hf_classify_batch: Batch Text Classification (In-Memory)

In huggingfaceR: Access 'Hugging Face' Models and Datasets

Batch Text Classification (In-Memory)

Description

Classify multiple texts in parallel. This function processes all inputs in memory and returns results in a single tibble.

Usage

hf_classify_batch(
  text,
  model = hf_default_model("classify"),
  token = NULL,
  batch_size = 100L,
  max_active = 10L,
  progress = TRUE,
  endpoint_url = NULL
)

Arguments

text	Character vector of text(s) to classify.
model	Character string. Model ID from Hugging Face Hub. Default: "distilbert/distilbert-base-uncased-finetuned-sst-2-english".
token	Character string or NULL. API token for authentication.
batch_size	Integer. Number of texts per API request. Default: 100.
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 - 'score': Confidence score - '.input_idx': Original position in input vector - '.error': TRUE if request failed - '.error_msg': Error message or NA

Examples

## Not run: 
# Classify many texts in parallel
texts <- c("I love this!", "This is terrible.", "Meh, it's okay.")
result <- hf_classify_batch(texts, max_active = 5)

# Check for errors
errors <- result[result$.error, ]

## End(Not run)

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