Integrate JSON and Hugging Face
Connect JSON and Hugging Face nodes in your workflow. Integrate with any tool or database and ship powerful backend logic and APIs instantly - No code required!
Node stack
Supported JSON and Hugging Face Nodes
Add any other tools or your preferred database nodes. If an integration is not available generate your own using AI
Append Array to JSON File
Appends an array of objects to a JSON file. If the file or folder path doesn't exist, it creates it
Concat Property Values
Extracts and concatenates specific property values from an array within a JSON object and returns the concatenated string.
Extract & Join By Key
Extracts a specific key from each object within the array and concatenates the values into a single string.
Is Valid JSON
Check if a given string is valid JSON
JSON to CSV
Creates a CSV file from an array of JSON objects, stores it in the GCP Storage Bucket, and returns the public download URL.
Parse JSON
Converts a JSON string back into a JavaScript object
Stringify JSON
Convert a JSON object to a string
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).
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.
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.
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.
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.
Quick start
How to integrate JSON and Hugging Face
Step 1 — Add the nodes to your workflow
Create a new workflow in BuildShip, click “Add node”, and select the JSON and Hugging Face actions you want to use.
Step 2 — Configure each node
Go to each node to authenticate (or add your API key) and fill in the required parameters.
Step 3 — Connect the nodes
Each node in BuildShip can connect to others by using their output variables. When you reference a variable from one node in another, BuildShip automatically links them in the workflow.
Step 4 — Test your workflow
Define your starting data in the Inputs node and choose what to do with the result in the Flow Output node. Finally, run a test to see your workflow in action.
blog posts & tutorials
Recommended Reads
Below are recommneded blogs that will help in your journey
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