jeff_topics

jeff_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:

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

topic_model.get_topic_info()

Topic overview

• Number of topics: 94
• Number of training documents: 36727

<details> <summary>Click here for an overview of all topics.</summary> </details>

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