rapidfit 0.1.1

Build multi-task classifiers and augment classification datasets with ease

RapidFit

Turn a handful of labeled examples into a production-ready multi-task classifier.

RapidFit handles the two biggest pain points in text classification: not enough data and too many separate models. Give it a few examples per class, and it will generate more training data using LLMs, then train a single model that handles all your classification tasks at once.

Installation

pip install rapidfit

Augment Your Data

Start with just a few examples. RapidFit uses LLMs to expand your dataset while preserving label quality.

from rapidfit import LLMAugmenter

seed_data = {
    "sentiment": [
        {"text": "I love this product!", "label": "positive"},
        {"text": "Terrible experience.", "label": "negative"},
    ],
    "emotion": [
        {"text": "This makes me so happy!", "label": "joy"},
        {"text": "I can't believe they did this.", "label": "anger"},
    ],
}

augmenter = LLMAugmenter(api_key="your-api-key")
augmented = augmenter.augment(seed_data)

Configure generation with optional parameters:

Parameter	Default	Description
model_id	gpt-4.1-mini	LLM to use for generation
max_samples_per_task	128	Target samples per task
batch_size	8	Samples per generation call
save_path	./saved	Output directory
save_format	json	Format: json, jsonl, or csv

Train a Classifier

One model, multiple tasks. The multihead architecture shares a single encoder across all your classification tasks, making it efficient and consistent.

from rapidfit import MultiheadClassifier

classifier = MultiheadClassifier()
classifier.train(augmented)
classifier.save("./model")

Customize training:

classifier = MultiheadClassifier({
    "model_name": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
    "epochs": 10,
    "freeze_epochs": 3,
    "learning_rate": 2e-5,
    "patience": 3,
})

Predict

classifier = MultiheadClassifier()
classifier.load("./model")

# Single task
classifier.predict(["Great product!"], task="sentiment")
# [{"label": "positive", "confidence": 0.95}]

# All tasks
classifier.predict_all_tasks(["Great product!"])

Extend It

Build custom augmenters or classifiers by extending the base classes:

from rapidfit import BaseAugmenter, BaseClassifier

License

MIT