Demo

Don’t believe us? Play around with some of these models:

Task:

Installation

If you use npm,
you can install it using:

npm i @xenova/transformers

Alternatively, you can use it in a

tag from a CDN, for example:

Basic example

It’s super easy to translate from existing code!

from transformers import pipeline

# Allocate a pipeline for sentiment-analysis
pipe = pipeline('sentiment-analysis')

out = pipe('I love transformers!')
# [{'label': 'POSITIVE', 'score': 0.999806941}]

import { pipeline } from "@xenova/transformers";

// Allocate a pipeline for sentiment-analysis
let pipe = await pipeline('sentiment-analysis');

let out = await pipe('I love transformers!');
// [{'label': 'POSITIVE', 'score': 0.999817686}]

In the same way as the Python library, you can use a different model by providing its
name as the second argument to the pipeline function. For example:

// Use a different model for sentiment-analysis
let pipe = await pipeline('sentiment-analysis', 'nlptown/bert-base-multilingual-uncased-sentiment');

Custom setup

By default, Transformers.js uses hosted models
precompiled
WASM binaries,
which should work out-of-the-box. You can override this behaviour as follows:

import { env } from "@xenova/transformers";

// Use a different host for models.
// - `remoteURL` defaults to use the HuggingFace Hub
// - `localURL` defaults to '/models/onnx/quantized/'
env.remoteURL = 'https://www.example.com/';
env.localURL = '/path/to/models/';

// Set whether to use remote or local models. Defaults to true.
//  - If true, use the path specified by `env.remoteURL`.
//  - If false, use the path specified by `env.localURL`.
env.remoteModels = false;

// Set parent path of .wasm files. Defaults to use a CDN.
env.onnx.wasm.wasmPaths = '/path/to/files/';

Usage

We currently support the following
tasks and
models, which can be used with the
pipeline
function.

1. sentiment-analysis (a.k.a. text-classification)

   Supported models: distilbert-base-uncased-finetuned-sst-2-english,
   nlptown/bert-base-multilingual-uncased-sentiment,
   distilgpt2.
   For more information, check out the
   
   Text Classification docs
   .

2. question-answering

   Supported models: distilbert-base-cased-distilled-squad,
   distilbert-base-uncased-distilled-squad.
   For more information, check out t