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BERTopic_Political
karinegabsschon/BERTopic_Political
BERTopic_Political is a text classification model from karinegabsschon. Use it when you need a label for a piece of text. It is set up for bertopic.
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.
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.json4.2 MB · 55%
At a glance
- Task
- Text Classification
- Library
- bertopic
- Access
- Public
- Created
- Jul 7, 2025
- Updated
- Sep 1, 2025
- SHA
- 2cfd8228
Try a prompt
Model card
From the Hugging Face model README
BERTopic_Political
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:
from bertopic import BERTopic
topic_model = BERTopic.load("karinegabsschon/BERTopic_Political")
topic_model.get_topic_info()
Topic overview
- Number of topics: 20
- Number of training documents: 619
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | electric - tariffs - vehicles - ev - car | 11 | -1_electric_tariffs_vehicles_ev |
| 0 | cars - spd - tax - electric - purchase | 97 | 0_cars_spd_tax_electric |
| 1 | charging - chargers - public - ev - points | 87 | 1_charging_chargers_public_ev |
| 2 | tax - car - new - electric - petrol | 72 | 2_tax_car_new_electric |
| 3 | tesla - musk - elon - elon musk - trump | 53 | 3_tesla_musk_elon_elon musk |
| 4 | moves - aid - electric - euros - plan | 49 | 4_moves_aid_electric_euros |
| 5 | byd - chinese - china - price - price war | 36 | 5_byd_chinese_china_price |
| 6 | targets - government - mandate - starmer - zero | 25 | 6_targets_government_mandate_starmer |
| 7 | euros - bonus - ecological - ecological bonus - electric | 21 | 7_euros_bonus_ecological_ecological bonus |
| 8 | california - trump - states - administration - electric | 21 | 8_california_trump_states_administration |
| 9 | tariffs - united states - united - states - plant | 20 | 9_tariffs_united states_united_states |
| 10 | ukraine - region - electric - ukrainian - vehicles | 18 | 10_ukraine_region_electric_ukrainian |
| 11 | tesla - city - toronto - canadian - chow | 16 | 11_tesla_city_toronto_canadian |
| 12 | eu - china - chinese - tariffs - minimum | 15 | 12_eu_china_chinese_tariffs |
| 13 | chinese - defence - security - spying - military | 15 | 13_chinese_defence_security_spying |
| 14 | european - eu - commission - industry - electric | 14 | 14_european_eu_commission_industry |
| 15 | huf - businesses - subsidies - hungary - battery | 13 | 15_huf_businesses_subsidies_hungary |
| 16 | cent - government - diesel - fleet - electric | 12 | 16_cent_government_diesel_fleet |
| 17 | credit - tax - electric - vehicles - electric vehicles | 12 | 17_credit_tax_electric_vehicles |
| 18 | british - trade - cars - government - tariffs | 12 | 18_british_trade_cars_government |
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
- Task
- Text Classification
- Library
- bertopic
- Created
- Jul 7, 2025
- Updated
- Sep 1, 2025