chromadb-default-embed/types/pipelines.d.ts

Chroma's fork of @xenova/transformers serving as our default embedding function

/**
 * @typedef {Object} BoundingBox
 * @property {number} xmin The minimum x coordinate of the bounding box.
 * @property {number} ymin The minimum y coordinate of the bounding box.
 * @property {number} xmax The maximum x coordinate of the bounding box.
 * @property {number} ymax The maximum y coordinate of the bounding box.
 */
/**
 * Helper function to convert list [xmin, xmax, ymin, ymax] into object { "xmin": xmin, ... }
 * @param {number[]} box The bounding box as a list.
 * @param {boolean} asInteger Whether to cast to integers.
 * @returns {BoundingBox} The bounding box as an object.
 */
export function get_bounding_box(box: number[], asInteger: boolean): BoundingBox;
/**
 * @typedef {keyof typeof SUPPORTED_TASKS} TaskType
 * @typedef {keyof typeof TASK_ALIASES} AliasType
 * @typedef {TaskType | AliasType} PipelineType All possible pipeline types.
 * @typedef {{[K in TaskType]: InstanceType<typeof SUPPORTED_TASKS[K]["pipeline"]>}} SupportedTasks A mapping of pipeline names to their corresponding pipeline classes.
 * @typedef {{[K in AliasType]: InstanceType<typeof SUPPORTED_TASKS[TASK_ALIASES[K]]["pipeline"]>}} AliasTasks A mapping from pipeline aliases to their corresponding pipeline classes.
 * @typedef {SupportedTasks & AliasTasks} AllTasks A mapping from all pipeline names and aliases to their corresponding pipeline classes.
 */
/**
 * Utility factory method to build a `Pipeline` object.
 *
 * @template {PipelineType} T The type of pipeline to return.
 * @param {T} task The task defining which pipeline will be returned. Currently accepted tasks are:
 *  - `"audio-classification"`: will return a `AudioClassificationPipeline`.
 *  - `"automatic-speech-recognition"`: will return a `AutomaticSpeechRecognitionPipeline`.
 *  - `"depth-estimation"`: will return a `DepthEstimationPipeline`.
 *  - `"document-question-answering"`: will return a `DocumentQuestionAnsweringPipeline`.
 *  - `"feature-extraction"`: will return a `FeatureExtractionPipeline`.
 *  - `"fill-mask"`: will return a `FillMaskPipeline`.
 *  - `"image-classification"`: will return a `ImageClassificationPipeline`.
 *  - `"image-segmentation"`: will return a `ImageSegmentationPipeline`.
 *  - `"image-to-text"`: will return a `ImageToTextPipeline`.
 *  - `"object-detection"`: will return a `ObjectDetectionPipeline`.
 *  - `"question-answering"`: will return a `QuestionAnsweringPipeline`.
 *  - `"summarization"`: will return a `SummarizationPipeline`.
 *  - `"text2text-generation"`: will return a `Text2TextGenerationPipeline`.
 *  - `"text-classification"` (alias "sentiment-analysis" available): will return a `TextClassificationPipeline`.
 *  - `"text-generation"`: will return a `TextGenerationPipeline`.
 *  - `"token-classification"` (alias "ner" available): will return a `TokenClassificationPipeline`.
 *  - `"translation"`: will return a `TranslationPipeline`.
 *  - `"translation_xx_to_yy"`: will return a `TranslationPipeline`.
 *  - `"zero-shot-classification"`: will return a `ZeroShotClassificationPipeline`.
 *  - `"zero-shot-audio-classification"`: will return a `ZeroShotAudioClassificationPipeline`.
 *  - `"zero-shot-image-classification"`: will return a `ZeroShotImageClassificationPipeline`.
 *  - `"zero-shot-object-detection"`: will return a `ZeroShotObjectDetectionPipeline`.
 * @param {string} [model=null] The name of the pre-trained model to use. If not specified, the default model for the task will be used.
 * @param {import('./utils/hub.js').PretrainedOptions} [options] Optional parameters for the pipeline.
 * @returns {Promise<AllTasks[T]>} A Pipeline object for the specified task.
 * @throws {Error} If an unsupported pipeline is requested.
 */
export function pipeline<T extends PipelineType>(task: T, model?: string, { quantized, progress_callback, config, cache_dir, local_files_only, revision, }?: import('./utils/hub.js').PretrainedOptions): Promise<AllTasks[T]>;
declare const Pipeline_base: new () => {
    (...args: any[]): any;
    _call(...args: any[]): any;
};
/**
 * The Pipeline class is the class from which all pipelines inherit.
 * Refer to this class for methods shared across different pipelines.
 * @extends Callable
 */
export class Pipeline extends Pipeline_base {
    /**
     * Create a new Pipeline.
     * @param {Object} options An object containing the following properties:
     * @param {string} [options.task] The task of the pipeline. Useful for specifying subtasks.
     * @param {PreTrainedModel} [options.model] The model to use.
     * @param {PreTrainedTokenizer} [options.tokenizer=null] The tokenizer to use (if any).
     * @param {Processor} [options.processor=null] The processor to use (if any).
     */
    constructor({ task, model, tokenizer, processor }: {
        task?: string;
        model?: PreTrainedModel;
        tokenizer?: PreTrainedTokenizer;
        processor?: Processor;
    });
    task: string;
    model: PreTrainedModel;
    tokenizer: PreTrainedTokenizer;
    processor: Processor;
    /**
     * Disposes the model.
     * @returns {Promise<void>} A promise that resolves when the model has been disposed.
     */
    dispose(): Promise<void>;
}
declare const TextClassificationPipeline_base: new (options: TextPipelineConstructorArgs) => TextClassificationPipelineCallback;
/**
 * @typedef {Object} ModelTokenizerConstructorArgs
 * @property {string} task The task of the pipeline. Useful for specifying subtasks.
 * @property {PreTrainedModel} model The model to use.
 * @property {PreTrainedTokenizer} tokenizer The tokenizer to use.
 *
 * @typedef {ModelTokenizerConstructorArgs} TextPipelineConstructorArgs An object used to instantiate a text-based pipeline.
 */
/**
 * @typedef {Object} ModelProcessorConstructorArgs
 * @property {string} task The task of the pipeline. Useful for specifying subtasks.
 * @property {PreTrainedModel} model The model to use.
 * @property {Processor} processor The processor to use.
 *
 * @typedef {ModelProcessorConstructorArgs} AudioPipelineConstructorArgs An object used to instantiate an audio-based pipeline.
 * @typedef {ModelProcessorConstructorArgs} ImagePipelineConstructorArgs An object used to instantiate an image-based pipeline.
 */
/**
 * @typedef {Object} ModelTokenizerProcessorConstructorArgs
 * @property {string} task The task of the pipeline. Useful for specifying subtasks.
 * @property {PreTrainedModel} model The model to use.
 * @property {PreTrainedTokenizer} tokenizer The tokenizer to use.
 * @property {Processor} processor The processor to use.
 *
 * @typedef {ModelTokenizerProcessorConstructorArgs} TextAudioPipelineConstructorArgs An object used to instantiate a text- and audio-based pipeline.
 * @typedef {ModelTokenizerProcessorConstructorArgs} TextImagePipelineConstructorArgs An object used to instantiate a text- and image-based pipeline.
 */
/**
 * @typedef {Object} TextClassificationSingle
 * @property {string} label The label predicted.
 * @property {number} score The corresponding probability.
 * @typedef {TextClassificationSingle[]} TextClassificationOutput
 *
 * @typedef {Object} TextClassificationPipelineOptions Parameters specific to text classification pipelines.
 * @property {number} [topk=1] The number of top predictions to be returned.
 *
 * @callback TextClassificationPipelineCallback Classify the text(s) given as inputs.
 * @param {string|string[]} texts The input text(s) to be classified.
 * @param {TextClassificationPipelineOptions} [options] The options to use for text classification.
 * @returns {Promise<TextClassificationOutput|TextClassificationOutput[]>} An array or object containing the predicted labels and scores.
 */
/**
 * Text classification pipeline using any `ModelForSequenceClassification`.
 *
 * **Example:** Sentiment-analysis w/ `Xenova/distilbert-base-uncased-finetuned-sst-2-english`.
 * ```javascript
 * 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).
 * ```javascript
 * 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).
 * ```javascript
 * 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 }
 * // ]
 * ```
 */
export class TextClassificationPipeline extends TextClassificationPipeline_base {
    _call(texts: string | string[], options?: TextClassificationPipelineOptions): Promise<TextClassificationOutput | TextClassificationOutput[]>;
}
declare const TokenClassificationPipeline_base: new (options: TextPipelineConstructorArgs) => TokenClassificationPipelineCallback;
/**
 * @typedef {Object} TokenClassificationSingle
 * @property {string} word The token/word classified. This is obtained by decoding the selected tokens.
 * @property {number} score The corresponding probability for `entity`.
 * @property {string} entity The entity predicted for that token/word.
 * @property {number} index The index of the corresponding token in the sentence.
 * @property {number} [start] The index of the start of the corresponding entity in the sentence.
 * @property {number} [end] The index of the end of the corresponding entity in the sentence.
 * @typedef {TokenClassificationSingle[]} TokenClassificationOutput
 *
 * @typedef {Object} TokenClassificationPipelineOptions Parameters specific to token classification pipelines.
 * @property {string[]} [ignore_labels] A list of labels to ignore.
 *
 * @callback TokenClassificationPipelineCallback Classify each token of the text(s) given as inputs.
 * @param {string|string[]} texts One or several texts (or one list of texts) for token classification.
 * @param {TokenClassificationPipelineOptions} [options] The options to use for token classification.
 * @returns {Promise<TokenClassificationOutput|TokenClassificationOutput[]>} The result.
 */
/**
 * Named Entity Recognition pipeline using any `ModelForTokenClassification`.
 *
 * **Example:** Perform named entity recognition with `Xenova/bert-base-NER`.
 * ```javascript
 * 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).
 * ```javascript
 * 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' }
 * // ]
 * ```
 */
export class TokenClassificationPipeline extends TokenClassificationPipeline_base {
    _call(texts: string | string[], options?: TokenClassificationPipelineOptions): Promise<TokenClassificationOutput | TokenClassificationOutput[]>;
}
declare const QuestionAnsweringPipeline_base: new (options: TextPipelineConstructorArgs) => QuestionAnsweringPipelineCallback;
/**
 * @typedef {Object} QuestionAnsweringOutput
 * @property {number} score The probability associated to the answer.
 * @property {number} [start] The character start index of the answer (in the tokenized version of the input).
 * @property {number} [end] The character end index of the answer (in the tokenized version of the input).
 * @property {string} answer The answer to the question.
 *
 * @typedef {Object} QuestionAnsweringPipelineOptions Parameters specific to question answering pipelines.
 * @property {number} [topk=1] The number of top answer predictions to be returned.
 *
 * @callback QuestionAnsweringPipelineCallback Answer the question(s) given as inputs by using the context(s).
 * @param {string|string[]} question One or several question(s) (must be used in conjunction with the `context` argument).
 * @param {string|string[]} context One or several context(s) associated with the question(s) (must be used in conjunction with the `question` argument).
 * @param {QuestionAnsweringPipelineOptions} [options] The options to use for question answering.
 * @returns {Promise<QuestionAnsweringOutput|QuestionAnsweringOutput[]>} An array or object containing the predicted answers and scores.
 */
/**
 * Question Answering pipeline using any `ModelForQuestionAnswering`.
 *
 * **Example:** Run question answering with `Xenova/distilbert-base-uncased-distilled-squad`.
 * ```javascript
 * 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
 * // }
 * ```
 */
export class QuestionAnsweringPipeline extends QuestionAnsweringPipeline_base {
    _call(question: string | string[], context: string | string[], options?: QuestionAnsweringPipelineOptions): Promise<QuestionAnsweringOutput | QuestionAnsweringOutput[]>;
}
declare const FillMaskPipeline_base: new (options: TextPipelineConstructorArgs) => FillMaskPipelineCallback;
/**
 * @typedef {Object} FillMaskSingle
 * @property {string} sequence The corresponding input with the mask token prediction.
 * @property {number} score The corresponding probability.
 * @property {number} token The predicted token id (to replace the masked one).
 * @property {string} token_str The predicted token (to replace the masked one).
 * @typedef {FillMaskSingle[]} FillMaskOutput
 *
 * @typedef {Object} FillMaskPipelineOptions Parameters specific to fill mask pipelines.
 * @property {number} [topk=5] When passed, overrides the number of predictions to return.
 *
 * @callback FillMaskPipelineCallback Fill the masked token in the text(s) given as inputs.
 * @param {string|string[]} texts One or several texts (or one list of prompts) with masked tokens.
 * @param {FillMaskPipelineOptions} [options] The options to use for masked language modelling.
 * @returns {Promise<FillMaskOutput|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.
 */
/**
 * Masked language modeling prediction pipeline using any `ModelWithLMHead`.
 *
 * **Example:** Perform masked language modelling (a.k.a. "fill-mask") with `Xenova/bert-base-uncased`.
 * ```javascript
 * 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).
 * ```javascript
 * 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.' }]
 * ```
 */
export class FillMaskPipeline extends FillMaskPipeline_base {
    _call(texts: string | string[], options?: FillMaskPipelineOptions): Promise<FillMaskOutput | FillMaskOutput[]>;
}
declare const Text2TextGenerationPipeline_base: new (options: TextPipelineConstructorArgs) => Text2TextGenerationPipelineCallback;
/**
 * @typedef {Object} Text2TextGenerationSingle
 * @property {string} generated_text The generated text.
 * @typedef {Text2TextGenerationSingle[]} Text2TextGenerationOutput
 *
 * @callback Text2TextGenerationPipelineCallback Generate the output text(s) using text(s) given as inputs.
 * @param {string|string[]} texts Input text for the encoder.
 * @param {import('./utils/generation.js').GenerationConfigType} [options] Additional keyword arguments to pass along to the generate method of the model.
 * @returns {Promise<Text2TextGenerationOutput|Text2TextGenerationOutput[]>}
 */
/**
 * 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`.
 * ```javascript
 * 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." }]
 * ```
 */
export class Text2TextGenerationPipeline extends Text2TextGenerationPipeline_base {
    /** @type {'generated_text'} */
    _key: 'generated_text';
    _call(texts: string | string[], options?: import('./utils/generation.js').GenerationConfigType): Promise<Text2TextGenerationOutput | Text2TextGenerationOutput[]>;
}
declare const SummarizationPipeline_base: new (options: TextPipelineConstructorArgs) => SummarizationGenerationPipelineCallback;
/**
 * @typedef {Object} SummarizationGenerationSingle
 * @property {string} summary_text The summary text.
 * @typedef {SummarizationGenerationSingle[]} SummarizationGenerationOutput
 *
 * @callback SummarizationGenerationPipelineCallback Summarize the text(s) given as inputs.
 * @param {string|string[]} texts One or several articles (or one list of articles) to summarize.
 * @param {import('./utils/generation.js').GenerationConfigType} [options] Additional keyword arguments to pass along to the generate method of the model.
 * @returns {Promise<SummarizationGenerationOutput|SummarizationGenerationOutput[]>}
 */
/**
 * A pipeline for summarization tasks, inheriting from Text2TextGenerationPipeline.
 *
 * **Example:** Summarization w/ `Xenova/distilbart-cnn-6-6`.
 * ```javascript
 * 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.' }]
 * ```
 */
export class SummarizationPipeline extends SummarizationPipeline_base {
    /** @type {'summary_text'} */
    _key: 'summary_text';
}
declare const TranslationPipeline_base: new (options: TextPipelineConstructorArgs) => TranslationGenerationPipelineCallback;
/**
 * @typedef {Object} TranslationGenerationSingle
 * @property {string} translation_text The translated text.
 * @typedef {TranslationGenerationSingle[]} TranslationGenerationOutput
 *
 * @callback TranslationGenerationPipelineCallback Translate the text(s) given as inputs.
 * @param {string|string[]} texts Texts to be translated.
 * @param {import('./utils/generation.js').GenerationConfigType} [options] Additional keyword arguments to pass along to the generate method of the model.
 * @returns {Promise<TranslationGenerationOutput|TranslationGenerationOutput[]>}
 */
/**
 * Translates text from one language to another.
 *
 * **Example:** Multilingual translation w/ `Xenova/nllb-200-distilled-600M`.
 *
 * See [here](https://github.com/facebookresearch/flores/blob/main/flores200/README.md#languages-in-flores-200)
 * for the full list of languages and their corresponding codes.
 *
 * ```javascript
 * 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](https://huggingface.co/facebook/m2m100_418M#languages-covered)
 * for the full list of languages and their corresponding codes.
 *
 * ```javascript
 * 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](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt#languages-covered)
 * for the full list of languages and their corresponding codes.
 *
 * ```javascript
 * 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.' }]
 * ```
 */
export class TranslationPipeline extends TranslationPipeline_base {
    /** @type {'translation_text'} */
    _key: 'translation_text';
}
declare const TextGenerationPipeline_base: new (options: TextPipelineConstructorArgs) => TextGenerationPipelineCallback;
/**
 * @typedef {Object} TextGenerationSingle
 * @property {string} generated_text The generated text.
 * @typedef {TextGenerationSingle[]} TextGenerationOutput
 *
 * @typedef {Object} TextGenerationSpecificParams Parameters specific to text-generation pipelines.
 * @property {boolean} [add_special_tokens] Whether or not to add special tokens when tokenizing the sequences.
 * @typedef {import('./utils/generation.js').GenerationConfigType & TextGenerationSpecificParams} TextGenerationConfig
 *
 * @callback TextGenerationPipelineCallback Complete the prompt(s) given as inputs.
 * @param {string|string[]} texts One or several prompts (or one list of prompts) to complete.
 * @param {TextGenerationConfig} [options] Additional keyword arguments to pass along to the generate method of the model.
 * @returns {Promise<TextGenerationOutput|TextGenerationOutput[]>} An array or object containing the generated texts.
 */
/**
 * 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`](./utils/generation#module_utils/generation.GenerationConfig).
 *
 * **Example:** Text generation with `Xenova/distilgpt2` (default settings).
 * ```javascript
 * 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).
 * ```javascript
 * 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`.
 * ```javascript
 * 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'
 * // }]
 * ```
 */
export class TextGenerationPipeline extends TextGenerationPipeline_base {
    _call(texts: string | string[], options?: TextGenerationConfig): Promise<TextGenerationOutput | TextGenerationOutput[]>;
}
declare const ZeroShotClassificationPipeline_base: new (options: TextPipelineConstructorArgs) => ZeroShotClassificationPipelineCallback;
/**
 * @typedef {Object} ZeroShotClassificationOutput
 * @property {string} sequence The sequence for which this is the output.
 * @property {string[]} labels The labels sorted by order of likelihood.
 * @property {number[]} scores The probabilities for each of the labels.
 *
 * @typedef {Object} ZeroShotClassificationPipelineOptions Parameters specific to zero-shot classification pipelines.
 * @property {string} [hypothesis_template="This example is {}."] The template used to turn each
 * candidate label into an NLI-style hypothesis. The candidate label will replace the {} placeholder.
 * @property {boolean} [multi_label=false] 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.
 *
 * @callback ZeroShotClassificationPipelineCallback Classify the sequence(s) given as inputs.
 * @param {string|string[]} texts The sequence(s) to classify, will be truncated if the model input is too large.
 * @param {string|string[]} candidate_labels The set of possible class labels to classify each sequence into.
 * Can be a single label, a string of comma-separated labels, or a list of labels.
 * @param {ZeroShotClassificationPipelineOptions} [options] The options to use for zero-shot classification.
 * @returns {Promise<ZeroShotClassificationOutput|ZeroShotClassificationOutput[]>} An array or object containing the predicted labels and scores.
 */
/**
 * 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`.
 * ```javascript
 * 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).
 * ```javascript
 * 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 ]
 * // }
 * ```
 */
export class ZeroShotClassificationPipeline extends ZeroShotClassificationPipeline_base {
    label2id: {
        [k: string]: any;
    };
    entailment_id: any;
    contradiction_id: any;
    _call(texts: string | string[], candidate_labels: string | string[], options?: ZeroShotClassificationPipelineOptions): Promise<ZeroShotClassificationOutput | ZeroShotClassificationOutput[]>;
}
declare const FeatureExtractionPipeline_base: new (options: TextPipelineConstructorArgs) => FeatureExtractionPipelineCallback;
/**
 * @typedef {Object} FeatureExtractionPipelineOptions Parameters specific to feature extraction pipelines.
 * @property {'none'|'mean'|'cls'} [pooling="none"] The pooling method to use.
 * @property {boolean} [normalize=false] Whether or not to normalize the embeddings in the last dimension.
 *
 * @callback FeatureExtractionPipelineCallback Extract the features of the input(s).
 * @param {string|string[]} texts One or several texts (or one list of texts) to get the features of.
 * @param {FeatureExtractionPipelineOptions} [options] The options to use for feature extraction.
 * @returns {Promise<Tensor>} The features computed by the model.
 */
/**
 * 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).
 * ```javascript
 * 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).
 * ```javascript
 * 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.
 * ```javascript
 * 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]
 * // }
 * ```
 */
export class FeatureExtractionPipeline extends FeatureExtractionPipeline_base {
    _call(texts: string | string[], options?: FeatureExtractionPipelineOptions): Promise<Tensor>;
}
declare const AudioClassificationPipeline_base: new (options: AudioPipelineConstructorArgs) => AudioClassificationPipelineCallback;
/**
 * @typedef {Object} AudioClassificationSingle
 * @property {string} label The label predicted.
 * @property {number} score The corresponding probability.
 * @typedef {AudioClassificationSingle[]} AudioClassificationOutput
 *
 * @typedef {Object} AudioClassificationPipelineOptions Parameters specific to audio classification pipelines.
 * @property {number} [topk=null] The number of top labels that will be returned by the pipeline.
 * If the provided number is `null` or higher than the number of labels available in the model configuration,
 * it will default to the number of labels.
 *
 * @callback AudioClassificationPipelineCallback Classify the sequence(s) given as inputs.
 * @param {AudioPipelineInputs} audio The input audio file(s) to be classified. The input is either:
 * - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate
 * to get the waveform using the [`AudioContext`](https://developer.mozilla.org/en-US/docs/Web/API/AudioContext) API.
 * If `AudioContext` is not available, you should pass the raw waveform in as a Float32Array of shape `(n, )`.
 * - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done).
 * @param {AudioClassificationPipelineOptions} [options] The options to use for audio classification.
 * @returns {Promise<AudioClassificationOutput|AudioClassificationOutput[]>} An array or object containing the predicted labels and scores.
 */
/**
 * 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`.
 * ```javascript
 * 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.
 * ```javascript
 * 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 },
 * // ]
 * ```
 */
export class AudioClassificationPipeline extends AudioClassificationPipeline_base {
    _call(audio: AudioPipelineInputs, options?: AudioClassificationPipelineOptions): Promise<AudioClassificationOutput | AudioClassificationOutput[]>;
}
declare const ZeroShotAudioClassificationPipeline_base: new (options: TextAudioPipelineConstructorArgs) => ZeroShotAudioClassificationPipelineCallback;
/**
 * @typedef {Object} ZeroShotAudioClassificationOutput
 * @property {string} label The label identified by the model. It is one of the suggested `candidate_label`.
 * @property {number} score The score attributed by the model for that label (between 0 and 1).
 *
 * @typedef {Object} ZeroShotAudioClassificationPipelineOptions Parameters specific to zero-shot audio classification pipelines.
 * @property {string} [hypothesis_template="This is a sound of {}."] The sentence used in conjunction with `candidate_labels`
 * to attempt the audio classification by replacing the placeholder with the candidate_labels.
 * Then likelihood is estimated by using `logits_per_audio`.
 *
 * @callback ZeroShotAudioClassificationPipelineCallback Classify the sequence(s) given as inputs.
 * @param {AudioPipelineInputs} audio The input audio file(s) to be classified. The input is either:
 * - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate
 * to get the waveform using the [`AudioContext`](https://developer.mozilla.org/en-US/docs/Web/API/AudioContext) API.
 * If `AudioContext` is not available, you should pass the raw waveform in as a Float32Array of shape `(n, )`.
 * - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done).
 * @param {string[]} candidate_labels The candidate labels for this audio.
 * @param {ZeroShotAudioClassificationPipelineOptions} [options] The options to use for zero-shot audio classification.
 * @returns {Promise<ZeroShotAudioClassificationOutput[]|ZeroShotAudioClassificationOutput[][]>} An array of objects containing the predicted labels and scores.
 */
/**
 * 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`.
 * ```javascript
 * 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' }
 * // ]
 * ```
 */
export class ZeroShotAudioClassificationPipeline extends ZeroShotAudioClassificationPipeline_base {
    _call(audio: AudioPipelineInputs, candidate_labels: string[], options?: ZeroShotAudioClassificationPipelineOptions): Promise<ZeroShotAudioClassificationOutput[] | ZeroShotAudioClassificationOutput[][]>;
}
declare const AutomaticSpeechRecognitionPipeline_base: new (options: TextAudioPipelineConstructorArgs) => AutomaticSpeechRecognitionPipelineCallback;
/**
 * @typedef {{stride: number[], input_features: Tensor, is_last: boolean, tokens?: number[], token_timestamps?: number[]}} ChunkCallbackItem
 * @callback ChunkCallback
 * @param {ChunkCallbackItem} chunk The chunk to process.
 */
/**
 * @typedef {Object} Chunk
 * @property {[number, number]} timestamp The start and end timestamp of the chunk in seconds.
 * @property {string} text The recognized text.
 */
/**
 * @typedef {Object} AutomaticSpeechRecognitionOutput
 * @property {string} text The recognized text.
 * @property {Chunk[]} [chunks] When using `return_timestamps`, the `chunks` will become a list
 * containing all the various text chunks identified by the model.
 *
 * @typedef {Object} AutomaticSpeechRecognitionSpecificParams Parameters specific to automatic-speech-recognition pipelines.
 * @property {boolean|'word'} [kwargs.return_timestamps] Whether to return timestamps or not. Default is `false`.
 * @property {number} [kwargs.chunk_length_s] The length of audio chunks to process in seconds. Default is 0 (no chunking).
 * @property {number} [kwargs.stride_length_s] The length of overlap between consecutive audio chunks in seconds. If not provided, defaults to `chunk_length_s / 6`.
 * @property {ChunkCallback} [kwargs.chunk_callback] Callback function to be called with each chunk processed.
 * @property {boolean} [kwargs.force_full_sequences] Whether to force outputting full sequences or not. Default is `false`.
 * @property {string} [kwargs.language] The source language. Default is `null`, meaning it should be auto-detected. Use this to potentially improve performance if the source language is known.
 * @property {string} [kwargs.task] The task to perform. Default is `null`, meaning it should be auto-detected.
 * @property {number[][]} [kwargs.forced_decoder_ids] 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.
 * @property {number} [num_frames] The number of frames in the input audio.
 * @typedef {import('./utils/generation.js').GenerationConfigType & AutomaticSpeechRecognitionSpecificParams} AutomaticSpeechRecognitionConfig
 *
 * @callback AutomaticSpeechRecognitionPipelineCallback Transcribe the audio sequence(s) given as inputs to text.
 * @param {AudioPipelineInputs} audio The input audio file(s) to be transcribed. The input is either:
 * - `string` or `URL` that is the filename/URL of the audio file, the file will be read at the processor's sampling rate
 * to get the waveform using the [`AudioContext`](https://developer.mozilla.org/en-US/docs/Web/API/AudioContext) API.
 * If `AudioContext` is not available, you should pass the raw waveform in as a Float32Array of shape `(n, )`.
 * - `Float32Array` or `Float64Array` of shape `(n, )`, representing the raw audio at the correct sampling rate (no further check will be done).
 * @param {AutomaticSpeechRecognitionConfig} [options] Additional keyword arguments to pass along to the generate method of the model.
 * @returns {Promise<AutomaticSpeechRecognitionOutput|AutomaticSpeechRecognitionOutput[]>} An object containing the transcription text and optionally timestamps if `return_timestamps` is `true`.
 */
/**
 * Pipeline that aims at extracting spoken text contained within some audio.
 *
 * **Example:** Transcribe English.
 * ```javascript
 * 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.
 * ```javascript
 * 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.
 * ```javascript
 * 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.
 * ```javascript
 * 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.
 * ```javascript
 * 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.
 * ```javascript
 * 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" }
 * ```
 */
export class AutomaticSpeechRecognitionPipeline extends AutomaticSpeechRecognitionPipeline_base {
    _call(audio: AudioPipelineInputs, options?: AutomaticSpeechRecognitionConfig): Promise<AutomaticSpeechRecognitionOutput | AutomaticSpeechRecognitionOutput[]>;
    /**
     * @type {AutomaticSpeechRecognitionPipelineCallback}
     * @private
     */
    private _call_wav2vec2;
    /**
     * @type {AutomaticSpeechRecognitionPipelineCallback}
     * @private
     */
    private _call_whisper;
}
declare const ImageToTextPipeline_base: new (options: TextImagePipelineConstructorArgs) => ImageToTextPipelineCallback;
/**
 * @typedef {Object} ImageToTextSingle
 * @property {string} generated_text The generated text.
 * @typedef {ImageToTextSingle[]} ImageToTextOutput
 *
 * @callback ImageToTextPipelineCallback Assign labels to the image(s) passed as inputs.
 * @param {ImagePipelineInputs} texts The images to be captioned.
 * @param {import('./utils/generation.js').GenerationConfigType} [options] Additional keyword arguments to pass along to the generate method of the model.
 * @returns {Promise<ImageToTextOutput|ImageToTextOutput[]>} An object (or array of objects) containing the generated text(s).
 */
/**
 * 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`.
 * ```javascript
 * 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`.
 * ```javascript
 * 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.' }]
 * ```
 */
export class ImageToTextPipeline extends ImageToTextPipeline_base {
    _call(texts: ImagePipelineInputs, options?: import('./utils/generation.js').GenerationConfigType): Promise<ImageToTextOutput | ImageToTextOutput[]>;
}
declare const ImageClassificationPipeline_base: new (options: ImagePipelineConstructorArgs) => ImageClassificati