pipelines

Pipelines provide a high-level, easy to use, API for running machine learning models.

Example: Instantiate pipeline using the pipeline function.

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

let classifier = await pipeline('sentiment-analysis');
let result = await classifier('I love transformers!');
// [{'label': 'POSITIVE', 'score': 0.999817686}]

pipelines.Pipeline ⇐ Callable

The Pipeline class is the class from which all pipelines inherit. Refer to this class for methods shared across different pipelines.

Kind: static class of pipelines
Extends: Callable


new Pipeline(task, tokenizer, model)

Create a new Pipeline.

ParamTypeDescription
taskstring

The task of the pipeline. Useful for specifying subtasks.

tokenizerPreTrainedTokenizer

The tokenizer to use.

modelPreTrainedModel

The model to use.


pipeline.dispose()Promise.<void>

Disposes the model.

Kind: instance method of Pipeline
Returns: Promise.<void> - A promise that resolves when the model has been disposed.


pipeline._call(texts)Promise.<any>

Executes the task associated with the pipeline.

Kind: instance method of Pipeline
Returns: Promise.<any> - A promise that resolves to an array containing the inputs and outputs of the task.

ParamTypeDescription
textsany

The input texts to be processed.


pipelines.TextClassificationPipeline ⇐ Pipeline

Text classification pipeline using any ModelForSequenceClassification.

Kind: static class of pipelines
Extends: Pipeline


textClassificationPipeline._call(texts, options)Promise.<(Array<Object>|Object)>

Executes the text classification task.

Kind: instance method of TextClassificationPipeline
Returns: Promise.<(Array<Object>|Object)> - A promise that resolves to an array or object containing the predicted labels and scores.

ParamTypeDefaultDescription
textsany

The input texts to be classified.

optionsObject

An optional object containing the following properties:

[options.topk]number1

The number of top predictions to be returned.


pipelines.TokenClassificationPipeline ⇐ Pipeline

Named Entity Recognition pipeline using any ModelForTokenClassification.

Kind: static class of pipelines
Extends: Pipeline


tokenClassificationPipeline._call(texts, options)Promise.<(Array<Object>|Object)>

Executes the token classification task.

Kind: instance method of TokenClassificationPipeline
Returns: Promise.<(Array<Object>|Object)> - A promise that resolves to an array or object containing the predicted labels and scores.

ParamTypeDescription
textsany

The input texts to be classified.

optionsObject

An optional object containing the following properties:


pipelines.QuestionAnsweringPipeline ⇐ Pipeline

Question Answering pipeline using any ModelForQuestionAnswering.

Example: Run question answering with distilbert-base-uncased-distilled-squad.

let question = 'Who was Jim Henson?';
let context = 'Jim Henson was a nice puppet.';

let answerer = await pipeline('question-answering', 'Xenova/distilbert-base-uncased-distilled-squad');
let outputs = await answerer(question, context);
console.log(outputs);
// {
//     "answer": "a nice puppet",
//     "score": 0.5768911502526741
// }

Kind: static class of pipelines
Extends: Pipeline


questionAnsweringPipeline._call(question, context, options)Promise.<any>

Executes the question answering task.

Kind: instance method of QuestionAnsweringPipeline
Returns: Promise.<any> - A promise that resolves to an array or object containing the predicted answers and scores.

ParamTypeDefaultDescription
questionstring | Array<string>

The question(s) to be answered.

contextstring | Array<string>

The context(s) where the answer(s) can be found.

optionsObject

An optional object containing the following properties:

[options.topk]number1

The number of top answer predictions to be returned.


pipelines.FillMaskPipeline ⇐ Pipeline

Masked language modeling prediction pipeline using any ModelWithLMHead.

Kind: static class of pipelines
Extends: Pipeline


fillMaskPipeline._call(texts, options)Promise.<(Array<Object>|Object)>

Fill the masked token in the text(s) given as inputs.

Kind: instance method of FillMaskPipeline
Returns: Promise.<(Array<Object>|Object)> - A promise that resolves to an array or object containing the predicted tokens and scores.

ParamTypeDefaultDescription
textsany

The masked input texts.

optionsObject

An optional object containing the following properties:

[options.topk]number5

The number of top predictions to be returned.


pipelines.Text2TextGenerationPipeline ⇐ Pipeline

Text2TextGenerationPipeline class for generating text using a model that performs text-to-text generation tasks.

Kind: static class of pipelines
Extends: Pipeline


text2TextGenerationPipeline._call(texts, [options])Promise.<any>

Fill the masked token in the text(s) given as inputs.

Kind: instance method of Text2TextGenerationPipeline
Returns: Promise.<any> - An array of objects containing the score, predicted token, predicted token string, and the sequence with the predicted token filled in, or an array of such arrays (one for each input text). If only one input text is given, the output will be an array of objects.
Throws:

ParamTypeDefaultDescription
textsstring | Array<string>

The text or array of texts to be processed.

[options]Object{}

Options for the fill-mask pipeline.

[options.topk]number5

The number of top-k predictions to return.


pipelines.SummarizationPipeline ⇐ Text2TextGenerationPipeline

A pipeline for summarization tasks, inheriting from Text2TextGenerationPipeline.

Kind: static class of pipelines
Extends: Text2TextGenerationPipeline


pipelines.TranslationPipeline ⇐ Text2TextGenerationPipeline

TranslationPipeline class to translate text from one language to another using the provided model and tokenizer.

Kind: static class of pipelines
Extends: Text2TextGenerationPipeline


pipelines.TextGenerationPipeline ⇐ Pipeline

Language generation pipeline using any ModelWithLMHead or ModelForCausalLM. This pipeline predicts the words that will follow a specified text prompt. NOTE: For the full list of generation parameters, see GenerationConfig.

Example: Text generation with Xenova/distilgpt2 (default settings).

let text = 'I enjoy walking with my cute dog,';
let classifier = await pipeline('text-generation', 'Xenova/distilgpt2');
let output = await classifier(text);
console.log(output);
// [{ generated_text: "I enjoy walking with my cute dog, and I love to play with the other dogs." }]

Example: Text generation with Xenova/distilgpt2 (custom settings).

let text = 'Once upon a time, there was';
let classifier = await pipeline('text-generation', 'Xenova/distilgpt2');
let output = await classifier(text, {
    temperature: 2,
    max_new_tokens: 10,
    repetition_penalty: 1.5,
    no_repeat_ngram_size: 2,
    num_beams: 2,
    num_return_sequences: 2,
});
console.log(output);
// [{
//   "generated_text": "Once upon a time, there was an abundance of information about the history and activities that"
// }, {
//   "generated_text": "Once upon a time, there was an abundance of information about the most important and influential"
// }]

Example: Run code generation with Xenova/codegen-350M-mono.

let text = 'def fib(n):';
let classifier = await pipeline('text-generation', 'Xenova/codegen-350M-mono');
let output = await classifier(text, {
    max_new_tokens: 40,
});
console.log(output[0].generated_text);
// def fib(n):
//     if n == 0:
//         return 0
//     if n == 1:
//         return 1
//     return fib(n-1) + fib(n-2)

Kind: static class of pipelines
Extends: Pipeline


textGenerationPipeline._call(texts, [generate_kwargs])Promise.<any>

Generates text based on an input prompt.

Kind: instance method of TextGenerationPipeline
Returns: Promise.<any> - The generated text or texts.

ParamTypeDefaultDescription
textsany

The input prompt or prompts to generate text from.

[generate_kwargs]Object{}

Additional arguments for text generation.


pipelines.ZeroShotClassificationPipeline ⇐ Pipeline

NLI-based zero-shot classification pipeline using a ModelForSequenceClassification trained on NLI (natural language inference) tasks. Equivalent of text-classification pipelines, but these models don’t require a hardcoded number of potential classes, they can be chosen at runtime. It usually means it’s slower but it is much more flexible.

Example: Zero shot classification with Xenova/mobilebert-uncased-mnli.

let text = 'Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.';
let labels = [ 'mobile', 'billing', 'website', 'account access' ];
let classifier = await pipeline('zero-shot-classification', 'Xenova/mobilebert-uncased-mnli');
let output = await classifier(text, labels);
console.log(output);
//  {
//    sequence: 'Last week I upgraded my iOS version and ever since then my phone has been overheating whenever I use your app.',
//    labels: [ 'mobile', 'website', 'billing', 'account access' ],
//    scores: [ 0.5562091040482018, 0.1843621307860853, 0.13942646639336376, 0.12000229877234923 ]
//  }

Example: Zero shot classification with Xenova/nli-deberta-v3-xsmall (multi-label).

let text = 'I have a problem with my iphone that needs to be resolved asap!';
let labels = [ 'urgent', 'not urgent', 'phone', 'tablet', 'computer' ];
let classifier = await pipeline('zero-shot-classification', 'Xenova/nli-deberta-v3-xsmall');
let output = await classifier(text, labels, { multi_label: true });
console.log(output);
// {
//   sequence: 'I have a problem with my iphone that needs to be resolved asap!',
//   labels: [ 'urgent', 'phone', 'computer', 'tablet', 'not urgent' ],
//   scores: [ 0.9958870956360275, 0.9923963400697035, 0.002333537946160235, 0.0015134138567598765, 0.0010699384208377163 ]
// }

Kind: static class of pipelines
Extends: Pipeline


new ZeroShotClassificationPipeline(task, tokenizer, model)

Create a new ZeroShotClassificationPipeline.

ParamTypeDescription
taskstring

The task of the pipeline. Useful for specifying subtasks.

tokenizerPreTrainedTokenizer

The tokenizer to use.

modelPreTrainedModel

The model to use.


zeroShotClassificationPipeline._call(texts, candidate_labels, options)Promise.<(Object|Array<Object>)>

Kind: instance method of ZeroShotClassificationPipeline
Returns: Promise.<(Object|Array<Object>)> - The prediction(s), as a map (or list of maps) from label to score.

ParamTypeDefaultDescription
textsArray.<any>
candidate_labelsArray.<string>
optionsObject

Additional options:

[options.hypothesis_template]string""This example is {}.""

The template used to turn each candidate label into an NLI-style hypothesis. The candidate label will replace the &lcub;} placeholder.

[options.multi_label]booleanfalse

Whether or not multiple candidate labels can be true. If false, the scores are normalized such that the sum of the label likelihoods for each sequence is 1. If true, the labels are considered independent and probabilities are normalized for each candidate by doing a softmax of the entailment score vs. the contradiction score.


pipelines.FeatureExtractionPipeline ⇐ Pipeline

Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base transformer, which can be used as features in downstream tasks.

Example: Run feature extraction with bert-base-uncased (without pooling/normalization).

let extractor = await pipeline('feature-extraction', 'Xenova/bert-base-uncased', { revision: 'default' });
let result = await extractor('This is a simple test.');
console.log(result);
// Tensor {
//     type: 'float32',
//     data: Float32Array [0.05939924716949463, 0.021655935794115067, ...],
//     dims: [1, 8, 768]
// }

Example: Run feature extraction with bert-base-uncased (with pooling/normalization).

let extractor = await pipeline('feature-extraction', 'Xenova/bert-base-uncased', { revision: 'default' });
let result = await extractor('This is a simple test.', { pooling: 'mean', normalize: true });
console.log(result);
// Tensor {
//     type: 'float32',
//     data: Float32Array [0.03373778983950615, -0.010106077417731285, ...],
//     dims: [1, 768]
// }

Example: Calculating embeddings with sentence-transformers models.

let extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
let result = await extractor('This is a simple test.', { pooling: 'mean', normalize: true });
console.log(result);
// Tensor {
//     type: 'float32',
//     data: Float32Array [0.09094982594251633, -0.014774246141314507, ...],
//     dims: [1, 384]
// }

Kind: static class of pipelines
Extends: Pipeline


featureExtractionPipeline._call(texts, options)

Extract the features of the input(s).

Kind: instance method of FeatureExtractionPipeline
Returns: The features computed by the model.

ParamTypeDefaultDescription
textsstring | Array<string>

The input texts

optionsObject

Additional options:

[options.pooling]string""none""

The pooling method to use. Can be one of: "none", "mean".

[options.normalize]booleanfalse

Whether or not to normalize the embeddings in the last dimension.


pipelines.AutomaticSpeechRecognitionPipeline ⇐ Pipeline

Pipeline that aims at extracting spoken text contained within some audio.

Example: Transcribe English.

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
let output = await transcriber(url);
// { text: " And so my fellow Americans ask not what your country can do for you, ask what you can do for your country." }

Example: Transcribe English w/ timestamps.

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
let output = await transcriber(url, { return_timestamps: true });
// {
//   text: " And so my fellow Americans ask not what your country can do for you, ask what you can do for your country."
//   chunks: [
//     { timestamp: [0, 8],  text: " And so my fellow Americans ask not what your country can do for you" }
//     { timestamp: [8, 11], text: " ask what you can do for your country." }
//   ]
// }

Example: Transcribe English w/ word-level timestamps.

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en', {
    revision: 'output_attentions',
});
let output = await transcriber(url, { return_timestamps: 'word' });
// {
//   "text": " And so my fellow Americans ask not what your country can do for you ask what you can do for your country.",
//   "chunks": [
//     { "text": " And", "timestamp": [0, 0.78] },
//     { "text": " so", "timestamp": [0.78, 1.06] },
//     { "text": " my", "timestamp": [1.06, 1.46] },
//     ...
//     { "text": " for", "timestamp": [9.72, 9.92] },
//     { "text": " your", "timestamp": [9.92, 10.22] },
//     { "text": " country.", "timestamp": [10.22, 13.5] }
//   ]
// }

Example: Transcribe French.

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/french-audio.mp3';
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small');
let output = await transcriber(url, { language: 'french', task: 'transcribe' });
// { text: " J'adore, j'aime, je n'aime pas, je déteste." }

Example: Translate French to English.

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/french-audio.mp3';
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small');
let output = await transcriber(url, { language: 'french', task: 'translate' });
// { text: " I love, I like, I don't like, I hate." }

Example: Transcribe/translate audio longer than 30 seconds.

let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/ted_60.wav';
let transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
let output = await transcriber(url, { chunk_length_s: 30, stride_length_s: 5 });
// { text: " So in college, I was a government major, which means [...] So I'd start off light and I'd bump it up" }

Kind: static class of pipelines
Extends: Pipeline


new AutomaticSpeechRecognitionPipeline(task, tokenizer, model, processor)

Create a new AutomaticSpeechRecognitionPipeline.

ParamTypeDescription
taskstring

The task of the pipeline. Useful for specifying subtasks.

tokenizerPreTrainedTokenizer

The tokenizer to use.

modelPreTrainedModel

The model to use.

processorProcessor

The processor to use.


automaticSpeechRecognitionPipeline._call(audio, [kwargs])Promise.<Object>

Asynchronously processes audio and generates text transcription using the model.

Kind: instance method of AutomaticSpeechRecognitionPipeline
Returns: Promise.<Object> - A Promise that resolves to an object containing the transcription text and optionally timestamps if return_timestamps is true.

ParamTypeDefaultDescription
audioFloat32Array | Array<Float32Array>

The audio to be transcribed. Can be a single Float32Array or an array of Float32Arrays.

[kwargs]Object{}

Optional arguments.

[kwargs.return_timestamps]boolean | 'word'

Whether to return timestamps or not. Default is false.

[kwargs.chunk_length_s]number

The length of audio chunks to process in seconds. Default is 0 (no chunking).

[kwargs.stride_length_s]number

The length of overlap between consecutive audio chunks in seconds. If not provided, defaults to chunk_length_s / 6.

[kwargs.chunk_callback]ChunkCallback

Callback function to be called with each chunk processed.

[kwargs.force_full_sequences]boolean

Whether to force outputting full sequences or not. Default is false.

[kwargs.language]string

The source language. Default is null, meaning it should be auto-detected. Use this to potentially improve performance if the source language is known.

[kwargs.task]string

The task to perform. Default is null, meaning it should be auto-detected.

[kwargs.forced_decoder_ids]Array.<Array<number>>

A list of pairs of integers which indicates a mapping from generation indices to token indices that will be forced before sampling. For example, [[1, 123]] means the second generated token will always be a token of index 123.


pipelines.ImageToTextPipeline ⇐ Pipeline

Image To Text pipeline using a AutoModelForVision2Seq. This pipeline predicts a caption for a given image.

Kind: static class of pipelines
Extends: Pipeline


new ImageToTextPipeline(task, tokenizer, model, processor)

Create a new ImageToTextPipeline.

ParamTypeDescription
taskstring

The task of the pipeline. Useful for specifying subtasks.

tokenizerPreTrainedTokenizer

The tokenizer to use.

modelPreTrainedModel

The model to use.

processorProcessor

The processor to use.


imageToTextPipeline._call(images, [generate_kwargs])Promise.<(Object|Array<Object>)>

Assign labels to the image(s) passed as inputs.

Kind: instance method of ImageToTextPipeline
Returns: Promise.<(Object|Array<Object>)> - A Promise that resolves to an object (or array of objects) containing the generated text(s).

ParamTypeDefaultDescription
imagesArray.<any>

The images to be captioned.

[generate_kwargs]Object{}

Optional generation arguments.


pipelines.ImageClassificationPipeline ⇐ Pipeline

Image classification pipeline using any AutoModelForImageClassification. This pipeline predicts the class of an image.

Example: Classify an image.

let classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
let outputs = await classifier(url);
// Array(1) [
//   {label: 'tiger, Panthera tigris', score: 0.632695734500885},
// ]

Example: Classify an image and return top n classes.

let classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
let outputs = await classifier(url, { topk: 3 });
// Array(3) [
//   {label: 'tiger, Panthera tigris', score: 0.632695734500885},
//   {label: 'tiger cat', score: 0.3634825646877289},
//   {label: 'lion, king of beasts, Panthera leo', score: 0.00045060308184474707},
// ]

Example: Classify an image and return all classes.

let classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
let url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
let outputs = await classifier(url, { topk: 0 });
// Array(1000) [
//   {label: 'tiger, Panthera tigris', score: 0.632695734500885},
//   {label: 'tiger cat', score: 0.3634825646877289},
//   {label: 'lion, king of beasts, Panthera leo', score: 0.00045060308184474707},
//   {label: 'jaguar, panther, Panthera onca, Felis onca', score: 0.00035465499968267977},
//   ...
// ]

Kind: static class of pipelines
Extends: Pipeline


new ImageClassificationPipeline(task, model, processor)

Create a new ImageClassificationPipeline.

ParamTypeDescription
taskstring

The task of the pipeline. Useful for specifying subtasks.

modelPreTrainedModel

The model to use.

processorProcessor

The processor to use.


imageClassificationPipeline._call(images, options)Promise.<any>

Classify the given images.

Kind: instance method of ImageClassificationPipeline
Returns: Promise.<any> - The top classification results for the images.

ParamTypeDefaultDescription
imagesany

The images to classify.

optionsObject

The options to use for classification.

[options.topk]number1

The number of top results to return.


pipelines.ImageSegmentationPipeline ⇐ Pipeline

Image segmentation pipeline using any AutoModelForXXXSegmentation. This pipeline predicts masks of objects and their classes.

Kind: static class of pipelines
Extends: Pipeline


new ImageSegmentationPipeline(task, model, processor)

Create a new ImageSegmentationPipeline.

ParamTypeDescription
taskstring

The task of the pipeline. Useful for specifying subtasks.

modelPreTrainedModel

The model to use.

processorProcessor

The processor to use.


imageSegmentationPipeline._call(images, options)Promise.<Array>

Segment the input images.

Kind: instance method of ImageSegmentationPipeline
Returns: Promise.<Array> - The annotated segments.

ParamTypeDefaultDescription
imagesArray

The input images.

optionsObject

The options to use for segmentation.

[options.threshold]number0.5

Probability threshold to filter out predicted masks.

[options.mask_threshold]number0.5

Threshold to use when turning the predicted masks into binary values.

[options.overlap_mask_area_threshold]number0.8

Mask overlap threshold to eliminate small, disconnected segments.

[options.subtask]null | string

Segmentation task to be performed. One of [panoptic, instance, and semantic], depending on model capabilities. If not set, the pipeline will attempt to resolve (in that order).

[options.label_ids_to_fuse]Array

List of label ids to fuse. If not set, do not fuse any labels.

[options.target_sizes]Array

List of target sizes for the input images. If not set, use the original image sizes.


pipelines.ZeroShotImageClassificationPipeline ⇐ Pipeline

Zero shot image classification pipeline. This pipeline predicts the class of an image when you provide an image and a set of candidate_labels.

Kind: static class of pipelines
Extends: Pipeline


new ZeroShotImageClassificationPipeline(task, tokenizer, model, processor)

Create a new ZeroShotImageClassificationPipeline.

ParamTypeDescription
taskstring

The task of the pipeline. Useful for specifying subtasks.

tokenizerPreTrainedTokenizer

The tokenizer to use.

modelPreTrainedModel

The model to use.

processorProcessor

The processor to use.


zeroShotImageClassificationPipeline._call(images, candidate_labels, options)Promise.<any>

Classify the input images with candidate labels using a zero-shot approach.

Kind: instance method of ZeroShotImageClassificationPipeline
Returns: Promise.<any> - An array of classifications for each input image or a single classification object if only one input image is provided.

ParamTypeDescription
imagesArray

The input images.

candidate_labelsArray

The candidate labels.

optionsObject

The options for the classification.

[options.hypothesis_template]string

The hypothesis template to use for zero-shot classification. Default: "This is a photo of &lcub;}".


pipelines.ObjectDetectionPipeline ⇐ Pipeline

Object detection pipeline using any AutoModelForObjectDetection. This pipeline predicts bounding boxes of objects and their classes.

Example: Run object-detection with facebook/detr-resnet-50.

let img = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';

let detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
let output = await detector(img, { threshold: 0.9 });
// [{
//   "score": 0.9976370930671692,
//   "label": "remote",
//   "box": { "xmin": 31, "ymin": 68, "xmax": 190, "ymax": 118 }
// },
// ...
// {
//   "score": 0.9984092116355896,
//   "label": "cat",
//   "box": { "xmin": 331, "ymin": 19, "xmax": 649, "ymax": 371 }
// }]

Kind: static class of pipelines
Extends: Pipeline


new ObjectDetectionPipeline(task, model, processor)

Create a new ObjectDetectionPipeline.

ParamTypeDescription
taskstring

The task of the pipeline. Useful for specifying subtasks.

modelPreTrainedModel

The model to use.

processorProcessor

The processor to use.


objectDetectionPipeline._call(images, options)

Detect objects (bounding boxes & classes) in the image(s) passed as inputs.

Kind: instance method of ObjectDetectionPipeline

ParamTypeDefaultDescription
imagesArray.<any>

The input images.

optionsObject

The options for the object detection.

[options.threshold]number0.9

The threshold used to filter boxes by score.

[options.percentage]booleanfalse

Whether to return the boxes coordinates in percentage (true) or in pixels (false).


pipelines.pipeline(task, [model], [options])Promise.<Pipeline>

Utility factory method to build a [Pipeline] object.

Kind: static method of pipelines
Returns: Promise.<Pipeline> - A Pipeline object for the specified task.
Throws:

ParamTypeDefaultDescription
taskstring

The task of the pipeline.

[model]stringnull

The name of the pre-trained model to use. If not specified, the default model for the task will be used.

[options]PretrainedOptions

Optional parameters for the pipeline.


pipelines~ChunkCallback : function

Kind: inner typedef of pipelines

ParamTypeDescription
chunkChunk

The chunk to process.


pipelines~PretrainedOptions : *

Kind: inner typedef of pipelines