Project Title

transformers.js — State-of-the-art Machine Learning for the Web without Server Dependencies

Overview

transformers.js is a JavaScript library that enables developers to run state-of-the-art machine learning models directly in the browser, eliminating the need for a server. It is functionally equivalent to Hugging Face's transformers Python library, allowing for the use of the same pretrained models with a similar API. This library supports a wide range of tasks across different modalities, including NLP, computer vision, audio, and multimodal tasks.

Key Features

Web-based Machine Learning: Run machine learning models directly in the browser.
Functionally Equivalent to Python Library: Use the same pretrained models and similar API as the transformers Python library.
Support for Multiple Modalities: Handles tasks in NLP, computer vision, audio, and multimodal domains.

Use Cases

NLP Applications: Developers can use transformers.js for text classification, named entity recognition, and question answering in web applications.
Image and Video Processing: Utilize the library for image classification, object detection, and segmentation in web-based applications.
Audio Processing: Integrate automatic speech recognition and audio classification features into web applications.

Advantages

No Server Required: Reduces infrastructure costs and deployment complexity.
Easy Model Conversion: Convert pretrained PyTorch, TensorFlow, or JAX models to ONNX using 🤗 Optimum.
Extensive Documentation: Comprehensive documentation available for easy integration and usage.

Limitations / Considerations

Browser Compatibility: May have limitations based on browser support for ONNX Runtime.
Performance: Running complex models in the browser may impact performance and speed compared to server-side execution.

TensorFlow.js: A JavaScript library for training and deploying machine learning models in the browser, differing in its focus on TensorFlow models.
Pyodide: A project that brings the Python scientific stack to the browser, allowing Python code to run in a web browser, differing in its approach of emulating a Python environment.
ONNX Runtime: A cross-platform, high-performance scoring engine for Open Neural Network Exchange (ONNX) models, which transformers.js utilizes for model execution in the browser.

Basic Information

GitHub: https://github.com/huggingface/transformers.js
Stars: 14,376
License: Unknown
Last Commit: 2025-08-20

📊 Project Information

Project Name: transformers.js
GitHub URL: https://github.com/huggingface/transformers.js
Programming Language: JavaScript
⭐ Stars: 14,376
🍴 Forks: 982
📅 Created: 2023-02-13
🔄 Last Updated: 2025-08-20

🏷️ Project Topics

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📚 Documentation

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transformers.js

Project Description