BERTopic-enron-5000

BERTopic-enron-5000

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("antulik/BERTopic-enron-5000")

topic_model.get_topic_info()

Topic overview

• Number of topics: 65
• Number of training documents: 5000

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

Training hyperparameters

• calculate_probabilities: False
• language: english
• low_memory: False
• min_topic_size: 10
• n_gram_range: (1, 1)
• nr_topics: None
• seed_topic_list: [['drug', 'cancer', 'drugs', 'doctor'], ['windows', 'drive', 'dos', 'file'], ['space', 'launch', 'orbit', 'lunar']]
• top_n_words: 10
• verbose: False
• zeroshot_min_similarity: 0.7
• zeroshot_topic_list: None

Framework versions

• Numpy: 1.23.5
• HDBSCAN: 0.8.33
• UMAP: 0.5.6
• Pandas: 2.0.3
• Scikit-Learn: 1.2.2
• Sentence-transformers: 2.7.0
• Transformers: 4.40.1
• Numba: 0.58.1
• Plotly: 5.15.0
• Python: 3.10.12