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Fusion 5.10
    Fusion 5.10

    Trains QnA model on a cold start (unsupervised) basis with with pre-trained or trained embeddings and deploys the trained model to the ML Model Service

    aMaxLen - integer

    Average length of question by number of tokens

    deployModelName - stringrequired

    Name of the model to be used for deployment (must be a valid DNS subdomain with no underscores)

    <= 30 characters

    Match pattern: [a-zA-Z][\-a-zA-Z0-9]*[a-zA-Z0-9]?

    extraTrainingArgs - string

    Add any additional arguments for the Python training scripts in this field

    id - stringrequired

    The ID for this job. Used in the API to reference this job. Allowed characters: a-z, A-Z, dash (-) and underscore (_)

    <= 63 characters

    Match pattern: [a-zA-Z][_\-a-zA-Z0-9]*[a-zA-Z0-9]?

    infBatch - integer

    The batch size used in the validation loop

    Default: 512

    lowerCases - boolean

    Whether to lower case all words in training, i.e. whether to treat upper case and lower case words equally.

    Default: true

    maxLen - integer

    Max length of question/answer by number of tokens

    maxTokensNum - integer

    Drop document if the total words is greater than this value

    >= 1

    exclusiveMinimum: false

    Default: 5000

    maxVocabSize - integer

    Maximum number of words in vocabulary, words will be trimmed if frequency is too low

    >= 1

    exclusiveMinimum: false

    Default: 100000

    minTokensNum - integer

    Drop document if the total words is lower than this value

    >= 1

    exclusiveMinimum: false

    Default: 1

    modelReplicas - integer

    How many replicas of the model should be deployed by Seldon Core

    Default: 1

    numClusters - integer

    Number of clusters to be used for fast dense vector retrieval. Note no clustering will be applied if this is set to 0. If left blank, cluster count will be inferred by the job depending on the data

    qMaxLen - integer

    Average length of question by number of tokens

    samplingProportion - number

    The proportion of data to be sampled from the full dataset. Use a value between 0 and 1 for a proportion (e.g. 0.5 for 50%), or for a specific number of examples, use an integer larger than 1. Leave blank for no sampling

    sparkConfig - array[object]

    Provide additional key/value pairs to be injected into the training JSON map at runtime. Values will be inserted as-is, so use " to surround string values

    object attributes:{key required : {
     display name: Parameter Name
     type: string
    value : {
     display name: Parameter Value
     type: string

    textColName - stringrequired

    Field which contains the documents that will be used to learn about the vocabulary. If multiple fields, please separate them by comma, e.g. question,answer.

    topKClusters - integer

    How many closest clusters the model can find for each query. At retrieval time, all answers in top k nearest clusters will be returned and reranked

    Default: 10

    trainingCollection - stringrequired

    Solr Collection containing content documents

    >= 1 characters

    type - stringrequired

    Default: argo-qna-coldstart

    Allowed values: argo-qna-coldstart

    useCustomEmbeddings - boolean

    Choose this option when there are many uncommon words or jargons in data. NOTE: please look at log for warning about percentage of covered vocabulary words, if this proportion is less than 80%, please set this parameter to true and do not use the pre-trained embeddings shipped with our package

    Default: false

    w2vEpochs - integer

    Number of epochs to train custom Word2Vec embeddings

    Default: 15

    w2vVectorSize - integer

    Word-vector dimensionality to represent text (suggested dimension ranges: 100~150

    Default: 150

    w2vWindowSize - integer

    The window size (context words from [-window, window]) for Word2Vec

    Default: 8