thomaskim1130/stella_en_1.5B_v5-FinanceRAG-v2

SentenceTransformer based on thomaskim1130/stellaen1.5B_v5-FinanceRAG

This is a sentence-transformers model finetuned from thomaskim1130/stella_en_1.5B_v5-FinanceRAG. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

Paper

Link https://arxiv.org/abs/2503.15191

Model Details

Model Description

• —Model Type: Sentence Transformer
• —Base model: thomaskim1130/stella_en_1.5B_v5-FinanceRAG <!-- at revision 07c45b205a2eb8e08bce8ac8dc0be08c7946db2a -->
• —Maximum Sequence Length: 512 tokens
• —Output Dimensionality: 1024 tokens
• —Similarity Function: Cosine Similarity <!-- - Training Dataset: Unknown --> <!-- - Language: Unknown --> <!-- - License: Unknown -->

Model Sources

• —Documentation: Sentence Transformers Documentation
• —Repository: Sentence Transformers on GitHub
• —Hugging Face: Sentence Transformers on Hugging Face

Full Model Architecture

SentenceTransformer(
  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: Qwen2Model 
  (1): Pooling({'word_embedding_dimension': 1536, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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): Dense({'in_features': 1536, 'out_features': 1024, 'bias': True, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

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("sentence_transformers_model_id")
# Run inference
sentences = [
    'Instruct: Given a web search query, retrieve relevant passages that answer the query.\nQuery: Title: \nText: | _id | q616570e0 |\n| title |  |\n| text | what was the ratio of the purchase in december 2012 to the purchase in january 2013\n\nwas ratio of purchase in december 2012 to purchase in january 2013\n\n\n',
    'Title: \nText: | _id | d61657130 |\n| title |  |\n| text | issuer purchases of equity securities during the three months ended december 31 , 2012 , we repurchased 619314 shares of our common stock for an aggregate of approximately $ 46.0 million , including commissions and fees , pursuant to our publicly announced stock repurchase program , as follows : period total number of shares purchased ( 1 ) average price paid per share ( 2 ) total number of shares purchased as part of publicly announced plans or programs approximate dollar value of shares that may yet be\n\ndollar value of shares that may yet be purchased under the plans or programs ( in millions ) .\n\nperiod               | total number of shares purchased ( 1 ) | average price paid per share ( 2 ) | total number of shares purchased as part of publicly announced plans orprograms | approximate dollar value of shares that may yet be purchased under the plans orprograms ( in millions )\n\n-------------------- | -------------------------------------- | ---------------------------------- | ------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------\n\noctober 2012         | 27524                                  | $ 72.62                            | 27524                                                                           | $ 1300.1\n\nnovember 2012        | 489390                                 | $ 74.22                            | 489390                                                                          | $ 1263.7\n\ndecember 2012        | 102400                                 | $ 74.83                            | 102400                                                                          | $ 1256.1\n\ntotal fourth quarter | 619314                                 | $ 74.25                            | 619314                                                                          | $ 1256.1\n\n( 1 ) repurchases made pursuant to the $ 1.5 billion stock repurchase program approved by our board of directors in march 2011 ( the 201c2011 buyback 201d ) .\nunder this program , our management is authorized to purchase shares from time to time through open market purchases or privately negotiated transactions at prevailing prices as permitted by securities laws and other legal requirements , and subject to market conditions and other factors .\n\nto facilitate repurchases , we make purchases pursuant to trading plans under rule 10b5-1 of the exchange act , which allows us to repurchase shares during periods when we otherwise might be prevented from doing so under insider trading laws or because of self-imposed trading blackout periods .\nthis program may be discontinued at any time .\n( 2 ) average price per share is calculated using the aggregate price , excluding commissions and fees .\n\nwe continued to repurchase shares of our common stock pursuant to our 2011 buyback subsequent to december 31 , 2012 .\nbetween january 1 , 2013 and january 21 , 2013 , we repurchased an additional 15790 shares of our common stock for an aggregate of $ 1.2 million , including commissions and fees , pursuant to the 2011 buyback .\n\nas a result , as of january 21 , 2013 , we had repurchased a total of approximately 4.3 million shares of our common stock under the 2011 buyback for an aggregate of $ 245.2 million , including commissions and fees .\nwe expect to continue to manage the pacing of the remaining $ 1.3 billion under the 2011 buyback in response to general market conditions and other relevant factors.\n\nissuer purchases of equity securities during three months ended december 31, 2012  repurchased 619314 shares of common stock for aggregate approximately $ 46. 0 million , including commissions and fees pursuant to our publicly announced stock repurchase program follows : period total number of shares purchased 1 ) average price paid per share ( 2 ) total number of shares purchased as part of publicly announced plans or programs approximate dollar value of shares be purchased under plans or programs ( in\n\nshares be purchased under plans or programs ( in millions ).\n\nrepurchases made to $ 1. 5 billion stock repurchase program approved by board of directors in march 2011 ( 201c2011 buyback 201d ).\n under program management authorized to purchase shares through open market purchases or privately negotiated transactions at prevailing prices as permitted by securities laws other legal requirements subject to market conditions other factors.\n\nfacilitate repurchases make purchases pursuant trading plans under rule 10b5-1 of exchange act  allows to repurchase shares during periods prevented from under insider trading laws or self-imposed trading blackout periods.\n program may be discontinued any time.\n average price per share calculated using aggregate price  excluding commissions and fees.\n continued to repurchase shares common stock pursuant to 2011 buyback subsequent to december 31 , 2012.\n\nbetween january 1 , 2013 and january 21 , 2013  repurchased additional 15790 shares common stock for aggregate of $ 1. 2 million , including commissions and fees pursuant to 2011 buyback.\n as of january 21 , 2013  had repurchased total of approximately 4. 3 million shares of common stock under 2011 buyback for aggregate of $ 245. 2 million , including commissions and fees.\n\nexpect to continue manage pacing remaining $ 1. 3 billion under 2011 buyback in response to general market conditions other relevant factors.\n\nperiod               | total number of shares purchased ( 1 ) | average price paid per share ( 2 ) | total number of shares purchased as part of publicly announced plans orprograms | approximate dollar value of shares that may yet be purchased under the plans orprograms ( in millions )\n\n-------------------- | -------------------------------------- | ---------------------------------- | ------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------\n\noctober 2012         | 27524                                  | $ 72.62                            | 27524                                                                           | $ 1300.1\n\nnovember 2012        | 489390                                 | $ 74.22                            | 489390                                                                          | $ 1263.7\n\ndecember 2012        | 102400                                 | $ 74.83                            | 102400                                                                          | $ 1256.1                                                                                               \ntotal fourth quarter | 619314                                 | $ 74.25                            | 619314                                                                          | $ 1256.1\n\n\n',
    'Title: \nText: | _id | d1a729e44 |\n| title |  |\n| text | Adjusted Revenue has limitations as a financial measure, should be considered as supplemental in nature, and is not meant as a substitute for the related financial information prepared in accordance with GAAP. These limitations include the following:\n\n• Adjusted Revenue is net of transaction-based costs, which is our largest cost of revenue item; • Adjusted Revenue is net of bitcoin costs, which could be a significant cost; • The deferred revenue adjustment that is added back to Adjusted Revenue will never be recognized as revenue by the Company; and • other companies, including companies in our industry, may calculate Adjusted Revenue differently or not at all, which reduces its usefulness as a comparative measure.\n\nBecause of these limitations, you should consider Adjusted Revenue alongside other financial performance measures, including total net revenue and our financial results presented in accordance with GAAP.\nThe following table presents a reconciliation of total net revenue to Adjusted Revenue for each of the periods indicated:\n\n|            |            | Year Ended December 31, |            |         \n--------------------------------------------------------------- | ---------- | ---------- | ----------------------- | ---------- | --------\n                                                                | 2018       | 2017       | 2016                    | 2015       | 2014\n\n|            |            | (in thousands)          |            |         \nTotal net revenue                                               | $3,298,177 | $2,214,253 | $1,708,721              | $1,267,118 | $850,192\nLess: Starbucks transaction-based revenue                       | —          | —          | 78,903                  | 142,283    | 123,024\n\nLess: transaction-based costs                                   | 1,558,562  | 1,230,290  | 943,200                 | 672,667    | 450,858 \nLess: bitcoin costs                                             | 164,827    | —          | —                       | —          | —       \nAdd: deferred revenue adjustment related to purchase accounting | $12,853    | $—         | $—                      | $—         | $—\n\nAdjusted Revenue                                                | $1,587,641 | $983,963   | $686,618                | $452,168   | $276,310\n\nAdjusted Revenue has limitations as financial measure, should be considered as supplemental in not substitute for related financial information prepared in accordance with GAAP. limitations include:\n\n• Adjusted Revenue is net of transaction-based costs, our largest cost of revenue item; • Adjusted Revenue net of bitcoin costs, could be significant cost; • deferred revenue adjustment added back to Adjusted Revenue never recognized as revenue by Company; • other companies, including companies in our industry, may calculate Adjusted Revenue differently or not, reduces its usefulness as comparative measure.\n\nof limitations consider Adjusted Revenue alongside other financial performance measures, including total net revenue and our financial results presented in accordance with GAAP.\n following table presents reconciliation of total net revenue to Adjusted Revenue for each periods indicated:\n\n|            |            | Year Ended December 31, |            |         \n--------------------------------------------------------------- | ---------- | ---------- | ----------------------- | ---------- | --------\n                                                                | 2018       | 2017       | 2016                    | 2015       | 2014    \n                                                                |            |            | (in thousands)          |            |\n\nTotal net revenue                                               | $3,298,177 | $2,214,253 | $1,708,721              | $1,267,118 | $850,192\nLess: Starbucks transaction-based revenue                       | —          | —          | 78,903                  | 142,283    | 123,024 \nLess: transaction-based costs                                   | 1,558,562  | 1,230,290  | 943,200                 | 672,667    | 450,858\n\nLess: bitcoin costs                                             | 164,827    | —          | —                       | —          | —       \nAdd: deferred revenue adjustment related to purchase accounting | $12,853    | $—         | $—                      | $—         | $—      \nAdjusted Revenue                                                | $1,587,641 | $983,963   | $686,618                | $452,168   | $276,310\n\n\n',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]

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

<!--

Direct Usage (Transformers)

<details><summary>Click to see the direct usage in Transformers</summary>

</details> -->

<!--

Downstream Usage (Sentence Transformers)

You can finetune this model on your own dataset.

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

</details> -->

<!--

Out-of-Scope Use

List how the model may foreseeably be misused and address what users ought not to do with the model. -->

Evaluation

Metrics

Information Retrieval

• —Dataset: Evaluate
• —Evaluated with <code>InformationRetrievalEvaluator</code>

<!--

Bias, Risks and Limitations

What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model. -->

<!--

Recommendations

What are recommendations with respect to the foreseeable issues? For example, filtering explicit content. -->

Training Details

Training Dataset

Unnamed Dataset

• —Size: 2,268 training samples
• —Columns: <code>sentence0</code> and <code>sentence1</code>
• —Approximate statistics based on the first 1000 samples: | | sentence0 | sentence1 | |:--------|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------| | type | string | string | | details | <ul><li>min: 49 tokens</li><li>mean: 88.95 tokens</li><li>max: 228 tokens</li></ul> | <ul><li>min: 60 tokens</li><li>mean: 472.9 tokens</li><li>max: 512 tokens</li></ul> |
• —Samples: | sentence0 | sentence1 | |:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| | <code>Instruct: Given a web search query, retrieve relevant passages that answer the query.<br>Query: Title: <br>Text: | id | q6168617e |<br>| title | |<br>| text | what is the total interest expense incurred by the senior unsecured notes that was redeemed in august 2005?<br><br>what total interest expense incurred by senior unsecured notes redeemed in august 2005?<br><br><br></code> | <code>Title: <br>Text: | id | d61661aea |<br>| title | |<br>| text | annual maturities as of december 31 , 2006 are scheduled as follows: .<br><br>2007 | $ 2.6 <br>-------------------- | --------<br>20081 | 2.8 <br>2009 | 257.0 <br>2010 | 240.9 <br>2011 | 500.0 <br>thereafter | 1247.9 <br>total long-term debt | $ 2251.2<br><br>1 in addition , holders of our $ 400.0 4.50% ( 4.50 % ) notes may require us to repurchase their 4.50% ( 4.50 % ) notes for cash at par in march 2008 .<br>these notes will mature in 2023 if not converted or repurchased .<br><br>redemption of long-term debt in august 2005 , we redeemed the remainder of our 7.875% ( 7.875 % ) senior unsecured notes with an aggregate principal amount of $ 250.0 at maturity for a total cost of $ 258.6 , which included the principal amount of the notes , accrued interest to the redemption date , and a prepayment penalty of $ 1.4 .<br>to redeem these notes we used the proceeds from the sale and issuance in july 2005 of $ 250.0 floating rate senior unsecured notes due 2008 .<br><br>floating rate senior unsecured notes in december 2006 , we exchanged all of our $ 250.0 floating rate notes due 2008 for $ 250.0 aggregate principal amount floating rate notes due 2010 .<br>the new floating rate notes mature on november 15 , 2010 and bear interest at a per annum rate equal to three-month libor plus 200 basis points , 125 basis points less than the interest rate on the old floating rate notes .<br><br>in connection with the exchange , we made an early participation payment of $ 41.25 ( actual amount ) in cash per $ 1000 ( actual amount ) principal amount of old floating rate notes for a total payment of $ 10.3 .<br>in accordance with eitf issue no .<br><br>in accordance with eitf issue no .<br>96-19 , debtor 2019s accounting for a modification or exchange of debt instruments ( 201ceitf 96-19 201d ) , this transaction is treated as an exchange of debt for accounting purposes because the present value of the remaining cash flows under the terms of the original instrument are not substantially different from those of the new instrument .<br><br>the new floating rate notes are reflected on our consolidated balance sheet net of the $ 10.3 early participation payment , which is amortized over the life of the new floating rate notes as a discount , using an effective interest method , and recorded in interest expense .<br>direct fees associated with the exchange of $ 3.5 were reflected in interest expense .<br><br>4.25% ( 4.25 % ) and 4.50% ( 4.50 % ) convertible senior notes in november 2006 , we exchanged $ 400.0 of our 4.50% ( 4.50 % ) convertible senior notes due 2023 ( the 201c4.50% ( 201c4.50 % ) notes 201d ) for $ 400.0 aggregate principal amount of 4.25% ( 4.25 % ) convertible senior notes due 2023 ( the 201c4.25% ( 201c4.25 % ) notes 201d ) .<br><br>as required by eitf 96-19 , this exchange is treated as an extinguishment of the 4.50% ( 4.50 % ) notes and an issuance of 4.25% ( 4.25 % ) notes for accounting purposes because the present value of the remaining cash flows plus the fair value of the embedded conversion option under the terms of the original instrument are substantially different from those of the new instrument .<br><br>as a result , the 4.25% ( 4.25 % ) notes are reflected on our consolidated balance sheet at their fair value at issuance , or $ 477.0 .<br>we recorded a non-cash charge in the fourth quarter of 2006 of $ 77.0 reflecting the difference between the fair value of the new debt and the carrying value of the old debt .<br><br>the difference between fair value and carrying value will be amortized through march 15 , 2012 , which is the first date holders may require us to repurchase the 4.25% ( 4.25 % ) notes , resulting in a reduction of reported interest expense in future periods .<br>we also recorded a non-cash charge of $ 3.8 for the extinguishment of unamortized debt issuance costs related to the exchanged 4.50% ( 4.50 % ) notes .<br><br>our 4.25% ( 4.25 % ) notes are convertible into our common stock at a conversion price of $ 12.42 per share , subject to adjustment in specified circumstances including any payment of cash dividends on our common stock .<br><br>the conversion rate of the new notes is also subject to adjustment for certain events arising from stock splits and combinations , stock dividends , certain cash dividends and certain other actions by us that modify our capital notes to consolidated financial statements 2014 ( continued ) ( amounts in millions , except per share amounts ) %%transmsg* transmitting job : y31000 pcn : 072000000 %%pcmsg|72 |00009|yes|no|02/28/2007 01:12|0|0|page is valid , no graphics -- color : d|<br><br>annual maturities of december 31 , 2006 scheduled:.<br> 2007 | $ 2. 6<br> -------------------- | --------<br> 20081 | 2. 8<br> 2009 | 257. 0<br> 2010 | 240. 9<br> 2011 | 500. 0<br> | 1247. 9<br> total long-term debt | $ 2251. 2<br> 1 holders of $ 400. 0 4. 50% ( 4. 50 % ) notes may require repurchase 4. 50% ( 4. 50 % ) notes for cash at march 2008.<br> notes mature 2023 if not converted or repurchased.<br><br>redemption of long-term debt august 2005 redeemed remainder of 7. 875% ( 7. 875 % ) senior unsecured notes with aggregate principal amount of $ 250. 0 at maturity for total cost of $ 258. 6 included principal amount notes accrued interest redemption date prepayment penalty of $ 1. 4.<br> redeem notes used proceeds from sale issuance july 2005 of $ 250. 0 floating rate senior unsecured notes due 2008.<br><br>december 2006 exchanged all $ 250. 0 floating rate notes due 2008 for $ 250. 0 aggregate principal amount floating rate notes due 2010.<br> new floating rate notes mature november 15 , 2010 bear interest per annum rate equal to three-month libor plus 200 basis points , 125 basis points less than interest rate old floating rate notes.<br><br>exchange made early participation payment of $ 41. 25 ( actual amount ) in cash per $ 1000 ( actual amount ) principal amount of old floating rate notes for total payment of $ 10. 3.<br> with eitf issue no.<br>96-19 , debtor 2019s accounting for modification or exchange of debt instruments ( 201ceitf 96-19 201d ) transaction treated as exchange of debt for accounting because present value of remaining cash flows under terms original instrument not substantially different from new instrument.<br><br>new floating rate notes reflected on consolidated balance sheet net of $ 10. 3 early participation payment amortized over life of new floating rate notes as discount effective interest method recorded in interest expense.<br> direct fees associated with exchange of $ 3. 5 reflected in interest expense.<br><br>4. 25% ( 4. 25 % ) and 4. 50% ( 4. 50 % ) convertible senior notes in november 2006 exchanged $ 400. 0 of 4. 50% ( 4. 50 % ) convertible senior notes due 2023 ( 201c4. 50% ( 201c4. 50 % ) notes 201d ) for $ 400. 0 aggregate principal amount of 4. 25% ( 4. 25 % ) convertible senior notes due 2023 ( 201c4. 25% 201c4. 25 % notes 201d ).<br><br>required by eitf 96-19 exchange treated as extinguishment of 4. 50% ( 4. 50 % ) notes and issuance of 4. 25% ( 4. 25 % ) notes for accounting because present value of remaining cash flows plus fair value of embedded conversion option under terms original instrument substantially different from new instrument.<br> 4. 25% ( 4. 25 % ) notes reflected on consolidated balance sheet at fair value at issuance , or $ 477. 0.<br><br>recorded non-cash charge in fourth quarter of 2006 of $ 77. 0 reflecting difference between fair value of new debt and carrying value of old debt.<br> difference fair value value amortized through march 15 , 2012 first date holders may require to repurchase 4. 25% ( 4.25 % ) notes resulting reduction of reported interest expense future periods.<br> recorded non-cash charge of $ 3. 8 for extinguishment unamortized debt issuance costs related to exchanged 4. 50% ( 4. 50 % ) notes.<br><br>4. 25% ( 4. 25 % ) notes convertible into common stock at conversion price $ 12. 42 per share subject to adjustment specified circumstances including payment of cash dividends common stock.<br><br>the conversion rate of the new notes is also subject to adjustment for certain events arising from stock splits and combinations , stock dividends , certain cash dividends and certain other actions by us that modify our capital notes to consolidated financial statements 2014 ( continued ) ( amounts in millions , except per share amounts ) %%transmsg transmitting job : y31000 pcn : 072000000 *%%pcmsg|72 |00009|yes|no|02/28/2007 01:12|0|0|page is valid , no graphics -- color : d|<br><br><br></code> | | <code>Instruct: Given a web search query, retrieve relevant passages that answer the query.<br>Query: Title: <br>Text: | id | q837b54d0 |<br>| title | |<br>| text | What is the total amount of Net sales of 2014, and Notes Payable of 2008 Carrying Amounts ?<br><br>total amount of Net sales of 2014, Notes Payable of 2008 Carrying Amounts ?<br><br><br></code> | <code>Title: <br>Text: | id | d813b8dac |<br>| title | |<br>| text | Table of Contents Mac The following table presents Mac net sales and unit sales information for 2014, 2013 and 2012 (dollars in millions and units in thousands):<br>| | 2014 | Change | 2013 | Change | 2012 |<br>| Net sales | $24,079 | 12% | $21,483 | -7% | $23,221 |<br>| Percentage of total net sales | 13% | | 13% | | 15% |<br>| Unit sales | 18,906 | 16% | 16,341 | -10% | 18,158 |<br><br>The year-over-year growth in Mac net sales and unit sales for 2014 was primarily driven by increased sales of MacBook Air, MacBook Pro and Mac Pro.<br>Mac net sales and unit sales increased in all of the Company’s operating segments.<br>Mac ASPs decreased during 2014 compared to 2013 primarily due to price reductions on certain Mac models and a shift in mix towards Mac portable systems.<br>Mac net sales and unit sales for 2013 were down or relatively flat in all of the Company’s operating segments.<br><br>Mac ASPs increased slightly partially offsetting the impact of lower unit sales on net sales.<br>The decline in Mac unit sales and net sales reflected the overall weakness in the market for personal computers.<br><br>Table of Contents Mac table presents Mac net sales unit sales information for 2014, 2013 2012 (dollars in millions units in thousands):<br> Mac net sales unit sales increased in all Company’s operating segments.<br> Mac ASPs decreased during 2014 compared to 2013 due to price reductions Mac models shift towards Mac portable systems.<br> Mac net sales unit sales for 2013 down or flat in all Company’s operating segments.<br> Mac ASPs increased slightly offsetting impact of lower unit sales on net sales.<br><br>decline in Mac unit sales net sales reflected weakness in market for personal computers.<br><br>| | 2014 | Change | 2013 | Change | 2012 |<br>| Net sales | $24,079 | 12% | $21,483 | -7% | $23,221 |<br>| Percentage of total net sales | 13% | | 13% | | 15% |<br>| Unit sales | 18,906 | 16% | 16,341 | -10% | 18,158 |<br><br><br></code> | | <code>Instruct: Given a web search query, retrieve relevant passages that answer the query.<br>Query: Title: <br>Text: | id | q1a73fa0a |<br>| title | |<br>| text | How many years did service revenue from APJC exceed $1,500 million?<br><br>many years service revenue APJC exceed $1,500 million?<br><br><br></code> | <code>Title: <br>Text: | id | d1a73fa6e |<br>| title | |<br>| text | Service Revenue by Segment<br>The following table presents the breakdown of service revenue by segment (in millions, except percentages):<br>Amounts may not sum and percentages may not recalculate due to rounding<br>Service revenue increased 2%, driven by an increase in software and solution support offerings. Service revenue increased in the Americas and EMEA segments, partially offset by decreased revenue in our APJC segment.<br><br>| | Years Ended | | 2019 vs. 2018 | <br>----------------------------- | ------------- | ------------- | ------------- | ------------------- | -------------------<br>Years Ended | July 27, 2019 | July 28, 2018 | July 29, 2017 | Variance in Dollars | Variance in Percent<br>Service revenue: | | | | |<br><br>Americas | $ 8,173 | $ 7,982 | $7,864 | $191 | 2% <br>Percentage of service revenue | 63.4% | 63.3% | 63.9% | | <br>EMEA | 2,854 | 2,754 | 2,635 | 100 | 4% <br>Percentage of service revenue | 22.1% | 21.8% | 21.4% | |<br><br>APJC | 1,872 | 1,885 | 1,801 | (13) | (1)% <br>Percentage of service revenue | 14.5% | 14.9% | 14.7% | | <br>Total | $ 12,899 | $ 12,621 | $12,300 | $278 | 2%<br><br>Service Revenue by Segment<br> table presents breakdown service revenue by segment (in millions except percentages):<br> Amounts not sum percentages not recalculate due to rounding<br> Service revenue increased 2% driven by increase software solution support offerings. Service revenue increased in Americas EMEA segments offset by decreased revenue in APJC segment.<br><br>| | Years Ended | | 2019 vs. 2018 | <br>----------------------------- | ------------- | ------------- | ------------- | ------------------- | -------------------<br>Years Ended | July 27, 2019 | July 28, 2018 | July 29, 2017 | Variance in Dollars | Variance in Percent<br>Service revenue: | | | | |<br><br>Americas | $ 8,173 | $ 7,982 | $7,864 | $191 | 2% <br>Percentage of service revenue | 63.4% | 63.3% | 63.9% | | <br>EMEA | 2,854 | 2,754 | 2,635 | 100 | 4% <br>Percentage of service revenue | 22.1% | 21.8% | 21.4% | |<br><br>APJC | 1,872 | 1,885 | 1,801 | (13) | (1)% <br>Percentage of service revenue | 14.5% | 14.9% | 14.7% | | <br>Total | $ 12,899 | $ 12,621 | $12,300 | $278 | 2%<br><br><br></code> |
• —Loss: <code>MultipleNegativesRankingLoss</code> with these parameters:

  {
      "scale": 20.0,
      "similarity_fct": "cos_sim"
  }

Training Hyperparameters

Non-Default Hyperparameters

• —eval_strategy: steps
• —per_device_train_batch_size: 4
• —per_device_eval_batch_size: 4
• —num_train_epochs: 2
• —fp16: True
• —batch_sampler: no_duplicates
• —multi_dataset_batch_sampler: round_robin

All Hyperparameters

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

• —overwrite_output_dir: False
• —do_predict: False
• —eval_strategy: steps
• —prediction_loss_only: True
• —per_device_train_batch_size: 4
• —per_device_eval_batch_size: 4
• —per_gpu_train_batch_size: None
• —per_gpu_eval_batch_size: None
• —gradient_accumulation_steps: 1
• —eval_accumulation_steps: None
• —torch_empty_cache_steps: None
• —learning_rate: 5e-05
• —weight_decay: 0.0
• —adam_beta1: 0.9
• —adam_beta2: 0.999
• —adam_epsilon: 1e-08
• —max_grad_norm: 1
• —num_train_epochs: 2
• —max_steps: -1
• —lr_scheduler_type: linear
• —lr_scheduler_kwargs: {}
• —warmup_ratio: 0.0
• —warmup_steps: 0
• —log_level: passive
• —log_level_replica: warning
• —log_on_each_node: True
• —logging_nan_inf_filter: True
• —save_safetensors: True
• —save_on_each_node: False
• —save_only_model: False
• —restore_callback_states_from_checkpoint: False
• —no_cuda: False
• —use_cpu: False
• —use_mps_device: False
• —seed: 42
• —data_seed: None
• —jit_mode_eval: False
• —use_ipex: False
• —bf16: False
• —fp16: True
• —fp16_opt_level: O1
• —half_precision_backend: auto
• —bf16_full_eval: False
• —fp16_full_eval: False
• —tf32: None
• —local_rank: 0
• —ddp_backend: None
• —tpu_num_cores: None
• —tpu_metrics_debug: False
• —debug: []
• —dataloader_drop_last: False
• —dataloader_num_workers: 0
• —dataloader_prefetch_factor: None
• —past_index: -1
• —disable_tqdm: False
• —remove_unused_columns: True
• —label_names: None
• —load_best_model_at_end: False
• —ignore_data_skip: False
• —fsdp: []
• —fsdp_min_num_params: 0
• —fsdp_config: {'minnumparams': 0, 'xla': False, 'xlafsdpv2': False, 'xlafsdpgrad_ckpt': False}
• —fsdp_transformer_layer_cls_to_wrap: None
• —accelerator_config: {'splitbatches': False, 'dispatchbatches': None, 'evenbatches': True, 'useseedablesampler': True, 'nonblocking': False, 'gradientaccumulationkwargs': None}
• —deepspeed: None
• —label_smoothing_factor: 0.0
• —optim: adamw_torch
• —optim_args: None
• —adafactor: False
• —group_by_length: False
• —length_column_name: length
• —ddp_find_unused_parameters: None
• —ddp_bucket_cap_mb: None
• —ddp_broadcast_buffers: False
• —dataloader_pin_memory: True
• —dataloader_persistent_workers: False
• —skip_memory_metrics: True
• —use_legacy_prediction_loop: False
• —push_to_hub: False
• —resume_from_checkpoint: None
• —hub_model_id: None
• —hub_strategy: every_save
• —hub_private_repo: False
• —hub_always_push: False
• —gradient_checkpointing: False
• —gradient_checkpointing_kwargs: None
• —include_inputs_for_metrics: False
• —eval_do_concat_batches: True
• —fp16_backend: auto
• —push_to_hub_model_id: None
• —push_to_hub_organization: None
• —mp_parameters:
• —auto_find_batch_size: False
• —full_determinism: False
• —torchdynamo: None
• —ray_scope: last
• —ddp_timeout: 1800
• —torch_compile: False
• —torch_compile_backend: None
• —torch_compile_mode: None
• —dispatch_batches: None
• —split_batches: None
• —include_tokens_per_second: False
• —include_num_input_tokens_seen: False
• —neftune_noise_alpha: None
• —optim_target_modules: None
• —batch_eval_metrics: False
• —eval_on_start: False
• —use_liger_kernel: False
• —eval_use_gather_object: False
• —batch_sampler: no_duplicates
• —multi_dataset_batch_sampler: round_robin

</details>

Training Logs

Framework Versions

• —Python: 3.10.12
• —Sentence Transformers: 3.1.1
• —Transformers: 4.45.2
• —PyTorch: 2.5.1+cu121
• —Accelerate: 1.1.1
• —Datasets: 3.1.0
• —Tokenizers: 0.20.3

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",
}

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}
}

<!--

Glossary

Clearly define terms in order to be accessible across audiences. -->

<!--

Model Card Authors

Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction. -->

<!--

Model Card Contact

Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors. -->