|
|
| --- |
| tags: |
| - bertopic |
| library_name: bertopic |
| pipeline_tag: text-classification |
| --- |
| |
| # BERTopic_Economic |
| |
| 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_Economic") |
| |
| topic_model.get_topic_info() |
| ``` |
| |
| ## Topic overview |
| |
| * Number of topics: 37 |
| * Number of training documents: 1290 |
| |
| <details> |
| <summary>Click here for an overview of all topics.</summary> |
| |
| | Topic ID | Topic Keywords | Topic Frequency | Label | |
| |----------|----------------|-----------------|-------| |
| | -1 | electric - car - cars - vehicles - new | 10 | -1_electric_car_cars_vehicles | |
| | 0 | byd - chinese - china - market - electric | 249 | 0_byd_chinese_china_market | |
| | 1 | tesla - sales - musk - year - europe | 131 | 1_tesla_sales_musk_year | |
| | 2 | new - used - year - car - month | 86 | 2_new_used_year_car | |
| | 3 | rivian - motley - motley fool - fool - stocks | 55 | 3_rivian_motley_motley fool_fool | |
| | 4 | charging - charging points - points - stations - charging stations | 52 | 4_charging_charging points_points_stations | |
| | 5 | tesla - musk - trump - elon - elon musk | 45 | 5_tesla_musk_trump_elon | |
| | 6 | spain - electric - moves - ebro - plan | 38 | 6_spain_electric_moves_ebro | |
| | 7 | charging - czech - ev charging - slovakia - czech republic | 37 | 7_charging_czech_ev charging_slovakia | |
| | 8 | units - ukraine - used - region - vehicles | 33 | 8_units_ukraine_used_region | |
| | 9 | tesla - musk - gerber - tsla - elon | 33 | 9_tesla_musk_gerber_tsla | |
| | 10 | hyundai - billion - honda - plant - nissan | 32 | 10_hyundai_billion_honda_plant | |
| | 11 | tax - car - pay - car tax - drivers | 31 | 11_tax_car_pay_car tax | |
| | 12 | percent - cars - previous year - registrations - previous | 30 | 12_percent_cars_previous year_registrations | |
| | 13 | million - iea - sales - global - electric | 29 | 13_million_iea_sales_global | |
| | 14 | cars - tax - purchase - federal - government | 29 | 14_cars_tax_purchase_federal | |
| | 15 | xiaomi - nio - li - chinese - yu7 | 28 | 15_xiaomi_nio_li_chinese | |
| | 16 | quarter - tesla - sales - electric vehicle - gm | 26 | 16_quarter_tesla_sales_electric vehicle | |
| | 17 | volvo - audi - jobs - cent - company | 23 | 17_volvo_audi_jobs_cent | |
| | 18 | public - charging - uk - charge - ev | 23 | 18_public_charging_uk_charge | |
| | 19 | discounts - combustion - dudenhöffer - cars - prices | 23 | 19_discounts_combustion_dudenhöffer_cars | |
| | 20 | euros - electric - french - aid - energy | 22 | 20_euros_electric_french_aid | |
| | 21 | china - shanghai - chinese - market - car | 22 | 21_china_shanghai_chinese_market | |
| | 22 | id - vw - every1 - id every1 - 000 euros | 19 | 22_id_vw_every1_id every1 | |
| | 23 | ferrari - stellantis - italy - elkann - october | 17 | 23_ferrari_stellantis_italy_elkann | |
| | 24 | foxconn - mitsubishi - japanese - nissan - mitsubishi motors | 17 | 24_foxconn_mitsubishi_japanese_nissan | |
| | 25 | belarus - charging - stations - electric - electric charging | 16 | 25_belarus_charging_stations_electric | |
| | 26 | volkswagen - europe - vw - group - percent | 16 | 26_volkswagen_europe_vw_group | |
| | 27 | german - vw - market - group - percent | 15 | 27_german_vw_market_group | |
| | 28 | used - used car - cars - percent - autoscout24 | 15 | 28_used_used car_cars_percent | |
| | 29 | vinfast - vf - vietnamese - vinfast auto - quarter | 14 | 29_vinfast_vf_vietnamese_vinfast auto | |
| | 30 | drivers - home - ev - petrol - charging | 13 | 30_drivers_home_ev_petrol | |
| | 31 | uk - car - government - mandate - evs | 13 | 31_uk_car_government_mandate | |
| | 32 | pod - pod point - point - edf - charging | 13 | 32_pod_pod point_point_edf | |
| | 33 | india - tata - ev - tata motors - plans | 12 | 33_india_tata_ev_tata motors | |
| | 34 | russia - electric - sales - passenger - voyah | 12 | 34_russia_electric_sales_passenger | |
| | 35 | analysts - gm - energy - general motors - general | 11 | 35_analysts_gm_energy_general motors | |
| |
| </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 |
| |
