BERTopic_Technological
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_Technological")
topic_model.get_topic_info()
Topic overview
- Number of topics: 28
- Number of training documents: 947
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | electric - battery - car - new - ev | 12 | -1_electric_battery_car_new |
| 0 | tesla - musk - elon - company - elon musk | 225 | 0_tesla_musk_elon_company |
| 1 | charging - ev - ev charging - solutions - infrastructure | 93 | 1_charging_ev_ev charging_solutions |
| 2 | byd - charging - minutes - new - tesla | 70 | 2_byd_charging_minutes_new |
| 3 | kia - electric - car - ev - cars | 55 | 3_kia_electric_car_ev |
| 4 | charging - car - battery - cars - mobility | 47 | 4_charging_car_battery_cars |
| 5 | xiaomi - su7 - yu7 - china - car | 45 | 5_xiaomi_su7_yu7_china |
| 6 | id - volkswagen - every1 - id every1 - vw | 39 | 6_id_volkswagen_every1_id every1 |
| 7 | leapmotor - ferrari - electric - c10 - stellantis | 30 | 7_leapmotor_ferrari_electric_c10 |
| 8 | cars - adac - vehicles - combustion - breakdown | 26 | 8_cars_adac_vehicles_combustion |
| 9 | electric - charging - renault - vehicles - iberdrola | 23 | 9_electric_charging_renault_vehicles |
| 10 | car - electric - fisker - ev - battery | 22 | 10_car_electric_fisker_ev |
| 11 | foxconn - mitsubishi - taiwan - software - ev | 22 | 11_foxconn_mitsubishi_taiwan_software |
| 12 | slate - truck - pickup - bezos - slate auto | 21 | 12_slate_truck_pickup_bezos |
| 13 | byd - chinese - tesla - electric - cars | 20 | 13_byd_chinese_tesla_electric |
| 14 | mercedes - cla - new - amg - eq | 19 | 14_mercedes_cla_new_amg |
| 15 | charging - octopus - pod - home - drivers | 17 | 15_charging_octopus_pod_home |
| 16 | nissan - micra - toyota - new - car | 17 | 16_nissan_micra_toyota_new |
| 17 | india - ev - tvs - mobility - infineon | 16 | 17_india_ev_tvs_mobility |
| 18 | mg - ev - s5 - mg4 - cyberster | 16 | 18_mg_ev_s5_mg4 |
| 19 | chinese - china - defence - cars - electric | 16 | 19_chinese_china_defence_cars |
| 20 | vinfast - vf - vietnam - vietnamese - electric | 15 | 20_vinfast_vf_vietnam_vietnamese |
| 21 | audi - rs6 - car - new - döllner | 15 | 21_audi_rs6_car_new |
| 22 | xpeng - ai - p7 - chinese - china | 15 | 22_xpeng_ai_p7_chinese |
| 23 | dolphin - surf - dolphin surf - byd - euros | 14 | 23_dolphin_surf_dolphin surf_byd |
| 24 | renault - r5 - car - r4 - electric | 13 | 24_renault_r5_car_r4 |
| 25 | bmw - sound - neue - neue klasse - klasse | 12 | 25_bmw_sound_neue_neue klasse |
| 26 | rivian - software - police - vehicle - electric | 12 | 26_rivian_software_police_vehicle |
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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