What is the Hugging Face Transformers pipeline API and how do you use it for common NLP and vision tasks?

The pipeline() function in Hugging Face Transformers is the highest-level API — it wraps model loading, tokenisation, inference, and post-processing into a single callable. It is the fastest way to get results from a pre-trained model and is ideal for prototyping and evaluation before committing to a custom training loop.

Pipelines support dozens of tasks out of the box including text generation, classification, named entity recognition, translation, summarisation, question answering, image classification, and zero-shot classification. Specifying a task without a model name loads the current recommended default for that task; specifying a model name loads exactly that checkpoint from the Hugging Face Hub.

from transformers import pipeline

# ── Text generation
gen = pipeline('text-generation', model='gpt2')
print(gen('The capital of France is', max_new_tokens=20))

# ── Sentiment / text classification
clf = pipeline('sentiment-analysis')  # loads recommended default
print(clf('I absolutely loved this product!'))
# [{'label': 'POSITIVE', 'score': 0.9998}]

# ── Named entity recognition
ner = pipeline('ner', aggregation_strategy='simple')
print(ner('Hugging Face is based in New York City.'))

# ── Summarisation
summ = pipeline('summarization', model='facebook/bart-large-cnn')
text = ('Scientists have discovered a new species of deep-sea fish '
        'near the Mariana Trench that can produce bioluminescent light...') * 3
print(summ(text, max_length=60, min_length=20))

# ── Zero-shot classification (no fine-tuning needed)
zsc = pipeline('zero-shot-classification', model='facebook/bart-large-mnli')
print(zsc(
    'The new iPhone has an impressive camera system.',
    candidate_labels=['technology', 'sports', 'politics'],
))

# ── Image classification
from transformers import pipeline as vp
img_clf = vp('image-classification', model='google/vit-base-patch16-224')
print(img_clf('https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/pipeline-cat-chonk.jpeg'))

# ── GPU acceleration
gen_gpu = pipeline('text-generation', model='mistralai/Mistral-7B-v0.1',
                    device=0,           # GPU 0
                    torch_dtype='auto') # auto selects bfloat16 on ampere+