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 = "distilbert/distilbert-base-uncased-finetuned-sst-2-english",
token = NULL,
batch_size = 100L,
max_active = 10L,
progress = TRUE
)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.
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
if (FALSE) { # \dontrun{
# 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, ]
} # }