|
|
| --- |
| tags: |
| - bertopic |
| library_name: bertopic |
| pipeline_tag: text-classification |
| --- |
| |
| # testing_b |
| |
| 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/testing_b") |
| |
| topic_model.get_topic_info() |
| ``` |
| |
| ## Topic overview |
| |
| * Number of topics: 20 |
| * 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 | 12 | -1_killiair_flight_service_customer | |
| | 0 | killiair - flight - airport - time - doha | 2257 | 0_killiair_flight_airport_time | |
| | 1 | bag - luggage - bags - cabin - pay | 847 | 1_bag_luggage_bags_cabin | |
| | 2 | jet - ryan - easy - air - flight | 499 | 2_jet_ryan_easy_air | |
| | 3 | refund - flight - cancelled - customer - service | 312 | 3_refund_flight_cancelled_customer | |
| | 4 | flight - delayed - delay - gatwick - hours | 229 | 4_flight_delayed_delay_gatwick | |
| | 5 | check - change - online - pay - fee | 173 | 5_check_change_online_pay | |
| | 6 | food - seat - meal - flight - plane | 151 | 6_food_seat_meal_flight | |
| | 7 | seats - seat - class - killiair - extra | 150 | 7_seats_seat_class_killiair | |
| | 8 | star - stressstress - stress - stars - zero | 149 | 8_star_stressstress_stress_stars | |
| | 9 | company - customer - worst - service - terrible | 74 | 9_company_customer_worst_service | |
| | 10 | thank - amazing - crew - flight - thanks | 73 | 10_thank_amazing_crew_flight | |
| | 11 | car - hire - rental - insurance - card | 62 | 11_car_hire_rental_insurance | |
| | 12 | stansted - flight - airport - parking - killiair | 39 | 12_stansted_flight_airport_parking | |
| | 13 | passport - date - son - gate - check | 33 | 13_passport_date_son_gate | |
| | 14 | chat - customer - service - reach - ai | 20 | 14_chat_customer_service_reach | |
| | 15 | voucher - rune - residual - booking - refund | 15 | 15_voucher_rune_residual_booking | |
| | 16 | band - word - easy - corporate - sue | 14 | 16_band_word_easy_corporate | |
| | 17 | malaga - page - alicante - taxi - killiair | 13 | 17_malaga_page_alicante_taxi | |
| | 18 | good - friendly - sh - service - late | 12 | 18_good_friendly_sh_service | |
| |
| </details> |
| |
| ## Training hyperparameters |
| |
| * calculate_probabilities: False |
| * language: None |
| * low_memory: False |
| * min_topic_size: 10 |
| * n_gram_range: (1, 1) |
| * nr_topics: 20 |
| * 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 |
| |
