Integrate OPENFORMAT and Hugging Face to automate workflows with scalable backend
Connect OPENFORMAT and Hugging Face nodes to in your workflow. Integrate with any tool or database and ship powerful backend logic and APIs instantly - No code required!
Getting Started
How To Connect OPENFORMAT and Hugging Face
Popular Templates With OPENFORMAT and Hugging Face
Explore our popular Google Sheets & templates below. Click. Remix. Ship!
Node stack
Supported Triggers & Actions
OPENFORMAT NODES
Check Missions
Checks whether the user has completed all of the actions that make up a mission, and whether they have already been rewarded for completing the mission. [Full Documentation Here](https://docs.openformat.tech/no-code/buildship/nodes/check-missions)
Create web3 Account
Creates a web3 account for your user. You can learn more about what this is and how it benefits your users here.
Get Leaderboard
Returns a leaderboard based on the amount of experience points (XP) users have earned.
Get User Information
Returns the user's experience point (XP) balance, the badges they have collected and the actions they have completed within that specific dApp.
Reward Badge
Reward a user with a badge for completing an action or mission. [Full documentation here](https://docs.openformat.tech/no-code/buildship/nodes/reward-badge)
Reward XP
Reward a user with experience points (XP) for completing an action.
Hugging Face NODES
Caption Image
Generate caption for the image using Hugging Face's [Salesforce/blip-image-captioning-large](https://huggingface.co/Salesforce/blip-image-captioning-large) model for image captioning pretrained on COCO dataset - base architecture (with ViT large backbone).
script
Image Classification
Get classification labels for your image using Hugging Face's [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) model which is a transformer encoder model (BERT-like) pretrained on a large collection of images in a supervised fashion, namely ImageNet-21k, at a resolution of 224x224 pixels. Next, the model was fine-tuned on ImageNet (also referred to as ILSVRC2012), a dataset comprising 1 million images and 1,000 classes, also at resolution 224x224.
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Text Summarization
Summarize long text using Hugging Face's [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) model which is a transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder. BART is pre-trained by (1) corrupting text with an arbitrary noising function, and (2) learning a model to reconstruct the original text.
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Text-To-Image
Generate image from text, using Hugging Face's [openskyml/dalle-3-xl](https://huggingface.co/openskyml/dalle-3-xl) test model very similar to Dall•E 3.
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Text-To-Music
Generate music from text using Hugging Face's [facebook/musicgen-small](https://huggingface.co/facebook/musicgen-small) model capable of generating high-quality music samples conditioned on text descriptions or audio prompts.
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Blog posts & Tutorials
Recommended
Reads
Below are recommneded blogs that will help in your journey
Support
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