bertopic_WGnews_Oct31
This is a 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:
from bertopic import BERTopic
topic_model = BERTopic.load("tyrealqian/bertopic_WGnews_Oct31")
topic_model.get_topic_info()
Topic overview
- Number of topics: 28
- Number of training documents: 6196
Click here for an overview of all topics.
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | beijing - winter - olympics - winter olympics - olympic | 18 | -1_beijing_winter_olympics_winter olympics |
| 0 | gold - medal - olympics - beijing - womens | 2054 | 0_gold_medal_olympics_beijing |
| 1 | covid - olympics - beijing - cases - winter | 633 | 1_covid_olympics_beijing_cases |
| 2 | gold - gu - womens - chinas - mens | 524 | 2_gold_gu_womens_chinas |
| 3 | president - xi - xi jinping - jinping - president xi | 388 | 3_president_xi_xi jinping_jinping |
| 4 | boycott - diplomatic - diplomatic boycott - boycott beijing - rights | 372 | 4_boycott_diplomatic_diplomatic boycott_boycott beijing |
| 5 | dwen - mascot - bing - bing dwen - dwen dwen | 328 | 5_dwen_mascot_bing_bing dwen |
| 6 | ceremony - opening - opening ceremony - beijing - ceremony beijing | 305 | 6_ceremony_opening_opening ceremony_beijing |
| 7 | kamila - valieva - kamila valieva - russian - figure | 249 | 7_kamila_valieva_kamila valieva_russian |
| 8 | torch - flame - relay - torch relay - olympic | 208 | 8_torch_flame_relay_torch relay |
| 9 | venue - ice - venues - zhangjiakou - beijing | 194 | 9_venue_ice_venues_zhangjiakou |
| 10 | sports - winter sports - winter - globalink - snow | 159 | 10_sports_winter sports_winter_globalink |
| 11 | food - robot - robots - served - serving | 122 | 11_food_robot_robots_served |
| 12 | green - carbon - games - beijing - winter | 120 | 12_green_carbon_games_beijing |
| 13 | coverage - heres - day - olympics - gold | 90 | 13_coverage_heres_day_olympics |
| 14 | bach - thomas bach - thomas - president thomas - ioc | 59 | 14_bach_thomas bach_thomas_president thomas |
| 15 | snow - snowfall - heavy - weather - heavy snowfall | 48 | 15_snow_snowfall_heavy_weather |
| 16 | bank - commemorative - digital - yuan - set | 43 | 16_bank_commemorative_digital_yuan |
| 17 | paralympic - paralympic games - games - paralympic winter - winter paralympic | 37 | 17_paralympic_paralympic games_games_paralympic winter |
| 18 | phones - personal - burner - app - smartphonelike | 34 | 18_phones_personal_burner_app |
| 19 | nbc - nbcuniversal - ads - ratings - nbcs | 31 | 19_nbc_nbcuniversal_ads_ratings |
| 20 | watch beijing - watch - athletes watch - know - names | 27 | 20_watch beijing_watch_athletes watch_know |
| 21 | ukraine - invasion - russian - invasion ukraine - ukraine beijing | 27 | 21_ukraine_invasion_russian_invasion ukraine |
| 22 | city - summer winter - summer - host summer - city host | 27 | 22_city_summer winter_summer_host summer |
| 23 | leduc - nonbinary - timothy leduc - timothy - openly | 26 | 23_leduc_nonbinary_timothy leduc_timothy |
| 24 | ralph lauren - lauren - ralph - uniforms - team | 26 | 24_ralph lauren_lauren_ralph_uniforms |
| 25 | peng - shuai - peng shuai - tennis - chinese tennis | 25 | 25_peng_shuai_peng shuai_tennis |
| 26 | women - female athletes - record - athletes - female | 22 | 26_women_female athletes_record_athletes |
Training hyperparameters
- calculate_probabilities: True
- 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: 1.26.4
- HDBSCAN: 0.8.39
- UMAP: 0.5.7
- Pandas: 2.2.2
- Scikit-Learn: 1.5.2
- Sentence-transformers: 3.2.1
- Transformers: 4.44.2
- Numba: 0.60.0
- Plotly: 5.24.1
- Python: 3.10.12
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