rapidfit 0.1.6

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
write_mode	overwrite	overwrite or append to existing data

When using append mode, RapidFit loads existing data from the save path and skips duplicate texts during generation.

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")

Or train directly from a saved data directory:

classifier = MultiheadClassifier()
classifier.train(data_save_dir="./saved")
classifier.save("./model")

Customize training:

from rapidfit import MultiheadConfig, TrainingConfig, LossConfig

config = MultiheadConfig(
    training=TrainingConfig(epochs=10, learning_rate=2e-5),
    loss=LossConfig(use_class_weights=True),
)
classifier = MultiheadClassifier(config)

For a complete guide on configuration options and use cases, see How the Multihead Classifier Works.

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!"])

Error Analysis

Understand where your model fails before deploying:

result = classifier.analyze()       # Analyze test set
classifier.display(result)          # Show confusion matrix, metrics, errors

Get per-class precision/recall, confusion matrices, and the actual samples that were misclassified. See Error Analysis Guide for details.

Production Deployment

Export to ONNX for faster inference:

pip install rapidfit[export]

# Export with INT8 quantization (3-4x faster, 4x smaller)
classifier.export_onnx("./onnx_models", quantize=True)

For deployment strategies, ONNX Runtime usage, and performance optimization, see Inference Guide.

Extend It

Build custom augmenters or classifiers by extending the base classes:

from rapidfit import BaseAugmenter, BaseClassifier

License

MIT