conduct of the firm’s businesses. Many of these proceedings are in early stages, and many of these cases seek an indeterminate amount of damages. Under ASC 450, an event is “reasonably possible” if “the chance of the future event or events occurring is more than remote but less than likely” and an event is “remote” if “the chance of the future event or events occurring is slight.” Thus, references to the upper end of the range of reasonably possible loss for cases in which the firm is able to estimate a range of reasonably possible loss mean the upper end of the range of loss for cases for which the firm believes the risk of loss is more than slight. THE GOLDMAN SACHS GROUP, INC. AND SUBSIDIARIES Notes to Consolidated Financial Statements 216 Goldman Sachs 2023 Form 10-K', |
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] |
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embeddings = model.encode(sentences) |
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print(embeddings.shape) |
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# [3, 768] |
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# Get the similarity scores for the embeddings |
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similarities = model.similarity(embeddings, embeddings) |
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print(similarities.shape) |
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# [3, 3] |
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``` |
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<!-- |
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### Direct Usage (Transformers) |
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<details><summary>Click to see the direct usage in Transformers</summary> |
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</details> |
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--> |
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<!-- |
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### Downstream Usage (Sentence Transformers) |
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You can finetune this model on your own dataset. |
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<details><summary>Click to expand</summary> |
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</details> |
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--> |
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<!-- |
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### Out-of-Scope Use |
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*List how the model may foreseeably be misused and address what users ought not to do with the model.* |
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--> |
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## Evaluation |
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### Metrics |
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#### Information Retrieval |
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* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator) |
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| Metric | Value | |
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|:--------------------|:-----------| |
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| cosine_accuracy@1 | 0.4419 | |
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| cosine_accuracy@3 | 0.6753 | |
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| cosine_accuracy@5 | 0.7626 | |
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| cosine_accuracy@10 | 0.8507 | |
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| cosine_precision@1 | 0.4419 | |
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| cosine_precision@3 | 0.2251 | |
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| cosine_precision@5 | 0.1525 | |
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| cosine_precision@10 | 0.0851 | |
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| cosine_recall@1 | 0.4419 | |
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| cosine_recall@3 | 0.6753 | |
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| cosine_recall@5 | 0.7626 | |
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| cosine_recall@10 | 0.8507 | |
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| cosine_ndcg@10 | 0.6432 | |
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| cosine_mrr@10 | 0.577 | |
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| **cosine_map@100** | **0.5835** | |
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| dot_accuracy@1 | 0.4419 | |
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| dot_accuracy@3 | 0.6753 | |
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| dot_accuracy@5 | 0.7626 | |
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| dot_accuracy@10 | 0.8507 | |
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| dot_precision@1 | 0.4419 | |
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| dot_precision@3 | 0.2251 | |
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| dot_precision@5 | 0.1525 | |
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| dot_precision@10 | 0.0851 | |
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| dot_recall@1 | 0.4419 | |
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| dot_recall@3 | 0.6753 | |
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| dot_recall@5 | 0.7626 | |
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| dot_recall@10 | 0.8507 | |
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| dot_ndcg@10 | 0.6432 | |
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| dot_mrr@10 | 0.577 | |
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| dot_map@100 | 0.5835 | |
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<!-- |
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## Bias, Risks and Limitations |
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*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.* |
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<!-- |
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### Recommendations |
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.* |
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--> |
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## Training Details |
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### Training Dataset |
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#### Unnamed Dataset |
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* Size: 98,400 training samples |
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* Columns: <code>sentence_0</code> and <code>sentence_1</code> |
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* Approximate statistics based on the first 1000 samples: |
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| | sentence_0 | sentence_1 | |
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|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| |
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| type | string | string | |
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| details | <ul><li>min: 13 tokens</li><li>mean: 24.55 tokens</li><li>max: 57 tokens</li></ul> | <ul><li>min: 26 tokens</li><li>mean: 468.34 tokens</li><li>max: 512 tokens</li></ul> | |
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* Samples: |
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| sentence_0 | sentence_1 | |
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|:---------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |
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| <code>What is the potential impact of converting Series A Preferred Stock on the Company's capital structure?</code> | <code>Company’s subsidiaries or consolidated affiliates) and implementing capital standards published by the Basel Committee on Banking Supervision, the SEC, the Board of Governors of the Federal Reserve System (the “Federal Reserve Board”) or any other United States national governmental body, or any other applicable regime based on capital standards published by the Basel Committee on Banking Supervision or its successor, or (iii) provides for a type of capital that in the Company’s judgment (after consultation with counsel of recognized standing) is substantially equivalent to such “Tier 1” capital (such capital described in either (ii) or (iii) is referred to below as “Tier 1 Capital Equivalent”), and • the Company affirmatively elects to qualify the Series A Preferred Stock for such Allowable Capital or Tier 1 Capital Equivalent treatment without any sublimit or other quantitative restriction on the inclusion of the Series A Preferred Stock in Allowable Capital or Tier 1 Capital Equivalent (other than any limitation the Company elects to accept and any limitation requiring that common equity or a specified form of common equity constitute the dominant form of Allowable Capital or Tier 1 Capital Equivalent) under such regulations, then, upon such affirmative election, the Series A Preferred Stock shall be convertible at the Company’s option into a new series of preferred stock having terms and provisions substantially identical to those of the Series A Preferred Stock, except that such new series may have such additional or modified rights, preferences, privileges and voting powers, and such limitations and restrictions thereof, as are necessary, in the Company’s judgment (after consultation with counsel of recognized standing), to comply with the Required Unrestricted Capital Provisions (defined below), provided that the Company will not cause any such conversion unless the Company determines that the rights, preferences, privileges and voting powers of such new series of preferred stock, taken as a whole, are not materially less favorable to the holders thereof than the rights, preferences, privileges and voting powers of the Series A Preferred Stock, taken as a whole. For example, the Company could agree to restrict its ability to pay dividends on or redeem the new series of preferred stock for a specified period or indefinitely, to the extent permitted by the terms and provisions of the new series of preferred stock, since such a restriction would be permitted in the Company’s discretion under the terms and provisions of the Series A Preferred Stock. The Company will provide notice to holders of the Series A Preferred Stock of any election to qualify the Series A Preferred Stock for Allowable Capital or Tier 1 Capital Equivalent treatment and of any determination to convert the Series A Preferred Stock into a new series of preferred stock, promptly upon the effectiveness of any such election or determination. A copy of any such notice and of the relevant regulations will be on file at the Company’s principal offices and, upon request, will be made available to any stockholder. As used above, the term “Required Unrestricted Capital Provisions” means the terms that are, in the Company’s judgment (after consultation with counsel of recognized standing), required for preferred stock to be treated as Allowable Capital or Tier 1 Capital Equivalent, as applicable, without any sublimit or other quantitative restriction on the inclusion of such preferred stock in Allowable Capital or Tier 1 Capital Equivalent (other than any limitation the Company elects to accept and any limitation requiring that common equity or a specified form of common equity constitute the dominant form of Allowable Capital or Tier 1 Capital Equivalent) pursuant to applicable regulations. Voting Rights Except as provided below, the holders of the Series A Preferred Stock have no voting rights. Whenever dividends on any shares of the Series A Preferred Stock shall have not been declared and paid for the equivalent of six or more dividend payments, whether or not for consecutive dividend periods (as used in this section, a “Nonpayment”), the holders of such shares, voting together as a class with holders of any and all other series of voting preferred stock (as defined below) then outstanding, will be entitled to vote for the election of a total of two additional members of the Company’s board of directors (as used in this section, the “Preferred Stock Directors”), provided that the election of any such directors shall not cause the Company to violate the corporate governance requirement of the New York Stock Exchange (or any other exchange on which the Company’s securities may be listed) that listed companies must have a majority of independent directors.</code> | |
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| <code>What is the age limit for general partners to serve in their capacity according to the Partnership Agreement?</code> | <code>PART III 74 ITEM 10. DIRECTORS, EXECUTIVE OF FICERS AND CORPORATE GOVERNANCE JFC does not have a board of directors. As of February 23, 2024, the Partners hip was composed of 33,857 individual partners, many of whom hold more than one type of partnership interest. Of those individuals, as of February 23, 2024, 610 were general partners, and 33,687 were limited par tners and 720 were subordinated limited partners. Managing Partner. Under the terms of the Partner ship Agreement, the Managing Partne r has primary responsibility for administering the Partnership’s business, determining its policies, and controlling the management and conduct of the Partnership’s business. Under the terms of the Partnership Agreement, the Managi ng Partner's powers include, without limitation, the power to admit and dismiss gene ral partners and the power to adjust t he proportion of their respective interest s in the Partnership. The Managing Partner serves for an indefin ite term and may be removed by a majority vote of the ELT (as discussed below) or a vote of the general partners holding a majority percentage ownership in the Partnership. If at any time the office of the Managing Partner is vacant, the ELT will succeed to all the powers and duties of the Managing Partner until a new Managing Partner is elected by a majority of the EL T. The Partnership’s operating subsidiaries are managed by JFC, under the leadership of the Managing Part ner, pursuant to services agreements. Enterprise Leadership Team. The ELT consists of the Managing Partner and up to 15 additional general partners appointed by the Managing Partner, with the specific number determined by the Managing Partner. Under the terms of the Partnership Agreement, the members of the ELT are the executive officers of the Partnership. The purpose of the ELT is to provide counsel and advice to the Managing Partner in discharging thei r functions, including the consideration of ownership of Partnership capital, ensuring the Part nership’s business risks are managed approp riately and helping to establish the strategic direction of the Partne rship. In addition, the ELT takes an active ro le in identifying, measuring and controlling the risks to which the Partnership is subject. ELT members serve for an indefinite term and may be removed by the Managing Partner or a vote of general partners holding a majority percent age ownership in the Partnership. Furthermore, in the event the position of Managing Partner is vacant, the ELT shall su cceed to all of the powers and duties of the Managing Partner until a new Managing Partner is elected by a majority of the ELT. The Partnership does not have a formal code of ethics that applies to its ELT members, as it relies on the core values and beliefs of the Partnership, as well as the Partnership Agreement. Throughout all of 2023, the ELT included Penny Pennington, Chairman, Andrew Miedler, Kenneth Cella, Jr., David Chubak, Lisa Dolan, David Gunn, Lena Haas, Tina Hrevus, Kristin Johnson, Francis LaQuinta, Hasan Malik, Suzan McDaniel, Timothy Rea and Wayne Roberts. Chris Lewis also serv ed as a member of the ELT prior to his retirement effective March 1, 2023. Effective January 8, 2024, the Managing Part ner appointed Keir Gumbs, General Counsel, to the ELT. The following table is a listing as of February 23, 2024 of t he members of the ELT, the year in which each member became a general partner and each member’s area of responsibility. Under the terms of the Partnership Agreement, all general partners, including the members of the ELT, are required to reti re in their capacity as general partners by the end of the calendar year during which they turn the age of 65. The members’ biographies are below. Enterprise General Name Age Leadership Team Partner Area of Responsibilit y Penn y Pennin gton 60 2014 2006 Mana ging Partner Andrew Miedler 46 2021 2011 Chief Financial Officer Kenneth Cella, Jr. 54 2014 2002 Head of External Affairs and Communit y Engagemen t David Chubak 43 2022 2022 Head of U.S.</code> | |
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| <code>How does the face value of the bond issued by Biogen, Inc. compare to that of the bond issued by KLA Corp.?</code> | <code>$20,000 1.65% 11/5/2030 16,844 0.00% Wyeth LLC $15,000 6.50% 1/2/2034 16,841 0.00% VeriSign, Inc. $20,000 2.70% 15/6/2031 16,655 0.00% Walgreens Boots Alliance, Inc. $20,000 4.80% 18/11/2044 16,573 0.00% Amgen, Inc. $20,000 4.20% 22/2/2052 16,571 0.00% UnitedHealth Group, Inc. $20,000 3.75% 15/10/2047 16,447 0.00% Motorola Solutions, Inc. $20,000 2.75% 24/5/2031 16,400 0.00% DH Europe Finance II Sarl $20,000 3.25% 15/11/2039 16,294 0.00% Corning, Inc. $20,000 4.38% 15/11/2057 16,262 0.00% Citigroup, Inc. CAD22,000 4.09% 9/6/2025 16,050 0.00% Humana, Inc. $20,000 2.15% 3/2/2032 15,781 0.00% Aptiv plc $25,000 3.10% 1/12/2051 15,700 0.00% JPMorgan Chase & Co. $15,000 5.50% 15/10/2040 15,465 0.00% Citigroup, Inc. $19,000 2.52% 3/11/2032 15,313 0.00% Bank of New York Mellon Corp. $15,000 5.80% 25/10/2028 15,280 0.00% QUALCOMM, Inc. $20,000 3.25% 20/5/2050 15,032 0.00% Biogen, Inc. $15,000 5.20% 15/9/2045 14,997 0.00% KLA Corp. $15,000 4.65% 15/7/2032 14,947 0.00% Stanley Black & Decker, Inc. $25,000 2.75% 15/11/2050 14,896 0.00% Simon Property Group LP $20,000 3.80% 15/7/2050 14,796 0.00% Wachovia Corp. $15,000 5.50% 1/8/2035 14,693 0.00% Alexandria Real Estate Equities, Inc. $20,000 1.88% 1/2/2033 14,636 0.00% Crown Castle, Inc. $15,000 3.20% 1/9/2024 14,536 0.00% Motorola Solutions, Inc. $15,000 4.60% 23/5/2029 14,515 0.00% Morgan Stanley $15,000 3.88% 27/1/2026 14,476 0.00% Truist Financial Corp. $15,000 2.50% 1/8/2024 14,466 0.00% Royalty Pharma plc $20,000 3.30% 2/9/2040 14,239 0.00% Boston Properties LP $15,000 3.20% 15/1/2025 14,227 0.00% Texas Instruments, Inc. $15,000 1.38% 12/3/2025 14,092 0.00% American Express Credit Corp. $15,000 3.30% 3/5/2027 14,079 0.00% McCormick & Co., Inc.</code> | |
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* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters: |
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```json |
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{ |
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"scale": 20.0, |
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"similarity_fct": "cos_sim" |
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} |
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``` |
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### Training Hyperparameters |
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#### Non-Default Hyperparameters |
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- `eval_strategy`: steps |
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- `per_device_train_batch_size`: 200 |
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- `per_device_eval_batch_size`: 200 |
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- `num_train_epochs`: 10 |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: round_robin |
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#### All Hyperparameters |
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<details><summary>Click to expand</summary> |
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- `overwrite_output_dir`: False |
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- `do_predict`: False |
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- `eval_strategy`: steps |
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- `prediction_loss_only`: True |
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- `per_device_train_batch_size`: 200 |
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- `per_device_eval_batch_size`: 200 |
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- `per_gpu_train_batch_size`: None |
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- `per_gpu_eval_batch_size`: None |
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- `gradient_accumulation_steps`: 1 |
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- `eval_accumulation_steps`: None |
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- `learning_rate`: 5e-05 |
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- `weight_decay`: 0.0 |
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- `adam_beta1`: 0.9 |
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- `adam_beta2`: 0.999 |
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- `adam_epsilon`: 1e-08 |
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- `max_grad_norm`: 1 |
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- `num_train_epochs`: 10 |
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- `max_steps`: -1 |
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- `lr_scheduler_type`: linear |
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- `lr_scheduler_kwargs`: {} |
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- `warmup_ratio`: 0.0 |
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- `warmup_steps`: 0 |
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- `log_level`: passive |
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- `log_level_replica`: warning |
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- `log_on_each_node`: True |
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- `logging_nan_inf_filter`: True |
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- `save_safetensors`: True |
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- `save_on_each_node`: False |
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- `save_only_model`: False |
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- `restore_callback_states_from_checkpoint`: False |
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- `no_cuda`: False |
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- `use_cpu`: False |
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- `use_mps_device`: False |
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- `seed`: 42 |
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- `data_seed`: None |
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- `jit_mode_eval`: False |
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- `use_ipex`: False |
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- `bf16`: False |
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- `fp16`: False |
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- `fp16_opt_level`: O1 |
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- `half_precision_backend`: auto |
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- `bf16_full_eval`: False |
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- `fp16_full_eval`: False |
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- `tf32`: None |
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- `local_rank`: 0 |
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- `ddp_backend`: None |
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- `tpu_num_cores`: None |
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- `tpu_metrics_debug`: False |
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- `debug`: [] |
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- `dataloader_drop_last`: False |
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- `dataloader_num_workers`: 0 |
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- `dataloader_prefetch_factor`: None |
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- `past_index`: -1 |
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- `disable_tqdm`: False |
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- `remove_unused_columns`: True |
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- `label_names`: None |
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- `load_best_model_at_end`: False |
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- `ignore_data_skip`: False |
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- `fsdp`: [] |
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- `fsdp_min_num_params`: 0 |
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- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False} |
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- `fsdp_transformer_layer_cls_to_wrap`: None |
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- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} |
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- `deepspeed`: None |
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- `label_smoothing_factor`: 0.0 |
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- `optim`: adamw_torch |
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- `optim_args`: None |
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- `adafactor`: False |
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- `group_by_length`: False |
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- `length_column_name`: length |
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- `ddp_find_unused_parameters`: None |
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- `ddp_bucket_cap_mb`: None |
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- `ddp_broadcast_buffers`: False |
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- `dataloader_pin_memory`: True |
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- `dataloader_persistent_workers`: False |
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- `skip_memory_metrics`: True |
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- `use_legacy_prediction_loop`: False |
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- `push_to_hub`: False |
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- `resume_from_checkpoint`: None |
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- `hub_model_id`: None |
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- `hub_strategy`: every_save |
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- `hub_private_repo`: False |
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- `hub_always_push`: False |
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- `gradient_checkpointing`: False |
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- `gradient_checkpointing_kwargs`: None |
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- `include_inputs_for_metrics`: False |
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- `eval_do_concat_batches`: True |
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- `fp16_backend`: auto |
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- `push_to_hub_model_id`: None |
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- `push_to_hub_organization`: None |
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- `mp_parameters`: |
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- `auto_find_batch_size`: False |
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- `full_determinism`: False |
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- `torchdynamo`: None |
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- `ray_scope`: last |
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- `ddp_timeout`: 1800 |
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- `torch_compile`: False |
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- `torch_compile_backend`: None |
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- `torch_compile_mode`: None |
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- `dispatch_batches`: None |
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- `split_batches`: None |
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- `include_tokens_per_second`: False |
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- `include_num_input_tokens_seen`: False |
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- `neftune_noise_alpha`: None |
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- `optim_target_modules`: None |
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- `batch_eval_metrics`: False |
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- `eval_on_start`: False |
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- `batch_sampler`: no_duplicates |
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- `multi_dataset_batch_sampler`: round_robin |
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</details> |
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### Training Logs |
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| Epoch | Step | Training Loss | cosine_map@100 | |
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|:------:|:----:|:-------------:|:--------------:| |
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| 0.2033 | 100 | - | 0.5090 | |
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| 0.4065 | 200 | - | 0.5376 | |
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| 0.6098 | 300 | - | 0.5487 | |
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| 0.8130 | 400 | - | 0.5595 | |
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| 1.0 | 492 | - | 0.5716 | |
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| 1.0163 | 500 | 2.1266 | 0.5707 | |
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| 1.2195 | 600 | - | 0.5745 | |
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| 1.4228 | 700 | - | 0.5784 | |
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| 1.6260 | 800 | - | 0.5789 | |
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| 1.8293 | 900 | - | 0.5807 | |
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| 2.0 | 984 | - | 0.5835 | |
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### Framework Versions |
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- Python: 3.10.13 |
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- Sentence Transformers: 3.0.1 |
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- Transformers: 4.42.3 |
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- PyTorch: 2.5.0.dev20240704+cu124 |
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- Accelerate: 0.32.1 |
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- Datasets: 2.20.0 |
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- Tokenizers: 0.19.1 |
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## Citation |
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### BibTeX |
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#### Sentence Transformers |
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```bibtex |
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@inproceedings{reimers-2019-sentence-bert, |
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title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks", |
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author = "Reimers, Nils and Gurevych, Iryna", |
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booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing", |
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month = "11", |
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year = "2019", |
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publisher = "Association for Computational Linguistics", |
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url = "https://arxiv.org/abs/1908.10084", |
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} |
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``` |
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#### MultipleNegativesRankingLoss |
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```bibtex |
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@misc{henderson2017efficient, |
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title={Efficient Natural Language Response Suggestion for Smart Reply}, |
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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}, |
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year={2017}, |
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eprint={1705.00652}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
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