ISSR_Dark_Web_7Topics

ISSR_Dark_Web_7Topics

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_7Topics")

topic_model.get_topic_info()

You can make predictions as follows:

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

Topic overview

• Number of topics: 8
• Number of training documents: 65529

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

Training hyperparameters

• calculate_probabilities: False
• language: None
• low_memory: False
• min_topic_size: 10
• n_gram_range: (1, 2)
• nr_topics: None
• seed_topic_list: [['tor site', 'drug', 'cocaine', 'ketamine', 'weed', 'trafficking', 'scammer', 'market', 'vendor', 'bitcoin', 'mdma', 'coke', 'lsd', 'heroine', 'xanax', 'tor node', 'tor site', 'gun', 'weapon', 'hacking']]
• top_n_words: 10
• verbose: True
• zeroshot_min_similarity: 0.05
• zeroshot_topic_list: [['burglary', 'buy drugs', 'buy weapons', 'child abuse', 'check sale', 'corruption', 'counterfeit money', 'drugs', 'espionage', 'fake IDs', 'find vendor', 'fraud', 'gun', 'hacking', 'kidnapping', 'murder', 'organ trafficking', 'pedophilia', 'rape', 'scammer', 'sell drugs', 'terrorism', 'trafficking']]

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