import * as tf from '@tensorflow/tfjs';
import * as types from '../../../types';
export declare class EmbeddingsModel {
    static setupModel(pretrainedNGramVectors: types.PretrainedDict, maxWords: number, maxNgrams: number, embeddingDimensions: number): tf.Sequential;
    tokenizer: types.IAidaTokenizer;
    private ngramToIdDictionary;
    private maxWords;
    private maxNgrams;
    private embeddingDimensions;
    private model;
    private inputModel;
    constructor(ngramToIdDictionary: {
        [key: string]: number;
    }, maxWords: number, maxNgrams: number, embeddingDimensions: number, tokenizer: types.IAidaTokenizer, pretrainedEmbeddingModel?: tf.LayersModel, pretrainedNGramVectors?: types.PretrainedDict);
    tfModel: () => tf.LayersModel;
    modelInput: () => tf.LayersModel;
    embed: (sentences: string[]) => tf.Tensor<tf.Rank.R3>;
    dictionary: () => {
        [key: string]: number;
    };
    private sentencesToWordIds;
    private generateWordIdsFromNGrams;
}