Optional examplesA list of examples with labels, in the following format: [[\"The movie is so interesting.\", \"Positive\"], [\"It is quite boring.\", \"Negative\"], ...] All the label strings will be normalized to be
capitalized. You should specify either examples or file, but not
both.
CreateClassificationRequest
Optional expandIf an object name is in the list, we provide the full information of the
object; otherwise, we only provide the object ID. Currently we support
completion and file objects for expansion.
CreateClassificationRequest
Optional fileThe ID of the uploaded file that contains training examples. See upload
file for how to upload a file of the
desired format and purpose. You should specify either examples or
file, but not both.
CreateClassificationRequest
Optional labelsThe set of categories being classified. If not specified, candidate labels will be automatically collected from the examples you provide. All the label strings will be normalized to be capitalized.
CreateClassificationRequest
Optional logit_Modify the likelihood of specified tokens appearing in the completion.
Accepts a json object that maps tokens (specified by their token ID in
the GPT tokenizer) to an associated bias value from -100 to 100. You can
use this tokenizer tool (which works for both
GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias
is added to the logits generated by the model prior to sampling. The
exact effect will vary per model, but values between -1 and 1 should
decrease or increase likelihood of selection; values like -100 or 100
should result in a ban or exclusive selection of the relevant token. As
an example, you can pass {\"50256\": -100} to prevent the <|endoftext|>
token from being generated.
CreateClassificationRequest
Optional logprobsInclude the log probabilities on the logprobs most likely tokens, as
well the chosen tokens. For example, if logprobs is 5, the API will
return a list of the 5 most likely tokens. The API will always return the
logprob of the sampled token, so there may be up to logprobs+1
elements in the response. The maximum value for logprobs is 5. If you
need more than this, please contact us through our Help
center and describe your use case. When
logprobs is set, completion will be automatically added into expand
to get the logprobs.
CreateClassificationRequest
Optional max_The maximum number of examples to be ranked by
Search when using file. Setting
it to a higher value leads to improved accuracy but with increased
latency and cost.
CreateClassificationRequest
ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.
CreateClassificationRequest
Query to be classified.
CreateClassificationRequest
Optional return_A special boolean flag for showing metadata. If set to true, each
document entry in the returned JSON will contain a "metadata" field.
This flag only takes effect when file is set.
CreateClassificationRequest
Optional return_If set to true, the returned JSON will include a "prompt" field
containing the final prompt that was used to request a completion. This
is mainly useful for debugging purposes.
CreateClassificationRequest
Optional search_ID of the model to use for Search.
You can select one of ada, babbage, curie, or davinci.
CreateClassificationRequest
Optional temperatureWhat sampling temperature to use. Higher values mean the model will
take more risks. Try 0.9 for more creative applications, and 0 (argmax
sampling) for ones with a well-defined answer.
CreateClassificationRequest
Optional userA unique identifier representing your end-user, which will help OpenAI to monitor and detect abuse. Learn more.
CreateClassificationRequest
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CreateClassificationRequest