Connect Mailparser and HuggingFace to Build Intelligent Automations
Ready to use Mailparser and HuggingFace automations
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Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
New Email Parsed One Request Per Row
Triggers when a new email is parsed in an inbox, and create one request for each row if the result contains a tabular result.
New Email Parsed One Request Per Email
Triggers when a new email is parsed in an inbox, and create one request for one email.
Do thisActions
Action is the task that follows automatically within your Mailparser 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.
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Frequently Asked Questions
To start, connect both your Mailparser and HuggingFace accounts to viaSocket. Once connected, you can set up a workflow where an event in Mailparser triggers actions in HuggingFace (or vice versa).
Absolutely. You can customize how Mailparser 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 Mailparser 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 Mailparser 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 Mailparser
Send emails to Mailparser, get structured data in return. Leads, Contact Forms, eCommerce, Shipping Notes, Attachments, Customer Info & more.
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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