
When this happens...

Automatically do this!
Text Classification
Language Translation
Enable Integrations or automations with these events of Gupshup and HuggingFace
Sends a message to a specified user through Gupshup.
Allow you to opt a user in or out.
Use any compatible model from the Hugging Face to classify input text using a ML model.
Use a compatible model from the Hugging Face Hub to translate text from one language to another.
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Workflow automation is the process of using technology to execute repetitive tasks with minimal human intervention, creating a seamless flow of activities.
To start, connect both your Gupshup and HuggingFace accounts to viaSocket. Once connected, you can set up a workflow where an event in Gupshup triggers actions in HuggingFace (or vice versa).
Absolutely. You can customize how Gupshup 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 Gupshup 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 Gupshup 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.
Gupshup is a comprehensive messaging platform that enables businesses to build conversational experiences across various messaging channels. It provides tools for creating chatbots, managing customer interactions, and automating communication processes, making it easier for businesses to engage with their audience effectively.
Learn MoreHugging 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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