--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # BERTopic_Technological 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_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