yelpreview_custom
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_custom")
topic_model.get_topic_info()
Topic overview
- Number of topics: 24
- Number of training documents: 10000
<details> <summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | food - good - place - great - like | 80 | -1_food_good_place_great |
| 0 | bar - place - beer - great - good | 3441 | 0_bar_place_beer_great |
| 1 | store - like - just - car - place | 893 | 1_store_like_just_car |
| 2 | mexican - tacos - food - salsa - burrito | 854 | 2_mexican_tacos_food_salsa |
| 3 | pizza - crust - good - place - like | 672 | 3_pizza_crust_good_place |
| 4 | food - good - great - place - service | 426 | 4_food_good_great_place |
| 5 | sushi - roll - rolls - place - good | 381 | 5_sushi_roll_rolls_place |
| 6 | burger - fries - burgers - good - like | 320 | 6_burger_fries_burgers_good |
| 7 | breakfast - good - chicken - food - toast | 316 | 7_breakfast_good_chicken_food |
| 8 | good - just - cheese - ordered - food | 264 | 8_good_just_cheese_ordered |
| 9 | coffee - starbucks - yogurt - place - coffee shop | 244 | 9_coffee_starbucks_yogurt_place |
| 10 | food - minutes - table - just - time | 238 | 10_food_minutes_table_just |
| 11 | massage - hair - nails - pedicure - nail | 220 | 11_massage_hair_nails_pedicure |
| 12 | scottsdale - food - place - good - great | 213 | 12_scottsdale_food_place_good |
| 13 | hotel - room - pool - stay - resort | 203 | 13_hotel_room_pool_stay |
| 14 | dr - care - dog - office - dentist | 202 | 14_dr_care_dog_office |
| 15 | thai - curry - pad - pad thai - food | 190 | 15_thai_curry_pad_pad thai |
| 16 | service - food - good - place - time | 162 | 16_service_food_good_place |
| 17 | cupcakes - cupcake - gelato - chocolate - frosting | 123 | 17_cupcakes_cupcake_gelato_chocolate |
| 18 | chinese - chinese food - food - rice - good | 120 | 18_chinese_chinese food_food_rice |
| 19 | bbq - brisket - ribs - sauce - pork | 117 | 19_bbq_brisket_ribs_sauce |
| 20 | place - great - food - staff - good | 111 | 20_place_great_food_staff |
| 21 | pho - vietnamese - spring - spring rolls - broth | 110 | 21_pho_vietnamese_spring_spring rolls |
| 22 | food - mojo - love - az - green | 100 | 22_food_mojo_love_az |
</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
