Dark B E R T

Introduction

DarkBERT is a BERT-like language model pretrained using a corpus from the Dark Web. It is intended for research and academic purposes, focusing on understanding language usage on the Dark Web.

Architecture

DarkBERT is based on the BERT architecture, leveraging its transformer-based model structure. It employs techniques like masked language modeling to predict missing words in a sentence, using a corpus derived from the Dark Web.

Training

The model was trained using a specialized corpus from the Dark Web. This involved preprocessing data to ensure relevance and accuracy in representing the linguistic characteristics of the Dark Web.

Guide: Running Locally

1. Install Transformers Library: Ensure you have the transformers library installed.

   pip install transformers
   

2. Download Model: Obtain DarkBERT from the Hugging Face model repository and place it in a local directory, e.g., DarkBERT.

3. Load Model and Tokenizer:

   from transformers import AutoModel, AutoTokenizer
   model = AutoModel.from_pretrained("DarkBERT")
   tokenizer = AutoTokenizer.from_pretrained("DarkBERT")
   

4. Perform Inference: Use the model for tasks like masked language modeling.

   from transformers import pipeline
   unmasker = pipeline('fill-mask', model="DarkBERT")
   result = unmasker("RagnarLocker, LockBit, and REvil are types of <mask>.")
   print(result)
   

5. Suggested Cloud GPUs: For enhanced performance, particularly with large datasets, consider using cloud GPU services such as AWS EC2, Google Cloud Platform, or Azure.

License

DarkBERT is distributed under the Creative Commons Attribution-NonCommercial 4.0 International License (cc-by-nc-4.0). This restricts use to non-commercial purposes, emphasizing ethical and research-focused applications.