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.