---
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