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

# Qatar_BERTopic

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/Qatar_BERTopic")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 22
* Number of training documents: 714

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | doha - qatar - airline - airlines - refund | 5 | -1_doha_qatar_airline_airlines | 
| 0 | doha - qatar - airline - airlines - flights | 211 | 0_doha_qatar_airline_airlines | 
| 1 | refund - refunded - refunds - booking - voucher | 78 | 1_refund_refunded_refunds_booking | 
| 2 | doha - qatar - baggage - luggage - airline | 72 | 2_doha_qatar_baggage_luggage | 
| 3 | airline - passengers - flights - attendant - steward | 49 | 3_airline_passengers_flights_attendant | 
| 4 | qatar - airline - airlines - flights - carriers | 44 | 4_qatar_airline_airlines_flights | 
| 5 | baggage - doha - airlines - airline - luggage | 39 | 5_baggage_doha_airlines_airline | 
| 6 | airline - airlines - flights - emirates - flight | 35 | 6_airline_airlines_flights_emirates | 
| 7 | refund - airline - flights - flight - cancel | 32 | 7_refund_airline_flights_flight | 
| 8 | airline - airlines - seats - qatar - seating | 28 | 8_airline_airlines_seats_qatar | 
| 9 | qatar - doha - airlines - flights - emirates | 18 | 9_qatar_doha_airlines_flights | 
| 10 | customer - complaints - service - terrible - horrible | 17 | 10_customer_complaints_service_terrible | 
| 11 | qatar - complaint - doha - complaints - airline | 15 | 11_qatar_complaint_doha_complaints | 
| 12 | avios - qatar - booking - compensation - aviso | 14 | 12_avios_qatar_booking_compensation | 
| 13 | airline - airlines - flight - airplane - horrible | 9 | 13_airline_airlines_flight_airplane | 
| 14 | doha - qatar - flights - cancellation - airlines | 8 | 14_doha_qatar_flights_cancellation | 
| 15 | doha - qatar - qatari - emirates - flight | 8 | 15_doha_qatar_qatari_emirates | 
| 16 | doha - qatar - airlines - bangkok - airport | 8 | 16_doha_qatar_airlines_bangkok | 
| 17 | seats - seating - airline - booked - seat | 7 | 17_seats_seating_airline_booked | 
| 18 | qatar - opodo - airline - refunded - voucher | 6 | 18_qatar_opodo_airline_refunded | 
| 19 | doha - qatar - flight - destinations - airways | 6 | 19_doha_qatar_flight_destinations | 
| 20 | qatar - airlines - disability - flight - wheelchair | 5 | 20_qatar_airlines_disability_flight |
  
</details>

## Training hyperparameters

* calculate_probabilities: False
* language: None
* low_memory: False
* min_topic_size: 5
* n_gram_range: (1, 1)
* nr_topics: None
* 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