Classy Classification

Have you every struggled with needing a Spacy TextCategorizer but didn't have the time to train one from scratch? Classy Classification is the way to go! For few-shot classification using sentence-transformers or spaCy models, provide a dictionary with labels and examples, or just provide a list of labels for zero shot-classification with Hugginface zero-shot classifiers.

Install

pip install classy-classification

Quickstart

SpaCy embeddings

import spacy
import classy_classification

data = {
    "furniture": ["This text is about chairs.",
               "Couches, benches and televisions.",
               "I really need to get a new sofa."],
    "kitchen": ["There also exist things like fridges.",
                "I hope to be getting a new stove today.",
                "Do you also have some ovens."]
}

nlp = spacy.load("en_core_web_md")
nlp.add_pipe(
    "text_categorizer", 
    config={
        "data": data, 
        "model": "spacy"
    }
) 

print(nlp("I am looking for kitchen appliances.")._.cats)

# Output:
#
# [{"label": "furniture", "score": 0.21}, {"label": "kitchen", "score": 0.79}]

Sentence-transfomer embeddings

import spacy
import classy_classification

data = {
    "furniture": ["This text is about chairs.",
               "Couches, benches and televisions.",
               "I really need to get a new sofa."],
    "kitchen": ["There also exist things like fridges.",
                "I hope to be getting a new stove today.",
                "Do you also have some ovens."]
}

nlp = spacy.blank("en")
nlp.add_pipe(
    "text_categorizer", 
    config={
        "data": data, 
        "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2"
    }
) 

print(nlp("I am looking for kitchen appliances.")._.cats)

# Output:
#
# [{"label": "furniture", "score": 0.21}, {"label": "kitchen", "score": 0.79}]

Hugginface zero-shot classifiers

import spacy
import classy_classification

data = ["furniture", "kitchen"]

nlp = spacy.blank("en")
nlp.add_pipe(
    "text_categorizer", 
    config={
        "data": data, 
        "model": "facebook/bart-large-mnli",
        "cat_type": "zero"
    }
) 

print(nlp("I am looking for kitchen appliances.")._.cats)

# Output:
#
# [{"label": "furniture", "score": 0.21}, {"label": "kitchen", "score": 0.79}]

Credits

Inspiration Drawn From

Huggingface does offer some nice models for few/zero-shot classification, but these are not tailored to multi-lingual approaches. Rasa NLU has a nice approach for this, but its too embedded in their codebase for easy usage outside of Rasa/chatbots. Additionally, it made sense to integrate sentence-transformers and Hugginface zero-shot, instead of default word embeddings. Finally, I decided to integrate with Spacy, since training a custom Spacy TextCategorizer seems like a lot of hassle if you want something quick and dirty.

• Scikit-learn
• Rasa NLU
• Sentence Transformers
• Spacy

Or buy me a coffee

Standalone usage without spaCy

from classy_classification import classyClassifier

data = {
    "furniture": ["This text is about chairs.",
               "Couches, benches and televisions.",
               "I really need to get a new sofa."],
    "kitchen": ["There also exist things like fridges.",
                "I hope to be getting a new stove today.",
                "Do you also have some ovens."]
}

classifier = classyClassifier(data=data)
classifier("I am looking for kitchen appliances.")
classifier.pipe(["I am looking for kitchen appliances."])

# overwrite training data
classifier.set_training_data(data=data)
classifier("I am looking for kitchen appliances.")

# overwrite [embedding model](https://www.sbert.net/docs/pretrained_models.html)
classifier.set_embedding_model(model="paraphrase-MiniLM-L3-v2")
classifier("I am looking for kitchen appliances.")

# overwrite SVC config
classifier.set_svc(
    config={                              
        "C": [1, 2, 5, 10, 20, 100],
        "kernels": ["linear"],                              
        "max_cross_validation_folds": 5
    }
)
classifier("I am looking for kitchen appliances.")

Todo

[ ] look into a way to integrate spacy trf models.

[ ] multiple clasifications datasets for a single input e.g. emotions and topic.