sentence-transformers safetensors bert sentence-similarity feature-extraction generated_from_trainer dataset_size:150 loss:MatryoshkaLoss loss:MultipleNegativesRankingLoss en arxiv:1908.10084 arxiv:2205.13147 arxiv:1705.00652 base_model:BAAI/bge-base-en-v1.5 base_model:finetune:BAAI/bge-base-en-v1.5 license:apache-2.0 model-index autotrain_compatible text-embeddings-inference endpoints_compatible region:us
---
base_model: BAAI/bge-base-en-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:150
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
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?
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: 1.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      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: 1.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      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: 1.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      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: 1.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      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: 1.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      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: 1.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      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: 1.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      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: 1.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      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: 1.0
      name: Cosine Accuracy@3
    - type: cosine_accuracy@5
      value: 1.0
      name: Cosine Accuracy@5
    - type: cosine_accuracy@10
      value: 1.0
      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: 1.0
      name: Cosine Recall@3
    - type: cosine_recall@5
      value: 1.0
      name: Cosine Recall@5
    - type: cosine_recall@10
      value: 1.0
      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](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
    - json
- **Language:** en
- **License:** apache-2.0

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

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

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("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]
```

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</details>
-->

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### Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

<details><summary>Click to expand</summary>

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### Out-of-Scope Use

*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation

### Metrics

#### Information Retrieval
* Dataset: `dim_768`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8933     |
| 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
* Dataset: `dim_512`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.8867     |
| 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
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9133     |
| 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
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.9267     |
| 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
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)

| Metric              | Value      |
|:--------------------|:-----------|
| cosine_accuracy@1   | 0.94       |
| 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** |

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## Bias, Risks and Limitations

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## Training Details

### Training Dataset

#### json

* Dataset: json
* Size: 150 training samples
* Columns: <code>context</code> and <code>question</code>
* Approximate statistics based on the first 150 samples:
  |         | context                                                                              | question                                                                            |
  |:--------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
  | type    | string                                                                               | string                                                                              |
  | details | <ul><li>min: 17 tokens</li><li>mean: 314.29 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 11 tokens</li><li>mean: 39.67 tokens</li><li>max: 175 tokens</li></ul> |
* Samples:
  | context                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                   | question                                                                                                                                                                                                                          |
  |:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
  | <code>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</code> | <code>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.</code>                                                  |
  | <code>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</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                               | <code>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.</code> |
  | <code>3M Company and SubsidiariesConsolidated Statement of IncomeYears ended December 31(Millions, except per share amounts)202220212020Net sales$34,229 $35,355 $32,184</code>                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                           | <code>Is 3M a capital-intensive business based on FY2022 data?</code>                                                                                                                                                             |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
  ```json
  {
      "loss": "MultipleNegativesRankingLoss",
      "matryoshka_dims": [
          768,
          512,
          256,
          128,
          64
      ],
      "matryoshka_weights": [
          1,
          1,
          1,
          1,
          1
      ],
      "n_dims_per_step": -1
  }
  ```

### Training Hyperparameters
#### Non-Default Hyperparameters

- `eval_strategy`: epoch
- `per_device_train_batch_size`: 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
<details><summary>Click to expand</summary>

- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 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

</details>

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

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

#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
    title={Efficient Natural Language Response Suggestion for Smart Reply},
    author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
    year={2017},
    eprint={1705.00652},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

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