diff --git "a/README.md" "b/README.md" new file mode 100644--- /dev/null +++ "b/README.md" @@ -0,0 +1,1751 @@ +--- +tags: +- sentence-transformers +- sentence-similarity +- feature-extraction +- generated_from_trainer +- dataset_size:752506 +- loss:CachedMultipleNegativesRankingLoss +base_model: avsolatorio/NoInstruct-small-Embedding-v0 +widget: +- source_sentence: Internationaler Währungsfonds außergewöhnliche Finanzierung + sentences: + - 'measurement_unit: US dollars | periodicity: Annual, Daily, Monthly, Quarterly + | idno: IMF_IRFCL_RACFACTFIO | topics: Prosperity, Finance, Financial Stability + and Integrity | name: Reserves, Contingent Short-term Net Drains on Foreign Currency + Assets (Nominal Value), Undrawn, Unconditional Credit Lines provided to, Banks + and Other Financial Institutions headquartered outside Reporting Country (–) | + methodology: Please refer to: http://www.imf.org/external/np/sta/ir/IRProcessWeb/dataguide.htm + | sources: International Monetary Fund (IMF) | time_periods: 1999–2024-Q2 | definition_long: + Please refer to: https://legacydata.imf.org/?sk=2DFB3380-3603-4D2C-90BE-A04D8BBCE237 + | ref_country: 37 economies | database_name: International Reserves and Foreign + Currency Liquidity (IRFCL)' + - 'database_name: Balance of Payments (BOP) and International Investment Position + (IIP) | ref_country: 197 economies | topics: Prosperity, Economic Policy, Macro-financial + Policies | idno: IMF_BOP_BFXF_BP6 | periodicity: Annual, Quarterly | measurement_unit: + USD | name: Supplementary Items, Financial Account, Net (Excluding Exceptional + Financing) | time_periods: 1948–2024-Q3 | definition_long: Please refer to: https://data.imf.org/en/datasets/IMF.STA:BOP_AGG' + - 'idno: IMF_IRFCL_RACFAMPCL | time_periods: 1999–2024-Q2 | measurement_unit: US + dollars | ref_country: 34 economies | sources: International Monetary Fund (IMF) + | methodology: Please refer to: http://www.imf.org/external/np/sta/ir/IRProcessWeb/dataguide.htm + | periodicity: Annual, Daily, Monthly, Quarterly | database_name: International + Reserves and Foreign Currency Liquidity (IRFCL) | name: Reserves, Contingent Short-term + Net Drains on Foreign Currency Assets (Nominal Value), Aggregate Short and Long + Positions of Options in Foreign Currencies vis-à-vis the Domestic Currency, PRO + MEMORIA: In-the-money Options, - 5 % (Appreciation of 5%), Long Position | definition_long: + Please refer to: https://legacydata.imf.org/?sk=2DFB3380-3603-4D2C-90BE-A04D8BBCE237' + - 'time_periods: 1995–2024-Q3 | periodicity: Quarterly, Annual | definition_long: + Please refer to: https://data.imf.org/en/datasets/IMF.STA:BOP_AGG | methodology: + Please refer to: https://www.imf.org/external/pubs/ft/bopman/bopman.pdf | topics: + Prosperity, Economic Policy, Macro-financial Policies | sources: International + Monetary Fund (IMF) | idno: IMF_BOP_BEFPDCBRP_BP6_USD | ref_country: 49 economies + | measurement_unit: USD, LCU, Euros | name: BOP, Memorandum Items, Exceptional + financing, Portfolio investment, Debt securities, Central bank, Rescheduling of + payments due in current reporting period | database_name: Balance of Payments + (BOP) and International Investment Position (IIP)' +- source_sentence: Sigorta taleplerinin karşılıklarındaki değişimler nasıl hesaplanır? + sentences: + - 'periodicity: Annual | measurement_unit: Domestic currency (Millions) | name: + Technical Reserves, Insurance corporations, Liabilities | ref_country: 109 economies + | definition_long: Technical Reserves Are financial institutions whose principal + function is to provide life, accident, sickness, fire, or other forms of coverage + to individual institutional units, groups of units, or reinsurance services to + other insurance corporations. For the purposes of the FAS, insurance corporations + are disaggregated into life and non-life. Obligations where one unit (the debtor) + is obliged, under specific circumstances, to provide funds or other resources + to another unit (the creditor). These include shares and other equity in corporations. + | time_periods: 2017–2025 | database_name: Financial Access Survey (FAS) | sources: + International Monetary Fund (IMF) | methodology: Please refer to https://www.imf.org/-/media/Files/Data/Home/financial-access-survey-guidelines-and-manual-english.ashx' + - 'periodicity: Annual, Monthly, Quarterly | definition_long: Please refer to: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI + | ref_country: 23 economies | methodology: Please refer to: https://www.imf.org/en/Data/Statistics/FSI-guide + | time_periods: 2010–2024-Q1 | sources: International Monetary Fund (IMF) | database_name: + Financial Soundness Indicators (FSIs) | name: Balance Sheets and Income Statements, + Other financial corporations, Insurance Corporations, Income and Expense Statement, + Claims incurred, net of reinsurance, Changes in reserves for claims outstanding + | idno: IMF_FSI_FS_OFM_IPF_IC_CIC | topics: Prosperity, Finance, Financial Stability + and Integrity' + - 'name: Adequacy of social insurance programs (% of total welfare of beneficiary + households) | methodology: ASPIRE performance indicators are generally based on + national representative household surveys (except for Argentina where the survey + is only urban representative) including household income expenditure/budget surveys, + Living Standard Measurement Surveys (LSMS), Multiple Indicator Cluster Surveys + (MICs), Surveys on Income and Living Conditions (SILCs), and Welfare Monitoring + Surveys. Efforts are made to ensure that welfare aggregates (either income or + consumption per capita) used to rank households are those harmonized by World + Bank regional poverty teams and are up-to-date. | ref_country: 113 economies | + measurement_unit: % of Total | periodicity: Annual | idno: WB_WDI_PER_SI_ALLSI_ADQ_POP_TOT + | definition_long: Adequacy of social insurance programs is measured by the total + transfer amount received by the population participating in social insurance programs + as a share of their total welfare. Welfare is defined as the total income or total + expenditure of beneficiary households. Social insurance programs include old age + contributory pensions (including survivors and disability) and social security + and health insurance benefits (including occupational injury benefits, paid sick + leave, maternity and other social insurance). Estimates include both direct and + indirect beneficiaries. | sources: World Bank (WB) | database_name: World Development + Indicators (WDI)' + - 'topics: Prosperity, Finance, Financial Stability and Integrity | time_periods: + 2010–2024-Q1 | name: Balance Sheets and Income Statements, Other financial corporations, + Life Insurance Corporations, Income and Expense Statement, Premiums earned, net + of reinsurance, Reinsurers'' share of gross premiums earned | sources: International + Monetary Fund (IMF) | idno: IMF_FSI_FS_OFM_IPF_LIC_PER | definition_long: Please + refer to: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI + | ref_country: 22 economies | periodicity: Annual, Monthly, Quarterly | methodology: + Please refer to: https://www.imf.org/en/Data/Statistics/FSI-guide | database_name: + Financial Soundness Indicators (FSIs)' +- source_sentence: IMF Ödemeler Dengesi Toplam Veri Tabanı + sentences: + - 'periodicity: Annual | methodology: Data related to the operations of the IMF + come from the IMF Treasurer''s Department and are converted from special drawing + rights (SDRs) into dollars using end-of-period exchange rates for stocks and average + over the period exchange rates for converting flows. DOD refers to disbursed and + outstanding debt; data are in current U.S. dollars. Data on external debt are + gathered through the World Bank''s Debtor Reporting System (DRS). Long term debt + data are compiled using the countries report on public and publicly guaranteed + borrowing on a loan-by-loan basis and private non guaranteed borrowing on an aggregate + basis. These data are supplemented by information from major multilateral banks + and official lending agencies in major | database_name: World Development Indicators + (WDI) | sources: World Bank (WB) | time_periods: 1970–2024 | name: Use of IMF + credit (DOD, current US$) | ref_country: 122 economies | definition_long: Use + of IMF Credit: Data related to the operations of the IMF are provided by the IMF + Treasurer’s Department. They are converted from special drawing rights into dollars + using end-of-period exchange rates for stocks and average-over-the-period exchange + rates for flows. IMF trust fund operations under the Enhanced Structural Adjustment + Facility, Extended Fund Facility, Poverty Reduction and Growth Facility, and Structural + Adjustment Facility (Enhanced Structural Adjustment Facility in 1999) are presented + together with all of the IMF’s special facilities (buffer stock, supplemental + reserve, compensatory and contingency facilities, oil facilities, and other facilities). + SDR allocations are also included in this category.' + - 'idno: IMF_BOPAGG_BCA_BP6 | name: Current Account, Total, Net | topics: Prosperity, + Economic Policy, Macro-financial Policies | sources: International Monetary Fund + (IMF) | periodicity: Annual | time_periods: 2005–2022 | measurement_unit: USD, + % of GDP | methodology: Refer to the Sixth edition of the IMF''s Balance of Payments + and International investment Position Manual | ref_country: 195 economies | database_name: + Balance of Payments (BOP), World and Regional Aggregates | definition_long: This + indicator is a consolidation of the following indicators from the source: Current + Account, Total, Net, Percent of GDP, Percent Current Account, Total, Net, US Dollars' + - 'measurement_unit: Domestic currency, Euros, US dollars | time_periods: 1997-12–2024-Q2 + | database_name: International Financial Statistics (IFS) | topics: Prosperity, + Economic Policy, Finance, Macro-financial Policies, Financial Stability and Integrity + | methodology: Please refer to: https://www.imf.org/en/Data/Manuals-and-Guides + | definition_long: Please refer to: https://data.imf.org/en/news/accessing%20international%20financial%20statistics + | periodicity: Annual, Monthly, Quarterly | name: Monetary, Central Bank Survey, + Net Claims on Central Government, Liabilities To Central Government | ref_country: + 171 economies | idno: IMF_IFS_MFS_SRF_FASLG' + - 'name: Monetary, Depository Corporations Survey, Domestic Claims, Net Claims on + Central Government, Liabilities to Central Government (refers to the Depository + Corporations) | time_periods: 1997-12–2024-Q2 | measurement_unit: Domestic currency, + Euros, US dollars | topics: Prosperity, Economic Policy, Finance, Fiscal Policy, + Financial Stability and Integrity | database_name: International Financial Statistics + (IFS) | ref_country: 171 economies | definition_long: Please refer to: https://data.imf.org/en/news/accessing%20international%20financial%20statistics + | periodicity: Annual, Monthly, Quarterly' +- source_sentence: IMF loans to Philippines balance of payments + sentences: + - 'name: BOP, Net lending (+) / net borrowing (-) (balance from current and capital + account) | sources: International Monetary Fund (IMF) | ref_country: 198 economies + | measurement_unit: USD, Euros, LCU | idno: IMF_BOP_BACK_BP6 | periodicity: Quarterly, + Annual | methodology: Please refer to: https://www.imf.org/external/pubs/ft/bopman/bopman.pdf + | time_periods: 1948–2024-Q3 | database_name: Balance of Payments (BOP) and International + Investment Position (IIP) | topics: Prosperity, Economic Policy, Macro-financial + Policies | definition_long: Please refer to: https://data.imf.org/en/datasets/IMF.STA:BOP_AGG' + - 'topics: Prosperity, Economic Policy, Macro-financial Policies | definition_long: + Please refer to: https://data.imf.org/en/datasets/IMF.STA:BOP_AGG | name: Supplementary + Items, Net Credit and Loans from the IMF (Excluding Reserve Position) | methodology: + Please refer to: https://www.imf.org/external/pubs/ft/bopman/bopman.pdf | time_periods: + 1948–2024-Q3 | sources: International Monetary Fund (IMF) | periodicity: Quarterly, + Annual | ref_country: 187 economies' + - 'time_periods: 1974–2024-Q3 | measurement_unit: USD, LCU, Euros | definition_long: + Please refer to: https://data.imf.org/en/datasets/IMF.STA:BOP_AGG | database_name: + Balance of Payments (BOP) and International Investment Position (IIP) | idno: + IMF_BOP_BEFTD_BP6_USD | sources: International Monetary Fund (IMF) | methodology: + Please refer to: https://www.imf.org/external/pubs/ft/bopman/bopman.pdf | name: + BOP, Memorandum Items, Exceptional financing, Capital transfers, Debt forgiveness' + - 'idno: IMF_BOP_ILCCDL_NRES_OTSOFC_1YOL_BP6_USD | sources: International Monetary + Fund (IMF) | time_periods: 2008–2024-Q3 | definition_long: Please refer to: https://data.imf.org/en/datasets/IMF.STA:BOP_AGG + | periodicity: Quarterly, Annual | topics: Prosperity, Economic Policy, Macro-financial + Policies | methodology: Please refer to: https://www.imf.org/external/pubs/ft/bopman/bopman.pdf + | measurement_unit: Euros, USD, LCU | ref_country: 17 economies | name: IIP, Currency + Composition, Debt Liabilities to Nonresidents by Sector, Other Sectors, Other + Financial Corporations, Of which one year or less | database_name: Balance of + Payments (BOP) and International Investment Position (IIP)' +- source_sentence: Kesenjangan pembelajaran Türkiye 2015 TIMSS matematika kelas 5 + sentences: + - 'database_name: Education Statistics | periodicity: Annual | sources: World Bank + (WB), International Association for the Evaluation of Educational Achievement + (IEA) | ref_country: 66 economies | time_periods: 2003–2019 | measurement_unit: + Index | idno: WB_EDSTATS_SE_CLO_8_LDSEV_M_TMS | name: Learning Deprivation Severity;TIMSS + for grade 8 using MPL Low (400 points) for math' + - 'name: Learning Deprivation Severity;PISA for grade 15Y using MPL Level 2 for + science | ref_country: 82 economies | database_name: Education Statistics | time_periods: + 2000–2018 | definition_long: Learning Deprivation Severity captures the inequality + of learning among the learning deprived population and is the gap squared in relation + to the minimum proficiency squared. This indicator is calculated based on the + data from Programme for International Student Assessment (PISA) assessments of + the extent to which 15-year-old students can handle science adeptly when confronted + with situations and problems, using Minimum proficiency level (MPL) 2 as the benchmark + of basic knowledge. | sources: World Bank (WB), Organisation for Economic Co-operation + and Development (OECD) | measurement_unit: Index | idno: WB_EDSTATS_SE_CLO_15Y_LDSEV_S_PS' + - 'ref_country: 6 economies: Malta, New Zealand, Norway, South Africa, Türkiye, + United Kingdom | definition_long: Learning Deprivation Gap, that measures the + average distance of a learning deprived child to the minimum proficiency level + and indicates the average increase in learning required to eliminate learning + deprivation. This indicator is calculated based on the data from International + Association for the Evaluation of Educational Achievement''s (IEA) TIMSS (Trends + in International Mathematics and Science Study) assessments on the skills in mathematics + at 5th grade, using Minimum proficiency level (MPL) Low (400 points) as the benchmark + of basic knowledge. | idno: WB_EDSTATS_SE_CLO_5_LDGAP_M_TMS | measurement_unit: + Index | database_name: Education Statistics | name: Learning Deprivation Gap;TIMSS + for grade 5 using MPL Low (400 points) for math | time_periods: 2011–2019 | periodicity: + Annual | sources: World Bank (WB), International Association for the Evaluation + of Educational Achievement (IEA)' + - 'measurement_unit: Index | idno: WB_EDSTATS_SE_CLO_6_LDSEV_M_TMS | periodicity: + Annual | ref_country: 3 economies: Botswana, Honduras, Yemen, Rep. | definition_long: + Learning Deprivation Severity captures the inequality of learning among the learning + deprived population and is the gap squared in relation to the minimum proficiency + squared. This indicator is calculated based on the data from International Association + for the Evaluation of Educational Achievement''s (IEA) TIMSS (Trends in International + Mathematics and Science Study) assessments on the skills in mathematics at 6th + grade, using Minimum proficiency level (MPL) Low (400 points) as the benchmark + of basic knowledge. | time_periods: 2011–2011 | database_name: Education Statistics + | name: Learning Deprivation Severity;TIMSS for grade 6 using MPL Low (400 points) + for math | sources: World Bank (WB), International Association for the Evaluation + of Educational Achievement (IEA)' +pipeline_tag: sentence-similarity +library_name: sentence-transformers +--- + +# SentenceTransformer based on avsolatorio/NoInstruct-small-Embedding-v0 + +This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [avsolatorio/NoInstruct-small-Embedding-v0](https://huggingface.co/avsolatorio/NoInstruct-small-Embedding-v0). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for retrieval. + +## Model Details + +### Model Description +- **Model Type:** Sentence Transformer +- **Base model:** [avsolatorio/NoInstruct-small-Embedding-v0](https://huggingface.co/avsolatorio/NoInstruct-small-Embedding-v0) +- **Maximum Sequence Length:** 512 tokens +- **Output Dimensionality:** 384 dimensions +- **Similarity Function:** Cosine Similarity +- **Supported Modality:** Text + + + + +### Model Sources + +- **Documentation:** [Sentence Transformers Documentation](https://sbert.net) +- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers) +- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers) + +### Full Model Architecture + +``` +SentenceTransformer( + (0): Transformer({'transformer_task': 'feature-extraction', 'modality_config': {'text': {'method': 'forward', 'method_output_name': 'last_hidden_state'}}, 'module_output_name': 'token_embeddings', 'architecture': 'BertModel'}) + (1): Pooling({'embedding_dimension': 384, 'pooling_mode': 'cls', 'include_prompt': True}) + (2): Pooling({'embedding_dimension': 384, 'pooling_mode': 'mean', 'include_prompt': True}) + (3): 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("ai4data/devdata-search-noinstruct-small-cmnrl") +# Run inference +sentences = [ + 'Kesenjangan pembelajaran Türkiye 2015 TIMSS matematika kelas 5', + "ref_country: 6 economies: Malta, New Zealand, Norway, South Africa, Türkiye, United Kingdom | definition_long: Learning Deprivation Gap, that measures the average distance of a learning deprived child to the minimum proficiency level and indicates the average increase in learning required to eliminate learning deprivation. This indicator is calculated based on the data from International Association for the Evaluation of Educational Achievement's (IEA) TIMSS (Trends in International Mathematics and Science Study) assessments on the skills in mathematics at 5th grade, using Minimum proficiency level (MPL) Low (400 points) as the benchmark of basic knowledge. | idno: WB_EDSTATS_SE_CLO_5_LDGAP_M_TMS | measurement_unit: Index | database_name: Education Statistics | name: Learning Deprivation Gap;TIMSS for grade 5 using MPL Low (400 points) for math | time_periods: 2011–2019 | periodicity: Annual | sources: World Bank (WB), International Association for the Evaluation of Educational Achievement (IEA)", + 'database_name: Education Statistics | periodicity: Annual | sources: World Bank (WB), International Association for the Evaluation of Educational Achievement (IEA) | ref_country: 66 economies | time_periods: 2003–2019 | measurement_unit: Index | idno: WB_EDSTATS_SE_CLO_8_LDSEV_M_TMS | name: Learning Deprivation Severity;TIMSS for grade 8 using MPL Low (400 points) for math', +] +embeddings = model.encode(sentences) +print(embeddings.shape) +# [3, 384] + +# Get the similarity scores for the embeddings +similarities = model.similarity(embeddings, embeddings) +print(similarities) +# tensor([[1.0000, 0.8073, 0.6102], +# [0.8073, 1.0000, 0.8010], +# [0.6102, 0.8010, 1.0000]]) +``` + + + + + + + + + + +## Training Details + +### Training Dataset + +#### Unnamed Dataset + +* Size: 752,506 training samples +* Columns: anchor, positive, negative_1, negative_2, and negative_3 +* Approximate statistics based on the first 100 samples: + | | anchor | positive | negative_1 | negative_2 | negative_3 | + |:---------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| + | type | string | string | string | string | string | + | modality | text | text | text | text | text | + | details | | | | | | +* Samples: + | anchor | positive | negative_1 | negative_2 | negative_3 | + |:-----------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | üç aylık hanehalk gelir verileri | topics: Prosperity, Finance, Financial Stability and Integrity \| name: Balance Sheets and Income Statements, Households, Gross disposable income, Wages and salaries from employment \| idno: IMF_FSI_FS_HH_IDGW \| periodicity: Annual, Quarterly \| database_name: Financial Soundness Indicators (FSIs) \| sources: International Monetary Fund (IMF) \| methodology: Please refer to: https://www.imf.org/en/Data/Statistics/FSI-guide \| measurement_unit: Euros, Domestic currency, US dollars | name: Core Set, Deposit Takers, Earnings and Profitability, Interest Margin to Gross Income \| sources: International Monetary Fund (IMF) \| definition_long: Please refer to: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI \| measurement_unit: Percentage of gross income \| time_periods: 2000–2024-Q2 \| methodology: Please refer to: https://www.imf.org/en/Data/Statistics/FSI-guide \| idno: IMF_FSI_FSEIM \| ref_country: 148 economies \| topics: Prosperity, Finance, Financial Stability and Integrity \| database_name: Financial Soundness Indicators (FSIs) | sources: Organisation for Economic Co-operation and Development (OECD) \| definition_long: Gini (market income) (Income definition until 2011) \| topics: Prosperity, Poverty, Inequality and Shared Prosperity \| periodicity: Annual \| methodology: https://www.oecd.org/social/income-distribution-database.htm \| name: Gini (market income) (Income definition until 2011) \| measurement_unit: Index 0-1 \| database_name: Income Distribution Database \| idno: OECD_IDD_INC_MRKT_GINI_METH2011 | measurement_unit: Index \| name: Household Survey on income, etc (Availability score over 10 years) \| methodology: The Statistical Performance Indicators (SPI) measure the performance of national statistical systems across five pillars: data use, data services, data products, data sources, and data infrastructure. The SPI framework includes 51 indicators that reflect both the outcomes and enabling factors of statistical performance. Each pillar is composed of several dimensions, with indicators aggregated using a simple average to form dimension, pillar, and overall index scores. The SPI draws on publicly available, internationally comparable data, and provides a holistic, forward-looking approach to assessing statistical capacity, as outlined in this World Bank working paper: https://documents1.worldbank.org/curated/en/440191616164007723/pdf/Statistical-Performance-Indicators-and-Index-A-New-Tool-to-Measure-Country-Statistical-Capacity.pdf \| database_name: Statistical Performance Indic... | + | 2018 سے 2023 تک براہ راست سرمایہ کاری کے بہاؤ | measurement_unit: USD, Euros, LCU \| name: BOP, Financial Account, Direct investment, Equity and investment fund shares, Equity other than reinvestment of earnings, Direct investment enterprises in direct investor (reverse investment) \| definition_long: Please refer to: https://data.imf.org/en/datasets/IMF.STA:BOP_AGG \| ref_country: 154 economies \| time_periods: 1967–2024-Q3 \| periodicity: Quarterly, Annual \| sources: International Monetary Fund (IMF) \| methodology: Please refer to: https://www.imf.org/external/pubs/ft/bopman/bopman.pdf \| idno: IMF_BOP_BFDEOR_BP6 \| topics: Prosperity, Economic Policy, Macro-financial Policies | idno: IMF_BOP_IDDR_BP6 \| periodicity: Annual, Quarterly \| methodology: Please refer to: https://www.imf.org/external/pubs/ft/bopman/bopman.pdf \| name: IIP, Direct investment, Debt instruments, Direct investment enterprises in direct investor (reverse investment) \| ref_country: 127 economies \| topics: Prosperity, Economic Policy, Macro-financial Policies \| time_periods: 1982–2024-Q3 \| sources: International Monetary Fund (IMF) | topics: Prosperity, Trade, Investment and Competitiveness, Investment and Business Climate \| database_name: World Development Indicators (WDI) \| methodology: Data on equity flows are based on balance of payments data reported by the International Monetary Fund (IMF). Foreign direct investment (FDI) data are supplemented by the World Bank staff estimates using data from the United Nations Conference on Trade and Development (UNCTAD) and official national sources. The internationally accepted definition of FDI (from the sixth edition of the IMF's Balance of Payments Manual [2009]), includes the following components: equity investment, including investment associated with equity that gives rise to control or influence; investment in indirectly influenced or controlled enterprises; investment in fellow enterprises; debt (except selected debt); and reverse investment. The Framework for Direct Investment Relationships provides criteria for determining whether cross-border ownership results i... | idno: IMF_CDIS_IW_BP6 \| database_name: Coordinated Direct Investment Survey (CDIS) \| topics: Prosperity, Economic Policy, Macro-financial Policies \| name: Direct Investment Positions \| time_periods: 2009–2023 \| definition_long: Please refer to: https://data.imf.org/en/datasets/IMF.STA:DIP \| periodicity: Annual \| sources: International Monetary Fund (IMF) \| methodology: Please refer to: https://www.imf.org/external/np/sta/pdf/cdisguide.pdf | + | WB_WDI_SP_POP_65UP_MA_ZS data | periodicity: Annual \| methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects. \| definition_long: Male population 65 years of age or older as a percentage of the total male population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. \| measurement_unit: Percentage \| time_periods: 1960–2024 \| name: Population ages 65 and above, male (% of male population) \| idno: WB_WDI_SP_POP_65UP_MA_ZS | database_name: World Development Indicators (WDI) \| name: Population ages 15-64 (% of total population) \| measurement_unit: Percentage \| ref_country: 217 economies \| topics: Infrastructure, Urban, Resilience and Land, Housing \| time_periods: 1960–2024 \| definition_long: Total population between the ages 15 to 64 as a percentage of the total population. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship. \| methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects. \| sources: United Nations (UN) \| periodicity: Annual | time_periods: 1960–2024 \| ref_country: 217 economies \| sources: United Nations (UN) \| methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects. \| definition_long: Male population between the ages 50 to 54 as a percentage of the total male population. \| measurement_unit: Percentage \| periodicity: Annual \| idno: WB_WDI_SP_POP_5054_MA_5Y \| name: Population ages 50-54, male (% of male population) \| database_name: World Development Indicators (WDI) | definition_long: Male population between the ages 25 to 29 as a percentage of the total male population. \| ref_country: 217 economies \| methodology: Population structure by age and sex in the World Bank's estimates is based on age/sex distributions of the population in United Nations Population Division's World Population Prospects. \| measurement_unit: Percentage \| sources: United Nations (UN) \| periodicity: Annual \| name: Population ages 25-29, male (% of male population) \| idno: WB_WDI_SP_POP_2529_MA_5Y \| database_name: World Development Indicators (WDI) \| time_periods: 1960–2024 | +* Loss: [CachedMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: + ```json + { + "scale": 20.0, + "similarity_fct": "cos_sim", + "mini_batch_size": 48, + "gather_across_devices": false, + "directions": [ + "query_to_doc" + ], + "partition_mode": "joint", + "hardness_mode": null, + "hardness_strength": 0.0 + } + ``` + +### Evaluation Dataset + +#### Unnamed Dataset + +* Size: 7,602 evaluation samples +* Columns: anchor, positive, negative_1, negative_2, and negative_3 +* Approximate statistics based on the first 100 samples: + | | anchor | positive | negative_1 | negative_2 | negative_3 | + |:---------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------| + | type | string | string | string | string | string | + | modality | text | text | text | text | text | + | details | | | | | | +* Samples: + | anchor | positive | negative_1 | negative_2 | negative_3 | + |:----------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | 公務員が現金のみで給与を受け取る割合はどのくらいですか？ | database_name: Global Findex Database \| measurement_unit: Percentage of public sector wage recipients, Percentage of respondents \| name: Received public sector wages: in cash only \| sources: World Bank (WB) \| periodicity: Triennial \| ref_country: 158 economies \| definition_long: The percentage of respondents who report being employed by the government, military, or public sector and receiving any money from their employer in the past year in the form of a salary or wages for doing work, and who received that money in cash only. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies. \| methodology: Global Findex indicators are derived from individual-level survey data collected by Gallup, Inc. as part of the Gallup World Poll (conducted annually since 2005 on approximately 1000 people in each of more than 160 economies and in more than 150 languages), with an added Global Findex module designed by the World Bank. Surveys are nation... | database_name: Global Findex Database \| sources: World Bank (WB) \| topics: Prosperity, Finance, Financial Inclusion, Infrastructure and Access \| definition_long: The percentage of respondents who report personally receiving any payment from the government (government transfers, public sector pension, or public sector wages) in the past year. This includes payments for educational or medical expenses, unemployment benefits, subsidy payments, or any kind of social benefits. It also includes pension payments from the government, military, or public sector as well as wages from employment in the government, military, or public sector. The respondents are the entire civilian, noninstitutionalized population age 15 and up in the target economies. \| time_periods: 2017–2024 \| ref_country: 152 economies \| periodicity: Triennial \| name: Received government payments \| idno: WB_FINDEX_FING2P \| methodology: Global Findex indicators are derived from individual-level survey data collected by Gallup, ... | methodology: Global Findex indicators are derived from individual-level survey data collected by Gallup, Inc. as part of the Gallup World Poll (conducted annually since 2005 on approximately 1000 people in each of more than 160 economies and in more than 150 languages), with an added Global Findex module designed by the World Bank. Surveys are nationally representative and cover the non-institutionalized civilian population aged 15 and above. For more information, see https://www.worldbank.org/en/publication/globalfindex/methodology. \| topics: Prosperity, Finance, Financial Inclusion, Infrastructure and Access \| name: Received private sector wages: into a bank or similar financial institution account \| idno: WB_FINDEX_FIN32_N33_34A \| measurement_unit: Percentage of private sector wage recipients, Percentage of respondents \| time_periods: 2014–2024 \| periodicity: Triennial \| sources: World Bank (WB) \| definition_long: The percentage of respondents who report being employed in the privat... | definition_long: All staff (teacher and non-teachers) compensation expressed as a percentage of direct expenditure in public educational institutions (instructional and non-instructional) of the specified level of education. Financial aid to students and other transfers are excluded from direct expenditure. Staff compensation includes salaries, contributions by employers for staff retirement programmes, and other allowances and benefits. Divide all staff compensation in public institutions of a given level of education (ex. primary, secondary, or all levels combined) by total expenditure (current and capital) in public institutions of the same level of education, and multiply by 100. \| idno: WB_EDSTATS_UIS_XSPENDP_56_FDPUB_FNS \| name: All staff compensation as % of total expenditure in tertiary public institutions (%) \| periodicity: Annual \| measurement_unit: Share (proportion) \| methodology: For more information, consult the UNESCO Institute of Statistics website: http://www.uis.unesc... | + | data asuransi kuartalan dan tahunan stabilitas keuangan | idno: IMF_FSI_FS_OFM_IPF_NLIC_A \| database_name: Financial Soundness Indicators (FSIs) \| time_periods: 2007–2024-Q1 \| name: Balance Sheets and Income Statements, Other financial corporations, Total financial system assets, Total Assets, Insurance corporations, Nonlife insurance corporations \| ref_country: 33 economies \| periodicity: Annual, Monthly, Quarterly \| methodology: Please refer to: https://www.imf.org/en/Data/Statistics/FSI-guide | methodology: Please refer to: https://www.imf.org/en/Data/Statistics/FSI-guide \| name: Balance Sheets and Income Statements, Other financial corporations, Life Insurance Corporations, Balance Sheet, Liabilities, Insurance, pensions, and standardized guarantee schemes, Net equity of households in life insurance reserves \| database_name: Financial Soundness Indicators (FSIs) \| sources: International Monetary Fund (IMF) \| definition_long: Please refer to: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI \| ref_country: 22 economies \| topics: Prosperity, Finance, Financial Stability and Integrity | measurement_unit: Domestic currency, Euros, US dollars \| topics: Prosperity, Finance, Financial Stability and Integrity \| time_periods: 2010–2024-Q1 \| ref_country: 26 economies \| periodicity: Annual, Monthly, Quarterly \| idno: IMF_FSI_FS_OFM_IPF_LIC_NI \| name: Balance Sheets and Income Statements, Other financial corporations, Life Insurance Corporations, Income and Expense Statement, Net income from insurance activity \| sources: International Monetary Fund (IMF) \| methodology: Please refer to: https://www.imf.org/en/Data/Statistics/FSI-guide \| definition_long: Please refer to: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI | definition_long: Please refer to: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI \| topics: Prosperity, Finance, Financial Stability and Integrity \| ref_country: 26 economies \| methodology: Please refer to: https://www.imf.org/en/Data/Statistics/FSI-guide \| periodicity: Annual, Monthly, Quarterly \| database_name: Financial Soundness Indicators (FSIs) \| measurement_unit: Domestic currency, Euros, US dollars \| idno: IMF_FSI_FS_OFM_IPF_IC_LIPI \| time_periods: 2008–2024-Q1 \| name: Balance Sheets and Income Statements, Other financial corporations, Insurance Corporations, Balance Sheet, Liabilities, Insurance, pensions, and standardized guarantee schemes, Prepayment of insurance premiums and insurance payable | + | What is the difference between lending rates and deposit rates charged by banks? | measurement_unit: Basis points \| name: Additional FSIs, Deposit Takers, Spread Between Reference Lending and Deposit Rates \| sources: International Monetary Fund (IMF) \| idno: IMF_FSI_FSSR \| definition_long: Please refer to: https://data.imf.org/en/datasets/IMF:EXTERNAL_DATASET_CARDS/IMF.STA:LFSI \| ref_country: 69 economies \| database_name: Financial Soundness Indicators (FSIs) \| methodology: Please refer to: https://www.imf.org/en/Data/Statistics/FSI-guide | ref_country: 152 economies \| definition_long: Deposit interest rate is the rate paid by commercial or similar banks for demand, time, or savings deposits. The terms and conditions attached to these rates differ by country, however, limiting their comparability. This indicator is expressed as a percentage (a÷b)*100. \| periodicity: Annual \| methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/ \| name: Deposit interest rate (%) \| idno: WB_WDI_FR_INR_DPST \| sources: International Monetary Fund (IMF) \| database_name: World Development Indicators (WDI) | measurement_unit: % \| definition_long: Lending rate is the bank rate that usually meets the short- and medium-term financing needs of the private sector. This rate is normally differentiated according to creditworthiness of borrowers and objectives of financing. The terms and conditions attached to these rates differ by country, however, limiting their comparability. \| database_name: World Development Indicators (WDI) \| idno: WB_WDI_FR_INR_LEND \| name: Lending interest rate (%) \| ref_country: 148 economies \| periodicity: Annual \| time_periods: 1960–2024 \| methodology: Monetary and Financial statistics are compiled in accordance with international standards: Monetary and Financial Statistics Manual, 2018 or 2004 versions. Specific information on how countries compile their Monetary and Finance statistics can be found on the IMF website: https://dsbb.imf.org/ \| sources: International Monetary Fund (IMF) | sources: International Monetary Fund (IMF) \| time_periods: 2017–2024 \| ref_country: 73 economies \| methodology: Please refer to https://www.imf.org/-/media/Files/Data/Home/financial-access-survey-guidelines-and-manual-english.ashx \| definition_long: Refers to the accounts of resident nonfinancial corporations (public and private) and individuals (household sector) that have obtained credit (loans) from the reporting institutions. This definition considers: (1) The actual number of loans that nonfinancial corporations and individuals have received from the reporting institutions are counted, as opposed to number of borrowers. (2) Overdraft accounts are also counted towards the total number of loan accounts. Are financial institutions that carry out a range of financial intermediation depending on national banking regulations and practices but typically include accepting deposits; offering checking and saving account services; granting business and personal loans; and offering other basi... | +* Loss: [CachedMultipleNegativesRankingLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cachedmultiplenegativesrankingloss) with these parameters: + ```json + { + "scale": 20.0, + "similarity_fct": "cos_sim", + "mini_batch_size": 48, + "gather_across_devices": false, + "directions": [ + "query_to_doc" + ], + "partition_mode": "joint", + "hardness_mode": null, + "hardness_strength": 0.0 + } + ``` + +### Training Hyperparameters +#### Non-Default Hyperparameters + +- `per_device_train_batch_size`: 128 +- `num_train_epochs`: 5.0 +- `learning_rate`: 3e-05 +- `warmup_steps`: 0.1 +- `bf16`: True +- `per_device_eval_batch_size`: 128 +- `load_best_model_at_end`: True +- `dataloader_num_workers`: 16 +- `dataloader_persistent_workers`: True +- `dataloader_prefetch_factor`: 4 + +#### All Hyperparameters +
Click to expand + +- `per_device_train_batch_size`: 128 +- `num_train_epochs`: 5.0 +- `max_steps`: -1 +- `learning_rate`: 3e-05 +- `lr_scheduler_type`: linear +- `lr_scheduler_kwargs`: None +- `warmup_steps`: 0.1 +- `optim`: adamw_torch_fused +- `optim_args`: None +- `weight_decay`: 0.0 +- `adam_beta1`: 0.9 +- `adam_beta2`: 0.999 +- `adam_epsilon`: 1e-08 +- `optim_target_modules`: None +- `gradient_accumulation_steps`: 1 +- `average_tokens_across_devices`: True +- `max_grad_norm`: 1.0 +- `label_smoothing_factor`: 0.0 +- `bf16`: True +- `fp16`: False +- `bf16_full_eval`: False +- `fp16_full_eval`: False +- `tf32`: None +- `gradient_checkpointing`: False +- `gradient_checkpointing_kwargs`: None +- `torch_compile`: False +- `torch_compile_backend`: None +- `torch_compile_mode`: None +- `use_liger_kernel`: False +- `liger_kernel_config`: None +- `use_cache`: False +- `neftune_noise_alpha`: None +- `torch_empty_cache_steps`: None +- `auto_find_batch_size`: False +- `log_on_each_node`: True +- `logging_nan_inf_filter`: True +- `include_num_input_tokens_seen`: no +- `log_level`: passive +- `log_level_replica`: warning +- `disable_tqdm`: False +- `project`: huggingface +- `trackio_space_id`: None +- `trackio_bucket_id`: None +- `trackio_static_space_id`: None +- `per_device_eval_batch_size`: 128 +- `prediction_loss_only`: True +- `eval_on_start`: False +- `eval_do_concat_batches`: True +- `eval_use_gather_object`: False +- `eval_accumulation_steps`: None +- `include_for_metrics`: [] +- `batch_eval_metrics`: False +- `save_only_model`: False +- `save_on_each_node`: False +- `enable_jit_checkpoint`: False +- `push_to_hub`: False +- `hub_private_repo`: None +- `hub_model_id`: None +- `hub_strategy`: every_save +- `hub_always_push`: False +- `hub_revision`: None +- `load_best_model_at_end`: True +- `ignore_data_skip`: False +- `restore_callback_states_from_checkpoint`: False +- `full_determinism`: False +- `seed`: 42 +- `data_seed`: None +- `use_cpu`: False +- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None} +- `parallelism_config`: None +- `dataloader_drop_last`: False +- `dataloader_num_workers`: 16 +- `dataloader_pin_memory`: True +- `dataloader_persistent_workers`: True +- `dataloader_prefetch_factor`: 4 +- `remove_unused_columns`: True +- `label_names`: None +- `train_sampling_strategy`: random +- `length_column_name`: length +- `ddp_find_unused_parameters`: None +- `ddp_bucket_cap_mb`: None +- `ddp_broadcast_buffers`: False +- `ddp_static_graph`: None +- `ddp_backend`: None +- `ddp_timeout`: 1800 +- `fsdp`: None +- `fsdp_config`: None +- `deepspeed`: None +- `debug`: [] +- `skip_memory_metrics`: True +- `do_predict`: False +- `resume_from_checkpoint`: None +- `warmup_ratio`: None +- `local_rank`: -1 +- `prompts`: None +- `batch_sampler`: batch_sampler +- `multi_dataset_batch_sampler`: proportional +- `router_mapping`: {} +- `learning_rate_mapping`: {} + +
+ +### Training Logs +
Click to expand + +| Epoch | Step | Training Loss | Validation Loss | +|:-------:|:---------:|:-------------:|:---------------:| +| 0.0043 | 25 | 5.6202 | - | +| 0.0085 | 50 | 5.6297 | - | +| 0.0128 | 75 | 5.5812 | - | +| 0.0170 | 100 | 5.5358 | - | +| 0.0213 | 125 | 5.4741 | - | +| 0.0255 | 150 | 5.4175 | - | +| 0.0298 | 175 | 5.3786 | - | +| 0.0340 | 200 | 5.2680 | - | +| 0.0383 | 225 | 5.1447 | - | +| 0.0425 | 250 | 5.0826 | - | +| 0.0468 | 275 | 5.0714 | - | +| 0.0510 | 300 | 4.9947 | - | +| 0.0553 | 325 | 4.8690 | - | +| 0.0595 | 350 | 4.8750 | - | +| 0.0638 | 375 | 4.8394 | - | +| 0.0680 | 400 | 4.7044 | - | +| 0.0723 | 425 | 4.6608 | - | +| 0.0765 | 450 | 4.7134 | - | +| 0.0808 | 475 | 4.6193 | - | +| 0.0850 | 500 | 4.5724 | - | +| 0.0893 | 525 | 4.4952 | - | +| 0.0936 | 550 | 4.5353 | - | +| 0.0978 | 575 | 4.4816 | - | +| 0.1021 | 600 | 4.4828 | - | +| 0.1063 | 625 | 4.4723 | - | +| 0.1106 | 650 | 4.4281 | - | +| 0.1148 | 675 | 4.3516 | - | +| 0.1191 | 700 | 4.3295 | - | +| 0.1233 | 725 | 4.3338 | - | +| 0.1276 | 750 | 4.2704 | - | +| 0.1318 | 775 | 4.2913 | - | +| 0.1361 | 800 | 4.2681 | - | +| 0.1403 | 825 | 4.1882 | - | +| 0.1446 | 850 | 4.2539 | - | +| 0.1488 | 875 | 4.2039 | - | +| 0.1531 | 900 | 4.1432 | - | +| 0.1573 | 925 | 4.1726 | - | +| 0.1616 | 950 | 4.0994 | - | +| 0.1658 | 975 | 4.1103 | - | +| 0.1701 | 1000 | 4.0969 | 3.9661 | +| 0.1743 | 1025 | 4.0586 | - | +| 0.1786 | 1050 | 4.1395 | - | +| 0.1829 | 1075 | 4.0937 | - | +| 0.1871 | 1100 | 4.0291 | - | +| 0.1914 | 1125 | 3.9959 | - | +| 0.1956 | 1150 | 4.0158 | - | +| 0.1999 | 1175 | 3.9518 | - | +| 0.2041 | 1200 | 3.9880 | - | +| 0.2084 | 1225 | 4.0043 | - | +| 0.2126 | 1250 | 3.9202 | - | +| 0.2169 | 1275 | 3.8903 | - | +| 0.2211 | 1300 | 3.9466 | - | +| 0.2254 | 1325 | 3.9467 | - | +| 0.2296 | 1350 | 3.8890 | - | +| 0.2339 | 1375 | 3.8788 | - | +| 0.2381 | 1400 | 3.9512 | - | +| 0.2424 | 1425 | 3.8926 | - | +| 0.2466 | 1450 | 3.8644 | - | +| 0.2509 | 1475 | 3.8489 | - | +| 0.2551 | 1500 | 3.8425 | - | +| 0.2594 | 1525 | 3.7923 | - | +| 0.2637 | 1550 | 3.7727 | - | +| 0.2679 | 1575 | 3.7259 | - | +| 0.2722 | 1600 | 3.7895 | - | +| 0.2764 | 1625 | 3.8200 | - | +| 0.2807 | 1650 | 3.7486 | - | +| 0.2849 | 1675 | 3.7641 | - | +| 0.2892 | 1700 | 3.7141 | - | +| 0.2934 | 1725 | 3.6630 | - | +| 0.2977 | 1750 | 3.7346 | - | +| 0.3019 | 1775 | 3.6728 | - | +| 0.3062 | 1800 | 3.7034 | - | +| 0.3104 | 1825 | 3.6520 | - | +| 0.3147 | 1850 | 3.6448 | - | +| 0.3189 | 1875 | 3.5979 | - | +| 0.3232 | 1900 | 3.6910 | - | +| 0.3274 | 1925 | 3.6337 | - | +| 0.3317 | 1950 | 3.4782 | - | +| 0.3359 | 1975 | 3.6399 | - | +| 0.3402 | 2000 | 3.5984 | 3.3923 | +| 0.3444 | 2025 | 3.5276 | - | +| 0.3487 | 2050 | 3.5744 | - | +| 0.3530 | 2075 | 3.5243 | - | +| 0.3572 | 2100 | 3.5960 | - | +| 0.3615 | 2125 | 3.5555 | - | +| 0.3657 | 2150 | 3.4883 | - | +| 0.3700 | 2175 | 3.4885 | - | +| 0.3742 | 2200 | 3.5119 | - | +| 0.3785 | 2225 | 3.4195 | - | +| 0.3827 | 2250 | 3.4388 | - | +| 0.3870 | 2275 | 3.4866 | - | +| 0.3912 | 2300 | 3.4012 | - | +| 0.3955 | 2325 | 3.3360 | - | +| 0.3997 | 2350 | 3.4636 | - | +| 0.4040 | 2375 | 3.4031 | - | +| 0.4082 | 2400 | 3.3435 | - | +| 0.4125 | 2425 | 3.4274 | - | +| 0.4167 | 2450 | 3.3644 | - | +| 0.4210 | 2475 | 3.3635 | - | +| 0.4252 | 2500 | 3.2441 | - | +| 0.4295 | 2525 | 3.3182 | - | +| 0.4337 | 2550 | 3.3000 | - | +| 0.4380 | 2575 | 3.2938 | - | +| 0.4423 | 2600 | 3.2769 | - | +| 0.4465 | 2625 | 3.3680 | - | +| 0.4508 | 2650 | 3.2518 | - | +| 0.4550 | 2675 | 3.2625 | - | +| 0.4593 | 2700 | 3.2735 | - | +| 0.4635 | 2725 | 3.2524 | - | +| 0.4678 | 2750 | 3.2918 | - | +| 0.4720 | 2775 | 3.2043 | - | +| 0.4763 | 2800 | 3.2213 | - | +| 0.4805 | 2825 | 3.2363 | - | +| 0.4848 | 2850 | 3.2321 | - | +| 0.4890 | 2875 | 3.1937 | - | +| 0.4933 | 2900 | 3.2054 | - | +| 0.4975 | 2925 | 3.1775 | - | +| 0.5018 | 2950 | 3.1145 | - | +| 0.5060 | 2975 | 3.1645 | - | +| 0.5103 | 3000 | 3.2082 | 2.9662 | +| 0.5145 | 3025 | 3.1032 | - | +| 0.5188 | 3050 | 3.1637 | - | +| 0.5230 | 3075 | 3.1067 | - | +| 0.5273 | 3100 | 3.1215 | - | +| 0.5316 | 3125 | 3.1186 | - | +| 0.5358 | 3150 | 3.0841 | - | +| 0.5401 | 3175 | 3.0317 | - | +| 0.5443 | 3200 | 3.1090 | - | +| 0.5486 | 3225 | 3.0962 | - | +| 0.5528 | 3250 | 3.0569 | - | +| 0.5571 | 3275 | 2.9969 | - | +| 0.5613 | 3300 | 3.0273 | - | +| 0.5656 | 3325 | 3.0095 | - | +| 0.5698 | 3350 | 2.9979 | - | +| 0.5741 | 3375 | 2.9043 | - | +| 0.5783 | 3400 | 3.0085 | - | +| 0.5826 | 3425 | 2.9580 | - | +| 0.5868 | 3450 | 2.9545 | - | +| 0.5911 | 3475 | 2.9912 | - | +| 0.5953 | 3500 | 2.9998 | - | +| 0.5996 | 3525 | 2.9806 | - | +| 0.6038 | 3550 | 2.9441 | - | +| 0.6081 | 3575 | 2.9543 | - | +| 0.6123 | 3600 | 2.9249 | - | +| 0.6166 | 3625 | 2.9102 | - | +| 0.6209 | 3650 | 2.9222 | - | +| 0.6251 | 3675 | 2.8535 | - | +| 0.6294 | 3700 | 2.8752 | - | +| 0.6336 | 3725 | 2.9121 | - | +| 0.6379 | 3750 | 2.9313 | - | +| 0.6421 | 3775 | 2.8627 | - | +| 0.6464 | 3800 | 2.8876 | - | +| 0.6506 | 3825 | 2.8514 | - | +| 0.6549 | 3850 | 2.8525 | - | +| 0.6591 | 3875 | 2.8042 | - | +| 0.6634 | 3900 | 2.8733 | - | +| 0.6676 | 3925 | 2.8174 | - | +| 0.6719 | 3950 | 2.7960 | - | +| 0.6761 | 3975 | 2.8830 | - | +| 0.6804 | 4000 | 2.8498 | 2.5842 | +| 0.6846 | 4025 | 2.7865 | - | +| 0.6889 | 4050 | 2.8118 | - | +| 0.6931 | 4075 | 2.8055 | - | +| 0.6974 | 4100 | 2.7622 | - | +| 0.7016 | 4125 | 2.7626 | - | +| 0.7059 | 4150 | 2.7271 | - | +| 0.7102 | 4175 | 2.7440 | - | +| 0.7144 | 4200 | 2.7554 | - | +| 0.7187 | 4225 | 2.7130 | - | +| 0.7229 | 4250 | 2.7314 | - | +| 0.7272 | 4275 | 2.6651 | - | +| 0.7314 | 4300 | 2.6917 | - | +| 0.7357 | 4325 | 2.7011 | - | +| 0.7399 | 4350 | 2.6582 | - | +| 0.7442 | 4375 | 2.7004 | - | +| 0.7484 | 4400 | 2.6605 | - | +| 0.7527 | 4425 | 2.6318 | - | +| 0.7569 | 4450 | 2.6118 | - | +| 0.7612 | 4475 | 2.6759 | - | +| 0.7654 | 4500 | 2.6467 | - | +| 0.7697 | 4525 | 2.6690 | - | +| 0.7739 | 4550 | 2.6306 | - | +| 0.7782 | 4575 | 2.6303 | - | +| 0.7824 | 4600 | 2.6329 | - | +| 0.7867 | 4625 | 2.6651 | - | +| 0.7910 | 4650 | 2.6603 | - | +| 0.7952 | 4675 | 2.6133 | - | +| 0.7995 | 4700 | 2.6152 | - | +| 0.8037 | 4725 | 2.6378 | - | +| 0.8080 | 4750 | 2.5875 | - | +| 0.8122 | 4775 | 2.5989 | - | +| 0.8165 | 4800 | 2.5947 | - | +| 0.8207 | 4825 | 2.5884 | - | +| 0.8250 | 4850 | 2.5881 | - | +| 0.8292 | 4875 | 2.5735 | - | +| 0.8335 | 4900 | 2.5513 | - | +| 0.8377 | 4925 | 2.6018 | - | +| 0.8420 | 4950 | 2.5512 | - | +| 0.8462 | 4975 | 2.5855 | - | +| 0.8505 | 5000 | 2.5376 | 2.3247 | +| 0.8547 | 5025 | 2.5259 | - | +| 0.8590 | 5050 | 2.4565 | - | +| 0.8632 | 5075 | 2.5615 | - | +| 0.8675 | 5100 | 2.5908 | - | +| 0.8717 | 5125 | 2.5035 | - | +| 0.8760 | 5150 | 2.4797 | - | +| 0.8803 | 5175 | 2.4456 | - | +| 0.8845 | 5200 | 2.4171 | - | +| 0.8888 | 5225 | 2.5017 | - | +| 0.8930 | 5250 | 2.4892 | - | +| 0.8973 | 5275 | 2.4838 | - | +| 0.9015 | 5300 | 2.4401 | - | +| 0.9058 | 5325 | 2.5462 | - | +| 0.9100 | 5350 | 2.4932 | - | +| 0.9143 | 5375 | 2.4448 | - | +| 0.9185 | 5400 | 2.4307 | - | +| 0.9228 | 5425 | 2.4651 | - | +| 0.9270 | 5450 | 2.4306 | - | +| 0.9313 | 5475 | 2.4974 | - | +| 0.9355 | 5500 | 2.4426 | - | +| 0.9398 | 5525 | 2.4623 | - 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| +| 4.2609 | 25050 | 1.4587 | - | +| 4.2652 | 25075 | 1.4704 | - | +| 4.2694 | 25100 | 1.4111 | - | +| 4.2737 | 25125 | 1.4016 | - | +| 4.2779 | 25150 | 1.4098 | - | +| 4.2822 | 25175 | 1.3714 | - | +| 4.2864 | 25200 | 1.4037 | - | +| 4.2907 | 25225 | 1.4237 | - | +| 4.2949 | 25250 | 1.4133 | - | +| 4.2992 | 25275 | 1.4287 | - | +| 4.3035 | 25300 | 1.3938 | - | +| 4.3077 | 25325 | 1.4145 | - | +| 4.3120 | 25350 | 1.3941 | - | +| 4.3162 | 25375 | 1.3521 | - | +| 4.3205 | 25400 | 1.4080 | - | +| 4.3247 | 25425 | 1.3736 | - | +| 4.3290 | 25450 | 1.4056 | - | +| 4.3332 | 25475 | 1.4525 | - | +| 4.3375 | 25500 | 1.4065 | - | +| 4.3417 | 25525 | 1.3697 | - | +| 4.3460 | 25550 | 1.3528 | - | +| 4.3502 | 25575 | 1.3955 | - | +| 4.3545 | 25600 | 1.3853 | - | +| 4.3587 | 25625 | 1.3618 | - | +| 4.3630 | 25650 | 1.4036 | - | +| 4.3672 | 25675 | 1.4140 | - | +| 4.3715 | 25700 | 1.4107 | - | +| 4.3757 | 25725 | 1.3978 | - | +| 4.3800 | 25750 | 1.4371 | - | +| 4.3842 | 25775 | 1.4303 | - | +| 4.3885 | 25800 | 1.4355 | - | +| 4.3928 | 25825 | 1.4051 | - | +| 4.3970 | 25850 | 1.3774 | - | +| 4.4013 | 25875 | 1.3830 | - | +| 4.4055 | 25900 | 1.3971 | - | +| 4.4098 | 25925 | 1.4202 | - | +| 4.4140 | 25950 | 1.4413 | - | +| 4.4183 | 25975 | 1.3869 | - | +| 4.4225 | 26000 | 1.3457 | 1.3278 | +| 4.4268 | 26025 | 1.3671 | - | +| 4.4310 | 26050 | 1.4497 | - | +| 4.4353 | 26075 | 1.4059 | - | +| 4.4395 | 26100 | 1.4408 | - | +| 4.4438 | 26125 | 1.4299 | - | +| 4.4480 | 26150 | 1.3738 | - | +| 4.4523 | 26175 | 1.3872 | - | +| 4.4565 | 26200 | 1.3619 | - | +| 4.4608 | 26225 | 1.3713 | - | +| 4.4650 | 26250 | 1.3950 | - | +| 4.4693 | 26275 | 1.3945 | - | +| 4.4735 | 26300 | 1.4500 | - | +| 4.4778 | 26325 | 1.4545 | - | +| 4.4821 | 26350 | 1.4104 | - | +| 4.4863 | 26375 | 1.4443 | - | +| 4.4906 | 26400 | 1.4241 | - | +| 4.4948 | 26425 | 1.4406 | - | +| 4.4991 | 26450 | 1.3797 | - | +| 4.5033 | 26475 | 1.3754 | - | +| 4.5076 | 26500 | 1.4013 | - | +| 4.5118 | 26525 | 1.3894 | - | +| 4.5161 | 26550 | 1.3945 | - | +| 4.5203 | 26575 | 1.3998 | - | +| 4.5246 | 26600 | 1.4291 | - | +| 4.5288 | 26625 | 1.4155 | - | +| 4.5331 | 26650 | 1.4235 | - | +| 4.5373 | 26675 | 1.3947 | - | +| 4.5416 | 26700 | 1.3977 | - | +| 4.5458 | 26725 | 1.4344 | - | +| 4.5501 | 26750 | 1.3368 | - | +| 4.5543 | 26775 | 1.3745 | - | +| 4.5586 | 26800 | 1.4084 | - | +| 4.5629 | 26825 | 1.4338 | - | +| 4.5671 | 26850 | 1.4514 | - | +| 4.5714 | 26875 | 1.4312 | - | +| 4.5756 | 26900 | 1.3723 | - | +| 4.5799 | 26925 | 1.3548 | - | +| 4.5841 | 26950 | 1.4124 | - | +| 4.5884 | 26975 | 1.4237 | - | +| 4.5926 | 27000 | 1.3547 | 1.3229 | +| 4.5969 | 27025 | 1.3545 | - | +| 4.6011 | 27050 | 1.3715 | - | +| 4.6054 | 27075 | 1.4043 | - | +| 4.6096 | 27100 | 1.4354 | - | +| 4.6139 | 27125 | 1.4149 | - | +| 4.6181 | 27150 | 1.3871 | - | +| 4.6224 | 27175 | 1.3520 | - | +| 4.6266 | 27200 | 1.4062 | - | +| 4.6309 | 27225 | 1.4240 | - | +| 4.6351 | 27250 | 1.3639 | - | +| 4.6394 | 27275 | 1.3706 | - | +| 4.6436 | 27300 | 1.4052 | - | +| 4.6479 | 27325 | 1.4344 | - | +| 4.6522 | 27350 | 1.4317 | - | +| 4.6564 | 27375 | 1.3400 | - | +| 4.6607 | 27400 | 1.3699 | - | +| 4.6649 | 27425 | 1.4571 | - | +| 4.6692 | 27450 | 1.3747 | - | +| 4.6734 | 27475 | 1.4174 | - | +| 4.6777 | 27500 | 1.3619 | - | +| 4.6819 | 27525 | 1.3580 | - | +| 4.6862 | 27550 | 1.3980 | - | +| 4.6904 | 27575 | 1.4027 | - | +| 4.6947 | 27600 | 1.4251 | - | +| 4.6989 | 27625 | 1.3975 | - | +| 4.7032 | 27650 | 1.4478 | - | +| 4.7074 | 27675 | 1.3643 | - | +| 4.7117 | 27700 | 1.3991 | - | +| 4.7159 | 27725 | 1.4057 | - | +| 4.7202 | 27750 | 1.3924 | - | +| 4.7244 | 27775 | 1.4177 | - | +| 4.7287 | 27800 | 1.3657 | - | +| 4.7329 | 27825 | 1.3697 | - | +| 4.7372 | 27850 | 1.3955 | - | +| 4.7415 | 27875 | 1.4104 | - | +| 4.7457 | 27900 | 1.3837 | - | +| 4.7500 | 27925 | 1.3858 | - | +| 4.7542 | 27950 | 1.4233 | - | +| 4.7585 | 27975 | 1.3754 | - | +| 4.7627 | 28000 | 1.4066 | 1.3196 | +| 4.7670 | 28025 | 1.3945 | - | +| 4.7712 | 28050 | 1.4068 | - | +| 4.7755 | 28075 | 1.3691 | - | +| 4.7797 | 28100 | 1.3916 | - | +| 4.7840 | 28125 | 1.3965 | - | +| 4.7882 | 28150 | 1.4089 | - | +| 4.7925 | 28175 | 1.3722 | - | +| 4.7967 | 28200 | 1.3841 | - | +| 4.8010 | 28225 | 1.4010 | - | +| 4.8052 | 28250 | 1.3621 | - | +| 4.8095 | 28275 | 1.3905 | - | +| 4.8137 | 28300 | 1.3783 | - | +| 4.8180 | 28325 | 1.4333 | - | +| 4.8222 | 28350 | 1.3624 | - | +| 4.8265 | 28375 | 1.3615 | - | +| 4.8308 | 28400 | 1.3919 | - | +| 4.8350 | 28425 | 1.3562 | - | +| 4.8393 | 28450 | 1.4188 | - | +| 4.8435 | 28475 | 1.3778 | - | +| 4.8478 | 28500 | 1.3769 | - | +| 4.8520 | 28525 | 1.3606 | - | +| 4.8563 | 28550 | 1.3213 | - | +| 4.8605 | 28575 | 1.3614 | - | +| 4.8648 | 28600 | 1.4117 | - | +| 4.8690 | 28625 | 1.4237 | - | +| 4.8733 | 28650 | 1.3886 | - | +| 4.8775 | 28675 | 1.3809 | - | +| 4.8818 | 28700 | 1.3843 | - | +| 4.8860 | 28725 | 1.3673 | - | +| 4.8903 | 28750 | 1.4163 | - | +| 4.8945 | 28775 | 1.3144 | - | +| 4.8988 | 28800 | 1.3955 | - | +| 4.9030 | 28825 | 1.3979 | - | +| 4.9073 | 28850 | 1.4144 | - | +| 4.9115 | 28875 | 1.3894 | - | +| 4.9158 | 28900 | 1.4170 | - | +| 4.9201 | 28925 | 1.4105 | - | +| 4.9243 | 28950 | 1.3590 | - | +| 4.9286 | 28975 | 1.3957 | - | +| 4.9328 | 29000 | 1.3997 | 1.3170 | +| 4.9371 | 29025 | 1.3671 | - | +| 4.9413 | 29050 | 1.3973 | - | +| 4.9456 | 29075 | 1.3643 | - | +| 4.9498 | 29100 | 1.3729 | - | +| 4.9541 | 29125 | 1.3583 | - | +| 4.9583 | 29150 | 1.4153 | - | +| 4.9626 | 29175 | 1.3598 | - | +| 4.9668 | 29200 | 1.3862 | - | +| 4.9711 | 29225 | 1.3994 | - | +| 4.9753 | 29250 | 1.3414 | - | +| 4.9796 | 29275 | 1.4035 | - | +| 4.9838 | 29300 | 1.4216 | - | +| 4.9881 | 29325 | 1.3692 | - | +| 4.9923 | 29350 | 1.3992 | - | +| 4.9966 | 29375 | 1.3844 | - | +| **5.0** | **29395** | **-** | **1.3169** | + +* The bold row denotes the saved checkpoint. +
+ +### Training Time +- **Training**: 10.3 hours +- **Evaluation**: 10.4 minutes +- **Total**: 10.5 hours + +### Framework Versions +- Python: 3.11.7 +- Sentence Transformers: 5.5.1 +- Transformers: 5.12.0 +- PyTorch: 2.12.0+cu130 +- Accelerate: 1.14.0 +- Datasets: 5.0.0 +- Tokenizers: 0.22.2 + +## 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", +} +``` + +#### CachedMultipleNegativesRankingLoss +```bibtex +@misc{gao2021scaling, + title={Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup}, + author={Luyu Gao and Yunyi Zhang and Jiawei Han and Jamie Callan}, + year={2021}, + eprint={2101.06983}, + archivePrefix={arXiv}, + primaryClass={cs.LG} +} +``` + + + + + + \ No newline at end of file