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

# BERTopic_Social

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("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 ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | new - electric - seat - car - manual | 5 | -1_new_electric_seat_car | 
| 0 | electric - car - ev - charging - cent | 25 | 0_electric_car_ev_charging | 
| 1 | tesla - musk - elon - elon musk - vehicle | 54 | 1_tesla_musk_elon_elon musk | 
| 2 | new - nissan - citroen - car - retro | 30 | 2_new_nissan_citroen_car | 
| 3 | percent - cars - car - private - electric | 15 | 3_percent_cars_car_private | 
| 4 | chinese - china - electric - xiaomi - cars | 15 | 4_chinese_china_electric_xiaomi | 
| 5 | electric - vehicles - french - electric car - price | 12 | 5_electric_vehicles_french_electric car | 
| 6 | renault - car - electric - mg - new | 12 | 6_renault_car_electric_mg | 
| 7 | german - trust - brands - quality - german brands | 9 | 7_german_trust_brands_quality | 
| 8 | units - electric - april - russia - electric vehicles | 8 | 8_units_electric_april_russia | 
| 9 | sharing - car sharing - car - audi - club | 8 | 9_sharing_car sharing_car_audi | 
| 10 | used - carmax - car - used car - cars | 6 | 10_used_carmax_car_used car | 
| 11 | best - ev9 - puma - edmunds - electric | 6 | 11_best_ev9_puma_edmunds |

</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: 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