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:
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
Click here for an overview of all topics.
| 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 |
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
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