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BGE-Finetuned-FinBench

Sentence Similarity
sentence-transformers
Safetensors
English
bert
feature-extraction
Generated from Trainer
dataset_size:150
loss:MatryoshkaLoss
loss:MultipleNegativesRankingLoss
Eval Results
text-embeddings-inference
Inference Endpoints
Model card Files Community
BGE-Finetuned-FinBench / README.md
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metadata
language:
  - en
license: apache-2.0
tags:
  - sentence-transformers
  - sentence-similarity
  - feature-extraction
  - generated_from_trainer
  - dataset_size:150
  - loss:MatryoshkaLoss
  - loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
  - source_sentence: >-
      Worldwide Sales Change By Business SegmentOrganic
      salesAcquisitionsDivestituresTranslationTotal sales changeSafety and
      Industrial1.0 % % %(4.2) %(3.2) %Transportation and Electronics1.2
      (0.5)(4.6)(3.9)Health Care3.2 (1.4)(3.8)(2.0)Consumer(0.9)
      (0.4)(2.6)(3.9)Total Company1.2 (0.5)(3.9)(3.2)
    sentences:
      - Has MGM Resorts paid dividends to common shareholders in FY2022?
      - >-
        If we exclude the impact of M&A, which segment has dragged down 3M's
        overall growth in 2022?
      - In 2022 Q2, which of JPM's business segments had the highest net income?
  - source_sentence: >-
      Table of ContentsConsolidated Statement of IncomePepsiCo, Inc. and
      SubsidiariesFiscal years ended December 31, 2022, December 25, 2021 and
      December 26, 2020(in millions except per share amounts)202220212020Net
      Revenue$86,392 $79,474 $70,372 Cost of sales40,576 37,075 31,797 Gross
      profit45,816 42,399 38,575 Selling, general and administrative
      expenses34,459 31,237 28,453 Gain associated with the Juice Transaction
      (see Note 13)(3,321) Impairment of intangible assets (see Notes 1 and
      4)3,166 42 Operating Profit11,512 11,162 10,080 Other pension and retiree
      medical benefits income132 522 117 Net interest expense and
      other(939)(1,863)(1,128)Income before income taxes10,705 9,821 9,069
      Provision for income taxes1,727 2,142 1,894 Net income8,978 7,679 7,175
      Less: Net income attributable to noncontrolling interests68 61 55 Net
      Income Attributable to PepsiCo$8,910 $7,618 $7,120 Net Income Attributable
      to PepsiCo per Common ShareBasic$6.45 $5.51 $5.14 Diluted$6.42 $5.49 $5.12
      Weighted-average common shares outstandingBasic1,380 1,382 1,385
      Diluted1,387 1,389 1,392 See accompanying notes to the consolidated
      financial statements.60
    sentences:
      - >-
        What is Amcor's year end FY2020 net AR (in USD millions)? Address the
        question by adopting the perspective of a financial analyst who can only
        use the details shown within the balance sheet.
      - >-
        What is the FY2022 unadjusted EBITDA less capex for PepsiCo? Define
        unadjusted EBITDA as unadjusted operating income + depreciation and
        amortization [from cash flow statement]. Answer in USD millions. Respond
        to the question by assuming the perspective of an investment analyst who
        can only use the details shown within the statement of cash flows and
        the income statement.
      - >-
        By how much did Pepsico increase its unsecured five year revolving
        credit agreement on May 26, 2023?
  - source_sentence: >-
      Lockheed Martin CorporationConsolidated Statements of Earnings(in
      millions, except per share data) Years Ended December 31,202220212020Net
      salesProducts$ 55,466 $ 56,435 $ 54,928 Services 10,518 10,609 10,470
      Total net sales 65,984 67,044 65,398 Cost of salesProducts (49,577)
      (50,273) (48,996) Services (9,280) (9,463) (9,371) Severance and other
      charges (100) (36) (27) Other unallocated, net 1,260 1,789 1,650 Total
      cost of sales (57,697) (57,983) (56,744) Gross profit 8,287 9,061 8,654
      Other income (expense), net 61 62 (10) Operating profit 8,348 9,123 8,644
      Interest expense (623) (569) (591) Non-service FAS pension (expense)
      income (971) (1,292) 219 Other non-operating (expense) income, net (74)
      288 (37) Earnings from continuing operations before income taxes 6,680
      7,550 8,235 Income tax expense (948) (1,235) (1,347) Net earnings from
      continuing operations 5,732 6,315 6,888 Net loss from discontinued
      operations  (55) Net earnings$ 5,732 $ 6,315 $ 6,833 Earnings (loss) per
      common shareBasicContinuing operations$ 21.74 $ 22.85 $ 24.60 Discontinued
      operations  (0.20) Basic earnings per common share$ 21.74 $ 22.85 $ 24.40
      DilutedContinuing operations$ 21.66 $ 22.76 $ 24.50 Discontinued
      operations  (0.20) Diluted earnings per common share$ 21.66 $ 22.76 $
      24.30 The accompanying notes are an integral part of these consolidated
      financial statements.Table of Contents 63
    sentences:
      - As of Q2'2023, is Pfizer spinning off any large business segments?
      - >-
        What is Lockheed Martin's 2 year total revenue CAGR from FY2020 to
        FY2022 (in units of percents and round to one decimal place)? Provide a
        response to the question by primarily using the statement of income.
      - >-
        What are the geographies that Pepsico primarily operates in as of
        FY2022?
  - source_sentence: >-
      The Kraft Heinz CompanyConsolidated Statements of Income(in millions,
      except per share data) December 28, 2019 December 29, 2018 December 30,
      2017Net sales$24,977 $26,268 $26,076Cost of products sold16,830 17,347
      17,043Gross profit8,147 8,921 9,033Selling, general and administrative
      expenses, excluding impairment losses3,178 3,190 2,927Goodwill impairment
      losses1,197 7,008 Intangible asset impairment losses702 8,928 49Selling,
      general and administrative expenses5,077 19,126 2,976Operating
      income/(loss)3,070 (10,205) 6,057Interest expense1,361 1,284 1,234Other
      expense/(income)(952) (168) (627)Income/(loss) before income taxes2,661
      (11,321) 5,450Provision for/(benefit from) income taxes728 (1,067)
      (5,482)Net income/(loss)1,933 (10,254) 10,932Net income/(loss)
      attributable to noncontrolling interest(2) (62) (9)Net income/(loss)
      attributable to common shareholders$1,935 $(10,192) $10,941Per share data
      applicable to common shareholders:  Basic earnings/(loss)$1.59 $(8.36)
      $8.98Diluted earnings/(loss)1.58 (8.36) 8.91See accompanying notes to the
      consolidated financial statements.45
    sentences:
      - >-
        What drove gross margin change as of the FY2022 for American Express? If
        gross margin is not a useful metric for a company like this, then please
        state that and explain why.
      - >-
        How much was the Real change in Sales for AMCOR in FY 2023 vs FY 2022,
        if we exclude the impact of FX movement, passthrough costs and one-off
        items?
      - >-
        What is Kraft Heinz's FY2019 inventory turnover ratio? Inventory
        turnover ratio is defined as: (FY2019 COGS) / (average inventory between
        FY2018 and FY2019). Round your answer to two decimal places. Please base
        your judgments on the information provided primarily in the balance
        sheet and the P&L statement.
  - source_sentence: >-
      3M Company and SubsidiariesConsolidated Statement of IncomeYears ended
      December 31(Millions, except per share amounts)202220212020Net
      sales$34,229 $35,355 $32,184
    sentences:
      - Is 3M a capital-intensive business based on FY2022 data?
      - >-
        What is Amazon's year-over-year change in revenue from FY2016 to FY2017
        (in units of percents and round to one decimal place)? Calculate what
        was asked by utilizing the line items clearly shown in the statement of
        income.
      - >-
        Among all of the derivative instruments that Verizon used to manage the
        exposure to fluctuations of foreign currencies exchange rates or
        interest rates, which one had the highest notional value in FY 2021?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
  - cosine_accuracy@1
  - cosine_accuracy@3
  - cosine_accuracy@5
  - cosine_accuracy@10
  - cosine_precision@1
  - cosine_precision@3
  - cosine_precision@5
  - cosine_precision@10
  - cosine_recall@1
  - cosine_recall@3
  - cosine_recall@5
  - cosine_recall@10
  - cosine_ndcg@10
  - cosine_mrr@10
  - cosine_map@100
model-index:
  - name: BGE Base - FinBench Finetuned
    results:
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 768
          type: dim_768
        metrics:
          - type: cosine_accuracy@1
            value: 0.8933333333333333
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8933333333333333
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33333333333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19999999999999996
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8933333333333333
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9588867770000028
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9444444444444444
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9444444444444445
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 512
          type: dim_512
        metrics:
          - type: cosine_accuracy@1
            value: 0.8866666666666667
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.8866666666666667
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33333333333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19999999999999996
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.8866666666666667
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9572991737142889
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9422222222222221
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9422222222222223
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 256
          type: dim_256
        metrics:
          - type: cosine_accuracy@1
            value: 0.9133333333333333
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9133333333333333
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33333333333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19999999999999996
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9133333333333333
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9671410469523832
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9555555555555554
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9555555555555556
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 128
          type: dim_128
        metrics:
          - type: cosine_accuracy@1
            value: 0.9266666666666666
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.9266666666666666
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33333333333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19999999999999996
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.9266666666666666
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9720619835714305
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9622222222222221
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9622222222222223
            name: Cosine Map@100
      - task:
          type: information-retrieval
          name: Information Retrieval
        dataset:
          name: dim 64
          type: dim_64
        metrics:
          - type: cosine_accuracy@1
            value: 0.94
            name: Cosine Accuracy@1
          - type: cosine_accuracy@3
            value: 1
            name: Cosine Accuracy@3
          - type: cosine_accuracy@5
            value: 1
            name: Cosine Accuracy@5
          - type: cosine_accuracy@10
            value: 1
            name: Cosine Accuracy@10
          - type: cosine_precision@1
            value: 0.94
            name: Cosine Precision@1
          - type: cosine_precision@3
            value: 0.33333333333333326
            name: Cosine Precision@3
          - type: cosine_precision@5
            value: 0.19999999999999996
            name: Cosine Precision@5
          - type: cosine_precision@10
            value: 0.09999999999999998
            name: Cosine Precision@10
          - type: cosine_recall@1
            value: 0.94
            name: Cosine Recall@1
          - type: cosine_recall@3
            value: 1
            name: Cosine Recall@3
          - type: cosine_recall@5
            value: 1
            name: Cosine Recall@5
          - type: cosine_recall@10
            value: 1
            name: Cosine Recall@10
          - type: cosine_ndcg@10
            value: 0.9769829201904777
            name: Cosine Ndcg@10
          - type: cosine_mrr@10
            value: 0.9688888888888888
            name: Cosine Mrr@10
          - type: cosine_map@100
            value: 0.9688888888888889
            name: Cosine Map@100

BGE Base - FinBench Finetuned

This is a sentence-transformers model finetuned from BAAI/bge-base-en-v1.5 on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Model Details

Model Description

  • Model Type: Sentence Transformer
  • Base model: BAAI/bge-base-en-v1.5
  • Maximum Sequence Length: 512 tokens
  • Output Dimensionality: 768 tokens
  • Similarity Function: Cosine Similarity
  • Training Dataset:
    • json
  • Language: en
  • License: apache-2.0

Model Sources

Full Model Architecture 

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel 
  (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
  (2): Normalize()
)

Usage

Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

pip install -U sentence-transformers

Then you can load this model and run inference.

from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("Snorkeler/BGE-Finetuned-FinBench")
# Run inference
sentences = [
    '3M Company and SubsidiariesConsolidated Statement of IncomeYears ended December 31(Millions, except per share amounts)202220212020Net sales$34,229 $35,355 $32,184',
    'Is 3M a capital-intensive business based on FY2022 data?',
    'Among all of the derivative instruments that Verizon used to manage the exposure to fluctuations of foreign currencies exchange rates or interest rates, which one had the highest notional value in FY 2021?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]

Evaluation

Metrics

Information Retrieval

Metric Value
cosine_accuracy@1 0.8933
cosine_accuracy@3 1.0
cosine_accuracy@5 1.0
cosine_accuracy@10 1.0
cosine_precision@1 0.8933
cosine_precision@3 0.3333
cosine_precision@5 0.2
cosine_precision@10 0.1
cosine_recall@1 0.8933
cosine_recall@3 1.0
cosine_recall@5 1.0
cosine_recall@10 1.0
cosine_ndcg@10 0.9589
cosine_mrr@10 0.9444
cosine_map@100 0.9444

Information Retrieval

Metric Value
cosine_accuracy@1 0.8867
cosine_accuracy@3 1.0
cosine_accuracy@5 1.0
cosine_accuracy@10 1.0
cosine_precision@1 0.8867
cosine_precision@3 0.3333
cosine_precision@5 0.2
cosine_precision@10 0.1
cosine_recall@1 0.8867
cosine_recall@3 1.0
cosine_recall@5 1.0
cosine_recall@10 1.0
cosine_ndcg@10 0.9573
cosine_mrr@10 0.9422
cosine_map@100 0.9422

Information Retrieval

Metric Value
cosine_accuracy@1 0.9133
cosine_accuracy@3 1.0
cosine_accuracy@5 1.0
cosine_accuracy@10 1.0
cosine_precision@1 0.9133
cosine_precision@3 0.3333
cosine_precision@5 0.2
cosine_precision@10 0.1
cosine_recall@1 0.9133
cosine_recall@3 1.0
cosine_recall@5 1.0
cosine_recall@10 1.0
cosine_ndcg@10 0.9671
cosine_mrr@10 0.9556
cosine_map@100 0.9556

Information Retrieval

Metric Value
cosine_accuracy@1 0.9267
cosine_accuracy@3 1.0
cosine_accuracy@5 1.0
cosine_accuracy@10 1.0
cosine_precision@1 0.9267
cosine_precision@3 0.3333
cosine_precision@5 0.2
cosine_precision@10 0.1
cosine_recall@1 0.9267
cosine_recall@3 1.0
cosine_recall@5 1.0
cosine_recall@10 1.0
cosine_ndcg@10 0.9721
cosine_mrr@10 0.9622
cosine_map@100 0.9622

Information Retrieval

Metric Value
cosine_accuracy@1 0.94
cosine_accuracy@3 1.0
cosine_accuracy@5 1.0
cosine_accuracy@10 1.0
cosine_precision@1 0.94
cosine_precision@3 0.3333
cosine_precision@5 0.2
cosine_precision@10 0.1
cosine_recall@1 0.94
cosine_recall@3 1.0
cosine_recall@5 1.0
cosine_recall@10 1.0
cosine_ndcg@10 0.977
cosine_mrr@10 0.9689
cosine_map@100 0.9689

Training Details

Training Dataset

json

  • Dataset: json
  • Size: 150 training samples
  • Columns: context and question
  • Approximate statistics based on the first 150 samples:
    context question
    type string string
    details
    • min: 17 tokens
    • mean: 314.29 tokens
    • max: 512 tokens
    • min: 11 tokens
    • mean: 39.67 tokens
    • max: 175 tokens
  • Samples:
    context question
    Table of Contents 3M Company and SubsidiariesConsolidated Statement of Cash Flow sYears ended December 31 (Millions) 2018 2017 2016 Cash Flows from Operating Activities Net income including noncontrolling interest $5,363 $4,869 $5,058 Adjustments to reconcile net income including noncontrolling interest to net cashprovided by operating activities Depreciation and amortization 1,488 1,544 1,474 Company pension and postretirement contributions (370) (967) (383) Company pension and postretirement expense 410 334 250 Stock-based compensation expense 302 324 298 Gain on sale of businesses (545) (586) (111) Deferred income taxes (57) 107 7 Changes in assets and liabilities Accounts receivable (305) (245) (313) Inventories (509) (387) 57 Accounts payable 408 24 148 Accrued income taxes (current and long-term) 134 967 101 Other net 120 256 76 Net cash provided by (used in) operating activities 6,439 6,240 6,662 Cash Flows from Investing Activities Purchases of property, plant and equipment (PP&E) (1,577) (1,373) (1,420) Proceeds from sale of PP&E and other assets 262 49 58 Acquisitions, net of cash acquired 13 (2,023) (16) Purchases of marketable securities and investments (1,828) (2,152) (1,410) Proceeds from maturities and sale of marketable securities and investments 2,497 1,354 1,247 Proceeds from sale of businesses, net of cash sold 846 1,065 142 Other net 9 (6) (4) Net cash provided by (used in) investing activities 222 (3,086) (1,403) Cash Flows from Financing Activities Change in short-term debt net (284) 578 (797) Repayment of debt (maturities greater than 90 days) (1,034) (962) (992) Proceeds from debt (maturities greater than 90 days) 2,251 1,987 2,832 Purchases of treasury stock (4,870) (2,068) (3,753) Proceeds from issuance of treasury stock pursuant to stock option and benefit plans 485 734 804 Dividends paid to shareholders (3,193) (2,803) (2,678) Other net (56) (121) (42) Net cash provided by (used in) financing activities (6,701) (2,655) (4,626) Effect of exchange rate changes on cash and cash equivalents (160) 156 (33) Net increase (decrease) in cash and cash equivalents (200) 655 600 Cash and cash equivalents at beginning of year 3,053 2,398 1,798 Cash and cash equivalents at end of period $2,853 $3,053 $2,398 The accompanying Notes to Consolidated Financial Statements are an integral part of this statement. 60 What is the FY2018 capital expenditure amount (in USD millions) for 3M? Give a response to the question by relying on the details shown in the cash flow statement.
    Table of Contents 3M Company and SubsidiariesConsolidated Balance Shee tAt December 31 December 31, December 31, (Dollars in millions, except per share amount) 2018 2017 Assets Current assets Cash and cash equivalents $2,853 $3,053 Marketable securities current 380 1,076 Accounts receivable net of allowances of $95 and $103 5,020 4,911 Inventories Finished goods 2,120 1,915 Work in process 1,292 1,218 Raw materials and supplies 954 901 Total inventories 4,366 4,034 Prepaids 741 937 Other current assets 349 266 Total current assets 13,709 14,277 Property, plant and equipment 24,873 24,914 Less: Accumulated depreciation (16,135) (16,048) Property, plant and equipment net 8,738 8,866 Goodwill 10,051 10,513 Intangible assets net 2,657 2,936 Other assets 1,345 1,395 Total assets $36,500 $37,987 Liabilities Current liabilities Short-term borrowings and current portion of long-term debt $1,211 $1,853 Accounts payable 2,266 1,945 Accrued payroll 749 870 Accrued income taxes 243 310 Other current liabilities 2,775 2,709 Total current liabilities 7,244 7,687 Long-term debt 13,411 12,096 Pension and postretirement benefits 2,987 3,620 Other liabilities 3,010 2,962 Total liabilities $26,652 $26,365 Commitments and contingencies (Note 16) Equity 3M Company shareholders equity: Common stock par value, $.01 par value $ 9 $ 9 Shares outstanding - 2018: 576,575,168 Shares outstanding - 2017: 594,884,237 Additional paid-in capital 5,643 5,352 Retained earnings 40,636 39,115 Treasury stock (29,626) (25,887) Accumulated other comprehensive income (loss) (6,866) (7,026) Total 3M Company shareholders equity 9,796 11,563 Noncontrolling interest 52 59 Total equity $9,848 $11,622 Total liabilities and equity $36,500 $37,987 The accompanying Notes to Consolidated Financial Statements are an integral part of this statement.58 Assume that you are a public equities analyst. Answer the following question by primarily using information that is shown in the balance sheet: what is the year end FY2018 net PPNE for 3M? Answer in USD billions.
    3M Company and SubsidiariesConsolidated Statement of IncomeYears ended December 31(Millions, except per share amounts)202220212020Net sales$34,229 $35,355 $32,184 Is 3M a capital-intensive business based on FY2022 data?
  • Loss: MatryoshkaLoss with these parameters:
    {
    "loss": "MultipleNegativesRankingLoss",
    "matryoshka_dims": [
        768,
        512,
        256,
        128,
        64
    ],
    "matryoshka_weights": [
        1,
        1,
        1,
        1,
        1
    ],
    "n_dims_per_step": -1
}

Training Hyperparameters

Non-Default Hyperparameters

  • eval_strategy: epoch
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 16
  • gradient_accumulation_steps: 16
  • learning_rate: 2e-05
  • num_train_epochs: 50
  • lr_scheduler_type: cosine
  • warmup_ratio: 0.1
  • fp16: True
  • tf32: False
  • load_best_model_at_end: True
  • optim: adamw_torch_fused
  • batch_sampler: no_duplicates

All Hyperparameters

Click to expand
  • overwrite_output_dir: False
  • do_predict: False
  • eval_strategy: epoch
  • prediction_loss_only: True
  • per_device_train_batch_size: 32
  • per_device_eval_batch_size: 16
  • per_gpu_train_batch_size: None
  • per_gpu_eval_batch_size: None
  • gradient_accumulation_steps: 16
  • eval_accumulation_steps: None
  • learning_rate: 2e-05
  • weight_decay: 0.0
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • max_grad_norm: 1.0
  • num_train_epochs: 50
  • max_steps: -1
  • lr_scheduler_type: cosine
  • lr_scheduler_kwargs: {}
  • warmup_ratio: 0.1
  • warmup_steps: 0
  • log_level: passive
  • log_level_replica: warning
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • save_safetensors: True
  • save_on_each_node: False
  • save_only_model: False
  • restore_callback_states_from_checkpoint: False
  • no_cuda: False
  • use_cpu: False
  • use_mps_device: False
  • seed: 42
  • data_seed: None
  • jit_mode_eval: False
  • use_ipex: False
  • bf16: False
  • fp16: True
  • fp16_opt_level: O1
  • half_precision_backend: auto
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: False
  • local_rank: 0
  • ddp_backend: None
  • tpu_num_cores: None
  • tpu_metrics_debug: False
  • debug: []
  • dataloader_drop_last: False
  • dataloader_num_workers: 0
  • dataloader_prefetch_factor: None
  • past_index: -1
  • disable_tqdm: False
  • remove_unused_columns: True
  • label_names: None
  • load_best_model_at_end: True
  • ignore_data_skip: False
  • fsdp: []
  • fsdp_min_num_params: 0
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • fsdp_transformer_layer_cls_to_wrap: None
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • deepspeed: None
  • label_smoothing_factor: 0.0
  • optim: adamw_torch_fused
  • optim_args: None
  • adafactor: False
  • group_by_length: False
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • skip_memory_metrics: True
  • use_legacy_prediction_loop: False
  • push_to_hub: False
  • resume_from_checkpoint: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_private_repo: 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
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional

Training Logs

Epoch Step Training Loss dim_768_cosine_map@100 dim_512_cosine_map@100 dim_256_cosine_map@100 dim_128_cosine_map@100 dim_64_cosine_map@100
0 0 - 0.4797 0.4762 0.4373 0.3948 0.2870
1.0 1 - 0.4796 0.4762 0.4374 0.3946 0.2869
2.0 2 - 0.5128 0.4990 0.4817 0.4673 0.3554
3.0 4 - 0.5387 0.5180 0.5362 0.5217 0.4156
1.0 1 - 0.5387 0.5180 0.5362 0.5217 0.4156
2.0 2 - 0.5509 0.5339 0.5399 0.5288 0.4394
3.0 4 - 0.5921 0.5763 0.5743 0.5709 0.5007
4.0 5 - 0.6112 0.6097 0.6068 0.6031 0.5435
5.0 6 - 0.6244 0.6383 0.6379 0.6478 0.5920
6.0 8 - 0.6763 0.6857 0.7064 0.7134 0.6909
7.0 9 - 0.6853 0.7161 0.7264 0.7463 0.7321
8.0 10 2.0247 - - - - -
8.2 11 - 0.7454 0.7757 0.7821 0.8181 0.7850
9.0 12 - 0.7661 0.7926 0.8071 0.8261 0.8165
10.0 13 - 0.7783 0.8061 0.8221 0.8396 0.8382
11.0 15 - 0.8221 0.8217 0.8600 0.8834 0.8903
12.0 16 - 0.8301 0.8393 0.8756 0.8908 0.9143
13.0 17 - 0.8454 0.8562 0.8943 0.9167 0.9261
14.0 19 - 0.8697 0.8861 0.9167 0.9311 0.9417
15.0 20 0.72 0.8808 0.8939 0.9217 0.9344 0.9522
16.2 22 - 0.9061 0.9 0.9439 0.9411 0.9556
17.0 23 - 0.9061 0.9061 0.9439 0.9444 0.9556
18.0 24 - 0.9111 0.9117 0.9444 0.9444 0.9589
19.0 26 - 0.9256 0.92 0.9478 0.9522 0.9589
20.0 27 - 0.9256 0.9233 0.9478 0.9489 0.9611
21.0 28 - 0.9289 0.9311 0.9478 0.9556 0.9644
22.0 30 0.3518 0.94 0.9344 0.9511 0.9556 0.9656
23.0 31 - 0.9411 0.9356 0.9544 0.9556 0.9656
24.2 33 - 0.9411 0.9389 0.9544 0.9589 0.9689
25.0 34 - 0.9378 0.9389 0.9556 0.9589 0.9689
26.0 35 - 0.9378 0.9389 0.9556 0.9589 0.9689
27.0 37 - 0.9444 0.9389 0.9556 0.9589 0.9689
28.0 38 - 0.9444 0.9389 0.9589 0.9589 0.9689
29.0 39 - 0.9444 0.9389 0.9589 0.9589 0.9689
29.4 40 0.2456 - - - - -
30.0 41 - 0.9444 0.9422 0.9589 0.9589 0.9689
31.0 42 - 0.9444 0.9422 0.9589 0.9622 0.9689
32.2 44 - 0.9444 0.9422 0.9556 0.9622 0.9689
33.0 45 - 0.9444 0.9422 0.9556 0.9622 0.9689
34.0 46 - 0.9444 0.9422 0.9556 0.9622 0.9689
35.0 48 - 0.9444 0.9422 0.9556 0.9622 0.9689
36.0 49 - 0.9444 0.9422 0.9556 0.9622 0.9689
37.0 50 0.2123 0.9444 0.9422 0.9556 0.9622 0.9689
  • The bold row denotes the saved checkpoint.

Framework Versions

  • Python: 3.10.12
  • Sentence Transformers: 3.2.1
  • Transformers: 4.41.2
  • PyTorch: 2.1.2+cu121
  • Accelerate: 1.1.1
  • Datasets: 2.19.1
  • Tokenizers: 0.19.1

Citation

BibTeX

Sentence Transformers 

@inproceedings{reimers-2019-sentence-bert,
    title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
    author = "Reimers, Nils and Gurevych, Iryna",
    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
    month = "11",
    year = "2019",
    publisher = "Association for Computational Linguistics",
    url = "https://arxiv.org/abs/1908.10084",
}

MatryoshkaLoss 

@misc{kusupati2024matryoshka,
    title={Matryoshka Representation Learning},
    author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
    year={2024},
    eprint={2205.13147},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

MultipleNegativesRankingLoss 

@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}
}