![]() ![]() You can refine your search by selecting the task you’re interested in (e.g., text-classification). To find compatible models on the Hub, select the “transformers.js” library tag in the filter menu (or visit this link). If you don’t see your task/model listed here or it is not yet supported, feel free Here is the list of all tasks and architectures currently supported by Transformers.js. Sentiment analysis (in-browser inference) Want to jump straight in? Get started with one of our sample applications/templates: Name API REFERENCE describes all classes and functions, as well as their available parameters and types.DEVELOPER GUIDES show you how to use the library to achieve a specific goal.TUTORIALS are a great place to start if you’re a beginner! We also include sample applications for you to play around with!.GET STARTED provides a quick tour of the library and installation instructions to get up and running.The documentation is organized into 4 sections: Python (original)Ĭopied // Use a different model for sentiment-analysis let pipe = await pipeline( 'sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment') Contents Pipelines group together a pretrained model with preprocessing of inputs and postprocessing of outputs, making it the easiest way to run models with the library. It’s super simple to translate from existing code! Just like the python library, we support the pipeline API. ![]() The best part about it, is that you can easily convert your pretrained PyTorch, TensorFlow, or JAX models to ONNX using □ Optimum.įor more information, check out the full documentation. Transformers.js uses ONNX Runtime to run models in the browser. □ Multimodal: zero-shot image classification.□️ Audio: automatic speech recognition and audio classification.□️ Computer Vision: image classification, object detection, and segmentation.□ Natural Language Processing: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation.These models support common tasks in different modalities, such as: 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. Run □ Transformers directly in your browser, with no need for a server! State-of-the-art Machine Learning for the web.
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