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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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# topic_model_bert_topic |
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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/topic_model_bert_topic") |
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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: 26 |
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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 | place - good - food - great - like | 82 | -1_place_good_food_great | |
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| 0 | store - like - car - just - great | 3133 | 0_store_like_car_just | |
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| 1 | mexican - tacos - food - salsa - good | 876 | 1_mexican_tacos_food_salsa | |
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| 2 | good - ordered - just - food - cheese | 725 | 2_good_ordered_just_food | |
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| 3 | pizza - good - crust - place - great | 568 | 3_pizza_good_crust_place | |
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| 4 | food - great - place - good - service | 536 | 4_food_great_place_good | |
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| 5 | hotel - room - pool - stay - airport | 445 | 5_hotel_room_pool_stay | |
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| 6 | burger - fries - burgers - good - like | 370 | 6_burger_fries_burgers_good | |
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| 7 | hair - dr - massage - nails - time | 339 | 7_hair_dr_massage_nails | |
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| 8 | coffee - starbucks - place - good - like | 269 | 8_coffee_starbucks_place_good | |
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| 9 | scottsdale - food - place - great - good | 262 | 9_scottsdale_food_place_great | |
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| 10 | sushi - roll - rolls - place - good | 258 | 10_sushi_roll_rolls_place | |
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| 11 | minutes - food - table - just - time | 241 | 11_minutes_food_table_just | |
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| 12 | ice - ice cream - cream - cupcakes - cupcake | 224 | 12_ice_ice cream_cream_cupcakes | |
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| 13 | breakfast - pancakes - eggs - good - place | 200 | 13_breakfast_pancakes_eggs_good | |
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| 14 | thai - curry - pad - food - pad thai | 199 | 14_thai_curry_pad_food | |
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| 15 | bbq - phoenix - food - brisket - good | 199 | 15_bbq_phoenix_food_brisket | |
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| 16 | beer - place - great - beers - food | 179 | 16_beer_place_great_beers | |
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| 17 | service - food - good - place - order | 152 | 17_service_food_good_place | |
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| 18 | pho - vietnamese - broth - spring - spring rolls | 147 | 18_pho_vietnamese_broth_spring | |
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| 19 | bar - music - place - night - cool | 106 | 19_bar_music_place_night | |
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| 20 | chinese - chinese food - food - soup - chicken | 101 | 20_chinese_chinese food_food_soup | |
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| 21 | greek - vegan - meat - food - place | 100 | 21_greek_vegan_meat_food | |
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| 22 | theater - movie - seats - movies - amc | 99 | 22_theater_movie_seats_movies | |
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| 23 | chinese - food - chinese food - pei - pei wei | 95 | 23_chinese_food_chinese food_pei | |
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| 24 | bar - good - food - happy - great | 95 | 24_bar_good_food_happy | |
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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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