ISSR_Dark_Web_121Topics

ISSR_Dark_Web_121Topics

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("D0men1c0/ISSR_Dark_Web_121Topics")

topic_model.get_topic_info()

You can make predictions as follows:

sentence = ['Fentanyl']
topic, _ = topic_model.transform(sentence)
topic_model.get_topic_info(topic[0])

Topic overview

• Number of topics: 122
• Number of training documents: 260996

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

Training hyperparameters

• calculate_probabilities: True
• language: None
• low_memory: True
• min_topic_size: 10
• n_gram_range: (1, 3)
• 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: 1.26.4
• HDBSCAN: 0.8.36
• UMAP: 0.5.6
• Pandas: 2.2.1
• Scikit-Learn: 1.4.1.post1
• Sentence-transformers: 3.0.1
• Transformers: 4.39.3
• Numba: 0.60.0
• Plotly: 5.22.0
• Python: 3.12.2