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

# BERTopic_Environmental

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_Environmental")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 26
* Number of training documents: 905

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

  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | electric - car - cars - charging - vehicles | 11 | -1_electric_car_cars_charging | 
| 0 | battery - batteries - lithium - catl - technology | 213 | 0_battery_batteries_lithium_catl | 
| 1 | byd - charging - dolphin - chinese - new | 61 | 1_byd_charging_dolphin_chinese | 
| 2 | charging - ev - chargers - ev charging - electric | 58 | 2_charging_ev_chargers_ev charging | 
| 3 | zero - government - uk - mandate - electric | 57 | 3_zero_government_uk_mandate | 
| 4 | electric - charging - points - france - car | 49 | 4_electric_charging_points_france | 
| 5 | battery - lithium - recycling - batteries - supply | 48 | 5_battery_lithium_recycling_batteries | 
| 6 | cars - combustion - study - electric - car | 36 | 6_cars_combustion_study_electric | 
| 7 | percent - cars - market - sales - vehicles | 33 | 7_percent_cars_market_sales | 
| 8 | fires - safety - battery - electric - cars | 29 | 8_fires_safety_battery_electric | 
| 9 | charging - electric - sweden - vehicles - circle | 29 | 9_charging_electric_sweden_vehicles | 
| 10 | tax - drivers - petrol - ev - rates | 25 | 10_tax_drivers_petrol_ev | 
| 11 | kia - car - model - electric - range | 25 | 11_kia_car_model_electric | 
| 12 | cent - car - petrol - evs - drivers | 23 | 12_cent_car_petrol_evs | 
| 13 | charging - stations - charging stations - charging points - points | 23 | 13_charging_stations_charging stations_charging points | 
| 14 | india - ev - green - mobility - electric | 23 | 14_india_ev_green_mobility | 
| 15 | indonesia - battery - lg - ev - ev battery | 20 | 15_indonesia_battery_lg_ev | 
| 16 | department - flames - police - car - tesla | 20 | 16_department_flames_police_car | 
| 17 | transport - ireland - council - ev - climate | 19 | 17_transport_ireland_council_ev | 
| 18 | toyota - electric - new - europe - hyundai | 19 | 18_toyota_electric_new_europe | 
| 19 | sales - new - electric - cent - car | 17 | 19_sales_new_electric_cent | 
| 20 | european - commission - eu - von - der | 15 | 20_european_commission_eu_von | 
| 21 | power - blackout - spain - homes - electricity | 14 | 21_power_blackout_spain_homes | 
| 22 | nissan - leaf - micra - new - generation | 13 | 22_nissan_leaf_micra_new | 
| 23 | ship - coast - vessel - coast guard - guard | 13 | 23_ship_coast_vessel_coast guard | 
| 24 | id - volkswagen - vw - every1 - id every1 | 12 | 24_id_volkswagen_vw_every1 |

</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