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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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# model_wolf |
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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("wongzien2000/model_wolf") |
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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: 19 |
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* Number of training documents: 2933 |
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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 | split - great - creatine - best - exercise | 37 | -1_split_great_creatine_best | |
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| 0 | cable - just - exercise - exercises - lateral | 468 | 0_cable_just_exercise_exercises | |
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| 1 | mike - dr - dr mike - darth - sith | 1267 | 1_mike_dr_dr mike_darth | |
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| 2 | sets - protein - week - muscle - volume | 166 | 2_sets_protein_week_muscle | |
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| 3 | deadlift - deadlifts - strength - hypertrophy - legs | 107 | 3_deadlift_deadlifts_strength_hypertrophy | |
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| 4 | tier - list - accent - tier list - pencil | 95 | 4_tier_list_accent_tier list | |
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| 5 | pistol - squats - pistol squats - squat - reverse | 80 | 5_pistol_squats_pistol squats_squat | |
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| 6 | tier - deadlift tier - deadlift - sticky ricky - ricky | 79 | 6_tier_deadlift tier_deadlift_sticky ricky | |
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| 7 | uncles - stamps - time stamps - comment - timestamps | 77 | 7_uncles_stamps_time stamps_comment | |
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| 8 | sound - audio - ai - milo - sound effects | 75 | 8_sound_audio_ai_milo | |
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| 9 | curl - incline - curls - preacher - preacher curl | 74 | 9_curl_incline_curls_preacher | |
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| 10 | milo - dr milo - hear - ending - miew | 73 | 10_milo_dr milo_hear_ending | |
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| 11 | leg - leg extension - extension - quads - quad | 54 | 11_leg_leg extension_extension_quads | |
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| 12 | calf - calf raise - seated calf - seated - calves | 54 | 12_calf_calf raise_seated calf_seated | |
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| 13 | partials - lengthened partials - lengthened - song - grandma | 50 | 13_partials_lengthened partials_lengthened_song | |
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| 14 | wolf - dr wolf - meadows - meadows row - dr | 46 | 14_wolf_dr wolf_meadows_meadows row | |
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| 15 | squats - squat - hack - sissy - sissy squats | 45 | 15_squats_squat_hack_sissy | |
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| 16 | app - myoadapt - december - waiting - coming | 43 | 16_app_myoadapt_december_waiting | |
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| 17 | mike - interviewing mike - bomb - interviewing - love mike | 43 | 17_mike_interviewing mike_bomb_interviewing | |
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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: 10 |
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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: 2.0.2 |
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* HDBSCAN: 0.8.40 |
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* UMAP: 0.5.7 |
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* Pandas: 2.2.2 |
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* Scikit-Learn: 1.6.1 |
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* Sentence-transformers: 3.4.1 |
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* Transformers: 4.50.2 |
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* Numba: 0.60.0 |
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* Plotly: 5.24.1 |
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* Python: 3.11.11 |
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