|
|
| --- |
| tags: |
| - bertopic |
| library_name: bertopic |
| pipeline_tag: text-classification |
| --- |
| |
| # topic_model_bert_topic |
| |
| 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("VegetaSama/topic_model_bert_topic") |
| |
| topic_model.get_topic_info() |
| ``` |
| |
| ## Topic overview |
| |
| * Number of topics: 26 |
| * Number of training documents: 10000 |
| |
| <details> |
| <summary>Click here for an overview of all topics.</summary> |
| |
| | Topic ID | Topic Keywords | Topic Frequency | Label | |
| |----------|----------------|-----------------|-------| |
| | -1 | place - good - food - great - like | 82 | -1_place_good_food_great | |
| | 0 | store - like - car - just - great | 3133 | 0_store_like_car_just | |
| | 1 | mexican - tacos - food - salsa - good | 876 | 1_mexican_tacos_food_salsa | |
| | 2 | good - ordered - just - food - cheese | 725 | 2_good_ordered_just_food | |
| | 3 | pizza - good - crust - place - great | 568 | 3_pizza_good_crust_place | |
| | 4 | food - great - place - good - service | 536 | 4_food_great_place_good | |
| | 5 | hotel - room - pool - stay - airport | 445 | 5_hotel_room_pool_stay | |
| | 6 | burger - fries - burgers - good - like | 370 | 6_burger_fries_burgers_good | |
| | 7 | hair - dr - massage - nails - time | 339 | 7_hair_dr_massage_nails | |
| | 8 | coffee - starbucks - place - good - like | 269 | 8_coffee_starbucks_place_good | |
| | 9 | scottsdale - food - place - great - good | 262 | 9_scottsdale_food_place_great | |
| | 10 | sushi - roll - rolls - place - good | 258 | 10_sushi_roll_rolls_place | |
| | 11 | minutes - food - table - just - time | 241 | 11_minutes_food_table_just | |
| | 12 | ice - ice cream - cream - cupcakes - cupcake | 224 | 12_ice_ice cream_cream_cupcakes | |
| | 13 | breakfast - pancakes - eggs - good - place | 200 | 13_breakfast_pancakes_eggs_good | |
| | 14 | thai - curry - pad - food - pad thai | 199 | 14_thai_curry_pad_food | |
| | 15 | bbq - phoenix - food - brisket - good | 199 | 15_bbq_phoenix_food_brisket | |
| | 16 | beer - place - great - beers - food | 179 | 16_beer_place_great_beers | |
| | 17 | service - food - good - place - order | 152 | 17_service_food_good_place | |
| | 18 | pho - vietnamese - broth - spring - spring rolls | 147 | 18_pho_vietnamese_broth_spring | |
| | 19 | bar - music - place - night - cool | 106 | 19_bar_music_place_night | |
| | 20 | chinese - chinese food - food - soup - chicken | 101 | 20_chinese_chinese food_food_soup | |
| | 21 | greek - vegan - meat - food - place | 100 | 21_greek_vegan_meat_food | |
| | 22 | theater - movie - seats - movies - amc | 99 | 22_theater_movie_seats_movies | |
| | 23 | chinese - food - chinese food - pei - pei wei | 95 | 23_chinese_food_chinese food_pei | |
| | 24 | bar - good - food - happy - great | 95 | 24_bar_good_food_happy | |
| |
| </details> |
| |
| ## Training hyperparameters |
| |
| * calculate_probabilities: True |
| * language: None |
| * low_memory: False |
| * min_topic_size: 10 |
| * n_gram_range: (1, 1) |
| * nr_topics: None |
| * seed_topic_list: None |
| * top_n_words: 5 |
| * verbose: True |
| * 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: 1.3.0 |
| * Sentence-transformers: 2.2.2 |
| * Transformers: 4.32.1 |
| * Numba: 0.58.1 |
| * Plotly: 5.9.0 |
| * Python: 3.11.5 |
| |
