|
|
| --- |
| tags: |
| - bertopic |
| library_name: bertopic |
| pipeline_tag: text-classification |
| --- |
| |
| # BERTopic_Environmental |
| |
| 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_Environmental") |
| |
| topic_model.get_topic_info() |
| ``` |
| |
| ## Topic overview |
| |
| * Number of topics: 26 |
| * Number of training documents: 905 |
| |
| <details> |
| <summary>Click here for an overview of all topics.</summary> |
| |
| | Topic ID | Topic Keywords | Topic Frequency | Label | |
| |----------|----------------|-----------------|-------| |
| | -1 | electric - car - cars - charging - vehicles | 11 | -1_electric_car_cars_charging | |
| | 0 | battery - batteries - lithium - catl - technology | 213 | 0_battery_batteries_lithium_catl | |
| | 1 | byd - charging - dolphin - chinese - new | 61 | 1_byd_charging_dolphin_chinese | |
| | 2 | charging - ev - chargers - ev charging - electric | 58 | 2_charging_ev_chargers_ev charging | |
| | 3 | zero - government - uk - mandate - electric | 57 | 3_zero_government_uk_mandate | |
| | 4 | electric - charging - points - france - car | 49 | 4_electric_charging_points_france | |
| | 5 | battery - lithium - recycling - batteries - supply | 48 | 5_battery_lithium_recycling_batteries | |
| | 6 | cars - combustion - study - electric - car | 36 | 6_cars_combustion_study_electric | |
| | 7 | percent - cars - market - sales - vehicles | 33 | 7_percent_cars_market_sales | |
| | 8 | fires - safety - battery - electric - cars | 29 | 8_fires_safety_battery_electric | |
| | 9 | charging - electric - sweden - vehicles - circle | 29 | 9_charging_electric_sweden_vehicles | |
| | 10 | tax - drivers - petrol - ev - rates | 25 | 10_tax_drivers_petrol_ev | |
| | 11 | kia - car - model - electric - range | 25 | 11_kia_car_model_electric | |
| | 12 | cent - car - petrol - evs - drivers | 23 | 12_cent_car_petrol_evs | |
| | 13 | charging - stations - charging stations - charging points - points | 23 | 13_charging_stations_charging stations_charging points | |
| | 14 | india - ev - green - mobility - electric | 23 | 14_india_ev_green_mobility | |
| | 15 | indonesia - battery - lg - ev - ev battery | 20 | 15_indonesia_battery_lg_ev | |
| | 16 | department - flames - police - car - tesla | 20 | 16_department_flames_police_car | |
| | 17 | transport - ireland - council - ev - climate | 19 | 17_transport_ireland_council_ev | |
| | 18 | toyota - electric - new - europe - hyundai | 19 | 18_toyota_electric_new_europe | |
| | 19 | sales - new - electric - cent - car | 17 | 19_sales_new_electric_cent | |
| | 20 | european - commission - eu - von - der | 15 | 20_european_commission_eu_von | |
| | 21 | power - blackout - spain - homes - electricity | 14 | 21_power_blackout_spain_homes | |
| | 22 | nissan - leaf - micra - new - generation | 13 | 22_nissan_leaf_micra_new | |
| | 23 | ship - coast - vessel - coast guard - guard | 13 | 23_ship_coast_vessel_coast guard | |
| | 24 | id - volkswagen - vw - every1 - id every1 | 12 | 24_id_volkswagen_vw_every1 | |
| |
| </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 |
| |
