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.
pip install classy-classification
or install with faster inference using onnx.
pip install classy-classification[onnx]
ONNX does show some issues when pickling the data.
Some installation issues might occur, which can be fixed by these commands.
brew install cmake
brew install protobuf
pip3 install onnx --no-use-pep517
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}]
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. Note that we use a multi-layer perceptron for this purpose instead of the default SVC
implementation, requiring a few more training samples.
import spacy
import classy_classification
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."],
"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."]
}
nlp = spacy.load("en_core_web_md")
nlp.add_pipe(
"text_categorizer",
config={
"data": data,
"model": "spacy",
"cat_type": "multi-label",
"config": {"hidden_layer_sizes": (64,), "seed": 42}
}
)
print(nlp("texts about dinner tables have multiple labels.")._.cats)
# Output:
#
# [{"label": "furniture", "score": 0.94}, {"label": "kitchen", "score": 0.97}]
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",
"device": "gpu"
}
)
print(nlp("I am looking for kitchen appliances.")._.cats)
# Output:
#
# [{"label": "furniture", "score": 0.21}, {"label": "kitchen", "score": 0.79}]
import spacy
import classy_classification
data = ["furniture", "kitchen"]
nlp = spacy.blank("en")
nlp.add_pipe(
"text_categorizer",
config={
"data": data,
"model": "typeform/distilbert-base-uncased-mnli",
"cat_type": "zero",
"device": "gpu"
}
)
print(nlp("I am looking for kitchen appliances.")._.cats)
# Output:
#
# [{"label": "furniture", "score": 0.21}, {"label": "kitchen", "score": 0.79}]
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.
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],
"kernels": ["linear"],
"max_cross_validation_folds": 5
}
)
classifier("I am looking for kitchen appliances.")
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.")
[ ] look into a way to integrate spacy trf models.