State-of-the-art Machine Learning for the Web
Run đ€ Transformers directly in your browser, with no need for a server! Transformers.js is designed to be functionally equivalent to Hugging Face's [transformers](https://github.com/huggingface/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: - đ **Natural Language Processing**: text classification, named entity recognition, question answering, language modeling, summarization, translation, multiple choice, and text generation. - đŒïž **Computer Vision**: image classification, object detection, segmentation, and depth estimation. - đŁïž **Audio**: automatic speech recognition, audio classification, and text-to-speech. - đ **Multimodal**: embeddings, zero-shot audio classification, zero-shot image classification, and zero-shot object detection. Transformers.js uses [ONNX Runtime](https://onnxruntime.ai/) to run models in the browser. The best part about it, is that you can easily [convert](#convert-your-models-to-onnx) your pretrained PyTorch, TensorFlow, or JAX models to ONNX using [đ€ Optimum](https://github.com/huggingface/optimum#onnx--onnx-runtime). For more information, check out the full [documentation](https://huggingface.co/docs/transformers.js). ## Installation To install via [NPM](https://www.npmjs.com/package/@huggingface/transformers), run: ```bash npm i @huggingface/transformers ``` Alternatively, you can use it in vanilla JS, without any bundler, by using a CDN or static hosting. For example, using [ES Modules](https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules), you can import the library with: ```html ``` ## Quick tour It's super simple to translate from existing code! Just like the python library, we support the `pipeline` API. 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.| Python (original) | Javascript (ours) |
|---|---|
| ```python from transformers import pipeline # Allocate a pipeline for sentiment-analysis pipe = pipeline('sentiment-analysis') out = pipe('I love transformers!') # [{'label': 'POSITIVE', 'score': 0.999806941}] ``` | ```javascript import { pipeline } from '@huggingface/transformers'; // Allocate a pipeline for sentiment-analysis const pipe = await pipeline('sentiment-analysis'); const out = await pipe('I love transformers!'); // [{'label': 'POSITIVE', 'score': 0.999817686}] ``` |