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Choose Best

The Choose Best endpoint uses likelihood to perform classification. Given a query text that you'd like to classify between a number of options, Choose Best will return a score between the query and each option. For some examples of Choose Best see classifiying questions and sentiment analysis.

Usage#

    Sample Response#

    {
    "scores": [
    -0.004,
    -5.379
    ],
    "tokens": [
    [" positive"],
    [" negative"]
    ],
    "token_log_likelihoods": [
    [-0.004],
    [-5.379]
    ]
    }

    Request#

    query#

    string

    Used to query the options.

    options#

    array of strings
    Each string concatenates to the query.

    mode#

    One of PREPEND_OPTION|APPEND_OPTION to specify where the option string will be placed and how to compute the log-likelihood.

    If PREPEND_OPTION is selected, the output will be the log-likelihood of the queryquery conditioned on the optionoption:   log p(queryoption)\log\ p(\textrm{query} | \textrm{option})

    If APPEND_OPTION is selected, the output will be the log-likelihood of the optionoption conditioned on the queryquery:   log p(optionquery)\log\ p(\textrm{option} | \textrm{query})

    See the Choose Best modes page for more details.

    Response#

    scores#

    array of floats
    An array of floats corresponding to a score for each of the options, where a higher score represents a more likely query-option pair. This score is computed as:
    score=log_likelihoodn_tokens\textrm{score} = \frac{\textrm{log\_likelihood}}{\textrm{n\_tokens}}

    where n_tokens is the number of tokens that are summed over to compute the log_likelihood.

    tokens#

    array of tokens
    An array of tokens corresponding to the tokens for each of the options. The tokens are represented as an array of strings.

    token_log_likelihoods#

    array of token log likelihoods

    An array of log likelihoods corresponding to the tokens of each of the options. The log likelihoods are represented as an array of floats.