stormtrooper 0.2.0

Transformer-based zero and few-shot classification in scikit-learn pipelines

stormtrooper

Transformer-based zero/few shot learning components for scikit-learn pipelines.

Example

pip install stormtrooper

class_labels = ["atheism/christianity", "astronomy/space"]
example_texts = [
    "God came down to earth to save us.",
    "A new nebula was recently discovered in the proximity of the Oort cloud."
]

Zero-shot learning

For zero-shot learning you can use zero-shot models:

from stormtrooper import ZeroShotClassifier
classifier = ZeroShotClassifier().fit(None, class_labels)

Generative models (GPT, Llama):

from stormtrooper import GenerativeZeroShotClassifier
# You can hand-craft prompts if it suits you better, but
# a default prompt is already available
prompt = """
### System:
You are a literary expert tasked with labeling texts according to
their content.
Please follow the user's instructions as precisely as you can.
### User:
Your task will be to classify a text document into one
of the following classes: {classes}.
Please respond with a single label that you think fits
the document best.
Classify the following piece of text:
'{X}'
### Assistant:
"""
classifier = GenerativeZeroShotClassifier(prompt=prompt).fit(None, class_labels)

Text2Text models (T5): If you are running low on resources I would personally recommend T5.

from stormtrooper import Text2TextZeroShotClassifier
# You can define a custom prompt, but a default one is available
prompt = "..."
classifier =Text2TextZeroShotClassifier(prompt=prompt).fit(None, class_labels)

predictions = classifier.predict(example_texts)

assert list(predictions) == ["atheism/christianity", "astronomy/space"]

Few-Shot Learning

For few-shot tasks you can only use Generative and Text2Text (aka. promptable) models.

from stormtrooper import GenerativeFewShotClassifier, Text2TextFewShotClassifier

classifier = Text2TextFewShotClassifier().fit(example_texts, class_labels)
predictions = model.predict(["Calvinists believe in predestination."])

assert list(predictions) == ["atheism/christianity"]

Fuzzy Matching

Models by default will fuzzy match results to the closest class label, you can disable this behavior by specifying fuzzy_match=False.

If you want fuzzy matching speedup, you should install python-Levenshtein.