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--- |
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tags: |
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- bertopic |
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library_name: bertopic |
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pipeline_tag: text-classification |
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--- |
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# BERTopic_Environmental |
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. |
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BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets. |
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## Usage |
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To use this model, please install BERTopic: |
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``` |
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pip install -U bertopic |
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``` |
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You can use the model as follows: |
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```python |
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from bertopic import BERTopic |
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topic_model = BERTopic.load("karinegabsschon/BERTopic_Environmental") |
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topic_model.get_topic_info() |
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``` |
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## Topic overview |
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* Number of topics: 26 |
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* Number of training documents: 905 |
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<details> |
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<summary>Click here for an overview of all topics.</summary> |
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| Topic ID | Topic Keywords | Topic Frequency | Label | |
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|----------|----------------|-----------------|-------| |
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| -1 | electric - car - cars - charging - vehicles | 11 | -1_electric_car_cars_charging | |
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| 0 | battery - batteries - lithium - catl - technology | 213 | 0_battery_batteries_lithium_catl | |
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| 1 | byd - charging - dolphin - chinese - new | 61 | 1_byd_charging_dolphin_chinese | |
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| 2 | charging - ev - chargers - ev charging - electric | 58 | 2_charging_ev_chargers_ev charging | |
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| 3 | zero - government - uk - mandate - electric | 57 | 3_zero_government_uk_mandate | |
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| 4 | electric - charging - points - france - car | 49 | 4_electric_charging_points_france | |
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| 5 | battery - lithium - recycling - batteries - supply | 48 | 5_battery_lithium_recycling_batteries | |
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| 6 | cars - combustion - study - electric - car | 36 | 6_cars_combustion_study_electric | |
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| 7 | percent - cars - market - sales - vehicles | 33 | 7_percent_cars_market_sales | |
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| 8 | fires - safety - battery - electric - cars | 29 | 8_fires_safety_battery_electric | |
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| 9 | charging - electric - sweden - vehicles - circle | 29 | 9_charging_electric_sweden_vehicles | |
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| 10 | tax - drivers - petrol - ev - rates | 25 | 10_tax_drivers_petrol_ev | |
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| 11 | kia - car - model - electric - range | 25 | 11_kia_car_model_electric | |
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| 12 | cent - car - petrol - evs - drivers | 23 | 12_cent_car_petrol_evs | |
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| 13 | charging - stations - charging stations - charging points - points | 23 | 13_charging_stations_charging stations_charging points | |
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| 14 | india - ev - green - mobility - electric | 23 | 14_india_ev_green_mobility | |
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| 15 | indonesia - battery - lg - ev - ev battery | 20 | 15_indonesia_battery_lg_ev | |
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| 16 | department - flames - police - car - tesla | 20 | 16_department_flames_police_car | |
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| 17 | transport - ireland - council - ev - climate | 19 | 17_transport_ireland_council_ev | |
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| 18 | toyota - electric - new - europe - hyundai | 19 | 18_toyota_electric_new_europe | |
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| 19 | sales - new - electric - cent - car | 17 | 19_sales_new_electric_cent | |
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| 20 | european - commission - eu - von - der | 15 | 20_european_commission_eu_von | |
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| 21 | power - blackout - spain - homes - electricity | 14 | 21_power_blackout_spain_homes | |
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| 22 | nissan - leaf - micra - new - generation | 13 | 22_nissan_leaf_micra_new | |
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| 23 | ship - coast - vessel - coast guard - guard | 13 | 23_ship_coast_vessel_coast guard | |
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| 24 | id - volkswagen - vw - every1 - id every1 | 12 | 24_id_volkswagen_vw_every1 | |
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</details> |
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## Training hyperparameters |
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* calculate_probabilities: False |
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* language: None |
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* low_memory: False |
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* min_topic_size: 10 |
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* n_gram_range: (1, 1) |
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* nr_topics: None |
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* seed_topic_list: None |
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* top_n_words: 10 |
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* verbose: True |
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* zeroshot_min_similarity: 0.7 |
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* zeroshot_topic_list: None |
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## Framework versions |
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* Numpy: 2.0.2 |
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* HDBSCAN: 0.8.40 |
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* UMAP: 0.5.8 |
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* Pandas: 2.2.2 |
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* Scikit-Learn: 1.6.1 |
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* Sentence-transformers: 4.1.0 |
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* Transformers: 4.53.0 |
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* Numba: 0.60.0 |
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* Plotly: 5.24.1 |
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* Python: 3.11.13 |
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