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Singapore_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/Singapore_BERTopic")

topic_model.get_topic_info()

Topic overview

  • Number of topics: 10
  • Number of training documents: 160
Click here for an overview of all topics.
Topic ID Topic Keywords Topic Frequency Label
-1 airline - airlines - flights - refund - flight 6 -1_airline_airlines_flights_refund
0 airline - airlines - flights - singapore - meals 31 0_airline_airlines_flights_singapore
1 refund - airline - airlines - complaint - singapore 43 1_refund_airline_airlines_complaint
2 baggage - luggage - airlines - airline - bags 20 2_baggage_luggage_airlines_airline
3 airlines - passengers - seats - flight - cabin 14 3_airlines_passengers_seats_flight
4 refund - repayment - sia - customer - complaints 11 4_refund_repayment_sia_customer
5 airlines - airline - fees - singapore - flights 10 5_airlines_airline_fees_singapore
6 refund - airline - cancellation - booking - cancel 9 6_refund_airline_cancellation_booking
7 miles - airlines - airline - mileage - loyalty 9 7_miles_airlines_airline_mileage
8 airline - flight - reviews - booking - customer 7 8_airline_flight_reviews_booking

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
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