How deeply does Hugging Face in Action cover zero-shot learning?

Explains the distinction from one-shot learning and demonstrates high-level pipeline calls, stopping short of model internals.

Zero-shot learning

As explained in Hugging Face in Action

Zero-shot classification is the assignment of labels to inputs without task-specific labeled examples during model adaptation or inference.

Zero-shot classification assigns inputs to labels without labeled examples for those categories during training or model adaptation. It is useful when new categories, such as Home Appliances or Electronics, are not included among a pretrained model’s fixed labels. Text models commonly use Natural Language Inference (NLI), and the approach also applies to images, audio, video, and graphs. section 3.3.5 section 3.4

Classify text with candidate labels

from transformers import pipeline classifier = pipeline("zero-shot-classification", model="joeddav/xlm-roberta-large-xnli") labels = ["technology", "politics", "business", "romance"] prediction = classifier(text1, labels, multi_label=True) The returned scores rank how well the paragraph matches each candidate label. Passing multiple paragraphs returns predictions for each input.

How Hugging Face in Action covers it

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Also explained in

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Introduction to Generative AI, Second EditionZero-shot learning is performing a task without task-specific labeled examples or demonstrations.
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Starting Data Analytics with Generative AI and PythonA zero-shot learning model performs a task or makes predictions for categories without task-specific examples for those categories during inference.
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Sutskever's ListA zero-shot setting evaluates a model on a task or query without providing task-specific examples in the input.
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LLMs in ProductionA learning or inference setting in which a model performs a task without being given task-specific examples.
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Knowledge Graphs and LLMs in ActionZero-shot learning is performing a task without task-specific examples, relying on a task description and pretrained knowledge.
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Generative AI in ActionZero-shot learning is the ability to perform a task or recognize classes not directly represented in task-specific training examples by relying on broader learned associations or descriptions.
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Transformers in ActionZero-shot learning is the ability to perform a task or recognize classes without task-specific examples during adaptation or inference.
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Build a Large Language Model (From Scratch)A learning setup where a model handles a task without having seen any task-specific examples beforehand.
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Reinforcement Learning from Human FeedbackZero-shot evaluation measures a model on questions or tasks without providing task-specific examples in the prompt.

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Related concepts

few-shot learningcontrasts with · Few-shot learning is the adaptation or performance of a model on a task using only a small number of task-specific examples.
prompt engineeringused by · Prompt engineering is the deliberate design and refinement of instructions, context, examples, or other inputs to guide an AI model’s behavior or outputs.
one-shot learningcontrasts with · One-shot learning is a machine-learning setting in which a model generalizes to recognize or perform a task from a single labeled or demonstrated example.
pretraininguses · Pretraining is the initial training of a model on broad data to learn general patterns or capabilities before adaptation to specific tasks or domains.
DetectGPTincludes · DetectGPT is a zero-shot method for detecting machine-generated text by analyzing changes in language-model likelihoods under perturbations.
NLIuses · Natural language inference is the task of determining whether a hypothesis is entailed by, contradicts, or is neutral with respect to a premise.

This explainer was generated by Manning's knowledge graph from the text of Hugging Face in Action. The section links point to the parts of the book it draws on.