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

# Emirates_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/Emirates_BERTopic")

topic_model.get_topic_info()
```

## Topic overview

* Number of topics: 11
* Number of training documents: 375

<details>
  <summary>Click here for an overview of all topics.</summary>
  
  | Topic ID | Topic Keywords | Topic Frequency | Label | 
|----------|----------------|-----------------|-------| 
| -1 | emirates - airline - airlines - flights - refund | 9 | -1_emirates_airline_airlines_flights | 
| 0 | emirates - airlines - airline - dubai - flights | 100 | 0_emirates_airlines_airline_dubai | 
| 1 | airline - airlines - flights - aviation - planes | 68 | 1_airline_airlines_flights_aviation | 
| 2 | emirates - meals - meal - attendant - airline | 35 | 2_emirates_meals_meal_attendant | 
| 3 | emirates - refund - cancel - booking - ticket | 34 | 3_emirates_refund_cancel_booking | 
| 4 | airline - refunded - refund - ticket - booking | 28 | 4_airline_refunded_refund_ticket | 
| 5 | emirates - dubai - baggage - luggage - airline | 26 | 5_emirates_dubai_baggage_luggage | 
| 6 | emirates - airline - refund - seats - flights | 26 | 6_emirates_airline_refund_seats | 
| 7 | emirates - airlines - airline - booking - fees | 23 | 7_emirates_airlines_airline_booking | 
| 8 | passengers - airline - emirates - stewardess - aisle | 14 | 8_passengers_airline_emirates_stewardess | 
| 9 | emirates - delayed - dubai - delays - flights | 12 | 9_emirates_delayed_dubai_delays |
  
</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