Add BERTopic model

README.md

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+
+---
+tags:
+- bertopic
+library_name: bertopic
+pipeline_tag: text-classification
+---
+
+# bertopic_WGnews_Oct31
+
+This is a [BERTopic](https://github.com/MaartenGr/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:
+
+```python
+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
+
+<details>
+  <summary>Click here for an overview of all topics.</summary>
+  
+  | 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 |
+  
+</details>
+
+## 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

config.json

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+{
+  "calculate_probabilities": true,
+  "language": null,
+  "low_memory": false,
+  "min_topic_size": 10,
+  "n_gram_range": [
+    1,
+    1
+  ],
+  "nr_topics": null,
+  "seed_topic_list": null,
+  "top_n_words": 10,
+  "verbose": true,
+  "zeroshot_min_similarity": 0.7,
+  "zeroshot_topic_list": null
+}

ctfidf.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:319e31ae05f7469702ae747d320da3d211470104342723bca7a293ac2fc9894f
+size 1658880

topic_embeddings.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:14fe682db798799fbf8db236803779e90f69539f67209985a4512339e8047bc5
+size 114776