import * as tf from '@tensorflow/tfjs';
import * as types from '../../../types';
import { EmbeddingsModel } from '../embeddings/EmbeddingsModel';
export default class NerModel extends types.PipelineModel implements types.IPipelineModel {
    private static setup;
    private config;
    private datasetParams;
    private model;
    private embeddingsModel;
    private logger;
    private nerTrainStatsHandler;
    constructor(config: types.INerModelParams & types.IDefaultModelParams, datasetParams: types.IDatasetParams, embeddingsModel: EmbeddingsModel, logger: types.IPipelineModelLogger, pretrainedModel?: tf.LayersModel, nerTrainStatsHandler?: types.ITrainStatsHandler['ner']);
    tfModel: () => tf.LayersModel;
    rawPrediction: (sentences: string[], classificationPred: types.IClassificationPred[]) => {
        highestIndex: number;
        confidence: number;
    }[][];
    predict: (sentences: string[], classificationPred: types.IClassificationPred[]) => types.ISlotReducer[];
    train: (trainDataset: types.ITrainingParams) => Promise<void>;
    test: (testExamples: types.ITestingParams, resultsHandler?: types.ITestPredictionsHandler | undefined) => Promise<types.IPredictionStats>;
    private defaultResultsLogger;
}