classy-classification 1.0.0

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!

Classy Classification

Have you ever 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

SetFit support

I got a lot of requests for SetFit support, but I decided to create a separate package for this. Feel free to check it out. ❤️

Quickstart

SpaCy embeddings

import spacy
# or import standalone
# 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."]
}

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

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

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

Sentence level classification

import spacy

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.add_pipe(
    "classy_classification",
    config={
        "data": data,
        "model": "spacy",
        "include_sent": True
    }
)

print(nlp("I am looking for kitchen appliances. And I love doing so.").sents[0]._.cats)

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

Define random seed and verbosity

nlp.add_pipe(
    "classy_classification",
    config={
        "data": data,
        "verbose": True,
        "config": {"seed": 42}
    }
)

Multi-label classification

Sometimes multiple labels are necessary to fully describe the contents of a text. In that case, we want to make use of the multi-label implementation, here the sum of label scores is not limited to 1. Just pass the same training data to multiple keys.

import spacy

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

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

print(nlp("I am looking for furniture and kitchen equipment.")._.cats)

# Output:
#
# [{"furniture": 0.92}, {"kitchen": 0.91}]

Outlier detection

Sometimes it is worth to be able to do outlier detection or binary classification. This can either be approached using a binary training dataset, however, I have also implemented support for a OneClassSVM for outlier detection using a single label. Not that this method does not return probabilities, but that the data is formatted like label-score value pair to ensure uniformity.

Approach 1:

import spacy

data_binary = {
    "inlier": ["This text is about chairs.",
               "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."]
}

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

print(nlp("This text is a random text")._.cats)

# Output:
#
# [{'inlier': 0.2926672385488411, 'outlier': 0.707332761451159}]

Approach 2:

import spacy

data_singular = {
    "furniture": ["This text is about chairs.",
               "Couches, benches and televisions.",
               "I really need to get a new sofa.",
               "We have a new dinner table."]
}
nlp = spacy.load("en_core_web_md")
nlp.add_pipe(
    "classy_classification",
    config={
        "data": data_singular,
    }
)

print(nlp("This text is a random text")._.cats)

# Output:
#
# [{'furniture': 0, 'not_furniture': 1}]

Sentence-transfomer embeddings

import spacy

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(
    "classy_classification",
    config={
        "data": data,
        "model": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
        "device": "gpu"
    }
)

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

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

Hugginface zero-shot classifiers

import spacy

data = ["furniture", "kitchen"]

nlp = spacy.blank("en")
nlp.add_pipe(
    "classy_classification",
    config={
        "data": data,
        "model": "typeform/distilbert-base-uncased-mnli",
        "cat_type": "zero",
        "device": "gpu"
    }
)

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

# Output:
#
# [{"furniture": 0.21}, {"kitchen": 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_classification_model(
    config={
        "C": [1, 2, 5, 10, 20, 100],
        "kernel": ["linear"],
        "max_cross_validation_folds": 5
    }
)
classifier("I am looking for kitchen appliances.")

Save and load models

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)

with open("./classifier.pkl", "wb") as f:
    pickle.dump(classifier, f)

f = open("./classifier.pkl", "rb")
classifier = pickle.load(f)
classifier("I am looking for kitchen appliances.")