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yelpreview_custom
Ayomidedeji/yelpreview_custom
yelpreview_custom is a text classification model from Ayomidedeji. Use it when you need a label for a piece of text. It is set up for bertopic.
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.
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Repo size
23.5 MB
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At a glance
- Task
- Text Classification
- Library
- bertopic
- Access
- Public
- Created
- Jun 5, 2024
- Updated
- Jun 5, 2024
- SHA
- 8066ad24
Try a prompt
Model card
From the Hugging Face model README
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("Ayomidedeji/yelpreview_custom")
topic_model.get_topic_info()
Topic overview
- Number of topics: 24
- Number of training documents: 10000
| Topic ID | Topic Keywords | Topic Frequency | Label |
|---|---|---|---|
| -1 | place - food - good - great - like | 88 | -1_place_food_good_great |
| 0 | store - like - just - great - time | 3337 | 0_store_like_just_great |
| 1 | mexican - food - tacos - salsa - good | 1212 | 1_mexican_food_tacos_salsa |
| 2 | pizza - good - crust - place - great | 815 | 2_pizza_good_crust_place |
| 3 | chinese - food - chicken - rice - chinese food | 573 | 3_chinese_food_chicken_rice |
| 4 | scottsdale - place - food - good - great | 323 | 4_scottsdale_place_food_good |
| 5 | food - place - great - good - service | 323 | 5_food_place_great_good |
| 6 | good - ordered - just - like - place | 304 | 6_good_ordered_just_like |
| 7 | food - good - great - place - service | 263 | 7_food_good_great_place |
| 8 | burger - fries - burgers - good - like | 262 | 8_burger_fries_burgers_good |
| 9 | sushi - roll - rolls - place - happy | 255 | 9_sushi_roll_rolls_place |
| 10 | bar - music - place - night - drinks | 242 | 10_bar_music_place_night |
| 11 | coffee - starbucks - place - coffee shop - good | 230 | 11_coffee_starbucks_place_coffee shop |
| 12 | cream - ice - ice cream - yogurt - cupcakes | 217 | 12_cream_ice_ice cream_yogurt |
| 13 | hotel - room - pool - stay - rooms | 206 | 13_hotel_room_pool_stay |
| 14 | beer - great - place - beers - food | 195 | 14_beer_great_place_beers |
| 15 | thai - pad - curry - pad thai - food | 190 | 15_thai_pad_curry_pad thai |
| 16 | table - minutes - food - time - came | 165 | 16_table_minutes_food_time |
| 17 | sandwich - sandwiches - subway - sub - bread | 152 | 17_sandwich_sandwiches_subway_sub |
| 18 | service - food - good - great - time | 140 | 18_service_food_good_great |
| 19 | bbq - brisket - ribs - sauce - good | 134 | 19_bbq_brisket_ribs_sauce |
| 20 | breakfast - eggs - pancakes - toast - place | 129 | 20_breakfast_eggs_pancakes_toast |
| 21 | pho - vietnamese - broth - rolls - spring rolls | 123 | 21_pho_vietnamese_broth_rolls |
| 22 | donuts - tea - boba - donut - bosa | 122 | 22_donuts_tea_boba_donut |
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: 1.25.2
- HDBSCAN: 0.8.36
- UMAP: 0.5.6
- Pandas: 2.0.3
- Scikit-Learn: 1.2.2
- Sentence-transformers: 3.0.0
- Transformers: 4.41.1
- Numba: 0.58.1
- Plotly: 5.15.0
- Python: 3.10.12
- Task
- Text Classification
- Library
- bertopic
- Created
- Jun 5, 2024
- Updated
- Jun 5, 2024