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

```python 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("encorewebtrf") nlp.addpipe( "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

```python 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.addpipe( "classyclassification", 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

```python

nlp.addpipe( "classyclassification", 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.

```python 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("encorewebmd") nlp.addpipe( "classyclassification", config={ "data": data, "model": "spacy", "multilabel": 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:

```python 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("encorewebmd") nlp.addpipe( "classyclassification", config={ "data": databinary, } )

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

Output:

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

```

Approach 2:

```python import spacy

datasingular = { "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("encorewebmd") nlp.addpipe( "classyclassification", config={ "data": data_singular, } )

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

Output:

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

```

Sentence-transfomer embeddings

```python 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.addpipe( "classyclassification", 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

```python import spacy

data = ["furniture", "kitchen"]

nlp = spacy.blank("en") nlp.addpipe( "classyclassification", 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

```python

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.settrainingdata(data=data) classifier("I am looking for kitchen appliances.")

overwrite embedding model

classifier.setembeddingmodel(model="paraphrase-MiniLM-L3-v2") classifier("I am looking for kitchen appliances.")

overwrite SVC config

classifier.setclassificationmodel( config={ "C": [1, 2, 5, 10, 20, 100], "kernel": ["linear"], "maxcrossvalidation_folds": 5 } ) classifier("I am looking for kitchen appliances.") ```

Save and load models

```python 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.") ```