Connect Shodan and HuggingFace to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Shodan
Choose an Action
Automatically do this!
Text Classification
Language Translation
Enable Integrations or automations with these events of Shodan and HuggingFace
Enable Integrations or automations with these events of Shodan and HuggingFace
Actions
Find Host
Find an existing host.
Find Host Count
Find an existing hose count.
List Filters
List all an existing filters.
List Protocols
List all an existing protocols.
List Providers
List all an existing providers.
Text Classification
Use any compatible model from the Hugging Face to classify input text using a ML model.
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Frequently Asked Questions
To start, connect both your Shodan and HuggingFace accounts to viaSocket. Once connected, you can set up a workflow where an event in Shodan triggers actions in HuggingFace (or vice versa).
Absolutely. You can customize how Shodan data is recorded in HuggingFace. This includes choosing which data fields go into which fields of HuggingFace, setting up custom formats, and filtering out unwanted information.
The data sync between Shodan and HuggingFace typically happens in real-time through instant triggers. And a maximum of 15 minutes in case of a scheduled trigger.
Yes, viaSocket allows you to add custom logic or use built-in filters to modify data according to your needs.
Yes, you can set conditional logic to control the flow of data between Shodan and HuggingFace. For instance, you can specify that data should only be sent if certain conditions are met, or you can create if/else statements to manage different outcomes.
About Shodan
Shodan is a search engine for Internet-connected devices, allowing users to discover and analyze devices and systems exposed to the public internet. It provides insights into the security and vulnerabilities of these devices, making it a valuable tool for cybersecurity professionals and researchers.
Learn MoreAbout HuggingFace
Hugging Face is a leading platform for natural language processing (NLP) and machine learning models. It provides a wide range of pre-trained models and tools to help developers and researchers build, train, and deploy state-of-the-art machine learning applications.
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