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

# Topic_Modelling_Airlines_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/Topic_Modelling_Airlines_BERTopic")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 17
* 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 | 23 | -1_killiair_flight_service_customer | 
| 0 | killiair - doha - flight - service - worst | 2399 | poor_customer_experience | 
| 1 | bag - luggage - cabin - bags - pay | 639 | luggage_fee | 
| 2 | flight - delayed - hours - delay - killiair | 386 | delays | 
| 3 | check - ryan - online - air - killiair | 334 | check_in_process | 
| 4 | refund - killiair - flight - cancelled - booking | 293 | refund | 
| 5 | jet - easy - flight - cancelled - refund | 237 | refund_cancelled_flights | 
| 6 | seats - seat - plane - flight - killiair | 227 | inflight_facilities | 
| 7 | luggage - lost - bag - killiair - baggage | 154 | luggage_lost | 
| 8 | holiday - holidays - hotel - killiair - booked | 102 | hotel | 
| 9 | thank - amazing - crew - flight - thanks | 81 | good_customer_experience | 
| 10 | change - price - 115 - fare - booking | 59 | change_ticket_fee | 
| 11 | food - meal - dubai - flight - killiair | 48 | inflight_service | 
| 12 | car - hire - rental - insurance - card | 47 | car | 
| 13 | seats - seat - paid - extra - window | 41 | seating_fees | 
| 14 | service - killiair - customer - zero - customers | 37 | poor_customer_experience | 
| 15 | stansted - flight - airport - parking - killiair | 27 | airport_facilities |
  
</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: 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