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karinegabsschon/BERTopic_Social

sourceHugging Faceupdated 1y agoView on Hugging Face
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tags:

  • bertopic libraryname: bertopic pipelinetag: text-classification ---

BERTopic_Social

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:

python
from bertopic import BERTopic
topic_model = BERTopic.load("karinegabsschon/BERTopic_Social")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 13
  • Number of training documents: 205

<details> <summary>Click here for an overview of all topics.</summary>

Topic IDTopic KeywordsTopic FrequencyLabel
-1new - electric - seat - car - manual5-1newelectricseatcar
0electric - car - ev - charging - cent250electriccarevcharging
1tesla - musk - elon - elon musk - vehicle541teslamuskelonelon musk
2new - nissan - citroen - car - retro302newnissancitroencar
3percent - cars - car - private - electric153percentcarscarprivate
4chinese - china - electric - xiaomi - cars154chinesechinaelectricxiaomi
5electric - vehicles - french - electric car - price125electricvehiclesfrenchelectric car
6renault - car - electric - mg - new126renaultcarelectricmg
7german - trust - brands - quality - german brands97germantrustbrandsquality
8units - electric - april - russia - electric vehicles88unitselectricaprilrussia
9sharing - car sharing - car - audi - club89sharingcar sharingcaraudi
10used - carmax - car - used car - cars610usedcarmaxcarused car
11best - ev9 - puma - edmunds - electric611bestev9pumaedmunds

</details>

Training hyperparameters

  • calculate_probabilities: False
  • language: None
  • low_memory: False
  • mintopicsize: 10
  • ngramrange: (1, 1)
  • nr_topics: None
  • seedtopiclist: None
  • topnwords: 10
  • verbose: True
  • zeroshotminsimilarity: 0.7
  • zeroshottopiclist: None

Framework versions

  • Numpy: 2.0.2
  • HDBSCAN: 0.8.40
  • UMAP: 0.5.8
  • Pandas: 2.2.2
  • Scikit-Learn: 1.6.1
  • Sentence-transformers: 4.1.0
  • Transformers: 4.53.0
  • Numba: 0.60.0
  • Plotly: 5.24.1
  • Python: 3.11.13