Fusion 5.10
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 Smart Answers model on a cold start (unsupervised) basis with with pre-trained or trained embeddings and deploys the trained model to the ML Model Service
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]?
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
}}
Options used when writing output to Solr or other sources
object attributes:{key required : {
display name: Parameter Name
type: string
}value : {
display name: Parameter Value
type: string
}}
Options used when reading input from Solr or other sources.
object attributes:{key required : {
display name: Parameter Name
type: string
}value : {
display name: Parameter Value
type: string
}}
Solr collection or cloud storage path where training data is present.
>= 1 characters
The format of the training data - solr, parquet etc.
>= 1 characters
Default: solr
Name of the secret used to access cloud storage as defined in the K8s namespace
>= 1 characters
Solr or SQL query to filter training data. Use solr query when solr collection is specified in Training Path. Use SQL query when cloud storage location is specified. The table name for SQL is `spark_input`
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.
Name of the model to be used for deployment (must be a valid lowercased DNS subdomain with no underscores).
<= 30 characters
Match pattern: ^[a-z0-9]([-a-z0-9]*[a-z0-9])?(\.[a-z0-9]([-a-z0-9]*[a-z0-9])?)*$
Specify one of these custom embeddings: ['word_custom', 'bpe_custom'] or choose one of the included pre-trained embeddings / models.
Default: word_en_300d_2M
Allowed values: word_custombpe_customword_en_300d_2Mbpe_en_300d_10Kbpe_en_300d_200Kbpe_ja_300d_100Kbpe_ko_300d_100Kbpe_zh_300d_50Kbpe_multi_300d_320Kdistilbert_endistilbert_multibiobert_v1.1
How many replicas of the model should be deployed by Seldon Core
Default: 1
Number of epochs to train custom Word2Vec embeddings
Default: 15
Word-vector dimensionality to represent text (suggested dimension ranges: 100~300)
Default: 150
The window size (context words from [-window, window]) for Word2Vec
Default: 8
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
Drop document if the total words is lower than this value
>= 1
exclusiveMinimum: false
Default: 1
Drop document if the total words is greater than this value
>= 1
exclusiveMinimum: false
Default: 5000
Whether to lower case all words in training, i.e. whether to treat upper case and lower case words equally. Only utilized for custom embeddings or for the default model base: word_en_300d_2M.
Default: true
Maximum number of words in vocabulary, words will be trimmed if frequency is too low
>= 1
exclusiveMinimum: false
Default: 100000
Max length of question/answer by number of tokens
The batch size used for encoding during the training
DEPRECATED: please, consider using Milvus for fast dense vector similarity search. 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
Default: 0
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
Use Unidecode library to transform Unicode input into ASCII transliterations. Only utilized for custom embeddings or for the default model base: word_en_300d_2M
Default: true
Determines how token vectors should be aggregated to obtain final content vector. Must be one of: [avg, max].
Default: avg
Allowed values: avgmax
Default: argo-qna-coldstart
Allowed values: argo-qna-coldstart