Batch Zero-Shot Classification (In-Memory) — hf_classify_zero_shot

Classify multiple texts into custom categories in parallel without training.

Usage

hf_classify_zero_shot_batch(
  text,
  labels,
  model = "facebook/bart-large-mnli",
  multi_label = FALSE,
  token = NULL,
  batch_size = 50L,
  max_active = 10L,
  progress = TRUE
)

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.

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

if (FALSE) { # \dontrun{
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)
} # }