hf_classify_zero_shot_batch: Batch Zero-Shot Classification (In-Memory)

In huggingfaceR: Access 'Hugging Face' Models and Datasets

Batch Zero-Shot Classification (In-Memory)

Description

Classify multiple texts into custom categories in parallel without training.

Usage

hf_classify_zero_shot_batch(
  text,
  labels,
  model = hf_default_model("zero_shot"),
  multi_label = FALSE,
  token = NULL,
  batch_size = 50L,
  max_active = 10L,
  progress = TRUE,
  endpoint_url = NULL
)

Arguments

text	Character vector of text(s) to classify.
labels	Character vector of candidate labels/categories.
model	Character string. Model ID from Hugging Face Hub. Default: "facebook/bart-large-mnli".
multi_label	Logical. If TRUE, allows multiple labels per text. Default: FALSE.
token	Character string or NULL. API token for authentication.
batch_size	Integer. Number of texts per API request. Default: 50.
max_active	Integer. Maximum concurrent requests. Default: 10.
progress	Logical. Show progress bar. Default: TRUE.
endpoint_url	Character string or NULL. A custom Inference Endpoint URL.

Value

A tibble with columns: - 'text': Original input text - 'label': Predicted label (or labels if multi_label) - 'score': Confidence score(s) - '.input_idx': Original position in input vector - '.error': TRUE if request failed - '.error_msg': Error message or NA

Examples

## Not run: 
texts <- c("I love my new laptop", "The game was exciting", "This recipe is delicious")
labels <- c("technology", "sports", "food")
result <- hf_classify_zero_shot_batch(texts, labels, max_active = 5)

## End(Not run)

huggingfaceR documentation built on Aug. 30, 2026, 1:06 a.m.