translateText {perplexR}

Large Language Model: Translate Text

Description

Large Language Model: Translate Text

Usage

translateText(
  text = clipr::read_clip(allow_non_interactive = TRUE),
  toLanguage = "German",
  PERPLEXITY_API_KEY = Sys.getenv("PERPLEXITY_API_KEY"),
  modelSelection = c("mistral-7b-instruct", "mixtral-8x7b-instruct",
    "codellama-70b-instruct", "sonar-small-chat", "sonar-small-online",
    "sonar-medium-chat", "sonar-medium-online"),
  systemRole = "You are a helpful assistant.",
  maxTokens = 265,
  temperature = 1,
  top_p = NULL,
  top_k = 100,
  presence_penalty = 0,
  frequency_penalty = NULL,
  proxy = NULL
)

Arguments

text

The text to be translated by Large Language Model. If not provided, it will use what's copied on the clipboard.

toLanguage

The language to be translated into.

PERPLEXITY_API_KEY

PERPLEXITY API key.

modelSelection

model choice. Default is mistral-7b-instruct.

systemRole

Role for model. Default is: "You are a helpful assistant."

maxTokens

The maximum integer of completion tokens returned by API.

temperature

The amount of randomness in the response, valued between 0 inclusive and 2 exclusive. Higher values are more random, and lower values are more deterministic. Set either temperature or top_p.

top_p

Nucleus sampling threshold, valued between 0 and 1 inclusive.

top_k

The number of tokens to keep for highest top-k filtering, specified as an integer between 0 and 2048 inclusive. If set to 0, top-k filtering is disabled.

presence_penalty

A value between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics. Incompatible with frequency_penalty.

frequency_penalty

A multiplicative penalty greater than 0. Values greater than 1.0 penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim. A value of 1.0 means no penalty.

proxy

Default value is NULL.

Value

A character value with the response generated by Large Language Model.

Examples

## Not run: 
translateText("Dear Recipient, I hope this message finds you well.")

## End(Not run)