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Dynamic category assignment

Dynamic category assignment enables a model to classify text into predefined categories, even without prior training for those categories.

Using Hugging Face’s pipeline() for the zero-shot-classification task, provide the text and predefined categories to identify the best match.

Build a classifier to predict the label for the input text, which is a news headline already loaded for you.

The pipelines from the transformers library is preloaded for your convenience.

Note: We are using a customized version of the pipeline to help you learn how to use these functions without needing to download the model.

Bu egzersiz

Working with Hugging Face

kursunun bir parçasıdır
Kursu Görüntüle

Egzersiz talimatları

  • Build the pipeline and save as classifier.
  • Create a list of the labels - "politics", "science", "sports" - and save as categories.
  • Predict the label of text using the classifier and predefined categories.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

text = "AI-powered robots assist in complex brain surgeries with precision."

# Create the pipeline
____ = pipeline(____="zero-shot-classification", ____="facebook/bart-large-mnli")

# Create the categories list
categories = ["politics", "____", "____"]

# Predict the output
output = ____(____, ____)

# Print the top label and its score
print(f"Top Label: {output['labels'][0]} with score: {output['scores'][0]}")
Kodu Düzenle ve Çalıştır