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fin-sparse-encoder-doc-v1

Feature Extraction
sentence-transformers
Safetensors
English
sparse-encoder
sparse
asymmetric
inference-free
splade
Generated from Trainer
dataset_size:18247
loss:SpladeLoss
loss:SparseMultipleNegativesRankingLoss
loss:FlopsLoss
Eval Results (legacy)
Model card Files
xet
Community

Financial Domain Sparse Encoder (doc-v3-gte fine-tuned)

This is a Asymmetric Inference-free SPLADE Sparse Encoder model finetuned from opensearch-project/opensearch-neural-sparse-encoding-doc-v3-gte on the financial-filings-sparse-retrieval-training dataset using the sentence-transformers library. It maps sentences & paragraphs to a 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.

Model Details

Model Description

Model Sources

Full Model Architecture 

SparseEncoder(
  (0): Router(
    (sub_modules): ModuleDict(
      (query): Sequential(
        (0): SparseStaticEmbedding({'frozen': False}, dim=30522, tokenizer=TokenizersBackend)
      )
      (document): Sequential(
        (0): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False, 'architecture': 'NewForMaskedLM'})
        (1): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'log1p_relu', 'word_embedding_dimension': 30522})
      )
    )
  )
)

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 SparseEncoder

# Download from the 🤗 Hub
model = SparseEncoder("oneryalcin/fin-sparse-encoder-doc-v1")
# Run inference
queries = [
    "How much did the company charge for depreciation of tangible assets in 2025?",
]
documents = [
    "CoolerAid Holdings Ltd\nNotes to the Consolidated Financial Statements (continued)\nYear ended 31 March 2025\n\n5. Operating profit\nOperating profit or loss is stated after charging/crediting\n\n|| 2025| 2024|\n|------------------------------------------------|---------|-----------|\n| Depreciation of tangible assets| 870,340 | 752,425   |\n| Loss/(gains) on disposal of tangible assets| 1,535   | (173,401) |\n| Impairment of trade debtors| 32,465  | 15,860|\n| Operating lease rentals| 112,292 | 139,908   |\n\n6. Auditor's remuneration\n\n|| 2025  | 2024  |\n|--------------------------------------------------------------|-------|-------|\n| Fees payable for the audit of the consolidated financial statements | 5,000 | 5,000 |\n\n7. Staff costs\nThe average number of persons employed by the group during the year, including the directors, amounted to:\n\n|| 2025 | 2024 |\n|---------------------|------|------|\n| Distribution staff  | 44   | 42   |\n| Administrative staff| 14   | 13   |\n| Management staff| 2| 2|\n| Directors| 4| 4|\n|| 64   | 61   |\n\nThe aggregate payroll costs incurred during the year, relating to the above, were:\n\n|| 2025| 2024|\n|---------------------|-----------|-----------|\n| Wages and salaries  | 2,828,063 | 2,596,870 |\n| Social security costs| 314,830   | 303,444   |\n| Other pension costs | 79,769| 68,413|\n|| 3,222,662 | 2,968,727 |\n\n8. Directors' remuneration\nThe directors' aggregate remuneration in respect of qualifying services was:\n\n|| 2025   | 2024   |\n|---------------|--------|--------|\n| Remuneration  | 18,880 | 18,880 |\n\n- 20 -",
    'Kenneth Forbes (Holdings) Limited\nNotes to the Financial Statements (continued)\nYear ended 30 April 2025\n\n13. Tangible assets.\n\n| Group and company | Freehold property £ | Plant and machinery £ | Fixtures and fittings £ | Motor vehicles £ | Assets held under the course of construction £ | Total £ |\n|---|---|---|---|---|---|---|\n| Cost |  |  |  |  |  |  |\n| At 1 May 2024 | 3,941,316 | 3,684,443 | 1,326,032 | 294,882 | 41,948 | 9,288,621 |\n| Additions | 570,435 | 395,946 | 187,049 | 194,503 | 106,519 | 1,454,452 |\n| Disposals |  | (317,265) | (214,785) | (164,244) |  | (696,294) |\n| At 30 Apr 2025 | 4,511,751 | 3,763,124 | 1,298,296 | 325,141 | 148,467 | 10,046,779 |\n| Depreciation |  |  |  |  |  |  |\n| At 1 May 2024 | 1,158,100 | 3,257,887 | 904,088 | 156,223 |  | 5,476,298 |\n| Charge for the year | 133,789 | 160,512 | 101,477 | 78,993 |  | 474,771 |\n| Disposals |  | (317,265) | (214,785) | (133,585) |  | (665,635) |\n| At 30 Apr 2025 | 1,291,889 | 3,101,134 | 790,780 | 101,631 |  | 5,285,434 |\n| Carrying amount |  |  |  |  |  |  |\n| At 30 Apr 2025 | 3,219,862 | 661,990 | 507,516 | 223,510 | 148,467 | 4,761,345 |\n| At 30 Apr 2024 | 2,783,216 | 426,556 | 421,944 | 138,659 | 41,948 | 3,812,323 |\n\nFreehold land amounting to £475,010 (2024: £475,010) is not depreciated.\nAll assets are held by the company but used by the group undertakings or associates.\n\nTangible assets held at valuation\n\nIn respect of tangible assets held at valuation, aggregate cost, depreciation and comparable carrying\namount that would have been recognised if the assets had been carried under the historical cost model are\nas follows:\n\nGroup and company\n\n|  | Freehold property £ |\n|---|---|\n| At 30 April 2025 |  |\n| Aggregate cost | 3,854,887 |\n| Aggregate depreciation | (1,963,225) |\n| Carrying value | 1,891,662 |\n| At 30 April 2024 |  |\n| Aggregate cost | 3,854,887 |\n| Aggregate depreciation | (1,700,013) |\n| Carrying value | 2,154,874 |\n\n1\n-24-\n1',
    'WHOCANFIXMYCAR.COM LTD\nNOTES TO THE FINANCIAL STATEMENTS\nFOR THE YEAR ENDED 31 MARCH 2025\n\n7. Tangible fixed assets\n\n| | Fixtures and fittings £ | Computer equipment £ | Right of use assets £ | Total £ | £ |\n|---|---|---|---|---|---|\n| **Cost** | | | | | |\n| At 1 April 2024 | 61,966 | 131,878 | - | 193,844 |  |\n| Additions | - | 3,187 | 287,367 | 290,554 |  |\n| At 31 March 2025 | 61,966 | 135,065 | 287,367 | 484,398 |  |\n| **Depreciation** | | | | | |\n| At 1 April 2024 | 61,966 | 99,890 | - | 161,856 |  |\n| Charge for the year | - | 12,982 | 82,657 | 95,639 |  |\n| At 31 March 2025 | 61,966 | 112,872 | 82,657 | 257,495 |  |\n| **Net book value** | | | | | |\n| At 31 March 2025 | - | 22,193 | 204,710 | 226,903 |  |\n| At 31 March 2024 | - | 31,988 | - | 31,988 |  |\n\nThe net book value of owned and leased assets included as "Tangible fixed assets" in the Balance Sheet is as follows:\n\n| | 2025 £ | 2024 £ |\n|---|---|---|\n| Tangible fixed assets owned | 22,193 | 31,988 |\n| Right-of-use tangible fixed assets | 204,710 | - |\n| | 226,903 | 31,988 |\n\nInformation about right-of-use assets is summarised below:\n\nNet book value\n\n| | 2025 £ | 2024 £ |\n|---|---|---|\n| Office and computer equipment | 204,710 | - |\n| | 204,710 | - |\n\nPage 11',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 30522] [3, 30522]

# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[18.4437, 21.8515, 17.4189]])

Evaluation

Domain Evaluation: Financial Document Retrieval

Evaluated on 2,028 held-out financial test examples (SEC filings + earnings call transcripts). Each example has 1 query + 1 positive + up to 7 hard negatives. We re-rank candidates per query using sparse dot-product similarity.

Metric Base Model Fine-tuned (2 epochs) Delta
acc@1 39.9% 55.2% +15.2%
acc@3 69.2% 84.0% +14.8%
acc@5 84.1% 93.7% +9.6%
mrr@10 0.580 0.710 +13.0%
ndcg@10 0.681 0.781 +10.0%
mean_rank 2.90 2.09 -0.81
median_rank 2.0 1.0 -1.0

The fine-tuned model ranks the correct document first (median_rank=1.0) more often than not, compared to position 2 for the base model. Sparsity also improved: corpus active dims decreased from ~2,500 to ~1,666, meaning faster inverted index lookups at inference time.

Metrics

Sparse Information Retrieval

Metric NanoNFCorpus NanoSciFact NanoFiQA2018
dot_accuracy@1 0.44 0.54 0.36
dot_accuracy@3 0.62 0.84 0.58
dot_accuracy@5 0.66 0.86 0.64
dot_accuracy@10 0.76 0.9 0.7
dot_precision@1 0.44 0.54 0.36
dot_precision@3 0.38 0.3 0.2733
dot_precision@5 0.356 0.188 0.208
dot_precision@10 0.314 0.1 0.12
dot_recall@1 0.0472 0.53 0.1872
dot_recall@3 0.1003 0.82 0.4071
dot_recall@5 0.1234 0.845 0.4956
dot_recall@10 0.1574 0.89 0.5478
dot_ndcg@10 0.3775 0.7393 0.4401
dot_mrr@10 0.5381 0.6897 0.4698
dot_map@100 0.1787 0.6905 0.3822
query_active_dims 4.76 19.04 12.06
query_sparsity_ratio 0.9998 0.9994 0.9996
corpus_active_dims 1493.3485 1752.0487 1723.0986
corpus_sparsity_ratio 0.9511 0.9426 0.9435
avg_flops 1.0107 4.7662 4.0714

Sparse Nano BEIR

  • Dataset: NanoBEIR_mean
  • Evaluated with SparseNanoBEIREvaluator with these parameters:
    {
    "dataset_names": [
        "nfcorpus",
        "scifact",
        "fiqa2018"
    ],
    "dataset_id": "sentence-transformers/NanoBEIR-en"
}
Metric Value
dot_accuracy@1 0.4467
dot_accuracy@3 0.68
dot_accuracy@5 0.72
dot_accuracy@10 0.7867
dot_precision@1 0.4467
dot_precision@3 0.3178
dot_precision@5 0.2507
dot_precision@10 0.178
dot_recall@1 0.2548
dot_recall@3 0.4425
dot_recall@5 0.488
dot_recall@10 0.5317
dot_ndcg@10 0.519
dot_mrr@10 0.5659
dot_map@100 0.4171
query_active_dims 11.9533
query_sparsity_ratio 0.9996
corpus_active_dims 1666.2235
corpus_sparsity_ratio 0.9454
avg_flops 2.3256

Training Details

Training Dataset

financial-filings-sparse-retrieval-training

  • Dataset: financial-filings-sparse-retrieval-training at 23e44ab
  • Size: 18,247 training samples
  • Columns: query, positive, negative_0, negative_1, negative_2, negative_3, negative_4, negative_5, and negative_6
  • Approximate statistics based on the first 1000 samples:
    query positive negative_0 negative_1 negative_2 negative_3 negative_4 negative_5 negative_6
    type string string string string string string string string string
    details
    • min: 9 tokens
    • mean: 20.51 tokens
    • max: 79 tokens
    • min: 51 tokens
    • mean: 331.12 tokens
    • max: 512 tokens
    • min: 54 tokens
    • mean: 360.35 tokens
    • max: 512 tokens
    • min: 56 tokens
    • mean: 357.16 tokens
    • max: 512 tokens
    • min: 2 tokens
    • mean: 303.81 tokens
    • max: 512 tokens
    • min: 2 tokens
    • mean: 263.98 tokens
    • max: 512 tokens
    • min: 2 tokens
    • mean: 221.87 tokens
    • max: 512 tokens
    • min: 2 tokens
    • mean: 193.47 tokens
    • max: 512 tokens
    • min: 2 tokens
    • mean: 166.03 tokens
    • max: 512 tokens
  • Samples:
    query positive negative_0 negative_1 negative_2 negative_3 negative_4 negative_5 negative_6
    What was the actuarial gain on defined benefit pension plans for the year 2021? 2021 £'000 2020 £'000 Loss for the financial year (9,731) (50,454) Other comprehensive income/(expense) for the financial period Actuarial gain/(loss) on defined benefit pension plans 31,200 (4,400) Deferred tax impact of actuarial gain/(loss) (10,920) 1,540 Other comprehensive income/(expense) 20,280 (2,860) Total comprehensive income/(expense) for the financial period 10,549 (53,314)

    STATEMENT OF COMPREHENSIVE INCOME FOR THE YEAR ENDED 31 DECEMBER 2021

    11    		 Defined benefit pension plans 2022 2021 Equities 12% 26% Bonds 56% 31% Liability driven investment 32% 39% Other - 4% 100% 100%

    Defined benefit pension plans 2022 €'000 2021 €'000 Actuarial losses from changes in financial assumptions 36 391 Actuarial gain/(losses) 3,629 (4,830) 3,665 (4,439)

    markdown Dunlop International Europe Limited Notes to the Financial Statements - continued for the year ended 31 December 2022<br><br>Defined benefit pension plans 2022 €'000 2021 €'000 Opening fair value of scheme assets 90,460 85,266 Contributions by employer 1,129 1,189 Expected return 1,697 1,183 Actuarial (losses) (33,630) (591) Benefits paid (3,397) (3,408) Exchange differences on foreign plans (4,631) 6,821 51,628 90,460<br><br>Changes in the fair value of scheme assets are as follows:<br><br>Page 24

    The major categories of scheme assets as a percentage of total scheme assets are as follows:

    Changes in the present value of the defined benefit obligation are as follows:

    Defined benefit pension pla...    		 The components of net periodic pension benefit cost recognized in our Consolidated Statements of Operations for the periods presented are as follows:

    Years Ended December 31, 2021 Projected benefit obligation, beginning of year $ 97,740 Service cost $ 1,282 Interest cost $ 1,452 Actuarial (gain) loss $ (8,682) Benefits paid $ (2,010) Translation adjustment $ (4,006) Projected benefit obligation, end of year $ 85,776 Fair value of plan assets, beginning of year $ 17,293 Actual return on plan assets $ 641 Contributions $ 1,775 Benefits paid $ (1,112) Actuarial gain $ 71 Translation adjustment $ (147) Fair value of plan assets, end of year $ 18,521 Funded status of plan $ (67,255)

    Our projected benefit obligation and plan assets for defined benefit pension plans and the related assumptions used to determine the related liabilities are as follows:

    Defined Benefit Plan

    We maintain defined benefit pension plans for certain of our non-U.S. employees in the U.K., Germany, and Philippines. ...
    What is the interest rate for the GBP short term loan with Canadian Natural Resources Limited? CREDITORS: amounts falling due within one year

    33

    DEBTORS

    The carrying amount of debtors is a reasonable approximation to fair value. Trade debtors and other receivables are not overdue as payment terms have not been exceeded. The expected credit loss on the trade debtor's balance was negligible and therefore no adjustment has been applied.

    Other creditors includes £4.9 million (2022 - £4.3 million) in respect of future share options (note 21). The short term loan with CNR International (U.K.) Developments Limited expired on 31 December 2023. Interest was generated at a rate of Secured Overnight Financing Rate (SOFR) + 1.75%.

    Amounts owed by group undertakings are unsecured and repayable on demand. The GBP short term loan with Canadian Natural Resources Limited generates interest at a rate of Sterling Overnight Index Average (SONIA) + 1.15% per annum. Trading balances are interest free. Management considered the expected credit loss on amounts due from Group undertakings at 31 Dec...    		 for the year ended 31 December 2022

    The share options creditor relates to amounts payable to the ultimate parent Canadian Natural Resources Limited relating to employee options to purchase stock in the aforementioned company. The provision is based on the specifics of the agreed plan with Canadian Natural Resources Limited. The current portion of this totalling £4.3 million (2021 - £3.7 million) is included in creditor amounts falling due within one year.

    34

    The Company settled an intercompany term loan on 24 October 2022. The amount drawn down by the Company at 24 October 2022 was US$440.0 million (2021 - US$440.0 million). Interest was charged at US$ LIBOR + 2.8175% per annum on amounts drawn down from this facility until settlement.

    2022 (£'000) 2021 (£'000) Share options 3,550 4,276 Lease liabilities (note 13) 4,786 6,428 Total 8,336 10,704

    The short term loan with CNR International (U.K.) Developments Limited generates interest at a rate of USS LIBOR + 1.75%.

    18. CREDITORS: ...    		 18 Cash and cash equivalents

    Group 31 Dec 2021 (£000) Group 31 Dec 2020 (£000) Company 31 Dec 2021 (£000) Company 31 Dec 2020 (£000) Sterling 19,641 37,349 19,498 37,347 United States Dollar 8,574 6,826 — — Euros 361 — — — Canadian Dollar 227 364 — — Polish Zloty 225 283 — — Singapore Dollar 29 — — — Japanese Yen 7 — — — Total 29,064 44,822 19,498 37,347

    Cash and cash equivalents are denominated in the following currencies:

    On 14 October 2021, the Group and Company entered into a loan agreement with Bank Of Ireland Group plc consisting of a £10 million term loan in addition to a revolving credit facility of £10 million. The loan is secured on the assets of the Group. Operating covenants are limited to the Group’s net debt leverage and interest cover. The term loan is repayable over five years with an initial 12-month repayment holiday followed by annual capital repayments of £1,250,000. At the end of the term, a bullet payment of £5 million is due. The loan is denominated in Pound S...
    What is the amount for Charges à imputer relative au personnel? Ventilation de la rubrique 492/3 du passif si celle-ci représente un montant important

    COMPTES DE RÉGULARISATION

    Description Montant Charges à imputer relative au personnel 128.084.824,70 Charges à imputer: intérêts courus et non échus 37.536.987,21 Charges à imputer diverses 12.774.405,94 Charges à imputer: protocoles et conventions avec autres opérateurs et réseaux 7.240.507,84 Produits à reporter divers 7.841.755,07 Produits à reporter relatifs au trafic 116.123.554,80 Produis à reporter: Hello Belgium Railpass 21.396.182,64 Produis à reporter: NPV 20.088.866,29 Produits à reporter: financements alternatifs 15.285.835,38

    72

    First - C-Cap2022 - 38 / 82    		 markdown N° 0203.430.576 C-Cap 6.9<br><br>Charges à imputer diverses Exercice Charges à imputer: protocoles et conventions avec autres opérateurs et reseaux 2.101.406,29 Charges à imputer relatives au personnel 1.378.996,29 Charges à imputer: intérêts courus et non échus 139.424.041,35 Produits à reporter divers 39.103.099,47 Produits à reporter relatifs au trafic 6.816.893,14 Produits à reporter: financements altematifs 146.853.054,23 Produis à reporter: NPV 9.576.751,43 15.185.940,48<br><br>71 Rapport annuel SNCB 2023

    COMPTES DE RÉGULARISATION

    Ventilation de la rubrique 492/3 du passif si celle-ci représente un montant important    		 DETTES FISCALES, SALARIALES ET SOCIALES (€)

    CODES BNB 2024 VENTILATION DE LA RUBRIQUE 492/3 DU PASSIF SI CELLE-CI REPRÉSENTE UN MONTANT IMPORTANT Intérêts crédits KBC à imputer Autres charges à imputer

    ÉTAT DES DETTES (€)

    COMPTES DE REGULARISATION (€)

    12.4 Etat des dettes et comptes de régularisation du passif

    CODES BNB 2024 VENTILATION DES DETTES A L'ORIGINE A PLUS D'UN AN, EN FONCTION DE LEUR DUREE RESDIEUELLE Dettes à plus d'un an échéant dans l'année Dettes financières 8801 Etablissements de crédit 8841 Total des dettes à plus d'un an échéant dans l'année (42) Dettes ayant plus d'un an mais 5 ans au plus à courir Dettes financières 8802 Etablissement de crédit 8842 Total des dettes ayant plus d'un an mais 5 ans au plus à courir 8912

    12.5 Résultats d'exploitation (en milliers €)

    DETTES GARANTIES (€)

    CODES BNB 2024 Rémunérations et charges sociales Autres dettes salariales et sociales 9077

    ```markdown

    CODES BNB 2023 2024 CHARGES D'EXPLOITATION Travailleurs pour lesquels la ...    		 Résultats financiers Charges financières récurrentes Ventilation des autres charges financières

    11 Dettes fiscales, salariales et sociales

    2022 2021 Impôts (rubriques 450/3 et 179 du passif) Dettes fiscales échues - - Dettes fiscales non échues - - Dettes fiscales estimées - - Rémunérations et charges sociales (rubriques 454/9 et 179 du passif) Dettes échues envers l'Office National de Sécurité Sociale - - Autres dettes salariales et sociales 28.500 25.000

    FINANCIÈRE DE TUBIZE – RAPPORT FINANCIER ANNUEL 2022

    Comptes de régularisation

    FINANCIÈRE DE TUBIZE - RAPPORT ANNUEL 2022 40 41

    2022 2021 Ventilation de la rubrique 492/3 du passif si celle-ci représente un montant important Charges à imputer: intérêts 345.843 40.556 Charges à imputer: commission de réservation 107.126 76.667

    2022 2021 Charges d'exploitation Travailleurs pour lesquels la société a introduit une déclaration DIMONA ou qui sont inscrits au registre général du personnel - - Nombre total à la date de clôture - - Ef...    		 ANNEXES DES COMPTES DE LA SOCIÉTÉ AU 31 MAI 2025

    SITUATION FISCALE DIFFEREE ET LATENTE

    | Accroissements de la dette future d'impôt | Montant |
    |---|---|
    | Impôt dû sur provisions réglementées: | |
    | Provisions pour hausse de prix | |
    | Provisions pour fluctuation des cours | |
    | Provisions pour investissements | |
    | Amortissements dérogatoires | 3 548 |
    | Subventions d'investissement | |
    | TOTAL ACCROISSEMENTS | 3 548 |

    | Allègements de la dette future d'impôt | Montant |
    |---|---|
    | Impôt payé d'avance sur: | |
    | Charges non déductibles temporairement (à déduire l'année suivante) | 1 200 |
    | Congés payés | |
    | Participation des salariés | 53 |
    | Autres | |
    | A déduire ultérieurement | |
    | Provisions pour propre assureur | |
    | Autres | |
    | TOTAL ALLÈGEMENTS | 1 254 |

    SITUATION FISCALE DIFFÉRÉE NETTE
    2 294

    IMPÔT DÙ SUR: Plus-values différées
    43 436

    CREDIT A IMPUTER SUR: Déficits reportables

    CREDIT A IMPUTER SUR: Moins-values à long terme

    SITUATION FISCALE LATENTE NETT...    		 d'un mois au plus

    Titres à revenu fixe émis par des établissements de crédit

    AUTRES PLACEMENTS DE TRÉSORERIE

    Autres placements de trésorerie non repris ci-avant

    Codes Exercice Exercice précédent 51 8681 8682 8683 52 18.088.896,34 55.716.775,84 8684 53 226.519.496,99 136.305.308,18 8686 85.000.000,00 801.112,99 8687 1.599.690,84 8688 139.919.806,15 135.504.195,19 8689

    Actions, parts et placements autres que placements à revenu fixe

    Titres à revenu fixe

    Actions et parts - Montant non appelé

    Avec une durée résiduelle ou de préavis

    Métaux précieux et œuvres d'art

    Ventilation de la rubrique 490/1 de l'actif si celle-ci représente un montant important

    PLACEMENTS DE TRÉSORERIE ET COMPTES DE RÉGULARISATION DE L'ACTIF

    64 Rapport annuel SNCB 2023

    Actions et parts - Valeur comptable augmentée du montant non appelé

    Comptes à terme détenus auprès des établissements de crédit

    de plus d'un mois à un an au plus

    COMPTES DE RÉGULARISATION

    Exercice Charges à reporter: redevance infrastru...    		 4.6. Accroissements et allégements de la dette future d'impôt

    Les éléments entraînant un décalage d'imposition conduisent à un accroissement de la dette future d'impôt de 21 278K€ calculé au taux de 25.82%.

    La situation fiscale latente s'analyse comme suit :

    | Base de calcul | Montants en K€ |
    |---|---|
    | BASE D'IMPOT SUR : | |
    | Provisions réglementées : | |
    | - Ecart de conversion Actif | 0 |
    | - Ecart de conversion Passif | -4 |
    | - Provision pour investissements | |
    | - Amortissements dérogatoires | 94 455 |
    | Subventions d'investissement | 3 283 |
    | Produits non imposables temporairement : | |
    | (à réintégrer l'année de leur acquisition) | |
    | - plafonnement TP | |
    | TOTAL ACCROISSEMENTS | 97 734 |
    | BASE D'IMPOT PAYE D'AVANCE SUR : | |
    | Charges non déductibles temporairement : | |
    | (à déduire l'année suivante) | |
    | - Provision pour risques et charges | -928 |
    | - Provision pour participation | -4 083 |
    | - Contribution solidarité | -869 |
    | - Provisions pou...
  • Loss: SpladeLoss with these parameters:
    {
    "loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score', gather_across_devices=False)",
    "document_regularizer_weight": 3e-05,
    "query_regularizer_weight": 0.0
}

Training Hyperparameters

Non-Default Hyperparameters

  • num_train_epochs: 2
  • learning_rate: 2e-05
  • warmup_steps: 114
  • weight_decay: 0.01
  • gradient_accumulation_steps: 4
  • bf16: True
  • tf32: True
  • eval_strategy: steps
  • dataloader_num_workers: 4
  • batch_sampler: no_duplicates
  • router_mapping: {'query': 'query', 'positive': 'document', 'negative_0': 'document', 'negative_1': 'document', 'negative_2': 'document', 'negative_3': 'document', 'negative_4': 'document', 'negative_5': 'document', 'negative_6': 'document'}
  • learning_rate_mapping: {'sub_modules\.query\..*': 0.001}

All Hyperparameters

Click to expand
  • per_device_train_batch_size: 8
  • num_train_epochs: 2
  • max_steps: -1
  • learning_rate: 2e-05
  • lr_scheduler_type: linear
  • lr_scheduler_kwargs: None
  • warmup_steps: 114
  • optim: adamw_torch_fused
  • optim_args: None
  • weight_decay: 0.01
  • adam_beta1: 0.9
  • adam_beta2: 0.999
  • adam_epsilon: 1e-08
  • optim_target_modules: None
  • gradient_accumulation_steps: 4
  • average_tokens_across_devices: True
  • max_grad_norm: 1.0
  • label_smoothing_factor: 0.0
  • bf16: True
  • fp16: False
  • bf16_full_eval: False
  • fp16_full_eval: False
  • tf32: True
  • gradient_checkpointing: False
  • gradient_checkpointing_kwargs: None
  • torch_compile: False
  • torch_compile_backend: None
  • torch_compile_mode: None
  • use_liger_kernel: False
  • liger_kernel_config: None
  • use_cache: False
  • neftune_noise_alpha: None
  • torch_empty_cache_steps: None
  • auto_find_batch_size: False
  • log_on_each_node: True
  • logging_nan_inf_filter: True
  • include_num_input_tokens_seen: no
  • log_level: passive
  • log_level_replica: warning
  • disable_tqdm: False
  • project: huggingface
  • trackio_space_id: trackio
  • eval_strategy: steps
  • per_device_eval_batch_size: 8
  • prediction_loss_only: True
  • eval_on_start: False
  • eval_do_concat_batches: True
  • eval_use_gather_object: False
  • eval_accumulation_steps: None
  • include_for_metrics: []
  • batch_eval_metrics: False
  • save_only_model: False
  • save_on_each_node: False
  • enable_jit_checkpoint: False
  • push_to_hub: False
  • hub_private_repo: None
  • hub_model_id: None
  • hub_strategy: every_save
  • hub_always_push: False
  • hub_revision: None
  • load_best_model_at_end: False
  • ignore_data_skip: False
  • restore_callback_states_from_checkpoint: False
  • full_determinism: False
  • seed: 42
  • data_seed: None
  • use_cpu: False
  • accelerator_config: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
  • parallelism_config: None
  • dataloader_drop_last: False
  • dataloader_num_workers: 4
  • dataloader_pin_memory: True
  • dataloader_persistent_workers: False
  • dataloader_prefetch_factor: None
  • remove_unused_columns: True
  • label_names: None
  • train_sampling_strategy: random
  • length_column_name: length
  • ddp_find_unused_parameters: None
  • ddp_bucket_cap_mb: None
  • ddp_broadcast_buffers: False
  • ddp_backend: None
  • ddp_timeout: 1800
  • fsdp: []
  • fsdp_config: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
  • deepspeed: None
  • debug: []
  • skip_memory_metrics: True
  • do_predict: False
  • resume_from_checkpoint: None
  • warmup_ratio: None
  • local_rank: -1
  • prompts: None
  • batch_sampler: no_duplicates
  • multi_dataset_batch_sampler: proportional
  • router_mapping: {'query': 'query', 'positive': 'document', 'negative_0': 'document', 'negative_1': 'document', 'negative_2': 'document', 'negative_3': 'document', 'negative_4': 'document', 'negative_5': 'document', 'negative_6': 'document'}
  • learning_rate_mapping: {'sub_modules\.query\..*': 0.001}

Training Logs

Click to expand
Epoch Step Training Loss NanoNFCorpus_dot_ndcg@10 NanoSciFact_dot_ndcg@10 NanoFiQA2018_dot_ndcg@10 NanoBEIR_mean_dot_ndcg@10
0.0175 10 2.5846 - - - -
0.0351 20 2.4596 - - - -
0.0526 30 2.5787 - - - -
0.0701 40 2.2135 - - - -
0.0877 50 2.1444 - - - -
0.1052 60 2.0011 - - - -
0.1228 70 1.8179 - - - -
0.1403 80 1.7744 - - - -
0.1578 90 1.7054 - - - -
0.1754 100 1.5427 - - - -
0.1929 110 1.6134 - - - -
0.2104 120 1.6381 - - - -
0.2280 130 1.6946 - - - -
0.2455 140 1.4456 - - - -
0.2630 150 1.4302 - - - -
0.2806 160 1.3097 - - - -
0.2981 170 1.5755 - - - -
0.3157 180 1.2906 - - - -
0.3332 190 1.3424 - - - -
0.3507 200 1.5477 - - - -
0.3683 210 1.3442 - - - -
0.3858 220 1.2810 - - - -
0.4033 230 1.3157 - - - -
0.4209 240 1.2839 - - - -
0.4384 250 1.2428 - - - -
0.4559 260 1.2376 - - - -
0.4735 270 1.1353 - - - -
0.4910 280 1.2513 - - - -
0.5085 290 1.0490 - - - -
0.5261 300 1.0669 - - - -
0.5436 310 1.2219 - - - -
0.5612 320 1.0313 - - - -
0.5787 330 1.2846 - - - -
0.5962 340 1.0939 - - - -
0.6138 350 1.0299 - - - -
0.6313 360 0.6464 - - - -
0.6488 370 0.7067 - - - -
0.6664 380 0.5505 - - - -
0.6839 390 0.6885 - - - -
0.7014 400 0.8663 - - - -
0.7190 410 0.8602 - - - -
0.7365 420 0.5517 - - - -
0.7541 430 0.3781 - - - -
0.7716 440 0.6533 - - - -
0.7891 450 1.1145 - - - -
0.8067 460 0.3240 - - - -
0.8242 470 0.5818 - - - -
0.8417 480 0.3394 - - - -
0.8593 490 0.8986 - - - -
0.8768 500 0.6177 0.3695 0.7388 0.3862 0.4982
0.8943 510 0.8443 - - - -
0.9119 520 0.5454 - - - -
0.9294 530 0.9840 - - - -
0.9470 540 0.6111 - - - -
0.9645 550 0.7095 - - - -
0.9820 560 0.8391 - - - -
0.9996 570 0.6461 - - - -
1.0158 580 1.3053 - - - -
1.0333 590 0.9817 - - - -
1.0509 600 1.0531 - - - -
1.0684 610 0.9087 - - - -
1.0859 620 0.9186 - - - -
1.1035 630 1.0373 - - - -
1.1210 640 0.9417 - - - -
1.1385 650 0.9963 - - - -
1.1561 660 0.9058 - - - -
1.1736 670 0.9252 - - - -
1.1911 680 1.0170 - - - -
1.2087 690 0.9957 - - - -
1.2262 700 0.8720 - - - -
1.2438 710 0.8776 - - - -
1.2613 720 0.8562 - - - -
1.2788 730 0.8772 - - - -
1.2964 740 0.9591 - - - -
1.3139 750 0.9495 - - - -
1.3314 760 0.9933 - - - -
1.3490 770 0.8449 - - - -
1.3665 780 0.7833 - - - -
1.3840 790 0.9574 - - - -
1.4016 800 0.7727 - - - -
1.4191 810 0.8997 - - - -
1.4367 820 0.8796 - - - -
1.4542 830 0.8535 - - - -
1.4717 840 1.0049 - - - -
1.4893 850 0.8912 - - - -
1.5068 860 0.9883 - - - -
1.5243 870 0.7190 - - - -
1.5419 880 0.9274 - - - -
1.5594 890 0.8372 - - - -
1.5769 900 0.7986 - - - -
1.5945 910 0.7205 - - - -
1.6120 920 0.5797 - - - -
1.6295 930 0.6741 - - - -
1.6471 940 0.5253 - - - -
1.6646 950 0.1963 - - - -
1.6822 960 0.4864 - - - -
1.6997 970 0.7439 - - - -
1.7172 980 0.6164 - - - -
1.7348 990 0.3680 - - - -
1.7523 1000 0.5521 0.3775 0.7393 0.4401 0.5190
1.7698 1010 0.2149 - - - -
1.7874 1020 0.5544 - - - -
1.8049 1030 0.8062 - - - -
1.8224 1040 0.2349 - - - -
1.8400 1050 0.5362 - - - -
1.8575 1060 0.8963 - - - -
1.8751 1070 0.5910 - - - -
1.8926 1080 0.3764 - - - -
1.9101 1090 0.5331 - - - -
1.9277 1100 1.0374 - - - -
1.9452 1110 0.6087 - - - -
1.9627 1120 0.4690 - - - -
1.9803 1130 0.4651 - - - -
1.9978 1140 0.5315 - - - -

Framework Versions

  • Python: 3.11.10
  • Sentence Transformers: 5.2.3
  • Transformers: 5.2.0
  • PyTorch: 2.10.0+cu128
  • Accelerate: 1.12.0
  • Datasets: 4.5.0
  • Tokenizers: 0.22.2

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",
}

SpladeLoss 

@misc{formal2022distillationhardnegativesampling,
      title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
      author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
      year={2022},
      eprint={2205.04733},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2205.04733},
}

SparseMultipleNegativesRankingLoss 

@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}
}

FlopsLoss 

@article{paria2020minimizing,
    title={Minimizing flops to learn efficient sparse representations},
    author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
    journal={arXiv preprint arXiv:2004.05665},
    year={2020}
}
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