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---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---
# bertopic_model_v1
This is a [BERTopic](https://github.com/MaartenGr/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:
```python
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
<details>
<summary>Click here for an overview of all topics.</summary>
| 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 |
</details>
## 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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