The pipeline API

The pipeline API

Just like the transformers Python library, Transformers.js provides users with a simple way to leverage the power of transformers. The pipeline() function is the easiest and fastest way to use a pretrained model for inference.

For the full list of available tasks/pipelines, check out this table.

The basics

Start by creating an instance of pipeline() and specifying a task you want to use it for. For example, to create a sentiment analysis pipeline, you can do:

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

let classifier = await pipeline('sentiment-analysis');

When running for the first time, the pipeline will download and cache the default pretrained model associated with the task. This can take a while, but subsequent calls will be much faster.

By default, models will be downloaded from the BOINC AI Hub and stored in browser cache, but there are ways to specify custom models and cache locations. For more information see here.

You can now use the classifier on your target text by calling it as a function:

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let result = await classifier('I love transformers!');
// [{'label': 'POSITIVE', 'score': 0.9998}]

If you have multiple inputs, you can pass them as an array:

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let result = await classifier(['I love transformers!', 'I hate transformers!']);
// [{'label': 'POSITIVE', 'score': 0.9998}, {'label': 'NEGATIVE', 'score': 0.9982}]

You can also specify a different model to use for the pipeline by passing it as the second argument to the pipeline() function. For example, to use a different model for sentiment analysis (like one trained to predict sentiment of a review as a number of stars between 1 and 5), you can do:

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let reviewer = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');

let result = await reviewer('The Shawshank Redemption is a true masterpiece of cinema.');
// [{label: '5 stars', score: 0.8167929649353027}]

Transformers.js supports loading any model hosted on the BOINC AI Hub, provided it has ONNX weights (located in a subfolder called onnx). For more information on how to convert your PyTorch, TensorFlow, or JAX model to ONNX, see the conversion section.

The pipeline() function is a great way to quickly use a pretrained model for inference, as it takes care of all the preprocessing and postprocessing for you. For example, if you want to perform Automatic Speech Recognition (ASR) using OpenAI’s Whisper model, you can do:

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// Allocate a pipeline for Automatic Speech Recognition
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small.en');

// Transcribe an audio file, loaded from a URL.
let result = await transcriber('https://boincai.com/datasets/Narsil/asr_dummy/resolve/main/mlk.flac');
// {text: ' I have a dream that one day this nation will rise up and live out the true meaning of its creed.'}

Pipeline options

Loading

We offer a variety of options to control how models are loaded from the BOINC AI Hub (or locally). By default, the quantized version of the model is used, which is smaller and faster, but usually less accurate. To override this behaviour (i.e., use the unquantized model), you can use a custom PretrainedOptions object as the third parameter to the pipeline function:

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// Allocation a pipeline for feature extraction, using the unquantized model
const pipe = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2', {
    quantized: false,
});

You can also specify which revision of the model to use, by passing a revision parameter. Since the BOINC AI Hub uses a git-based versioning system, you can use any valid git revision specifier (e.g., branch name or commit hash)

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let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en', {
    revision: 'output_attentions',
});

For the full list of options, check out the PretrainedOptions documentation.

Running

Many pipelines have additional options that you can specify. For example, when using a model that does multilingual translation, you can specify the source and target languages like this:

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// Allocation a pipeline for translation
let translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');

// Translate from English to Greek
let result = await translator('I like to walk my dog.', {
    src_lang: 'eng_Latn',
    tgt_lang: 'ell_Grek'
});
// [ { translation_text: 'Μου αρέσει να περπατάω το σκυλί μου.' } ]

// Translate back to English
let result2 = await translator(result[0].translation_text, {
    src_lang: 'ell_Grek',
    tgt_lang: 'eng_Latn'
});
// [ { translation_text: 'I like to walk my dog.' } ]

When using models that support auto-regressive generation, you can specify generation parameters like the number of new tokens, sampling methods, temperature, repetition penalty, and much more. For a full list of available parameters, see to the GenerationConfig class.

For example, to generate a poem using LaMini-Flan-T5-783M, you can do:

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// Allocate a pipeline for text2text-generation
let poet = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
let result = await poet('Write me a love poem about cheese.', {
    max_new_tokens: 200,
    temperature: 0.9,
    repetition_penalty: 2.0,
    no_repeat_ngram_size: 3,

    // top_k: 20,
    // do_sample: true,
});

which outputs:

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Cheese, oh cheese! You're the perfect comfort food.
Your texture so smooth and creamy you can never get old.
With every bite it melts in your mouth like buttery delights
that make me feel right at home with this sweet treat of mine. 

From classic to bold flavor combinations,
I love how versatile you are as an ingredient too?
Cheddar is my go-to for any occasion or mood; 
It adds depth and richness without being overpowering its taste buds alone

For more information on the available options for each pipeline, refer to the API Reference. If you would like more control over the inference process, you can use the AutoModel, AutoTokenizer, or AutoProcessor classes instead.

Available tasks

Tasks

Natural Language Processing

Task
ID
Description
Supported?

conversational

Generating conversational text that is relevant, coherent and knowledgable given a prompt.

fill-mask

Masking some of the words in a sentence and predicting which words should replace those masks.

question-answering

Retrieve the answer to a question from a given text.

sentence-similarity

Determining how similar two texts are.

summarization

Producing a shorter version of a document while preserving its important information.

table-question-answering

Answering a question about information from a given table.

text-classification or sentiment-analysis

Assigning a label or class to a given text.

text-generation

Producing new text by predicting the next word in a sequence.

text2text-generation

Converting one text sequence into another text sequence.

token-classification or ner

Assigning a label to each token in a text.

translation

Converting text from one language to another.

zero-shot-classification

Classifying text into classes that are unseen during training.

Vision

Task
ID
Description
Supported?

depth-estimation

Predicting the depth of objects present in an image.

image-classification

Assigning a label or class to an entire image.

image-segmentation

Divides an image into segments where each pixel is mapped to an object. This task has multiple variants such as instance segmentation, panoptic segmentation and semantic segmentation.

image-to-image

Transforming a source image to match the characteristics of a target image or a target image domain.

mask-generation

Generate masks for the objects in an image.

object-detection

Identify objects of certain defined classes within an image.

n/a

Assigning a label or class to an entire video.

n/a

Generating images with no condition in any context (like a prompt text or another image).

Audio

Task
ID
Description
Supported?

audio-classification

Assigning a label or class to a given audio.

n/a

Generating audio from an input audio source.

automatic-speech-recognition

Transcribing a given audio into text.

text-to-speech or text-to-audio

Generating natural-sounding speech given text input.

Tabular

Task
ID
Description
Supported?

n/a

Classifying a target category (a group) based on set of attributes.

n/a

Predicting a numerical value given a set of attributes.

Multimodal

Task
ID
Description
Supported?

document-question-answering

Answering questions on document images.

feature-extraction

Transforming raw data into numerical features that can be processed while preserving the information in the original dataset.

image-to-text

Output text from a given image.

text-to-image

Generates images from input text.

visual-question-answering

Answering open-ended questions based on an image.

zero-shot-image-classification

Classifying images into classes that are unseen during training.

Reinforcement Learning

Task
ID
Description
Supported?

n/a

Learning from actions by interacting with an environment through trial and error and receiving rewards (negative or positive) as feedback.

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