🤵🔥 Classy-Fire 🔥🤵

Classy-fire is a pretrained multiclass text classification approach that leverages Azure OpenAI's LLM APIs using clever parameter tuning and prompting for classification.

Start here

Installation

pip install classy-fire

Usage example

from classy_fire import LLMClassifier
classifier = LLMClassifier(["Banana", "Watermelon", "Apple", "Grape"])

result = classifier("Has an elongated shape")
print(result)
>>> ('Banana', 0)

The premise behind classy-fire

In Classy-fire, we instruct the LLM to provide the most likely classification for an input string to a set of predetermined classes (also strings). Formally, given a string instance $x_i$ and a set of $k$ classes provided as strings, $C=(C_1, ..., C_k)$, classy-fire determines $argmax_j Pr[x_i \in C_j | C, \Theta]$ Where $\Theta$ is the parameters (knowledge of the world) of the language model.

• Classy-fire does this efficiently by mapping class strings to single tokens and providing a strong prior probability for these tokens. We instruct the model to generate a single token response, which allows for optimized inference runtime.
• Classy-fire does this deterministically and with less sensitivity to confabulation (hallucination) by setting the model temperature to 0, thereby guaranteeing the returned response is the argmax of the model posterior probability.

Quality of results

See evaluate.ipynb for a sample showcasing the power of this method on a public text classification dataset.

Contributing

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