Integrate BuildShip Database and Hugging Face to automate workflows with scalable backend
Connect BuildShip Database 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 BuildShip Database and Hugging Face
Popular Templates With BuildShip Database and Hugging Face
Explore our popular Google Sheets & templates below. Click. Remix. Ship!
Node stack
Supported Triggers & Actions
BuildShip Database NODES
Add Object to Array
Adds an object to an existing array in a Firestore document
BuildShip Document Count
Get the count of documents in a collection for a given filter
Collection Query
Get documents of a query in Firestore with multiple filters. See [documentation](https://docs.buildship.com/basics/buildship-tables#querying-collection) for full details.
Create Document
Creates or updates a document in a specified Firestore collection (with Document Reference field type support).
Delete Document
Delete a document from a BuildShip's Firestore collection
Field Average
Average of field in collection for given filter
Field Sum
Sum of field in collection for given filter
Get Document
Fetch a document from BuildShip's Firestore DB by its collection name and ID
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Get Field Value
Fetches a specific field value from a document in a BuildShip's Firestore collection
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Query Count
Get server count of a query in Firestore with multiple filters
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Update Field Value
Updates a field value in BuildShip's Firestore document with a given object
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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).
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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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