---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# alpinetest

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("Wscherm19/alpinetest")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 37
* Number of training documents: 2788

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | unto - people - men - man - great | 10 | -1_unto_people_men_man | 
| 0 | florentines - duke - pope - florence - city | 1004 | 0_florentines_duke_pope_florence | 
| 1 | man - men - things - good - virtue | 274 | 1_man_men_things_good | 
| 2 | army - enemy - soldiers - romans - chap | 212 | 2_army_enemy_soldiers_romans | 
| 3 | law - power - sovereign - commonwealth - nature | 135 | 3_law_power_sovereign_commonwealth | 
| 4 | prince - thou - ought - subjects - princes | 118 | 4_prince_thou_ought_subjects | 
| 5 | adam - right - power - dominion - children | 79 | 5_adam_right_power_dominion | 
| 6 | commonweal - aristotle - oligarchie - democratie - government | 67 | 6_commonweal_aristotle_oligarchie_democratie | 
| 7 | king - unto - charles - france - kingdom | 57 | 7_king_unto_charles_france | 
| 8 | turk - turks - forces - princes - christians | 54 | 8_turk_turks_forces_princes | 
| 9 | parliament - law - house - england - page | 51 | 9_parliament_law_house_england | 
| 10 | church - ceremonies - churches - things - god | 49 | 10_church_ceremonies_churches_things | 
| 11 | commonwealth - people - unto - senate - popular | 48 | 11_commonwealth_people_unto_senate | 
| 12 | trade - east - england - wares - india | 48 | 12_trade_east_england_wares | 
| 13 | scripture - church - god - christ - things | 45 | 13_scripture_church_god_christ | 
| 14 | earl - king - did - henry - richard | 43 | 14_earl_king_did_henry | 
| 15 | magistrates - power - unto - law - judges | 36 | 15_magistrates_power_unto_law | 
| 16 | god - moses - israel - kingdom - king | 36 | 16_god_moses_israel_kingdom | 
| 17 | prince - unto - tyrant - subjects - good | 33 | 17_prince_unto_tyrant_subjects | 
| 18 | fol - cities - city - great - miles | 33 | 18_fol_cities_city_great | 
| 19 | god - spirit - resurrection - shall - scripture | 31 | 19_god_spirit_resurrection_shall | 
| 20 | lord - oh - hamlet - thy - thou | 31 | 20_lord_oh_hamlet_thy | 
| 21 | silver - money - deniers - gold - thousand | 30 | 21_silver_money_deniers_gold | 
| 22 | church - christian - pope - civil - sovereign | 27 | 22_church_christian_pope_civil | 
| 23 | shall - unto - council - tribe - ballot | 26 | 23_shall_unto_council_tribe | 
| 24 | thou - war - thy - tac - hist | 25 | 24_thou_war_thy_tac | 
| 25 | chap - people - romans - senate - rome | 23 | 25_chap_people_romans_senate | 
| 26 | laws - king - monarchy - government - power | 20 | 26_laws_king_monarchy_government | 
| 27 | town - people - did - men - athenians | 19 | 27_town_people_did_men | 
| 28 | page - majesty - highness - chap - royal | 19 | 28_page_majesty_highness_chap | 
| 29 | christ - god - jesus - savior - apostles | 19 | 29_christ_god_jesus_savior | 
| 30 | senate - people - unto - power - magistrates | 17 | 30_senate_people_unto_power | 
| 31 | noble - alexander - man - wise - men | 17 | 31_noble_alexander_man_wise | 
| 32 | law - god - laws - things - reason | 14 | 32_law_god_laws_things | 
| 33 | citisens - slaves - citisen - strangers - unto | 13 | 33_citisens_slaves_citisen_strangers | 
| 34 | enquest - say - judges - man - law | 13 | 34_enquest_say_judges_man | 
| 35 | princes - empire - emperor - alliance - swissers | 12 | 35_princes_empire_emperor_alliance |
  
</details>

## Training hyperparameters

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

## Framework versions

* Numpy: 1.26.4
* HDBSCAN: 0.8.39
* UMAP: 0.5.7
* Pandas: 2.2.2
* Scikit-Learn: 1.5.2
* Sentence-transformers: 3.2.1
* Transformers: 4.46.1
* Numba: 0.60.0
* Plotly: 5.24.1
* Python: 3.10.12