Changelog

Source: NEWS.md

huggingfaceR 2.1.0

New features

• 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).

Improvements

• 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.

huggingfaceR 2.0.0

Breaking changes

• 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.

New features

• 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.

Improvements

• 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.