Hugging Face Transformers tutorial: pipeline, tokenizer, and models in 2026

Hugging Face Transformers v5 modernizes the library while retaining its core pipeline-tokenizer-model mental model, but it removes TensorFlow and JAX/Flax support in favor of PyTorch, eliminates several older pipelines such as summarization and question answering, and changes or deprecates numerous APIs commonly found in 2022 tutorials. The tutorial explains how pipelines provide a simple interface for tasks including classification, generation, zero-shot classification, speech recognition, and multimodal workloads, while AutoTokenizer and AutoModel classes offer greater control over tokenization, model inference, batching, devices, and logits. It covers tokenizer outputs such as input IDs and attention masks, PyTorch inference practices, model saving through mandatory safetensors serialization, and navigating the rapidly expanded Hugging Face Hub, which hosts millions of models. For fine-tuning, it highlights v5 changes including eval_strategy replacing evaluation_strategy and processing_class replacing tokenizer in Trainer. The discussion also contrasts self-hosting speech-recognition models with hosted APIs, arguing that production requirements such as diarization, timestamps, long-audio processing, and infrastructure can make managed services more practical at lower volumes, while self-hosting may become more economical for organizations with sufficient scale and engineering capacity.

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