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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_kmean-20topics |
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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("hts98/bertopic_kmean-20topics") |
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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: 20 |
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* Number of training documents: 529579 |
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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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| 0 | hanoi - quarter - old - bay - lake | 70447 | 0_hanoi_quarter_old_bay | |
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| 1 | vietnam - vietnamese - best - stayed - mekong | 61694 | 1_vietnam_vietnamese_best_stayed | |
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| 2 | location - hotel - good - old - breakfast | 50809 | 2_location_hotel_good_old | |
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| 3 | good - clean - location - helpful - friendly | 44027 | 3_good_clean_location_helpful | |
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| 4 | pool - beach - view - massage - spa | 43959 | 4_pool_beach_view_massage | |
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| 5 | room - told - said - asked - shower | 40332 | 5_room_told_said_asked | |
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| 6 | thank - service - staff - ms - helpful | 36010 | 6_thank_service_staff_ms | |
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| 7 | hoi - homestay - town - bikes - free | 28816 | 7_hoi_homestay_town_bikes | |
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| 8 | saigon - minh - chi - ho - city | 28655 | 8_saigon_minh_chi_ho | |
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| 9 | resort - villa - beach - villas - island | 20536 | 9_resort_villa_beach_villas | |
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| 10 | bikes - beach - town - bike - free | 19495 | 10_bikes_beach_town_bike | |
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| 11 | hostel - dorm - dalat - dorms - beds | 17662 | 11_hostel_dorm_dalat_dorms | |
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| 12 | bay - halong - ha - cruise - kiem | 12629 | 12_bay_halong_ha_cruise | |
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| 13 | nang - da - danang - naman - dragon | 12005 | 13_nang_da_danang_naman | |
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| 14 | phu - quoc - resort - mui - ne | 9228 | 14_phu_quoc_resort_mui | |
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| 15 | hcmc - hcm - tau - vung - silverland | 8368 | 15_hcmc_hcm_tau_vung | |
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| 16 | phong - ninh - binh - nha - coc | 8121 | 16_phong_ninh_binh_nha | |
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| 17 | hue - citadel - imperial - jade - serene | 8072 | 17_hue_citadel_imperial_jade | |
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| 18 | nha - trang - sheraton - beach - russian | 6163 | 18_nha_trang_sheraton_beach | |
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| 19 | la - siesta - residencia - trendy - selva | 2551 | 19_la_siesta_residencia_trendy | |
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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: 15 |
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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: 1.24.3 |
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* HDBSCAN: 0.8.33 |
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* UMAP: 0.5.5 |
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* Pandas: 2.0.3 |
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* Scikit-Learn: 1.2.2 |
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* Sentence-transformers: 2.2.2 |
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* Transformers: 4.35.2 |
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* Numba: 0.57.1 |
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* Plotly: 5.16.1 |
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* Python: 3.10.12 |
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