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bertopic_model_v1

This is a BERTopic model. BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.

Usage

To use this model, please install BERTopic:

pip install -U bertopic

You can use the model as follows:

from bertopic import BERTopic
topic_model = BERTopic.load("ivanleomk/bertopic_model_v1")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 25
  • Number of training documents: 1358
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
-1 events - invites - national - volunteer - week 12 -1_events_invites_national_volunteer
0 gworks - software - hub - the - and 34 0_gworks_software_hub_the
1 upflow - upflows - can - how - and 226 1_upflow_upflows_can_how
2 banking - compliance - are - what - unit 188 2_banking_compliance_are_what
3 canal - canals - for - what - shop 142 3_canal_canals_for_what
4 pricing - roi - details - upflows - of 135 4_pricing_roi_details_upflows
5 sama - task - delivery - annotation - quality 77 5_sama_task_delivery_annotation
6 collection - cash - processes - ar - collections 68 6_collection_cash_processes_ar
7 case - studies - we - have - do 46 7_case_studies_we_have
8 naro - naros - platform - answers - docebo 40 8_naro_naros_platform_answers
9 invoices - invoice - invoicing - upflow - handling 40 9_invoices_invoice_invoicing_upflow
10 recipient - renewal - the - sender - agreement 39 10_recipient_renewal_the_sender
11 payment - upflows - gateway - features - upflow 39 11_payment_upflows_gateway_features
12 builder - tool - audience - presentation - the 37 12_builder_tool_audience_presentation
13 where - found - be - deck - promised 34 13_where_found_be_deck
14 stripe - express - hubspot - billing - payment 30 14_stripe_express_hubspot_billing
15 retention - unit - ottimate - increase - customer 28 15_retention_unit_ottimate_increase
16 netsuite - with - upflow - integration - synchronization 24 16_netsuite_with_upflow_integration
17 email - inbox - ar - success - up 23 17_email_inbox_ar_success
18 chargebee - with - upflow - synchronized - integration 20 18_chargebee_with_upflow_synchronized
19 budget - allocation - iq - plate - ppl 17 19_budget_allocation_iq_plate
20 receivable - accounts - jjjworks - upflow - resources 16 20_receivable_accounts_jjjworks_upflow
21 card - cards - status - program - the 15 21_card_cards_status_program
22 project - projects - growth - roadmap - create 14 22_project_projects_growth_roadmap
23 nps - docebo - employee - scores - improving 14 23_nps_docebo_employee_scores

Training hyperparameters

  • calculate_probabilities: False
  • language: english
  • low_memory: False
  • min_topic_size: 10
  • n_gram_range: (1, 1)
  • nr_topics: None
  • seed_topic_list: None
  • top_n_words: 10
  • verbose: False
  • zeroshot_min_similarity: 0.7
  • zeroshot_topic_list: None

Framework versions

  • Numpy: 1.26.4
  • HDBSCAN: 0.8.37
  • UMAP: 0.5.6
  • Pandas: 2.2.2
  • Scikit-Learn: 1.5.0
  • Sentence-transformers: 3.0.1
  • Transformers: 4.41.2
  • Numba: 0.60.0
  • Plotly: 5.22.0
  • Python: 3.12.3
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