Hugging Face's logo

tyrealqian
/
bertopic_WGnews_Oct31

Text Classification
BERTopic
Model card Files Community
bertopic_WGnews_Oct31
File size: 4,306 Bytes 
f8624a3
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
 

---
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