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';

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

• pipelines
  • static
    • .Pipeline ⇐ Callable
      • new Pipeline(options)
      • .dispose() : DisposeType
    • .TextClassificationPipeline
      • new TextClassificationPipeline(options)
      • ._call() : TextClassificationPipelineCallback
    • .TokenClassificationPipeline
      • new TokenClassificationPipeline(options)
      • ._call() : TokenClassificationPipelineCallback
    • .QuestionAnsweringPipeline
      • new QuestionAnsweringPipeline(options)
      • ._call() : QuestionAnsweringPipelineCallback
    • .FillMaskPipeline
      • new FillMaskPipeline(options)
      • ._call() : FillMaskPipelineCallback
    • .Text2TextGenerationPipeline
      • new Text2TextGenerationPipeline(options)
      • ._key : ’generated_text’
      • ._call() : Text2TextGenerationPipelineCallback
    • .SummarizationPipeline
      • new SummarizationPipeline(options)
      • ._key : ’summary_text’
    • .TranslationPipeline
      • new TranslationPipeline(options)
      • ._key : ’translation_text’
    • .TextGenerationPipeline
      • new TextGenerationPipeline(options)
      • ._call() : TextGenerationPipelineCallback
    • .ZeroShotClassificationPipeline
      • new ZeroShotClassificationPipeline(options)
      • .model : any
      • ._call() : ZeroShotClassificationPipelineCallback
    • .FeatureExtractionPipeline
      • new FeatureExtractionPipeline(options)
      • ._call() : FeatureExtractionPipelineCallback
    • .ImageFeatureExtractionPipeline
      • new ImageFeatureExtractionPipeline(options)
      • ._call() : ImageFeatureExtractionPipelineCallback
    • .AudioClassificationPipeline
      • new AudioClassificationPipeline(options)
      • ._call() : AudioClassificationPipelineCallback
    • .ZeroShotAudioClassificationPipeline
      • new ZeroShotAudioClassificationPipeline(options)
      • ._call() : ZeroShotAudioClassificationPipelineCallback
    • .AutomaticSpeechRecognitionPipeline
      • new AutomaticSpeechRecognitionPipeline(options)
      • ._call() : AutomaticSpeechRecognitionPipelineCallback
    • .ImageToTextPipeline
      • new ImageToTextPipeline(options)
      • ._call() : ImageToTextPipelineCallback
    • .ImageClassificationPipeline
      • new ImageClassificationPipeline(options)
      • ._call() : ImageClassificationPipelineCallback
    • .ImageSegmentationPipeline
      • new ImageSegmentationPipeline(options)
      • ._call() : ImageSegmentationPipelineCallback
    • .ZeroShotImageClassificationPipeline
      • new ZeroShotImageClassificationPipeline(options)
      • ._call() : ZeroShotImageClassificationPipelineCallback
    • .ObjectDetectionPipeline
      • new ObjectDetectionPipeline(options)
      • ._call() : ObjectDetectionPipelineCallback
    • .ZeroShotObjectDetectionPipeline
      • new ZeroShotObjectDetectionPipeline(options)
      • ._call() : ZeroShotObjectDetectionPipelineCallback
    • .DocumentQuestionAnsweringPipeline
      • new DocumentQuestionAnsweringPipeline(options)
      • ._call() : DocumentQuestionAnsweringPipelineCallback
    • .TextToAudioPipeline
      • new TextToAudioPipeline(options)
      • ._call() : TextToAudioPipelineCallback
    • .ImageToImagePipeline
      • new ImageToImagePipeline(options)
      • ._call() : ImageToImagePipelineCallback
    • .DepthEstimationPipeline
      • new DepthEstimationPipeline(options)
      • ._call() : DepthEstimationPipelineCallback
    • .pipeline(task, [model], [options]) ⇒ *
  • inner
    • ~x[1] : number
    • ~ImagePipelineInputs : string | RawImage | URL
    • ~AudioPipelineInputs : string | URL | Float32Array | Float64Array
    • ~BoundingBox : Object
    • ~Disposable ⇒ Promise.<void>
    • ~TextPipelineConstructorArgs : Object
    • ~ImagePipelineConstructorArgs : Object
    • ~TextImagePipelineConstructorArgs : Object
    • ~TextClassificationPipelineType ⇒ Promise.<(TextClassificationOutput|Array<TextClassificationOutput>)>
    • ~TokenClassificationPipelineType ⇒ Promise.<(TokenClassificationOutput|Array<TokenClassificationOutput>)>
    • ~QuestionAnsweringPipelineType ⇒ Promise.<(QuestionAnsweringOutput|Array<QuestionAnsweringOutput>)>
    • ~FillMaskPipelineType ⇒ Promise.<(FillMaskOutput|Array<FillMaskOutput>)>
    • ~Text2TextGenerationPipelineType ⇒ Promise.<(Text2TextGenerationOutput|Array<Text2TextGenerationOutput>)>
    • ~SummarizationPipelineType ⇒ Promise.<(SummarizationOutput|Array<SummarizationOutput>)>
    • ~TranslationPipelineType ⇒ Promise.<(TranslationOutput|Array<TranslationOutput>)>
    • ~TextGenerationPipelineType ⇒ Promise.<(TextGenerationOutput|Array<TextGenerationOutput>)>
    • ~ZeroShotClassificationPipelineType ⇒ Promise.<(ZeroShotClassificationOutput|Array<ZeroShotClassificationOutput>)>
    • ~FeatureExtractionPipelineType ⇒ Promise.<Tensor>
    • ~ImageFeatureExtractionPipelineType ⇒ Promise.<Tensor>
    • ~AudioClassificationPipelineType ⇒ Promise.<(AudioClassificationOutput|Array<AudioClassificationOutput>)>
    • ~ZeroShotAudioClassificationPipelineType ⇒ Promise.<(Array<ZeroShotAudioClassificationOutput>|Array<Array<ZeroShotAudioClassificationOutput>>)>
    • ~ChunkCallback : function
    • ~Chunk : Object
    • ~AutomaticSpeechRecognitionPipelineType ⇒ Promise.<(AutomaticSpeechRecognitionOutput|Array<AutomaticSpeechRecognitionOutput>)>
    • ~ImageToTextPipelineType ⇒ Promise.<(ImageToTextOutput|Array<ImageToTextOutput>)>
    • ~ImageClassificationPipelineType ⇒ Promise.<(ImageClassificationOutput|Array<ImageClassificationOutput>)>
    • ~ImageSegmentationPipelineType ⇒ Promise.<Array<ImageSegmentationPipelineOutput>>
    • ~ZeroShotImageClassificationPipelineType ⇒ Promise.<(Array<ZeroShotImageClassificationOutput>|Array<Array<ZeroShotImageClassificationOutput>>)>
    • ~ObjectDetectionPipelineType ⇒ Promise.<(ObjectDetectionPipelineOutput|Array<ObjectDetectionPipelineOutput>)>
    • ~ZeroShotObjectDetectionPipelineType ⇒ Promise.<(Array<ZeroShotObjectDetectionOutput>|Array<Array<ZeroShotObjectDetectionOutput>>)>
    • ~DocumentQuestionAnsweringPipelineType ⇒ Promise.<(DocumentQuestionAnsweringOutput|Array<DocumentQuestionAnsweringOutput>)>
    • ~TextToAudioPipelineConstructorArgs : Object
    • ~TextToAudioPipelineType ⇒ Promise.<TextToAudioOutput>
    • ~ImageToImagePipelineType ⇒ Promise.<(RawImage|Array<RawImage>)>
    • ~DepthEstimationPipelineType ⇒ Promise.<(DepthEstimationPipelineOutput|Array<DepthEstimationPipelineOutput>)>
    • ~AllTasks : *

pipelines.Pipeline ⇐ <code> Callable </code>

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

• .Pipeline ⇐ Callable
  • new Pipeline(options)
  • .dispose() : DisposeType

new Pipeline(options)

Create a new Pipeline.

pipeline.dispose() : <code> DisposeType </code>

Kind: instance method of Pipeline

pipelines.TextClassificationPipeline

Text classification pipeline using any ModelForSequenceClassification.

Example: Sentiment-analysis w/ Xenova/distilbert-base-uncased-finetuned-sst-2-english.

const classifier = await pipeline('sentiment-analysis', 'Xenova/distilbert-base-uncased-finetuned-sst-2-english');
const output = await classifier('I love transformers!');
// [{ label: 'POSITIVE', score: 0.999788761138916 }]

Example: Multilingual sentiment-analysis w/ Xenova/bert-base-multilingual-uncased-sentiment (and return top 5 classes).

const classifier = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
const output = await classifier('Le meilleur film de tous les temps.', { topk: 5 });
// [
//   { label: '5 stars', score: 0.9610759615898132 },
//   { label: '4 stars', score: 0.03323351591825485 },
//   { label: '3 stars', score: 0.0036155181005597115 },
//   { label: '1 star', score: 0.0011325967498123646 },
//   { label: '2 stars', score: 0.0009423971059732139 }
// ]

Example: Toxic comment classification w/ Xenova/toxic-bert (and return all classes).

const classifier = await pipeline('text-classification', 'Xenova/toxic-bert');
const output = await classifier('I hate you!', { topk: null });
// [
//   { label: 'toxic', score: 0.9593140482902527 },
//   { label: 'insult', score: 0.16187334060668945 },
//   { label: 'obscene', score: 0.03452680632472038 },
//   { label: 'identity_hate', score: 0.0223250575363636 },
//   { label: 'threat', score: 0.019197041168808937 },
//   { label: 'severe_toxic', score: 0.005651099607348442 }
// ]

Kind: static class of pipelines

• .TextClassificationPipeline
  • new TextClassificationPipeline(options)
  • ._call() : TextClassificationPipelineCallback

new TextClassificationPipeline(options)

Create a new TextClassificationPipeline.

textClassificationPipeline._call() : <code> TextClassificationPipelineCallback </code>

Kind: instance method of TextClassificationPipeline

pipelines.TokenClassificationPipeline

Named Entity Recognition pipeline using any ModelForTokenClassification.

Example: Perform named entity recognition with Xenova/bert-base-NER.

const classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
const output = await classifier('My name is Sarah and I live in London');
// [
//   { entity: 'B-PER', score: 0.9980202913284302, index: 4, word: 'Sarah' },
//   { entity: 'B-LOC', score: 0.9994474053382874, index: 9, word: 'London' }
// ]

Example: Perform named entity recognition with Xenova/bert-base-NER (and return all labels).

const classifier = await pipeline('token-classification', 'Xenova/bert-base-NER');
const output = await classifier('Sarah lives in the United States of America', { ignore_labels: [] });
// [
//   { entity: 'B-PER', score: 0.9966587424278259, index: 1, word: 'Sarah' },
//   { entity: 'O', score: 0.9987385869026184, index: 2, word: 'lives' },
//   { entity: 'O', score: 0.9990072846412659, index: 3, word: 'in' },
//   { entity: 'O', score: 0.9988298416137695, index: 4, word: 'the' },
//   { entity: 'B-LOC', score: 0.9995510578155518, index: 5, word: 'United' },
//   { entity: 'I-LOC', score: 0.9990395307540894, index: 6, word: 'States' },
//   { entity: 'I-LOC', score: 0.9986724853515625, index: 7, word: 'of' },
//   { entity: 'I-LOC', score: 0.9975294470787048, index: 8, word: 'America' }
// ]

Kind: static class of pipelines

• .TokenClassificationPipeline
  • new TokenClassificationPipeline(options)
  • ._call() : TokenClassificationPipelineCallback

new TokenClassificationPipeline(options)

Create a new TokenClassificationPipeline.

tokenClassificationPipeline._call() : <code> TokenClassificationPipelineCallback </code>

Kind: instance method of TokenClassificationPipeline

pipelines.QuestionAnsweringPipeline

Question Answering pipeline using any ModelForQuestionAnswering.

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

const answerer = await pipeline('question-answering', 'Xenova/distilbert-base-uncased-distilled-squad');
const question = 'Who was Jim Henson?';
const context = 'Jim Henson was a nice puppet.';
const output = await answerer(question, context);
// {
//   answer: "a nice puppet",
//   score: 0.5768911502526741
// }

Kind: static class of pipelines

• .QuestionAnsweringPipeline
  • new QuestionAnsweringPipeline(options)
  • ._call() : QuestionAnsweringPipelineCallback

new QuestionAnsweringPipeline(options)

Create a new QuestionAnsweringPipeline.

questionAnsweringPipeline._call() : <code> QuestionAnsweringPipelineCallback </code>

Kind: instance method of QuestionAnsweringPipeline

pipelines.FillMaskPipeline

Masked language modeling prediction pipeline using any ModelWithLMHead.

Example: Perform masked language modelling (a.k.a. “fill-mask”) with Xenova/bert-base-uncased.

const unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased');
const output = await unmasker('The goal of life is [MASK].');
// [
//   { token_str: 'survival', score: 0.06137419492006302, token: 8115, sequence: 'The goal of life is survival.' },
//   { token_str: 'love', score: 0.03902450203895569, token: 1567, sequence: 'The goal of life is love.' },
//   { token_str: 'happiness', score: 0.03253183513879776, token: 9266, sequence: 'The goal of life is happiness.' },
//   { token_str: 'freedom', score: 0.018736306577920914, token: 4438, sequence: 'The goal of life is freedom.' },
//   { token_str: 'life', score: 0.01859794743359089, token: 1297, sequence: 'The goal of life is life.' }
// ]

Example: Perform masked language modelling (a.k.a. “fill-mask”) with Xenova/bert-base-cased (and return top result).

const unmasker = await pipeline('fill-mask', 'Xenova/bert-base-cased');
const output = await unmasker('The Milky Way is a [MASK] galaxy.', { topk: 1 });
// [{ token_str: 'spiral', score: 0.6299987435340881, token: 14061, sequence: 'The Milky Way is a spiral galaxy.' }]

Kind: static class of pipelines

• .FillMaskPipeline
  • new FillMaskPipeline(options)
  • ._call() : FillMaskPipelineCallback

new FillMaskPipeline(options)

Create a new FillMaskPipeline.

fillMaskPipeline._call() : <code> FillMaskPipelineCallback </code>

Kind: instance method of FillMaskPipeline

pipelines.Text2TextGenerationPipeline

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

Example: Text-to-text generation w/ Xenova/LaMini-Flan-T5-783M.

const generator = await pipeline('text2text-generation', 'Xenova/LaMini-Flan-T5-783M');
const output = await generator('how can I become more healthy?', {
  max_new_tokens: 100,
});
// [{ generated_text: "To become more healthy, you can: 1. Eat a balanced diet with plenty of fruits, vegetables, whole grains, lean proteins, and healthy fats. 2. Stay hydrated by drinking plenty of water. 3. Get enough sleep and manage stress levels. 4. Avoid smoking and excessive alcohol consumption. 5. Regularly exercise and maintain a healthy weight. 6. Practice good hygiene and sanitation. 7. Seek medical attention if you experience any health issues." }]

Kind: static class of pipelines

• .Text2TextGenerationPipeline
  • new Text2TextGenerationPipeline(options)
  • ._key : ’generated_text’
  • ._call() : Text2TextGenerationPipelineCallback

new Text2TextGenerationPipeline(options)

Create a new Text2TextGenerationPipeline.

text2TextGenerationPipeline._key : <code> ’ generated_text ’ </code>

Kind: instance property of Text2TextGenerationPipeline

text2TextGenerationPipeline._call() : <code> Text2TextGenerationPipelineCallback </code>

Kind: instance method of Text2TextGenerationPipeline

pipelines.SummarizationPipeline

A pipeline for summarization tasks, inheriting from Text2TextGenerationPipeline.

Example: Summarization w/ Xenova/distilbart-cnn-6-6.

const generator = await pipeline('summarization', 'Xenova/distilbart-cnn-6-6');
const text = 'The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, ' +
  'and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. ' +
  'During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest ' +
  'man-made structure in the world, a title it held for 41 years until the Chrysler Building in New ' +
  'York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to ' +
  'the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the ' +
  'Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second ' +
  'tallest free-standing structure in France after the Millau Viaduct.';
const output = await generator(text, {
  max_new_tokens: 100,
});
// [{ summary_text: ' The Eiffel Tower is about the same height as an 81-storey building and the tallest structure in Paris. It is the second tallest free-standing structure in France after the Millau Viaduct.' }]

Kind: static class of pipelines

• .SummarizationPipeline
  • new SummarizationPipeline(options)
  • ._key : ’summary_text’

new SummarizationPipeline(options)

Create a new SummarizationPipeline.

summarizationPipeline._key : <code> ’ summary_text ’ </code>

Kind: instance property of SummarizationPipeline

pipelines.TranslationPipeline

Translates text from one language to another.

Example: Multilingual translation w/ Xenova/nllb-200-distilled-600M.

See here for the full list of languages and their corresponding codes.

const translator = await pipeline('translation', 'Xenova/nllb-200-distilled-600M');
const output = await translator('जीवन एक चॉकलेट बॉक्स की तरह है।', {
  src_lang: 'hin_Deva', // Hindi
  tgt_lang: 'fra_Latn', // French
});
// [{ translation_text: 'La vie est comme une boîte à chocolat.' }]

Example: Multilingual translation w/ Xenova/m2m100_418M.

See here for the full list of languages and their corresponding codes.

const translator = await pipeline('translation', 'Xenova/m2m100_418M');
const output = await translator('生活就像一盒巧克力。', {
  src_lang: 'zh', // Chinese
  tgt_lang: 'en', // English
});
// [{ translation_text: 'Life is like a box of chocolate.' }]

Example: Multilingual translation w/ Xenova/mbart-large-50-many-to-many-mmt.

See here for the full list of languages and their corresponding codes.

const translator = await pipeline('translation', 'Xenova/mbart-large-50-many-to-many-mmt');
const output = await translator('संयुक्त राष्ट्र के प्रमुख का कहना है कि सीरिया में कोई सैन्य समाधान नहीं है', {
  src_lang: 'hi_IN', // Hindi
  tgt_lang: 'fr_XX', // French
});
// [{ translation_text: 'Le chef des Nations affirme qu 'il n 'y a military solution in Syria.' }]

Kind: static class of pipelines

• .TranslationPipeline
  • new TranslationPipeline(options)
  • ._key : ’translation_text’

new TranslationPipeline(options)

Create a new TranslationPipeline.

translationPipeline._key : <code> ’ translation_text ’ </code>

Kind: instance property of TranslationPipeline

pipelines.TextGenerationPipeline

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).

const generator = await pipeline('text-generation', 'Xenova/distilgpt2');
const text = 'I enjoy walking with my cute dog,';
const output = await generator(text);
// [{ 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).

const generator = await pipeline('text-generation', 'Xenova/distilgpt2');
const text = 'Once upon a time, there was';
const output = await generator(text, {
  temperature: 2,
  max_new_tokens: 10,
  repetition_penalty: 1.5,
  no_repeat_ngram_size: 2,
  num_beams: 2,
  num_return_sequences: 2,
});
// [{
//   "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.

const generator = await pipeline('text-generation', 'Xenova/codegen-350M-mono');
const text = 'def fib(n):';
const output = await generator(text, {
  max_new_tokens: 44,
});
// [{
//   generated_text: 'def fib(n):\n' +
//     '    if n == 0:\n' +
//     '        return 0\n' +
//     '    elif n == 1:\n' +
//     '        return 1\n' +
//     '    else:\n' +
//     '        return fib(n-1) + fib(n-2)\n'
// }]

Kind: static class of pipelines

• .TextGenerationPipeline
  • new TextGenerationPipeline(options)
  • ._call() : TextGenerationPipelineCallback

new TextGenerationPipeline(options)

Create a new TextGenerationPipeline.

textGenerationPipeline._call() : <code> TextGenerationPipelineCallback </code>

Kind: instance method of TextGenerationPipeline

pipelines.ZeroShotClassificationPipeline

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.

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

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

• .ZeroShotClassificationPipeline
  • new ZeroShotClassificationPipeline(options)
  • .model : any
  • ._call() : ZeroShotClassificationPipelineCallback

new ZeroShotClassificationPipeline(options)

Create a new ZeroShotClassificationPipeline.

zeroShotClassificationPipeline.model : <code> any </code>

Kind: instance property of ZeroShotClassificationPipeline

zeroShotClassificationPipeline._call() : <code> ZeroShotClassificationPipelineCallback </code>

Kind: instance method of ZeroShotClassificationPipeline

pipelines.FeatureExtractionPipeline

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).

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

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

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

Example: Calculating embeddings with sentence-transformers models.

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

Example: Calculating binary embeddings with sentence-transformers models.

const extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2');
const output = await extractor('This is a simple test.', { pooling: 'mean', quantize: true, precision: 'binary' });
// Tensor {
//   type: 'int8',
//   data: Int8Array [49, 108, 24, ...],
//   dims: [1, 48]
// }

Kind: static class of pipelines

• .FeatureExtractionPipeline
  • new FeatureExtractionPipeline(options)
  • ._call() : FeatureExtractionPipelineCallback

new FeatureExtractionPipeline(options)

Create a new FeatureExtractionPipeline.

featureExtractionPipeline._call() : <code> FeatureExtractionPipelineCallback </code>

Kind: instance method of FeatureExtractionPipeline

pipelines.ImageFeatureExtractionPipeline

Image 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: Perform image feature extraction with Xenova/vit-base-patch16-224-in21k.

const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/vit-base-patch16-224-in21k');
const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png';
const features = await image_feature_extractor(url);
// Tensor {
//   dims: [ 1, 197, 768 ],
//   type: 'float32',
//   data: Float32Array(151296) [ ... ],
//   size: 151296
// }

Example: Compute image embeddings with Xenova/clip-vit-base-patch32.

const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/clip-vit-base-patch32');
const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png';
const features = await image_feature_extractor(url);
// Tensor {
//   dims: [ 1, 512 ],
//   type: 'float32',
//   data: Float32Array(512) [ ... ],
//   size: 512
// }

Kind: static class of pipelines

• .ImageFeatureExtractionPipeline
  • new ImageFeatureExtractionPipeline(options)
  • ._call() : ImageFeatureExtractionPipelineCallback

new ImageFeatureExtractionPipeline(options)

Create a new ImageFeatureExtractionPipeline.

imageFeatureExtractionPipeline._call() : <code> ImageFeatureExtractionPipelineCallback </code>

Kind: instance method of ImageFeatureExtractionPipeline

pipelines.AudioClassificationPipeline

Audio classification pipeline using any AutoModelForAudioClassification. This pipeline predicts the class of a raw waveform or an audio file.

Example: Perform audio classification with Xenova/wav2vec2-large-xlsr-53-gender-recognition-librispeech.

const classifier = await pipeline('audio-classification', 'Xenova/wav2vec2-large-xlsr-53-gender-recognition-librispeech');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const output = await classifier(url);
// [
//   { label: 'male', score: 0.9981542229652405 },
//   { label: 'female', score: 0.001845747814513743 }
// ]

Example: Perform audio classification with Xenova/ast-finetuned-audioset-10-10-0.4593 and return top 4 results.

const classifier = await pipeline('audio-classification', 'Xenova/ast-finetuned-audioset-10-10-0.4593');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cat_meow.wav';
const output = await classifier(url, { topk: 4 });
// [
//   { label: 'Meow', score: 0.5617874264717102 },
//   { label: 'Cat', score: 0.22365376353263855 },
//   { label: 'Domestic animals, pets', score: 0.1141069084405899 },
//   { label: 'Animal', score: 0.08985692262649536 },
// ]

Kind: static class of pipelines

• .AudioClassificationPipeline
  • new AudioClassificationPipeline(options)
  • ._call() : AudioClassificationPipelineCallback

new AudioClassificationPipeline(options)

Create a new AudioClassificationPipeline.

audioClassificationPipeline._call() : <code> AudioClassificationPipelineCallback </code>

Kind: instance method of AudioClassificationPipeline

pipelines.ZeroShotAudioClassificationPipeline

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

Example: Perform zero-shot audio classification with Xenova/clap-htsat-unfused.

const classifier = await pipeline('zero-shot-audio-classification', 'Xenova/clap-htsat-unfused');
const audio = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/dog_barking.wav';
const candidate_labels = ['dog', 'vaccum cleaner'];
const scores = await classifier(audio, candidate_labels);
// [
//   { score: 0.9993992447853088, label: 'dog' },
//   { score: 0.0006007603369653225, label: 'vaccum cleaner' }
// ]

Kind: static class of pipelines

• .ZeroShotAudioClassificationPipeline
  • new ZeroShotAudioClassificationPipeline(options)
  • ._call() : ZeroShotAudioClassificationPipelineCallback

new ZeroShotAudioClassificationPipeline(options)

Create a new ZeroShotAudioClassificationPipeline.

zeroShotAudioClassificationPipeline._call() : <code> ZeroShotAudioClassificationPipelineCallback </code>

Kind: instance method of ZeroShotAudioClassificationPipeline

pipelines.AutomaticSpeechRecognitionPipeline

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

Example: Transcribe English.

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const 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.

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const 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.

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/jfk.wav';
const 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.

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

Example: Translate French to English.

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-small');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/french-audio.mp3';
const 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.

const transcriber = await pipeline('automatic-speech-recognition', 'Xenova/whisper-tiny.en');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/ted_60.wav';
const 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

• .AutomaticSpeechRecognitionPipeline
  • new AutomaticSpeechRecognitionPipeline(options)
  • ._call() : AutomaticSpeechRecognitionPipelineCallback

new AutomaticSpeechRecognitionPipeline(options)

Create a new AutomaticSpeechRecognitionPipeline.

automaticSpeechRecognitionPipeline._call() : <code> AutomaticSpeechRecognitionPipelineCallback </code>

Kind: instance method of AutomaticSpeechRecognitionPipeline

pipelines.ImageToTextPipeline

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

Example: Generate a caption for an image w/ Xenova/vit-gpt2-image-captioning.

const captioner = await pipeline('image-to-text', 'Xenova/vit-gpt2-image-captioning');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const output = await captioner(url);
// [{ generated_text: 'a cat laying on a couch with another cat' }]

Example: Optical Character Recognition (OCR) w/ Xenova/trocr-small-handwritten.

const captioner = await pipeline('image-to-text', 'Xenova/trocr-small-handwritten');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/handwriting.jpg';
const output = await captioner(url);
// [{ generated_text: 'Mr. Brown commented icily.' }]

Kind: static class of pipelines

• .ImageToTextPipeline
  • new ImageToTextPipeline(options)
  • ._call() : ImageToTextPipelineCallback

new ImageToTextPipeline(options)

Create a new ImageToTextPipeline.

imageToTextPipeline._call() : <code> ImageToTextPipelineCallback </code>

Kind: instance method of ImageToTextPipeline

pipelines.ImageClassificationPipeline

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

Example: Classify an image.

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

Example: Classify an image and return top n classes.

const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url, { topk: 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.

const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url, { topk: 0 });
// [
//   { 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

• .ImageClassificationPipeline
  • new ImageClassificationPipeline(options)
  • ._call() : ImageClassificationPipelineCallback

new ImageClassificationPipeline(options)

Create a new ImageClassificationPipeline.

imageClassificationPipeline._call() : <code> ImageClassificationPipelineCallback </code>

Kind: instance method of ImageClassificationPipeline

pipelines.ImageSegmentationPipeline

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

Example: Perform image segmentation with Xenova/detr-resnet-50-panoptic.

const segmenter = await pipeline('image-segmentation', 'Xenova/detr-resnet-50-panoptic');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const output = await segmenter(url);
// [
//   { label: 'remote', score: 0.9984649419784546, mask: RawImage { ... } },
//   { label: 'cat', score: 0.9994316101074219, mask: RawImage { ... } }
// ]

Kind: static class of pipelines

• .ImageSegmentationPipeline
  • new ImageSegmentationPipeline(options)
  • ._call() : ImageSegmentationPipelineCallback

new ImageSegmentationPipeline(options)

Create a new ImageSegmentationPipeline.

imageSegmentationPipeline._call() : <code> ImageSegmentationPipelineCallback </code>

Kind: instance method of ImageSegmentationPipeline

pipelines.ZeroShotImageClassificationPipeline

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

Example: Zero shot image classification w/ Xenova/clip-vit-base-patch32.

const classifier = await pipeline('zero-shot-image-classification', 'Xenova/clip-vit-base-patch32');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url, ['tiger', 'horse', 'dog']);
// [
//   { score: 0.9993917942047119, label: 'tiger' },
//   { score: 0.0003519294841680676, label: 'horse' },
//   { score: 0.0002562698791734874, label: 'dog' }
// ]

Kind: static class of pipelines

• .ZeroShotImageClassificationPipeline
  • new ZeroShotImageClassificationPipeline(options)
  • ._call() : ZeroShotImageClassificationPipelineCallback

new ZeroShotImageClassificationPipeline(options)

Create a new ZeroShotImageClassificationPipeline.

zeroShotImageClassificationPipeline._call() : <code> ZeroShotImageClassificationPipelineCallback </code>

Kind: instance method of ZeroShotImageClassificationPipeline

pipelines.ObjectDetectionPipeline

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

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

const detector = await pipeline('object-detection', 'Xenova/detr-resnet-50');
const img = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const 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

• .ObjectDetectionPipeline
  • new ObjectDetectionPipeline(options)
  • ._call() : ObjectDetectionPipelineCallback

new ObjectDetectionPipeline(options)

Create a new ObjectDetectionPipeline.

objectDetectionPipeline._call() : <code> ObjectDetectionPipelineCallback </code>

Kind: instance method of ObjectDetectionPipeline

pipelines.ZeroShotObjectDetectionPipeline

Zero-shot object detection pipeline. This pipeline predicts bounding boxes of objects when you provide an image and a set of candidate_labels.

Example: Zero-shot object detection w/ Xenova/owlvit-base-patch32.

const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/astronaut.png';
const candidate_labels = ['human face', 'rocket', 'helmet', 'american flag'];
const output = await detector(url, candidate_labels);
// [
//   {
//     score: 0.24392342567443848,
//     label: 'human face',
//     box: { xmin: 180, ymin: 67, xmax: 274, ymax: 175 }
//   },
//   {
//     score: 0.15129457414150238,
//     label: 'american flag',
//     box: { xmin: 0, ymin: 4, xmax: 106, ymax: 513 }
//   },
//   {
//     score: 0.13649864494800568,
//     label: 'helmet',
//     box: { xmin: 277, ymin: 337, xmax: 511, ymax: 511 }
//   },
//   {
//     score: 0.10262022167444229,
//     label: 'rocket',
//     box: { xmin: 352, ymin: -1, xmax: 463, ymax: 287 }
//   }
// ]

Example: Zero-shot object detection w/ Xenova/owlvit-base-patch32 (returning top 4 matches and setting a threshold).

const detector = await pipeline('zero-shot-object-detection', 'Xenova/owlvit-base-patch32');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/beach.png';
const candidate_labels = ['hat', 'book', 'sunglasses', 'camera'];
const output = await detector(url, candidate_labels, { topk: 4, threshold: 0.05 });
// [
//   {
//     score: 0.1606510728597641,
//     label: 'sunglasses',
//     box: { xmin: 347, ymin: 229, xmax: 429, ymax: 264 }
//   },
//   {
//     score: 0.08935828506946564,
//     label: 'hat',
//     box: { xmin: 38, ymin: 174, xmax: 258, ymax: 364 }
//   },
//   {
//     score: 0.08530698716640472,
//     label: 'camera',
//     box: { xmin: 187, ymin: 350, xmax: 260, ymax: 411 }
//   },
//   {
//     score: 0.08349756896495819,
//     label: 'book',
//     box: { xmin: 261, ymin: 280, xmax: 494, ymax: 425 }
//   }
// ]

Kind: static class of pipelines

• .ZeroShotObjectDetectionPipeline
  • new ZeroShotObjectDetectionPipeline(options)
  • ._call() : ZeroShotObjectDetectionPipelineCallback

new ZeroShotObjectDetectionPipeline(options)

Create a new ZeroShotObjectDetectionPipeline.

zeroShotObjectDetectionPipeline._call() : <code> ZeroShotObjectDetectionPipelineCallback </code>

Kind: instance method of ZeroShotObjectDetectionPipeline

pipelines.DocumentQuestionAnsweringPipeline

Document Question Answering pipeline using any AutoModelForDocumentQuestionAnswering. The inputs/outputs are similar to the (extractive) question answering pipeline; however, the pipeline takes an image (and optional OCR’d words/boxes) as input instead of text context.

Example: Answer questions about a document with Xenova/donut-base-finetuned-docvqa.

const qa_pipeline = await pipeline('document-question-answering', 'Xenova/donut-base-finetuned-docvqa');
const image = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/invoice.png';
const question = 'What is the invoice number?';
const output = await qa_pipeline(image, question);
// [{ answer: 'us-001' }]

Kind: static class of pipelines

• .DocumentQuestionAnsweringPipeline
  • new DocumentQuestionAnsweringPipeline(options)
  • ._call() : DocumentQuestionAnsweringPipelineCallback

new DocumentQuestionAnsweringPipeline(options)

Create a new DocumentQuestionAnsweringPipeline.

documentQuestionAnsweringPipeline._call() : <code> DocumentQuestionAnsweringPipelineCallback </code>

Kind: instance method of DocumentQuestionAnsweringPipeline

pipelines.TextToAudioPipeline

Text-to-audio generation pipeline using any AutoModelForTextToWaveform or AutoModelForTextToSpectrogram. This pipeline generates an audio file from an input text and optional other conditional inputs.

Example: Generate audio from text with Xenova/speecht5_tts.

const synthesizer = await pipeline('text-to-speech', 'Xenova/speecht5_tts', { quantized: false });
const speaker_embeddings = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/speaker_embeddings.bin';
const out = await synthesizer('Hello, my dog is cute', { speaker_embeddings });
// {
//   audio: Float32Array(26112) [-0.00005657337896991521, 0.00020583874720614403, ...],
//   sampling_rate: 16000
// }

You can then save the audio to a .wav file with the wavefile package:

import wavefile from 'wavefile';
import fs from 'fs';

const wav = new wavefile.WaveFile();
wav.fromScratch(1, out.sampling_rate, '32f', out.audio);
fs.writeFileSync('out.wav', wav.toBuffer());

Example: Multilingual speech generation with Xenova/mms-tts-fra. See here for the full list of available languages (1107).

const synthesizer = await pipeline('text-to-speech', 'Xenova/mms-tts-fra');
const out = await synthesizer('Bonjour');
// {
//   audio: Float32Array(23808) [-0.00037693005288019776, 0.0003325853613205254, ...],
//   sampling_rate: 16000
// }

Kind: static class of pipelines

• .TextToAudioPipeline
  • new TextToAudioPipeline(options)
  • ._call() : TextToAudioPipelineCallback

new TextToAudioPipeline(options)

Create a new TextToAudioPipeline.

textToAudioPipeline._call() : <code> TextToAudioPipelineCallback </code>

Kind: instance method of TextToAudioPipeline

pipelines.ImageToImagePipeline

Image to Image pipeline using any AutoModelForImageToImage. This pipeline generates an image based on a previous image input.

Example: Super-resolution w/ Xenova/swin2SR-classical-sr-x2-64

const upscaler = await pipeline('image-to-image', 'Xenova/swin2SR-classical-sr-x2-64');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/butterfly.jpg';
const output = await upscaler(url);
// RawImage {
//   data: Uint8Array(786432) [ 41, 31, 24,  43, ... ],
//   width: 512,
//   height: 512,
//   channels: 3
// }

Kind: static class of pipelines

• .ImageToImagePipeline
  • new ImageToImagePipeline(options)
  • ._call() : ImageToImagePipelineCallback

new ImageToImagePipeline(options)

Create a new ImageToImagePipeline.

imageToImagePipeline._call() : <code> ImageToImagePipelineCallback </code>

Kind: instance method of ImageToImagePipeline

pipelines.DepthEstimationPipeline

Depth estimation pipeline using any AutoModelForDepthEstimation. This pipeline predicts the depth of an image.

Example: Depth estimation w/ Xenova/dpt-hybrid-midas

const depth_estimator = await pipeline('depth-estimation', 'Xenova/dpt-hybrid-midas');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const out = await depth_estimator(url);
// {
//   predicted_depth: Tensor {
//     dims: [ 384, 384 ],
//     type: 'float32',
//     data: Float32Array(147456) [ 542.859130859375, 545.2833862304688, 546.1649169921875, ... ],
//     size: 147456
//   },
//   depth: RawImage {
//     data: Uint8Array(307200) [ 86, 86, 86, ... ],
//     width: 640,
//     height: 480,
//     channels: 1
//   }
// }

Kind: static class of pipelines

• .DepthEstimationPipeline
  • new DepthEstimationPipeline(options)
  • ._call() : DepthEstimationPipelineCallback

new DepthEstimationPipeline(options)

Create a new DepthEstimationPipeline.

depthEstimationPipeline._call() : <code> DepthEstimationPipelineCallback </code>

Kind: instance method of DepthEstimationPipeline

pipelines.pipeline(task, [model], [options]) ⇒ <code> * </code>

Utility factory method to build a Pipeline object.

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

• Error If an unsupported pipeline is requested.

pipelines~x[1] : <code> number </code>

Kind: inner property of pipelines

pipelines~ImagePipelineInputs : <code> string </code> | <code> RawImage </code> | <code> URL </code>

Kind: inner typedef of pipelines

pipelines~AudioPipelineInputs : <code> string </code> | <code> URL </code> | <code> Float32Array </code> | <code> Float64Array </code>

Kind: inner typedef of pipelines

pipelines~BoundingBox : <code> Object </code>

Kind: inner typedef of pipelines
Properties

pipelines~Disposable ⇒ <code> Promise. < void > </code>

Kind: inner typedef of pipelines
Returns: Promise.<void> - A promise that resolves when the item has been disposed.
Properties

pipelines~TextPipelineConstructorArgs : <code> Object </code>

An object used to instantiate a text-based pipeline.

Kind: inner typedef of pipelines
Properties

pipelines~ImagePipelineConstructorArgs : <code> Object </code>

An object used to instantiate an audio-based pipeline.

Kind: inner typedef of pipelines
Properties

pipelines~TextImagePipelineConstructorArgs : <code> Object </code>

An object used to instantiate a text- and audio-based pipeline.

Kind: inner typedef of pipelines
Properties

pipelines~TextClassificationPipelineType ⇒ <code> Promise. < (TextClassificationOutput|Array < TextClassificationOutput > ) > </code>

Parameters specific to text classification pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(TextClassificationOutput|Array<TextClassificationOutput>)> - An array or object containing the predicted labels and scores.

Properties

pipelines~TokenClassificationPipelineType ⇒ <code> Promise. < (TokenClassificationOutput|Array < TokenClassificationOutput > ) > </code>

Parameters specific to token classification pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(TokenClassificationOutput|Array<TokenClassificationOutput>)> - The result.

Properties

pipelines~QuestionAnsweringPipelineType ⇒ <code> Promise. < (QuestionAnsweringOutput|Array < QuestionAnsweringOutput > ) > </code>

Parameters specific to question answering pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(QuestionAnsweringOutput|Array<QuestionAnsweringOutput>)> - An array or object containing the predicted answers and scores.

Properties

pipelines~FillMaskPipelineType ⇒ <code> Promise. < (FillMaskOutput|Array < FillMaskOutput > ) > </code>

Parameters specific to fill mask pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(FillMaskOutput|Array<FillMaskOutput>)> - 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:

• Error When the mask token is not found in the input text.

Properties

pipelines~Text2TextGenerationPipelineType ⇒ <code> Promise. < (Text2TextGenerationOutput|Array < Text2TextGenerationOutput > ) > </code>

Kind: inner typedef of pipelines

Properties

pipelines~SummarizationPipelineType ⇒ <code> Promise. < (SummarizationOutput|Array < SummarizationOutput > ) > </code>

Kind: inner typedef of pipelines

Properties

pipelines~TranslationPipelineType ⇒ <code> Promise. < (TranslationOutput|Array < TranslationOutput > ) > </code>

Kind: inner typedef of pipelines

Properties

pipelines~TextGenerationPipelineType ⇒ <code> Promise. < (TextGenerationOutput|Array < TextGenerationOutput > ) > </code>

Parameters specific to text-generation pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(TextGenerationOutput|Array<TextGenerationOutput>)> - An array or object containing the generated texts.

Properties

pipelines~ZeroShotClassificationPipelineType ⇒ <code> Promise. < (ZeroShotClassificationOutput|Array < ZeroShotClassificationOutput > ) > </code>

Parameters specific to zero-shot classification pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(ZeroShotClassificationOutput|Array<ZeroShotClassificationOutput>)> - An array or object containing the predicted labels and scores.

Properties

pipelines~FeatureExtractionPipelineType ⇒ <code> Promise. < Tensor > </code>

Parameters specific to feature extraction pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<Tensor> - The features computed by the model.

Properties

pipelines~ImageFeatureExtractionPipelineType ⇒ <code> Promise. < Tensor > </code>

Parameters specific to image feature extraction pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<Tensor> - The image features computed by the model.

Properties

pipelines~AudioClassificationPipelineType ⇒ <code> Promise. < (AudioClassificationOutput|Array < AudioClassificationOutput > ) > </code>

Parameters specific to audio classification pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(AudioClassificationOutput|Array<AudioClassificationOutput>)> - An array or object containing the predicted labels and scores.

Properties

pipelines~ZeroShotAudioClassificationPipelineType ⇒ <code> Promise. < (Array < ZeroShotAudioClassificationOutput > |Array < Array < ZeroShotAudioClassificationOutput > > ) > </code>

Parameters specific to zero-shot audio classification pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(Array<ZeroShotAudioClassificationOutput>|Array<Array<ZeroShotAudioClassificationOutput>>)> - An array of objects containing the predicted labels and scores.

Properties

pipelines~ChunkCallback : <code> function </code>

Kind: inner typedef of pipelines

pipelines~Chunk : <code> Object </code>

Kind: inner typedef of pipelines
Properties

pipelines~AutomaticSpeechRecognitionPipelineType ⇒ <code> Promise. < (AutomaticSpeechRecognitionOutput|Array < AutomaticSpeechRecognitionOutput > ) > </code>

Parameters specific to automatic-speech-recognition pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(AutomaticSpeechRecognitionOutput|Array<AutomaticSpeechRecognitionOutput>)> - An object containing the transcription text and optionally timestamps if return_timestamps is true.

Properties

pipelines~ImageToTextPipelineType ⇒ <code> Promise. < (ImageToTextOutput|Array < ImageToTextOutput > ) > </code>

Kind: inner typedef of pipelines
Returns: Promise.<(ImageToTextOutput|Array<ImageToTextOutput>)> - An object (or array of objects) containing the generated text(s).

Properties

pipelines~ImageClassificationPipelineType ⇒ <code> Promise. < (ImageClassificationOutput|Array < ImageClassificationOutput > ) > </code>

Parameters specific to image classification pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(ImageClassificationOutput|Array<ImageClassificationOutput>)> - An array or object containing the predicted labels and scores.

Properties

pipelines~ImageSegmentationPipelineType ⇒ <code> Promise. < Array < ImageSegmentationPipelineOutput > > </code>

Parameters specific to image segmentation pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<Array<ImageSegmentationPipelineOutput>> - The annotated segments.

Properties

pipelines~ZeroShotImageClassificationPipelineType ⇒ <code> Promise. < (Array < ZeroShotImageClassificationOutput > |Array < Array < ZeroShotImageClassificationOutput > > ) > </code>

Parameters specific to zero-shot image classification pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(Array<ZeroShotImageClassificationOutput>|Array<Array<ZeroShotImageClassificationOutput>>)> - An array of objects containing the predicted labels and scores.

Properties

pipelines~ObjectDetectionPipelineType ⇒ <code> Promise. < (ObjectDetectionPipelineOutput|Array < ObjectDetectionPipelineOutput > ) > </code>

Parameters specific to object detection pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(ObjectDetectionPipelineOutput|Array<ObjectDetectionPipelineOutput>)> - A list of objects or a list of list of objects.

Properties

pipelines~ZeroShotObjectDetectionPipelineType ⇒ <code> Promise. < (Array < ZeroShotObjectDetectionOutput > |Array < Array < ZeroShotObjectDetectionOutput > > ) > </code>

Parameters specific to zero-shot object detection pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<(Array<ZeroShotObjectDetectionOutput>|Array<Array<ZeroShotObjectDetectionOutput>>)> - An array of objects containing the predicted labels, scores, and bounding boxes.

Properties

pipelines~DocumentQuestionAnsweringPipelineType ⇒ <code> Promise. < (DocumentQuestionAnsweringOutput|Array < DocumentQuestionAnsweringOutput > ) > </code>

Kind: inner typedef of pipelines
Returns: Promise.<(DocumentQuestionAnsweringOutput|Array<DocumentQuestionAnsweringOutput>)> - An object (or array of objects) containing the answer(s).

Properties

pipelines~TextToAudioPipelineConstructorArgs : <code> Object </code>

Kind: inner typedef of pipelines
Properties

pipelines~TextToAudioPipelineType ⇒ <code> Promise. < TextToAudioOutput > </code>

Parameters specific to text-to-audio pipelines.

Kind: inner typedef of pipelines
Returns: Promise.<TextToAudioOutput> - An object containing the generated audio and sampling rate.

Properties

pipelines~ImageToImagePipelineType ⇒ <code> Promise. < (RawImage|Array < RawImage > ) > </code>

Kind: inner typedef of pipelines
Returns: Promise.<(RawImage|Array<RawImage>)> - The transformed image or list of images.

pipelines~DepthEstimationPipelineType ⇒ <code> Promise. < (DepthEstimationPipelineOutput|Array < DepthEstimationPipelineOutput > ) > </code>

Kind: inner typedef of pipelines
Returns: Promise.<(DepthEstimationPipelineOutput|Array<DepthEstimationPipelineOutput>)> - An image or a list of images containing result(s).

Properties

pipelines~AllTasks : <code> * </code>

All possible pipeline types.

Kind: inner typedef of pipelines