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wongzien2000/
wolf_topics

Name: wolf_topics
Availability: InStock

bertopic

text-classification

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wolf_topics

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:

python

from bertopic import BERTopic
topic_model = BERTopic.load("wongzien2000/wolf_topics")

topic_model.get_topic_info()

Topic overview

Number of topics: 3
Number of training documents: 2933

Click here for an overview of all topics.

Topic ID	Topic Keywords	Topic Frequency	Label
-1	lengthened partials - partials - lengthened - partials bitch - fucking partials	277	-1_lengthened partials_partials_lengthened_partials bitch
0	pull ups - deadlifts - deadlift - biceps - curls	148	0_pull ups_deadlifts_deadlift_biceps
1	pistol squats - pistol squat - sissy squats - sissy squat - squats	2508	1_pistol squats_pistol squat_sissy squats_sissy squat

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: 5
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