Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,955 @@
+---
+base_model: BAAI/bge-large-en-v1.5
+datasets: []
+language: []
+library_name: sentence-transformers
+metrics:
+- cosine_accuracy@1
+- cosine_accuracy@3
+- cosine_accuracy@5
+- cosine_accuracy@10
+- cosine_precision@1
+- cosine_precision@3
+- cosine_precision@5
+- cosine_precision@10
+- cosine_recall@1
+- cosine_recall@3
+- cosine_recall@5
+- cosine_recall@10
+- cosine_ndcg@10
+- cosine_mrr@10
+- cosine_map@100
+- dot_accuracy@1
+- dot_accuracy@3
+- dot_accuracy@5
+- dot_accuracy@10
+- dot_precision@1
+- dot_precision@3
+- dot_precision@5
+- dot_precision@10
+- dot_recall@1
+- dot_recall@3
+- dot_recall@5
+- dot_recall@10
+- dot_ndcg@10
+- dot_mrr@10
+- dot_map@100
+pipeline_tag: sentence-similarity
+tags:
+- sentence-transformers
+- sentence-similarity
+- feature-extraction
+- generated_from_trainer
+- dataset_size:98560
+- loss:MultipleNegativesRankingLoss
+widget:
+- source_sentence: Which bond has the longest maturity date, and what is the name
+    of the issuing company?
+  sentences:
+  - 'Vanguard EUR Corporate Bond UCITS ETF Managed by Vanguard Global Advisers, LLC.497
+    Investment Objective Vanguard EUR Corporate Bond UCITS ETF seeks to track the
+    performance of the Bloomberg Euro-Aggregate: Corporates Index, a widely recognised
+    benchmark designed to reflect the total universe of publicly traded, fixed-coupon,
+    euro-denominated, investment-grade corporate bonds with maturities greater than
+    1 year and a minimum issue size of €300 million. 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. • The ETF ’s benchmark index returned 0.14% for
+    the 12-month period. • Among the largest constituents in the index, Italy, Austria
+    and Belgium performed better than the index as a whole. Among the laggards finishing
+    in negative territory were Sweden, the United States and Finland. • By sector,
+    bonds issued by utilities returned more than those issued by industrial companies
+    and financial institutions. • Longer-dated and lower-quality bonds generally performed
+    better. Benchmark returns in the commentary above are in euros. Benchmark: Bloomberg
+    Euro-Aggregate: Corporates Index Total Returns Periods Ended 30 June 2023 (Annualised
+    for periods over one year) One Year Five YearsTen Years or Since Inception1 EUR
+    Accumulating -0.08 % — -1.60 % Benchmark 0.14 — -1.48 Tracking Difference* -0.22
+    EUR Distributing -0.08 % -1.15 % -0.04 % Benchmark 0.14 -1.06 0.05 Tracking Difference*
+    -0.22 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 Accumulating, 19 February
+    2019; EUR Distributing, 24 February 2016.'
+  - We continue to evaluate the expanded use of strategic locations, including cities
+    in which we do not currently have a presence. As of December 2023 , 41% of our
+    employees were working in strategic locations. We believe our investment in these
+    strategic locations enables us to build centers of excellence around specific
+    capabilities that support our business initiatives. Sustainability We have a long-standing
+    commitment to sustainability. Our two priorities in this area are helping clients
+    across industries decarbonize their businesses to support their transition to
+    a low-carbon economy (Climate Transition) and to advance solutions that expand
+    access, increase affordability, and drive outcomes to support sustainable economic
+    growth (Inclusive Growth). Our strategy is to advance these two priorities through
+    our work with our clients, and with strategic partners whose strengths and areas
+    of focus complement our own, as well as through our supply chain. We established
+    a Sustainable Finance Group (SFG), which serves as the centralized group that
+    drives climate strategy and sustainability efforts across our firm, including
+    commercial efforts alongside our businesses, to advance Climate Transition and
+    Inclusive Growth. Since establishing SFG, our sustainable finance-related efforts
+    have continued to evolve. For example, within Global Banking & Markets, we established
+    the Sustainable Banking Group, a group focused on supporting our corporate clients
+    in reducing their direct and indirect carbon emissions. Within Asset & Wealth
+    Management there are multiple teams that specialize in sustainable investing.
+    The Sustainability & Impact Solutions team in Asset & Wealth Management also helps
+    mobilize the full range of insights, advisory services and investment solutions
+    across our asset management client segments.THE GOLDMAN SACHS GROUP, INC. AND
+    SUBSIDIARIES Goldman Sachs 2023 Form 10-K 7
+  - $75,000 1.40% 1/9/2030 59,430 0.01% Brighthouse Financial, Inc. $78,000 4.70%
+    22/6/2047 59,389 0.01% Conagra Brands, Inc. $70,000 1.38% 1/11/2027 59,352 0.01%
+    CVS Health Corp. $85,000 2.70% 21/8/2040 59,267 0.01% Microsoft Corp. $65,000
+    3.45% 8/8/2036 59,262 0.01% Athene Holding Ltd. $65,000 4.13% 12/1/2028 59,256
+    0.01% Microsoft Corp. $60,000 4.45% 3/11/2045 59,157 0.01% Chubb Corp. $55,000
+    6.00% 11/5/2037 59,057 0.01% Apple, Inc.1$80,000 2.38% 8/2/2041 59,017 0.01% Home
+    Depot, Inc. $95,000 2.38% 15/3/2051 58,966 0.01% Gilead Sciences, Inc. $87,000
+    2.80% 1/10/2050 58,926 0.01% Lowe's Cos, Inc. $65,000 3.75% 1/4/2032 58,772 0.01%
+    Bank of America NA $55,000 6.00% 15/10/2036 58,768 0.01% Morgan Stanley $67,000
+    4.30% 27/1/2045 58,612 0.01% Amazon.com, Inc. $65,000 1.00% 12/5/2026 58,448 0.01%
+    Cigna Group $60,000 4.13% 15/11/2025 58,429 0.01% Mastercard, Inc. $70,000 3.65%
+    1/6/2049 58,395 0.01% Marsh & McLennan Cos, Inc. $70,000 2.25% 15/11/2030 58,218
+    0.01% Amgen, Inc. $70,000 2.30% 25/2/2031 58,168 0.01% Crown Castle, Inc. $85,000
+    3.25% 15/1/2051 58,134 0.01% Comcast Corp. $70,000 4.00% 1/11/2049 58,108 0.01%
+    American Express Co. $60,000 2.50% 30/7/2024 58,013 0.01% FedEx Corp. $65,000
+    4.75% 15/11/2045 58,009 0.01% Comcast Corp. $60,000 3.38% 15/8/2025 57,927 0.01%
+    Zimmer Biomet Holdings, Inc. $60,000 3.55% 1/4/2025 57,869 0.01% Charter Communications
+    Operating LLC/Charter Communications Operating Capital $70,000 5.38% 1/5/2047
+    57,847 0.01% Huntington National Bank $60,000 5.65% 10/1/2030 57,626 0.01% Intel
+    Corp. $90,000 3.10% 15/2/2060 57,605 0.01% Northern Trust Corp. $70,000 1.95%
+    1/5/2030 57,586 0.01% Eli Lilly & Co.
+- source_sentence: What is the interest rate of the bond issued by Public Service
+    Co. of Colorado, and when does it mature?
+  sentences:
+  - CAD50,000 5.70% 9/11/2027 38,543 0.00% Hyundai Capital Canada, Inc. CAD55,000
+    3.20% 16/2/2027 38,219 0.00% TELUS Corp. CAD55,000 3.30% 2/5/2029 37,952 0.00%
+    Royal Bank of Canada CAD60,000 1.67% 28/1/2033 37,930 0.00% Inter Pipeline Ltd.
+    CAD50,000 5.71% 29/5/2030 37,831 0.00% Inter Pipeline Ltd. CAD50,000 5.85% 18/5/2032
+    37,676 0.00% Enbridge Gas, Inc. CAD55,000 2.90% 1/4/2030 37,496 0.00% Province
+    of New Brunswick Canada CAD50,000 3.95% 14/8/2032 37,469 0.00% Canadian Tire Corp.,
+    Ltd. CAD50,000 5.61% 4/9/2035 37,458 0.00% Toronto-Dominion Bank CAD50,000 3.23%
+    24/7/2024 36,962 0.00% Dream Summit Industrial LP CAD55,000 2.25% 12/1/2027 36,942
+    0.00% Enbridge Gas, Inc. CAD50,000 4.55% 17/8/2052 36,928 0.00% OMERS Realty Corp.
+    CAD50,000 4.54% 9/4/2029 36,924 0.00% TELUS Corp. CAD60,000 4.10% 5/4/2051 36,720
+    0.00% Federation des Caisses Desjardins du Quebec CAD50,000 4.41% 19/5/2027 36,691
+    0.00% Canada Housing Trust No 1 CAD55,000 1.90% 15/3/2031 36,424 0.00% Enbridge
+    Gas, Inc. CAD50,000 4.15% 17/8/2032 36,396 0.00% Province of New Brunswick Canada
+    CAD45,000 4.80% 3/6/2041 36,265 0.00% CT CAD60,000 2.37% 6/1/2031 36,222 0.00%
+    Finning International, Inc. CAD50,000 5.08% 13/6/2042 36,143 0.00% Bell Telephone
+    Co. of Canada or Bell Canada CAD50,000 3.80% 21/8/2028 35,730 0.00% TransCanada
+    PipeLines Ltd. $40,000 5.00% 16/10/2043 35,705 0.00% EPCOR Utilities, Inc. CAD55,000
+    2.41% 30/6/2031 35,588 0.00% Rogers Communications, Inc. CAD50,000 5.25% 15/4/2052
+    35,436 0.00% Bank of Montreal CAD50,000 2.08% 17/6/2030 35,111 0.00%
+  - 50,000 2.65% 15/1/2032 37,370 0.00% Public Service Co. of Colorado 40,000 4.10%
+    1/6/2032 37,223 0.00% Phillips Edison Grocery Center Operating Partnership I LP
+    50,000 2.63% 15/11/2031 37,172 0.00% Corning, Inc. 40,000 4.70% 15/3/2037 37,091
+    0.00% Blackstone Holdings Finance Co. LLC 50,000 4.00% 2/10/2047 37,025 0.00%
+    Oncor Electric Delivery Co. LLC 40,000 4.55% 1/12/2041 36,833 0.00% Entergy Mississippi
+    LLC 50,000 3.50% 1/6/2051 36,806 0.00% Banner Health 50,000 2.91% 1/1/2042 36,544
+    0.00% Orlando Health Obligated Group 50,000 3.33% 1/10/2050 36,540 0.00% Arizona
+    Public Service Co. 40,000 2.95% 15/9/2027 36,388 0.00% MultiCare Health System
+    60,000 2.80% 15/8/2050 36,301 0.00% West Virginia United Health System Obligated
+    Group 55,000 3.13% 1/6/2050 36,285 0.00% Public Service Electric & Gas Co. 50,000
+    3.15% 1/1/2050 36,198 0.00% Leggett & Platt, Inc. 50,000 3.50% 15/11/2051 36,166
+    0.00% University of Chicago 50,000 3.00% 1/10/2052 36,075 0.00% STORE Capital
+    Corp. 50,000 2.75% 18/11/2030 36,057 0.00% ERP Operating LP 46,000 4.00% 1/8/2047
+    36,025 0.00% Emory University 50,000 2.97% 1/9/2050 35,873 0.00% San Diego Gas
+    & Electric Co. 50,000 3.32% 15/4/2050 35,755 0.00% OneAmerica Financial Partners,
+    Inc. 50,000 4.25% 15/10/2050 35,752 0.00% Arizona Public Service Co. 45,000 4.25%
+    1/3/2049 35,649 0.00% W R Berkley Corp. 50,000 3.55% 30/3/2052 35,567 0.00%
+  - 375,000 4.75% 1/12/2025 368,735 0.02% Vulcan Materials Co. 375,000 4.50% 1/4/2025
+    367,796 0.02% National Fuel Gas Co. 375,000 5.20% 15/7/2025 367,544 0.02% Trane
+    Technologies Luxembourg Finance SA 385,000 3.50% 21/3/2026 367,408 0.02% CNO Global
+    Funding 395,000 1.65% 6/1/2025 367,190 0.02% Prudential Financial, Inc. 400,000
+    1.50% 10/3/2026 365,739 0.02% Hanover Insurance Group, Inc. 375,000 4.50% 15/4/2026
+    363,094 0.02% Prudential Insurance Co. of America 350,000 8.30% 1/7/2025 362,568
+    0.02% Protective Life Global Funding 400,000 1.17% 15/7/2025 362,098 0.02% GLP
+    Capital LP/GLP Financing II, Inc. 375,000 3.35% 1/9/2024 362,002 0.02% Healthpeak
+    OP LLC 375,000 4.00% 1/6/2025 361,843 0.02% AutoNation, Inc. 375,000 3.50% 15/11/2024
+    361,718 0.02% United Airlines 2020-1 Class B Pass Through Trust 376,750 4.88%
+    15/1/2026 361,301 0.02% Teledyne Technologies, Inc. 400,000 1.60% 1/4/2026 361,204
+    0.02% American Electric Power Co., Inc. 400,000 1.00% 1/11/2025 361,128 0.02%
+    Analog Devices, Inc. 375,000 2.95% 1/4/2025 360,435 0.02% Sherwin-Williams Co.
+    375,000 3.45% 1/8/2025 360,112 0.02% Met Tower Global Funding 375,000 3.70% 13/6/2025
+    359,524 0.02% Sherwin-Williams Co. 367,000 4.25% 8/8/2025 359,319 0.02% Principal
+    Financial Group, Inc. 375,000 3.40% 15/5/2025 358,948 0.02% Georgia Power Co.
+    375,000 2.20% 15/9/2024 358,479 0.02% New York Life Global Funding 381,000 1.45%
+    14/1/2025 358,138 0.02% Cardinal Health, Inc. 370,000 3.50% 15/11/2024 357,994
+    0.02% Protective Life Global Funding 375,000 3.22% 28/3/2025 357,964 0.02% Franklin
+    Resources, Inc. 375,000 2.85% 30/3/2025 356,865 0.02% Laboratory Corp. of America
+    Holdings 400,000 1.55% 1/6/2026 356,846 0.02% Laboratory Corp. of America Holdings
+    375,000 2.30% 1/12/2024 356,466 0.02% Jackson National Life Global Funding 375,000
+    3.88% 11/6/2025 355,865 0.02% Principal Life Global Funding II 375,000 2.25% 21/11/2024
+    355,855 0.02% PACCAR Financial Corp. 375,000 1.80% 6/2/2025 354,610 0.02% TCI
+    Communications, Inc.
+- source_sentence: Discuss the impact of economic conditions on the performance of
+    bonds issued by entities like Henan Water Conservancy Investment Group Co., Ltd.
+  sentences:
+  - On November 20, 2023, the court denied the plaintiffs’ motion for a rehearing
+    or for leave to amend.Rivian Automotive Inc. GS&Co. is among the underwriters
+    named as defendants in putative securities class actions filed on March 7, 2022
+    and February 28, 2023 in the U.S. District Court for the Central District of California
+    and in the Superior Court of the State of California, County of Orange, respectively,
+    relating to Rivian Automotive Inc.’s (Rivian) approximately $13.7 billion November
+    2021 initial public offering. In addition to the underwriters, the defendants
+    include Rivian and certain of its officers and directors. GS&Co. underwrote 44,733,050
+    shares of common stock representing an aggregate offering price of approximately
+    $3.5 billion . On March 2, 2023, the plaintiffs in the federal court action filed
+    an amended consolidated complaint, and on July 3, 2023, the court denied the defendants’
+    motion to dismiss the amended consolidated complaint. On June 30, 2023, the court
+    in the state court action granted the defendants’ motion to dismiss the complaint,
+    and on September 1, 2023, the plaintiffs appealed. On December 1, 2023, the plaintiffs
+    moved for class certification. Natera Inc. GS&Co. is among the underwriters named
+    as defendants in putative securities class actions in New York Supreme Court,
+    County of New York and the U.S. District Court for the Western District of Texas
+    filed on March 10, 2022 and October 7, 2022, respectively, relating to Natera
+    Inc.’s (Natera) approximately $585 million July 2021 public offering of common
+    stock. In addition to the underwriters, the defendants include Natera and certain
+    of its officers and directors. GS&Co. underwrote 1,449,000 shares of common stock
+    representing an aggregate offering price of approximately $164 million . On July
+    15, 2022, the parties in the state court action filed a stipulation and proposed
+    order approving the discontinuance of the action without prejudice. On September
+    11, 2023, the federal court granted in part and denied in part the defendants’
+    motion to dismiss. Robinhood Markets, Inc. GS&Co. is among the underwriters named
+    as defendants in a putative securities class action filed on December 17, 2021
+    in the U.S. District Court for the Northern District of California relating to
+    Robinhood Markets, Inc.’s (Robinhood) approximately $2.2 billion July 2021 initial
+    public offering. In addition to the underwriters, the defendants include Robinhood
+    and certain of its officers and directors. GS&Co. underwrote 18,039,706 shares
+    of common stock representing an aggregate offering price of approximately $686
+    million . On February 10, 2023, the court granted the defendants’ motion to dismiss
+    the complaint with leave to amend, and on March 13, 2023, the plaintiffs filed
+    a second amended complaint. On January 24, 2024, the court granted the defendants’
+    motion to dismiss the second amended complaint without leave to amend.THE GOLDMAN
+    SACHS GROUP, INC. AND SUBSIDIARIES Notes to Consolidated Financial Statements
+    222 Goldman Sachs 2023 Form 10-K
+  - 200,000 3.50% 5/7/2027 187,785 0.03% Huarong Finance 2019 Co., Ltd. 200,000 3.25%
+    13/11/2024 187,354 0.03% Henan Water Conservancy Investment Group Co., Ltd. 200,000
+    2.80% 18/9/2025 185,811 0.03% CCBL Cayman 1 Corp., Ltd. 200,000 1.99% 21/7/2025
+    185,505 0.03% Hengjian International Investment Ltd. 200,000 1.88% 23/6/2025 185,112
+    0.03% Sinopec Group Overseas Development 2016 Ltd. 200,000 2.75% 29/9/2026 184,261
+    0.03% ICBCIL Finance Co., Ltd. 200,000 2.70% 27/1/2027 184,153 0.03% CDBL Funding
+    2 200,000 2.00% 4/3/2026 182,327 0.03% Chongqing International Logistics Hub Park
+    Construction Co., Ltd. 200,000 4.30% 26/9/2024 182,270 0.03% Sunny Express Enterprises
+    Corp. 200,000 3.13% 23/4/2030 180,767 0.03% China Construction Bank Corp. 200,000
+    1.46% 22/4/2026 180,688 0.03% Chalco Hong Kong Investment Co., Ltd. 200,000 2.10%
+    28/7/2026 180,686 0.03% Bank of China Ltd. 200,000 1.40% 28/4/2026 180,090 0.03%
+    Shenwan Hongyuan International Finance Ltd. 200,000 1.80% 14/7/2026 179,360 0.03%
+    SFG International Holdings Co., Ltd. 200,000 2.40% 3/6/2026 179,078 0.03% Sinopec
+    Capital 2013 Ltd. 200,000 4.25% 24/4/2043 178,983 0.03% ICBCIL Finance Co., Ltd.
+    200,000 1.75% 2/8/2026 178,698 0.03% CSSC Capital 2015 Ltd. 200,000 2.10% 27/7/2026
+    178,662 0.03% Sinochem Offshore Capital Co., Ltd. 200,000 2.25% 24/11/2026 178,126
+    0.03% AVIC International Finance & Investment Ltd. 200,000 2.50% 17/11/2026 177,158
+    0.03% Huarong Finance II Co., Ltd. 200,000 4.88% 22/11/2026 176,528 0.03% Bright
+    Galaxy International Ltd. 200,000 3.25% 15/7/2026 175,325 0.03% Central Plaza
+    Development Ltd. 200,000 5.75% Perpetual 175,287 0.03% China Great Wall International
+    Holdings III Ltd. 200,000 3.88% 31/8/2027 175,105 0.03% China Huaneng Group Hong
+    Kong Treasury Management Holding Ltd. 200,000 2.70% 20/1/2031 172,985 0.03% Guoren
+    Property & Casualty Insurance Co., Ltd. 200,000 3.35% 1/6/2026 171,853 0.03% Three
+    Gorges Finance I Cayman Islands Ltd. 200,000 2.15% 22/9/2030 170,989 0.03% Blossom
+    Joy Ltd.
+  - 'The list includes the investments constituting the greatest proportion of investments
+    of the financial product during the reference period which is: From 11 October
+    2022 to 30 June 2023What were the top investments of this financial product? The
+    top investments of this Fund are set out below. These figures are percentages
+    of net assets and are weighted averages of the market value as at the end of December
+    2022, March 2023 and June 2023. Largest investments Sector % Assets Country SAMSUNG
+    ELECTRONICS CO LTD Telecommunications 3.88% Korea TOYOTA MOTOR CORP Consumer Discretionary
+    2.92% Japan AIA GROUP LTD Financials 2.08% Hong Kong COMMONWEALTH BANK OF AUSTRAL
+    Financials 1.90% Australia SONY GROUP CORP Consumer Discretionary 1.74% Japan
+    CSL LTD Health Care 1.53% Australia KEYENCE CORP Industrials 1.47% Japan MITSUBISHI
+    UFJ FINANCIAL GRO Financials 1.32% Japan DAIICHI SANKYO CO LTD Health Care 1.04%
+    Japan NATIONAL AUSTRALIA BANK LTD Financials 0.99% Australia SHIN-ETSU CHEMICAL
+    CO LTD Basic Materials 0.96% Japan HONG KONG EXCHANGES & CLEAR Financials 0.88%
+    Hong Kong SUMITOMO MITSUI FINANCIAL GR Financials 0.87% Japan TOKYO ELECTRON LTD
+    Technology 0.87% Japan WESTPAC BANKING CORP Financials 0.86% Australia Asset allocation
+    describes the share of investments in specific assets.What was the proportion
+    of sustainability-related investments? Please see the information below in this
+    respect. What was the asset allocation? The Fund invested 96.97% of its net assets
+    in line with the ESG requirements of the Index, which is consistent with the environmental
+    and/or social characteristics promoted by the Fund. The Fund did not commit to
+    make any sustainable investments. The Fund did not use indirect exposures (including
+    derivatives) to attain the environmental or social characteristics promoted by
+    the Fund. The remaining 3.03% of the Fund’s net assets are in other investments
+    (“#2 Other”), which are not aligned with environmental and/or social characteristics
+    promoted by the Fund. Investments #1 Aligned with E/S characteristics 96.97% #2
+    Other 3.03% #1A Sustainable N/A #1B Other E/S characteristics N/A #1 Aligned with
+    E/S characteristics includes the investments of the financial product used to
+    attain the environmental or social characteristics promoted by the financial product.
+    1002'
+- source_sentence: Identify the company with the highest number of shares listed in
+    the provided context and state its fair value.
+  sentences:
+  - 'we do not have latitude in carrier selection and establishing rates with the
+    Carrier. Revenue is recognized on a net basis for these transactions. Advertising
+    Revenue We derive the majority of our advertising revenue from sponsored listing
+    fees paid by Merchants and brands in exchange for advertising on our platform.
+    Advertising revenue is recognized when an end-user engages with the sponsored
+    listing based on the number of clicks. Revenue is presented on a gross basis in
+    the amount billed to Merchants and brands as we control the advertisement before
+    it is transferred to the end-user. Incentives to Customers Incentives provided
+    to customers are recorded as a reduction of revenue if we do not receive a distinct
+    good or service or cannot reasonably estimate the fair value of the good or service
+    received. Incentives to customers that are not provided in exchange for a distinct
+    good or service are evaluated as variable consideration, in the most likely amount
+    to be earned by the customer at the time or as they are earned by customers, depending
+    on the type of incentive. Since incentives are earned over a short period of time,
+    there is limited uncertainty when estimating variable consideration. Incentives
+    earned by customers for referring new customers are paid in exchange for a distinct
+    service and are accounted for as customer acquisition costs. We expense such referral
+    payments as incurred in sales and marketing expenses in the consolidated statements
+    of operations. We expense costs to acquire new customer contracts as incurred
+    because the amortization period would be one year or less. The amount recorded
+    as an expense is the lesser of the amount of the incentive paid or the established
+    fair value of the service received. Fair value of the service is established using
+    amounts paid to vendors for similar services. The amounts paid to customers presented
+    as sales and marketing expenses for the years ended December 31, 2021, 2022 and
+    2023 were immaterial. In some transactions, incentives and payments made to customers
+    may exceed the revenue earned in the transaction. In these transactions, the resulting
+    shortfall amount is recorded as a reduction of revenue. End-User Discounts and
+    Promotions We offer discounts and promotions to end-users to encourage use of
+    our platform. These are offered in various forms of discounts and promotions and
+    include: Targeted end-user discounts and promotions: These discounts and promotions
+    are offered to a limited number of end-users in a market to acquire, re-engage,
+    or generally increase end-users use of the Platform, and are akin to a coupon.
+    An example is an offer providing a discount on a limited number of rides or deliveries
+    during a limited time period. We record the cost of these discounts and promotions
+    to end-users who are not our customers as sales and marketing expenses at the
+    time they are redeemed by the end-user. End-user referrals: These referrals are
+    earned when an existing end-user (the referring end-user) refers a new end-user
+    (the referred end-user) to the platform and the new end-user who is not our customer
+    completes their first transaction on the platform. These referrals are typically
+    paid in the form of a credit given to the referring end-user. These referrals
+    are offered to attract new end-users to the Platform. We record the liability
+    for these referrals and corresponding expenses as sales and marketing expenses
+    at the time the referral is earned by the referring end-user. Market-wide promotions:
+    These promotions are pricing actions in the form of discounts that reduce the
+    end-user fare charged by Drivers and Merchants to end-users who are not our customers
+    for all or substantially all Mobility or Delivery offerings in a specific market.
+    This also includes any discounts offered under our subscription offerings and
+    certain discounts within the Uber Rewards programs, which enable end-users to
+    receive a fixed fare or a discount on all eligible rides. Accordingly, we record
+    the cost of these promotions as a reduction of revenue at the time the transaction
+    is completed. Refunds and Credits Refunds and credits to end-users due to end-user
+    dissatisfaction with the Platform are recorded as sales and marketing expenses
+    or as a reduction of revenue depending on whether the end-user is considered a
+    customer based on the market. Refunds to end-users that we recover from Drivers
+    and Merchants are recorded as a reduction of revenue. Other We have elected to
+    exclude from revenue, taxes assessed by a governmental authority that are both
+    imposed on and are concurrent with specific revenue producing transactions, and
+    collected from Drivers, Merchants and end-users and remitted to governmental authorities.
+    Accordingly, such amounts are not included as a component of revenue or cost of
+    revenue. Practical Expedients We have utilized the practical expedient available
+    under ASC 606-10-50-14 and do not disclose the value of unsatisfied performance
+    obligations for contracts with an original expected length of one year or less.
+    We have no significant financing components in our contracts with customers. 89'
+  - 78 Vanguard ESG Emerging Markets All Cap UCITS ETF..Number of SharesFair Value
+    US Dollars ($)% of Total Net Assets Yangzijiang Financial Holding Ltd. 9,300 2,336
+    0.01% Shanghai Jinjiang International Hotels Co., Ltd. Class A 400 2,328 0.01%
+    Shanghai Junshi Biosciences Co., Ltd. Class H 800 2,328 0.01% China Risun Group
+    Ltd. 5,000 2,322 0.01% Shoucheng Holdings Ltd. 10,000 2,322 0.01% Zoomlion Heavy
+    Industry Science & Technology Co., Ltd. Class A 2,500 2,320 0.01% China Meidong
+    Auto Holdings Ltd. 2,000 2,310 0.01% Zhejiang Century Huatong Group Co., Ltd.
+    Class A 2,200 2,295 0.01% Huayu Automotive Systems Co., Ltd. Class A 900 2,284
+    0.01% Montage Technology Co., Ltd. Class A 289 2,281 0.01% PAX Global Technology
+    Ltd. 3,000 2,274 0.01% China Railway Signal & Communication Corp., Ltd. Class
+    H 6,000 2,266 0.01% People's Insurance Co. Group of China Ltd. Class A 2,800 2,248
+    0.01% Eoptolink Technology, Inc. Ltd. Class A 240 2,242 0.01% China Construction
+    Bank Corp. Class A 2,600 2,237 0.01% AsiaInfo Technologies Ltd. 1,600 2,225 0.01%
+    China Eastern Airlines Corp., Ltd. Class A 3,400 2,225 0.01% Towngas Smart Energy
+    Co., Ltd. 5,000 2,220 0.01% Zhongyu Energy Holdings Ltd. 3,000 2,217 0.01% Kunlun
+    Tech Co., Ltd. Class A 400 2,215 0.01% GoerTek, Inc. Class A 900 2,196 0.01% Remegen
+    Co., Ltd. Class H 500 2,185 0.01% BAIC Motor Corp., Ltd. Class H 9,000 2,182 0.01%
+    Zhejiang Dahua Technology Co., Ltd. Class A 800 2,172 0.01% Xinyi Energy Holdings
+    Ltd. 6,600 2,156 0.01% Huizhou Desay Sv Automotive Co., Ltd. Class A 100 2,142
+    0.01% JCET Group Co., Ltd. Class A 500 2,142 0.01% China Jushi Co., Ltd. Class
+    A 1,100 2,141 0.01% Ecovacs Robotics Co., Ltd. Class A 200 2,138 0.01% Haichang
+    Ocean Park Holdings Ltd. 14,000 2,126 0.01% Bloomage Biotechnology Corp., Ltd.
+    Class A 173 2,120 0.01% Lens Technology Co., Ltd. Class A 1,300 2,102 0.01% Hangzhou
+    Steam Turbine Power Group Co., Ltd. Class B 1,800 2,097 0.01% Angang Steel Co.,
+    Ltd. Class H 8,000 2,093 0.01% Great Wall Motor Co., Ltd. Class A 600 2,076 0.01%
+    Sun Art Retail Group Ltd. 8,000 2,062 0.01% Noah Holdings Ltd. ADR 144 2,028 0.01%
+    Anjoy Foods Group Co., Ltd. Class A 100 2,018 0.01% Chaozhou Three-Circle Group
+    Co., Ltd.
+  - We are regularly under audit by tax authorities in different jurisdictions. Although
+    we believe that our provision for income taxes and our tax estimates are reasonable,
+    tax authorities may disagree with certain positions we have taken. In addition,
+    econom ic and political pressures to increase tax revenue in various jurisdictions
+    may make resolving tax disputes favorably more difficult. We are currently under
+    Internal Revenue Service audit for prior tax years, with the primary unresolved
+    issues relating to transfer pricing. The final resolution of those audits, and
+    other audits or litigation, may differ from the amounts recorded in our consolidated
+    financial statements and may materially affect our consolidated financial statements
+    in the period or periods in which that determina tion is made. We earn a significant
+    amount of our operating income outside the U.S. A change in the mix of earnings
+    and losses in countries with differing statutory tax rates, changes in our business
+    or structure, or the expiration of or disputes about certain tax agreements in
+    a particular country may result in higher effective tax rates for the company.
+    In addition, changes in U.S. federal and state or international tax laws applicable
+    to corporate multinationals, other fundamental law changes currently being considere
+    d by many countries, including in the U.S., and changes in taxing jurisdictions’
+    administrative interpretations, decisions, policies, and positions may materially
+    adversely impact our consolidated financial statements.
+- source_sentence: What are the advantages and disadvantages of investing in corporate
+    bonds compared to government bonds?
+  sentences:
+  - €100,000 2.88% 15/1/2038 92,965 0.02% Bank of New York Mellon Corp. $95,000 4.97%
+    26/4/2034 92,748 0.02% WPC Eurobond BV €100,000 1.35% 15/4/2028 92,674 0.02% Amgen,
+    Inc.1$100,000 2.60% 19/8/2026 92,601 0.02% AT&T, Inc. €100,000 2.05% 19/5/2032
+    92,593 0.02% Aon Corp. $100,000 3.75% 2/5/2029 92,563 0.02% Chubb INA Holdings,
+    Inc. $102,000 4.35% 3/11/2045 92,352 0.02% Bank of America Corp. $96,000 4.38%
+    27/4/2028 92,301 0.02% Verizon Communications, Inc. $117,000 1.50% 18/9/2030 92,243
+    0.02% Medtronic Global Holdings SCA €100,000 0.38% 15/10/2028 92,231 0.02% Intel
+    Corp. $100,000 4.90% 5/8/2052 92,209 0.02% KeyBank NA $100,000 4.15% 8/8/2025
+    92,200 0.02% Aetna, Inc. $95,000 3.50% 15/11/2024 92,178 0.02% AT&T, Inc. $110,000
+    4.35% 15/6/2045 92,161 0.02% PepsiCo, Inc. €100,000 1.13% 18/3/2031 92,095 0.02%
+    Ally Financial, Inc. $115,000 2.20% 2/11/2028 92,042 0.02% JPMorgan Chase & Co.
+    £100,000 1.90% 28/4/2033 92,039 0.02% Westinghouse Air Brake Technologies Corp.
+    $95,000 4.95% 15/9/2028 92,021 0.02% Viatris , Inc. $139,000 4.00% 22/6/2050 91,948
+    0.02% Amazon.com, Inc. $90,000 4.80% 5/12/2034 91,936 0.02% General Motors Financial
+    Co., Inc. $95,000 3.50% 7/11/2024 91,890 0.02% US Bancorp $120,000 1.38% 22/7/2030
+    91,848 0.02% Goldman Sachs Group, Inc. $105,000 4.41% 23/4/2039 91,826 0.02% Blackstone
+    Holdings Finance Co. LLC €100,000 1.50% 10/4/2029 91,820 0.02%
+  - 171 Vanguard ESG North America All Cap UCITS ETF.Number of SharesFair Value US
+    Dollars ($)% of Total Net Assets Topgolf Callaway Brands Corp. 339 6,729 0.01%
+    LGI Homes, Inc. 49 6,610 0.01% Sirius XM Holdings, Inc. 1,417 6,419 0.01% American
+    Airlines Group, Inc. 356 6,387 0.01% Lyft, Inc. Class A 661 6,339 0.01% Columbia
+    Sportswear Co. 82 6,334 0.01% Helen of Troy Ltd. 58 6,265 0.01% Signet Jewelers
+    Ltd. 95 6,200 0.01% MDC Holdings, Inc. 132 6,174 0.01% Kohl's Corp. 264 6,085
+    0.01% Frontdoor, Inc. 187 5,965 0.01% Carter's, Inc. 80 5,808 0.01% Boot Barn
+    Holdings, Inc. 68 5,759 0.01% Rush Enterprises, Inc. Class A 94 5,710 0.01% Aritzia,
+    Inc. 204 5,670 0.01% Papa John's International, Inc. 76 5,611 0.01% Cavco Industries,
+    Inc. 19 5,605 0.01% Inter Parfums, Inc. 41 5,544 0.01% AMC Entertainment Holdings,
+    Inc. Class A 1,239 5,452 0.01% Steven Madden Ltd. 164 5,361 0.01% Nordstrom, Inc.
+    257 5,261 0.01% Linamar Corp. 99 5,209 0.01% QuantumScape Corp. Class A 640 5,114
+    0.01% Foot Locker, Inc. 188 5,097 0.01% Peloton Interactive, Inc. Class A 654
+    5,029 0.01% Century Communities, Inc. 64 4,904 0.01% SeaWorld Entertainment, Inc.
+    87 4,873 0.01% Kontoor Brands, Inc. 115 4,842 0.01% Dorman Products, Inc. 61 4,809
+    0.01% Urban Outfitters, Inc. 145 4,804 0.01% Under Armour, Inc. Class A 651 4,700
+    0.01% Jack in the Box, Inc. 48 4,681 0.01% Cracker Barrel Old Country Store, Inc.
+    50 4,659 0.01% Dana, Inc. 272 4,624 0.01% Graham Holdings Co. Class B 8 4,572
+    0.01% Farfetch Ltd. Class A 751 4,536 0.01% American Eagle Outfitters, Inc. 379
+    4,472 0.01% Sonos, Inc. 270 4,409 0.01% Cinemark Holdings, Inc. 264 4,356 0.01%
+    Winnebago Industries, Inc. 65 4,335 0.01% TripAdvisor, Inc. 258 4,254 0.01% Gap,
+    Inc. 476 4,251 0.01% Liberty Media Corp.-Liberty Braves Class C 105 4,160 0.01%
+    OPENLANE, Inc. 268 4,079 0.01% PriceSmart, Inc. 55 4,073 0.01% Leslie's, Inc.
+    432 4,056 0.01% National Vision Holdings, Inc. 164 3,984 0.01% Six Flags Entertainment
+    Corp. 153 3,975 0.01% Luminar Technologies, Inc.
+  - 924 Vanguard USD Treasury Bond UCITS ETF Principal US Dollars ($) CouponMaturity
+    DateFair Value US Dollars ($)% of Total Net Assets United States Treasury Note
+    8,475,000 1.88% 28/2/2027 7,769,854 0.47% United States Treasury Bond 9,088,000
+    3.00% 15/8/2052 7,731,900 0.46% United States Treasury Note 8,907,000 1.38% 31/12/2028
+    7,726,823 0.46% United States Treasury Note 8,184,000 1.13% 15/1/2025 7,696,477
+    0.46% United States Treasury Bond 10,590,000 1.88% 15/2/2041 7,692,642 0.46% United
+    States Treasury Note 8,466,000 0.25% 30/9/2025 7,668,344 0.46% United States Treasury
+    Note 8,266,700 1.63% 15/2/2026 7,659,614 0.46% United States Treasury Note 8,957,000
+    0.63% 31/12/2027 7,654,036 0.46% United States Treasury Note 8,087,000 0.38% 15/8/2024
+    7,651,060 0.46% United States Treasury Note 8,000,500 2.13% 15/5/2025 7,596,412
+    0.46% United States Treasury Note 8,035,000 2.50% 31/3/2027 7,530,302 0.45% United
+    States Treasury Note 8,138,700 1.63% 15/5/2026 7,511,766 0.45% United States Treasury
+    Note 8,464,000 1.75% 31/1/2029 7,483,366 0.45% United States Treasury Note 8,296,000
+    0.88% 30/6/2026 7,474,826 0.45% United States Treasury Note 8,078,000 2.00% 15/11/2026
+    7,472,781 0.45% United States Treasury Note 7,874,000 1.50% 15/2/2025 7,432,010
+    0.45% United States Treasury Note 7,794,000 1.75% 15/3/2025 7,372,332 0.44% United
+    States Treasury Bond 10,008,000 2.00% 15/11/2041 7,327,732 0.44% United States
+    Treasury Note 8,106,000 0.38% 30/11/2025 7,316,932 0.44% United States Treasury
+    Note 7,738,000 2.75% 30/4/2027 7,309,992 0.44% United States Treasury Bond 11,011,000
+    1.88% 15/11/2051 7,270,701 0.44% United States Treasury Bond 10,047,000 2.25%
+    15/2/2052 7,263,667 0.44% United States Treasury Note 7,416,000 3.25% 30/6/2027
+    7,132,686 0.43% United States Treasury Note 7,559,000 2.63% 31/5/2027 7,103,098
+    0.43% United States Treasury Note 7,729,500 2.38% 15/5/2029 7,046,526 0.42% United
+    States Treasury Note 7,855,000 1.88% 28/2/2029 6,986,041 0.42% United States Treasury
+    Note 7,023,000 4.13% 30/9/2027 6,984,044 0.42% United States Treasury Note 7,000,000
+    4.00% 30/6/2028 6,961,
+model-index:
+- name: SentenceTransformer based on BAAI/bge-large-en-v1.5
+  results:
+  - task:
+      type: information-retrieval
+      name: Information Retrieval
+    dataset:
+      name: Unknown
+      type: unknown
+    metrics:
+    - type: cosine_accuracy@1
+      value: 0.4509054895302773
+      name: Cosine Accuracy@1
+    - type: cosine_accuracy@3
+      value: 0.694538766270515
+      name: Cosine Accuracy@3
+    - type: cosine_accuracy@5
+      value: 0.784804753820034
+      name: Cosine Accuracy@5
+    - type: cosine_accuracy@10
+      value: 0.8679966044142614
+      name: Cosine Accuracy@10
+    - type: cosine_precision@1
+      value: 0.4509054895302773
+      name: Cosine Precision@1
+    - type: cosine_precision@3
+      value: 0.23151292209017169
+      name: Cosine Precision@3
+    - type: cosine_precision@5
+      value: 0.1569609507640068
+      name: Cosine Precision@5
+    - type: cosine_precision@10
+      value: 0.08679966044142615
+      name: Cosine Precision@10
+    - type: cosine_recall@1
+      value: 0.4509054895302773
+      name: Cosine Recall@1
+    - type: cosine_recall@3
+      value: 0.694538766270515
+      name: Cosine Recall@3
+    - type: cosine_recall@5
+      value: 0.784804753820034
+      name: Cosine Recall@5
+    - type: cosine_recall@10
+      value: 0.8679966044142614
+      name: Cosine Recall@10
+    - type: cosine_ndcg@10
+      value: 0.6578616098378955
+      name: Cosine Ndcg@10
+    - type: cosine_mrr@10
+      value: 0.5905876923491564
+      name: Cosine Mrr@10
+    - type: cosine_map@100
+      value: 0.5964470032488818
+      name: Cosine Map@100
+    - type: dot_accuracy@1
+      value: 0.4509054895302773
+      name: Dot Accuracy@1
+    - type: dot_accuracy@3
+      value: 0.694538766270515
+      name: Dot Accuracy@3
+    - type: dot_accuracy@5
+      value: 0.784804753820034
+      name: Dot Accuracy@5
+    - type: dot_accuracy@10
+      value: 0.8679966044142614
+      name: Dot Accuracy@10
+    - type: dot_precision@1
+      value: 0.4509054895302773
+      name: Dot Precision@1
+    - type: dot_precision@3
+      value: 0.23151292209017169
+      name: Dot Precision@3
+    - type: dot_precision@5
+      value: 0.1569609507640068
+      name: Dot Precision@5
+    - type: dot_precision@10
+      value: 0.08679966044142615
+      name: Dot Precision@10
+    - type: dot_recall@1
+      value: 0.4509054895302773
+      name: Dot Recall@1
+    - type: dot_recall@3
+      value: 0.694538766270515
+      name: Dot Recall@3
+    - type: dot_recall@5
+      value: 0.784804753820034
+      name: Dot Recall@5
+    - type: dot_recall@10
+      value: 0.8679966044142614
+      name: Dot Recall@10
+    - type: dot_ndcg@10
+      value: 0.6578616098378955
+      name: Dot Ndcg@10
+    - type: dot_mrr@10
+      value: 0.5905876923491564
+      name: Dot Mrr@10
+    - type: dot_map@100
+      value: 0.5964470032488818
+      name: Dot Map@100
+---
+
+# SentenceTransformer based on BAAI/bge-large-en-v1.5
+
+This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5). It maps sentences & paragraphs to a 1024-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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) <!-- at revision d4aa6901d3a41ba39fb536a557fa166f842b0e09 -->
+- **Maximum Sequence Length:** 512 tokens
+- **Output Dimensionality:** 1024 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': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+  (2): Normalize()
+)
+```
+
+## Usage
+
+### Direct Usage (Sentence Transformers)
+
+First install the Sentence Transformers library:
+
+```bash
+pip install -U sentence-transformers
+```
+
+Then you can load this model and run inference.
+```python
+from sentence_transformers import SentenceTransformer
+
+# Download from the 🤗 Hub
+model = SentenceTransformer("sujet-ai/Marsilia-Embedding-EN-Large")
+# Run inference
+sentences = [
+    'What are the advantages and disadvantages of investing in corporate bonds compared to government bonds?',
+    '€100,000 2.88% 15/1/2038 92,965 0.02% Bank of New York Mellon Corp. $95,000 4.97% 26/4/2034 92,748 0.02% WPC Eurobond BV €100,000 1.35% 15/4/2028 92,674 0.02% Amgen, Inc.1$100,000 2.60% 19/8/2026 92,601 0.02% AT&T, Inc. €100,000 2.05% 19/5/2032 92,593 0.02% Aon Corp. $100,000 3.75% 2/5/2029 92,563 0.02% Chubb INA Holdings, Inc. $102,000 4.35% 3/11/2045 92,352 0.02% Bank of America Corp. $96,000 4.38% 27/4/2028 92,301 0.02% Verizon Communications, Inc. $117,000 1.50% 18/9/2030 92,243 0.02% Medtronic Global Holdings SCA €100,000 0.38% 15/10/2028 92,231 0.02% Intel Corp. $100,000 4.90% 5/8/2052 92,209 0.02% KeyBank NA $100,000 4.15% 8/8/2025 92,200 0.02% Aetna, Inc. $95,000 3.50% 15/11/2024 92,178 0.02% AT&T, Inc. $110,000 4.35% 15/6/2045 92,161 0.02% PepsiCo, Inc. €100,000 1.13% 18/3/2031 92,095 0.02% Ally Financial, Inc. $115,000 2.20% 2/11/2028 92,042 0.02% JPMorgan Chase & Co. £100,000 1.90% 28/4/2033 92,039 0.02% Westinghouse Air Brake Technologies Corp. $95,000 4.95% 15/9/2028 92,021 0.02% Viatris , Inc. $139,000 4.00% 22/6/2050 91,948 0.02% Amazon.com, Inc. $90,000 4.80% 5/12/2034 91,936 0.02% General Motors Financial Co., Inc. $95,000 3.50% 7/11/2024 91,890 0.02% US Bancorp $120,000 1.38% 22/7/2030 91,848 0.02% Goldman Sachs Group, Inc. $105,000 4.41% 23/4/2039 91,826 0.02% Blackstone Holdings Finance Co. LLC €100,000 1.50% 10/4/2029 91,820 0.02%',
+    '924 Vanguard USD Treasury Bond UCITS ETF Principal US Dollars ($) CouponMaturity DateFair Value US Dollars ($)% of Total Net Assets United States Treasury Note 8,475,000 1.88% 28/2/2027 7,769,854 0.47% United States Treasury Bond 9,088,000 3.00% 15/8/2052 7,731,900 0.46% United States Treasury Note 8,907,000 1.38% 31/12/2028 7,726,823 0.46% United States Treasury Note 8,184,000 1.13% 15/1/2025 7,696,477 0.46% United States Treasury Bond 10,590,000 1.88% 15/2/2041 7,692,642 0.46% United States Treasury Note 8,466,000 0.25% 30/9/2025 7,668,344 0.46% United States Treasury Note 8,266,700 1.63% 15/2/2026 7,659,614 0.46% United States Treasury Note 8,957,000 0.63% 31/12/2027 7,654,036 0.46% United States Treasury Note 8,087,000 0.38% 15/8/2024 7,651,060 0.46% United States Treasury Note 8,000,500 2.13% 15/5/2025 7,596,412 0.46% United States Treasury Note 8,035,000 2.50% 31/3/2027 7,530,302 0.45% United States Treasury Note 8,138,700 1.63% 15/5/2026 7,511,766 0.45% United States Treasury Note 8,464,000 1.75% 31/1/2029 7,483,366 0.45% United States Treasury Note 8,296,000 0.88% 30/6/2026 7,474,826 0.45% United States Treasury Note 8,078,000 2.00% 15/11/2026 7,472,781 0.45% United States Treasury Note 7,874,000 1.50% 15/2/2025 7,432,010 0.45% United States Treasury Note 7,794,000 1.75% 15/3/2025 7,372,332 0.44% United States Treasury Bond 10,008,000 2.00% 15/11/2041 7,327,732 0.44% United States Treasury Note 8,106,000 0.38% 30/11/2025 7,316,932 0.44% United States Treasury Note 7,738,000 2.75% 30/4/2027 7,309,992 0.44% United States Treasury Bond 11,011,000 1.88% 15/11/2051 7,270,701 0.44% United States Treasury Bond 10,047,000 2.25% 15/2/2052 7,263,667 0.44% United States Treasury Note 7,416,000 3.25% 30/6/2027 7,132,686 0.43% United States Treasury Note 7,559,000 2.63% 31/5/2027 7,103,098 0.43% United States Treasury Note 7,729,500 2.38% 15/5/2029 7,046,526 0.42% United States Treasury Note 7,855,000 1.88% 28/2/2029 6,986,041 0.42% United States Treasury Note 7,023,000 4.13% 30/9/2027 6,984,044 0.42% United States Treasury Note 7,000,000 4.00% 30/6/2028 6,961,',
+]
+embeddings = model.encode(sentences)
+print(embeddings.shape)
+# [3, 1024]
+
+# Get the similarity scores for the embeddings
+similarities = model.similarity(embeddings, embeddings)
+print(similarities.shape)
+# [3, 3]
+```
+
+<!--
+### Direct Usage (Transformers)
+
+<details><summary>Click to see the direct usage in Transformers</summary>
+
+</details>
+-->
+
+<!--
+### Downstream Usage (Sentence Transformers)
+
+You can finetune this model on your own dataset.
+
+<details><summary>Click to expand</summary>
+
+</details>
+-->
+
+<!--
+### Out-of-Scope Use
+
+*List how the model may foreseeably be misused and address what users ought not to do with the model.*
+-->
+
+## Evaluation
+
+### Metrics
+
+#### Information Retrieval
+
+* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
+
+| Metric              | Value      |
+|:--------------------|:-----------|
+| cosine_accuracy@1   | 0.4509     |
+| cosine_accuracy@3   | 0.6945     |
+| cosine_accuracy@5   | 0.7848     |
+| cosine_accuracy@10  | 0.868      |
+| cosine_precision@1  | 0.4509     |
+| cosine_precision@3  | 0.2315     |
+| cosine_precision@5  | 0.157      |
+| cosine_precision@10 | 0.0868     |
+| cosine_recall@1     | 0.4509     |
+| cosine_recall@3     | 0.6945     |
+| cosine_recall@5     | 0.7848     |
+| cosine_recall@10    | 0.868      |
+| cosine_ndcg@10      | 0.6579     |
+| cosine_mrr@10       | 0.5906     |
+| **cosine_map@100**  | **0.5964** |
+| dot_accuracy@1      | 0.4509     |
+| dot_accuracy@3      | 0.6945     |
+| dot_accuracy@5      | 0.7848     |
+| dot_accuracy@10     | 0.868      |
+| dot_precision@1     | 0.4509     |
+| dot_precision@3     | 0.2315     |
+| dot_precision@5     | 0.157      |
+| dot_precision@10    | 0.0868     |
+| dot_recall@1        | 0.4509     |
+| dot_recall@3        | 0.6945     |
+| dot_recall@5        | 0.7848     |
+| dot_recall@10       | 0.868      |
+| dot_ndcg@10         | 0.6579     |
+| dot_mrr@10          | 0.5906     |
+| dot_map@100         | 0.5964     |
+
+<!--
+## Bias, Risks and Limitations
+
+*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
+-->
+
+<!--
+### Recommendations
+
+*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
+-->
+
+## Training Details
+
+### Training Dataset
+
+#### Unnamed Dataset
+
+
+* Size: 98,560 training samples
+* Columns: <code>sentence_0</code> and <code>sentence_1</code>
+* Approximate statistics based on the first 1000 samples:
+  |         | sentence_0                                                                         | sentence_1                                                                           |
+  |:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
+  | type    | string                                                                             | string                                                                               |
+  | details | <ul><li>min: 13 tokens</li><li>mean: 24.64 tokens</li><li>max: 50 tokens</li></ul> | <ul><li>min: 22 tokens</li><li>mean: 467.11 tokens</li><li>max: 512 tokens</li></ul> |
+* Samples:
+  | sentence_0                                                                                                                                        | sentence_1                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                     |
+  |:--------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
+  | <code>Define "Bankruptcy Law" as mentioned in the context. What are its implications for the company?</code>                                      | <code>(5) a court of competent jurisdiction enters an order or decree under any Bankruptcy Law that: •is for relief against us in an involuntary case, or adjudicates us insolvent or bankrupt; •appoints a custodian of us or for all or substantially all of our property; or •orders the winding-up or liquidation of us (or any similar relief is granted under any foreign laws); and the order or decree remains unstayed and in effect for 90 days (or, in the case of the 2018 Indenture, 90 consecutive days); or (6) any other event of default provided with respect to debt securities of such series occurs. “Bankruptcy Law” means Title 11, United States Code or any similar federal or state or foreign law for the relief of debtors. “Custodian” means any custodian, receiver, trustee, assignee, liquidator or other similar official under any Bankruptcy Law. If an event of default with respect to debt securities of any series (other than an event of default relating to certain events of bankruptcy, insolvency, or reorganization of us) occurs and is continuing, the trustee by notice to us, or the holders of, in the case of the 2013 Indenture, at least 25% in aggregate principal amount of the outstanding debt securities of such series , and in the case of the 2018 Indenture, at least 33% in aggregate principal amount of the outstanding debt securities of such series, by notice to us and the trustee, may, and the trustee at the request of these holders will, declare the principal of and premium, if any, and accrued and unpaid interest on all the debt securities of such series to be due and payable. Upon such a declaration, such principal, premium and accrued and unpaid interest will be due and payable immediately. If an event of default relating to certain events of bankruptcy, insolvency, or reorganization of us occurs and is continuing, the principal of and premium, if any, and accrued and unpaid interest on the debt securities of such series will become and be immediately due and payable without any declaration or other act on the part of the trustee or any holders. The holders of not less than a majority in aggregate principal amount of the outstanding debt securities of any series may rescind a declaration of acceleration and its consequences, if we have deposited certain sums with the trustee and all events of default with respect to the debt securities of such series, other than the non-payment of the principal or interest which have become due solely by such acceleration, have been cured or waived, as provided in the Indentures. An event of default for a particular series of debt securities does not necessarily constitute an event of default for any other series of debt securities issued under the Indentures. We are required to furnish the trustee annually within 120 days after the end of our fiscal year a statement by one of our officers to the effect that, to the best knowledge of such officer, we are not in default in the fulfillment of any of our obligations under the applicable Indenture or, if there has been a default in the fulfillment of any such obligation, specifying each such default and the nature and status thereof. No holder of any debt securities of any series will have any right to institute any judicial or other proceeding with respect to the applicable Indenture, or for the appointment of a receiver or trustee, or for any other remedy unless: (1) an event of default has occurred and is continuing and such holder has given the trustee prior written notice of such continuing event of default with respect to the debt securities of such series; (2) in the case of the 2013 Indenture, the holders of not less than 25% of the aggregate principal amount of the outstanding debt securities of such series, and in the case of the 2018 Indenture, the holders of not less than 33% of the aggregate principal amount of the outstanding debt securities of such series have requested the trustee to institute proceedings in respect of such event of default; (3) the trustee has been offered indemnity reasonably satisfactory to it against its costs, expenses and liabilities in complying with such request; (4) the trustee has failed to institute proceedings 60 days after the receipt of such notice, request and offer of indemnity; and 10</code> |
+  | <code>What is the total amount of derivative liabilities for currencies as of December 2023, and how does it compare to the previous year?</code> | <code>The tables below present the gross fair value and the notional amounts of derivative contracts by major product type, the amounts of counterparty and cash collateral netting in the consolidated balance sheets, as well as cash and securities collateral posted and received under enforceable credit support agreements that do not meet the criteria for netting under U.S. GAAP. As of December 2023 As of December 2022 $ in millionsDerivative Assets Derivative Liabilities Derivative Assets Derivative Liabilities Not accounted for as hedges Exchange-traded $ 3,401 $ 1,129 $ 675 $ 1,385 OTC-cleared 67,815 64,490 74,297 72,979 Bilateral OTC 171,109 149,444 195,052 174,687 Total interest rates 242,325 215,063 270,024 249,051 OTC-cleared 1,271 1,533 1,516 1,802 Bilateral OTC 11,554 8,601 10,751 9,478 Total credit 12,825 10,134 12,267 11,280 Exchange-traded 708 15 1,041 22 OTC-cleared 1,033 1,632 520 589 Bilateral OTC 88,158 95,742 102,301 111,276 Total currencies 89,899 97,389 103,862 111,887 Exchange-traded 5,468 5,998 9,225 9,542 OTC-cleared 635 711 698 838 Bilateral OTC 10,739 11,234 30,017 22,745 Total commodities 16,842 17,943 39,940 33,125 Exchange-traded 31,315 39,247 26,302 26,607 OTC-cleared 122 171 685 19 Bilateral OTC 28,601 40,696 23,574 30,157 Total equities 60,038 80,114 50,561 56,783 Subtotal 421,929 420,643 476,654 462,126 Accounted for as hedges Bilateral OTC 298 9 335 11 Total interest rates 298 9 335 11 OTC-cleared – 7 29 29 Bilateral OTC 5 208 53 256 Total currencies 5 215 82 285 Subtotal 303 224 417 296 Total gross fair value $ 422,232 $ 420,867 $ 477,071 $ 462,422 Offset in the consolidated balance sheets Exchange-traded $ (32,722) $ (32,722) $ (31,229) $ (31,229) OTC-cleared (67,272) (67,272) (75,349) (75,349) Bilateral OTC (221,395) (221,395) (254,304) (254,304) Counterparty netting (321,389) (321,389) (360,882) (360,882) OTC-cleared (1,335) (486) (1,388) (406) Bilateral OTC (48,388) (42,238) (55,388) (46,399) Cash collateral netting (49,723) (42,724) (56,776) (46,805) Total amounts offset $ (371,112) $ (364,113) $ (417,658) $ (407,687) Included in the consolidated balance sheets Exchange-traded $ 8,170 $ 13,667 $ 6,014 $ 6,327 OTC-cleared 2,269 786 1,008 501 Bilateral OTC 40,681 42,</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                         |
+  | <code>What is the significance of the percentage of total net assets for each company listed in the context?</code>                               | <code>325 Vanguard FTSE Emerging Markets UCITS ETF..Number of SharesFair Value US Dollars ($)% of Total Net Assets Tongkun Group Co., Ltd. Class A 61,400 111,831 0.01% Bluefocus Intelligent Communications Group Co., Ltd. Class A 84,000 111,657 0.01% Beijing United Information Technology Co., Ltd. Class A 21,953 111,443 0.01% Shanghai Zhenhua Heavy Industries Co., Ltd. Class B 464,425 110,998 0.01% Wuchan Zhongda Group Co., Ltd. Class A 162,600 110,415 0.01% Hubei Energy Group Co., Ltd. Class A 175,917 110,269 0.01% Offshore Oil Engineering Co., Ltd. Class A 137,100 110,248 0.01% Siasun Robot & Automation Co., Ltd. Class A 48,100 110,220 0.01% Zhejiang Huahai Pharmaceutical Co., Ltd. Class A 43,432 109,911 0.01% Amlogic Shanghai Co., Ltd. Class A 9,446 109,486 0.01% Beijing Wantai Biological Pharmacy Enterprise Co., Ltd. Class A 11,923 109,432 0.01% Sailun Group Co., Ltd. Class A 69,800 109,284 0.01% Shenzhen Kedali Industry Co., Ltd. Class A 6,000 109,075 0.01% Shan Xi Hua Yang Group New Energy Co., Ltd. Class A 100,050 108,786 0.00% Changzhou Xingyu Automotive Lighting Systems Co., Ltd. Class A 6,400 108,737 0.00% Xiamen C & D, Inc. Class A 72,500 108,728 0.00% Jointown Pharmaceutical Group Co., Ltd. Class A 75,841 108,213 0.00% Shenzhen Kangtai Biological Products Co., Ltd. Class A 31,000 108,194 0.00% Ningxia Baofeng Energy Group Co., Ltd. Class A 61,803 107,128 0.00% Huaxin Cement Co., Ltd. Class H 123,400 106,920 0.00% Shanghai Junshi Biosciences Co., Ltd. Class A 20,128 106,633 0.00% StarPower Semiconductor Ltd. Class A 3,600 106,494 0.00% People.cn Co., Ltd. Class A 26,525 106,468 0.00% Asymchem Laboratories Tianjin Co., Ltd. Class A 6,500 105,307 0.00% China National Accord Medicines Corp., Ltd. Class A 17,550 105,206 0.00% Bloomage Biotechnology Corp., Ltd. Class A 8,582 105,181 0.00% Jason Furniture Hangzhou Co., Ltd. Class A 19,980 104,778 0.00% Chengxin Lithium Group Co., Ltd. Class A 23,900 104,703 0.00% Shanghai Friendess Electronic Technology Corp., Ltd. Class A 4,037 104,637 0.00% Flat Glass Group Co., Ltd. Class A 19,700 104,284 0.00% Guangdong Electric Power Development Co., Ltd. Class A 103,900 104,260 0.00% Hangzhou Lion Electronics Co., Ltd. Class A 20,628 104,149 0.00% YongXing Special Materials Technology Co., Ltd. Class A 12,090 104,052 0.00% Hongfa Technology Co., Ltd.</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                          |
+* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
+  ```json
+  {
+      "scale": 20.0,
+      "similarity_fct": "cos_sim"
+  }
+  ```
+
+### Training Hyperparameters
+#### Non-Default Hyperparameters
+
+- `eval_strategy`: steps
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `num_train_epochs`: 10
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: round_robin
+
+#### All Hyperparameters
+<details><summary>Click to expand</summary>
+
+- `overwrite_output_dir`: False
+- `do_predict`: False
+- `eval_strategy`: steps
+- `prediction_loss_only`: True
+- `per_device_train_batch_size`: 64
+- `per_device_eval_batch_size`: 64
+- `per_gpu_train_batch_size`: None
+- `per_gpu_eval_batch_size`: None
+- `gradient_accumulation_steps`: 1
+- `eval_accumulation_steps`: None
+- `learning_rate`: 5e-05
+- `weight_decay`: 0.0
+- `adam_beta1`: 0.9
+- `adam_beta2`: 0.999
+- `adam_epsilon`: 1e-08
+- `max_grad_norm`: 1
+- `num_train_epochs`: 10
+- `max_steps`: -1
+- `lr_scheduler_type`: linear
+- `lr_scheduler_kwargs`: {}
+- `warmup_ratio`: 0.0
+- `warmup_steps`: 0
+- `log_level`: passive
+- `log_level_replica`: warning
+- `log_on_each_node`: True
+- `logging_nan_inf_filter`: True
+- `save_safetensors`: True
+- `save_on_each_node`: False
+- `save_only_model`: False
+- `restore_callback_states_from_checkpoint`: False
+- `no_cuda`: False
+- `use_cpu`: False
+- `use_mps_device`: False
+- `seed`: 42
+- `data_seed`: None
+- `jit_mode_eval`: False
+- `use_ipex`: False
+- `bf16`: False
+- `fp16`: False
+- `fp16_opt_level`: O1
+- `half_precision_backend`: auto
+- `bf16_full_eval`: False
+- `fp16_full_eval`: False
+- `tf32`: None
+- `local_rank`: 0
+- `ddp_backend`: None
+- `tpu_num_cores`: None
+- `tpu_metrics_debug`: False
+- `debug`: []
+- `dataloader_drop_last`: False
+- `dataloader_num_workers`: 0
+- `dataloader_prefetch_factor`: None
+- `past_index`: -1
+- `disable_tqdm`: False
+- `remove_unused_columns`: True
+- `label_names`: None
+- `load_best_model_at_end`: False
+- `ignore_data_skip`: False
+- `fsdp`: []
+- `fsdp_min_num_params`: 0
+- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
+- `fsdp_transformer_layer_cls_to_wrap`: None
+- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
+- `deepspeed`: None
+- `label_smoothing_factor`: 0.0
+- `optim`: adamw_torch
+- `optim_args`: None
+- `adafactor`: False
+- `group_by_length`: False
+- `length_column_name`: length
+- `ddp_find_unused_parameters`: None
+- `ddp_bucket_cap_mb`: None
+- `ddp_broadcast_buffers`: False
+- `dataloader_pin_memory`: True
+- `dataloader_persistent_workers`: False
+- `skip_memory_metrics`: True
+- `use_legacy_prediction_loop`: False
+- `push_to_hub`: False
+- `resume_from_checkpoint`: None
+- `hub_model_id`: None
+- `hub_strategy`: every_save
+- `hub_private_repo`: False
+- `hub_always_push`: False
+- `gradient_checkpointing`: False
+- `gradient_checkpointing_kwargs`: None
+- `include_inputs_for_metrics`: False
+- `eval_do_concat_batches`: True
+- `fp16_backend`: auto
+- `push_to_hub_model_id`: None
+- `push_to_hub_organization`: None
+- `mp_parameters`: 
+- `auto_find_batch_size`: False
+- `full_determinism`: False
+- `torchdynamo`: None
+- `ray_scope`: last
+- `ddp_timeout`: 1800
+- `torch_compile`: False
+- `torch_compile_backend`: None
+- `torch_compile_mode`: None
+- `dispatch_batches`: None
+- `split_batches`: None
+- `include_tokens_per_second`: False
+- `include_num_input_tokens_seen`: False
+- `neftune_noise_alpha`: None
+- `optim_target_modules`: None
+- `batch_eval_metrics`: False
+- `eval_on_start`: False
+- `batch_sampler`: no_duplicates
+- `multi_dataset_batch_sampler`: round_robin
+
+</details>
+
+### Training Logs
+| Epoch  | Step | Training Loss | cosine_map@100 |
+|:------:|:----:|:-------------:|:--------------:|
+| 0.0649 | 100  | -             | 0.5166         |
+| 0.1299 | 200  | -             | 0.5361         |
+| 0.1948 | 300  | -             | 0.5431         |
+| 0.2597 | 400  | -             | 0.5565         |
+| 0.3247 | 500  | 1.4823        | 0.5605         |
+| 0.3896 | 600  | -             | 0.5703         |
+| 0.4545 | 700  | -             | 0.5738         |
+| 0.5195 | 800  | -             | 0.5776         |
+| 0.5844 | 900  | -             | 0.5763         |
+| 0.6494 | 1000 | 0.9938        | 0.5771         |
+| 0.7143 | 1100 | -             | 0.5783         |
+| 0.7792 | 1200 | -             | 0.5826         |
+| 0.8442 | 1300 | -             | 0.5864         |
+| 0.9091 | 1400 | -             | 0.5902         |
+| 0.9740 | 1500 | 0.8712        | 0.5919         |
+| 1.0    | 1540 | -             | 0.5906         |
+| 1.0390 | 1600 | -             | 0.5833         |
+| 1.1039 | 1700 | -             | 0.5876         |
+| 1.1688 | 1800 | -             | 0.5964         |
+
+
+### Framework Versions
+- Python: 3.10.13
+- Sentence Transformers: 3.0.1
+- Transformers: 4.42.3
+- PyTorch: 2.5.0.dev20240704+cu124
+- Accelerate: 0.32.1
+- Datasets: 2.20.0
+- Tokenizers: 0.19.1
+
+## Citation
+
+### BibTeX
+
+#### Sentence Transformers
+```bibtex
+@inproceedings{reimers-2019-sentence-bert,
+    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
+    author = "Reimers, Nils and Gurevych, Iryna",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
+    month = "11",
+    year = "2019",
+    publisher = "Association for Computational Linguistics",
+    url = "https://arxiv.org/abs/1908.10084",
+}
+```
+
+#### MultipleNegativesRankingLoss
+```bibtex
+@misc{henderson2017efficient,
+    title={Efficient Natural Language Response Suggestion for Smart Reply}, 
+    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
+    year={2017},
+    eprint={1705.00652},
+    archivePrefix={arXiv},
+    primaryClass={cs.CL}
+}
+```
+
+<!--
+## Glossary
+
+*Clearly define terms in order to be accessible across audiences.*
+-->
+
+<!--
+## Model Card Authors
+
+*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
+-->
+
+<!--
+## Model Card Contact
+
+*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
+-->

config.json

@@ -0,0 +1,32 @@
+{
+  "_name_or_path": "bge_large_finetune",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.3",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

config_sentence_transformers.json

@@ -0,0 +1,10 @@
+{
+  "__version__": {
+    "sentence_transformers": "3.0.1",
+    "transformers": "4.42.3",
+    "pytorch": "2.5.0.dev20240704+cu124"
+  },
+  "prompts": {},
+  "default_prompt_name": null,
+  "similarity_fn_name": null
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e78c593fa236c5a3d0a8441dcd6d0f8482ce2e62f5f97be3a06f7173563b9de6
+size 1340612432

modules.json

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

sentence_bert_config.json

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

special_tokens_map.json

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

tokenizer_config.json

@@ -0,0 +1,64 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "max_length": 512,
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_to_multiple_of": null,
+  "pad_token": "[PAD]",
+  "pad_token_type_id": 0,
+  "padding_side": "right",
+  "sep_token": "[SEP]",
+  "stride": 0,
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "truncation_side": "right",
+  "truncation_strategy": "longest_first",
+  "unk_token": "[UNK]"
+}