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--- |
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tags: |
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- bertopic |
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library_name: bertopic |
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pipeline_tag: text-classification |
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--- |
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# bert_key |
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This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model. |
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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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## Usage |
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To use this model, please install BERTopic: |
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``` |
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pip install -U bertopic |
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``` |
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You can use the model as follows: |
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```python |
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from bertopic import BERTopic |
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topic_model = BERTopic.load("VegetaSama/bert_key") |
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topic_model.get_topic_info() |
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``` |
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## Topic overview |
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* Number of topics: 17 |
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* Number of training documents: 10000 |
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<details> |
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<summary>Click here for an overview of all topics.</summary> |
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| Topic ID | Topic Keywords | Topic Frequency | Label | |
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|----------|----------------|-----------------|-------| |
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| -1 | restaurant - meal - sandwich - food - lunch | 86 | -1_restaurant_meal_sandwich_food | |
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| 0 | restaurant - drinks - dinner - bar - steak | 2059 | 0_restaurant_drinks_dinner_bar | |
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| 1 | mexican food - tacos - taco - chips salsa - salsa | 2789 | 1_mexican food_tacos_taco_chips salsa | |
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| 2 | shop - shopping - nordstrom - store - customer service | 731 | 2_shop_shopping_nordstrom_store | |
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| 3 | thai food - chinese food - pad thai - thai - fried rice | 701 | 3_thai food_chinese food_pad thai_thai | |
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| 4 | best pizza - pizza good - good pizza - pizza - pizzeria | 594 | 4_best pizza_pizza good_good pizza_pizza | |
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| 5 | scottsdale - phoenix - restaurant - bbq - arizona | 586 | 5_scottsdale_phoenix_restaurant_bbq | |
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| 6 | burger - good burger - burgers - burger fries - restaurant | 443 | 6_burger_good burger_burgers_burger fries | |
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| 7 | restaurant - hostess - dinner - waiter - waitress | 354 | 7_restaurant_hostess_dinner_waiter | |
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| 8 | best sushi - sushi - sushi place - sushi bar - spicy tuna | 321 | 8_best sushi_sushi_sushi place_sushi bar | |
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| 9 | manicure - massage - pedicure - salon - nail | 294 | 9_manicure_massage_pedicure_salon | |
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| 10 | hotels - hotel - resort - marriott - amenities | 288 | 10_hotels_hotel_resort_marriott | |
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| 11 | coffee shop - coffee - starbucks - coffee shops - good coffee | 215 | 11_coffee shop_coffee_starbucks_coffee shops | |
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| 12 | breakfast - pancakes - protein pancakes - bakery - lunch | 211 | 12_breakfast_pancakes_protein pancakes_bakery | |
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| 13 | hike - hiking - trails - trail - south mountain | 135 | 13_hike_hiking_trails_trail | |
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| 14 | downtown phoenix - central phoenix - restaurants - phoenix area - phoenix | 105 | 14_downtown phoenix_central phoenix_restaurants_phoenix area | |
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| 15 | vets - vet - veterinary - pets - petsmart | 88 | 15_vets_vet_veterinary_pets | |
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</details> |
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## Training hyperparameters |
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* calculate_probabilities: True |
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* language: None |
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* low_memory: False |
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* min_topic_size: 10 |
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* n_gram_range: (1, 1) |
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* nr_topics: None |
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* seed_topic_list: None |
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* top_n_words: 5 |
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* verbose: True |
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* zeroshot_min_similarity: 0.7 |
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* zeroshot_topic_list: None |
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## Framework versions |
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* Numpy: 1.24.3 |
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* HDBSCAN: 0.8.33 |
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* UMAP: 0.5.5 |
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* Pandas: 2.0.3 |
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* Scikit-Learn: 1.3.0 |
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* Sentence-transformers: 2.2.2 |
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* Transformers: 4.32.1 |
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* Numba: 0.58.1 |
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* Plotly: 5.9.0 |
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* Python: 3.11.5 |
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