hf_ez_summarization_local_inference: Summarization Local Inference

In farach/huggingfaceR: Hugging Face State-of-the-Art Models in R

Summarization Local Inference

Description

Summarization Local Inference

Usage

hf_ez_summarization_local_inference(
  string,
  min_length = NULL,
  max_length = NULL,
  top_k = NULL,
  top_p = NULL,
  temperature = 1,
  repetition_penalty = NULL,
  max_time = NULL,
  tidy = TRUE,
  ...
)

Arguments

string	a string to be summarized
min_length	Integer to define the minimum length in tokens of the output summary. Default: NULL
max_length	Integer to define the maximum length in tokens of the output summary. Default: NULL
top_k	Integer to define the top tokens considered within the sample operation to create new text. Default: NULL
top_p	Float to define the tokens that are within the sample operation of text generation. Add tokens in the sample for more probable to least probable until the sum of the probabilities is greater than top_p. Default: NULL
temperature	Float (0.0-100.0). The temperature of the sampling operation. 1 means regular sampling, 0 means always take the highest score, 100.0 is getting closer to uniform probability. Default: 1.0
repetition_penalty	Float (0.0-100.0). The more a token is used within generation the more it is penalized to not be picked in successive generation passes. Default: NULL
max_time	Float (0-120.0). The amount of time in seconds that the query should take maximum. Network can cause some overhead so it will be a soft limit. Default: NULL
tidy	Whether to tidy the results into a tibble. Default: TRUE (tidy the results)

Value

The results of the inference

See Also

https://huggingface.co/docs/transformers/main/en/pipeline_tutorial

farach/huggingfaceR documentation built on Feb. 4, 2023, 10:31 p.m.