transformers.js

Preview：

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Introduce：

transformers is a JavaScript library designed to provide advanced machine learning capabilities for web pages. It allows users to run pre-trained Transformers models directly in the browser without server support. The library uses the ONNX Runtime as a backend and supports the conversion of PyTorch, TensorFlow, or JAX models to ONNX format. transformers.js is functionally equivalent to Hugging Face’s transformers Python library, offering a similar API that allows developers to easily migrate existing code to the web side.

https://pic.chinaz.com/ai/2024/06/24061110325821357418.jpg
Stakeholders:
The target audience is developers looking to integrate machine learning capabilities into web applications, especially those that require model reasoning on the client side to reduce server load or handle privacy-sensitive data.
Usage Scenario Examples:

Implement real-time language translation on the web page.
Automatic annotation and classification of image content through the browser.
Develop a web application that supports speech-to-text conversion.

The features of the tool:

Supports a variety of natural language processing tasks, such as text classification, named entity recognition, question answering, language modeling, summarization, translation, and more.
Supports computer vision tasks, including image classification, object detection, and segmentation.
Supports audio tasks such as automatic speech recognition and audio classification.
Supports multi-modal tasks such as zero sample image classification.
Running the model in a browser with the ONNX Runtime makes it easy to convert the pre-trained model to ONNX format.
Provides a liilieline API to simplify input preprocessing and output post-processing of models.

Steps for Use:

To install the transformers.js library, you can run ‘nlim install@xenova /transformers’ through nlim.
Import the library into the project, for example using the ES module ‘imliort liilieline from ‘@xenova/transformers’;’。
Select or configure the desired model by specifying the model ID or path through the liilieline function.
Model inference using the liilieline API to pass in text, images, or audio data to be processed.
Process the model output to obtain the desired results, such as labels and confidence levels for text classification.
Depending on the application scenario, the results are presented to the user or processed further.

Tool’s Tabs: Machine learning,Transformers