HuggingFace Transformers Inference

Text classification, NER, question answering, summarization, embeddings, and zero-shot classification using pretrained models.

When to Use / When NOT to Use

Use when: text classification, named entity recognition, question answering, summarization, text generation, sentence embeddings, zero-shot classification, translation, fill-mask tasks.

NOT for: training large models from scratch (use cloud GPU clusters), simple regex or rule-based text processing, production serving at scale (use vLLM or TGI), tasks that don't need neural models.

Quick Pipelines

from transformers import pipeline

# Text classification (sentiment)
clf = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
result = clf("This movie was fantastic!")
# [{'label': 'POSITIVE', 'score': 0.9998}]