yelpreview_repKB
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("Kamaljp/yelpreview_repKB")
topic_model.get_topic_info()
Topic overview
- Number of topics: 21
- Number of training documents: 10000
<details> <summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | restaurant - bbq - meal - chicken - meat | 93 | -1_restaurant_bbq_meal_chicken |
| 0 | great food - good food - food great - food good - restaurant | 2866 | 0_great food_good food_food great_food good |
| 1 | mexican food - burrito - salsa - tacos - chips salsa | 868 | 1_mexican food_burrito_salsa_tacos |
| 2 | shop - shopping - store - stores - mall | 777 | 2_shop_shopping_store_stores |
| 3 | beer selection - beers - drinks - beer - bartenders | 659 | 3_beer selection_beers_drinks_beer |
| 4 | restaurant - appetizer - meal - dinner - dish | 636 | 4_restaurant_appetizer_meal_dinner |
| 5 | best pizza - pizza good - good pizza - pizza - pizzeria | 567 | 5_best pizza_pizza good_good pizza_pizza |
| 6 | restaurant - bbq - wines - drinks - wine | 559 | 6_restaurant_bbq_wines_drinks |
| 7 | good burger - burger - burger fries - burgers - fries | 481 | 7_good burger_burger_burger fries_burgers |
| 8 | best sushi - sushi - sushi bar - spicy tuna - tuna roll | 333 | 8_best sushi_sushi_sushi bar_spicy tuna |
| 9 | better starbucks - coffee - starbucks - coffee shop - coffee shops | 317 | 9_better starbucks_coffee_starbucks_coffee shop |
| 10 | manicure - gel manicure - nail - pedicure - nails | 234 | 10_manicure_gel manicure_nail_pedicure |
| 11 | chinese food - chinese restaurant - fried rice - orange chicken - rice | 229 | 11_chinese food_chinese restaurant_fried rice_orange chicken |
| 12 | restaurant - dinner - waiter - hostess - meal | 218 | 12_restaurant_dinner_waiter_hostess |
| 13 | hotels - hotel - resort - amenities - rooms | 217 | 13_hotels_hotel_resort_amenities |
| 14 | ice cream - flavors - flavor - ice - chocolate | 213 | 14_ice cream_flavors_flavor_ice |
| 15 | thai food - thai restaurant - pad thai - thai - thai place | 205 | 15_thai food_thai restaurant_pad thai_thai |
| 16 | breakfast food - breakfast - pancakes - lunch - protein pancakes | 179 | 16_breakfast food_breakfast_pancakes_lunch |
| 17 | movie theater - theaters - theater - theatre - amc | 149 | 17_movie theater_theaters_theater_theatre |
| 18 | vietnamese cuisine - vietnamese restaurants - vietnamese restaurant - vietnamese food - beef pho | 101 | 18_vietnamese cuisine_vietnamese restaurants_vietnamese restaurant_vietnamese food |
| 19 | sandwich great - sandwiches good - sandwich shop - sandwiches - sandwich | 99 | 19_sandwich great_sandwiches good_sandwich shop_sandwiches |
</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
Framework versions
- Numpy: 1.22.4
- HDBSCAN: 0.8.29
- UMAP: 0.5.3
- Pandas: 1.5.3
- Scikit-Learn: 1.2.2
- Sentence-transformers: 2.2.2
- Transformers: 4.30.2
- Numba: 0.56.4
- Plotly: 5.13.1
- Python: 3.10.12
