spaCy-SetFit
spaCy v3 
spaCy-SetFit is a Python library that extends spaCy’s text categorization capabilities by incorporating SetFit for few-shot classification. It allows you to train a text categorizer using a intuitive dictionary.
The library integrates with spaCy’s pipeline architecture, enabling easy integration and configuration of the text categorizer component. You can provide a training dataset containing inlier and outlier examples, and spaCy-SetFit will use the paraphrase-MiniLM-L3-v2 model for training the text categorizer with SetFit. Once trained, you can use the categorizer to classify new text and obtain category probabilities.
Example
import spacy # Create some example data train_dataset = { "inlier": [ "Text about furniture", "Couches, benches and televisions.", "I really need to get a new sofa." ], "outlier": [ "Text about kitchen equipment", "This text is about politics", "Comments about AI and stuff." ] } # Load the spaCy language model: nlp = spacy.load("en_core_web_sm") # Add the "spacy_setfit" pipeline component to the spaCy model, and configure it with SetFit parameters: nlp.add_pipe("spacy_setfit", config={ "pretrained_model_name_or_path": "paraphrase-MiniLM-L3-v2", "setfit_trainer_args": { "train_dataset": train_dataset } }) doc = nlp("I really need to get a new sofa.") doc.cats # {'inlier': 0.902350975129, 'outlier': 0.097649024871}
Categories pipeline
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