Connect 3Scribe and HuggingFace to Build Intelligent Automations
Ready to use 3Scribe and HuggingFace automations
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Actions and Triggers
When this happensTriggers
A trigger is an event that starts a workflow.
Job Complete
Triggers when a transcription job has completed.
Do thisActions
Action is the task that follows automatically within your 3Scribe integrations.
Create Transcription Job
Posts a file to 3Scribe to be transcribed
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 3Scribe and HuggingFace accounts to viaSocket. Once connected, you can set up a workflow where an event in 3Scribe triggers actions in HuggingFace (or vice versa).
Absolutely. You can customize how 3Scribe 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 3Scribe 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 3Scribe 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 3Scribe
3Scribe is the most accurate, automated text transcription available. We convert audio and video files and give near human accuracy at automated prices.
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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