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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_Social |
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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_Social") |
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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: 13 |
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* Number of training documents: 205 |
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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 | new - electric - seat - car - manual | 5 | -1_new_electric_seat_car | |
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| 0 | electric - car - ev - charging - cent | 25 | 0_electric_car_ev_charging | |
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| 1 | tesla - musk - elon - elon musk - vehicle | 54 | 1_tesla_musk_elon_elon musk | |
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| 2 | new - nissan - citroen - car - retro | 30 | 2_new_nissan_citroen_car | |
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| 3 | percent - cars - car - private - electric | 15 | 3_percent_cars_car_private | |
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| 4 | chinese - china - electric - xiaomi - cars | 15 | 4_chinese_china_electric_xiaomi | |
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| 5 | electric - vehicles - french - electric car - price | 12 | 5_electric_vehicles_french_electric car | |
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| 6 | renault - car - electric - mg - new | 12 | 6_renault_car_electric_mg | |
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| 7 | german - trust - brands - quality - german brands | 9 | 7_german_trust_brands_quality | |
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| 8 | units - electric - april - russia - electric vehicles | 8 | 8_units_electric_april_russia | |
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| 9 | sharing - car sharing - car - audi - club | 8 | 9_sharing_car sharing_car_audi | |
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| 10 | used - carmax - car - used car - cars | 6 | 10_used_carmax_car_used car | |
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| 11 | best - ev9 - puma - edmunds - electric | 6 | 11_best_ev9_puma_edmunds | |
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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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