Emirates_BERTopic

This is a 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:

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

Click here for an overview of all topics.

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

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