model_wolf
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("wongzien2000/model_wolf")
topic_model.get_topic_info()
Topic overview
- Number of topics: 19
- Number of training documents: 2933
Click here for an overview of all topics.
Topic ID | Topic Keywords | Topic Frequency | Label |
---|---|---|---|
-1 | split - great - creatine - best - exercise | 37 | -1_split_great_creatine_best |
0 | cable - just - exercise - exercises - lateral | 468 | 0_cable_just_exercise_exercises |
1 | mike - dr - dr mike - darth - sith | 1267 | 1_mike_dr_dr mike_darth |
2 | sets - protein - week - muscle - volume | 166 | 2_sets_protein_week_muscle |
3 | deadlift - deadlifts - strength - hypertrophy - legs | 107 | 3_deadlift_deadlifts_strength_hypertrophy |
4 | tier - list - accent - tier list - pencil | 95 | 4_tier_list_accent_tier list |
5 | pistol - squats - pistol squats - squat - reverse | 80 | 5_pistol_squats_pistol squats_squat |
6 | tier - deadlift tier - deadlift - sticky ricky - ricky | 79 | 6_tier_deadlift tier_deadlift_sticky ricky |
7 | uncles - stamps - time stamps - comment - timestamps | 77 | 7_uncles_stamps_time stamps_comment |
8 | sound - audio - ai - milo - sound effects | 75 | 8_sound_audio_ai_milo |
9 | curl - incline - curls - preacher - preacher curl | 74 | 9_curl_incline_curls_preacher |
10 | milo - dr milo - hear - ending - miew | 73 | 10_milo_dr milo_hear_ending |
11 | leg - leg extension - extension - quads - quad | 54 | 11_leg_leg extension_extension_quads |
12 | calf - calf raise - seated calf - seated - calves | 54 | 12_calf_calf raise_seated calf_seated |
13 | partials - lengthened partials - lengthened - song - grandma | 50 | 13_partials_lengthened partials_lengthened_song |
14 | wolf - dr wolf - meadows - meadows row - dr | 46 | 14_wolf_dr wolf_meadows_meadows row |
15 | squats - squat - hack - sissy - sissy squats | 45 | 15_squats_squat_hack_sissy |
16 | app - myoadapt - december - waiting - coming | 43 | 16_app_myoadapt_december_waiting |
17 | mike - interviewing mike - bomb - interviewing - love mike | 43 | 17_mike_interviewing mike_bomb_interviewing |
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: 10
- verbose: True
- zeroshot_min_similarity: 0.7
- zeroshot_topic_list: None
Framework versions
- Numpy: 2.0.2
- HDBSCAN: 0.8.40
- UMAP: 0.5.7
- Pandas: 2.2.2
- Scikit-Learn: 1.6.1
- Sentence-transformers: 3.4.1
- Transformers: 4.50.2
- Numba: 0.60.0
- Plotly: 5.24.1
- Python: 3.11.11
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