Connect HuggingFace and Recras to Build Intelligent Automations
Ready to use HuggingFace and Recras automations
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Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Booking
Triggers when a new booking is created.
New Contact
Triggers when a new contact is created.
Invoice Sent
Triggers when an invoice is (marked as) sent.
New Start Moment
Triggers when a new start moment is created.
New Attachment
Triggers when a new attachment is created.
New Company
Triggers when a new company is created.
Do thisActions
Action is the task that follows automatically within your HuggingFace integrations.
Text Classification
Use any compatible model from the Hugging Face to classify input text using a ML model.
Language Translation
Use a compatible model from the Hugging Face Hub to translate text from one language to another.
Create Start moment
Creates a start moment in recras.
Update Start moment
Updates a start moment in recras.
Find Customer
Find a customer using their ID.
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Frequently Asked Questions
To start, connect both your HuggingFace and Recras accounts to viaSocket. Once connected, you can set up a workflow where an event in HuggingFace triggers actions in Recras (or vice versa).
Absolutely. You can customize how HuggingFace data is recorded in Recras. This includes choosing which data fields go into which fields of Recras, setting up custom formats, and filtering out unwanted information.
The data sync between HuggingFace and Recras 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 HuggingFace and Recras. 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 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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