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bertopic_kmean-20topics

Text Classification
BERTopic
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bertopic_kmean-20topics
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---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# bertopic_kmean-20topics

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("hts98/bertopic_kmean-20topics")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 20
* Number of training documents: 529579

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| 0 | hanoi - quarter - old - bay - lake | 70447 | 0_hanoi_quarter_old_bay | 
| 1 | vietnam - vietnamese - best - stayed - mekong | 61694 | 1_vietnam_vietnamese_best_stayed | 
| 2 | location - hotel - good - old - breakfast | 50809 | 2_location_hotel_good_old | 
| 3 | good - clean - location - helpful - friendly | 44027 | 3_good_clean_location_helpful | 
| 4 | pool - beach - view - massage - spa | 43959 | 4_pool_beach_view_massage | 
| 5 | room - told - said - asked - shower | 40332 | 5_room_told_said_asked | 
| 6 | thank - service - staff - ms - helpful | 36010 | 6_thank_service_staff_ms | 
| 7 | hoi - homestay - town - bikes - free | 28816 | 7_hoi_homestay_town_bikes | 
| 8 | saigon - minh - chi - ho - city | 28655 | 8_saigon_minh_chi_ho | 
| 9 | resort - villa - beach - villas - island | 20536 | 9_resort_villa_beach_villas | 
| 10 | bikes - beach - town - bike - free | 19495 | 10_bikes_beach_town_bike | 
| 11 | hostel - dorm - dalat - dorms - beds | 17662 | 11_hostel_dorm_dalat_dorms | 
| 12 | bay - halong - ha - cruise - kiem | 12629 | 12_bay_halong_ha_cruise | 
| 13 | nang - da - danang - naman - dragon | 12005 | 13_nang_da_danang_naman | 
| 14 | phu - quoc - resort - mui - ne | 9228 | 14_phu_quoc_resort_mui | 
| 15 | hcmc - hcm - tau - vung - silverland | 8368 | 15_hcmc_hcm_tau_vung | 
| 16 | phong - ninh - binh - nha - coc | 8121 | 16_phong_ninh_binh_nha | 
| 17 | hue - citadel - imperial - jade - serene | 8072 | 17_hue_citadel_imperial_jade | 
| 18 | nha - trang - sheraton - beach - russian | 6163 | 18_nha_trang_sheraton_beach | 
| 19 | la - siesta - residencia - trendy - selva | 2551 | 19_la_siesta_residencia_trendy |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 15
* verbose: True
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None

## Framework versions

* Numpy: 1.24.3
* HDBSCAN: 0.8.33
* UMAP: 0.5.5
* Pandas: 2.0.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.2.2
* Transformers: 4.35.2
* Numba: 0.57.1
* Plotly: 5.16.1
* Python: 3.10.12