Connect Pipefy and HuggingFace to Build Intelligent Automations
Choose a Trigger
When this happens...
- Request a new Trigger for Pipefy
Choose an Action
Automatically do this!
Text Classification
Language Translation
Enable Integrations or automations with these events of Pipefy and HuggingFace
Enable Integrations or automations with these events of Pipefy and HuggingFace
Actions
Create Record
Creates a new table record.
Create Card
Creates a new card.
Delete Card
Delete a card using card id.
Update Card
Updates a card Title.
Find Card
Finds a card by Title.
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 Pipefy and HuggingFace accounts to viaSocket. Once connected, you can set up a workflow where an event in Pipefy triggers actions in HuggingFace (or vice versa).
Absolutely. You can customize how Pipefy 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 Pipefy 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 Pipefy 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 Pipefy
The easy button for processes and workflows Easily organize and run all your processes in one place, leaving the inefficient patchwork of apps, forms, spreadsheets and e-mail threads forever in the past
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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