Changelog

Source: NEWS.md

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.