Transformers.js 文档
pipelines
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pipelines
Pipelines 提供了一个高级、易于使用的 API,用于运行机器学习模型。
示例: 使用 pipeline
函数实例化 pipeline。
import { pipeline } from '@huggingface/transformers';
const classifier = await pipeline('sentiment-analysis');
const output = await classifier('I love transformers!');
// [{'label': 'POSITIVE', 'score': 0.999817686}]
- pipelines
- 静态
- .Pipeline
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
- .BackgroundRemovalPipeline
new BackgroundRemovalPipeline(options)
._call()
:BackgroundRemovalPipelineCallback
- .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])
⇒*
- .Pipeline
- inner
~ImagePipelineInputs
:string
|RawImage
|URL
|Blob
|HTMLCanvasElement
|OffscreenCanvas
~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>>)>
~Chunk
:Object
~AutomaticSpeechRecognitionPipelineType
⇒Promise.<(AutomaticSpeechRecognitionOutput|Array<AutomaticSpeechRecognitionOutput>)>
~ImageToTextPipelineType
⇒Promise.<(ImageToTextOutput|Array<ImageToTextOutput>)>
~ImageClassificationPipelineType
⇒Promise.<(ImageClassificationOutput|Array<ImageClassificationOutput>)>
~ImageSegmentationPipelineType
⇒Promise.<Array<ImageSegmentationPipelineOutput>>
~BackgroundRemovalPipelineType
⇒Promise.<Array<RawImage>>
~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
:*
- 静态
pipelines.Pipeline
Pipeline 类是所有 pipeline 继承的类。请参阅此类以获取不同 pipeline 之间共享的方法。
Kind: pipelines
的静态类
- .Pipeline
new Pipeline(options)
.dispose()
:DisposeType
new Pipeline(options)
创建一个新的 Pipeline。
参数 | 类型 | 默认值 | 描述 |
---|---|---|---|
options | Object | 包含以下属性的对象 | |
[options.task] | string | pipeline 的任务。用于指定子任务。 | |
[options.model] | PreTrainedModel | pipeline 使用的模型。 | |
[options.tokenizer] | PreTrainedTokenizer |
| pipeline 使用的分词器(如果有)。 |
[options.processor] | Processor |
| pipeline 使用的处理器(如果有)。 |
pipeline.dispose() : <code> DisposeType </code>
Kind: Pipeline
的实例方法
pipelines.TextClassificationPipeline
文本分类 pipeline,使用任何 ModelForSequenceClassification
。
示例: 使用 Xenova/distilbert-base-uncased-finetuned-sst-2-english
进行情感分析。
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 }]
示例: 使用 Xenova/bert-base-multilingual-uncased-sentiment
进行多语言情感分析(并返回前 5 个类别)。
const classifier = await pipeline('sentiment-analysis', 'Xenova/bert-base-multilingual-uncased-sentiment');
const output = await classifier('Le meilleur film de tous les temps.', { top_k: 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 }
// ]
示例: 使用 Xenova/toxic-bert
进行有毒评论分类(并返回所有类别)。
const classifier = await pipeline('text-classification', 'Xenova/toxic-bert');
const output = await classifier('I hate you!', { top_k: 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: pipelines
的静态类
- .TextClassificationPipeline
new TextClassificationPipeline(options)
._call()
:TextClassificationPipelineCallback
new TextClassificationPipeline(options)
创建一个新的 TextClassificationPipeline。
参数 | 类型 | 描述 |
---|---|---|
options | TextPipelineConstructorArgs | 用于实例化 pipeline 的对象。 |
textClassificationPipeline._call() : <code> TextClassificationPipelineCallback </code>
Kind: TextClassificationPipeline
的实例方法
pipelines.TokenClassificationPipeline
命名实体识别 pipeline,使用任何 ModelForTokenClassification
。
示例: 使用 Xenova/bert-base-NER
执行命名实体识别。
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' }
// ]
示例: 使用 Xenova/bert-base-NER
执行命名实体识别(并返回所有标签)。
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: pipelines
的静态类
- .TokenClassificationPipeline
new TokenClassificationPipeline(options)
._call()
:TokenClassificationPipelineCallback
new TokenClassificationPipeline(options)
创建一个新的 TokenClassificationPipeline。
参数 | 类型 | 描述 |
---|---|---|
options | TextPipelineConstructorArgs | 用于实例化 pipeline 的对象。 |
tokenClassificationPipeline._call() : <code> TokenClassificationPipelineCallback </code>
Kind: TokenClassificationPipeline
的实例方法
pipelines.QuestionAnsweringPipeline
问答 pipeline,使用任何 ModelForQuestionAnswering
。
示例: 使用 Xenova/distilbert-base-uncased-distilled-squad
运行问答。
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: pipelines
的静态类
- .QuestionAnsweringPipeline
new QuestionAnsweringPipeline(options)
._call()
:QuestionAnsweringPipelineCallback
new QuestionAnsweringPipeline(options)
创建一个新的 QuestionAnsweringPipeline。
参数 | 类型 | 描述 |
---|---|---|
options | TextPipelineConstructorArgs | 用于实例化 pipeline 的对象。 |