model_jeff

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_jeff")

topic_model.get_topic_info()

Topic overview

• Number of topics: 26
• Number of training documents: 36727

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	jeff - just - great - love - thank	256	-1_jeff_just_great_love
0	protein - fat - weight - eat - calories	8978	0_protein_fat_weight_eat
1	sets - reps - exercise - week - sets reps	9577	1_sets_reps_exercise_week
2	content - great - thank - science - informative	5017	2_content_great_thank_science
3	breaking bad - breaking - bad - better - better breaking	1473	3_breaking bad_breaking_bad_better
4	level - ready - ready level - level ready - level level	1425	4_level_ready_ready level_level ready
5	bench - press - chest - bench press - grip	1234	5_bench_press_chest_bench press
6	thank - great - man - amazing - thanks	951	6_thank_great_man_amazing
7	fitness - videos - science - thank - jeff	818	7_fitness_videos_science_thank
8	pro - elite - average - lost - level	741	8_pro_elite_average_lost
9	app - macros - macrofactor - macro - fat	641	9_app_macros_macrofactor_macro
10	jeff - jeff jeff - bro jeff - bro - love jeff	578	10_jeff_jeff jeff_bro jeff_bro
11	videos - love - love videos - youtube - quality	524	11_videos_love_love videos_youtube
12	kiwi - kiwi juice - juice - snort - snorting	468	12_kiwi_kiwi juice_juice_snort
13	steroids - testosterone - steroid - anabolic - use steroids	440	13_steroids_testosterone_steroid_anabolic
14	book - program - bought - books - ordered	395	14_book_program_bought_books
15	roids - women - habits - atomic - atomic habits	393	15_roids_women_habits_atomic
16	tall - short - look - height - dude	382	16_tall_short_look_height
17	mask - wearing - wearing mask - hoodie - shirt	340	17_mask_wearing_wearing mask_hoodie
18	shapiro - ben - ben shapiro - jacked - nerd	336	18_shapiro_ben_ben shapiro_jacked
19	mom - chili - macros - recipe - plastic	314	19_mom_chili_macros_recipe
20	jeff - thanks jeff - thanks - thank jeff - thank	308	20_jeff_thanks jeff_thanks_thank jeff
21	sleep - hours - sleeping - work - job	301	21_sleep_hours_sleeping_work
22	steph - bro steph - ber - bro - ber ber	288	22_steph_bro steph_ber_bro
23	voice - sound - match - voice doesn - sounds	282	23_voice_sound_match_voice doesn
24	thumbnail - picture - look - pic - photo	267	24_thumbnail_picture_look_pic

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