Maintainer: s2w-ai

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Last updated 4/29/2024


Model LinkView on HuggingFace
API SpecView on HuggingFace
Github LinkNo Github link provided
Paper LinkNo paper link provided

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Model overview

DarkBERT is a BERT-like language model that has been pretrained on a corpus of dark web data, as described in the research paper "DarkBERT: A Language Model for the Dark Side of the Internet (ACL 2023)". It was developed by the organization s2w-ai. This model differs from standard BERT models in that it has been exposed to a dataset focused on the darker corners of the internet, potentially giving it unique capabilities for understanding and processing that type of content.

The DarkBERT model shares similarities with other well-known BERT-based models like BERT-large, uncased, whole-word masking, BERT-base, uncased, BERT-base, cased, and DistilBERT-base, uncased. Like these models, DarkBERT uses a masked language modeling (MLM) objective during pretraining, which allows it to learn rich contextual representations of text.

Model inputs and outputs


  • Text sequences of up to 512 tokens


  • Predicted tokens to fill masked positions in the input text
  • Confidence scores for each predicted token


The DarkBERT model has been specifically trained on a dark web corpus, meaning it may have unique capabilities for understanding and processing content related to cybercrime, underground marketplaces, and other illicit activities found on the dark web. This could make it useful for tasks like detecting and analyzing mentions of specific dark web entities, understanding the sentiment and intent behind dark web-related communications, or identifying potential threats or illegal activities.

What can I use it for?

The DarkBERT model could be a valuable tool for researchers, security professionals, and law enforcement agencies working to better understand and combat dark web-related activities. It could be used to aid in the analysis of dark web forum posts, dark web marketplace listings, and other dark web-related text data. Additionally, the model could be fine-tuned for specific tasks like named entity recognition, relation extraction, or text classification to further enhance its capabilities in this domain.

Things to try

One interesting thing to try with DarkBERT would be to compare its performance on dark web-related tasks to that of standard BERT models. This could help shed light on the unique insights the model has gained from its specialized pretraining. You could also experiment with fine-tuning DarkBERT on different dark web-related datasets or tasks to further explore its capabilities.

This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!

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