Add BERTopic model
Browse files- README.md +94 -0
- config.json +17 -0
- topic_embeddings.safetensors +3 -0
- topics.json +2598 -0
README.md
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---
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tags:
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- bertopic
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library_name: bertopic
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pipeline_tag: text-classification
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---
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# bertopic_model_v1
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
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BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
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## Usage
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To use this model, please install BERTopic:
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```
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pip install -U bertopic
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```
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You can use the model as follows:
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```python
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from bertopic import BERTopic
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topic_model = BERTopic.load("ivanleomk/bertopic_model_v1")
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topic_model.get_topic_info()
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```
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## Topic overview
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* Number of topics: 25
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* Number of training documents: 1358
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<details>
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<summary>Click here for an overview of all topics.</summary>
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| Topic ID | Topic Keywords | Topic Frequency | Label |
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|----------|----------------|-----------------|-------|
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| -1 | events - invites - national - volunteer - week | 12 | -1_events_invites_national_volunteer |
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| 0 | gworks - software - hub - the - and | 34 | 0_gworks_software_hub_the |
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| 1 | upflow - upflows - can - how - and | 226 | 1_upflow_upflows_can_how |
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| 2 | banking - compliance - are - what - unit | 188 | 2_banking_compliance_are_what |
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| 3 | canal - canals - for - what - shop | 142 | 3_canal_canals_for_what |
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| 4 | pricing - roi - details - upflows - of | 135 | 4_pricing_roi_details_upflows |
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| 5 | sama - task - delivery - annotation - quality | 77 | 5_sama_task_delivery_annotation |
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| 6 | collection - cash - processes - ar - collections | 68 | 6_collection_cash_processes_ar |
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| 7 | case - studies - we - have - do | 46 | 7_case_studies_we_have |
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| 8 | naro - naros - platform - answers - docebo | 40 | 8_naro_naros_platform_answers |
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| 9 | invoices - invoice - invoicing - upflow - handling | 40 | 9_invoices_invoice_invoicing_upflow |
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| 10 | recipient - renewal - the - sender - agreement | 39 | 10_recipient_renewal_the_sender |
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| 11 | payment - upflows - gateway - features - upflow | 39 | 11_payment_upflows_gateway_features |
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| 12 | builder - tool - audience - presentation - the | 37 | 12_builder_tool_audience_presentation |
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| 13 | where - found - be - deck - promised | 34 | 13_where_found_be_deck |
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| 14 | stripe - express - hubspot - billing - payment | 30 | 14_stripe_express_hubspot_billing |
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| 15 | retention - unit - ottimate - increase - customer | 28 | 15_retention_unit_ottimate_increase |
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| 16 | netsuite - with - upflow - integration - synchronization | 24 | 16_netsuite_with_upflow_integration |
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| 17 | email - inbox - ar - success - up | 23 | 17_email_inbox_ar_success |
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| 18 | chargebee - with - upflow - synchronized - integration | 20 | 18_chargebee_with_upflow_synchronized |
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| 19 | budget - allocation - iq - plate - ppl | 17 | 19_budget_allocation_iq_plate |
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| 20 | receivable - accounts - jjjworks - upflow - resources | 16 | 20_receivable_accounts_jjjworks_upflow |
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| 21 | card - cards - status - program - the | 15 | 21_card_cards_status_program |
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| 22 | project - projects - growth - roadmap - create | 14 | 22_project_projects_growth_roadmap |
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| 23 | nps - docebo - employee - scores - improving | 14 | 23_nps_docebo_employee_scores |
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</details>
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## Training hyperparameters
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* calculate_probabilities: False
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* language: english
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* low_memory: False
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* min_topic_size: 10
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* n_gram_range: (1, 1)
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* nr_topics: None
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* seed_topic_list: None
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* top_n_words: 10
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* verbose: False
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* zeroshot_min_similarity: 0.7
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* zeroshot_topic_list: None
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## Framework versions
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* Numpy: 1.26.4
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* HDBSCAN: 0.8.37
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* UMAP: 0.5.6
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* Pandas: 2.2.2
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* Scikit-Learn: 1.5.0
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* Sentence-transformers: 3.0.1
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* Transformers: 4.41.2
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* Numba: 0.60.0
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* Plotly: 5.22.0
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* Python: 3.12.3
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config.json
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{
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"calculate_probabilities": false,
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"language": "english",
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"low_memory": false,
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"min_topic_size": 10,
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"n_gram_range": [
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1,
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1
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],
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"nr_topics": null,
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"seed_topic_list": null,
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"top_n_words": 10,
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"verbose": false,
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"zeroshot_min_similarity": 0.7,
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"zeroshot_topic_list": null,
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"embedding_model": "sentence-transformers/all-MiniLM-L6-v2"
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}
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topic_embeddings.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:a31e70740edf39a3206d6103b77d0b64f85eaab67e77d31ea4d5f44d675b1897
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size 38488
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topics.json
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1415 |
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1429 |
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1660 |
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1666 |
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1667 |
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1758 |
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1763 |
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1765 |
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1767 |
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1768 |
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1769 |
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1771 |
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1772 |
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1773 |
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1779 |
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1780 |
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1781 |
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1782 |
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1783 |
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1784 |
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1785 |
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1786 |
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1787 |
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1788 |
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1789 |
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1791 |
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1792 |
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1793 |
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1794 |
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1795 |
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1796 |
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1797 |
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13,
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1798 |
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2,
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1799 |
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5,
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1800 |
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0,
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1801 |
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18,
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1802 |
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1803 |
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1804 |
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1805 |
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1806 |
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1807 |
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1808 |
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1809 |
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1810 |
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1811 |
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1812 |
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1813 |
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1814 |
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1815 |
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1816 |
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1817 |
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1818 |
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12,
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1819 |
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1820 |
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1821 |
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1822 |
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1823 |
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1824 |
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1825 |
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1826 |
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1827 |
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1828 |
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1829 |
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1831 |
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1832 |
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1833 |
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1834 |
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1836 |
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1837 |
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1841 |
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1842 |
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1843 |
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1845 |
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1846 |
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1847 |
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1848 |
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1849 |
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1850 |
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1851 |
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1852 |
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1853 |
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1854 |
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1855 |
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1856 |
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1857 |
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1858 |
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1859 |
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6,
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1860 |
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1861 |
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1862 |
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1863 |
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1864 |
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1865 |
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2,
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1866 |
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17,
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1867 |
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1,
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1868 |
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1869 |
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23,
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1870 |
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10,
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1871 |
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4,
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1872 |
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2,
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1873 |
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1874 |
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18,
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1875 |
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3,
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1876 |
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1,
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1877 |
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1,
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1878 |
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1879 |
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14,
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1880 |
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1,
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1881 |
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1,
|
1882 |
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1,
|
1883 |
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0,
|
1884 |
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3,
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1885 |
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2,
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1886 |
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12,
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1887 |
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1888 |
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4,
|
1889 |
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0,
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1890 |
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1891 |
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2,
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1892 |
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3,
|
1893 |
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0,
|
1894 |
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6,
|
1895 |
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1896 |
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0,
|
1897 |
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1,
|
1898 |
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2,
|
1899 |
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1,
|
1900 |
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1,
|
1901 |
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5,
|
1902 |
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2,
|
1903 |
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8,
|
1904 |
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14,
|
1905 |
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2,
|
1906 |
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7,
|
1907 |
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5,
|
1908 |
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3,
|
1909 |
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12,
|
1910 |
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5,
|
1911 |
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3,
|
1912 |
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0,
|
1913 |
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0,
|
1914 |
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0,
|
1915 |
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0,
|
1916 |
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11,
|
1917 |
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2,
|
1918 |
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6,
|
1919 |
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12,
|
1920 |
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4,
|
1921 |
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-1,
|
1922 |
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1,
|
1923 |
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12,
|
1924 |
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0,
|
1925 |
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1,
|
1926 |
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3,
|
1927 |
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16,
|
1928 |
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22,
|
1929 |
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1,
|
1930 |
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10,
|
1931 |
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2,
|
1932 |
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0,
|
1933 |
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9,
|
1934 |
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20,
|
1935 |
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0,
|
1936 |
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1,
|
1937 |
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13,
|
1938 |
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0,
|
1939 |
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17,
|
1940 |
+
21,
|
1941 |
+
1,
|
1942 |
+
9,
|
1943 |
+
11,
|
1944 |
+
0,
|
1945 |
+
0,
|
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