/**
 * Supported in-browser ML models for specific tasks
 * These models are optimized for browser use and are well-tested with transformers.js
 * All models use the Xenova quantized versions for optimal browser performance
 */
export declare enum MLModel {
    /**
     * Zero-shot classification model for text classification tasks
     * Model: distilbert-base-uncased-mnli
     * Size: ~60-80MB quantized
     * Use case: Tool detection, text classification
     */
    ZERO_SHOT_CLASSIFICATION = "Xenova/distilbert-base-uncased-mnli",
    /**
     * Feature extraction model for embeddings
     * Model: all-MiniLM-L6-v2
     * Size: ~23MB quantized
     * Use case: Text embeddings, semantic search
     */
    FEATURE_EXTRACTION = "Xenova/all-MiniLM-L6-v2",
    /**
     * Text classification model
     * Model: distilbert-base-uncased-finetuned-sst-2-english
     * Size: ~67MB quantized
     * Use case: Sentiment analysis, text classification
     */
    TEXT_CLASSIFICATION = "Xenova/distilbert-base-uncased-finetuned-sst-2-english",
    /**
     * Question answering model
     * Model: distilbert-base-cased-distilled-squad
     * Size: ~125MB quantized
     * Use case: Question answering, reading comprehension
     */
    QUESTION_ANSWERING = "Xenova/distilbert-base-cased-distilled-squad",
    /**
     * Summarization model
     * Model: distilbart-cnn-6-6
     * Size: ~300MB quantized
     * Use case: Text summarization
     */
    SUMMARIZATION = "Xenova/distilbart-cnn-6-6",
    /**
     * Translation model (English to French)
     * Model: opus-mt-en-fr
     * Size: ~200MB quantized
     * Use case: Machine translation
     */
    TRANSLATION_EN_FR = "Xenova/opus-mt-en-fr",
    /**
     * Fill-mask model
     * Model: distilbert-base-uncased
     * Size: ~67MB quantized
     * Use case: Masked language modeling, text completion
     */
    FILL_MASK = "Xenova/distilbert-base-uncased",
    /**
     * Sentence similarity model
     * Model: all-MiniLM-L6-v2 (same as feature extraction)
     * Size: ~23MB quantized
     * Use case: Sentence similarity, semantic search
     */
    SENTENCE_SIMILARITY = "Xenova/all-MiniLM-L6-v2",
    /**
     * Named entity recognition (token classification)
     * Model: distilbert-base-uncased-finetuned-conll03-english
     * Size: ~67MB quantized
     * Use case: Named entity recognition, token classification
     */
    TOKEN_CLASSIFICATION = "Xenova/distilbert-base-uncased-finetuned-conll03-english",
    /**
     * Image classification model
     * Model: vit-base-patch16-224
     * Size: ~330MB quantized
     * Use case: Image classification
     */
    IMAGE_CLASSIFICATION = "Xenova/vit-base-patch16-224",
    /**
     * Object detection model
     * Model: detr-resnet-50
     * Size: ~160MB quantized
     * Use case: Object detection in images
     */
    OBJECT_DETECTION = "Xenova/detr-resnet-50",
    /**
     * Object detection model (YOLO-style, default for YOLO flows)
     * Uses YOLOS-tiny (public, no auth). For YOLOv9-web see OBJECT_DETECTION_YOLOV9_WEB (may require HF token).
     */
    OBJECT_DETECTION_YOLOV9 = "Xenova/yolos-tiny",
    /**
     * Object detection model (YOLOv9 web). May require Hugging Face token if repo is gated.
     */
    OBJECT_DETECTION_YOLOV9_WEB = "Xenova/yolov9-web",
    /**
     * Object detection model (YOLOS tiny)
     * Model: yolos-tiny
     * Use case: Lightweight object detection, ViT-based
     */
    OBJECT_DETECTION_YOLOS_TINY = "Xenova/yolos-tiny",
    /**
     * Object detection model (YOLOS small)
     * Model: yolos-small
     * Use case: Object detection, higher accuracy than tiny
     */
    OBJECT_DETECTION_YOLOS_SMALL = "Xenova/yolos-small",
    /**
     * Object detection model (YOLOS base)
     * Model: yolos-base
     * Use case: Object detection, higher accuracy
     */
    OBJECT_DETECTION_YOLOS_BASE = "Xenova/yolos-base",
    /**
     * Object detection model (GELAN / YOLOv9-based). Not supported by pipeline() API.
     * Use OBJECT_DETECTION_YOLOS_* or OBJECT_DETECTION (DETR) instead.
     */
    OBJECT_DETECTION_GELAN = "Xenova/gelan-c_all",
    /**
     * Image segmentation model (panoptic)
     * Model: detr-resnet-50-panoptic
     * Use case: Segment objects in images, each with label and mask
     */
    IMAGE_SEGMENTATION = "Xenova/detr-resnet-50-panoptic",
    /**
     * Automatic speech recognition model
     * Model: whisper-tiny
     * Size: ~75MB quantized
     * Use case: Speech-to-text transcription
     */
    AUTOMATIC_SPEECH_RECOGNITION = "Xenova/whisper-tiny",
    /**
     * Audio classification model
     * Model: wav2vec2-base
     * Size: ~95MB quantized
     * Use case: Audio classification, sound recognition
     */
    AUDIO_CLASSIFICATION = "Xenova/wav2vec2-base"
}
/**
 * ML task types supported by transformers.js
 * These correspond to pipeline types available in transformers.js
 */
export declare enum MLTask {
    ZERO_SHOT_CLASSIFICATION = "zero-shot-classification",
    FEATURE_EXTRACTION = "feature-extraction",
    TEXT_CLASSIFICATION = "text-classification",
    SENTIMENT_ANALYSIS = "sentiment-analysis",
    QUESTION_ANSWERING = "question-answering",
    SUMMARIZATION = "summarization",
    TRANSLATION = "translation",
    FILL_MASK = "fill-mask",
    SENTENCE_SIMILARITY = "sentence-similarity",
    TOKEN_CLASSIFICATION = "token-classification",
    IMAGE_CLASSIFICATION = "image-classification",
    OBJECT_DETECTION = "object-detection",
    AUTOMATIC_SPEECH_RECOGNITION = "automatic-speech-recognition",
    AUDIO_CLASSIFICATION = "audio-classification",
    IMAGE_TO_TEXT = "image-to-text",
    TEXT_TO_IMAGE = "text-to-image",
    IMAGE_SEGMENTATION = "image-segmentation",
    DEPTH_ESTIMATION = "depth-estimation"
}
/**
 * Get the recommended model for a specific task
 * Returns the default model for the task, or null if no default is specified
 */
export declare function getModelForTask(task: MLTask): MLModel | null;
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