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Add BERTopic model

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README.md ADDED
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+
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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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+
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+ # BERTopic_Economic
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+
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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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+
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+ ## Usage
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+
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+ To use this model, please install BERTopic:
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+
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+ ```
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+ pip install -U bertopic
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+ ```
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+
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+ You can use the model as follows:
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+
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+ ```python
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+ from bertopic import BERTopic
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+ topic_model = BERTopic.load("karinegabsschon/BERTopic_Economic")
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+
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+ topic_model.get_topic_info()
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+ ```
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+
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+ ## Topic overview
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+
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+ * Number of topics: 37
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+ * Number of training documents: 1290
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+
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+ <details>
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+ <summary>Click here for an overview of all topics.</summary>
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+
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+ | Topic ID | Topic Keywords | Topic Frequency | Label |
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+ |----------|----------------|-----------------|-------|
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+ | -1 | electric - car - cars - vehicles - new | 10 | -1_electric_car_cars_vehicles |
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+ | 0 | byd - chinese - china - market - electric | 249 | 0_byd_chinese_china_market |
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+ | 1 | tesla - sales - musk - year - europe | 131 | 1_tesla_sales_musk_year |
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+ | 2 | new - used - year - car - month | 86 | 2_new_used_year_car |
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+ | 3 | rivian - motley - motley fool - fool - stocks | 55 | 3_rivian_motley_motley fool_fool |
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+ | 4 | charging - charging points - points - stations - charging stations | 52 | 4_charging_charging points_points_stations |
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+ | 5 | tesla - musk - trump - elon - elon musk | 45 | 5_tesla_musk_trump_elon |
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+ | 6 | spain - electric - moves - ebro - plan | 38 | 6_spain_electric_moves_ebro |
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+ | 7 | charging - czech - ev charging - slovakia - czech republic | 37 | 7_charging_czech_ev charging_slovakia |
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+ | 8 | units - ukraine - used - region - vehicles | 33 | 8_units_ukraine_used_region |
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+ | 9 | tesla - musk - gerber - tsla - elon | 33 | 9_tesla_musk_gerber_tsla |
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+ | 10 | hyundai - billion - honda - plant - nissan | 32 | 10_hyundai_billion_honda_plant |
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+ | 11 | tax - car - pay - car tax - drivers | 31 | 11_tax_car_pay_car tax |
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+ | 12 | percent - cars - previous year - registrations - previous | 30 | 12_percent_cars_previous year_registrations |
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+ | 13 | million - iea - sales - global - electric | 29 | 13_million_iea_sales_global |
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+ | 14 | cars - tax - purchase - federal - government | 29 | 14_cars_tax_purchase_federal |
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+ | 15 | xiaomi - nio - li - chinese - yu7 | 28 | 15_xiaomi_nio_li_chinese |
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+ | 16 | quarter - tesla - sales - electric vehicle - gm | 26 | 16_quarter_tesla_sales_electric vehicle |
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+ | 17 | volvo - audi - jobs - cent - company | 23 | 17_volvo_audi_jobs_cent |
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+ | 18 | public - charging - uk - charge - ev | 23 | 18_public_charging_uk_charge |
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+ | 19 | discounts - combustion - dudenhöffer - cars - prices | 23 | 19_discounts_combustion_dudenhöffer_cars |
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+ | 20 | euros - electric - french - aid - energy | 22 | 20_euros_electric_french_aid |
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+ | 21 | china - shanghai - chinese - market - car | 22 | 21_china_shanghai_chinese_market |
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+ | 22 | id - vw - every1 - id every1 - 000 euros | 19 | 22_id_vw_every1_id every1 |
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+ | 23 | ferrari - stellantis - italy - elkann - october | 17 | 23_ferrari_stellantis_italy_elkann |
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+ | 24 | foxconn - mitsubishi - japanese - nissan - mitsubishi motors | 17 | 24_foxconn_mitsubishi_japanese_nissan |
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+ | 25 | belarus - charging - stations - electric - electric charging | 16 | 25_belarus_charging_stations_electric |
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+ | 26 | volkswagen - europe - vw - group - percent | 16 | 26_volkswagen_europe_vw_group |
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+ | 27 | german - vw - market - group - percent | 15 | 27_german_vw_market_group |
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+ | 28 | used - used car - cars - percent - autoscout24 | 15 | 28_used_used car_cars_percent |
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+ | 29 | vinfast - vf - vietnamese - vinfast auto - quarter | 14 | 29_vinfast_vf_vietnamese_vinfast auto |
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+ | 30 | drivers - home - ev - petrol - charging | 13 | 30_drivers_home_ev_petrol |
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+ | 31 | uk - car - government - mandate - evs | 13 | 31_uk_car_government_mandate |
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+ | 32 | pod - pod point - point - edf - charging | 13 | 32_pod_pod point_point_edf |
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+ | 33 | india - tata - ev - tata motors - plans | 12 | 33_india_tata_ev_tata motors |
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+ | 34 | russia - electric - sales - passenger - voyah | 12 | 34_russia_electric_sales_passenger |
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+ | 35 | analysts - gm - energy - general motors - general | 11 | 35_analysts_gm_energy_general motors |
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+
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+ </details>
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+
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+ ## Training hyperparameters
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+
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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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+
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+ ## Framework versions
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+
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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
config.json ADDED
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+ {
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+ "calculate_probabilities": false,
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+ "language": null,
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+ "low_memory": false,
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+ "min_topic_size": 10,
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+ "n_gram_range": [
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+ 1,
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+ 1
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+ ],
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+ "nr_topics": null,
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+ "seed_topic_list": null,
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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": null,
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+ "embedding_model": "ibm-granite/granite-embedding-125m-english"
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+ }
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topics.json ADDED
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