Fusion 5.10
Legacy Product
Train a Smart Answers model on a cold start (unsupervised) basis, with pre-trained or trained embeddings, and deploy the trained model to the ML Model Service.
See Train A Smart Answers Cold Start Model for configuration instructions.
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
Average length of question by number of tokens
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]?
Add any additional arguments for the Python training scripts in this field
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]?
The batch size used in the validation loop
Default: 512
Whether to lower case all words in training, i.e. whether to treat upper case and lower case words equally.
Default: true
Max length of question/answer by number of tokens
Drop document if the total words is greater than this value
>= 1
exclusiveMinimum: false
Default: 5000
Maximum number of words in vocabulary, words will be trimmed if frequency is too low
>= 1
exclusiveMinimum: false
Default: 100000
Drop document if the total words is lower than this value
>= 1
exclusiveMinimum: false
Default: 1
How many replicas of the model should be deployed by Seldon Core
Default: 1
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
Average length of question by number of tokens
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
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
}}
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.
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
Solr Collection containing content documents
>= 1 characters
Default: argo-qna-coldstart
Allowed values: argo-qna-coldstart
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
Number of epochs to train custom Word2Vec embeddings
Default: 15
Word-vector dimensionality to represent text (suggested dimension ranges: 100~150
Default: 150
The window size (context words from [-window, window]) for Word2Vec
Default: 8