High-Performance Open-Source Archive
hf_extract() turns unstructured text into tidy
columns with chat-model structured JSON output. Pass a lightweight named
schema such as c(name = "string", score = "number") or a
full JSON Schema list, and the function returns one row per input text
with one column per schema field (#55).
hf_chat() now supports tool/function calling,
streaming callbacks, and image inputs for vision-capable chat models.
New helpers hf_tool(), hf_run_tools(), and
hf_describe_image() make these capabilities available from
R pipelines (#55).
Multimodal inference wrappers. New functions add
audio, image, and generation workflows: hf_transcribe(),
hf_text_to_image(), hf_classify_image(),
hf_caption_image(), hf_detect_objects(), and
hf_text_to_speech() (#55). Live verification passed for
ASR, text-to-image, image classification, captioning, and object
detection; public hosted TTS provider support is currently blocked, so
hf_text_to_speech() is documented for compatible providers
or dedicated Inference Endpoints.
Hub files, providers, and guarded writes. New
Hub helpers include hf_hub_download(),
hf_list_repo_files(), hf_search_spaces(),
hf_search_papers(), hf_list_providers(),
hf_create_repo(), hf_upload_file(),
hf_push_dataset(), and guarded
hf_delete_repo() (#55). Search helpers now follow Hub
pagination links, and write/destructive operations require
confirm = TRUE.
First-class text tasks. New API-first,
tidyverse-native wrappers round out the text toolkit, each accepting
character vectors and returning tibbles: hf_summarize()
(summarization), hf_translate() (translation),
hf_ner() (named-entity recognition, one tidy row per entity
with character offsets), hf_question_answer() (extractive
QA), and hf_table_question_answer() (ask a data frame a
question in plain language).
Centralized default models. A new exported
helper, hf_default_model(), is the single source of truth
for every task’s default model. All hf_* functions now
resolve their model default through it (no behavior change
— the resolved values are identical), so defaults can be audited or
updated in one place. Call hf_default_model() to see the
whole registry, or hf_default_model("translate") for a
single task’s default.
hf_whoami() now returns billing/pro status and
token-role metadata so users can check whether their token is read-only
or write-capable before Hub write operations.
Beginner-friendly default translation model.
hf_translate() now defaults to
Helsinki-NLP/opus-mt-en-fr (English to French) instead of
facebook/nllb-200-distilled-600M. The Helsinki-NLP
opus-mt-* family encodes the translation direction in the
model ID, so hf_translate("Hello") works with no FLORES-200
language codes — a smoother first experience. NLLB remains fully
supported for multilingual translation via the model,
source, and target arguments.
Unified request engine with inference-provider
routing. Internal request construction is consolidated in
R/request.R (hf_parse_model(),
hf_inference_url(), hf_error_body(),
hf_is_transient(), hf_task_request()). As a
result, the model = "id:provider" suffix now selects an
inference provider for all serverless tasks — including
embeddings, classification, and the new text tasks — not just chat.
Retries now back off only on genuinely transient status codes (429/5xx),
and error messages are consistent across every inference
function.
The package no longer requires Python or reticulate for core functionality. All inference is handled through the Hugging Face Inference API via httr2. Legacy functions that depend on Python/reticulate remain available but are not required for new workflows.
Default chat and generation model changed from
HuggingFaceTB/SmolLM3-3B to
meta-llama/Llama-3.1-8B-Instruct, which has broader
provider support.
API-first architecture: All core functions
(hf_classify(), hf_embed(),
hf_chat(), hf_generate(),
hf_fill_mask()) use the Hugging Face Inference API
directly. No Python installation needed.
Text classification: hf_classify()
for sentiment analysis and hf_classify_zero_shot() for
custom label classification without training.
Embeddings and similarity:
hf_embed() generates dense vector representations.
hf_similarity() computes pairwise cosine similarity.
hf_nearest_neighbors(), hf_cluster_texts(),
and hf_extract_topics() provide higher-level semantic
analysis. hf_embed_umap() reduces embeddings to 2D for
visualization.
Chat and generation: hf_chat() for
single-turn LLM interaction with system prompts.
hf_conversation() and chat() for multi-turn
conversations with persistent history. hf_generate() for
text completion. hf_fill_mask() for BERT-style masked token
prediction.
Hub discovery: hf_search_models(),
hf_model_info(), hf_search_datasets(),
hf_dataset_info(), and hf_list_tasks() for
exploring the Hugging Face Hub from R.
Datasets: hf_load_dataset() loads
dataset rows directly into tibbles, with support for splits, pagination,
and column selection.
Batch processing: hf_embed_batch(),
hf_classify_batch(), and
hf_classify_zero_shot_batch() process large inputs with
parallel requests. hf_embed_chunks() and
hf_classify_chunks() add disk checkpointing for datasets
too large to hold in memory.
tidymodels integration:
step_hf_embed() recipe step embeds text columns as part of
a tidymodels preprocessing pipeline.
tidytext integration:
hf_embed_text() works directly with data frame text columns
for tidytext-style workflows.
Model availability checking:
hf_check_inference() queries model metadata to verify
whether a model supports the free serverless Inference API before you
make inference calls.
Dedicated Inference Endpoints: All inference
functions accept an endpoint_url parameter to route
requests to a dedicated Inference Endpoint instead of the public
serverless API. This supports models not available on the free tier and
production workloads requiring dedicated capacity.
All functions return tibbles and accept character vectors, enabling natural composition with dplyr, tidyr, and the rest of the tidyverse.
Improved error messages for 404 responses explain that the model
may exist on the Hub but not be available for serverless inference, and
suggest using hf_check_inference().
Documentation updated to clarify that the Inference API serves a curated subset of the Hub’s 500,000+ models, not all of them.
Need mirroring services?
Contact our team at info@vpspulse.com.
Mirror powered by VPSpulse
Infrastructure sponsored by VPSPulse & Secure Payments by ArionPay.