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Use Transformers.js from Blazor WebAssembly to run pretrained models with the ONNX Runtime in the browser.

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LostBeard/SpawnDev.BlazorJS.TransformersJS

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SpawnDev.BlazorJS.TransformersJS

NuGet

State-of-the-art Machine Learning for the Web in Blazor WebAssembly

SpawnDev.BlazorJS.TrasnformersJS brings the awesome Transformers.js library from Hugging Face to Blazor WebAssembly.

Transformers.js is designed to be functionally equivalent to Hugging Face’s transformers python library, meaning you can run the same pretrained models using a very similar API. These models support common tasks in different modalities, such as:

  • πŸ“ Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.
  • πŸ–ΌοΈ Computer Vision: image classification, object detection, segmentation, and depth estimation.
  • πŸ—£οΈ Audio: automatic speech recognition, audio classification, and text-to-speech.
  • πŸ™ Multimodal: embeddings, zero-shot audio classification, zero-shot image classification, and zero-shot object detection.

Transformers.js uses ONNX Runtime to run models in the browser. The best part about it, is that you can easily convert your pretrained PyTorch, TensorFlow, or JAX models to ONNX using πŸ€— Optimum.

Demo

The current demo app uses Transformers.js, Blazor, and WebGL.

NOTE: The models used can be large. A fast connection is recommended.

WIP

If you are interested in this project, please start an issue to suggest features or areas of interest.

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