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testing_b
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---
tags:
- bertopic
library_name: bertopic
pipeline_tag: text-classification
---

# testing_b

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("sneakykilli/testing_b")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 20
* Number of training documents: 5134

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | killiair - flight - service - customer - airport | 12 | -1_killiair_flight_service_customer | 
| 0 | killiair - flight - airport - time - doha | 2257 | 0_killiair_flight_airport_time | 
| 1 | bag - luggage - bags - cabin - pay | 847 | 1_bag_luggage_bags_cabin | 
| 2 | jet - ryan - easy - air - flight | 499 | 2_jet_ryan_easy_air | 
| 3 | refund - flight - cancelled - customer - service | 312 | 3_refund_flight_cancelled_customer | 
| 4 | flight - delayed - delay - gatwick - hours | 229 | 4_flight_delayed_delay_gatwick | 
| 5 | check - change - online - pay - fee | 173 | 5_check_change_online_pay | 
| 6 | food - seat - meal - flight - plane | 151 | 6_food_seat_meal_flight | 
| 7 | seats - seat - class - killiair - extra | 150 | 7_seats_seat_class_killiair | 
| 8 | star - stressstress - stress - stars - zero | 149 | 8_star_stressstress_stress_stars | 
| 9 | company - customer - worst - service - terrible | 74 | 9_company_customer_worst_service | 
| 10 | thank - amazing - crew - flight - thanks | 73 | 10_thank_amazing_crew_flight | 
| 11 | car - hire - rental - insurance - card | 62 | 11_car_hire_rental_insurance | 
| 12 | stansted - flight - airport - parking - killiair | 39 | 12_stansted_flight_airport_parking | 
| 13 | passport - date - son - gate - check | 33 | 13_passport_date_son_gate | 
| 14 | chat - customer - service - reach - ai | 20 | 14_chat_customer_service_reach | 
| 15 | voucher - rune - residual - booking - refund | 15 | 15_voucher_rune_residual_booking | 
| 16 | band - word - easy - corporate - sue | 14 | 16_band_word_easy_corporate | 
| 17 | malaga - page - alicante - taxi - killiair | 13 | 17_malaga_page_alicante_taxi | 
| 18 | good - friendly - sh - service - late | 12 | 18_good_friendly_sh_service |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: 20
* seed_topic_list: None
* top_n_words: 10
* verbose: False
* 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: 2.3.1
* Transformers: 4.36.2
* Numba: 0.57.1
* Plotly: 5.16.1
* Python: 3.10.12