classy-fire 0.2.1

Classy-fire is multiclass text classification approach leveraging OpenAI LLM model APIs optimally using clever parameter tuning and prompting.

🤵🔥 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.

Why?

• Tired of having to beg your LLM to pick from a set of options / actions?
• Tired of working hard on cleaning and parsing its responses to trigger a flow?
• Struggling to strip unhelpful prefixes (such as "Sure! " or "I am just a language model!")?
• Having to wait on retries in cases of unexpected outputs?
• Getting random responses on the same query?
• Need a "quick and dirty" text classifier? Don't have enough training data?

Start here

Installation

pip install classy-fire

Usage example

from classy_fire import LLMClassifier, MCMCClassifier

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

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

mcmc_classifier = MCMCClassifier(["Banana", "Watermelon", "Apple", "Grape"])
result = mcmc_classifier("Has an oval or spherical shape")
print(result)
>>> [('Banana', 0, 0.0), ('Watermelon', 1, 0.02), ('Apple', 2, 0.81), ('Grape', 3, 0.17)]

Prerequisites

make sure you have OPENAI_API_BASE, OPENAI_API_VERSION, OPENAI_API_TYPE and OPENAI_API_KEY environment variables populated beforehand and a deployment of gpt-3.5-turbo named gpt-35-turbo-0301 (or pass deployment_name and model_name parameters to the LLMClassifier constructor). One way to accomplish this:

1. Create a file called ".env" in the path of your script containing:

OPENAI_API_BASE=https://<endpoint>/
OPENAI_API_VERSION=2023-05-15  # for example
OPENAI_API_TYPE=azure  # or openai
OPENAI_API_KEY=<private key guid>

2. install python-dotenv
3. add to the beginning your script

load_dotenv()

Continue here

LLMClassifier optional parameters

LLMClassifier can be initialized with added parameters that can help instruct and ground it to the classification task at hand.

• task_description = "Ability to provide additional context on the classification options and overall context on inputs"
• few_shot_examples = "Ability to provide instances of inputs and corresponding expected output values as a string"

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.
• New! MCMCClassifier can now estimate the posterior distribution over classes using a Markov Chain Monte Carlo approach!

Quality of results

We ran a preliminary experiment to classify a sample of 100 tweets from the tweet_eval dataset. The results appear to beat the SOTA.

	precision	recall	f1-score	support
anger	0.97	0.81	0.88	42
joy	0.74	0.92	0.82	25
optimism	1.00	0.25	0.40	8
sadness	0.75	1.00	0.86	21
accuracy			0.83	96
macro avg	0.87	0.74	0.74	96
weighted avg	0.87	0.83	0.82	96

See evaluate.ipynb for the details behind this experiment.

• We encourage the community to benchmark and explore this method against larger or more standardized datasets.
• We have not evaluated alternative prompting strategies or the impact of adding few shot examples, there may be flexibility in the achievable quality.

Other stuff

Contributing

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